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Created December 13, 2020 16:54
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My Solution to Kaggle Data Science Competition 2020
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Kaggle_DataScience_Competition_2020.ipynb
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"source": [
"<H1 style=\"text-align: center\"> Introduction </H1>\n",
" \n",
"\n",
"In the past decade, computer science has evolved and the importance of **Data Science** has become the new norm, every company s looking to invest more in **Machine learning**. This is where someone like me who never had an Interest before got curious and started learning more about this world of Kaggle, it took me 3 months to get hold of some of the basic and advanced tools used by **Data Scientists**. I am using those same tools to evaluate this data set and come up with the best conclusion. In this Notebook, I will be telling you the story of data and I will be sharing my own experience so that any beginner can learn from my mistake and get ahead.\n",
"\n",
"<center><img src='https://www.yorkhotel.com.sg/uploads/9/8/1/8/98182264/accomplishment-celebrate-ceremony-267885_orig.jpg' alt=\"Degree\" style=\"width: 1000px\"> </center><br>\n",
"\n",
"> www.yorkhotel.com.sg\n",
"\n",
"\n",
"\n",
"## Big Question \n",
"\n",
"The big question is how do we determine whether getting a degree is necessary for getting into Data Science and eventually scoring Data Science Job. My sole focus will be on survey participants who had no formal degree and comparing them with other participants. In my opinion degree, the online platform has improved and due to COVID19, people get their skills through the online platform such as Kaggle, Udacity, DataCamp, edx, DataQuest, Udemy, Codecademy, and Coursera. These platforms are beginner friendly and it has paved the way for the less privileged people to get skills free. The ost COVID19 era will look different as more people will learn online.\n",
"\n",
"## To do list\n",
"- Analyzing Dataset\n",
"- Analyzing different Age groups\n",
"- Finding the relation between age groups and different professions\n",
"- Analyzing Different Sex\n",
"- Comparing Job titles of participants with a degree and without a degree\n",
"- Finding if coding experience improves job opportunities and comparing it with Degree and without.\n",
"- Finding the unemployment rate in each category\n",
"- Analysing Past survey data\n",
"- giving my conclusion of the weather getting a degree is important to score a better job and better opportunities."
]
},
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"text": [
"Collecting seaborn==0.11.0\r\n",
" Downloading seaborn-0.11.0-py3-none-any.whl (283 kB)\r\n",
"\u001b[K |████████████████████████████████| 283 kB 910 kB/s \r\n",
"\u001b[?25hRequirement already satisfied: scipy>=1.0 in /opt/conda/lib/python3.7/site-packages (from seaborn==0.11.0) (1.4.1)\r\n",
"Requirement already satisfied: matplotlib>=2.2 in /opt/conda/lib/python3.7/site-packages (from seaborn==0.11.0) (3.2.1)\r\n",
"Requirement already satisfied: pandas>=0.23 in /opt/conda/lib/python3.7/site-packages (from seaborn==0.11.0) (1.1.4)\r\n",
"Requirement already satisfied: numpy>=1.15 in /opt/conda/lib/python3.7/site-packages (from seaborn==0.11.0) (1.18.5)\r\n",
"Requirement already satisfied: numpy>=1.15 in /opt/conda/lib/python3.7/site-packages (from seaborn==0.11.0) (1.18.5)\r\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn==0.11.0) (1.2.0)\r\n",
"Requirement already satisfied: python-dateutil>=2.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn==0.11.0) (2.8.1)\r\n",
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"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn==0.11.0) (2.4.7)\r\n",
"Requirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from cycler>=0.10->matplotlib>=2.2->seaborn==0.11.0) (1.14.0)\r\n",
"Requirement already satisfied: numpy>=1.15 in /opt/conda/lib/python3.7/site-packages (from seaborn==0.11.0) (1.18.5)\r\n",
"Requirement already satisfied: python-dateutil>=2.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn==0.11.0) (2.8.1)\r\n",
"Requirement already satisfied: pytz>=2017.2 in /opt/conda/lib/python3.7/site-packages (from pandas>=0.23->seaborn==0.11.0) (2019.3)\r\n",
"Requirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from cycler>=0.10->matplotlib>=2.2->seaborn==0.11.0) (1.14.0)\r\n",
"Requirement already satisfied: numpy>=1.15 in /opt/conda/lib/python3.7/site-packages (from seaborn==0.11.0) (1.18.5)\r\n",
"Installing collected packages: seaborn\r\n",
" Attempting uninstall: seaborn\r\n",
" Found existing installation: seaborn 0.10.0\r\n",
" Uninstalling seaborn-0.10.0:\r\n",
" Successfully uninstalled seaborn-0.10.0\r\n",
"Successfully installed seaborn-0.11.0\r\n"
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"source": [
"!pip install seaborn==0.11.0"
]
},
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"source": [
"# Preparing Kernal and Exploring Data set"
]
},
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"source": [
"import numpy as np \n",
"\n",
"import pandas as pd \n",
"pd.set_option('display.max_columns', None)\n",
"\n",
"import seaborn as sns\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
"import warnings\n",
"warnings.simplefilter(action='ignore')\n",
"\n"
]
},
{
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"source": [
"## Cleaning Data"
]
},
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"source": [
"First thing first I need to look at data and description, so I went through all the documentation of Kaggle **Dataset** of **2020**. I even looked at other people's notebooks and got some Ideas of my own. I was hard for me to get started but when I started finding new things it became a passion project. I made some changes in time columns and made a data frame to work on. I also used an older data set to compare progress or different trends."
]
},
{
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"tags": []
},
"source": [
"### 2020 DataSet"
]
},
{
"cell_type": "markdown",
"metadata": {
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"end_time": "2020-12-05T10:04:54.241531",
"exception": false,
"start_time": "2020-12-05T10:04:54.179047",
"status": "completed"
},
"tags": []
},
"source": [
"Exploring and cleaning the 2020 Data Set and further changes will be made later in the project."
]
},
{
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"status": "completed"
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"outputs": [
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" <th>Q37_Part_10</th>\n",
" <th>Q37_Part_11</th>\n",
" <th>Q37_OTHER</th>\n",
" <th>Q38</th>\n",
" <th>Q39_Part_1</th>\n",
" <th>Q39_Part_2</th>\n",
" <th>Q39_Part_3</th>\n",
" <th>Q39_Part_4</th>\n",
" <th>Q39_Part_5</th>\n",
" <th>Q39_Part_6</th>\n",
" <th>Q39_Part_7</th>\n",
" <th>Q39_Part_8</th>\n",
" <th>Q39_Part_9</th>\n",
" <th>Q39_Part_10</th>\n",
" <th>Q39_Part_11</th>\n",
" <th>Q39_OTHER</th>\n",
" <th>Q26_B_Part_1</th>\n",
" <th>Q26_B_Part_2</th>\n",
" <th>Q26_B_Part_3</th>\n",
" <th>Q26_B_Part_4</th>\n",
" <th>Q26_B_Part_5</th>\n",
" <th>Q26_B_Part_6</th>\n",
" <th>Q26_B_Part_7</th>\n",
" <th>Q26_B_Part_8</th>\n",
" <th>Q26_B_Part_9</th>\n",
" <th>Q26_B_Part_10</th>\n",
" <th>Q26_B_Part_11</th>\n",
" <th>Q26_B_OTHER</th>\n",
" <th>Q27_B_Part_1</th>\n",
" <th>Q27_B_Part_2</th>\n",
" <th>Q27_B_Part_3</th>\n",
" <th>Q27_B_Part_4</th>\n",
" <th>Q27_B_Part_5</th>\n",
" <th>Q27_B_Part_6</th>\n",
" <th>Q27_B_Part_7</th>\n",
" <th>Q27_B_Part_8</th>\n",
" <th>Q27_B_Part_9</th>\n",
" <th>Q27_B_Part_10</th>\n",
" <th>Q27_B_Part_11</th>\n",
" <th>Q27_B_OTHER</th>\n",
" <th>Q28_B_Part_1</th>\n",
" <th>Q28_B_Part_2</th>\n",
" <th>Q28_B_Part_3</th>\n",
" <th>Q28_B_Part_4</th>\n",
" <th>Q28_B_Part_5</th>\n",
" <th>Q28_B_Part_6</th>\n",
" <th>Q28_B_Part_7</th>\n",
" <th>Q28_B_Part_8</th>\n",
" <th>Q28_B_Part_9</th>\n",
" <th>Q28_B_Part_10</th>\n",
" <th>Q28_B_OTHER</th>\n",
" <th>Q29_B_Part_1</th>\n",
" <th>Q29_B_Part_2</th>\n",
" <th>Q29_B_Part_3</th>\n",
" <th>Q29_B_Part_4</th>\n",
" <th>Q29_B_Part_5</th>\n",
" <th>Q29_B_Part_6</th>\n",
" <th>Q29_B_Part_7</th>\n",
" <th>Q29_B_Part_8</th>\n",
" <th>Q29_B_Part_9</th>\n",
" <th>Q29_B_Part_10</th>\n",
" <th>Q29_B_Part_11</th>\n",
" <th>Q29_B_Part_12</th>\n",
" <th>Q29_B_Part_13</th>\n",
" <th>Q29_B_Part_14</th>\n",
" <th>Q29_B_Part_15</th>\n",
" <th>Q29_B_Part_16</th>\n",
" <th>Q29_B_Part_17</th>\n",
" <th>Q29_B_OTHER</th>\n",
" <th>Q31_B_Part_1</th>\n",
" <th>Q31_B_Part_2</th>\n",
" <th>Q31_B_Part_3</th>\n",
" <th>Q31_B_Part_4</th>\n",
" <th>Q31_B_Part_5</th>\n",
" <th>Q31_B_Part_6</th>\n",
" <th>Q31_B_Part_7</th>\n",
" <th>Q31_B_Part_8</th>\n",
" <th>Q31_B_Part_9</th>\n",
" <th>Q31_B_Part_10</th>\n",
" <th>Q31_B_Part_11</th>\n",
" <th>Q31_B_Part_12</th>\n",
" <th>Q31_B_Part_13</th>\n",
" <th>Q31_B_Part_14</th>\n",
" <th>Q31_B_OTHER</th>\n",
" <th>Q33_B_Part_1</th>\n",
" <th>Q33_B_Part_2</th>\n",
" <th>Q33_B_Part_3</th>\n",
" <th>Q33_B_Part_4</th>\n",
" <th>Q33_B_Part_5</th>\n",
" <th>Q33_B_Part_6</th>\n",
" <th>Q33_B_Part_7</th>\n",
" <th>Q33_B_OTHER</th>\n",
" <th>Q34_B_Part_1</th>\n",
" <th>Q34_B_Part_2</th>\n",
" <th>Q34_B_Part_3</th>\n",
" <th>Q34_B_Part_4</th>\n",
" <th>Q34_B_Part_5</th>\n",
" <th>Q34_B_Part_6</th>\n",
" <th>Q34_B_Part_7</th>\n",
" <th>Q34_B_Part_8</th>\n",
" <th>Q34_B_Part_9</th>\n",
" <th>Q34_B_Part_10</th>\n",
" <th>Q34_B_Part_11</th>\n",
" <th>Q34_B_OTHER</th>\n",
" <th>Q35_B_Part_1</th>\n",
" <th>Q35_B_Part_2</th>\n",
" <th>Q35_B_Part_3</th>\n",
" <th>Q35_B_Part_4</th>\n",
" <th>Q35_B_Part_5</th>\n",
" <th>Q35_B_Part_6</th>\n",
" <th>Q35_B_Part_7</th>\n",
" <th>Q35_B_Part_8</th>\n",
" <th>Q35_B_Part_9</th>\n",
" <th>Q35_B_Part_10</th>\n",
" <th>Q35_B_OTHER</th>\n",
" <th>Year</th>\n",
" </tr>\n",
" <tr>\n",
" <th>time</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>00:30:38</th>\n",
" <td>35-39</td>\n",
" <td>Man</td>\n",
" <td>Colombia</td>\n",
" <td>Doctoral degree</td>\n",
" <td>Student</td>\n",
" <td>5-10 years</td>\n",
" <td>Python</td>\n",
" <td>R</td>\n",
" <td>SQL</td>\n",
" <td>C</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Javascript</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>MATLAB</td>\n",
" <td>NaN</td>\n",
" <td>Other</td>\n",
" <td>Python</td>\n",
" <td>Jupyter (JupyterLab, Jupyter Notebooks, etc)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Visual Studio Code (VSCode)</td>\n",
" <td>NaN</td>\n",
" <td>Spyder</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Kaggle Notebooks</td>\n",
" <td>Colab Notebooks</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>A cloud computing platform (AWS, Azure, GCP, h...</td>\n",
" <td>GPUs</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2-5 times</td>\n",
" <td>Matplotlib</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Geoplotlib</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1-2 years</td>\n",
" <td>NaN</td>\n",
" <td>TensorFlow</td>\n",
" <td>Keras</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Xgboost</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Decision Trees or Random Forests</td>\n",
" <td>Gradient Boosting Machines (xgboost, lightgbm,...</td>\n",
" <td>Bayesian Approaches</td>\n",
" <td>NaN</td>\n",
" <td>Dense Neural Networks (MLPs, etc)</td>\n",
" <td>Convolutional Neural Networks</td>\n",
" <td>NaN</td>\n",
" <td>Recurrent Neural Networks</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Image classification and other general purpose...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Word embeddings/vectors (GLoVe, fastText, word...</td>\n",
" <td>NaN</td>\n",
" <td>Contextualized embeddings (ELMo, CoVe)</td>\n",
" <td>Transformer language models (GPT-3, BERT, XLne...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Coursera</td>\n",
" <td>NaN</td>\n",
" <td>Kaggle Learn Courses</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>University Courses (resulting in a university ...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Basic statistical software (Microsoft Excel, G...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Kaggle (notebooks, forums, etc)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Journal Publications (peer-reviewed journals, ...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Amazon Web Services (AWS)</td>\n",
" <td>Microsoft Azure</td>\n",
" <td>Google Cloud Platform (GCP)</td>\n",
" <td>IBM Cloud / Red Hat</td>\n",
" <td>NaN</td>\n",
" <td>SAP Cloud</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Azure Cloud Services</td>\n",
" <td>Microsoft Azure Container Instances</td>\n",
" <td>Azure Functions</td>\n",
" <td>Google Cloud Compute Engine</td>\n",
" <td>Google Cloud Functions</td>\n",
" <td>Google Cloud Run</td>\n",
" <td>Google Cloud App Engine</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Amazon SageMaker</td>\n",
" <td>Amazon Forecast</td>\n",
" <td>Amazon Rekognition</td>\n",
" <td>Azure Machine Learning Studio</td>\n",
" <td>Azure Cognitive Services</td>\n",
" <td>Google Cloud AI Platform / Google Cloud ML En...</td>\n",
" <td>Google Cloud Video AI</td>\n",
" <td>Google Cloud Natural Language</td>\n",
" <td>Google Cloud Vision AI</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>MongoDB</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Microsoft SQL Server</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Google Cloud BigQuery</td>\n",
" <td>Google Cloud SQL</td>\n",
" <td>Google Cloud Firestore</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Microsoft Power BI</td>\n",
" <td>Amazon QuickSight</td>\n",
" <td>Google Data Studio</td>\n",
" <td>NaN</td>\n",
" <td>Tableau</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>SAP Analytics Cloud</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Automated data augmentation (e.g. imgaug, albu...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Automated hyperparameter tuning (e.g. hyperopt...</td>\n",
" <td>Automation of full ML pipelines (e.g. Google C...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Google Cloud AutoML</td>\n",
" <td>NaN</td>\n",
" <td>Databricks AutoML</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Auto-Keras</td>\n",
" <td>Auto-Sklearn</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>TensorBoard</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>08:21:27</th>\n",
" <td>30-34</td>\n",
" <td>Man</td>\n",
" <td>United States of America</td>\n",
" <td>Master’s degree</td>\n",
" <td>Data Engineer</td>\n",
" <td>5-10 years</td>\n",
" <td>Python</td>\n",
" <td>R</td>\n",
" <td>SQL</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Python</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Visual Studio</td>\n",
" <td>NaN</td>\n",
" <td>PyCharm</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Sublime Text</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Colab Notebooks</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>A personal computer or laptop</td>\n",
" <td>GPUs</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2-5 times</td>\n",
" <td>Matplotlib</td>\n",
" <td>Seaborn</td>\n",
" <td>NaN</td>\n",
" <td>Ggplot / ggplot2</td>\n",
" <td>Shiny</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1-2 years</td>\n",
" <td>Scikit-learn</td>\n",
" <td>TensorFlow</td>\n",
" <td>Keras</td>\n",
" <td>PyTorch</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Linear or Logistic Regression</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Convolutional Neural Networks</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Transformer Networks (BERT, gpt-3, etc)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Image segmentation methods (U-Net, Mask R-CNN,...</td>\n",
" <td>NaN</td>\n",
" <td>Image classification and other general purpose...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Contextualized embeddings (ELMo, CoVe)</td>\n",
" <td>Transformer language models (GPT-3, BERT, XLne...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>10,000 or more employees</td>\n",
" <td>20+</td>\n",
" <td>We have well established ML methods (i.e., mod...</td>\n",
" <td>Analyze and understand data to influence produ...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Do research that advances the state of the art...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100,000-124,999</td>\n",
" <td>$100,000 or more ($USD)</td>\n",
" <td>Amazon Web Services (AWS)</td>\n",
" <td>Microsoft Azure</td>\n",
" <td>Google Cloud Platform (GCP)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Amazon EC2</td>\n",
" <td>AWS Lambda</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Azure Functions</td>\n",
" <td>Google Cloud Compute Engine</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Amazon SageMaker</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>PostgresSQL</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Amazon Redshift</td>\n",
" <td>Amazon Athena</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>PostgresSQL</td>\n",
" <td>Amazon QuickSight</td>\n",
" <td>Microsoft Power BI</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Tableau</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Microsoft Power BI</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>No / None</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>No / None</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>GitHub</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Coursera</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>DataCamp</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Udemy</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Business intelligence software (Salesforce, Ta...</td>\n",
" <td>Twitter (data science influencers)</td>\n",
" <td>NaN</td>\n",
" <td>Reddit (r/machinelearning, etc)</td>\n",
" <td>Kaggle (notebooks, forums, etc)</td>\n",
" <td>Course Forums (forums.fast.ai, Coursera forums...</td>\n",
" <td>YouTube (Kaggle YouTube, Cloud AI Adventures, ...</td>\n",
" <td>NaN</td>\n",
" <td>Blogs (Towards Data Science, Analytics Vidhya,...</td>\n",
" <td>NaN</td>\n",
" <td>Slack Communities (ods.ai, kagglenoobs, etc)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>00:14:20</th>\n",
" <td>35-39</td>\n",
" <td>Man</td>\n",
" <td>Argentina</td>\n",
" <td>Bachelor’s degree</td>\n",
" <td>Software Engineer</td>\n",
" <td>10-20 years</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Java</td>\n",
" <td>Javascript</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Bash</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>R</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Visual Studio Code (VSCode)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Notepad++</td>\n",
" <td>Sublime Text</td>\n",
" <td>Vim / Emacs</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>A personal computer or laptop</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>Never</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>D3 js</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>I do not use machine learning methods</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1000-9,999 employees</td>\n",
" <td>0</td>\n",
" <td>No (we do not use ML methods)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>None of these activities are an important part...</td>\n",
" <td>NaN</td>\n",
" <td>15,000-19,999</td>\n",
" <td>$0 ($USD)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>MySQL</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>No / None</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>No / None</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Coursera</td>\n",
" <td>edX</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Udacity</td>\n",
" <td>Udemy</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Basic statistical software (Microsoft Excel, G...</td>\n",
" <td>NaN</td>\n",
" <td>Email newsletters (Data Elixir, O'Reilly Data ...</td>\n",
" <td>NaN</td>\n",
" <td>Kaggle (notebooks, forums, etc)</td>\n",
" <td>NaN</td>\n",
" <td>YouTube (Kaggle YouTube, Cloud AI Adventures, ...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>MySQL</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Microsoft SQL Server</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>2020</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Q1 Q2 Q3 Q4 \\\n",
"time \n",
"00:30:38 35-39 Man Colombia Doctoral degree \n",
"08:21:27 30-34 Man United States of America Master’s degree \n",
"00:14:20 35-39 Man Argentina Bachelor’s degree \n",
"\n",
" Q5 Q6 Q7_Part_1 Q7_Part_2 Q7_Part_3 \\\n",
"time \n",
"00:30:38 Student 5-10 years Python R SQL \n",
"08:21:27 Data Engineer 5-10 years Python R SQL \n",
"00:14:20 Software Engineer 10-20 years NaN NaN NaN \n",
"\n",
" Q7_Part_4 Q7_Part_5 Q7_Part_6 Q7_Part_7 Q7_Part_8 Q7_Part_9 \\\n",
"time \n",
"00:30:38 C NaN NaN Javascript NaN NaN \n",
"08:21:27 NaN NaN NaN NaN NaN NaN \n",
"00:14:20 NaN NaN Java Javascript NaN NaN \n",
"\n",
" Q7_Part_10 Q7_Part_11 Q7_Part_12 Q7_OTHER Q8 \\\n",
"time \n",
"00:30:38 NaN MATLAB NaN Other Python \n",
"08:21:27 NaN NaN NaN NaN Python \n",
"00:14:20 Bash NaN NaN NaN R \n",
"\n",
" Q9_Part_1 Q9_Part_2 \\\n",
"time \n",
"00:30:38 Jupyter (JupyterLab, Jupyter Notebooks, etc) NaN \n",
"08:21:27 NaN NaN \n",
"00:14:20 NaN NaN \n",
"\n",
" Q9_Part_3 Q9_Part_4 Q9_Part_5 Q9_Part_6 \\\n",
"time \n",
"00:30:38 NaN Visual Studio Code (VSCode) NaN Spyder \n",
"08:21:27 Visual Studio NaN PyCharm NaN \n",
"00:14:20 NaN Visual Studio Code (VSCode) NaN NaN \n",
"\n",
" Q9_Part_7 Q9_Part_8 Q9_Part_9 Q9_Part_10 \\\n",
"time \n",
"00:30:38 NaN NaN NaN NaN \n",
"08:21:27 NaN Sublime Text NaN NaN \n",
"00:14:20 Notepad++ Sublime Text Vim / Emacs NaN \n",
"\n",
" Q9_Part_11 Q9_OTHER Q10_Part_1 Q10_Part_2 Q10_Part_3 \\\n",
"time \n",
"00:30:38 NaN NaN Kaggle Notebooks Colab Notebooks NaN \n",
"08:21:27 NaN NaN NaN Colab Notebooks NaN \n",
"00:14:20 NaN NaN NaN NaN NaN \n",
"\n",
" Q10_Part_4 Q10_Part_5 Q10_Part_6 Q10_Part_7 Q10_Part_8 Q10_Part_9 \\\n",
"time \n",
"00:30:38 NaN NaN NaN NaN NaN NaN \n",
"08:21:27 NaN NaN NaN NaN NaN NaN \n",
"00:14:20 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q10_Part_10 Q10_Part_11 Q10_Part_12 Q10_Part_13 Q10_OTHER \\\n",
"time \n",
"00:30:38 NaN NaN NaN NaN NaN \n",
"08:21:27 NaN NaN NaN NaN NaN \n",
"00:14:20 NaN NaN NaN None NaN \n",
"\n",
" Q11 Q12_Part_1 \\\n",
"time \n",
"00:30:38 A cloud computing platform (AWS, Azure, GCP, h... GPUs \n",
"08:21:27 A personal computer or laptop GPUs \n",
"00:14:20 A personal computer or laptop NaN \n",
"\n",
" Q12_Part_2 Q12_Part_3 Q12_OTHER Q13 Q14_Part_1 Q14_Part_2 \\\n",
"time \n",
"00:30:38 NaN NaN NaN 2-5 times Matplotlib NaN \n",
"08:21:27 NaN NaN NaN 2-5 times Matplotlib Seaborn \n",
"00:14:20 NaN None NaN Never NaN NaN \n",
"\n",
" Q14_Part_3 Q14_Part_4 Q14_Part_5 Q14_Part_6 Q14_Part_7 \\\n",
"time \n",
"00:30:38 NaN NaN NaN NaN NaN \n",
"08:21:27 NaN Ggplot / ggplot2 Shiny NaN NaN \n",
"00:14:20 NaN NaN NaN D3 js NaN \n",
"\n",
" Q14_Part_8 Q14_Part_9 Q14_Part_10 Q14_Part_11 Q14_OTHER \\\n",
"time \n",
"00:30:38 NaN Geoplotlib NaN NaN NaN \n",
"08:21:27 NaN NaN NaN NaN NaN \n",
"00:14:20 NaN NaN NaN NaN NaN \n",
"\n",
" Q15 Q16_Part_1 \\\n",
"time \n",
"00:30:38 1-2 years NaN \n",
"08:21:27 1-2 years Scikit-learn \n",
"00:14:20 I do not use machine learning methods NaN \n",
"\n",
" Q16_Part_2 Q16_Part_3 Q16_Part_4 Q16_Part_5 Q16_Part_6 \\\n",
"time \n",
"00:30:38 TensorFlow Keras NaN NaN NaN \n",
"08:21:27 TensorFlow Keras PyTorch NaN NaN \n",
"00:14:20 NaN NaN NaN NaN NaN \n",
"\n",
" Q16_Part_7 Q16_Part_8 Q16_Part_9 Q16_Part_10 Q16_Part_11 Q16_Part_12 \\\n",
"time \n",
"00:30:38 Xgboost NaN NaN NaN NaN NaN \n",
"08:21:27 NaN NaN NaN NaN NaN NaN \n",
"00:14:20 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q16_Part_13 Q16_Part_14 Q16_Part_15 Q16_OTHER \\\n",
"time \n",
"00:30:38 NaN NaN NaN NaN \n",
"08:21:27 NaN NaN NaN NaN \n",
"00:14:20 NaN NaN NaN NaN \n",
"\n",
" Q17_Part_1 Q17_Part_2 \\\n",
"time \n",
"00:30:38 NaN Decision Trees or Random Forests \n",
"08:21:27 Linear or Logistic Regression NaN \n",
"00:14:20 NaN NaN \n",
"\n",
" Q17_Part_3 \\\n",
"time \n",
"00:30:38 Gradient Boosting Machines (xgboost, lightgbm,... \n",
"08:21:27 NaN \n",
"00:14:20 NaN \n",
"\n",
" Q17_Part_4 Q17_Part_5 Q17_Part_6 \\\n",
"time \n",
"00:30:38 Bayesian Approaches NaN Dense Neural Networks (MLPs, etc) \n",
"08:21:27 NaN NaN NaN \n",
"00:14:20 NaN NaN NaN \n",
"\n",
" Q17_Part_7 Q17_Part_8 Q17_Part_9 \\\n",
"time \n",
"00:30:38 Convolutional Neural Networks NaN Recurrent Neural Networks \n",
"08:21:27 Convolutional Neural Networks NaN NaN \n",
"00:14:20 NaN NaN NaN \n",
"\n",
" Q17_Part_10 Q17_Part_11 Q17_OTHER \\\n",
"time \n",
"00:30:38 NaN NaN NaN \n",
"08:21:27 Transformer Networks (BERT, gpt-3, etc) NaN NaN \n",
"00:14:20 NaN NaN NaN \n",
"\n",
" Q18_Part_1 Q18_Part_2 \\\n",
"time \n",
"00:30:38 NaN NaN \n",
"08:21:27 NaN Image segmentation methods (U-Net, Mask R-CNN,... \n",
"00:14:20 NaN NaN \n",
"\n",
" Q18_Part_3 Q18_Part_4 \\\n",
"time \n",
"00:30:38 NaN Image classification and other general purpose... \n",
"08:21:27 NaN Image classification and other general purpose... \n",
"00:14:20 NaN NaN \n",
"\n",
" Q18_Part_5 Q18_Part_6 Q18_OTHER \\\n",
"time \n",
"00:30:38 NaN NaN NaN \n",
"08:21:27 NaN NaN NaN \n",
"00:14:20 NaN NaN NaN \n",
"\n",
" Q19_Part_1 Q19_Part_2 \\\n",
"time \n",
"00:30:38 Word embeddings/vectors (GLoVe, fastText, word... NaN \n",
"08:21:27 NaN NaN \n",
"00:14:20 NaN NaN \n",
"\n",
" Q19_Part_3 \\\n",
"time \n",
"00:30:38 Contextualized embeddings (ELMo, CoVe) \n",
"08:21:27 Contextualized embeddings (ELMo, CoVe) \n",
"00:14:20 NaN \n",
"\n",
" Q19_Part_4 Q19_Part_5 \\\n",
"time \n",
"00:30:38 Transformer language models (GPT-3, BERT, XLne... NaN \n",
"08:21:27 Transformer language models (GPT-3, BERT, XLne... NaN \n",
"00:14:20 NaN NaN \n",
"\n",
" Q19_OTHER Q20 Q21 \\\n",
"time \n",
"00:30:38 NaN NaN NaN \n",
"08:21:27 NaN 10,000 or more employees 20+ \n",
"00:14:20 NaN 1000-9,999 employees 0 \n",
"\n",
" Q22 \\\n",
"time \n",
"00:30:38 NaN \n",
"08:21:27 We have well established ML methods (i.e., mod... \n",
"00:14:20 No (we do not use ML methods) \n",
"\n",
" Q23_Part_1 Q23_Part_2 \\\n",
"time \n",
"00:30:38 NaN NaN \n",
"08:21:27 Analyze and understand data to influence produ... NaN \n",
"00:14:20 NaN NaN \n",
"\n",
" Q23_Part_3 Q23_Part_4 Q23_Part_5 \\\n",
"time \n",
"00:30:38 NaN NaN NaN \n",
"08:21:27 NaN NaN NaN \n",
"00:14:20 NaN NaN NaN \n",
"\n",
" Q23_Part_6 \\\n",
"time \n",
"00:30:38 NaN \n",
"08:21:27 Do research that advances the state of the art... \n",
"00:14:20 NaN \n",
"\n",
" Q23_Part_7 Q23_OTHER \\\n",
"time \n",
"00:30:38 NaN NaN \n",
"08:21:27 NaN NaN \n",
"00:14:20 None of these activities are an important part... NaN \n",
"\n",
" Q24 Q25 \\\n",
"time \n",
"00:30:38 NaN NaN \n",
"08:21:27 100,000-124,999 $100,000 or more ($USD) \n",
"00:14:20 15,000-19,999 $0 ($USD) \n",
"\n",
" Q26_A_Part_1 Q26_A_Part_2 \\\n",
"time \n",
"00:30:38 NaN NaN \n",
"08:21:27 Amazon Web Services (AWS) Microsoft Azure \n",
"00:14:20 NaN NaN \n",
"\n",
" Q26_A_Part_3 Q26_A_Part_4 Q26_A_Part_5 \\\n",
"time \n",
"00:30:38 NaN NaN NaN \n",
"08:21:27 Google Cloud Platform (GCP) NaN NaN \n",
"00:14:20 NaN NaN NaN \n",
"\n",
" Q26_A_Part_6 Q26_A_Part_7 Q26_A_Part_8 Q26_A_Part_9 Q26_A_Part_10 \\\n",
"time \n",
"00:30:38 NaN NaN NaN NaN NaN \n",
"08:21:27 NaN NaN NaN NaN NaN \n",
"00:14:20 NaN NaN NaN NaN NaN \n",
"\n",
" Q26_A_Part_11 Q26_A_OTHER Q27_A_Part_1 Q27_A_Part_2 Q27_A_Part_3 \\\n",
"time \n",
"00:30:38 NaN NaN NaN NaN NaN \n",
"08:21:27 NaN NaN Amazon EC2 AWS Lambda NaN \n",
"00:14:20 None NaN NaN NaN NaN \n",
"\n",
" Q27_A_Part_4 Q27_A_Part_5 Q27_A_Part_6 \\\n",
"time \n",
"00:30:38 NaN NaN NaN \n",
"08:21:27 NaN NaN Azure Functions \n",
"00:14:20 NaN NaN NaN \n",
"\n",
" Q27_A_Part_7 Q27_A_Part_8 Q27_A_Part_9 \\\n",
"time \n",
"00:30:38 NaN NaN NaN \n",
"08:21:27 Google Cloud Compute Engine NaN NaN \n",
"00:14:20 NaN NaN NaN \n",
"\n",
" Q27_A_Part_10 Q27_A_Part_11 Q27_A_OTHER Q28_A_Part_1 \\\n",
"time \n",
"00:30:38 NaN NaN NaN NaN \n",
"08:21:27 NaN NaN NaN Amazon SageMaker \n",
"00:14:20 NaN NaN NaN NaN \n",
"\n",
" Q28_A_Part_2 Q28_A_Part_3 Q28_A_Part_4 Q28_A_Part_5 Q28_A_Part_6 \\\n",
"time \n",
"00:30:38 NaN NaN NaN NaN NaN \n",
"08:21:27 NaN NaN NaN NaN NaN \n",
"00:14:20 NaN NaN NaN NaN NaN \n",
"\n",
" Q28_A_Part_7 Q28_A_Part_8 Q28_A_Part_9 Q28_A_Part_10 Q28_A_OTHER \\\n",
"time \n",
"00:30:38 NaN NaN NaN NaN NaN \n",
"08:21:27 NaN NaN NaN NaN NaN \n",
"00:14:20 NaN NaN NaN NaN NaN \n",
"\n",
" Q29_A_Part_1 Q29_A_Part_2 Q29_A_Part_3 Q29_A_Part_4 Q29_A_Part_5 \\\n",
"time \n",
"00:30:38 NaN NaN NaN NaN NaN \n",
"08:21:27 NaN PostgresSQL NaN NaN NaN \n",
"00:14:20 MySQL NaN NaN NaN NaN \n",
"\n",
" Q29_A_Part_6 Q29_A_Part_7 Q29_A_Part_8 Q29_A_Part_9 Q29_A_Part_10 \\\n",
"time \n",
"00:30:38 NaN NaN NaN NaN NaN \n",
"08:21:27 NaN NaN NaN NaN NaN \n",
"00:14:20 NaN NaN NaN NaN NaN \n",
"\n",
" Q29_A_Part_11 Q29_A_Part_12 Q29_A_Part_13 Q29_A_Part_14 \\\n",
"time \n",
"00:30:38 NaN NaN NaN NaN \n",
"08:21:27 Amazon Redshift Amazon Athena NaN NaN \n",
"00:14:20 NaN NaN NaN NaN \n",
"\n",
" Q29_A_Part_15 Q29_A_Part_16 Q29_A_Part_17 Q29_A_OTHER Q30 \\\n",
"time \n",
"00:30:38 NaN NaN NaN NaN NaN \n",
"08:21:27 NaN NaN NaN NaN PostgresSQL \n",
"00:14:20 NaN NaN NaN NaN NaN \n",
"\n",
" Q31_A_Part_1 Q31_A_Part_2 Q31_A_Part_3 Q31_A_Part_4 \\\n",
"time \n",
"00:30:38 NaN NaN NaN NaN \n",
"08:21:27 Amazon QuickSight Microsoft Power BI NaN NaN \n",
"00:14:20 NaN NaN NaN NaN \n",
"\n",
" Q31_A_Part_5 Q31_A_Part_6 Q31_A_Part_7 Q31_A_Part_8 Q31_A_Part_9 \\\n",
"time \n",
"00:30:38 NaN NaN NaN NaN NaN \n",
"08:21:27 Tableau NaN NaN NaN NaN \n",
"00:14:20 NaN NaN NaN NaN NaN \n",
"\n",
" Q31_A_Part_10 Q31_A_Part_11 Q31_A_Part_12 Q31_A_Part_13 \\\n",
"time \n",
"00:30:38 NaN NaN NaN NaN \n",
"08:21:27 NaN NaN NaN NaN \n",
"00:14:20 NaN NaN NaN NaN \n",
"\n",
" Q31_A_Part_14 Q31_A_OTHER Q32 Q33_A_Part_1 \\\n",
"time \n",
"00:30:38 NaN NaN NaN NaN \n",
"08:21:27 NaN NaN Microsoft Power BI NaN \n",
"00:14:20 None NaN NaN NaN \n",
"\n",
" Q33_A_Part_2 Q33_A_Part_3 Q33_A_Part_4 Q33_A_Part_5 Q33_A_Part_6 \\\n",
"time \n",
"00:30:38 NaN NaN NaN NaN NaN \n",
"08:21:27 NaN NaN NaN NaN NaN \n",
"00:14:20 NaN NaN NaN NaN NaN \n",
"\n",
" Q33_A_Part_7 Q33_A_OTHER Q34_A_Part_1 Q34_A_Part_2 Q34_A_Part_3 \\\n",
"time \n",
"00:30:38 NaN NaN NaN NaN NaN \n",
"08:21:27 No / None NaN NaN NaN NaN \n",
"00:14:20 No / None NaN NaN NaN NaN \n",
"\n",
" Q34_A_Part_4 Q34_A_Part_5 Q34_A_Part_6 Q34_A_Part_7 Q34_A_Part_8 \\\n",
"time \n",
"00:30:38 NaN NaN NaN NaN NaN \n",
"08:21:27 NaN NaN NaN NaN NaN \n",
"00:14:20 NaN NaN NaN NaN NaN \n",
"\n",
" Q34_A_Part_9 Q34_A_Part_10 Q34_A_Part_11 Q34_A_OTHER Q35_A_Part_1 \\\n",
"time \n",
"00:30:38 NaN NaN NaN NaN NaN \n",
"08:21:27 NaN NaN NaN NaN NaN \n",
"00:14:20 NaN NaN NaN NaN NaN \n",
"\n",
" Q35_A_Part_2 Q35_A_Part_3 Q35_A_Part_4 Q35_A_Part_5 Q35_A_Part_6 \\\n",
"time \n",
"00:30:38 NaN NaN NaN NaN NaN \n",
"08:21:27 NaN NaN NaN NaN NaN \n",
"00:14:20 NaN NaN NaN NaN NaN \n",
"\n",
" Q35_A_Part_7 Q35_A_Part_8 Q35_A_Part_9 Q35_A_Part_10 Q35_A_OTHER \\\n",
"time \n",
"00:30:38 NaN NaN NaN NaN NaN \n",
"08:21:27 NaN NaN NaN No / None NaN \n",
"00:14:20 NaN NaN NaN No / None NaN \n",
"\n",
" Q36_Part_1 Q36_Part_2 Q36_Part_3 Q36_Part_4 Q36_Part_5 Q36_Part_6 \\\n",
"time \n",
"00:30:38 NaN NaN NaN NaN NaN NaN \n",
"08:21:27 NaN NaN NaN GitHub NaN NaN \n",
"00:14:20 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q36_Part_7 Q36_Part_8 Q36_Part_9 Q36_OTHER Q37_Part_1 Q37_Part_2 \\\n",
"time \n",
"00:30:38 NaN NaN NaN NaN Coursera NaN \n",
"08:21:27 NaN NaN NaN NaN Coursera NaN \n",
"00:14:20 NaN NaN NaN NaN Coursera edX \n",
"\n",
" Q37_Part_3 Q37_Part_4 Q37_Part_5 Q37_Part_6 Q37_Part_7 \\\n",
"time \n",
"00:30:38 Kaggle Learn Courses NaN NaN NaN NaN \n",
"08:21:27 NaN DataCamp NaN NaN Udemy \n",
"00:14:20 NaN NaN NaN Udacity Udemy \n",
"\n",
" Q37_Part_8 Q37_Part_9 \\\n",
"time \n",
"00:30:38 NaN NaN \n",
"08:21:27 NaN NaN \n",
"00:14:20 NaN NaN \n",
"\n",
" Q37_Part_10 Q37_Part_11 \\\n",
"time \n",
"00:30:38 University Courses (resulting in a university ... NaN \n",
"08:21:27 NaN NaN \n",
"00:14:20 NaN NaN \n",
"\n",
" Q37_OTHER Q38 \\\n",
"time \n",
"00:30:38 NaN Basic statistical software (Microsoft Excel, G... \n",
"08:21:27 NaN Business intelligence software (Salesforce, Ta... \n",
"00:14:20 NaN Basic statistical software (Microsoft Excel, G... \n",
"\n",
" Q39_Part_1 \\\n",
"time \n",
"00:30:38 NaN \n",
"08:21:27 Twitter (data science influencers) \n",
"00:14:20 NaN \n",
"\n",
" Q39_Part_2 \\\n",
"time \n",
"00:30:38 NaN \n",
"08:21:27 NaN \n",
"00:14:20 Email newsletters (Data Elixir, O'Reilly Data ... \n",
"\n",
" Q39_Part_3 Q39_Part_4 \\\n",
"time \n",
"00:30:38 NaN Kaggle (notebooks, forums, etc) \n",
"08:21:27 Reddit (r/machinelearning, etc) Kaggle (notebooks, forums, etc) \n",
"00:14:20 NaN Kaggle (notebooks, forums, etc) \n",
"\n",
" Q39_Part_5 \\\n",
"time \n",
"00:30:38 NaN \n",
"08:21:27 Course Forums (forums.fast.ai, Coursera forums... \n",
"00:14:20 NaN \n",
"\n",
" Q39_Part_6 Q39_Part_7 \\\n",
"time \n",
"00:30:38 NaN NaN \n",
"08:21:27 YouTube (Kaggle YouTube, Cloud AI Adventures, ... NaN \n",
"00:14:20 YouTube (Kaggle YouTube, Cloud AI Adventures, ... NaN \n",
"\n",
" Q39_Part_8 \\\n",
"time \n",
"00:30:38 NaN \n",
"08:21:27 Blogs (Towards Data Science, Analytics Vidhya,... \n",
"00:14:20 NaN \n",
"\n",
" Q39_Part_9 \\\n",
"time \n",
"00:30:38 Journal Publications (peer-reviewed journals, ... \n",
"08:21:27 NaN \n",
"00:14:20 NaN \n",
"\n",
" Q39_Part_10 Q39_Part_11 Q39_OTHER \\\n",
"time \n",
"00:30:38 NaN NaN NaN \n",
"08:21:27 Slack Communities (ods.ai, kagglenoobs, etc) NaN NaN \n",
"00:14:20 NaN NaN NaN \n",
"\n",
" Q26_B_Part_1 Q26_B_Part_2 \\\n",
"time \n",
"00:30:38 Amazon Web Services (AWS) Microsoft Azure \n",
"08:21:27 NaN NaN \n",
"00:14:20 NaN NaN \n",
"\n",
" Q26_B_Part_3 Q26_B_Part_4 Q26_B_Part_5 \\\n",
"time \n",
"00:30:38 Google Cloud Platform (GCP) IBM Cloud / Red Hat NaN \n",
"08:21:27 NaN NaN NaN \n",
"00:14:20 NaN NaN NaN \n",
"\n",
" Q26_B_Part_6 Q26_B_Part_7 Q26_B_Part_8 Q26_B_Part_9 Q26_B_Part_10 \\\n",
"time \n",
"00:30:38 SAP Cloud NaN NaN NaN NaN \n",
"08:21:27 NaN NaN NaN NaN NaN \n",
"00:14:20 NaN NaN NaN NaN NaN \n",
"\n",
" Q26_B_Part_11 Q26_B_OTHER Q27_B_Part_1 Q27_B_Part_2 Q27_B_Part_3 \\\n",
"time \n",
"00:30:38 NaN NaN NaN NaN NaN \n",
"08:21:27 NaN NaN NaN NaN NaN \n",
"00:14:20 None NaN NaN NaN NaN \n",
"\n",
" Q27_B_Part_4 Q27_B_Part_5 \\\n",
"time \n",
"00:30:38 Azure Cloud Services Microsoft Azure Container Instances \n",
"08:21:27 NaN NaN \n",
"00:14:20 NaN NaN \n",
"\n",
" Q27_B_Part_6 Q27_B_Part_7 \\\n",
"time \n",
"00:30:38 Azure Functions Google Cloud Compute Engine \n",
"08:21:27 NaN NaN \n",
"00:14:20 NaN NaN \n",
"\n",
" Q27_B_Part_8 Q27_B_Part_9 \\\n",
"time \n",
"00:30:38 Google Cloud Functions Google Cloud Run \n",
"08:21:27 NaN NaN \n",
"00:14:20 NaN NaN \n",
"\n",
" Q27_B_Part_10 Q27_B_Part_11 Q27_B_OTHER \\\n",
"time \n",
"00:30:38 Google Cloud App Engine NaN NaN \n",
"08:21:27 NaN NaN NaN \n",
"00:14:20 NaN NaN NaN \n",
"\n",
" Q28_B_Part_1 Q28_B_Part_2 Q28_B_Part_3 \\\n",
"time \n",
"00:30:38 Amazon SageMaker Amazon Forecast Amazon Rekognition \n",
"08:21:27 NaN NaN NaN \n",
"00:14:20 NaN NaN NaN \n",
"\n",
" Q28_B_Part_4 Q28_B_Part_5 \\\n",
"time \n",
"00:30:38 Azure Machine Learning Studio Azure Cognitive Services \n",
"08:21:27 NaN NaN \n",
"00:14:20 NaN NaN \n",
"\n",
" Q28_B_Part_6 \\\n",
"time \n",
"00:30:38 Google Cloud AI Platform / Google Cloud ML En... \n",
"08:21:27 NaN \n",
"00:14:20 NaN \n",
"\n",
" Q28_B_Part_7 Q28_B_Part_8 \\\n",
"time \n",
"00:30:38 Google Cloud Video AI Google Cloud Natural Language \n",
"08:21:27 NaN NaN \n",
"00:14:20 NaN NaN \n",
"\n",
" Q28_B_Part_9 Q28_B_Part_10 Q28_B_OTHER Q29_B_Part_1 \\\n",
"time \n",
"00:30:38 Google Cloud Vision AI NaN NaN NaN \n",
"08:21:27 NaN NaN NaN NaN \n",
"00:14:20 NaN NaN NaN MySQL \n",
"\n",
" Q29_B_Part_2 Q29_B_Part_3 Q29_B_Part_4 Q29_B_Part_5 Q29_B_Part_6 \\\n",
"time \n",
"00:30:38 NaN NaN NaN MongoDB NaN \n",
"08:21:27 NaN NaN NaN NaN NaN \n",
"00:14:20 NaN NaN NaN NaN NaN \n",
"\n",
" Q29_B_Part_7 Q29_B_Part_8 Q29_B_Part_9 Q29_B_Part_10 \\\n",
"time \n",
"00:30:38 NaN Microsoft SQL Server NaN NaN \n",
"08:21:27 NaN NaN NaN NaN \n",
"00:14:20 NaN Microsoft SQL Server NaN NaN \n",
"\n",
" Q29_B_Part_11 Q29_B_Part_12 Q29_B_Part_13 Q29_B_Part_14 \\\n",
"time \n",
"00:30:38 NaN NaN NaN Google Cloud BigQuery \n",
"08:21:27 NaN NaN NaN NaN \n",
"00:14:20 NaN NaN NaN NaN \n",
"\n",
" Q29_B_Part_15 Q29_B_Part_16 Q29_B_Part_17 \\\n",
"time \n",
"00:30:38 Google Cloud SQL Google Cloud Firestore NaN \n",
"08:21:27 NaN NaN NaN \n",
"00:14:20 NaN NaN NaN \n",
"\n",
" Q29_B_OTHER Q31_B_Part_1 Q31_B_Part_2 \\\n",
"time \n",
"00:30:38 NaN Microsoft Power BI Amazon QuickSight \n",
"08:21:27 NaN NaN NaN \n",
"00:14:20 NaN NaN NaN \n",
"\n",
" Q31_B_Part_3 Q31_B_Part_4 Q31_B_Part_5 Q31_B_Part_6 \\\n",
"time \n",
"00:30:38 Google Data Studio NaN Tableau NaN \n",
"08:21:27 NaN NaN NaN NaN \n",
"00:14:20 NaN NaN NaN NaN \n",
"\n",
" Q31_B_Part_7 Q31_B_Part_8 Q31_B_Part_9 Q31_B_Part_10 Q31_B_Part_11 \\\n",
"time \n",
"00:30:38 NaN NaN NaN NaN NaN \n",
"08:21:27 NaN NaN NaN NaN NaN \n",
"00:14:20 NaN NaN NaN NaN NaN \n",
"\n",
" Q31_B_Part_12 Q31_B_Part_13 Q31_B_Part_14 Q31_B_OTHER \\\n",
"time \n",
"00:30:38 NaN SAP Analytics Cloud NaN NaN \n",
"08:21:27 NaN NaN NaN NaN \n",
"00:14:20 NaN NaN None NaN \n",
"\n",
" Q33_B_Part_1 Q33_B_Part_2 \\\n",
"time \n",
"00:30:38 Automated data augmentation (e.g. imgaug, albu... NaN \n",
"08:21:27 NaN NaN \n",
"00:14:20 NaN NaN \n",
"\n",
" Q33_B_Part_3 Q33_B_Part_4 \\\n",
"time \n",
"00:30:38 NaN NaN \n",
"08:21:27 NaN NaN \n",
"00:14:20 NaN NaN \n",
"\n",
" Q33_B_Part_5 \\\n",
"time \n",
"00:30:38 Automated hyperparameter tuning (e.g. hyperopt... \n",
"08:21:27 NaN \n",
"00:14:20 NaN \n",
"\n",
" Q33_B_Part_6 Q33_B_Part_7 \\\n",
"time \n",
"00:30:38 Automation of full ML pipelines (e.g. Google C... NaN \n",
"08:21:27 NaN NaN \n",
"00:14:20 NaN None \n",
"\n",
" Q33_B_OTHER Q34_B_Part_1 Q34_B_Part_2 Q34_B_Part_3 \\\n",
"time \n",
"00:30:38 NaN Google Cloud AutoML NaN Databricks AutoML \n",
"08:21:27 NaN NaN NaN NaN \n",
"00:14:20 NaN NaN NaN NaN \n",
"\n",
" Q34_B_Part_4 Q34_B_Part_5 Q34_B_Part_6 Q34_B_Part_7 \\\n",
"time \n",
"00:30:38 NaN NaN Auto-Keras Auto-Sklearn \n",
"08:21:27 NaN NaN NaN NaN \n",
"00:14:20 NaN NaN NaN NaN \n",
"\n",
" Q34_B_Part_8 Q34_B_Part_9 Q34_B_Part_10 Q34_B_Part_11 Q34_B_OTHER \\\n",
"time \n",
"00:30:38 NaN NaN NaN NaN NaN \n",
"08:21:27 NaN NaN NaN NaN NaN \n",
"00:14:20 NaN NaN NaN NaN NaN \n",
"\n",
" Q35_B_Part_1 Q35_B_Part_2 Q35_B_Part_3 Q35_B_Part_4 Q35_B_Part_5 \\\n",
"time \n",
"00:30:38 NaN NaN NaN NaN TensorBoard \n",
"08:21:27 NaN NaN NaN NaN NaN \n",
"00:14:20 NaN NaN NaN NaN NaN \n",
"\n",
" Q35_B_Part_6 Q35_B_Part_7 Q35_B_Part_8 Q35_B_Part_9 Q35_B_Part_10 \\\n",
"time \n",
"00:30:38 NaN NaN NaN NaN NaN \n",
"08:21:27 NaN NaN NaN NaN NaN \n",
"00:14:20 NaN NaN NaN NaN None \n",
"\n",
" Q35_B_OTHER Year \n",
"time \n",
"00:30:38 NaN 2020 \n",
"08:21:27 NaN 2020 \n",
"00:14:20 NaN 2020 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Kaggle=pd.read_csv(\"../input/kaggle-survey-2020/kaggle_survey_2020_responses.csv\")\n",
"Kaggle.drop([0],axis=0,inplace=True)\n",
"Kaggle['time'] = Kaggle['Time from Start to Finish (seconds)'].astype(int)\n",
"Kaggle.drop(\"Time from Start to Finish (seconds)\",axis=1,inplace=True)\n",
"Kaggle['time'] = pd.to_datetime(Kaggle['time'], unit='s').dt.time\n",
"first_col=Kaggle.pop('time')\n",
"Kaggle.insert(0, 'time', first_col)\n",
"Kaggle.set_index('time',inplace=True)\n",
"Kaggle[\"Year\"]=\"2020\"\n",
"Kaggle.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.065595,
"end_time": "2020-12-05T10:04:56.620416",
"exception": false,
"start_time": "2020-12-05T10:04:56.554821",
"status": "completed"
},
"tags": []
},
"source": [
"### 2019 DataSet"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.067164,
"end_time": "2020-12-05T10:04:56.752327",
"exception": false,
"start_time": "2020-12-05T10:04:56.685163",
"status": "completed"
},
"tags": []
},
"source": [
"Exploring and cleaning the 2019 Data Set and further changes will be made later in the project. This data set is quite different from 2020 and I have to open a CSV file to find similar columns to compare. In the end, when I recognize similar columns it becomes easy for me to handle data."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_kg_hide-input": true,
"execution": {
"iopub.execute_input": "2020-12-05T10:04:56.899441Z",
"iopub.status.busy": "2020-12-05T10:04:56.898777Z",
"iopub.status.idle": "2020-12-05T10:04:58.198910Z",
"shell.execute_reply": "2020-12-05T10:04:58.199606Z"
},
"papermill": {
"duration": 1.379362,
"end_time": "2020-12-05T10:04:58.199763",
"exception": false,
"start_time": "2020-12-05T10:04:56.820401",
"status": "completed"
},
"tags": []
},
"outputs": [
{
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" <th>Q17_Part_3</th>\n",
" <th>Q17_Part_4</th>\n",
" <th>Q17_Part_5</th>\n",
" <th>Q17_Part_6</th>\n",
" <th>Q17_Part_7</th>\n",
" <th>Q17_Part_8</th>\n",
" <th>Q17_Part_9</th>\n",
" <th>Q17_Part_10</th>\n",
" <th>Q17_Part_11</th>\n",
" <th>Q17_Part_12</th>\n",
" <th>Q17_OTHER_TEXT</th>\n",
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" <th>Q18_Part_2</th>\n",
" <th>Q18_Part_3</th>\n",
" <th>Q18_Part_4</th>\n",
" <th>Q18_Part_5</th>\n",
" <th>Q18_Part_6</th>\n",
" <th>Q18_Part_7</th>\n",
" <th>Q18_Part_8</th>\n",
" <th>Q18_Part_9</th>\n",
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" <th>Q19</th>\n",
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" <th>Q22</th>\n",
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" <th>Q24_Part_1</th>\n",
" <th>Q24_Part_2</th>\n",
" <th>Q24_Part_3</th>\n",
" <th>Q24_Part_4</th>\n",
" <th>Q24_Part_5</th>\n",
" <th>Q24_Part_6</th>\n",
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" <th>Q27_Part_3</th>\n",
" <th>Q27_Part_4</th>\n",
" <th>Q27_Part_5</th>\n",
" <th>Q27_Part_6</th>\n",
" <th>Q27_OTHER_TEXT</th>\n",
" <th>Q28_Part_1</th>\n",
" <th>Q28_Part_2</th>\n",
" <th>Q28_Part_3</th>\n",
" <th>Q28_Part_4</th>\n",
" <th>Q28_Part_5</th>\n",
" <th>Q28_Part_6</th>\n",
" <th>Q28_Part_7</th>\n",
" <th>Q28_Part_8</th>\n",
" <th>Q28_Part_9</th>\n",
" <th>Q28_Part_10</th>\n",
" <th>Q28_Part_11</th>\n",
" <th>Q28_Part_12</th>\n",
" <th>Q28_OTHER_TEXT</th>\n",
" <th>Q29_Part_1</th>\n",
" <th>Q29_Part_2</th>\n",
" <th>Q29_Part_3</th>\n",
" <th>Q29_Part_4</th>\n",
" <th>Q29_Part_5</th>\n",
" <th>Q29_Part_6</th>\n",
" <th>Q29_Part_7</th>\n",
" <th>Q29_Part_8</th>\n",
" <th>Q29_Part_9</th>\n",
" <th>Q29_Part_10</th>\n",
" <th>Q29_Part_11</th>\n",
" <th>Q29_Part_12</th>\n",
" <th>Q29_OTHER_TEXT</th>\n",
" <th>Q30_Part_1</th>\n",
" <th>Q30_Part_2</th>\n",
" <th>Q30_Part_3</th>\n",
" <th>Q30_Part_4</th>\n",
" <th>Q30_Part_5</th>\n",
" <th>Q30_Part_6</th>\n",
" <th>Q30_Part_7</th>\n",
" <th>Q30_Part_8</th>\n",
" <th>Q30_Part_9</th>\n",
" <th>Q30_Part_10</th>\n",
" <th>Q30_Part_11</th>\n",
" <th>Q30_Part_12</th>\n",
" <th>Q30_OTHER_TEXT</th>\n",
" <th>Q31_Part_1</th>\n",
" <th>Q31_Part_2</th>\n",
" <th>Q31_Part_3</th>\n",
" <th>Q31_Part_4</th>\n",
" <th>Q31_Part_5</th>\n",
" <th>Q31_Part_6</th>\n",
" <th>Q31_Part_7</th>\n",
" <th>Q31_Part_8</th>\n",
" <th>Q31_Part_9</th>\n",
" <th>Q31_Part_10</th>\n",
" <th>Q31_Part_11</th>\n",
" <th>Q31_Part_12</th>\n",
" <th>Q31_OTHER_TEXT</th>\n",
" <th>Q32_Part_1</th>\n",
" <th>Q32_Part_2</th>\n",
" <th>Q32_Part_3</th>\n",
" <th>Q32_Part_4</th>\n",
" <th>Q32_Part_5</th>\n",
" <th>Q32_Part_6</th>\n",
" <th>Q32_Part_7</th>\n",
" <th>Q32_Part_8</th>\n",
" <th>Q32_Part_9</th>\n",
" <th>Q32_Part_10</th>\n",
" <th>Q32_Part_11</th>\n",
" <th>Q32_Part_12</th>\n",
" <th>Q32_OTHER_TEXT</th>\n",
" <th>Q33_Part_1</th>\n",
" <th>Q33_Part_2</th>\n",
" <th>Q33_Part_3</th>\n",
" <th>Q33_Part_4</th>\n",
" <th>Q33_Part_5</th>\n",
" <th>Q33_Part_6</th>\n",
" <th>Q33_Part_7</th>\n",
" <th>Q33_Part_8</th>\n",
" <th>Q33_Part_9</th>\n",
" <th>Q33_Part_10</th>\n",
" <th>Q33_Part_11</th>\n",
" <th>Q33_Part_12</th>\n",
" <th>Q33_OTHER_TEXT</th>\n",
" <th>Q34_Part_1</th>\n",
" <th>Q34_Part_2</th>\n",
" <th>Q34_Part_3</th>\n",
" <th>Q34_Part_4</th>\n",
" <th>Q34_Part_5</th>\n",
" <th>Q34_Part_6</th>\n",
" <th>Q34_Part_7</th>\n",
" <th>Q34_Part_8</th>\n",
" <th>Q34_Part_9</th>\n",
" <th>Q34_Part_10</th>\n",
" <th>Q34_Part_11</th>\n",
" <th>Q34_Part_12</th>\n",
" <th>Q34_OTHER_TEXT</th>\n",
" <th>Year</th>\n",
" </tr>\n",
" <tr>\n",
" <th>time</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>00:08:30</th>\n",
" <td>22-24</td>\n",
" <td>Male</td>\n",
" <td>-1</td>\n",
" <td>France</td>\n",
" <td>Master’s degree</td>\n",
" <td>Software Engineer</td>\n",
" <td>-1</td>\n",
" <td>1000-9,999 employees</td>\n",
" <td>0</td>\n",
" <td>I do not know</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>30,000-39,999</td>\n",
" <td>$0 (USD)</td>\n",
" <td>Twitter (data science influencers)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Kaggle (forums, blog, social media, etc)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Blogs (Towards Data Science, Medium, Analytics...</td>\n",
" <td>Journal Publications (traditional publications...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>Coursera</td>\n",
" <td>NaN</td>\n",
" <td>DataCamp</td>\n",
" <td>NaN</td>\n",
" <td>Kaggle Courses (i.e. Kaggle Learn)</td>\n",
" <td>NaN</td>\n",
" <td>Udemy</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>Basic statistical software (Microsoft Excel, G...</td>\n",
" <td>0</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>1-2 years</td>\n",
" <td>Jupyter (JupyterLab, Jupyter Notebooks, etc)</td>\n",
" <td>RStudio</td>\n",
" <td>PyCharm</td>\n",
" <td>NaN</td>\n",
" <td>MATLAB</td>\n",
" <td>NaN</td>\n",
" <td>Spyder</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>Python</td>\n",
" <td>R</td>\n",
" <td>SQL</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Java</td>\n",
" <td>Javascript</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>MATLAB</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>Python</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>Matplotlib</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>CPUs</td>\n",
" <td>GPUs</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>Never</td>\n",
" <td>1-2 years</td>\n",
" <td>Linear or Logistic Regression</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>2019</td>\n",
" </tr>\n",
" <tr>\n",
" <th>00:07:03</th>\n",
" <td>40-44</td>\n",
" <td>Male</td>\n",
" <td>-1</td>\n",
" <td>India</td>\n",
" <td>Professional degree</td>\n",
" <td>Software Engineer</td>\n",
" <td>-1</td>\n",
" <td>&gt; 10,000 employees</td>\n",
" <td>20+</td>\n",
" <td>We have well established ML methods (i.e., mod...</td>\n",
" <td>Analyze and understand data to influence produ...</td>\n",
" <td>Build and/or run the data infrastructure that ...</td>\n",
" <td>Build prototypes to explore applying machine l...</td>\n",
" <td>Build and/or run a machine learning service th...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>5,000-7,499</td>\n",
" <td>&gt; $100,000 ($USD)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Kaggle (forums, blog, social media, etc)</td>\n",
" <td>NaN</td>\n",
" <td>YouTube (Cloud AI Adventures, Siraj Raval, etc)</td>\n",
" <td>Podcasts (Chai Time Data Science, Linear Digre...</td>\n",
" <td>Blogs (Towards Data Science, Medium, Analytics...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>Coursera</td>\n",
" <td>NaN</td>\n",
" <td>DataCamp</td>\n",
" <td>NaN</td>\n",
" <td>Kaggle Courses (i.e. Kaggle Learn)</td>\n",
" <td>NaN</td>\n",
" <td>Udemy</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>Cloud-based data software &amp; APIs (AWS, GCP, Az...</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>0</td>\n",
" <td>-1</td>\n",
" <td>I have never written code</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>2019</td>\n",
" </tr>\n",
" <tr>\n",
" <th>00:01:23</th>\n",
" <td>55-59</td>\n",
" <td>Female</td>\n",
" <td>-1</td>\n",
" <td>Germany</td>\n",
" <td>Professional degree</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>2019</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Q1 Q2 Q2_OTHER_TEXT Q3 Q4 \\\n",
"time \n",
"00:08:30 22-24 Male -1 France Master’s degree \n",
"00:07:03 40-44 Male -1 India Professional degree \n",
"00:01:23 55-59 Female -1 Germany Professional degree \n",
"\n",
" Q5 Q5_OTHER_TEXT Q6 Q7 \\\n",
"time \n",
"00:08:30 Software Engineer -1 1000-9,999 employees 0 \n",
"00:07:03 Software Engineer -1 > 10,000 employees 20+ \n",
"00:01:23 NaN -1 NaN NaN \n",
"\n",
" Q8 \\\n",
"time \n",
"00:08:30 I do not know \n",
"00:07:03 We have well established ML methods (i.e., mod... \n",
"00:01:23 NaN \n",
"\n",
" Q9_Part_1 \\\n",
"time \n",
"00:08:30 NaN \n",
"00:07:03 Analyze and understand data to influence produ... \n",
"00:01:23 NaN \n",
"\n",
" Q9_Part_2 \\\n",
"time \n",
"00:08:30 NaN \n",
"00:07:03 Build and/or run the data infrastructure that ... \n",
"00:01:23 NaN \n",
"\n",
" Q9_Part_3 \\\n",
"time \n",
"00:08:30 NaN \n",
"00:07:03 Build prototypes to explore applying machine l... \n",
"00:01:23 NaN \n",
"\n",
" Q9_Part_4 Q9_Part_5 \\\n",
"time \n",
"00:08:30 NaN NaN \n",
"00:07:03 Build and/or run a machine learning service th... NaN \n",
"00:01:23 NaN NaN \n",
"\n",
" Q9_Part_6 Q9_Part_7 Q9_Part_8 Q9_OTHER_TEXT Q10 \\\n",
"time \n",
"00:08:30 NaN NaN NaN -1 30,000-39,999 \n",
"00:07:03 NaN NaN NaN -1 5,000-7,499 \n",
"00:01:23 NaN NaN NaN -1 NaN \n",
"\n",
" Q11 Q12_Part_1 Q12_Part_2 \\\n",
"time \n",
"00:08:30 $0 (USD) Twitter (data science influencers) NaN \n",
"00:07:03 > $100,000 ($USD) NaN NaN \n",
"00:01:23 NaN NaN NaN \n",
"\n",
" Q12_Part_3 Q12_Part_4 Q12_Part_5 \\\n",
"time \n",
"00:08:30 NaN Kaggle (forums, blog, social media, etc) NaN \n",
"00:07:03 NaN Kaggle (forums, blog, social media, etc) NaN \n",
"00:01:23 NaN NaN NaN \n",
"\n",
" Q12_Part_6 \\\n",
"time \n",
"00:08:30 NaN \n",
"00:07:03 YouTube (Cloud AI Adventures, Siraj Raval, etc) \n",
"00:01:23 NaN \n",
"\n",
" Q12_Part_7 \\\n",
"time \n",
"00:08:30 NaN \n",
"00:07:03 Podcasts (Chai Time Data Science, Linear Digre... \n",
"00:01:23 NaN \n",
"\n",
" Q12_Part_8 \\\n",
"time \n",
"00:08:30 Blogs (Towards Data Science, Medium, Analytics... \n",
"00:07:03 Blogs (Towards Data Science, Medium, Analytics... \n",
"00:01:23 NaN \n",
"\n",
" Q12_Part_9 Q12_Part_10 \\\n",
"time \n",
"00:08:30 Journal Publications (traditional publications... NaN \n",
"00:07:03 NaN NaN \n",
"00:01:23 NaN NaN \n",
"\n",
" Q12_Part_11 Q12_Part_12 Q12_OTHER_TEXT Q13_Part_1 Q13_Part_2 \\\n",
"time \n",
"00:08:30 NaN NaN -1 NaN Coursera \n",
"00:07:03 NaN NaN -1 NaN Coursera \n",
"00:01:23 NaN NaN -1 NaN NaN \n",
"\n",
" Q13_Part_3 Q13_Part_4 Q13_Part_5 Q13_Part_6 \\\n",
"time \n",
"00:08:30 NaN DataCamp NaN Kaggle Courses (i.e. Kaggle Learn) \n",
"00:07:03 NaN DataCamp NaN Kaggle Courses (i.e. Kaggle Learn) \n",
"00:01:23 NaN NaN NaN NaN \n",
"\n",
" Q13_Part_7 Q13_Part_8 Q13_Part_9 Q13_Part_10 Q13_Part_11 Q13_Part_12 \\\n",
"time \n",
"00:08:30 NaN Udemy NaN NaN NaN NaN \n",
"00:07:03 NaN Udemy NaN NaN NaN NaN \n",
"00:01:23 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q13_OTHER_TEXT Q14 \\\n",
"time \n",
"00:08:30 -1 Basic statistical software (Microsoft Excel, G... \n",
"00:07:03 -1 Cloud-based data software & APIs (AWS, GCP, Az... \n",
"00:01:23 -1 NaN \n",
"\n",
" Q14_Part_1_TEXT Q14_Part_2_TEXT Q14_Part_3_TEXT Q14_Part_4_TEXT \\\n",
"time \n",
"00:08:30 0 -1 -1 -1 \n",
"00:07:03 -1 -1 -1 -1 \n",
"00:01:23 -1 -1 -1 -1 \n",
"\n",
" Q14_Part_5_TEXT Q14_OTHER_TEXT Q15 \\\n",
"time \n",
"00:08:30 -1 -1 1-2 years \n",
"00:07:03 0 -1 I have never written code \n",
"00:01:23 -1 -1 NaN \n",
"\n",
" Q16_Part_1 Q16_Part_2 Q16_Part_3 \\\n",
"time \n",
"00:08:30 Jupyter (JupyterLab, Jupyter Notebooks, etc) RStudio PyCharm \n",
"00:07:03 NaN NaN NaN \n",
"00:01:23 NaN NaN NaN \n",
"\n",
" Q16_Part_4 Q16_Part_5 Q16_Part_6 Q16_Part_7 Q16_Part_8 Q16_Part_9 \\\n",
"time \n",
"00:08:30 NaN MATLAB NaN Spyder NaN NaN \n",
"00:07:03 NaN NaN NaN NaN NaN NaN \n",
"00:01:23 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q16_Part_10 Q16_Part_11 Q16_Part_12 Q16_OTHER_TEXT Q17_Part_1 \\\n",
"time \n",
"00:08:30 NaN NaN NaN -1 NaN \n",
"00:07:03 NaN NaN NaN -1 NaN \n",
"00:01:23 NaN NaN NaN -1 NaN \n",
"\n",
" Q17_Part_2 Q17_Part_3 Q17_Part_4 Q17_Part_5 Q17_Part_6 Q17_Part_7 \\\n",
"time \n",
"00:08:30 NaN NaN NaN NaN NaN NaN \n",
"00:07:03 NaN NaN NaN NaN NaN NaN \n",
"00:01:23 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q17_Part_8 Q17_Part_9 Q17_Part_10 Q17_Part_11 Q17_Part_12 \\\n",
"time \n",
"00:08:30 NaN NaN NaN None NaN \n",
"00:07:03 NaN NaN NaN NaN NaN \n",
"00:01:23 NaN NaN NaN NaN NaN \n",
"\n",
" Q17_OTHER_TEXT Q18_Part_1 Q18_Part_2 Q18_Part_3 Q18_Part_4 \\\n",
"time \n",
"00:08:30 -1 Python R SQL NaN \n",
"00:07:03 -1 NaN NaN NaN NaN \n",
"00:01:23 -1 NaN NaN NaN NaN \n",
"\n",
" Q18_Part_5 Q18_Part_6 Q18_Part_7 Q18_Part_8 Q18_Part_9 Q18_Part_10 \\\n",
"time \n",
"00:08:30 NaN Java Javascript NaN NaN MATLAB \n",
"00:07:03 NaN NaN NaN NaN NaN NaN \n",
"00:01:23 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q18_Part_11 Q18_Part_12 Q18_OTHER_TEXT Q19 Q19_OTHER_TEXT \\\n",
"time \n",
"00:08:30 NaN NaN -1 Python -1 \n",
"00:07:03 NaN NaN -1 NaN -1 \n",
"00:01:23 NaN NaN -1 NaN -1 \n",
"\n",
" Q20_Part_1 Q20_Part_2 Q20_Part_3 Q20_Part_4 Q20_Part_5 Q20_Part_6 \\\n",
"time \n",
"00:08:30 NaN Matplotlib NaN NaN NaN NaN \n",
"00:07:03 NaN NaN NaN NaN NaN NaN \n",
"00:01:23 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q20_Part_7 Q20_Part_8 Q20_Part_9 Q20_Part_10 Q20_Part_11 Q20_Part_12 \\\n",
"time \n",
"00:08:30 NaN NaN NaN NaN NaN NaN \n",
"00:07:03 NaN NaN NaN NaN NaN NaN \n",
"00:01:23 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q20_OTHER_TEXT Q21_Part_1 Q21_Part_2 Q21_Part_3 Q21_Part_4 \\\n",
"time \n",
"00:08:30 -1 CPUs GPUs NaN NaN \n",
"00:07:03 -1 NaN NaN NaN NaN \n",
"00:01:23 -1 NaN NaN NaN NaN \n",
"\n",
" Q21_Part_5 Q21_OTHER_TEXT Q22 Q23 \\\n",
"time \n",
"00:08:30 NaN -1 Never 1-2 years \n",
"00:07:03 NaN -1 NaN NaN \n",
"00:01:23 NaN -1 NaN NaN \n",
"\n",
" Q24_Part_1 Q24_Part_2 Q24_Part_3 Q24_Part_4 \\\n",
"time \n",
"00:08:30 Linear or Logistic Regression NaN NaN NaN \n",
"00:07:03 NaN NaN NaN NaN \n",
"00:01:23 NaN NaN NaN NaN \n",
"\n",
" Q24_Part_5 Q24_Part_6 Q24_Part_7 Q24_Part_8 Q24_Part_9 Q24_Part_10 \\\n",
"time \n",
"00:08:30 NaN NaN NaN NaN NaN NaN \n",
"00:07:03 NaN NaN NaN NaN NaN NaN \n",
"00:01:23 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q24_Part_11 Q24_Part_12 Q24_OTHER_TEXT Q25_Part_1 Q25_Part_2 \\\n",
"time \n",
"00:08:30 NaN NaN -1 NaN NaN \n",
"00:07:03 NaN NaN -1 NaN NaN \n",
"00:01:23 NaN NaN -1 NaN NaN \n",
"\n",
" Q25_Part_3 Q25_Part_4 Q25_Part_5 Q25_Part_6 Q25_Part_7 Q25_Part_8 \\\n",
"time \n",
"00:08:30 NaN NaN NaN NaN None NaN \n",
"00:07:03 NaN NaN NaN NaN NaN NaN \n",
"00:01:23 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q25_OTHER_TEXT Q26_Part_1 Q26_Part_2 Q26_Part_3 Q26_Part_4 \\\n",
"time \n",
"00:08:30 -1 NaN NaN NaN NaN \n",
"00:07:03 -1 NaN NaN NaN NaN \n",
"00:01:23 -1 NaN NaN NaN NaN \n",
"\n",
" Q26_Part_5 Q26_Part_6 Q26_Part_7 Q26_OTHER_TEXT Q27_Part_1 \\\n",
"time \n",
"00:08:30 NaN NaN NaN -1 NaN \n",
"00:07:03 NaN NaN NaN -1 NaN \n",
"00:01:23 NaN NaN NaN -1 NaN \n",
"\n",
" Q27_Part_2 Q27_Part_3 Q27_Part_4 Q27_Part_5 Q27_Part_6 \\\n",
"time \n",
"00:08:30 NaN NaN NaN NaN NaN \n",
"00:07:03 NaN NaN NaN NaN NaN \n",
"00:01:23 NaN NaN NaN NaN NaN \n",
"\n",
" Q27_OTHER_TEXT Q28_Part_1 Q28_Part_2 Q28_Part_3 Q28_Part_4 \\\n",
"time \n",
"00:08:30 -1 NaN NaN NaN NaN \n",
"00:07:03 -1 NaN NaN NaN NaN \n",
"00:01:23 -1 NaN NaN NaN NaN \n",
"\n",
" Q28_Part_5 Q28_Part_6 Q28_Part_7 Q28_Part_8 Q28_Part_9 Q28_Part_10 \\\n",
"time \n",
"00:08:30 NaN NaN NaN NaN NaN NaN \n",
"00:07:03 NaN NaN NaN NaN NaN NaN \n",
"00:01:23 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q28_Part_11 Q28_Part_12 Q28_OTHER_TEXT Q29_Part_1 Q29_Part_2 \\\n",
"time \n",
"00:08:30 None NaN -1 NaN NaN \n",
"00:07:03 NaN NaN -1 NaN NaN \n",
"00:01:23 NaN NaN -1 NaN NaN \n",
"\n",
" Q29_Part_3 Q29_Part_4 Q29_Part_5 Q29_Part_6 Q29_Part_7 Q29_Part_8 \\\n",
"time \n",
"00:08:30 NaN NaN NaN NaN NaN NaN \n",
"00:07:03 NaN NaN NaN NaN NaN NaN \n",
"00:01:23 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q29_Part_9 Q29_Part_10 Q29_Part_11 Q29_Part_12 Q29_OTHER_TEXT \\\n",
"time \n",
"00:08:30 NaN NaN NaN NaN -1 \n",
"00:07:03 NaN NaN NaN NaN -1 \n",
"00:01:23 NaN NaN NaN NaN -1 \n",
"\n",
" Q30_Part_1 Q30_Part_2 Q30_Part_3 Q30_Part_4 Q30_Part_5 Q30_Part_6 \\\n",
"time \n",
"00:08:30 NaN NaN NaN NaN NaN NaN \n",
"00:07:03 NaN NaN NaN NaN NaN NaN \n",
"00:01:23 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q30_Part_7 Q30_Part_8 Q30_Part_9 Q30_Part_10 Q30_Part_11 Q30_Part_12 \\\n",
"time \n",
"00:08:30 NaN NaN NaN NaN NaN NaN \n",
"00:07:03 NaN NaN NaN NaN NaN NaN \n",
"00:01:23 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q30_OTHER_TEXT Q31_Part_1 Q31_Part_2 Q31_Part_3 Q31_Part_4 \\\n",
"time \n",
"00:08:30 -1 NaN NaN NaN NaN \n",
"00:07:03 -1 NaN NaN NaN NaN \n",
"00:01:23 -1 NaN NaN NaN NaN \n",
"\n",
" Q31_Part_5 Q31_Part_6 Q31_Part_7 Q31_Part_8 Q31_Part_9 Q31_Part_10 \\\n",
"time \n",
"00:08:30 NaN NaN NaN NaN NaN NaN \n",
"00:07:03 NaN NaN NaN NaN NaN NaN \n",
"00:01:23 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q31_Part_11 Q31_Part_12 Q31_OTHER_TEXT Q32_Part_1 Q32_Part_2 \\\n",
"time \n",
"00:08:30 NaN NaN -1 NaN NaN \n",
"00:07:03 NaN NaN -1 NaN NaN \n",
"00:01:23 NaN NaN -1 NaN NaN \n",
"\n",
" Q32_Part_3 Q32_Part_4 Q32_Part_5 Q32_Part_6 Q32_Part_7 Q32_Part_8 \\\n",
"time \n",
"00:08:30 NaN NaN NaN NaN NaN NaN \n",
"00:07:03 NaN NaN NaN NaN NaN NaN \n",
"00:01:23 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q32_Part_9 Q32_Part_10 Q32_Part_11 Q32_Part_12 Q32_OTHER_TEXT \\\n",
"time \n",
"00:08:30 NaN NaN NaN NaN -1 \n",
"00:07:03 NaN NaN NaN NaN -1 \n",
"00:01:23 NaN NaN NaN NaN -1 \n",
"\n",
" Q33_Part_1 Q33_Part_2 Q33_Part_3 Q33_Part_4 Q33_Part_5 Q33_Part_6 \\\n",
"time \n",
"00:08:30 NaN NaN NaN NaN NaN NaN \n",
"00:07:03 NaN NaN NaN NaN NaN NaN \n",
"00:01:23 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q33_Part_7 Q33_Part_8 Q33_Part_9 Q33_Part_10 Q33_Part_11 Q33_Part_12 \\\n",
"time \n",
"00:08:30 NaN NaN NaN NaN NaN NaN \n",
"00:07:03 NaN NaN NaN NaN NaN NaN \n",
"00:01:23 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q33_OTHER_TEXT Q34_Part_1 Q34_Part_2 Q34_Part_3 Q34_Part_4 \\\n",
"time \n",
"00:08:30 -1 NaN NaN NaN NaN \n",
"00:07:03 -1 NaN NaN NaN NaN \n",
"00:01:23 -1 NaN NaN NaN NaN \n",
"\n",
" Q34_Part_5 Q34_Part_6 Q34_Part_7 Q34_Part_8 Q34_Part_9 Q34_Part_10 \\\n",
"time \n",
"00:08:30 NaN NaN NaN NaN NaN NaN \n",
"00:07:03 NaN NaN NaN NaN NaN NaN \n",
"00:01:23 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q34_Part_11 Q34_Part_12 Q34_OTHER_TEXT Year \n",
"time \n",
"00:08:30 NaN NaN -1 2019 \n",
"00:07:03 NaN NaN -1 2019 \n",
"00:01:23 NaN NaN -1 2019 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Kaggle19=pd.read_csv(\"../input/kaggle-survey-2019/multiple_choice_responses.csv\")\n",
"Kaggle19.drop([0],axis=0,inplace=True)\n",
"Kaggle19['time'] = Kaggle19['Time from Start to Finish (seconds)'].astype(int)\n",
"Kaggle19.drop(\"Time from Start to Finish (seconds)\",axis=1,inplace=True)\n",
"Kaggle19['time'] = pd.to_datetime(Kaggle19['time'], unit='s').dt.time\n",
"first_col=Kaggle19.pop('time')\n",
"Kaggle19.insert(0, 'time', first_col)\n",
"Kaggle19.set_index('time',inplace=True)\n",
"Kaggle19[\"Year\"]=\"2019\"\n",
"Kaggle19.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.104132,
"end_time": "2020-12-05T10:04:58.408425",
"exception": false,
"start_time": "2020-12-05T10:04:58.304293",
"status": "completed"
},
"tags": []
},
"source": [
"### 2018 Dataset"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.102322,
"end_time": "2020-12-05T10:04:58.613608",
"exception": false,
"start_time": "2020-12-05T10:04:58.511286",
"status": "completed"
},
"tags": []
},
"source": [
"Exploring and cleaning the 2018 Data Set and further changes will be made later in the project. This data set is quite similar from 2019 and it took me no time to find similar columns such as pay gap and demography columns."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_kg_hide-input": true,
"execution": {
"iopub.execute_input": "2020-12-05T10:04:58.834219Z",
"iopub.status.busy": "2020-12-05T10:04:58.833378Z",
"iopub.status.idle": "2020-12-05T10:05:01.221630Z",
"shell.execute_reply": "2020-12-05T10:05:01.222328Z"
},
"papermill": {
"duration": 2.506165,
"end_time": "2020-12-05T10:05:01.222486",
"exception": false,
"start_time": "2020-12-05T10:04:58.716321",
"status": "completed"
},
"tags": []
},
"outputs": [
{
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" <th>Q13_Part_12</th>\n",
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" <th>Q14_Part_9</th>\n",
" <th>Q14_Part_10</th>\n",
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" <th>Q14_OTHER_TEXT</th>\n",
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" <th>Q15_Part_2</th>\n",
" <th>Q15_Part_3</th>\n",
" <th>Q15_Part_4</th>\n",
" <th>Q15_Part_5</th>\n",
" <th>Q15_Part_6</th>\n",
" <th>Q15_Part_7</th>\n",
" <th>Q15_OTHER_TEXT</th>\n",
" <th>Q16_Part_1</th>\n",
" <th>Q16_Part_2</th>\n",
" <th>Q16_Part_3</th>\n",
" <th>Q16_Part_4</th>\n",
" <th>Q16_Part_5</th>\n",
" <th>Q16_Part_6</th>\n",
" <th>Q16_Part_7</th>\n",
" <th>Q16_Part_8</th>\n",
" <th>Q16_Part_9</th>\n",
" <th>Q16_Part_10</th>\n",
" <th>Q16_Part_11</th>\n",
" <th>Q16_Part_12</th>\n",
" <th>Q16_Part_13</th>\n",
" <th>Q16_Part_14</th>\n",
" <th>Q16_Part_15</th>\n",
" <th>Q16_Part_16</th>\n",
" <th>Q16_Part_17</th>\n",
" <th>Q16_Part_18</th>\n",
" <th>Q16_OTHER_TEXT</th>\n",
" <th>Q17</th>\n",
" <th>Q17_OTHER_TEXT</th>\n",
" <th>Q18</th>\n",
" <th>Q18_OTHER_TEXT</th>\n",
" <th>Q19_Part_1</th>\n",
" <th>Q19_Part_2</th>\n",
" <th>Q19_Part_3</th>\n",
" <th>Q19_Part_4</th>\n",
" <th>Q19_Part_5</th>\n",
" <th>Q19_Part_6</th>\n",
" <th>Q19_Part_7</th>\n",
" <th>Q19_Part_8</th>\n",
" <th>Q19_Part_9</th>\n",
" <th>Q19_Part_10</th>\n",
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" <th>Q19_Part_12</th>\n",
" <th>Q19_Part_13</th>\n",
" <th>Q19_Part_14</th>\n",
" <th>Q19_Part_15</th>\n",
" <th>Q19_Part_16</th>\n",
" <th>Q19_Part_17</th>\n",
" <th>Q19_Part_18</th>\n",
" <th>Q19_Part_19</th>\n",
" <th>Q19_OTHER_TEXT</th>\n",
" <th>Q20</th>\n",
" <th>Q20_OTHER_TEXT</th>\n",
" <th>Q21_Part_1</th>\n",
" <th>Q21_Part_2</th>\n",
" <th>Q21_Part_3</th>\n",
" <th>Q21_Part_4</th>\n",
" <th>Q21_Part_5</th>\n",
" <th>Q21_Part_6</th>\n",
" <th>Q21_Part_7</th>\n",
" <th>Q21_Part_8</th>\n",
" <th>Q21_Part_9</th>\n",
" <th>Q21_Part_10</th>\n",
" <th>Q21_Part_11</th>\n",
" <th>Q21_Part_12</th>\n",
" <th>Q21_Part_13</th>\n",
" <th>Q21_OTHER_TEXT</th>\n",
" <th>Q22</th>\n",
" <th>Q22_OTHER_TEXT</th>\n",
" <th>Q23</th>\n",
" <th>Q24</th>\n",
" <th>Q25</th>\n",
" <th>Q26</th>\n",
" <th>Q27_Part_1</th>\n",
" <th>Q27_Part_2</th>\n",
" <th>Q27_Part_3</th>\n",
" <th>Q27_Part_4</th>\n",
" <th>Q27_Part_5</th>\n",
" <th>Q27_Part_6</th>\n",
" <th>Q27_Part_7</th>\n",
" <th>Q27_Part_8</th>\n",
" <th>Q27_Part_9</th>\n",
" <th>Q27_Part_10</th>\n",
" <th>Q27_Part_11</th>\n",
" <th>Q27_Part_12</th>\n",
" <th>Q27_Part_13</th>\n",
" <th>Q27_Part_14</th>\n",
" <th>Q27_Part_15</th>\n",
" <th>Q27_Part_16</th>\n",
" <th>Q27_Part_17</th>\n",
" <th>Q27_Part_18</th>\n",
" <th>Q27_Part_19</th>\n",
" <th>Q27_Part_20</th>\n",
" <th>Q27_OTHER_TEXT</th>\n",
" <th>Q28_Part_1</th>\n",
" <th>Q28_Part_2</th>\n",
" <th>Q28_Part_3</th>\n",
" <th>Q28_Part_4</th>\n",
" <th>Q28_Part_5</th>\n",
" <th>Q28_Part_6</th>\n",
" <th>Q28_Part_7</th>\n",
" <th>Q28_Part_8</th>\n",
" <th>Q28_Part_9</th>\n",
" <th>Q28_Part_10</th>\n",
" <th>Q28_Part_11</th>\n",
" <th>Q28_Part_12</th>\n",
" <th>Q28_Part_13</th>\n",
" <th>Q28_Part_14</th>\n",
" <th>Q28_Part_15</th>\n",
" <th>Q28_Part_16</th>\n",
" <th>Q28_Part_17</th>\n",
" <th>Q28_Part_18</th>\n",
" <th>Q28_Part_19</th>\n",
" <th>Q28_Part_20</th>\n",
" <th>Q28_Part_21</th>\n",
" <th>Q28_Part_22</th>\n",
" <th>Q28_Part_23</th>\n",
" <th>Q28_Part_24</th>\n",
" <th>Q28_Part_25</th>\n",
" <th>Q28_Part_26</th>\n",
" <th>Q28_Part_27</th>\n",
" <th>Q28_Part_28</th>\n",
" <th>Q28_Part_29</th>\n",
" <th>Q28_Part_30</th>\n",
" <th>Q28_Part_31</th>\n",
" <th>Q28_Part_32</th>\n",
" <th>Q28_Part_33</th>\n",
" <th>Q28_Part_34</th>\n",
" <th>Q28_Part_35</th>\n",
" <th>Q28_Part_36</th>\n",
" <th>Q28_Part_37</th>\n",
" <th>Q28_Part_38</th>\n",
" <th>Q28_Part_39</th>\n",
" <th>Q28_Part_40</th>\n",
" <th>Q28_Part_41</th>\n",
" <th>Q28_Part_42</th>\n",
" <th>Q28_Part_43</th>\n",
" <th>Q28_OTHER_TEXT</th>\n",
" <th>Q29_Part_1</th>\n",
" <th>Q29_Part_2</th>\n",
" <th>Q29_Part_3</th>\n",
" <th>Q29_Part_4</th>\n",
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" <th>Q29_Part_15</th>\n",
" <th>Q29_Part_16</th>\n",
" <th>Q29_Part_17</th>\n",
" <th>Q29_Part_18</th>\n",
" <th>Q29_Part_19</th>\n",
" <th>Q29_Part_20</th>\n",
" <th>Q29_Part_21</th>\n",
" <th>Q29_Part_22</th>\n",
" <th>Q29_Part_23</th>\n",
" <th>Q29_Part_24</th>\n",
" <th>Q29_Part_25</th>\n",
" <th>Q29_Part_26</th>\n",
" <th>Q29_Part_27</th>\n",
" <th>Q29_Part_28</th>\n",
" <th>Q29_OTHER_TEXT</th>\n",
" <th>Q30_Part_1</th>\n",
" <th>Q30_Part_2</th>\n",
" <th>Q30_Part_3</th>\n",
" <th>Q30_Part_4</th>\n",
" <th>Q30_Part_5</th>\n",
" <th>Q30_Part_6</th>\n",
" <th>Q30_Part_7</th>\n",
" <th>Q30_Part_8</th>\n",
" <th>Q30_Part_9</th>\n",
" <th>Q30_Part_10</th>\n",
" <th>Q30_Part_11</th>\n",
" <th>Q30_Part_12</th>\n",
" <th>Q30_Part_13</th>\n",
" <th>Q30_Part_14</th>\n",
" <th>Q30_Part_15</th>\n",
" <th>Q30_Part_16</th>\n",
" <th>Q30_Part_17</th>\n",
" <th>Q30_Part_18</th>\n",
" <th>Q30_Part_19</th>\n",
" <th>Q30_Part_20</th>\n",
" <th>Q30_Part_21</th>\n",
" <th>Q30_Part_22</th>\n",
" <th>Q30_Part_23</th>\n",
" <th>Q30_Part_24</th>\n",
" <th>Q30_Part_25</th>\n",
" <th>Q30_OTHER_TEXT</th>\n",
" <th>Q31_Part_1</th>\n",
" <th>Q31_Part_2</th>\n",
" <th>Q31_Part_3</th>\n",
" <th>Q31_Part_4</th>\n",
" <th>Q31_Part_5</th>\n",
" <th>Q31_Part_6</th>\n",
" <th>Q31_Part_7</th>\n",
" <th>Q31_Part_8</th>\n",
" <th>Q31_Part_9</th>\n",
" <th>Q31_Part_10</th>\n",
" <th>Q31_Part_11</th>\n",
" <th>Q31_Part_12</th>\n",
" <th>Q31_OTHER_TEXT</th>\n",
" <th>Q32</th>\n",
" <th>Q32_OTHER</th>\n",
" <th>Q33_Part_1</th>\n",
" <th>Q33_Part_2</th>\n",
" <th>Q33_Part_3</th>\n",
" <th>Q33_Part_4</th>\n",
" <th>Q33_Part_5</th>\n",
" <th>Q33_Part_6</th>\n",
" <th>Q33_Part_7</th>\n",
" <th>Q33_Part_8</th>\n",
" <th>Q33_Part_9</th>\n",
" <th>Q33_Part_10</th>\n",
" <th>Q33_Part_11</th>\n",
" <th>Q33_OTHER_TEXT</th>\n",
" <th>Q34_Part_1</th>\n",
" <th>Q34_Part_2</th>\n",
" <th>Q34_Part_3</th>\n",
" <th>Q34_Part_4</th>\n",
" <th>Q34_Part_5</th>\n",
" <th>Q34_Part_6</th>\n",
" <th>Q34_OTHER_TEXT</th>\n",
" <th>Q35_Part_1</th>\n",
" <th>Q35_Part_2</th>\n",
" <th>Q35_Part_3</th>\n",
" <th>Q35_Part_4</th>\n",
" <th>Q35_Part_5</th>\n",
" <th>Q35_Part_6</th>\n",
" <th>Q35_OTHER_TEXT</th>\n",
" <th>Q36_Part_1</th>\n",
" <th>Q36_Part_2</th>\n",
" <th>Q36_Part_3</th>\n",
" <th>Q36_Part_4</th>\n",
" <th>Q36_Part_5</th>\n",
" <th>Q36_Part_6</th>\n",
" <th>Q36_Part_7</th>\n",
" <th>Q36_Part_8</th>\n",
" <th>Q36_Part_9</th>\n",
" <th>Q36_Part_10</th>\n",
" <th>Q36_Part_11</th>\n",
" <th>Q36_Part_12</th>\n",
" <th>Q36_Part_13</th>\n",
" <th>Q36_OTHER_TEXT</th>\n",
" <th>Q37</th>\n",
" <th>Q37_OTHER_TEXT</th>\n",
" <th>Q38_Part_1</th>\n",
" <th>Q38_Part_2</th>\n",
" <th>Q38_Part_3</th>\n",
" <th>Q38_Part_4</th>\n",
" <th>Q38_Part_5</th>\n",
" <th>Q38_Part_6</th>\n",
" <th>Q38_Part_7</th>\n",
" <th>Q38_Part_8</th>\n",
" <th>Q38_Part_9</th>\n",
" <th>Q38_Part_10</th>\n",
" <th>Q38_Part_11</th>\n",
" <th>Q38_Part_12</th>\n",
" <th>Q38_Part_13</th>\n",
" <th>Q38_Part_14</th>\n",
" <th>Q38_Part_15</th>\n",
" <th>Q38_Part_16</th>\n",
" <th>Q38_Part_17</th>\n",
" <th>Q38_Part_18</th>\n",
" <th>Q38_Part_19</th>\n",
" <th>Q38_Part_20</th>\n",
" <th>Q38_Part_21</th>\n",
" <th>Q38_Part_22</th>\n",
" <th>Q38_OTHER_TEXT</th>\n",
" <th>Q39_Part_1</th>\n",
" <th>Q39_Part_2</th>\n",
" <th>Q40</th>\n",
" <th>Q41_Part_1</th>\n",
" <th>Q41_Part_2</th>\n",
" <th>Q41_Part_3</th>\n",
" <th>Q42_Part_1</th>\n",
" <th>Q42_Part_2</th>\n",
" <th>Q42_Part_3</th>\n",
" <th>Q42_Part_4</th>\n",
" <th>Q42_Part_5</th>\n",
" <th>Q42_OTHER_TEXT</th>\n",
" <th>Q43</th>\n",
" <th>Q44_Part_1</th>\n",
" <th>Q44_Part_2</th>\n",
" <th>Q44_Part_3</th>\n",
" <th>Q44_Part_4</th>\n",
" <th>Q44_Part_5</th>\n",
" <th>Q44_Part_6</th>\n",
" <th>Q45_Part_1</th>\n",
" <th>Q45_Part_2</th>\n",
" <th>Q45_Part_3</th>\n",
" <th>Q45_Part_4</th>\n",
" <th>Q45_Part_5</th>\n",
" <th>Q45_Part_6</th>\n",
" <th>Q46</th>\n",
" <th>Q47_Part_1</th>\n",
" <th>Q47_Part_2</th>\n",
" <th>Q47_Part_3</th>\n",
" <th>Q47_Part_4</th>\n",
" <th>Q47_Part_5</th>\n",
" <th>Q47_Part_6</th>\n",
" <th>Q47_Part_7</th>\n",
" <th>Q47_Part_8</th>\n",
" <th>Q47_Part_9</th>\n",
" <th>Q47_Part_10</th>\n",
" <th>Q47_Part_11</th>\n",
" <th>Q47_Part_12</th>\n",
" <th>Q47_Part_13</th>\n",
" <th>Q47_Part_14</th>\n",
" <th>Q47_Part_15</th>\n",
" <th>Q47_Part_16</th>\n",
" <th>Q48</th>\n",
" <th>Q49_Part_1</th>\n",
" <th>Q49_Part_2</th>\n",
" <th>Q49_Part_3</th>\n",
" <th>Q49_Part_4</th>\n",
" <th>Q49_Part_5</th>\n",
" <th>Q49_Part_6</th>\n",
" <th>Q49_Part_7</th>\n",
" <th>Q49_Part_8</th>\n",
" <th>Q49_Part_9</th>\n",
" <th>Q49_Part_10</th>\n",
" <th>Q49_Part_11</th>\n",
" <th>Q49_Part_12</th>\n",
" <th>Q49_OTHER_TEXT</th>\n",
" <th>Q50_Part_1</th>\n",
" <th>Q50_Part_2</th>\n",
" <th>Q50_Part_3</th>\n",
" <th>Q50_Part_4</th>\n",
" <th>Q50_Part_5</th>\n",
" <th>Q50_Part_6</th>\n",
" <th>Q50_Part_7</th>\n",
" <th>Q50_Part_8</th>\n",
" <th>Q50_OTHER_TEXT</th>\n",
" <th>Year</th>\n",
" </tr>\n",
" <tr>\n",
" <th>time</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>00:11:50</th>\n",
" <td>Female</td>\n",
" <td>-1</td>\n",
" <td>45-49</td>\n",
" <td>United States of America</td>\n",
" <td>Doctoral degree</td>\n",
" <td>Other</td>\n",
" <td>Consultant</td>\n",
" <td>-1</td>\n",
" <td>Other</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>I do not know</td>\n",
" <td>Analyze and understand data to influence produ...</td>\n",
" <td>Build and/or run a machine learning service th...</td>\n",
" <td>Build and/or run the data infrastructure that ...</td>\n",
" <td>NaN</td>\n",
" <td>Do research that advances the state of the art...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>Cloud-based data software &amp; APIs (AWS, GCP, Az...</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>0</td>\n",
" <td>-1</td>\n",
" <td>Jupyter/IPython</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Microsoft Azure</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>Python</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>Matplotlib</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>0% of my time</td>\n",
" <td>I have never written code but I want to learn</td>\n",
" <td>I have never studied machine learning but plan...</td>\n",
" <td>Maybe</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Azure Machine Learning Studio</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Microsoft Access</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>Twitter</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>Much better</td>\n",
" <td>Much worse</td>\n",
" <td>Independent projects are equally important as ...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>2018</td>\n",
" </tr>\n",
" <tr>\n",
" <th>00:07:14</th>\n",
" <td>Male</td>\n",
" <td>-1</td>\n",
" <td>30-34</td>\n",
" <td>Indonesia</td>\n",
" <td>Bachelor’s degree</td>\n",
" <td>Engineering (non-computer focused)</td>\n",
" <td>Other</td>\n",
" <td>0</td>\n",
" <td>Manufacturing/Fabrication</td>\n",
" <td>-1</td>\n",
" <td>5-10</td>\n",
" <td>10-20,000</td>\n",
" <td>No (we do not use ML methods)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>None of these activities are an important part...</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>Basic statistical software (Microsoft Excel, G...</td>\n",
" <td>1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>I have not used any cloud providers</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>SQL</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>Python</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>1% to 25% of my time</td>\n",
" <td>I have never written code but I want to learn</td>\n",
" <td>I have never studied machine learning but plan...</td>\n",
" <td>Definitely not</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>None/I do not know</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>Slightly worse</td>\n",
" <td>No opinion; I do not know</td>\n",
" <td>Independent projects are equally important as ...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>2018</td>\n",
" </tr>\n",
" <tr>\n",
" <th>00:11:58</th>\n",
" <td>Female</td>\n",
" <td>-1</td>\n",
" <td>30-34</td>\n",
" <td>United States of America</td>\n",
" <td>Master’s degree</td>\n",
" <td>Computer science (software engineering, etc.)</td>\n",
" <td>Data Scientist</td>\n",
" <td>-1</td>\n",
" <td>I am a student</td>\n",
" <td>-1</td>\n",
" <td>0-1</td>\n",
" <td>0-10,000</td>\n",
" <td>I do not know</td>\n",
" <td>Analyze and understand data to influence produ...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>Local or hosted development environments (RStu...</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>0</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>MATLAB</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>I have not used any cloud providers</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>R</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Java</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>MATLAB</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>Java</td>\n",
" <td>-1</td>\n",
" <td>Python</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>ggplot2</td>\n",
" <td>Matplotlib</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Seaborn</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>ggplot2</td>\n",
" <td>-1</td>\n",
" <td>75% to 99% of my time</td>\n",
" <td>5-10 years</td>\n",
" <td>&lt; 1 year</td>\n",
" <td>Definitely yes</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>Categorical Data</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Numerical Data</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Text Data</td>\n",
" <td>Time Series Data</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>Time Series Data</td>\n",
" <td>-1</td>\n",
" <td>Government websites</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Dataset aggregator/platform (Socrata, Kaggle P...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>GitHub</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>20</td>\n",
" <td>50</td>\n",
" <td>20</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>100</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>DataCamp</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Udemy</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>DataCamp</td>\n",
" <td>-1</td>\n",
" <td>Twitter</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>Slightly worse</td>\n",
" <td>Slightly better</td>\n",
" <td>Independent projects are equally important as ...</td>\n",
" <td>Very important</td>\n",
" <td>Very important</td>\n",
" <td>Very important</td>\n",
" <td>NaN</td>\n",
" <td>Metrics that consider accuracy</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>0-10</td>\n",
" <td>Lack of communication between individuals who ...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>When determining whether it is worth it to put...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>10-20</td>\n",
" <td>NaN</td>\n",
" <td>Examine feature correlations</td>\n",
" <td>Examine feature importances</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Plot predicted vs. actual results</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>I am confident that I can explain the outputs ...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Make sure the code is human-readable</td>\n",
" <td>Define all random seeds</td>\n",
" <td>NaN</td>\n",
" <td>Include a text file describing all dependencies</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>Too time-consuming</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>2018</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Q1 Q1_OTHER_TEXT Q2 Q3 \\\n",
"time \n",
"00:11:50 Female -1 45-49 United States of America \n",
"00:07:14 Male -1 30-34 Indonesia \n",
"00:11:58 Female -1 30-34 United States of America \n",
"\n",
" Q4 Q5 \\\n",
"time \n",
"00:11:50 Doctoral degree Other \n",
"00:07:14 Bachelor’s degree Engineering (non-computer focused) \n",
"00:11:58 Master’s degree Computer science (software engineering, etc.) \n",
"\n",
" Q6 Q6_OTHER_TEXT Q7 \\\n",
"time \n",
"00:11:50 Consultant -1 Other \n",
"00:07:14 Other 0 Manufacturing/Fabrication \n",
"00:11:58 Data Scientist -1 I am a student \n",
"\n",
" Q7_OTHER_TEXT Q8 Q9 Q10 \\\n",
"time \n",
"00:11:50 0 NaN NaN I do not know \n",
"00:07:14 -1 5-10 10-20,000 No (we do not use ML methods) \n",
"00:11:58 -1 0-1 0-10,000 I do not know \n",
"\n",
" Q11_Part_1 \\\n",
"time \n",
"00:11:50 Analyze and understand data to influence produ... \n",
"00:07:14 NaN \n",
"00:11:58 Analyze and understand data to influence produ... \n",
"\n",
" Q11_Part_2 \\\n",
"time \n",
"00:11:50 Build and/or run a machine learning service th... \n",
"00:07:14 NaN \n",
"00:11:58 NaN \n",
"\n",
" Q11_Part_3 Q11_Part_4 \\\n",
"time \n",
"00:11:50 Build and/or run the data infrastructure that ... NaN \n",
"00:07:14 NaN NaN \n",
"00:11:58 NaN NaN \n",
"\n",
" Q11_Part_5 \\\n",
"time \n",
"00:11:50 Do research that advances the state of the art... \n",
"00:07:14 NaN \n",
"00:11:58 NaN \n",
"\n",
" Q11_Part_6 Q11_Part_7 \\\n",
"time \n",
"00:11:50 NaN NaN \n",
"00:07:14 None of these activities are an important part... NaN \n",
"00:11:58 NaN NaN \n",
"\n",
" Q11_OTHER_TEXT Q12_MULTIPLE_CHOICE \\\n",
"time \n",
"00:11:50 -1 Cloud-based data software & APIs (AWS, GCP, Az... \n",
"00:07:14 -1 Basic statistical software (Microsoft Excel, G... \n",
"00:11:58 -1 Local or hosted development environments (RStu... \n",
"\n",
" Q12_Part_1_TEXT Q12_Part_2_TEXT Q12_Part_3_TEXT Q12_Part_4_TEXT \\\n",
"time \n",
"00:11:50 -1 -1 -1 -1 \n",
"00:07:14 1 -1 -1 -1 \n",
"00:11:58 -1 -1 -1 0 \n",
"\n",
" Q12_Part_5_TEXT Q12_OTHER_TEXT Q13_Part_1 Q13_Part_2 \\\n",
"time \n",
"00:11:50 0 -1 Jupyter/IPython NaN \n",
"00:07:14 -1 -1 NaN NaN \n",
"00:11:58 -1 -1 NaN NaN \n",
"\n",
" Q13_Part_3 Q13_Part_4 Q13_Part_5 Q13_Part_6 Q13_Part_7 Q13_Part_8 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN MATLAB NaN \n",
"\n",
" Q13_Part_9 Q13_Part_10 Q13_Part_11 Q13_Part_12 Q13_Part_13 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN NaN \n",
"\n",
" Q13_Part_14 Q13_Part_15 Q13_OTHER_TEXT Q14_Part_1 Q14_Part_2 \\\n",
"time \n",
"00:11:50 NaN NaN -1 NaN NaN \n",
"00:07:14 None NaN -1 NaN NaN \n",
"00:11:58 NaN NaN -1 NaN NaN \n",
"\n",
" Q14_Part_3 Q14_Part_4 Q14_Part_5 Q14_Part_6 Q14_Part_7 Q14_Part_8 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q14_Part_9 Q14_Part_10 Q14_Part_11 Q14_OTHER_TEXT Q15_Part_1 \\\n",
"time \n",
"00:11:50 NaN None NaN -1 NaN \n",
"00:07:14 NaN None NaN -1 NaN \n",
"00:11:58 NaN None NaN -1 NaN \n",
"\n",
" Q15_Part_2 Q15_Part_3 Q15_Part_4 Q15_Part_5 \\\n",
"time \n",
"00:11:50 NaN Microsoft Azure NaN NaN \n",
"00:07:14 NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN \n",
"\n",
" Q15_Part_6 Q15_Part_7 Q15_OTHER_TEXT \\\n",
"time \n",
"00:11:50 NaN NaN -1 \n",
"00:07:14 I have not used any cloud providers NaN -1 \n",
"00:11:58 I have not used any cloud providers NaN -1 \n",
"\n",
" Q16_Part_1 Q16_Part_2 Q16_Part_3 Q16_Part_4 Q16_Part_5 Q16_Part_6 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN SQL NaN NaN NaN \n",
"00:11:58 NaN R NaN NaN Java NaN \n",
"\n",
" Q16_Part_7 Q16_Part_8 Q16_Part_9 Q16_Part_10 Q16_Part_11 Q16_Part_12 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN MATLAB NaN NaN NaN \n",
"\n",
" Q16_Part_13 Q16_Part_14 Q16_Part_15 Q16_Part_16 Q16_Part_17 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN None \n",
"00:07:14 NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN NaN \n",
"\n",
" Q16_Part_18 Q16_OTHER_TEXT Q17 Q17_OTHER_TEXT Q18 \\\n",
"time \n",
"00:11:50 NaN -1 NaN -1 Python \n",
"00:07:14 NaN -1 NaN -1 Python \n",
"00:11:58 NaN -1 Java -1 Python \n",
"\n",
" Q18_OTHER_TEXT Q19_Part_1 Q19_Part_2 Q19_Part_3 Q19_Part_4 \\\n",
"time \n",
"00:11:50 -1 NaN NaN NaN NaN \n",
"00:07:14 -1 NaN NaN NaN NaN \n",
"00:11:58 -1 NaN NaN NaN NaN \n",
"\n",
" Q19_Part_5 Q19_Part_6 Q19_Part_7 Q19_Part_8 Q19_Part_9 Q19_Part_10 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q19_Part_11 Q19_Part_12 Q19_Part_13 Q19_Part_14 Q19_Part_15 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN NaN \n",
"\n",
" Q19_Part_16 Q19_Part_17 Q19_Part_18 Q19_Part_19 Q19_OTHER_TEXT Q20 \\\n",
"time \n",
"00:11:50 NaN NaN None NaN -1 NaN \n",
"00:07:14 NaN NaN None NaN -1 NaN \n",
"00:11:58 NaN NaN None NaN -1 NaN \n",
"\n",
" Q20_OTHER_TEXT Q21_Part_1 Q21_Part_2 Q21_Part_3 Q21_Part_4 \\\n",
"time \n",
"00:11:50 -1 NaN Matplotlib NaN NaN \n",
"00:07:14 -1 NaN NaN NaN NaN \n",
"00:11:58 -1 ggplot2 Matplotlib NaN NaN \n",
"\n",
" Q21_Part_5 Q21_Part_6 Q21_Part_7 Q21_Part_8 Q21_Part_9 Q21_Part_10 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN Seaborn NaN NaN \n",
"\n",
" Q21_Part_11 Q21_Part_12 Q21_Part_13 Q21_OTHER_TEXT Q22 \\\n",
"time \n",
"00:11:50 NaN NaN NaN -1 NaN \n",
"00:07:14 NaN None NaN -1 NaN \n",
"00:11:58 NaN NaN NaN -1 ggplot2 \n",
"\n",
" Q22_OTHER_TEXT Q23 \\\n",
"time \n",
"00:11:50 -1 0% of my time \n",
"00:07:14 -1 1% to 25% of my time \n",
"00:11:58 -1 75% to 99% of my time \n",
"\n",
" Q24 \\\n",
"time \n",
"00:11:50 I have never written code but I want to learn \n",
"00:07:14 I have never written code but I want to learn \n",
"00:11:58 5-10 years \n",
"\n",
" Q25 Q26 \\\n",
"time \n",
"00:11:50 I have never studied machine learning but plan... Maybe \n",
"00:07:14 I have never studied machine learning but plan... Definitely not \n",
"00:11:58 < 1 year Definitely yes \n",
"\n",
" Q27_Part_1 Q27_Part_2 Q27_Part_3 Q27_Part_4 Q27_Part_5 Q27_Part_6 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q27_Part_7 Q27_Part_8 Q27_Part_9 Q27_Part_10 Q27_Part_11 Q27_Part_12 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q27_Part_13 Q27_Part_14 Q27_Part_15 Q27_Part_16 Q27_Part_17 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN NaN \n",
"\n",
" Q27_Part_18 Q27_Part_19 Q27_Part_20 Q27_OTHER_TEXT Q28_Part_1 \\\n",
"time \n",
"00:11:50 NaN None NaN -1 NaN \n",
"00:07:14 NaN NaN NaN -1 NaN \n",
"00:11:58 NaN NaN NaN -1 NaN \n",
"\n",
" Q28_Part_2 Q28_Part_3 Q28_Part_4 Q28_Part_5 Q28_Part_6 Q28_Part_7 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q28_Part_8 Q28_Part_9 Q28_Part_10 Q28_Part_11 Q28_Part_12 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN NaN \n",
"\n",
" Q28_Part_13 Q28_Part_14 Q28_Part_15 Q28_Part_16 Q28_Part_17 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN NaN \n",
"\n",
" Q28_Part_18 Q28_Part_19 Q28_Part_20 Q28_Part_21 Q28_Part_22 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN NaN \n",
"\n",
" Q28_Part_23 Q28_Part_24 Q28_Part_25 Q28_Part_26 \\\n",
"time \n",
"00:11:50 NaN NaN NaN Azure Machine Learning Studio \n",
"00:07:14 NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN \n",
"\n",
" Q28_Part_27 Q28_Part_28 Q28_Part_29 Q28_Part_30 Q28_Part_31 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN NaN \n",
"\n",
" Q28_Part_32 Q28_Part_33 Q28_Part_34 Q28_Part_35 Q28_Part_36 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN NaN \n",
"\n",
" Q28_Part_37 Q28_Part_38 Q28_Part_39 Q28_Part_40 Q28_Part_41 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN NaN \n",
"\n",
" Q28_Part_42 Q28_Part_43 Q28_OTHER_TEXT Q29_Part_1 Q29_Part_2 \\\n",
"time \n",
"00:11:50 NaN NaN -1 NaN NaN \n",
"00:07:14 NaN NaN -1 NaN NaN \n",
"00:11:58 NaN NaN -1 NaN NaN \n",
"\n",
" Q29_Part_3 Q29_Part_4 Q29_Part_5 Q29_Part_6 Q29_Part_7 Q29_Part_8 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q29_Part_9 Q29_Part_10 Q29_Part_11 Q29_Part_12 Q29_Part_13 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN NaN \n",
"\n",
" Q29_Part_14 Q29_Part_15 Q29_Part_16 Q29_Part_17 Q29_Part_18 \\\n",
"time \n",
"00:11:50 NaN Microsoft Access NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN NaN \n",
"\n",
" Q29_Part_19 Q29_Part_20 Q29_Part_21 Q29_Part_22 Q29_Part_23 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN NaN \n",
"\n",
" Q29_Part_24 Q29_Part_25 Q29_Part_26 Q29_Part_27 Q29_Part_28 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN NaN \n",
"\n",
" Q29_OTHER_TEXT Q30_Part_1 Q30_Part_2 Q30_Part_3 Q30_Part_4 \\\n",
"time \n",
"00:11:50 -1 NaN NaN NaN NaN \n",
"00:07:14 -1 NaN NaN NaN NaN \n",
"00:11:58 -1 NaN NaN NaN NaN \n",
"\n",
" Q30_Part_5 Q30_Part_6 Q30_Part_7 Q30_Part_8 Q30_Part_9 Q30_Part_10 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q30_Part_11 Q30_Part_12 Q30_Part_13 Q30_Part_14 Q30_Part_15 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN NaN \n",
"\n",
" Q30_Part_16 Q30_Part_17 Q30_Part_18 Q30_Part_19 Q30_Part_20 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN NaN \n",
"\n",
" Q30_Part_21 Q30_Part_22 Q30_Part_23 Q30_Part_24 Q30_Part_25 \\\n",
"time \n",
"00:11:50 NaN NaN NaN None NaN \n",
"00:07:14 NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN NaN \n",
"\n",
" Q30_OTHER_TEXT Q31_Part_1 Q31_Part_2 Q31_Part_3 Q31_Part_4 \\\n",
"time \n",
"00:11:50 -1 NaN NaN NaN NaN \n",
"00:07:14 -1 NaN NaN NaN NaN \n",
"00:11:58 -1 NaN Categorical Data NaN NaN \n",
"\n",
" Q31_Part_5 Q31_Part_6 Q31_Part_7 Q31_Part_8 Q31_Part_9 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN \n",
"00:11:58 NaN Numerical Data NaN NaN Text Data \n",
"\n",
" Q31_Part_10 Q31_Part_11 Q31_Part_12 Q31_OTHER_TEXT \\\n",
"time \n",
"00:11:50 NaN NaN NaN -1 \n",
"00:07:14 NaN NaN NaN -1 \n",
"00:11:58 Time Series Data NaN NaN -1 \n",
"\n",
" Q32 Q32_OTHER Q33_Part_1 Q33_Part_2 \\\n",
"time \n",
"00:11:50 NaN -1 NaN NaN \n",
"00:07:14 NaN -1 NaN NaN \n",
"00:11:58 Time Series Data -1 Government websites NaN \n",
"\n",
" Q33_Part_3 Q33_Part_4 \\\n",
"time \n",
"00:11:50 NaN NaN \n",
"00:07:14 NaN NaN \n",
"00:11:58 NaN Dataset aggregator/platform (Socrata, Kaggle P... \n",
"\n",
" Q33_Part_5 Q33_Part_6 Q33_Part_7 Q33_Part_8 Q33_Part_9 Q33_Part_10 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN GitHub NaN \n",
"\n",
" Q33_Part_11 Q33_OTHER_TEXT Q34_Part_1 Q34_Part_2 Q34_Part_3 \\\n",
"time \n",
"00:11:50 NaN -1 NaN NaN NaN \n",
"00:07:14 NaN -1 NaN NaN NaN \n",
"00:11:58 NaN -1 2 3 20 \n",
"\n",
" Q34_Part_4 Q34_Part_5 Q34_Part_6 Q34_OTHER_TEXT Q35_Part_1 \\\n",
"time \n",
"00:11:50 NaN NaN NaN -1 NaN \n",
"00:07:14 NaN NaN NaN -1 NaN \n",
"00:11:58 50 20 0 1 0 \n",
"\n",
" Q35_Part_2 Q35_Part_3 Q35_Part_4 Q35_Part_5 Q35_Part_6 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN \n",
"00:11:58 0 0 100 0 0 \n",
"\n",
" Q35_OTHER_TEXT Q36_Part_1 Q36_Part_2 Q36_Part_3 Q36_Part_4 \\\n",
"time \n",
"00:11:50 -1 NaN NaN NaN NaN \n",
"00:07:14 -1 NaN NaN NaN NaN \n",
"00:11:58 -1 NaN NaN NaN DataCamp \n",
"\n",
" Q36_Part_5 Q36_Part_6 Q36_Part_7 Q36_Part_8 Q36_Part_9 Q36_Part_10 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN Udemy NaN \n",
"\n",
" Q36_Part_11 Q36_Part_12 Q36_Part_13 Q36_OTHER_TEXT Q37 \\\n",
"time \n",
"00:11:50 NaN NaN NaN -1 NaN \n",
"00:07:14 NaN NaN NaN -1 NaN \n",
"00:11:58 NaN NaN NaN -1 DataCamp \n",
"\n",
" Q37_OTHER_TEXT Q38_Part_1 Q38_Part_2 Q38_Part_3 Q38_Part_4 \\\n",
"time \n",
"00:11:50 -1 Twitter NaN NaN NaN \n",
"00:07:14 -1 NaN NaN NaN NaN \n",
"00:11:58 -1 Twitter NaN NaN NaN \n",
"\n",
" Q38_Part_5 Q38_Part_6 Q38_Part_7 Q38_Part_8 Q38_Part_9 Q38_Part_10 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q38_Part_11 Q38_Part_12 Q38_Part_13 Q38_Part_14 Q38_Part_15 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN NaN \n",
"\n",
" Q38_Part_16 Q38_Part_17 Q38_Part_18 Q38_Part_19 Q38_Part_20 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN NaN \n",
"\n",
" Q38_Part_21 Q38_Part_22 Q38_OTHER_TEXT Q39_Part_1 \\\n",
"time \n",
"00:11:50 NaN NaN -1 Much better \n",
"00:07:14 None/I do not know NaN -1 Slightly worse \n",
"00:11:58 NaN NaN -1 Slightly worse \n",
"\n",
" Q39_Part_2 \\\n",
"time \n",
"00:11:50 Much worse \n",
"00:07:14 No opinion; I do not know \n",
"00:11:58 Slightly better \n",
"\n",
" Q40 Q41_Part_1 \\\n",
"time \n",
"00:11:50 Independent projects are equally important as ... NaN \n",
"00:07:14 Independent projects are equally important as ... NaN \n",
"00:11:58 Independent projects are equally important as ... Very important \n",
"\n",
" Q41_Part_2 Q41_Part_3 Q42_Part_1 \\\n",
"time \n",
"00:11:50 NaN NaN NaN \n",
"00:07:14 NaN NaN NaN \n",
"00:11:58 Very important Very important NaN \n",
"\n",
" Q42_Part_2 Q42_Part_3 Q42_Part_4 Q42_Part_5 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN \n",
"00:11:58 Metrics that consider accuracy NaN NaN NaN \n",
"\n",
" Q42_OTHER_TEXT Q43 \\\n",
"time \n",
"00:11:50 -1 NaN \n",
"00:07:14 -1 NaN \n",
"00:11:58 -1 0-10 \n",
"\n",
" Q44_Part_1 Q44_Part_2 \\\n",
"time \n",
"00:11:50 NaN NaN \n",
"00:07:14 NaN NaN \n",
"00:11:58 Lack of communication between individuals who ... NaN \n",
"\n",
" Q44_Part_3 Q44_Part_4 Q44_Part_5 Q44_Part_6 Q45_Part_1 Q45_Part_2 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q45_Part_3 Q45_Part_4 \\\n",
"time \n",
"00:11:50 NaN NaN \n",
"00:07:14 NaN NaN \n",
"00:11:58 When determining whether it is worth it to put... NaN \n",
"\n",
" Q45_Part_5 Q45_Part_6 Q46 Q47_Part_1 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN \n",
"00:11:58 NaN NaN 10-20 NaN \n",
"\n",
" Q47_Part_2 Q47_Part_3 \\\n",
"time \n",
"00:11:50 NaN NaN \n",
"00:07:14 NaN NaN \n",
"00:11:58 Examine feature correlations Examine feature importances \n",
"\n",
" Q47_Part_4 Q47_Part_5 Q47_Part_6 Q47_Part_7 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN \n",
"\n",
" Q47_Part_8 Q47_Part_9 Q47_Part_10 \\\n",
"time \n",
"00:11:50 NaN NaN NaN \n",
"00:07:14 NaN NaN NaN \n",
"00:11:58 Plot predicted vs. actual results NaN NaN \n",
"\n",
" Q47_Part_11 Q47_Part_12 Q47_Part_13 Q47_Part_14 Q47_Part_15 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN NaN \n",
"\n",
" Q47_Part_16 Q48 \\\n",
"time \n",
"00:11:50 NaN NaN \n",
"00:07:14 NaN NaN \n",
"00:11:58 NaN I am confident that I can explain the outputs ... \n",
"\n",
" Q49_Part_1 Q49_Part_2 Q49_Part_3 Q49_Part_4 Q49_Part_5 Q49_Part_6 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN NaN \n",
"00:11:58 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q49_Part_7 Q49_Part_8 \\\n",
"time \n",
"00:11:50 NaN NaN \n",
"00:07:14 NaN NaN \n",
"00:11:58 Make sure the code is human-readable Define all random seeds \n",
"\n",
" Q49_Part_9 Q49_Part_10 \\\n",
"time \n",
"00:11:50 NaN NaN \n",
"00:07:14 NaN NaN \n",
"00:11:58 NaN Include a text file describing all dependencies \n",
"\n",
" Q49_Part_11 Q49_Part_12 Q49_OTHER_TEXT Q50_Part_1 \\\n",
"time \n",
"00:11:50 NaN NaN -1 NaN \n",
"00:07:14 NaN NaN -1 NaN \n",
"00:11:58 NaN NaN -1 NaN \n",
"\n",
" Q50_Part_2 Q50_Part_3 Q50_Part_4 Q50_Part_5 Q50_Part_6 \\\n",
"time \n",
"00:11:50 NaN NaN NaN NaN NaN \n",
"00:07:14 NaN NaN NaN NaN NaN \n",
"00:11:58 Too time-consuming NaN NaN NaN NaN \n",
"\n",
" Q50_Part_7 Q50_Part_8 Q50_OTHER_TEXT Year \n",
"time \n",
"00:11:50 NaN NaN -1 2018 \n",
"00:07:14 NaN NaN -1 2018 \n",
"00:11:58 NaN NaN -1 2018 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Kaggle18=pd.read_csv(\"../input/kaggle-survey-2018/multipleChoiceResponses.csv\")\n",
"Kaggle18.drop([0],axis=0,inplace=True)\n",
"Kaggle18['time'] = Kaggle18['Time from Start to Finish (seconds)'].astype(int)\n",
"Kaggle18.drop(\"Time from Start to Finish (seconds)\",axis=1,inplace=True)\n",
"Kaggle18['time'] = pd.to_datetime(Kaggle18['time'], unit='s').dt.time\n",
"first_col=Kaggle18.pop('time')\n",
"Kaggle18.insert(0, 'time', first_col)\n",
"Kaggle18.set_index('time',inplace=True)\n",
"Kaggle18[\"Year\"]=\"2018\"\n",
"Kaggle18.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.109349,
"end_time": "2020-12-05T10:05:01.443200",
"exception": false,
"start_time": "2020-12-05T10:05:01.333851",
"status": "completed"
},
"tags": []
},
"source": [
"## Exploring DataFrame"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.109935,
"end_time": "2020-12-05T10:05:01.661424",
"exception": false,
"start_time": "2020-12-05T10:05:01.551489",
"status": "completed"
},
"tags": []
},
"source": [
"Dividing the 2020 data frame into two different pandas data frame based on Education (With formal Degree/ Without Formal Degree). I used this so that I can keep all the content in the data and split them into two other data frames. In the end, no data lost."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_kg_hide-input": true,
"execution": {
"iopub.execute_input": "2020-12-05T10:05:01.864726Z",
"iopub.status.busy": "2020-12-05T10:05:01.856651Z",
"iopub.status.idle": "2020-12-05T10:05:02.443507Z",
"shell.execute_reply": "2020-12-05T10:05:02.442739Z"
},
"papermill": {
"duration": 0.67068,
"end_time": "2020-12-05T10:05:02.443651",
"exception": false,
"start_time": "2020-12-05T10:05:01.772971",
"status": "completed"
},
"tags": []
},
"outputs": [
{
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" <th>Q16_Part_10</th>\n",
" <th>Q16_Part_11</th>\n",
" <th>Q16_Part_12</th>\n",
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" <th>Q16_Part_14</th>\n",
" <th>Q16_Part_15</th>\n",
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" <th>Q17_Part_3</th>\n",
" <th>Q17_Part_4</th>\n",
" <th>Q17_Part_5</th>\n",
" <th>Q17_Part_6</th>\n",
" <th>Q17_Part_7</th>\n",
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" <th>Q23_Part_6</th>\n",
" <th>Q23_Part_7</th>\n",
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" <th>Q24</th>\n",
" <th>Q25</th>\n",
" <th>Q26_A_Part_1</th>\n",
" <th>Q26_A_Part_2</th>\n",
" <th>Q26_A_Part_3</th>\n",
" <th>Q26_A_Part_4</th>\n",
" <th>Q26_A_Part_5</th>\n",
" <th>Q26_A_Part_6</th>\n",
" <th>Q26_A_Part_7</th>\n",
" <th>Q26_A_Part_8</th>\n",
" <th>Q26_A_Part_9</th>\n",
" <th>Q26_A_Part_10</th>\n",
" <th>Q26_A_Part_11</th>\n",
" <th>Q26_A_OTHER</th>\n",
" <th>Q27_A_Part_1</th>\n",
" <th>Q27_A_Part_2</th>\n",
" <th>Q27_A_Part_3</th>\n",
" <th>Q27_A_Part_4</th>\n",
" <th>Q27_A_Part_5</th>\n",
" <th>Q27_A_Part_6</th>\n",
" <th>Q27_A_Part_7</th>\n",
" <th>Q27_A_Part_8</th>\n",
" <th>Q27_A_Part_9</th>\n",
" <th>Q27_A_Part_10</th>\n",
" <th>Q27_A_Part_11</th>\n",
" <th>Q27_A_OTHER</th>\n",
" <th>Q28_A_Part_1</th>\n",
" <th>Q28_A_Part_2</th>\n",
" <th>Q28_A_Part_3</th>\n",
" <th>Q28_A_Part_4</th>\n",
" <th>Q28_A_Part_5</th>\n",
" <th>Q28_A_Part_6</th>\n",
" <th>Q28_A_Part_7</th>\n",
" <th>Q28_A_Part_8</th>\n",
" <th>Q28_A_Part_9</th>\n",
" <th>Q28_A_Part_10</th>\n",
" <th>Q28_A_OTHER</th>\n",
" <th>Q29_A_Part_1</th>\n",
" <th>Q29_A_Part_2</th>\n",
" <th>Q29_A_Part_3</th>\n",
" <th>Q29_A_Part_4</th>\n",
" <th>Q29_A_Part_5</th>\n",
" <th>Q29_A_Part_6</th>\n",
" <th>Q29_A_Part_7</th>\n",
" <th>Q29_A_Part_8</th>\n",
" <th>Q29_A_Part_9</th>\n",
" <th>Q29_A_Part_10</th>\n",
" <th>Q29_A_Part_11</th>\n",
" <th>Q29_A_Part_12</th>\n",
" <th>Q29_A_Part_13</th>\n",
" <th>Q29_A_Part_14</th>\n",
" <th>Q29_A_Part_15</th>\n",
" <th>Q29_A_Part_16</th>\n",
" <th>Q29_A_Part_17</th>\n",
" <th>Q29_A_OTHER</th>\n",
" <th>Q30</th>\n",
" <th>Q31_A_Part_1</th>\n",
" <th>Q31_A_Part_2</th>\n",
" <th>Q31_A_Part_3</th>\n",
" <th>Q31_A_Part_4</th>\n",
" <th>Q31_A_Part_5</th>\n",
" <th>Q31_A_Part_6</th>\n",
" <th>Q31_A_Part_7</th>\n",
" <th>Q31_A_Part_8</th>\n",
" <th>Q31_A_Part_9</th>\n",
" <th>Q31_A_Part_10</th>\n",
" <th>Q31_A_Part_11</th>\n",
" <th>Q31_A_Part_12</th>\n",
" <th>Q31_A_Part_13</th>\n",
" <th>Q31_A_Part_14</th>\n",
" <th>Q31_A_OTHER</th>\n",
" <th>Q32</th>\n",
" <th>Q33_A_Part_1</th>\n",
" <th>Q33_A_Part_2</th>\n",
" <th>Q33_A_Part_3</th>\n",
" <th>Q33_A_Part_4</th>\n",
" <th>Q33_A_Part_5</th>\n",
" <th>Q33_A_Part_6</th>\n",
" <th>Q33_A_Part_7</th>\n",
" <th>Q33_A_OTHER</th>\n",
" <th>Q34_A_Part_1</th>\n",
" <th>Q34_A_Part_2</th>\n",
" <th>Q34_A_Part_3</th>\n",
" <th>Q34_A_Part_4</th>\n",
" <th>Q34_A_Part_5</th>\n",
" <th>Q34_A_Part_6</th>\n",
" <th>Q34_A_Part_7</th>\n",
" <th>Q34_A_Part_8</th>\n",
" <th>Q34_A_Part_9</th>\n",
" <th>Q34_A_Part_10</th>\n",
" <th>Q34_A_Part_11</th>\n",
" <th>Q34_A_OTHER</th>\n",
" <th>Q35_A_Part_1</th>\n",
" <th>Q35_A_Part_2</th>\n",
" <th>Q35_A_Part_3</th>\n",
" <th>Q35_A_Part_4</th>\n",
" <th>Q35_A_Part_5</th>\n",
" <th>Q35_A_Part_6</th>\n",
" <th>Q35_A_Part_7</th>\n",
" <th>Q35_A_Part_8</th>\n",
" <th>Q35_A_Part_9</th>\n",
" <th>Q35_A_Part_10</th>\n",
" <th>Q35_A_OTHER</th>\n",
" <th>Q36_Part_1</th>\n",
" <th>Q36_Part_2</th>\n",
" <th>Q36_Part_3</th>\n",
" <th>Q36_Part_4</th>\n",
" <th>Q36_Part_5</th>\n",
" <th>Q36_Part_6</th>\n",
" <th>Q36_Part_7</th>\n",
" <th>Q36_Part_8</th>\n",
" <th>Q36_Part_9</th>\n",
" <th>Q36_OTHER</th>\n",
" <th>Q37_Part_1</th>\n",
" <th>Q37_Part_2</th>\n",
" <th>Q37_Part_3</th>\n",
" <th>Q37_Part_4</th>\n",
" <th>Q37_Part_5</th>\n",
" <th>Q37_Part_6</th>\n",
" <th>Q37_Part_7</th>\n",
" <th>Q37_Part_8</th>\n",
" <th>Q37_Part_9</th>\n",
" <th>Q37_Part_10</th>\n",
" <th>Q37_Part_11</th>\n",
" <th>Q37_OTHER</th>\n",
" <th>Q38</th>\n",
" <th>Q39_Part_1</th>\n",
" <th>Q39_Part_2</th>\n",
" <th>Q39_Part_3</th>\n",
" <th>Q39_Part_4</th>\n",
" <th>Q39_Part_5</th>\n",
" <th>Q39_Part_6</th>\n",
" <th>Q39_Part_7</th>\n",
" <th>Q39_Part_8</th>\n",
" <th>Q39_Part_9</th>\n",
" <th>Q39_Part_10</th>\n",
" <th>Q39_Part_11</th>\n",
" <th>Q39_OTHER</th>\n",
" <th>Q26_B_Part_1</th>\n",
" <th>Q26_B_Part_2</th>\n",
" <th>Q26_B_Part_3</th>\n",
" <th>Q26_B_Part_4</th>\n",
" <th>Q26_B_Part_5</th>\n",
" <th>Q26_B_Part_6</th>\n",
" <th>Q26_B_Part_7</th>\n",
" <th>Q26_B_Part_8</th>\n",
" <th>Q26_B_Part_9</th>\n",
" <th>Q26_B_Part_10</th>\n",
" <th>Q26_B_Part_11</th>\n",
" <th>Q26_B_OTHER</th>\n",
" <th>Q27_B_Part_1</th>\n",
" <th>Q27_B_Part_2</th>\n",
" <th>Q27_B_Part_3</th>\n",
" <th>Q27_B_Part_4</th>\n",
" <th>Q27_B_Part_5</th>\n",
" <th>Q27_B_Part_6</th>\n",
" <th>Q27_B_Part_7</th>\n",
" <th>Q27_B_Part_8</th>\n",
" <th>Q27_B_Part_9</th>\n",
" <th>Q27_B_Part_10</th>\n",
" <th>Q27_B_Part_11</th>\n",
" <th>Q27_B_OTHER</th>\n",
" <th>Q28_B_Part_1</th>\n",
" <th>Q28_B_Part_2</th>\n",
" <th>Q28_B_Part_3</th>\n",
" <th>Q28_B_Part_4</th>\n",
" <th>Q28_B_Part_5</th>\n",
" <th>Q28_B_Part_6</th>\n",
" <th>Q28_B_Part_7</th>\n",
" <th>Q28_B_Part_8</th>\n",
" <th>Q28_B_Part_9</th>\n",
" <th>Q28_B_Part_10</th>\n",
" <th>Q28_B_OTHER</th>\n",
" <th>Q29_B_Part_1</th>\n",
" <th>Q29_B_Part_2</th>\n",
" <th>Q29_B_Part_3</th>\n",
" <th>Q29_B_Part_4</th>\n",
" <th>Q29_B_Part_5</th>\n",
" <th>Q29_B_Part_6</th>\n",
" <th>Q29_B_Part_7</th>\n",
" <th>Q29_B_Part_8</th>\n",
" <th>Q29_B_Part_9</th>\n",
" <th>Q29_B_Part_10</th>\n",
" <th>Q29_B_Part_11</th>\n",
" <th>Q29_B_Part_12</th>\n",
" <th>Q29_B_Part_13</th>\n",
" <th>Q29_B_Part_14</th>\n",
" <th>Q29_B_Part_15</th>\n",
" <th>Q29_B_Part_16</th>\n",
" <th>Q29_B_Part_17</th>\n",
" <th>Q29_B_OTHER</th>\n",
" <th>Q31_B_Part_1</th>\n",
" <th>Q31_B_Part_2</th>\n",
" <th>Q31_B_Part_3</th>\n",
" <th>Q31_B_Part_4</th>\n",
" <th>Q31_B_Part_5</th>\n",
" <th>Q31_B_Part_6</th>\n",
" <th>Q31_B_Part_7</th>\n",
" <th>Q31_B_Part_8</th>\n",
" <th>Q31_B_Part_9</th>\n",
" <th>Q31_B_Part_10</th>\n",
" <th>Q31_B_Part_11</th>\n",
" <th>Q31_B_Part_12</th>\n",
" <th>Q31_B_Part_13</th>\n",
" <th>Q31_B_Part_14</th>\n",
" <th>Q31_B_OTHER</th>\n",
" <th>Q33_B_Part_1</th>\n",
" <th>Q33_B_Part_2</th>\n",
" <th>Q33_B_Part_3</th>\n",
" <th>Q33_B_Part_4</th>\n",
" <th>Q33_B_Part_5</th>\n",
" <th>Q33_B_Part_6</th>\n",
" <th>Q33_B_Part_7</th>\n",
" <th>Q33_B_OTHER</th>\n",
" <th>Q34_B_Part_1</th>\n",
" <th>Q34_B_Part_2</th>\n",
" <th>Q34_B_Part_3</th>\n",
" <th>Q34_B_Part_4</th>\n",
" <th>Q34_B_Part_5</th>\n",
" <th>Q34_B_Part_6</th>\n",
" <th>Q34_B_Part_7</th>\n",
" <th>Q34_B_Part_8</th>\n",
" <th>Q34_B_Part_9</th>\n",
" <th>Q34_B_Part_10</th>\n",
" <th>Q34_B_Part_11</th>\n",
" <th>Q34_B_OTHER</th>\n",
" <th>Q35_B_Part_1</th>\n",
" <th>Q35_B_Part_2</th>\n",
" <th>Q35_B_Part_3</th>\n",
" <th>Q35_B_Part_4</th>\n",
" <th>Q35_B_Part_5</th>\n",
" <th>Q35_B_Part_6</th>\n",
" <th>Q35_B_Part_7</th>\n",
" <th>Q35_B_Part_8</th>\n",
" <th>Q35_B_Part_9</th>\n",
" <th>Q35_B_Part_10</th>\n",
" <th>Q35_B_OTHER</th>\n",
" <th>Year</th>\n",
" </tr>\n",
" <tr>\n",
" <th>time</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>00:02:30</th>\n",
" <td>22-24</td>\n",
" <td>Man</td>\n",
" <td>China</td>\n",
" <td>No formal education past high school</td>\n",
" <td>Student</td>\n",
" <td>&lt; 1 years</td>\n",
" <td>Python</td>\n",
" <td>NaN</td>\n",
" <td>SQL</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Python</td>\n",
" <td>Jupyter (JupyterLab, Jupyter Notebooks, etc)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>PyCharm</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>00:11:34</th>\n",
" <td>35-39</td>\n",
" <td>Man</td>\n",
" <td>South Africa</td>\n",
" <td>Some college/university study without earning ...</td>\n",
" <td>Data Analyst</td>\n",
" <td>&lt; 1 years</td>\n",
" <td>Python</td>\n",
" <td>NaN</td>\n",
" <td>SQL</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Python</td>\n",
" <td>Jupyter (JupyterLab, Jupyter Notebooks, etc)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Visual Studio Code (VSCode)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Kaggle Notebooks</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>A personal computer or laptop</td>\n",
" <td>GPUs</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Never</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Other</td>\n",
" <td>I do not use machine learning methods</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0-49 employees</td>\n",
" <td>3-4</td>\n",
" <td>I do not know</td>\n",
" <td>Analyze and understand data to influence produ...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>10,000-14,999</td>\n",
" <td>$0 ($USD)</td>\n",
" <td>Amazon Web Services (AWS)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>No / None</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>No / None</td>\n",
" <td>NaN</td>\n",
" <td>MySQL</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Tableau</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>No / None</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>No / None</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Kaggle</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Kaggle Learn Courses</td>\n",
" <td>DataCamp</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Udemy</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Business intelligence software (Salesforce, Ta...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>YouTube (Kaggle YouTube, Cloud AI Adventures, ...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Amazon Web Services (AWS)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Amazon EC2</td>\n",
" <td>AWS Lambda</td>\n",
" <td>Amazon Elastic Container Service</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Amazon SageMaker</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Amazon Athena</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Amazon QuickSight</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Tableau</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Automated model selection (e.g. auto-sklearn, ...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>2020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>00:06:51</th>\n",
" <td>30-34</td>\n",
" <td>Woman</td>\n",
" <td>Other</td>\n",
" <td>Some college/university study without earning ...</td>\n",
" <td>Student</td>\n",
" <td>3-5 years</td>\n",
" <td>Python</td>\n",
" <td>NaN</td>\n",
" <td>SQL</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Java</td>\n",
" <td>Javascript</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>MATLAB</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Python</td>\n",
" <td>Jupyter (JupyterLab, Jupyter Notebooks, etc)</td>\n",
" <td>NaN</td>\n",
" <td>Visual Studio</td>\n",
" <td>Visual Studio Code (VSCode)</td>\n",
" <td>PyCharm</td>\n",
" <td>Spyder</td>\n",
" <td>NaN</td>\n",
" <td>Sublime Text</td>\n",
" <td>Vim / Emacs</td>\n",
" <td>MATLAB</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Binder / JupyterHub</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Google Cloud AI Platform Notebooks</td>\n",
" <td>Google Cloud Datalab Notebooks</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>A personal computer or laptop</td>\n",
" <td>GPUs</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Never</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>1-2 years</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>Linear or Logistic Regression</td>\n",
" <td>Decision Trees or Random Forests</td>\n",
" <td>NaN</td>\n",
" <td>Bayesian Approaches</td>\n",
" <td>NaN</td>\n",
" <td>Dense Neural Networks (MLPs, etc)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Microsoft Azure</td>\n",
" <td>Google Cloud Platform (GCP)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>VMware Cloud</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Amazon EC2</td>\n",
" <td>NaN</td>\n",
" <td>Amazon Elastic Container Service</td>\n",
" <td>Azure Cloud Services</td>\n",
" <td>Microsoft Azure Container Instances</td>\n",
" <td>NaN</td>\n",
" <td>Google Cloud Compute Engine</td>\n",
" <td>Google Cloud Functions</td>\n",
" <td>Google Cloud Run</td>\n",
" <td>Google Cloud App Engine</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Amazon Rekognition</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Google Cloud AI Platform / Google Cloud ML En...</td>\n",
" <td>NaN</td>\n",
" <td>Google Cloud Natural Language</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2020</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Q1 Q2 Q3 \\\n",
"time \n",
"00:02:30 22-24 Man China \n",
"00:11:34 35-39 Man South Africa \n",
"00:06:51 30-34 Woman Other \n",
"\n",
" Q4 Q5 \\\n",
"time \n",
"00:02:30 No formal education past high school Student \n",
"00:11:34 Some college/university study without earning ... Data Analyst \n",
"00:06:51 Some college/university study without earning ... Student \n",
"\n",
" Q6 Q7_Part_1 Q7_Part_2 Q7_Part_3 Q7_Part_4 Q7_Part_5 \\\n",
"time \n",
"00:02:30 < 1 years Python NaN SQL NaN NaN \n",
"00:11:34 < 1 years Python NaN SQL NaN NaN \n",
"00:06:51 3-5 years Python NaN SQL NaN NaN \n",
"\n",
" Q7_Part_6 Q7_Part_7 Q7_Part_8 Q7_Part_9 Q7_Part_10 Q7_Part_11 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN NaN \n",
"00:06:51 Java Javascript NaN NaN NaN MATLAB \n",
"\n",
" Q7_Part_12 Q7_OTHER Q8 \\\n",
"time \n",
"00:02:30 NaN NaN Python \n",
"00:11:34 NaN NaN Python \n",
"00:06:51 NaN NaN Python \n",
"\n",
" Q9_Part_1 Q9_Part_2 \\\n",
"time \n",
"00:02:30 Jupyter (JupyterLab, Jupyter Notebooks, etc) NaN \n",
"00:11:34 Jupyter (JupyterLab, Jupyter Notebooks, etc) NaN \n",
"00:06:51 Jupyter (JupyterLab, Jupyter Notebooks, etc) NaN \n",
"\n",
" Q9_Part_3 Q9_Part_4 Q9_Part_5 Q9_Part_6 \\\n",
"time \n",
"00:02:30 NaN NaN PyCharm NaN \n",
"00:11:34 NaN Visual Studio Code (VSCode) NaN NaN \n",
"00:06:51 Visual Studio Visual Studio Code (VSCode) PyCharm Spyder \n",
"\n",
" Q9_Part_7 Q9_Part_8 Q9_Part_9 Q9_Part_10 Q9_Part_11 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN \n",
"00:06:51 NaN Sublime Text Vim / Emacs MATLAB NaN \n",
"\n",
" Q9_OTHER Q10_Part_1 Q10_Part_2 Q10_Part_3 Q10_Part_4 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN Kaggle Notebooks NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q10_Part_5 Q10_Part_6 Q10_Part_7 Q10_Part_8 Q10_Part_9 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN \n",
"00:06:51 Binder / JupyterHub NaN NaN NaN NaN \n",
"\n",
" Q10_Part_10 Q10_Part_11 \\\n",
"time \n",
"00:02:30 NaN NaN \n",
"00:11:34 NaN NaN \n",
"00:06:51 Google Cloud AI Platform Notebooks Google Cloud Datalab Notebooks \n",
"\n",
" Q10_Part_12 Q10_Part_13 Q10_OTHER Q11 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN A personal computer or laptop \n",
"00:06:51 NaN NaN NaN A personal computer or laptop \n",
"\n",
" Q12_Part_1 Q12_Part_2 Q12_Part_3 Q12_OTHER Q13 Q14_Part_1 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN NaN \n",
"00:11:34 GPUs NaN NaN NaN Never NaN \n",
"00:06:51 GPUs NaN NaN NaN Never NaN \n",
"\n",
" Q14_Part_2 Q14_Part_3 Q14_Part_4 Q14_Part_5 Q14_Part_6 Q14_Part_7 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q14_Part_8 Q14_Part_9 Q14_Part_10 Q14_Part_11 Q14_OTHER \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN Other \n",
"00:06:51 NaN NaN NaN None NaN \n",
"\n",
" Q15 Q16_Part_1 Q16_Part_2 \\\n",
"time \n",
"00:02:30 NaN NaN NaN \n",
"00:11:34 I do not use machine learning methods NaN NaN \n",
"00:06:51 1-2 years NaN NaN \n",
"\n",
" Q16_Part_3 Q16_Part_4 Q16_Part_5 Q16_Part_6 Q16_Part_7 Q16_Part_8 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q16_Part_9 Q16_Part_10 Q16_Part_11 Q16_Part_12 Q16_Part_13 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q16_Part_14 Q16_Part_15 Q16_OTHER Q17_Part_1 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN \n",
"00:06:51 NaN None NaN Linear or Logistic Regression \n",
"\n",
" Q17_Part_2 Q17_Part_3 Q17_Part_4 \\\n",
"time \n",
"00:02:30 NaN NaN NaN \n",
"00:11:34 NaN NaN NaN \n",
"00:06:51 Decision Trees or Random Forests NaN Bayesian Approaches \n",
"\n",
" Q17_Part_5 Q17_Part_6 Q17_Part_7 Q17_Part_8 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN \n",
"00:06:51 NaN Dense Neural Networks (MLPs, etc) NaN NaN \n",
"\n",
" Q17_Part_9 Q17_Part_10 Q17_Part_11 Q17_OTHER Q18_Part_1 Q18_Part_2 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q18_Part_3 Q18_Part_4 Q18_Part_5 Q18_Part_6 Q18_OTHER Q19_Part_1 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q19_Part_2 Q19_Part_3 Q19_Part_4 Q19_Part_5 Q19_OTHER \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q20 Q21 Q22 \\\n",
"time \n",
"00:02:30 NaN NaN NaN \n",
"00:11:34 0-49 employees 3-4 I do not know \n",
"00:06:51 NaN NaN NaN \n",
"\n",
" Q23_Part_1 Q23_Part_2 \\\n",
"time \n",
"00:02:30 NaN NaN \n",
"00:11:34 Analyze and understand data to influence produ... NaN \n",
"00:06:51 NaN NaN \n",
"\n",
" Q23_Part_3 Q23_Part_4 Q23_Part_5 Q23_Part_6 Q23_Part_7 Q23_OTHER \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q24 Q25 Q26_A_Part_1 Q26_A_Part_2 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN \n",
"00:11:34 10,000-14,999 $0 ($USD) Amazon Web Services (AWS) NaN \n",
"00:06:51 NaN NaN NaN NaN \n",
"\n",
" Q26_A_Part_3 Q26_A_Part_4 Q26_A_Part_5 Q26_A_Part_6 Q26_A_Part_7 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q26_A_Part_8 Q26_A_Part_9 Q26_A_Part_10 Q26_A_Part_11 Q26_A_OTHER \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q27_A_Part_1 Q27_A_Part_2 Q27_A_Part_3 Q27_A_Part_4 Q27_A_Part_5 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q27_A_Part_6 Q27_A_Part_7 Q27_A_Part_8 Q27_A_Part_9 Q27_A_Part_10 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q27_A_Part_11 Q27_A_OTHER Q28_A_Part_1 Q28_A_Part_2 Q28_A_Part_3 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 No / None NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q28_A_Part_4 Q28_A_Part_5 Q28_A_Part_6 Q28_A_Part_7 Q28_A_Part_8 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q28_A_Part_9 Q28_A_Part_10 Q28_A_OTHER Q29_A_Part_1 Q29_A_Part_2 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN No / None NaN MySQL NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q29_A_Part_3 Q29_A_Part_4 Q29_A_Part_5 Q29_A_Part_6 Q29_A_Part_7 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q29_A_Part_8 Q29_A_Part_9 Q29_A_Part_10 Q29_A_Part_11 Q29_A_Part_12 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q29_A_Part_13 Q29_A_Part_14 Q29_A_Part_15 Q29_A_Part_16 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN \n",
"\n",
" Q29_A_Part_17 Q29_A_OTHER Q30 Q31_A_Part_1 Q31_A_Part_2 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q31_A_Part_3 Q31_A_Part_4 Q31_A_Part_5 Q31_A_Part_6 Q31_A_Part_7 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN Tableau NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q31_A_Part_8 Q31_A_Part_9 Q31_A_Part_10 Q31_A_Part_11 Q31_A_Part_12 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q31_A_Part_13 Q31_A_Part_14 Q31_A_OTHER Q32 Q33_A_Part_1 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q33_A_Part_2 Q33_A_Part_3 Q33_A_Part_4 Q33_A_Part_5 Q33_A_Part_6 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q33_A_Part_7 Q33_A_OTHER Q34_A_Part_1 Q34_A_Part_2 Q34_A_Part_3 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 No / None NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q34_A_Part_4 Q34_A_Part_5 Q34_A_Part_6 Q34_A_Part_7 Q34_A_Part_8 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q34_A_Part_9 Q34_A_Part_10 Q34_A_Part_11 Q34_A_OTHER Q35_A_Part_1 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q35_A_Part_2 Q35_A_Part_3 Q35_A_Part_4 Q35_A_Part_5 Q35_A_Part_6 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q35_A_Part_7 Q35_A_Part_8 Q35_A_Part_9 Q35_A_Part_10 Q35_A_OTHER \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN No / None NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q36_Part_1 Q36_Part_2 Q36_Part_3 Q36_Part_4 Q36_Part_5 Q36_Part_6 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN Kaggle \n",
"00:06:51 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q36_Part_7 Q36_Part_8 Q36_Part_9 Q36_OTHER Q37_Part_1 Q37_Part_2 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Q37_Part_3 Q37_Part_4 Q37_Part_5 Q37_Part_6 Q37_Part_7 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 Kaggle Learn Courses DataCamp NaN NaN Udemy \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q37_Part_8 Q37_Part_9 Q37_Part_10 Q37_Part_11 Q37_OTHER \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q38 Q39_Part_1 \\\n",
"time \n",
"00:02:30 NaN NaN \n",
"00:11:34 Business intelligence software (Salesforce, Ta... NaN \n",
"00:06:51 NaN NaN \n",
"\n",
" Q39_Part_2 Q39_Part_3 Q39_Part_4 Q39_Part_5 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN \n",
"\n",
" Q39_Part_6 Q39_Part_7 \\\n",
"time \n",
"00:02:30 NaN NaN \n",
"00:11:34 YouTube (Kaggle YouTube, Cloud AI Adventures, ... NaN \n",
"00:06:51 NaN NaN \n",
"\n",
" Q39_Part_8 Q39_Part_9 Q39_Part_10 Q39_Part_11 Q39_OTHER \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q26_B_Part_1 Q26_B_Part_2 \\\n",
"time \n",
"00:02:30 NaN NaN \n",
"00:11:34 Amazon Web Services (AWS) NaN \n",
"00:06:51 NaN Microsoft Azure \n",
"\n",
" Q26_B_Part_3 Q26_B_Part_4 Q26_B_Part_5 \\\n",
"time \n",
"00:02:30 NaN NaN NaN \n",
"00:11:34 NaN NaN NaN \n",
"00:06:51 Google Cloud Platform (GCP) NaN NaN \n",
"\n",
" Q26_B_Part_6 Q26_B_Part_7 Q26_B_Part_8 Q26_B_Part_9 Q26_B_Part_10 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN \n",
"00:06:51 NaN VMware Cloud NaN NaN NaN \n",
"\n",
" Q26_B_Part_11 Q26_B_OTHER Q27_B_Part_1 Q27_B_Part_2 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN \n",
"00:11:34 NaN NaN Amazon EC2 AWS Lambda \n",
"00:06:51 NaN NaN Amazon EC2 NaN \n",
"\n",
" Q27_B_Part_3 Q27_B_Part_4 \\\n",
"time \n",
"00:02:30 NaN NaN \n",
"00:11:34 Amazon Elastic Container Service NaN \n",
"00:06:51 Amazon Elastic Container Service Azure Cloud Services \n",
"\n",
" Q27_B_Part_5 Q27_B_Part_6 \\\n",
"time \n",
"00:02:30 NaN NaN \n",
"00:11:34 NaN NaN \n",
"00:06:51 Microsoft Azure Container Instances NaN \n",
"\n",
" Q27_B_Part_7 Q27_B_Part_8 \\\n",
"time \n",
"00:02:30 NaN NaN \n",
"00:11:34 NaN NaN \n",
"00:06:51 Google Cloud Compute Engine Google Cloud Functions \n",
"\n",
" Q27_B_Part_9 Q27_B_Part_10 Q27_B_Part_11 \\\n",
"time \n",
"00:02:30 NaN NaN NaN \n",
"00:11:34 NaN NaN NaN \n",
"00:06:51 Google Cloud Run Google Cloud App Engine NaN \n",
"\n",
" Q27_B_OTHER Q28_B_Part_1 Q28_B_Part_2 Q28_B_Part_3 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN \n",
"00:11:34 NaN Amazon SageMaker NaN NaN \n",
"00:06:51 NaN NaN NaN Amazon Rekognition \n",
"\n",
" Q28_B_Part_4 Q28_B_Part_5 \\\n",
"time \n",
"00:02:30 NaN NaN \n",
"00:11:34 NaN NaN \n",
"00:06:51 NaN NaN \n",
"\n",
" Q28_B_Part_6 Q28_B_Part_7 \\\n",
"time \n",
"00:02:30 NaN NaN \n",
"00:11:34 NaN NaN \n",
"00:06:51 Google Cloud AI Platform / Google Cloud ML En... NaN \n",
"\n",
" Q28_B_Part_8 Q28_B_Part_9 Q28_B_Part_10 \\\n",
"time \n",
"00:02:30 NaN NaN NaN \n",
"00:11:34 NaN NaN NaN \n",
"00:06:51 Google Cloud Natural Language NaN NaN \n",
"\n",
" Q28_B_OTHER Q29_B_Part_1 Q29_B_Part_2 Q29_B_Part_3 Q29_B_Part_4 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q29_B_Part_5 Q29_B_Part_6 Q29_B_Part_7 Q29_B_Part_8 Q29_B_Part_9 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q29_B_Part_10 Q29_B_Part_11 Q29_B_Part_12 Q29_B_Part_13 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN \n",
"00:11:34 NaN NaN Amazon Athena NaN \n",
"00:06:51 NaN NaN NaN NaN \n",
"\n",
" Q29_B_Part_14 Q29_B_Part_15 Q29_B_Part_16 Q29_B_Part_17 Q29_B_OTHER \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q31_B_Part_1 Q31_B_Part_2 Q31_B_Part_3 Q31_B_Part_4 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN \n",
"00:11:34 NaN Amazon QuickSight NaN NaN \n",
"00:06:51 NaN NaN NaN NaN \n",
"\n",
" Q31_B_Part_5 Q31_B_Part_6 Q31_B_Part_7 Q31_B_Part_8 Q31_B_Part_9 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 Tableau NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q31_B_Part_10 Q31_B_Part_11 Q31_B_Part_12 Q31_B_Part_13 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN \n",
"\n",
" Q31_B_Part_14 Q31_B_OTHER Q33_B_Part_1 Q33_B_Part_2 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN \n",
"\n",
" Q33_B_Part_3 Q33_B_Part_4 \\\n",
"time \n",
"00:02:30 NaN NaN \n",
"00:11:34 Automated model selection (e.g. auto-sklearn, ... NaN \n",
"00:06:51 NaN NaN \n",
"\n",
" Q33_B_Part_5 Q33_B_Part_6 Q33_B_Part_7 Q33_B_OTHER Q34_B_Part_1 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q34_B_Part_2 Q34_B_Part_3 Q34_B_Part_4 Q34_B_Part_5 Q34_B_Part_6 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q34_B_Part_7 Q34_B_Part_8 Q34_B_Part_9 Q34_B_Part_10 Q34_B_Part_11 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN None \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q34_B_OTHER Q35_B_Part_1 Q35_B_Part_2 Q35_B_Part_3 Q35_B_Part_4 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q35_B_Part_5 Q35_B_Part_6 Q35_B_Part_7 Q35_B_Part_8 Q35_B_Part_9 \\\n",
"time \n",
"00:02:30 NaN NaN NaN NaN NaN \n",
"00:11:34 NaN NaN NaN NaN NaN \n",
"00:06:51 NaN NaN NaN NaN NaN \n",
"\n",
" Q35_B_Part_10 Q35_B_OTHER Year \n",
"time \n",
"00:02:30 NaN NaN 2020 \n",
"00:11:34 None NaN 2020 \n",
"00:06:51 NaN NaN 2020 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Kaggle_NDegree=Kaggle[(Kaggle.Q4 != \"Doctoral degree\") &\n",
" (Kaggle.Q4 != \"Master’s degree\") &\n",
" (Kaggle.Q4 != \"Bachelor’s degree\")&\n",
" (Kaggle.Q4 != \"Professional degree\")]\n",
"Kaggle_WDegree=Kaggle[(Kaggle.Q4 == \"Doctoral degree\") |\n",
" (Kaggle.Q4 == \"Master’s degree\") |\n",
" (Kaggle.Q4 == \"Bachelor’s degree\")|\n",
" (Kaggle.Q4 == \"Professional degree\")]\n",
"Kaggle_WDegree.head(3);\n",
"Kaggle_NDegree.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.114743,
"end_time": "2020-12-05T10:05:02.674674",
"exception": false,
"start_time": "2020-12-05T10:05:02.559931",
"status": "completed"
},
"tags": []
},
"source": [
"# Age Group and Sex"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.11411,
"end_time": "2020-12-05T10:05:02.904524",
"exception": false,
"start_time": "2020-12-05T10:05:02.790414",
"status": "completed"
},
"tags": []
},
"source": [
"Exploring the Age group and sex mentioned in the survey. I used a simple bar chart to explain the distribution of people using Kaggle in 2020 with and without Degree. "
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.119958,
"end_time": "2020-12-05T10:05:03.138366",
"exception": false,
"start_time": "2020-12-05T10:05:03.018408",
"status": "completed"
},
"tags": []
},
"source": [
"## Exploring Participantas with and without Degree"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_kg_hide-input": true,
"execution": {
"iopub.execute_input": "2020-12-05T10:05:03.385789Z",
"iopub.status.busy": "2020-12-05T10:05:03.382994Z",
"iopub.status.idle": "2020-12-05T10:05:03.402648Z",
"shell.execute_reply": "2020-12-05T10:05:03.403435Z"
},
"papermill": {
"duration": 0.149505,
"end_time": "2020-12-05T10:05:03.403638",
"exception": false,
"start_time": "2020-12-05T10:05:03.254133",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"D=Kaggle\n",
"D.Q4[(D.Q4 == \"Doctoral degree\") |\n",
" (D.Q4 == \"Master’s degree\") |\n",
" (D.Q4 == \"Bachelor’s degree\")|\n",
" (D.Q4 == \"Professional degree\")] = 'With Degree'\n",
"\n",
"D.Q4[D.Q4 != \"With Degree\"] = 'Without Degree'\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_kg_hide-input": true,
"execution": {
"iopub.execute_input": "2020-12-05T10:05:03.679255Z",
"iopub.status.busy": "2020-12-05T10:05:03.678277Z",
"iopub.status.idle": "2020-12-05T10:05:04.521577Z",
"shell.execute_reply": "2020-12-05T10:05:04.520718Z"
},
"papermill": {
"duration": 0.982322,
"end_time": "2020-12-05T10:05:04.521705",
"exception": false,
"start_time": "2020-12-05T10:05:03.539383",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1,1,figsize=(15,5))\n",
"ax1=sns.histplot(D.sort_values(by=\"Q1\"), x=\"Q1\", kde=True, hue='Q4', palette=\"viridis\")\n",
"ax1.set_title('Age Group with and without Degree',fontsize=16, fontweight='bold')\n",
"ax1.set(xlabel='',ylabel=\"Age Group\");\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.116846,
"end_time": "2020-12-05T10:05:04.755697",
"exception": false,
"start_time": "2020-12-05T10:05:04.638851",
"status": "completed"
},
"tags": []
},
"source": [
"As you can see in Histplot 〽 how Kaggle users are mostly with University degrees, but the trend of the age group is different from people without a degree. We can see that the people without a degree are mostly young generation and the age grows the number reduce ↘, may be they opt to get into college to improve the skills. For participants with formal degree peaked at 25-29 years and then the number slowly reduces. The difference between participants with degrees and without is quite high."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_kg_hide-input": true,
"execution": {
"iopub.execute_input": "2020-12-05T10:05:04.995824Z",
"iopub.status.busy": "2020-12-05T10:05:04.994988Z",
"iopub.status.idle": "2020-12-05T10:05:05.017850Z",
"shell.execute_reply": "2020-12-05T10:05:05.017238Z"
},
"papermill": {
"duration": 0.146026,
"end_time": "2020-12-05T10:05:05.017984",
"exception": false,
"start_time": "2020-12-05T10:05:04.871958",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Q4</th>\n",
" <th>With Degree</th>\n",
" <th>Without Degree</th>\n",
" <th>With Degree %</th>\n",
" <th>Without Degree %</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Q1</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>18-21</th>\n",
" <td>2758</td>\n",
" <td>711</td>\n",
" <td>15.5</td>\n",
" <td>32.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22-24</th>\n",
" <td>3420</td>\n",
" <td>366</td>\n",
" <td>19.2</td>\n",
" <td>16.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25-29</th>\n",
" <td>3720</td>\n",
" <td>291</td>\n",
" <td>20.9</td>\n",
" <td>13.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30-34</th>\n",
" <td>2573</td>\n",
" <td>238</td>\n",
" <td>14.4</td>\n",
" <td>10.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35-39</th>\n",
" <td>1817</td>\n",
" <td>174</td>\n",
" <td>10.2</td>\n",
" <td>7.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40-44</th>\n",
" <td>1261</td>\n",
" <td>136</td>\n",
" <td>7.1</td>\n",
" <td>6.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45-49</th>\n",
" <td>876</td>\n",
" <td>112</td>\n",
" <td>4.9</td>\n",
" <td>5.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50-54</th>\n",
" <td>631</td>\n",
" <td>67</td>\n",
" <td>3.5</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55-59</th>\n",
" <td>354</td>\n",
" <td>57</td>\n",
" <td>2.0</td>\n",
" <td>2.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60-69</th>\n",
" <td>363</td>\n",
" <td>35</td>\n",
" <td>2.0</td>\n",
" <td>1.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70+</th>\n",
" <td>65</td>\n",
" <td>11</td>\n",
" <td>0.4</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Q4 With Degree Without Degree With Degree % Without Degree %\n",
"Q1 \n",
"18-21 2758 711 15.5 32.3\n",
"22-24 3420 366 19.2 16.7\n",
"25-29 3720 291 20.9 13.2\n",
"30-34 2573 238 14.4 10.8\n",
"35-39 1817 174 10.2 7.9\n",
"40-44 1261 136 7.1 6.2\n",
"45-49 876 112 4.9 5.1\n",
"50-54 631 67 3.5 3.0\n",
"55-59 354 57 2.0 2.6\n",
"60-69 363 35 2.0 1.6\n",
"70+ 65 11 0.4 0.5"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"catD=D.groupby(\"Q1\")[\"Q4\"].value_counts().unstack()\n",
"catD[\"With Degree %\"]= ((catD[\"With Degree\"]/sum(catD[\"With Degree\"]))*100).round(1)\n",
"catD[\"Without Degree %\"]= ((catD[\"Without Degree\"]/sum(catD[\"Without Degree\"]))*100).round(1)\n",
"catD.sort_index(inplace=True)\n",
"catD"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_kg_hide-input": true,
"execution": {
"iopub.execute_input": "2020-12-05T10:05:05.198651Z",
"iopub.status.busy": "2020-12-05T10:05:05.192953Z",
"iopub.status.idle": "2020-12-05T10:05:05.460654Z",
"shell.execute_reply": "2020-12-05T10:05:05.459934Z"
},
"papermill": {
"duration": 0.36219,
"end_time": "2020-12-05T10:05:05.460777",
"exception": false,
"start_time": "2020-12-05T10:05:05.098587",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.ticker as mtick\n",
"\n",
"catNew=catD[[\"With Degree %\",\"Without Degree %\"]].stack()\n",
"catNew=pd.DataFrame(catNew).reset_index().rename(columns={0:\"count\"})\n",
"fig, ax = plt.subplots(1,1,figsize=(15,5))\n",
"ax1=sns.barplot( data=catNew.sort_values(by=\"Q1\"),x='Q1',y='count', hue='Q4', palette=\"viridis\")\n",
"ax1.set_title('Age Group with and without Degree Normalized',fontsize=16, fontweight='bold')\n",
"ax1.set(xlabel='Age Group',ylabel=\"\")\n",
"ax1.legend(title=\"\");\n",
"ax.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.0f%%'));"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.117816,
"end_time": "2020-12-05T10:05:05.697112",
"exception": false,
"start_time": "2020-12-05T10:05:05.579296",
"status": "completed"
},
"tags": []
},
"source": [
"When we look deeper into the percentage of participants with and without Degree it makes it clear that most of the people who are using Kaggle are young generation and without a degree, with more than 30+ % of participants are in the age range of 18-21 years. We will be looking at more parameters to determine the reasons and look deep into the specific pattern."
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.117966,
"end_time": "2020-12-05T10:05:05.933372",
"exception": false,
"start_time": "2020-12-05T10:05:05.815406",
"status": "completed"
},
"tags": []
},
"source": [
"## Going deep into Particpants without Degree"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_kg_hide-input": true,
"execution": {
"iopub.execute_input": "2020-12-05T10:05:06.183144Z",
"iopub.status.busy": "2020-12-05T10:05:06.179970Z",
"iopub.status.idle": "2020-12-05T10:05:06.299914Z",
"shell.execute_reply": "2020-12-05T10:05:06.299187Z"
},
"papermill": {
"duration": 0.248738,
"end_time": "2020-12-05T10:05:06.300044",
"exception": false,
"start_time": "2020-12-05T10:05:06.051306",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"f, axes = plt.subplots(1, 1,figsize=(10,5))\n",
"sns.set_style(\"whitegrid\", {'axes.grid' : False})\n",
"\n",
"\n",
"sex=Kaggle_NDegree.Q2.value_counts().sort_values(ascending=False).to_frame()\n",
"ax1=sns.barplot(data=sex,x=sex.index,y='Q2',palette=\"coolwarm\")\n",
"ax1.set_title('Different type of Sex in Survey',fontsize=21, fontweight='bold')\n",
"ax1.set_xlabel('Sex')\n",
"ax1.set_ylabel('')\n",
"ax1.set_xticklabels(ax1.get_xticklabels(),rotation=90);\n",
"ax1.set_yticks([])\n",
"for p in ax1.patches:\n",
" ax1.annotate(format(p.get_height(), '1.0f'), \n",
" (p.get_x() + p.get_width() / 2., p.get_height()), \n",
" ha = 'center', va = 'center', \n",
" xytext = (0, 9), \n",
" textcoords = 'offset points')\n",
"for s in ['top', 'left', 'right', 'bottom']:\n",
" ax1.spines[s].set_visible(False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.118731,
"end_time": "2020-12-05T10:05:06.537949",
"exception": false,
"start_time": "2020-12-05T10:05:06.419218",
"status": "completed"
},
"tags": []
},
"source": [
"## Male and Female VS Age Group"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_kg_hide-input": true,
"execution": {
"iopub.execute_input": "2020-12-05T10:05:06.804460Z",
"iopub.status.busy": "2020-12-05T10:05:06.800545Z",
"iopub.status.idle": "2020-12-05T10:05:07.327526Z",
"shell.execute_reply": "2020-12-05T10:05:07.326851Z"
},
"papermill": {
"duration": 0.669051,
"end_time": "2020-12-05T10:05:07.327631",
"exception": false,
"start_time": "2020-12-05T10:05:06.658580",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x720 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1,1, figsize=(15,10))\n",
"age_sex=Kaggle_NDegree.groupby(['Q1'])['Q2'].value_counts().unstack().sort_index()\n",
"man=age_sex[\"Man\"].to_frame()\n",
"woman=-age_sex[\"Woman\"].to_frame()\n",
"\n",
"ax=sns.barplot(data=man,x='Man',y=man.index,color=\"#006699\",label='Male')\n",
"ax=sns.barplot(data=woman,x='Woman',y=woman.index,color=\"#ff3333\",label='Female')\n",
"ax.set_xlim(-200, 600)\n",
"\n",
" \n",
"ax.set_xlabel('Number of Particepants')\n",
"ax.set_ylabel('Age Group',fontsize=15)\n",
"ax.set_title('Number of Male and Female Vs Age Group',fontsize=16, fontweight='bold')\n",
"for s in ['top', 'right', 'bottom']:\n",
" ax.spines[s].set_visible(False)\n",
"#annotate\n",
"# for p in ax.patches:\n",
"# width = p.get_width()\n",
"# plt.text(5+p.get_width(), p.get_y()+0.55*p.get_height(),\n",
"# '{:1.0f}'.format(abs(width)),\n",
"# ha='center', va='center',rotation=90)\n",
"\n",
"ax.legend();"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.082817,
"end_time": "2020-12-05T10:05:07.494443",
"exception": false,
"start_time": "2020-12-05T10:05:07.411626",
"status": "completed"
},
"tags": []
},
"source": [
"The Bar plot clearly shows that this platform is more populated by males and at very young generation. People who join Kaggle find it more related to Reddit and other social media platforms and as you can see this website is attracting a lot of young generation that is between for machine learning and data science. As far as the difference is Male vs female, we can see that This platform is more dominant by Male as most programmers or data scientists are in general are mal"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.082341,
"end_time": "2020-12-05T10:05:07.659738",
"exception": false,
"start_time": "2020-12-05T10:05:07.577397",
"status": "completed"
},
"tags": []
},
"source": [
"## Current Job of participants without Degree"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.083891,
"end_time": "2020-12-05T10:05:07.826232",
"exception": false,
"start_time": "2020-12-05T10:05:07.742341",
"status": "completed"
},
"tags": []
},
"source": [
"In this part, we will be looking at some of the Current jobs held by people without a University Degree. "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_kg_hide-input": true,
"execution": {
"iopub.execute_input": "2020-12-05T10:05:08.010391Z",
"iopub.status.busy": "2020-12-05T10:05:08.009420Z",
"iopub.status.idle": "2020-12-05T10:05:08.433222Z",
"shell.execute_reply": "2020-12-05T10:05:08.432331Z"
},
"papermill": {
"duration": 0.526226,
"end_time": "2020-12-05T10:05:08.433403",
"exception": false,
"start_time": "2020-12-05T10:05:07.907177",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x720 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1,1, figsize=(15,10))\n",
"XP=Kaggle_NDegree.Q5.value_counts().sort_values(ascending=False).to_frame()\n",
"ax=sns.barplot(data=XP,x=XP.index,y='Q5',palette=\"viridis\")\n",
"ax.set_title('Different type of Jobs in Survey',fontsize=16, fontweight='bold')\n",
"\n",
"ax.set_xlabel('Current role')\n",
"ax.set_ylabel('Number of Particpents')\n",
"ax.set_xticklabels(ax.get_xticklabels(),rotation=90)\n",
"for p in ax.patches:\n",
" ax.annotate(format(p.get_height(), '1.0f'), \n",
" (p.get_x() + p.get_width() / 2., p.get_height()), \n",
" ha = 'center', va = 'center', \n",
" xytext = (0, 9), \n",
" textcoords = 'offset points')\n",
"for s in ['top', 'left', 'right', 'bottom']:\n",
" ax.spines[s].set_visible(False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.084785,
"end_time": "2020-12-05T10:05:08.645572",
"exception": false,
"start_time": "2020-12-05T10:05:08.560787",
"status": "completed"
},
"tags": []
},
"source": [
"We can see from data that most of the participants were either students who are getting their formal degree online or at the campus or getting certifications and unemployed due to Covid 19 or lack of opportunities. I will be comparing Unemployment in the next part, but for now, Software Engineering and Data Science are runner-ups. I think it due to the low barrier of entry. "
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.084261,
"end_time": "2020-12-05T10:05:08.813823",
"exception": false,
"start_time": "2020-12-05T10:05:08.729562",
"status": "completed"
},
"tags": []
},
"source": [
"## Comparing Current job with and without Degree"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.082004,
"end_time": "2020-12-05T10:05:08.978557",
"exception": false,
"start_time": "2020-12-05T10:05:08.896553",
"status": "completed"
},
"tags": []
},
"source": [
"In this part we will be comparing the results of a survey based on Education, mostly focusing on People with a degree and without."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_kg_hide-input": true,
"execution": {
"iopub.execute_input": "2020-12-05T10:05:09.166783Z",
"iopub.status.busy": "2020-12-05T10:05:09.161840Z",
"iopub.status.idle": "2020-12-05T10:05:09.543018Z",
"shell.execute_reply": "2020-12-05T10:05:09.542161Z"
},
"papermill": {
"duration": 0.47906,
"end_time": "2020-12-05T10:05:09.543147",
"exception": false,
"start_time": "2020-12-05T10:05:09.064087",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1,1, figsize=(15,6))\n",
"\n",
"XP=Kaggle_NDegree.Q5.value_counts(normalize=True).sort_index(ascending=False).to_frame()\n",
"XP1=Kaggle_WDegree.Q5.value_counts(normalize=True).sort_index(ascending=False).to_frame()\n",
"ax=sns.barplot(data=XP,y='Q5',x=XP.index,color=\"#006699\",label='Without Degree')\n",
"ax=sns.barplot(data=-XP1,y='Q5',x=XP1.index,color=\"#ff3333\",label='With Degree')\n",
"\n",
"ax.set_ylabel('')\n",
"ax.set_xlabel('')\n",
"ax.set_title('Participants With Degree and Without Degree',fontsize=16, fontweight='bold')\n",
"ax.set_xticklabels(ax.get_xticklabels(),rotation=90)\n",
"for s in ['top', 'left', 'right', 'bottom']:\n",
" ax.spines[s].set_visible(False)\n",
"#annotate\n",
"for p in ax.patches:\n",
" ax.annotate('{:.1f}%'.format(abs(100*p.get_height())), \n",
" (p.get_x() + p.get_width() / 2.,p.get_height()), \n",
" ha = 'center', va = 'center', \n",
" xytext = (0,6), \n",
" textcoords = 'offset points')\n",
"ax.set_yticks([]) \n",
"ax.legend(loc='lower right');"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.127265,
"end_time": "2020-12-05T10:05:09.797912",
"exception": false,
"start_time": "2020-12-05T10:05:09.670647",
"status": "completed"
},
"tags": []
},
"source": [
"Will comparing the impact of education on getting a job, we can clearly see that participants with a degree are more evenly spread, most of them are students and then Data Scientist, but If we look at the red of participants without a degree which is more saturated towards student and unemployed combined 51+ % shared by these groups, compared to 33+% of Participants with Degree. "
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.124543,
"end_time": "2020-12-05T10:05:10.048200",
"exception": false,
"start_time": "2020-12-05T10:05:09.923657",
"status": "completed"
},
"tags": []
},
"source": [
"# Coding Experience and Job Relationship "
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.126787,
"end_time": "2020-12-05T10:05:10.303222",
"exception": false,
"start_time": "2020-12-05T10:05:10.176435",
"status": "completed"
},
"tags": []
},
"source": [
"In this part, we will be exploring the effect of having programming experience on getting jobs or getting better opportunities for both With a degree and without degree holders."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_kg_hide-input": true,
"execution": {
"iopub.execute_input": "2020-12-05T10:05:10.579478Z",
"iopub.status.busy": "2020-12-05T10:05:10.568197Z",
"iopub.status.idle": "2020-12-05T10:05:11.524455Z",
"shell.execute_reply": "2020-12-05T10:05:11.525159Z"
},
"papermill": {
"duration": 1.097442,
"end_time": "2020-12-05T10:05:11.525309",
"exception": false,
"start_time": "2020-12-05T10:05:10.427867",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x360 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1,2, figsize=(10,5))\n",
"K_heat = []\n",
"for i in Kaggle_NDegree.Q6.value_counts().index.to_list():\n",
" K_heat.append(Kaggle_NDegree.Q5.loc[Kaggle_NDegree.Q6 == str(i)].value_counts().to_frame().rename(columns={'Q5':str(i)}))\n",
"res_K_heat = pd.concat(K_heat, axis=1)\n",
"K_heat_W = []\n",
"for i in Kaggle_WDegree.Q6.value_counts().index.to_list():\n",
" K_heat_W.append(Kaggle_WDegree.Q5.loc[Kaggle_WDegree.Q6 == str(i)].value_counts().to_frame().rename(columns={'Q5':str(i)}))\n",
"res_K_heat_W = pd.concat(K_heat_W, axis=1)\n",
"\n",
"\n",
"ax0 = sns.heatmap(res_K_heat.sort_index(axis=1), linewidths=1.2, cbar=False, annot=True, fmt='g',cmap=sns.cubehelix_palette(as_cmap=True),ax=ax[0])\n",
"ax1= sns.heatmap(res_K_heat_W.sort_index(axis=1), linewidths=1.2, cbar=False, annot=True, fmt='g',cmap=sns.cubehelix_palette(as_cmap=True),ax=ax[1])\n",
"\n",
"ax0.set_title('Participants without degree',fontsize=16, fontweight='bold')\n",
"ax1.set_title('Participants with degree',fontsize=16, fontweight='bold')\n",
"ax1.set_yticks([]);"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.091078,
"end_time": "2020-12-05T10:05:11.707989",
"exception": false,
"start_time": "2020-12-05T10:05:11.616911",
"status": "completed"
},
"tags": []
},
"source": [
"Looking at the heat map it clearly shows how Kaggle is dominated by students who just start coding, but it doesn't give us a clear picture about on having coding experience will improve the chance of getting better opportunities, for that we need to go deep."
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.089923,
"end_time": "2020-12-05T10:05:11.889214",
"exception": false,
"start_time": "2020-12-05T10:05:11.799291",
"status": "completed"
},
"tags": []
},
"source": [
"## Exploring data coding experience without a Degree "
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.088763,
"end_time": "2020-12-05T10:05:12.067125",
"exception": false,
"start_time": "2020-12-05T10:05:11.978362",
"status": "completed"
},
"tags": []
},
"source": [
"Focusing on participants without a degree will make things simple to understand and later we will be comparing those results."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_kg_hide-input": true,
"execution": {
"iopub.execute_input": "2020-12-05T10:05:12.262753Z",
"iopub.status.busy": "2020-12-05T10:05:12.255223Z",
"iopub.status.idle": "2020-12-05T10:05:12.568134Z",
"shell.execute_reply": "2020-12-05T10:05:12.567484Z"
},
"papermill": {
"duration": 0.41117,
"end_time": "2020-12-05T10:05:12.568248",
"exception": false,
"start_time": "2020-12-05T10:05:12.157078",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1,1, figsize=(15,5))\n",
"XP=Kaggle_NDegree.Q6.value_counts().sort_index(ascending=False).to_frame()\n",
"ax=sns.barplot(data=XP,x=XP.index,y='Q6',palette=\"mako\")\n",
"ax.set_title('Coding Experince',fontsize=16, fontweight='bold')\n",
"\n",
"ax.set_xlabel('')\n",
"ax.set_ylabel('Number of Particpents',fontsize=21)\n",
"ax.set_xticklabels(ax.get_xticklabels())\n",
"for p in ax.patches:\n",
" ax.annotate(format(p.get_height(), '1.0f'), \n",
" (p.get_x() + p.get_width() / 2., p.get_height()), \n",
" ha = 'center', va = 'center', \n",
" xytext = (0, 9), \n",
" textcoords = 'offset points')\n",
"for s in ['top', 'left', 'right', 'bottom']:\n",
" ax.spines[s].set_visible(False)\n",
" \n",
"ax.set_xticklabels([\"I have never written code\",\"<1 years\",\"1-2 years\",\"3-5 years\",\"5-10 years\",\"10-20 years\",\"20+ years\"],rotation=90);"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.094753,
"end_time": "2020-12-05T10:05:12.751711",
"exception": false,
"start_time": "2020-12-05T10:05:12.656958",
"status": "completed"
},
"tags": []
},
"source": [
"We can see new development in this bar chat as you can see two peaks, on is on <1 year of coding experience and the other one is 20+ years of experience which is quite amazing, as we can assume people who have been coding for a while are transitioning into Data Science platforms. After this result, I am so curious about the 20+ years of XP and I want to find out what job they have."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"_kg_hide-input": true,
"execution": {
"iopub.execute_input": "2020-12-05T10:05:12.950278Z",
"iopub.status.busy": "2020-12-05T10:05:12.944787Z",
"iopub.status.idle": "2020-12-05T10:05:13.390523Z",
"shell.execute_reply": "2020-12-05T10:05:13.389836Z"
},
"papermill": {
"duration": 0.546182,
"end_time": "2020-12-05T10:05:13.390676",
"exception": false,
"start_time": "2020-12-05T10:05:12.844494",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Y20=Kaggle_NDegree[(Kaggle_NDegree.Q6 == \"20+ years\")]\n",
"\n",
"fig, ax = plt.subplots(1,1, figsize=(15,6))\n",
"XP=Y20.Q5.value_counts().sort_values(ascending=False).to_frame()\n",
"ax=sns.barplot(data=XP,x=XP.index,y='Q5',palette=\"mako\")\n",
"ax.set_title('Current job with coding experience greater then 20 years',fontsize=16, fontweight='bold')\n",
"\n",
"ax.set_xlabel('Current role')\n",
"ax.set_ylabel('Number of Particpents')\n",
"ax.set_xticklabels(ax.get_xticklabels(),rotation=90)\n",
"for p in ax.patches:\n",
" ax.annotate(format(p.get_height(), '1.0f'), \n",
" (p.get_x() + p.get_width() / 2., p.get_height()), \n",
" ha = 'center', va = 'center', \n",
" xytext = (0, 9), \n",
" textcoords = 'offset points')\n",
"for s in ['top', 'left', 'right', 'bottom']:\n",
" ax.spines[s].set_visible(False)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.132063,
"end_time": "2020-12-05T10:05:13.655457",
"exception": false,
"start_time": "2020-12-05T10:05:13.523394",
"status": "completed"
},
"tags": []
},
"source": [
"The majority of them are software engineers who are transitioning into the field of Data Science."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"_kg_hide-input": true,
"execution": {
"iopub.execute_input": "2020-12-05T10:05:13.934851Z",
"iopub.status.busy": "2020-12-05T10:05:13.934010Z",
"iopub.status.idle": "2020-12-05T10:05:13.946259Z",
"shell.execute_reply": "2020-12-05T10:05:13.945517Z"
},
"papermill": {
"duration": 0.157108,
"end_time": "2020-12-05T10:05:13.946412",
"exception": false,
"start_time": "2020-12-05T10:05:13.789304",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"MostXP=Kaggle_NDegree[(Kaggle_NDegree.Q6 != \"1-2 years\") &\n",
" (Kaggle_NDegree.Q6 != \"<1years\") &\n",
" (Kaggle_NDegree.Q6 != \"3-5 years\")&\n",
" (Kaggle_NDegree.Q6 != \"I have never written code\")]"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.093707,
"end_time": "2020-12-05T10:05:14.138364",
"exception": false,
"start_time": "2020-12-05T10:05:14.044657",
"status": "completed"
},
"tags": []
},
"source": [
"## Job titles with 5+ years of coding experience without Degree"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.091977,
"end_time": "2020-12-05T10:05:14.323476",
"exception": false,
"start_time": "2020-12-05T10:05:14.231499",
"status": "completed"
},
"tags": []
},
"source": [
"Dividing Data Set into two major groups, participants with greater than 5 years of experience and participants with less than 5 years of experience.In this part, we will be looking at participants without a degree but who have more than 5 years of experience."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"_kg_hide-input": true,
"execution": {
"iopub.execute_input": "2020-12-05T10:05:14.519939Z",
"iopub.status.busy": "2020-12-05T10:05:14.514279Z",
"iopub.status.idle": "2020-12-05T10:05:14.932130Z",
"shell.execute_reply": "2020-12-05T10:05:14.931303Z"
},
"papermill": {
"duration": 0.518027,
"end_time": "2020-12-05T10:05:14.932259",
"exception": false,
"start_time": "2020-12-05T10:05:14.414232",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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UWGL37bffmpq5JVewYEGtXLnSOCbJm8RZrVYtWrRIDRs2tBlTykTt6NGjqlixopEsOjo6GqWJx44d04svvmiqAXR0dMzUdr755ptUzZE6d+6cqdHgKlWqpCVLlqhkyZKKjIxU9+7djZv1oKAgxcbGZqqWevr06aaErFOnTvr000+NJpjx8fGmG87AwEBTAUr9+vU1f/58ubm5KS4uTiNGjNCOHTskJSZ8q1atko+PjyRp0aJFpm1PmzbN2Pd169ZlWGuUFc7OzlqyZImeffZZPXz4UG+++ab27dsnSYqJidHJkyeNplnff/+9qTT5H//4h5F8nT17Vn369DEdn8zavXu3qeZ3+PDhGjJkiDH95Zdfau7cuZKkgIAAvfXWW0YBRfny5fXRRx8Zydz27dvl4+OjkJAQY/++/PLLDJsLOjg4aN68eca5nPL4zp8/X926dTP+TiqRd3Bw0MKFC43jExsbK19fX2Nfpk2bpt69exs3Uym98MIL+vrrr42btPSuccn997//NW4qy5QpoxUrVhi1S+Hh4fL29lZUVJRiY2P17bffZnj9mTp1qjp06CBJ+te//iV/f39Jid//w4cPG9fU+fPnm34bGjdurKlTpxpJntVqVWBgoJF0nzt3zqjtl6TevXtr0qRJRjK7bNkyTZw4UVLi92/Xrl164YUXbO57Tp9/w4YNM9XCx8bGPlLszz//vPbt22dKaKTE4zN69GgFBARIkn766SejCezHH38sKXVz/7T6heXkZ5/WvmckL66/CQkJmjp1qvG+Z555Rt9//70KFy4sKfEzfvnllxUXF6eoqCgtWLAg3etgVq5rjyohIUHz58+XpExfl1977TXNmzdP8fHxiouL05o1a/Tmm29KSkxU4+PjJSVeY5I3r32UcyDpkTwpC853796tQYMGSUq8bwgMDMywqWta92YpC5bXr19vqvWOjo7W/v37c6xFCjKPZA82pVebltuefvppjR071tRvJL2b8ZEjR5r6p7m4uOjhw4emmg9XV9dUSU/ym4bTp0/rxo0baZZQly1b1tTMs1WrVqZkLzNNTXPKgQMHTHG/9NJLRqInSb6+vlqwYIFxY7Zv3z49fPgwzRqD7t27G4meJA0aNEgLFiwwakIPHDiQqZjKly+vsmXLGqX+x44dU69evYxkr169erp27ZpCQ0N19OhRvfjii6Yb+9q1a6fZXy0nDRw40LgBK1asmOrWrWv0JUy6cUjZBySllOdUoUKF9Mknn5hid3JyUs+ePY3plLVvgwcPNpZ3dnY2JXtJy/v4+Cg8PFyXLl0y5terV890k9WjRw/NmjUrR55z2blzZz377LOSEm8sWrRoYdwUSebzO/k54eTkZLrRq1Wrlrp162YkDFmR9FkkOXHihKlfYlJylWTPnj2p+pHs2rVLmzdvlpTYHDnJu+++q9q1a2e4/eeff95UaJHy+P7666+6c+eOChcubKrxeeqpp7R06VItXbrUmJe81vf69es6ffq0vLy8Um3TyclJEyZMMI20aavA4caNG6Z9c3R01KeffmpaJvl3fe/evel+/xs2bGgkelLidS35Z5f8c0/ZnNHPz890rbRYLKbzM+XyISEhGjlypDF9//590+u7d+/OVLKXk+dflSpVTAUKUuLxf5TYS5YsqT179ujTTz/VqVOndO3atVTLS4nXkpCQEJvnZXI5+dmnt++5JbPX39OnT5vOu4SEBH344YemdTk7Oxu/gbt37043ucrKdc0Wd3d39enTRy+++KKqVq0qd3d3hYSEaObMmdq0aZOx3OLFi9W/f/8Mu74kKVWqlNq3b28011y1apUGDhwoi8ViWufzzz9vrO9Rz4GiRYvq888/18GDB3X58mXdvXs3VbNOSRn+rqR3b5ZyjIAvvvhCbdq0UeXKlVW5cmW5ubk9UjN/ZB/JHlIpXry4qbN4WFiYKZnIK7Vr1870cONpdSiOjIw0NdO4fv16qv6IKYWGhqaZ7NWsWdN0AU0qZUySmdL4nJKyI3/yJm5S4g1Q5cqVjWQvJiZGUVFRaTavSfleFxcXVahQwRig59atW8bgArY0adLEaMKRlOQl/f+3v/1N169fN5K9hw8fmvry5cWokSlvqrLzGUZGRuru3bvGdPXq1VOtJ6WU/ZuSJ9dSYjMoBwcHo6Q2afmkAW+SL5dStWrVciTZS3lsUibeyY9N8rhKlixpNIdOknL/MivleZ3yhjullMdVkiZNmqSjR4+abuIaN26sAQMG2Nx+yu9C0ryk42u1WhUeHq7ChQubYo2JicnUdSWtZM/T0zNTN4Up15XclStXMnxgdUbf/6x8J5Jvo2TJkjYHf0oZZ/ImaWlJ6/NMS06efymb5Cd5lNjTG6QrLSn7kduSk599evueWzJ7rqXcn6Q+6OnJ6LzJynXNlrRquWrUqKGvvvpKISEhRv/z+Ph4/fzzz5kelMrHx8dI9i5duqSDBw+qWrVqpsLQl156yfj7Uc6BM2fOyMfHR3fu3LEZV/LfuZTSuzdLSoiTmq5v2LDBaOXi4OCgZ555Rj169NBrr72W5uBPyD302UMqKW9KkpeEZVXKEqOUTUQzkrIZTFaXzc7z/9Jr/pPyhiIz/WpyS8r9SusHO7P7npkf+8zeECRP2C5evKizZ88aP0wNGzY0mmn++uuvOn78uGlQiNwenEVKLNFMLr1mdVmRmWPzJDyHMrvHJidvFrN6nNL6roaFhenWrVumeSEhIanmpSUnvwsppXddyco1Lkl2zqf0+ial/Nxz8rqWE5+nLY96/qV3/LMbe0REhGbOnGl6LWnwjo4dO6YqsMnqdnLys8/OufcocuP6K2V83uTWNpNzcHBI1Q8/KzWGDRs2NPUDXblypbZs2WLcOz399NN68cUXjdcf5Rz48ssvTYne008/rZYtW6bZFzOj7WR07kyYMEGfffaZWrRoYTr+Dx8+1OnTp/Xpp5/qiy++yPI+4NGQWiOVjh07mkaB8vf3z7BZQsq+TsmbWCS/oZdkqs2xJSs3HmktW6xYMRUsWNC40P3tb39LNRpYbsnNEtOUg7+cP3/eNB0fH28MkCIl9stLmawmOXfunGk6NjbWNChD0aJFjcFubEme7FmtVqN/lZR47JMS/bi4OKNvg5RYE5mZ/npJ8qtZsZT6nAoODjaa9qUn5RDT58+fN9WKXLhwwdSpP+l7lrKvbFojNmZnFMdHVaZMGaN56dWrV41+R0lSnlOZlfK83rp1qypWrJjp99+/f1+jR49OVVofERGh8ePH2+w3m1bcyY+vxWIxmvmWLVtWv//+u6TEz2n37t2ZjjO57CRXKc+n7t27G310c1O5cuWMa821a9d0+fLlDGv3Un6eCxYs0PPPP//IceTk+Zfe8c9u7MePHzcVcPr4+JiGzp8wYcIjfWdz8rPPbmKf29fflMd+8ODBevfdd3N1m7YkJCTIwcEh3X1PWbuY1S4J/fv3N5qibtu2zbi2SImfcfL7q0c5B5Lff5UqVUqbNm0yYj1+/HimB/zK6NxxcHBQz549ja4MkZGROnfunKZNm2YMZPfDDz/ogw8+yNdC878ajjRS6dixo5555hlj+s6dOxowYECqUZ0SEhK0detW/f3vfzfNL1GihPH3kSNHjGZQv/32mykByG2Ojo5q3ry5MR0UFKQ1a9akWi4iIkILFy7UjBkzcmzbBQoUSLWNnNKsWTPTqFarV6829e1avHixrl+/bkw3b9483Yvqhg0bTDdH3333nankLyvNKz09PU0/1El9pypXrix3d3dVq1bNSDq3b99uLFenTp0s/TimHGQjo9HYcpqDg4Nat25tTN+9e1f/+te/TM2x4uLiTIUKKUtMZ82aZTSRiYuLS9WPNGn50qVLm0bIO3nypKlp4//+9z/TTUFeSd6vLT4+Xt9++60xffbs2XQfgWJLyuPk5+eXqinRgwcPFBgYqLfeeivV+z///HMjGXFyclLfvn2N17Zs2WI8wzE9P/30k6k/WMrjW7t2bWOk1eSxhoeH69tvv001Cl9kZKRWrlxpGnUxJ5QsWdLUPG3Lli1ptr74448/NGPGjBwr4GrTpo1peuzYsaaWGlarVQEBAcZouyk/z6+//tp4LUlSk7fRo0enaracntw6/5LLbuxJA2okSV5Q9ttvv9kcKTT5tS2t61p+ffbpxZhenI+iTp06pmanP/zwg+m5v0nOnj2ryZMnm35LcktYWJh69eqlTZs2pSpM2rRpk2nEZCmxcDMrunTpYnQfSTnQycsvv2xa9lHOgeTnp5OTk5FE3r9/P9NNjzNy9+5dzZkzxxgYS0osIG3SpImpQPf+/fupvk/IXdTsIRUHBwdNmzZNr7zyivGF/P3339WjRw/VqlVLZcqU0d27d40HHqfUpEkTrV+/XlJin4Ru3bqpZMmSCg8Pz/MmbUOGDNGuXbsUFxcnq9WqsWPHatasWapcubLi4+MVEhKiy5cvy2q1mgbVeFQpayT69u2rZ555Ro6OjurVq1eqG6escHd3V79+/bRw4UJJif3qevbsKS8vL92+fdv0w+js7Ky333473XXFxMTo5ZdfVv369XXr1i1TQm+xWDJ89mBamjZtaiTUST8sSRd5i8WiBg0aaOfOnaYb46w24XR3d1fhwoWNpHT16tUKCQkxaiEnT56cpfVl1ZAhQ7Rz505j0IXNmzfrwIEDqlWrlhISEnTu3DlFRUUZyUbbtm1Vt25do0/HL7/8og4dOqhWrVq6ePGiqQ9G2bJlTT/u/fv3Nz2E/p133lH9+vX18OHDVI9IySs+Pj5avXq1UXv/3Xffac+ePXJ3d9fx48fTHIwiM9q2bav69esbj17YuXOnWrdubfQPuX79uoKDg02P60iye/du0wApb775pkaOHKnLly8bN0KffPKJGjdunO5IcA8fPtTAgQPl5eWl+Pj4VMc3+XOnBgwYoDVr1hjXv6lTp2rlypWqVq2aHBwcFBoaqt9//10PHz7MlSbKI0aM0ODBg2W1WhUbG6s33nhDNWrUkKenp2JjY/X7778bQ6dn9lmZtgwYMECrV682Bso5fPiwXnzxRdWuXVvOzs46f/68rl27ph07dsjd3V21atVSp06dTAPmJA3zXqRIEUVGRuq3334zaskzW3uTW+dfctmNvW7durJYLMbv3Ny5c3XkyBG5uLjo6NGjqZLBlCpUqGD0T9u/f7/69u1rNJmbPHmyXF1d8+WzTy63r79OTk4aOnSocd2LiopSr169VLt2bZUqVUp3797VhQsXjALN7PYRzqpff/1V7777rgoWLKjatWurcOHCunTpUqo+061atTIVlmeGi4uLXn31VVPBhZSY+CZ/nFKS7J4D9erVM/oChoaGqlOnTqpWrZrOnDmTI4PMPXjwQF999ZW++uorlSlTRqVLl1axYsV0/fp106AyRYoUSbe1EXIHNXtIU/ny5eXv72+MZCUlltyeOXNGgYGBOnjwoHGjk7Jpw6BBg0ylf/Hx8QoLC5PVajU9VyYvPPPMM6ZhzaXEEq9du3Zp3759CgkJMX6Yc7JJQceOHU3HIDQ0VNu3b9eWLVtMtXDZ9d5776lz587GdExMjA4cOGBK9J566ilNnjw51fOzkuvatasSEhJ08ODBVDW3b7/9dpoD32QkrZrA5KWcaZV4ZvVm2GKxmGqTExISdODAAW3ZssU0qmVuqV69ur755hvT8/SioqJ04MABHT58ONWD1h0cHDRjxgzVqFHDmHf9+nXt27cvVaI3e/Zs07ma8iHgCQkJOnbsmH755Rd5eHjkycA2KVWrVi3V6Hfnzp0zBrFIWWiS2WcrWSwWTZ8+3XS+3rlzRwcPHtTOnTt18uRJI9FL/l29ceOGxo4da0zXrFnTGGXw008/NZrYxsTE6P3330/3hrtbt24qUqSIjh49quPHj5sKpry9vU3nXKlSpTRz5kzTYE5hYWHau3evdu/erfPnzxsFGrnRVKlNmzb66KOPTMc2ODhYO3fu1P79+03PyMqp7RcvXjzVA8RjYmJ05MgR/fzzz2neLP7nP/8xPaf1/v37OnLkiAIDAxUUFGTqT5bZ/lS5df7lROwVKlTQK6+8Ysy3Wq06duyYDhw4oOLFi5teS0uPHj1M08eOHf4YP9YAACAASURBVNOWLVu0ZcsWI7nNj88+uby4/vbr109vvfWWcW9htVp1+vRp494jecuVvGgKmPweJ+mc37lzZ6pEr1GjRvrqq6+ytY2+ffumOleTD8ySXHbPgZEjR5oGRgkNDdXu3bt17dq1TD0+IyvCwsIUFBRkPHoo+fV01KhRDNCSxzjaSFe5cuW0YsUK7d69W5s3b9axY8d0/fp13bt3T4ULF1blypX13HPPydvb2/S+atWq6fvvv9d///tf/fLLL5ISm0ANGjRIVatW1YoVK/J0Pzp06CAvLy8tW7ZM+/fv1x9//KGYmBi5urrK09NTdevWVevWrTM17HdmlS1bVt99952mTZumU6dOZXnUNcn8WIiUF0ZnZ2dNmTJF3t7eWr16tU6cOKGbN2/K2dlZZcuWVbNmzdS/f3+bz7Np0aKFBg4cqG+++UbHjh3T/fv3Va1aNfn6+qp79+5ZjjmtB6Mnb76RMnnMan+9JB988IFcXV21efNmhYeH2ywxz2mtW7fWpk2btHz5cu3du1eXLl1STEyMihYtqooVK6bq31O6dGmtWrVKq1ev1ubNm/Xbb78pOjpaBQsWVJUqVdSuXTv17ds3Vd8/BwcHffPNN1qwYIFWr16t0NBQPf3003rhhRc0YsSIPOmrlRYfHx95enpqzpw5OnPmjAoUKKCGDRtq5MiRRm1IkqwMBOHh4aEVK1Zo48aN2rRpk86cOaOoqCg5ODioRIkSqlGjhp577jlTQce4ceOM5oTOzs76/PPPjZug0qVL68MPPzSev/fLL7/o22+/NT3SIUmlSpX0f//3f5oyZYr27Nmj27dvq2LFinr11VfTHImvUaNG2rRpk3744Qft2rVLv//+u6Kjo1WgQAGVKVNGtWvXVosWLdS+fftM739W9OvXT82aNdOyZct08OBBhYaG6v79+3Jzc1O5cuX07LPPqk2bNqam7I+qXr16xoOkAwMDdf78eaPPXLly5dSsWTMVK1bMWL5QoUKaN2+etm/frvXr1+vkyZO6efOmrFarihcvrqpVq6pJkybq1KlTlkYlza3zL7nsxv7xxx+rXLlyWrVqlf78809jEIxRo0YZoxSmx9fXVwkJCVq7dq0uX75s+g1ILj8+++Ty4vo7atQotW/fXj/88IOOHj2qiIgIxcbGqnDhwqpUqZIaNGigdu3aZblAMjs8PT3l7++vHTt2KCgoSOHh4bp27Zri4uLk7u6uOnXqqFu3burcuXO2k08PDw916NDBeA5jgQIFMvwNzs450LhxYy1atEhTp07VyZMnZbFYVLt2bb311luqUqXKIz8PuEiRIvriiy905MgRnTx5UteuXVNUVJTR39nLy0t9+/bNkwHZYGaxPglDxQF/MQkJCWrVqpVRgtmwYUMtW7Ysn6MCEoWHh6tkyZKpamOuX7+uXr16GX1UXV1d9fPPP2d6kJ+8lPLh0EOHDs3x0m3kDns4/4CU3njjDaPZeV4NvIS/Bmr2gMfIzZs35e/vr8OHD5uaqqRVYwbkl2+//Vbbtm3Tc889p9KlS8vZ2VmhoaEKDAw0NW8bOHAgN9rIcZx/sBebNm3SlStXdOrUKdNAK/3798/HqGBvSPaAx0hERIS+/vpr07xSpUrJx8cnnyIC0nbz5k1t2rQp3df79u2rd955Jw8jwl8J5x/swfLly41HEiR59dVXUz3vGHgUJHvAY8hisahYsWJq3ry5Ro0aZeoHA+Q3b29vWSwWBQUF6dq1a7p9+7YKFCigsmXLqkGDBnrppZdMgzsBOYnzD/bG2dlZ5cqV08svv2wa+RfICfTZAwAAAAA7xKMXAAAAAMAOkewBAAAAgB0i2QMAAAAAO0Syl4GwsDD5+Pioc+fO6tq1qxYtWiRJmjZtmlq2bClvb295e3tr9+7dkqTIyEj5+PioQYMGmjRpUn6GDgAAAOAvjtE4M+Do6KgxY8aoTp06io6O1ksvvaTmzZtLkl5//XW98cYbpuULFCigESNG6Ny5czp37lx+hAwAAAAAkqjZy5CHh4fq1KkjSXJzc1OVKlUUERGR7vIFCxZUo0aNVKBAgbwKEQAAAADSRLKXSVeuXNGZM2dUv359SdLSpUvVvXt3jR07Vrdu3crn6AAAAADAjGQvE+7evavhw4dr3LhxcnNzU9++fbVt2zatX79eHh4e+uyzz/I7RAAAAAAwIdmzIS4uTsOHD1f37t3VoUMHSVKJEiXk6OgoBwcH9e7dWydPnsznKAEAAADAjGQvA1arVR9++KGqVKkiX19fY/7Vq1eNv7dv367q1avnR3gAAAAAkC6L1Wq15ncQj6sjR46oX79+qlGjhhwcEvPiUaNGaePGjTp79qwkydPTU5MmTZKHh4ckqW3btoqOjlZcXJwKFy6s+fPnq1q1avm2DwAAAAD+mkj2AAAAAMAO0YwTAAAAAOwQyR4AAAAA2KFcS/bCwsLk4+Ojzp07q2vXrlq0aJEkadq0aWrZsqW8vb3l7e2t3bt3G++ZPXu22rdvr44dO2rv3r25FRoAAAAA2D2n3Fqxo6OjxowZozp16ig6OlovvfSSmjdvLkl6/fXX9cYbb5iWP3/+vAICAhQQEKCIiAj5+vpqy5YtcnR0zK0QAQAAAMBu5VrNnoeHh+rUqSNJcnNzU5UqVRQREZHu8jt27FDXrl3l4uKi8uXLq2LFijpx4kRuhQcAAAAAdi1P+uxduXJFZ86cUf369SVJS5cuVffu3TV27FjdunVLkhQREaHSpUsb7ylVqlSGyaEtsQ/iHi3ox4g97QsAAACAvJFrzTiT3L17V8OHD9e4cePk5uamvn376p133pHFYtHUqVP12Wefyc/PT2k9AcJisWR7uy4FnNWpxsBHCf2xsTl4Xn6HAAAAAOAJk6s1e3FxcRo+fLi6d++uDh06SJJKlCghR0dHOTg4qHfv3jp58qQkqXTp0goPDzfeGxERYTyoHAAAAACQNbmW7FmtVn344YeqUqWKfH19jflXr141/t6+fbuqV68uSWrbtq0CAgIUGxury5cv69KlS/Ly8sqt8AAAAADAruVaM86jR49q/fr1qlGjhry9vSVJo0aN0saNG3X27FlJkqenpyZNmiRJql69ujp37qwuXbrI0dFR48ePZyROAAAAAMgmizWtznJ2gj57AAAAAP6q8mQ0TgAAAABA3iLZAwAAAAA7RLIHAAAAAHaIZA8AAAAA7BDJHgAAAADYIZI9AAAAALBDJHsAAAAAYIdI9gAAAADADpHsAQAAAIAdItkDAAAAADtEsgcAAAAAdohkDwAAAADsEMkeAAAAANghkj0AAAAAsEMkewAAAABgh0j2AAAAAMAOkewBAAAAgB0i2QMAAAAAO0SyBwAAAAB2iGQPAAAAAOwQyR4AAAAA2CGSPQAAAACwQyR7AAAAAGCHSPYAAAAAwA6R7AEAAACAHSLZAwAAAAA7RLIHAAAAAHaIZA8AAAAA7BDJHgAAAADYIZI9AAAAALBDJHsAAAAAYIdI9gAAAADADpHsAQAAAIAdItkDAAAAADtEsgcAAAAAdohkDwAAAADsEMkeAAAAANghkj0AAAAAsEMkewAAAABgh0j2AAAAAMAOkewBAAAAgB0i2QMAAAAAO0SyBwAAAAB2iGQPAAAAAOwQyR4AAAAA2CGSPQAAAACwQyR7AAAAAGCHSPYAAAAAwA6R7AEAAACAHSLZAwAAAAA7RLIHAAAAAHaIZA8AAAAA7BDJHgAAAADYoVxL9sLCwuTj46POnTura9euWrRokSQpKipKvr6+6tChg3x9fXXr1i3jPbNnz1b79u3VsWNH7d27N7dCAwAAAAC7l2vJnqOjo8aMGaMff/xRK1as0LJly3T+/HnNmTNHzZo109atW9WsWTPNmTNHknT+/HkFBAQoICBA8+bN08SJE5WQkJBb4QEAAACAXcu1ZM/Dw0N16tSRJLm5ualKlSqKiIjQjh071KNHD0lSjx49tH37dknSjh071LVrV7m4uKh8+fKqWLGiTpw4kVvhAQAAAIBdy5M+e1euXNGZM2dUv3593bhxQx4eHpISE8KbN29KkiIiIlS6dGnjPaVKlVJERERehAcAAAAAdifXk727d+9q+PDhGjdunNzc3NJdzmq1pppnsVhyMzQAAAAAsFu5muzFxcVp+PDh6t69uzp06CBJKl68uK5evSpJunr1qtzd3SVJpUuXVnh4uPHeiIgIowYQAAAAAJA1uZbsWa1Wffjhh6pSpYp8fX2N+W3bttW6deskSevWrVO7du2M+QEBAYqNjdXly5d16dIleXl55VZ4AAAAAGDXnHJrxUePHtX69etVo0YNeXt7S5JGjRqlQYMGaeTIkVq1apXKlCmjqVOnSpKqV6+uzp07q0uXLnJ0dNT48ePl6OiYW+EBAAAAgF2zWNPqLGcnOtUYmN8h5IjNwfPyOwQAAAAAT5g8GY0TAAAAAJC3SPYAAAAAwA6R7AEAAACAHSLZAwAAAAA7RLIHAAAAAHaIZA8AAAAA7BDJHgAAAADYIZI9AAAAALBDJHsAAAAAYIdI9gAAAADADpHsAQAAAIAdItkDAAAAADtEsgcAAAAAdohkDwAAAADsEMkeAAAAANghkj0AAAAAsEMkewAAAABgh0j2AAAAAMAOkewBAAAAgB0i2QMAAAAAO0SyBwAAAAB2iGQPAAAAAOwQyR4AAAAA2CGSPQAAAACwQ1lK9m7duqWzZ8/mViwAAAAAgBxiM9nz8fFRdHS0oqKi5O3trXHjxsnPzy8vYgMAAAAAZJPNZO/OnTtyc3PTtm3b1KtXL61Zs0Y//fRTXsQGAAAAAMgmm8leQkKCrl69qh9//FFt2rTJg5AAAAAAAI/KZrI3ZMgQvfHGG6pQoYK8vLx0+fJlVapUKQ9CAwAAAABkl5OtBUqWLKkNGzYY0+XLl9frr7+emzEBAAAAAB6RzZq9Tz75JFPzAAAAAACPj3Rr9oKCghQUFKSbN29qwYIFxvzo6GglJCTkSXAAAAAAgOxJN9mLi4tTTEyMEhISdPfuXWO+m5ubvvnmmzwJDgAAAACQPRar1WrNaIHQ0FB5enrmVTw5qlONgfkdQo7YHDwvv0MAAAAA8ISxOUBLbGysPvroI4WGhio+Pt6Yv3jx4lwNDAAAAACQfTaTvREjRqhPnz7q3bu3HBxsjucCAAAAAHgM2Ez2nJyc9Nprr+VFLAAAAACAHGKzqu6FF17Q0qVLdfXqVUVFRRn/AAAAAACPL5s1e2vXrpUkfffdd8Y8i8WiHTt25F5UAAAAAIBHYjPZCwwMzIs4AAAAAAA5yGYzznv37unbb7/VRx99JEm6dOmSdu7cmeuBAQAAAACyz2ayN3bsWDk7OysoKEiSVLp0aU2ZMiXXAwMAAAAAZJ/NZC8kJERvvvmmnJwSW3w+9dRTsvEcdgAAAABAPrOZ7Lm4uOj+/fuyWCySEpM/FxeXXA8MAAAAAJB9NgdoGTZsmAYOHKiwsDCNHj1aQUFB8vPzy4vYAAAAAADZZLFmok1mZGSkjh8/LqvVqvr168vd3T0vYntknWoMzO8QcsTm4Hn5HQIAAACAJ4zNmj1JOnz4sI4ePSqLxaL4+Hi1b98+t+MCAAAAADwCm332Pv74Y/3www+qUaOGqlevrh9++EETJ07Mi9gAAAAAANlks2bv8OHD2rhxozFAS8+ePdW9e/dcDwwAAAAAkH02a/YqV66sP//805gOCwtTzZo1czUoAAAAAMCjsVmzFxUVpS5dusjLy0uSdPLkST377LMaPHiwJGnWrFm5GyEAAAAAIMtsJnvDhw/PizgAAAAAADnIZrJXpkwZeXh4qECBApKk+/fv6/r16ypXrlyuBwcAAAAAyB6bffZGjBhhDM4iSQ4ODhoxYoTNFY8dO1bNmjVTt27djHnTpk1Ty5Yt5e3tLW9vb+3evdt4bfbs2Wrfvr06duyovXv3ZnU/AAAAAADJ2KzZS0hIkIuLizHt4uKiuLg4myvu1auX/vGPf+iDDz4wzX/99df1xhtvmOadP39eAQEBCggIUEREhHx9fbVlyxY5Ojpmdj8AAAAAAMnYrNlzd3fXjh07jOnt27erWLFiNlfcuHFjFS1aNFNB7NixQ127dpWLi4vKly+vihUr6sSJE5l6LwAAAAAgNZs1exMnTtR7772nf//737JarSpTpowmT56c7Q0uXbpU69atU926dTVmzBgVLVpUERERql+/vrFMqVKlFBERke1tAAAAAMBfnc1kr0KFClq5cqXu3r0rq9UqNze3bG+sb9++euedd2SxWDR16lR99tln8vPzk9VqTbVs8n6CAAAAAICsSTfZW79+vby9vbVgwYI0X/f19c3yxkqUKGH83bt3b+NZfaVLl1Z4eLjxWkREhDw8PLK8fgAAAABAonT77N27d0+SdPfu3TT/ZcfVq1eNv7dv367q1atLktq2bauAgADFxsbq8uXLunTpkvEQdwAAAABA1qVbs9enTx9JUrNmzdSwYUPTa0ePHrW54lGjRunQoUOKjIxUq1atNGzYMB06dEhnz56VJHl6emrSpEmSpOrVq6tz587q0qWLHB0dNX78eEbiBAAAAIBHYLGm1WEumZ49e2rt2rU25z2OOtUYmN8h5IjNwfPyOwQAAAAAT5h0a/aCgoIUFBSkmzdvmvrtRUdHKyEhIU+CAwAAAABkT7rJXlxcnGJiYpSQkGDqo+fm5qZvvvkmT4IDAAAAAGRPuslekyZN1LBhQwUHB2vo0KF5GRMAAAAA4BGlOxqnJDk6OioqKiqvYgEAAAAA5BCbD1WvXbu2Bg8erE6dOqlgwYLG/A4dOuRqYAAAAACA7LOZ7N26dUvFihXTwYMHTfNJ9gAAAADg8WUz2fPz88uLOAAAAAAAOchmsvfgwQOtWrVK586d04MHD4z5JIEAAAAA8PjKcIAWSXr//fd17do17du3T02aNFFERIQKFSqUF7EBAAAAALLJZrIXEhKikSNHytXVVT179tTs2bMVHBycF7EBAAAAALLJZrLn5JTY0rNIkSIKDg7WnTt3FBoamuuBAQAAAACyz2afvVdffVW3bt3SiBEj9PbbbysmJkYjRozIi9gAAAAAANmUYbK3fft23b59WydOnFDLli21Y8eOvIoLAAAAAPAI0m3G+fHHH2vhwoWKiorS1KlTNWPGjLyMCwAAAADwCNKt2Tty5IjWr18vR0dH3bt3T/369dOQIUPyMjbks7Fjx2rXrl0qXry4Nm7cKEmaPHmydu7cKWdnZ1WoUEF+fn4qUqSITpw4oY8++kiSZLVaNWzYMLVv3z4/wwcAAAD+0tKt2XN2dpajo6MkydXVVVarNc+CwuOhV69emjdvnmle8+bNtXHjRm3YsEGVKlXS7NmzJUnVq1fX6tWrtX79es2bN0/jx49XfHx8foQNAAAAQBnU7P3+++/q3r27MR0SEmKa3rBhQ+5GhnzXuHFjXblyxTSvRYsWxt/PPvusNm/eLCmxQCDJgwcPZLFY8iZIAAAAAGlKN9nbtGlTXsaBJ9Dq1avVuXNnY/r48eMaN26c/vzzT33++efGYzsAAAAA5L1078Y9PT3zMg48YWbOnClHR0f9/e9/N+bVr19fAQEBunDhgj744AO1atVKBQoUyMcoAQAAgL8umw9VB1Jau3atdu3apS+//DLN5ppVq1aVq6urgoOD8yE6AAAAABLJHrJoz549mjt3rmbOnGnqp3f58mVjQJbQ0FBdvHiR2mEAAAAgH6XbjPOf//ynFi1apC+++ELvv/9+XsaEx8SoUaN06NAhRUZGqlWrVho2bJjmzJmj2NhY+fr6Skpsujlp0iQdPXpUc+fOlZOTkxwcHPTxxx/L3d09n/cAAAAA+OtKN9m7du2aDh06pMDAQHXt2jXVoxfq1KmT68Ehf3399dep5vXu3TvNZXv06KEePXrkdkgAAAAAMindZG/48OGaM2eOwsPD5efnZ3rNYrFo8eLFuR4cAAAAACB7LFYbT0ufMWOGhgwZklfx5KhONQbmdwg5YnPwPNsLAQAAAEAyNh+ENmTIEO3YsUNHjhyRJDVp0kQvvPBCrgcGAAAAAMg+m6NxfvXVV1q8eLGqVq2qqlWravHixfrqq6/yIjYAAAAAQDbZrNnbtWuX1q9fLweHxLywZ8+e6tGjh0aPHp3rwQEAAAAAsidTz9m7ffu28fedO3dyLRjknNgHcfkdQo6yt/0BAAAAcpvNmr233npLPXv2VNOmTWW1WnX48GFq9Z4ALgWc1a3F/+V3GDlm477P8zsEAAAA4IliM9nr1q2bmjRpopMnT8pqteq9995TyZIl8yI2AAAAAEA22Uz2JMnDw0Pt2rXL7VgAAAAAADkkU332AAAAAABPFpI9AAAAALBDGSZ7Dx8+VLdu3fIqFgAAAABADskw2XNwcFDNmjX1559/5lU8AAAAAIAcYHOAlmvXrqlr167y8vKSq6urMX/WrFm5GhgAAAAAIPtsJntDhw7NizgAAAAAADnIZrLXpEkThYaG6o8//tDzzz+ve/fuKSEhIS9iAwAAAABkk83ROFeuXKnhw4dr/PjxkqSIiAgNGTIk1wMDAAAAAGSfzWRv6dKlWr58udzc3CRJlSpV0s2bN3M9MAAAAABA9tlM9lxcXOTi4mJMx8fH52pAAAAAAIBHZ7PPXuPGjTVr1izdv39f+/fv17Jly9S2bdu8iA0AAAAAkE02a/bee+89ubu7q0aNGlqxYoVat26tkSNH5kVsAAAAAIBsslmz5+DgoB49esjLy0sWi0WVK1eWxWLJi9gAAAAAANlkM9nbtWuXJkyYoAoVKshqterKlSuaOHGiWrdunRfxAQAAAACywWay99lnn2nx4sWqWLGiJCkkJESDBg0i2QMAAACAx5jNPnvFixc3Ej1JKl++vIoXL56rQQEAAAAAHk26NXtbt26VJFWrVk1vvvmmOnfuLIvFos2bN6tevXp5FiAAAAAAIOvSTfZ27txp/F2iRAkdPnxYkuTu7q5bt27lfmQAAAAAgGxLN9nz8/PLyziAJ8KiRYvk7+8vq9Wq3r176/XXXzde++677/T555/r559/lru7e/4FCQAAACgTA7RcvnxZS5YsUWhoqOLj4435s2bNytXAgMdNcHCw/P395e/vL2dnZw0cOFBt2rRRpUqVFBYWpp9++klly5bN7zABAAAASZlI9oYMGaKXX35ZL7zwghwcbI7nAtitCxcuqH79+nJ1dZUkNW7cWNu2bdObb74pPz8/vf/++3rnnXfyOUoAAAAgkc1kr0CBAurfv3+WVzx27Fjt2rVLxYsX18aNGyVJUVFRevfddxUaGipPT09NmTJFRYsWlSTNnj1bq1atkoODg/71r3+pZcuWWd4mkJtq1KihKVOmKDIyUk899ZT27NmjunXraseOHfLw8FCtWrXyO0QAAADAYLOqrn///po+fbqCgoJ0+vRp458tvXr10rx580zz5syZo2bNmmnr1q1q1qyZ5syZI0k6f/68AgICFBAQoHnz5mnixIlKSEjI5i4BuaNq1aoaOHCgBgwYoIEDB6pmzZpydHTUrFmzNGLEiPwODwAAADCxWbMXHBys9evX68CBA7JYLJIki8WixYsXZ/i+xo0b68qVK6Z5O3bs0Pfffy9J6tGjh3x8fPT+++9rx44d6tq1q1xcXFS+fHlVrFhRJ06cUIMGDbK7X0Cu6N27t3r37i1J+vrrr1W8eHFt2LBB3t7ekqTw8HD16tVL/v7+KlmyZH6GCgAAgL84m8netm3btH37drm4uDzyxm7cuCEPDw9JkoeHh27evClJioiIUP369Y3lSpUqpYiIiEfeHpDTbty4oeLFi+vPP//U1q1btWLFCv3zn/80Xm/btq1WrVrFaJwAAADIdzaTvVq1aunOnTsqXrx4rgVhtVpTzUuqRQQeJ8OGDVNUVJScnJw0YcIEo88pAAAA8LixmezduHFDnTt3Vr169eTs7GzMz86jF4oXL66rV6/Kw8NDV69eNWo/SpcurfDwcGO5iIgIowYQeJwsW7Ysw9cDAwPzKBIAAAAgYzaTvWHDhuXYxtq2bat169Zp0KBBWrdundq1a2fMHz16tHx9fRUREaFLly7Jy8srx7YLAAAAAH81NpO9Jk2aZGvFo0aN0qFDhxQZGalWrVpp2LBhGjRokEaOHKlVq1apTJkymjp1qiSpevXq6ty5s7p06SJHR0eNHz9ejo6O2douAAAAAECyWNPqMJdMgwYNjP5zcXFxio+Pl6urq44dO5YnAT6KTjUG5ncIOWJz8DzbC6WhW4v/y+FI8s/GfZ/ndwgAAADAE8VmzV5QUJBpevv27Tpx4kSuBQQAAAAAeHQ2H6qe0osvvqgDBw7kRiwAAAAAgBxis2Zv69atxt8PHz7UqVOneCwCngixsXFycXG2veATwJ72BQAAAHnDZrK3c+dO429HR0d5enrq22+/zdWggJzg4uKsLt4f53cYOWLT+o/zOwQAAAA8YWwme35+fnkRBwAAAAAgB6Wb7E2fPj3dN1ksFg0ZMiRXAgIAAAAAPLp0k72CBQummhcTE6PVq1crKiqKZA8AAAAAHmPpJnsDBgww/o6OjtbixYu1Zs0adenSxfQaAAAAAODxk2GfvaioKC1YsEAbNmxQz549tXbtWhUtWjSvYgMAAAAAZFO6yd7kyZO1bds2vfLKK9qwYYMKFSqUl3EBAAAAAB5BusneggUL5OLiopkzZ2rWrFnGfKvVKovFomPHjuVJgAAAAACArEs32Tt79mxexgEAAAAAyEEO+R0AAAAAACDnkewBAAAAgB0i2QMAAAAAO5ThoxcAIMnvv/+ud99915i+fPmyhg8froiICO3cuVPOzs6qUKGC/Pz8VKRIkXyMFAAAABI1ewAyqUqVKlq/fr3Wr1+vNWvWyNXVVe3bt1fz5s21ceNGbdiwQZUqVdLs6MtZ+QAAIABJREFU2bPzO1QAAACIZA9ANvz8888qX768PD091aJFCzk5JTYSePbZZxUeHp7P0QEAAEAi2QOQDQEBAerWrVuq+atXr1arVq3yISIAAACkRLIHIEtiY2MVGBioTp06mebPnDlTjo6O+vvf/55PkQEAACA5BmgBkCV79uxRnTp1VKJECWPe2rVrtWvXLi1cuFAWiyUfowMAAEASkj0AWRIQEKCuXbsa03v27NHcuXO1ZMkSubq65mNkAAAASI5mnAAy7d69e/rpp5/UoUMHY96///1v3b17V76+vvL29tb48ePzMUIAAAAkoWYPQKa5urrq4MGDpnnbtm3Lp2gAAACQEWr2AAAAAMAOkewBAAAAgB0i2QMAAAAAO0SyBwAAAAB2iGQPAAAAAOwQyR5gp2Jj4/M7hBxjT/sCAACQV3j0AmCnXFyc1Lb/v/M7jBwRuPij/A4BAADgiUPNHgAAAADYIZI9AAAAALBDJHsAAAAAYIdI9gAAAADADpHsAQAAAIAdItkDAAAAADtEsgcAAAAAdohkDwAAAADsEMkeAAAAANghkj0AAAAAsENO+R0AADwp2rZtq0KFCsnBwUGOjo5as2aNfvzxR02fPl0XLlyQv7+/6tWrl99h5iqOAQAATw6SPQDIgkWLFsnd3d2YrlGjhqZNm6YJEybkY1R5i2MAAMCTgWQPAB5B1apV8zuEfMcxAADg8USfPQDIgjfeeEO9evXSihUr8juUfMMxAADgyUDN3v9j787jak7///E/TqVFWYqENIYGeWsYskyT5T2NJIoWkSVLxttYpowwk6hGsi+DzMSMIfvWnhbSyO4zCIOaGUQJRQvt5fT6/dGv1zjKTM1Xryvnet5vN7db5zrvud0e57x5dT1fr+t6XoQQUkcHDhyAgYEBcnJyMG3aNHTu3Bn9+vVjHUtS9B0QQggh7w56skcIIXVkYGAAAGjVqhWsrKxw48YNxomkR98BIYQQ8u6gYo8QQuqguLgYhYWF4s/nzp1Dly5dGKeSFn0HhBBCyLuFlnESQkgd5OTkYM6cOQAAuVwOW1tbDB48GCdOnIC/vz9yc3Mxc+ZMdO/eHTt27GCctmHQd0AIIYS8W6jYI4SQOjAyMkJkZGSNcSsrK1hZWTFIJD36DgghhJB3Cy3jJIQQQgghhBAlRE/2CCGEkHqQy+VwcnKCgYEBtm3bhu+++w4nT56EiooKWrVqhZUrV4qNbAghhBCWmBR7lpaW0NbWhoqKClRVVREaGor8/Hx89dVXyMzMhKGhIb777ju0aNGCRTxCCCHkjXbv3g1jY2OxWc3nn3+OefPmie9t3boVy5YtYxmREEIIAcBwGWdwcDAiIiIQGhoKANi+fTvMzc1x/PhxmJubY/v27ayiEUIIIbV68uQJTp06hTFjxohjOjo64s8lJSWQyWQsohFCCCE1NJo9eydPnoS9vT0AwN7eHgkJCYwTEUIIIYpWrFiBhQsXQkVF8dfnxo0bMWTIEERFRcHDw4NROkIIIUQRs2Jv+vTpcHR0xKFDhwBUtfRu06YNAKBNmzbIzc1lFY0QogTKKl6yjvBW/ZvPo0zfQWP4LL/88gv09PRgampa472vvvoKSUlJsLOzw969exmkI4QQQmpismfvwIEDMDAwQE5ODqZNm4bOnTuziEEIUWIaTdRgMcefdYy35tzWpfX+bzSaqKHf18qxd+zX1T6sI+Dq1atITEzE6dOnUVZWhsLCQixYsADr1q0T/ze2traYOXMm3N3dGSYlhBBCqjB5slfdpaxVq1awsrLCjRs30KpVK2RnZwMAsrOzoaenxyIaIYQQUitPT0+cPn0aiYmJ2LBhAz7++GOsW7cO9+/fF/83iYmJdAOTEEJIoyF5sVdcXCx2MCsuLsa5c+fQpUsXWFpaIjw8HAAQHh6Ozz77TOpohBBCSL2tX78etra2sLOzw7lz5+Dt7c06EiGEEAKAwTLOnJwczJkzB0DVWUW2trYYPHgwPvzwQ8ybNw9Hjx5Fu3btsGnTJqmjEUIIIXUyYMAADBgwAACwZcsWxmkIIYSQ2kle7BkZGSEyMrLGuK6uLoKDg6WOQwghhBBCCCFKqdEcvUAIIYQQQggh5O2hYo8QQgghhBBClBAVe4QQQgghhBCihKjYI4QQQgghhBAlRMUeIYQQpVT2soJ1hLdK2T4PIYSQhid5N05CCCFEChpqTfDRcj/WMd6aa0v8WEdAWVkZJk6ciPLycsjlclhbW8Pd3R2xsbEIDAzE3bt3ceTIEXz44YesoxJCCAEVe4QQQgipI3V1dQQHB0NbWxsVFRWYMGECBg8ejK5du2LLli3w9fVlHZEQQsgrqNgjhBBCSJ3IZDJoa2sDAF6+fImXL19CJpPB2NiYcTJCCCG1oT17hBBCCKkzuVyO0aNH45NPPsEnn3yCXr16sY5ECCHkDajYI4QQQkidqaqqIiIiAklJSbhx4wb++OMP1pEIIYS8ARV7hBBCCKm35s2bY8CAAThz5gzrKIQQQt6Aij1CCCGE1Elubi5evHgBACgtLcX58+fRuXNnxqkIIYS8CTVoIYQQQkidZGdn45tvvoFcLocgCBg+fDg+/fRTnDhxAv7+/sjNzcXMmTPRvXt37Nixg3VcQgjhHhV7hBBCCKkTExMThIeH1xi3srKClZUVg0SEEEL+Di3jJIQQQgghhBAlRMUeIYQQQgghhCghKvYIIYQQQgghRAlRsUcIIYQQQgghSoiKPUIIIYQQQghRQlTsEUIIIUqo7GUF6whvlbJ9HkIIkQIdvUAIIYQoIQ21Jvhosy/rGG/NNfdvWUeAl5cXTp06hVatWiE6OhoAkJqaCl9fXxQXF8PQ0BDr1q2Djo4O46SEEFKFnuwRQgghhNSBo6MjfvrpJ4Uxb29veHp6IioqCkOHDq3xPiGEsETFHiGEEEJIHfTr1w8tWrRQGEtLS0O/fv0AABYWFjh+/DiLaIQQUisq9gghhBBC/qWuXbvi5MmTAIC4uDg8fvyYcSJCCPkLFXuEEEIIIf9SQEAA9u/fD0dHRxQVFUFdXZ11JEIIEVGDFkIIIYSQf8nY2Bg///wzgKolnadOnWIbiBBCXkFP9gghhBBC/qWcnBwAQGVlJX744Qe4uLgwTtSwvLy8YG5uDltb2xrv7dixA926dUNubi6DZNLg/fOTdw8Ve4QQQgghdTB//ny4uLggLS0NgwcPxpEjRxAdHQ1ra2vY2NigTZs2cHJyYh2zQdXWkRQAHj9+jPPnz6N9+/YMUkmH989P3j20jJMQQgghpA42bNhQ6/iUKVMkTsJOv3798PDhwxrjK1euxMKFCzF79mwGqaTD++cn7x56skcIIYQQQv61kydPok2bNjAxMWEdhQnePz9p3KjYI4QQQggh/0pJSQmCgoLg4eHBOgoTvH3+2vYsxsbGYuTIkTAxMcFvv/3GMB2pDRV7hBBCCCHkX0lPT8fDhw8xevRoWFpa4smTJ3B0dMTTp09ZR5MEb5+/tj2LXbt2xZYtW9CvXz9GqcjfoT17hBBCCCHkX+nWrRsuXLggvra0tMTRo0ehp6fHMJV0ePv8te1ZNDY2ZpSG1AU92SOEEEIIIXVSW0dSnvD++UlNu3btwsiRI2Fra4v58+ejrKyMdSQF9GSPEEIIIUqpTF4BDdUmrGO8FY3ls7ypI2m1xMREiZKwwfvnJ4qysrKwe/duxMTEQFNTEx4eHjh27BgcHR1ZRxNRsUcIIYQQpaSh2gSDd3uzjvFWnJ4cwDoCIaQWcrkcpaWlUFNTQ2lpKdq0acM6kgIq9gghhBBCCCGkngwMDODm5oZPP/0UGhoasLCwwMCBA1nHUkB79gghhBBCCCH/qLY9iydOnMDgwYORnJyMmTNnYvr06axjSub58+c4efIkTp48iTNnzqCkpAQRERGsYymgJ3uEEEIIIYSQf/SmPYtWVlYSJ2kczp8/jw4dOojdV4cNG4bk5GSMHj2acbK/0JM9QgghhBBCCKmn9u3b4/r16ygpKYEgCLhw4UKjO4qCnuwRQgghhCihcnkF1BtBB8+35d98norKCjRRUY7vQJk+i7Lo1asXrK2t4eDgADU1NXTv3h3jxo1jHUsBFXuEEEIIIUpIXbUJJkQtYB3jrdlvt67e/00TlSZYkvS/BkgjveVDtrOOQGrh7u4Od3d31jHeiJZxEkIIIYQQQogSomKPEEIIIYQQQpQQFXuEEEIIIYQooZeV5awjvFXK9nmkQHv2CCGEEEIIUUJqKur48cIo1jHemhnmkawjvHPoyR4hhBBCCCGEKCEq9gghhBBCCCFKR15ZxjrCW/VvPg8t4ySEEEIIIYQoHVUVDST8X3/WMd6aof3/r97/DT3ZI4QQQgghhBAlRMUeIYQQQgghhCghKvYIIYQQQgghRAlRsUcIIYQQQgghSqjRFXunT5+GtbU1rKyssH37dtZxCCGEEEIIIeSd1KiKPblcjmXLluGnn37CsWPHEB0djTt37rCORQghhBBCCCHvnEZV7N24cQMdO3aEkZER1NXVMXLkSJw8eZJ1LEIIIYQQQgh55zSqYi8rKwtt27YVXxsYGCArK4thIkIIIYQQQgh5N8kEQRBYh6gWGxuLs2fPIiAgAAAQHh6O3377DUuXLmWcjBBCCCGEEELeLY3qyV7btm3x5MkT8XVWVhbatGnDMBEhhBBCCCGEvJsaVbH34Ycf4v79+8jIyEB5eTmOHTsGS0tL1rEIIYQQQggh5J2jxjrAq9TU1ODj44PPP/8ccrkcTk5O6NKlC+tYhBBCCCGEEPLOaVR79gghhBBCCCGEvB2NahknIYQQQgghhJC3g4o9QgghhBBCCFFCVOwRQgghhBBCiBKiYo/UmVwux+rVq1nHYCI/P/9v/xB+lJeX12lMWS1cuLBOY8pKLpdj165drGMwFRwcXKcxZVVZWYmYmBjWMZhau3ZtncaUEc9zoWqVlZW4evUq6xikjqjYqyeeJ3qqqqq4desWeOzp4+joCCcnJzg6OsLc3BzW1tawtraGubk5HB0dWceTDF3ggXHjxtVpTFnduXNH4bVcLsetW7cYpZGeqqoqTp48yToGU+Hh4TXGwsLCGCRhQ0VFBfv27WMdg6nz58/XGDt9+jSDJNLjeS5UTUVFhfuC913SqI5eeBeMGzeuxi+12saU1X/+8x/MmjULw4cPR9OmTcXxYcOGMUzV8BITEwEAPj4++OyzzzBkyBAAQFJSEi5cuMAymqSqL/CHDh1iHUVyT58+RVZWFkpLS3H79m3xF31hYSFKSkoYp2t427ZtQ1BQEMrKytCnTx8AgCAIUFdXx9ixYxmnk1afPn2wbNkyjBgxAlpaWuJ4jx49GKZqeNHR0YiOjsbDhw/xxRdfiONFRUVo2bIlw2TS++STT7Bjx44afweU/XvYv38/Dhw4gIyMDNjZ2YnjRUVF4nWBB7zOhV5lYWGB+Ph4DBs2DDKZjHUcJo4fP45169YhJycHgiBAEATIZLJGd1Ocir064n2iV+358+fQ1dXFpUuXFMZ5ucDdvHkTy5YtE18PGTIEmzZtYphIerxe4M+ePYvQ0FA8efIEq1atEq8B2tramD9/PuN0DW/mzJmYOXMm1q9fD09PT9ZxmKr+Rf7qv32ZTIbdu3eziiSJ3r17Q19fH3l5eXBzcxPHtbW10a1bN4bJpBcSEgIACk/4ZDKZ0j/1tbOzw+DBg7FhwwaF64C2trbSF7qv4n0uBAA7d+5ESUkJVFVVoaGh0WgLnYa0du1aBAUFwdjYmHWUv0Xn7NVRWFgYQkNDcfPmTZiamorj2tracHR05OofOM+mT58OMzMzjBo1CjKZDJGRkbh8+TJ27NjBOppkevfuzfUFPj4+HtbW1qxjMHPlyhV0794dTZs2RUREBG7fvo3JkyfD0NCQdTQikeLiYmhqakJFRQVpaWm4d+8eBg8ejCZNmrCORiSSnp6Otm3bQl1dHZcuXcLvv/8Oe3t7NG/enHU0QiTj4uKCgwcPso7xj6jYqyfeJ3ppaWnw8/NDTk4OoqOjkZqaisTERMyePZt1NEnk5+cjMDAQly9fhkwmQ9++fTFnzhyu7mjyLjg4GE5OTtDW1saSJUtw+/ZteHp6YuDAgayjScLOzg6RkZH4/fffsXDhQowZMwYnTpzA3r17WUeTzLNnz7BhwwZkZ2fjp59+wp07d5CcnAxnZ2fW0STh6OiIffv24cWLFxg7dixMTU2hqamJ9evXs44mmZKSEuzcuROPHz+Gv78/7t+/j7S0NHz66aeso0li9OjRCAkJQWZmJqZPnw5LS0ukpaXhxx9/ZB1NErzPhYCqZfyRkZF4+PAh5syZg8ePH+Pp06fo2bMn62iSWb58OZ49e4ahQ4dCXV1dHG9sD4CoQUs9ffrpp4iKikJQUBACAwPFP7xYunQpPD09oaZWtQLYxMSEq65kLVu2xJIlS7Bv3z6EhYXB29ubu0JPEARERERg69atAIDHjx/jxo0bjFNJJyQkBDo6Ojh79ixycnKwcuVKria5ampqkMlkSEhIwOTJkzFlyhQUFRWxjiWpb775BgMHDkR2djYA4P3331f6JZyvEgQBWlpaOH78OCZNmoStW7fi7t27rGNJysvLC02aNEFycjIAoG3btvjuu+8Yp5KOiooK1NTUcPz4cUyZMgWLFy/G06dPWceSDO9zIQDw8/PDtWvXEB0dDQBo2rQpvv32W8appFVUVAQtLS2cO3cOv/zyi/insaE9e/U0a9YsNGvWDD169FCo4nlRUlJS466NqqoqozTSu3r1KpYsWYLi4mKcOnUKqampOHjwIPz8/FhHk4yfnx9UVFRw8eJFzJkzR7zAV+9hUXbViyGSkpLg5OQEExMTrrqyaWtrY9u2bYiKisLevXshl8vx8uVL1rEklZeXhxEjRmD79u0AqgpgFRV+7p0KgoDk5GRERUUhICAAQFVXVp6kp6fju+++w7FjxwAAmpqaXF0H1NTUEB0djYiICPzwww8AwNV1gPe5EADcuHEDYWFhsLe3BwC0aNECFRUVjFNJa+XKlawj1AkVe/WUlZXF1f6s1+nq6iI9PV1szBEXFwd9fX3GqaSzcuVK7NixA7NmzQJQdTfv8uXLjFNJi/cLvKmpKdzc3PDw4UN4enqisLCQq4n+xo0bER0djYCAAOjr6+PRo0eYPn0661iSatq0KfLy8sTr4LVr19CsWTPGqaTj7e2Nbdu2YejQoejSpQsyMjIwYMAA1rEkpa6ujtLSUvHvQHp6Olc3gFeuXImDBw/iiy++gJGRETIyMjBq1CjWsSTD+1wIqCr45XK5+B3k5uZy9bsQAMrKynD06FH8+eefKCsrE8cbWxFIxV499e7dG7///jt3nceq+fr6YunSpbh37x4GDRqEDh06cHOQarV27dopvObt4sb7BT4gIAApKSkwMjKClpYW8vLysGLFCtaxJKOvr49p06aJr9u3by8W/rz45ptvMGvWLKSnp8PFxQV5eXlcdeVt0aIFgoKCxNdGRkZYsmQJw0TS+/LLL/H555/j8ePH8PT0RHJycqOb4DWkDz74QOH/cyMjI/zvf/9jmEhaNBcCXF1dMWfOHDx79gwbN25EXFwc5s2bxzqWpBYuXIjOnTvj7NmzmDNnDqKiotC5c2fWsWqgBi31NGLECKSnp8PQ0FDhLl5UVBTDVNIrLi5GZWUldHR0WEeRlLu7O6ZOnQp/f38cOnQIe/bswc2bN7Fx40bW0SQTGRmJmJgY3L59Gw4ODuIF3sbGhnU0Sbi7u8PR0RGDBw/mqsgdP348Dhw4gN69eyscucFbN9ZqL1++RFpaGgRBQKdOnbjqRDl+/HhUVFTAwcEBdnZ23HZgzMvLw/Xr1yEIAnr16gU9PT3WkRqch4cHNm3apHDG3qtoLsSXu3fv4uLFixAEAebm5o3+CIK3zd7eHuHh4bCzs0NUVBQqKiowffr0RreHm4q9esrMzKx1nJe247x3ocvNzUVAQAAuXLgAQRBgYWEBb29v6Orqso4mKZ4v8OfPn0dISAiuX7+O4cOHw8HBgavPT/7qxPjo0SMsX76cu06MQFU3wtDQUMTFxaFnz55wcHDgpiMtANy6davGWLNmzdC+fXuxaYcyys7ORps2bWguxPlcqNrly5fx4MEDODk5ITc3F0VFRTAyMmIdSzJjxozB0aNHMXHiRPj6+qJ169ZwdnZufOdtCqTefv31V+Ho0aOCIAhCTk6OkJ6ezjiRdKZPny4cO3ZMsLOzEwRBECoqKgRbW1vGqaSTl5fHOkKj8PLlS+HJkydCZmam+Ic3L168EPbv3y8MHjxYGDdunHD06FGhvLycdawGt2DBgjqNKTMPDw9h+/btwsiRIwVBEISSkhJh1KhRjFNJ7+XLl0JcXJwwcOBAYfjw4YK1tbUQHx/POpYknJ2dhR49eggODg6Cg4OD0KNHD8HR0VGwtLQUzpw5wzpeg1uzZk2dxpQV73MhQRCELVu2CDNnzhSGDRsmCIIgPHnyRBg3bhzjVNI6fPiwkJ+fL1y6dEmwtLQUPv74Y2H//v2sY9XAzxqktyQwMBA//fST2IWtoqICCxcuZJxKOtVd6KqXr/HWhW7s2LFwd3dHUlISV53XXrVnzx588skncHNzw8yZM8U/PMnLy0NoaCiOHDmC7t27Y/Lkybh9+zbc3NxYR2twd+7cUXj98uXLWp9yKLP09HTMmDFDfILDWyfG1NRUrFixAiNGjMDFixcRFBSE2NhYBAcHc7NvzdDQEGFhYQgNDUVoaCjCw8PRtWtX7Nq1i4u9W+fPn68xdvr0aQZJ2OB9LgQAJ06cwA8//AAtLS0AgIGBAXfH8Dg7O6NFixbo378/Tp48iQsXLmD8+PGsY9WgvGsNGsiJEycQHh4OBwcHAPz95ea9C118fLy4jM/f3x8jRoyAg4MDOnXqxDqaZHbv3o24uDjulq5Wmzt3Lu7du4fRo0cjKCgIbdq0AVC1n9fR0ZFxuoazbds2BAUFoaysDH369AFQtV9PXV0dY8eOZZxOWrx3YvT394ezszPmz58PTU1NcdzAwAAeHh4Mk0nn3r176NKli/j6gw8+wO3bt5V+Cdv+/ftx4MABZGRkKOzbKyoqEq8LPOB9LgQATZo0gUwmE7+D4uJixomkExERgdGjR2Pnzp21vv9qE7PGgIq9euL5LzdAXehkMhksLCxgYWGBixcvYuHChdi/fz9MTEzg6emJ3r17s47Y4Nq2bcvdL7VXTZw4Eebm5rW+FxoaKnEa6VQ/wV2/fj08PT1Zx2GK906M+/bte+N7vHRm7dSpE3x9fTFy5EgAQExMDN5//32Ul5cr9Z49Ozs7DB48GBs2bFC4Dmhra6Nly5YMk0mL97kQANjY2MDHxwcvXrzA4cOHERISws2Nv5KSEgB4Zx72UIOWetqxYwcePHiAc+fOYebMmQgJCYGtrS1cXV1ZR5MMz13o8vLyEBkZiYiICLRu3RpjxoyBpaUlUlJS4OHhgcTERNYRG0z1Haw///wTaWlp+O9//6vwNKOx3cl6244fP/637w8bNkyiJOxlZWUhMzNT4SDtfv36MUwkPR47Mb6pA2M1njoxlpaWYv/+/bhy5QoEQYCZmRkmTJgADQ0NlJSUQFtbm3XEBieXy/Hs2TOF60D79u0ZJpIWz3OhaufOncPZs2cBAAMHDoSFhQXjRKQ2VOz9C7z/5b569WqNiR4vd3Otra0xatQoODk5oW3btgrvbd++XanPGQoMDPzb9+fOnStREja8vLz+9n1enuysW7cOMTExMDY2hqqqqjj+6rlrPOCx4H1TB8ZqvHRiJMDevXuxZcsWtG7dWmGvGk8FP89zIVLl66+/hre3t3j8zPPnz7Fq1apGNx+gYo/Uy8KFC5GRkQETExNxoieTybg5UFf4/88UKywshEwm4+Lu7etiY2NrnKlX2xhRTtbW1oiKiuJqj9rr1q5di9jYWHzwwQcKE13eCl6e3b9/Hxs2bMCdO3dQVlYmjje6lusNxMrKCocPH+Z27zbvcyGgarXLunXrkJOTA0EQuDxztfqcvX8aY015F5a/Za8fJPw6Xv5y37x5EzExMX/7XSizP//8E4sWLcLz588hCAL09PSwatUqdO3alXU0yWzfvr1GYVfbmDI7deoU/vzzT4VJnrI/2axmZGSEiooKrou9hIQExMXFcfsdXLt2Df7+/rh37x4qKiogl8uhpaXFze9BoOpJv7u7O1asWIHdu3cjNDSUq46svO/d5n0uBFTd9AoKCuL6nNnKyko8f/4cLVq0AADk5+crPOltLKjYq6Pk5GQAwKZNm9C6dWuMHj0aABAZGfnObNB8G7p06YKnT5+KHQh54+Pjg2+++QYff/wxAODSpUvw8fHBwYMHGSdreElJSTh9+jSysrKwfPlycbywsFBhOZ+y8/HxQWlpKS5dugRnZ2fEx8fjww8/ZB1LMlpaWrC3t4e5ublCscPTHW3eC95ly5Zh48aN8PDwQEhICMLDw5Gens46lqTKysrERk2Ghob48ssvMWHCBLi7uzNOJg0jIyO4urpyt3e7Gu9zIQBo1aoV14UeALi5ucHFxQXW1tYAgLi4OHzxxReMU9VExV49nT17FkeOHBFfT5gwAc7OzpgxYwbDVNLJy8vDyJEj0bNnT4XNyLwsXyouLhYLPQAYMGAANx1ZDQwMYGpqiri4OLz//vuQyWRQUVFB69at/3E/mzJJTk5GVFQU7OzsMHfuXEybNg1ffvkl61iSsbS0hKWlJesYTFHBC3Ts2BFyuRyqqqpwcnKCi4sL60iSUldXR2VlJTp27Ii9e/fCwMAAOTk5rGNJpn379mjfvj0qKipQUVHBOo7keJ8LAYCpqSnmzZuHoUOHKlwHeWpWZm9vD1NTU1y8eBGCICAwMBAffPAB61g1ULFXT6qqqoiMjMTIkSMhk8kQHR3N1VMNniYN3PxVAAAgAElEQVS1tTEyMsLWrVsVnux26NCBcSppGBsbIzIyEhUVFQgJCYEgCHj8+DEcHR3x3//+l3U8yVSfK6alpYWsrCzo6uri4cOHjFNJx8HBAaWlpXj06BE6d+7MOg4TvBe8WlpaKC8vR/fu3bFmzRq0adOGm5te1RYvXoySkhIsWbIEmzZtwsWLF7F69WrWsSRTvWy9uLgYTZs2ZZxGerzPhYCqYwe0tLRw7tw5hXEeir3CwkLo6OggPz8frVu3hq2trfhefn5+ozuGhBq01NPDhw8REBCAq1evQiaToU+fPli8eDE3E37ePX/+HFu2bBHbbfft2xdffvmluF5bma1YsQLFxcX45ptvoKOjA6Dqgrd69WpoaGhw81Rj69atcHV1xYULF7Bs2TLIZDKMGTMG8+bNYx1NEomJiVi9ejUqKiqQmJiIlJQUbNq0ias72rzLzMxE69atUVFRgV27dqGgoAATJkxAx44dWUcjEklOToa3tzeKi4tx6tQppKam4uDBg/Dz82MdjZAGN3PmTGzbtg2WlpYK+zarm9Q0tkZNVOyROhk/fjwOHDhQo1ENj92XeDVs2DDEx8fX2JAul8thY2Pzj+fQKaPy8nKUlZVx1ajA0dERwcHBcHV1FTuO2dnZcdFy3cPDA5s2bXrjeXM8fAekSlpaGnbs2IFHjx7h5cuX4vju3bsZppKOs7MzNm/ejFmzZonXAVtbW0RHRzNO1rBoLgT8+OOPmDFjBvz9/WttUMPLjd93CS3jrKc37U1qbGdqvG0HDhwA8FejGt7804ZbHp5qyGSyWi/sqqqq3HUk4/l8JVVVVa6K21d5e3sD4OPf+9+5cuUKAgMDaxQ6je1udkPy8PCAi4sLxo4dq3D8Bk/atWun8JqH74H3uRAAsSmLqakp4yTsTZkyBcHBwf84xhoVe/X06t6ksrIyJCQkcNWNKT8/v8aYtra2wgZlZeTm5sY6AnPGxsYIDw+vUdRERESgU6dOjFJJ703nK/FS7HXp0gVRUVGQy+W4f/8+9uzZg969e7OOJYnqaz3vh4d7e3vDy8sLpqamXEzwa6OmpoYJEyawjsFMu3btxO0s5eXl2LNnD1edGXmdCwEQ9ys7ODgwTsJOWVkZSkpKkJeXJx7FBVRtbcnOzmacriZaxvn/qLKyElOnTuVm6YalpSUeP36M5s2bAwBevHgBfX19tGrVCv7+/lzc6SkvL8e9e/cgk8nQqVMnbtqvZ2VlYe7cudDU1ESPHj0gk8nw22+/obS0FFu3boWBgQHriJKwsbHh+nylkpISBAUF4ezZsxAEAYMGDcLs2bOhoaHBOppkajt3tVmzZjA1NcU333wDIyMjRsmk4ezsrNCVmkdbtmyBnp4erKysFH4HNLbGDA0lNzcXAQEBuHDhAgRBgIWFBby9vbk5ZJ3mQrWveKq+Drq4uCj174Tg4GAEBwcjOzsbBgYGYrGno6ODsWPHYtKkSYwTKqJi7//RvXv3MHPmTJw4cYJ1FEn4+PjAysoKgwYNAlB1FMWZM2dgY2ODgIAApZ8AnDp1Cr6+vnjvvfcgCAIePnyIb7/9FkOGDGEdTTIXLlzAnTt3IAgCunTpIp41xQt3d3csWbKEqyf6RNHmzZvRpk0bsQPbsWPH8PTpU3Tu3BkHDhzAnj17GCdsWOvWrYNcLsewYcMUCp0ePXowTCWt2rqxNsbGDKRh8D4XAoDly5eLR1AAQExMDPT19VFaWorCwkKsXbuWccKGt2fPHri6urKO8Y9oGWc9vX5HV19fHwsWLGCYSFo3b97EsmXLxNcDBw7Ehg0b4OXlhfLycobJpLFq1Srs3r1b7DqXnp6O//3vf1wVe+bm5twVeK/i9XylgIAAeHt7v3H/qrJ//ledOXNGYTI3btw4jB07FnPnzuXie7h+/TqAqt8H1WQyGTcrXICqrrQ8ouYcVXifCwFASkoK9u3bJ762tLTExIkTsW/fPrEAVHatW7cWj2H4/vvvcfv2bcyaNavR3fiiYq+eeN6UC1QtUdm+fbvCnZwWLVpALpdzsXejVatWCu3FjYyM0KpVK4aJiNR4PV+p+mxJ2r9a1YgiJiYGw4cPBwDExcWJ7/GwvLe2J5fPnj1jkIStP/74A3fu3FGY3Cv73l1qzlGF97kQULWU99GjR2jfvj0A4NGjR8jLywMALvYuAsD3338PGxsbXL58GWfPnoWbmxv8/Pwa3ZNdWsZZT+9K552Gkpubi61bt4rnzJmZmWHu3LnQ0dHB48ePlf6cJV9fXzx69Ag2NjaQyWSIi4tDp06d0KdPHwB8HCZKFF25cgXR0dHw9fVlHUUSxcXF0NTUFCc0crkc5eXl0NLSYpxMOhkZGQgICEBycjJkMhk++ugjeHl5wcDAADdv3kTfvn1ZR5REQUEB4uPjER0djbt37+LMmTOsI0kmMDAQly5dwt27dzFkyBCcPn0aZmZm2Lx5M+toRAK8z4UAICkpCb6+vuIe5YcPH8LX1xf9+/fH4cOHMXXqVLYBJWBvb4/w8HCsX78eXbt2hZ2dnTjWmFCxV0fVnXcmT56MPXv2KHTemTFjBmJjYxknJFJ409Eb1ZT9CA5SJSUlBVFRUYiLi4OhoSGsra0b3YbshjJ27Fjs3LkT2traAICioiJMnz4dBw8eZJyMSKG0tBSJiYmIiorC7du3UVRUhK1bt6Jfv37cPNEAqs6WjIiIgL29PSIjI/Hs2TMsWbKEi2W8ADBt2jRs2rRJbFDy/PlzzJ8/Hzt27GCcjEipumGdIAjo3LmzUjdlqc3MmTNhYGCA8+fPIzQ0FJqamhgzZgwiIyNZR1NAyzjr6ODBg2LnHUdHR3FcW1sbEydOZJhMWmlpafj555+RmZnJ5UGyVMzxKy0tDceOHcOxY8fQsmVLjBgxAoIgKH0zjteVlZWJhR5QdQ0sKSlhmEh6ubm5OHz4cI3roLJfHzw9PXH58mVYWFhg0qRJ+Pjjj2FlZYUBAwawjiY5DQ0NqKioQE1NDYWFhWjVqhUyMjJYx5JMbm6uWOgBQIsWLZCTk8MwkbR4nwtVu3nzpnjm7O+//w5A+Zcyv+q7777DmTNn4ObmhubNmyM7OxuLFi1iHasGKvbqqHfv3rCxsUF8fDxcXV0RFhaG+Ph4dOjQQezIxoPqg2SdnZ25uotbLSMjA3v37q1xgeflbi7PbGxs0LdvXwQFBYlLdHbt2sU2FANaWlq4deuWuAH95s2b0NTUZJxKWrNnz4aZmRnMzc3FsxZ58Oeff6J58+YwNjZG586doaqqysUexdqYmprixYsXcHZ2hqOjI5o2bYqePXuyjiUZVVVVhf1amZmZXP1d4H0uBPB95mx1U5aysjL0798fQNXZi+rq6o1yPyst46wjBwcH7Ny5Ey1btsSvv/6Kr776CkuXLkVKSgru3bvHzTp9R0dHhIaGso7BzKhRozBmzBh07dpV4QJf/Y+dKK8TJ07g2LFjSE5OxqBBgzBy5Eh4e3tz15Xvxo0bmD9/vnj0xNOnT7Fx48ZG+QuuoYwePRoRERGsYzBx9+5dREdHIyYmBq1atcK9e/cQFRUFfX191tGYefjwIQoLC2FiYsI6imROnz4NHx8f9OvXDwBw+fJlLFu2TDyKQNnxPhcC+D5zdubMmdi2bRssLS0hk8nwainVGI9goWKvjkaNGiWuwf3222+hp6cnduXj6Rc/7wfJ0mHCpLi4GAkJCTh27BguXrwIe3t7WFlZYeDAgayjSaaiogJpaWniPg1eOq9V27hxI/r06cPVkSu1+e2333Ds2DHExcWhbdu2XOzbvHXr1t++39harjek3Nxc8RiOXr16QU9Pj3Ei6fA+FwLozNl3CRV7dWRra4vw8HCoqalh+PDh8Pf3F+9o2draIjo6mnFCafB+kGxUVBQePHgACwsLbg8TJn/Jz89HXFwcYmJilH6vxo0bN9CuXTvxCU54eDji4+NhaGiIuXPncjXJ6d27N0pKSqCurg41NTUIggCZTIarV6+yjsaEIAj49ddfuVjhYGJigi5dukBXVxcAatzRV/brQGZmJpo3b45mzZoBAC5evIiEhAQYGhpi4sSJCr8XlRnvcyEAcHV1RWpqKndnzr7qXenQT8VeHf3www9ISkqCrq4uHj9+jLCwMMhkMjx48ABff/01F3c0CbB+/XpERETgvffeE5cu8PALnhBayk5I1T7d+Ph4NGvWDCNGjICVlZVCwyJl5+zsjMDAQBgYGCAlJQVTp07FzJkz8fvvv0NNTQ0BAQGsIxKJ/N///V+t4zzc9HnXOvRTg5Y6mjVrFszNzfH06VNYWFiIE/3KykosXbqUcbqG9+OPP2LGjBkAgNjYWNjY2IjvbdiwAfPnz2cVTVInTpxAQkICN3cvCakml8vFp3cxMTEYN24crK2tYW1tLR64ruwiIiLEz3rlyhWYmZmJ7+3du5eb4zd4NnXqVEydOhUZGRk4duwYpk6divbt2+OLL75A9+7dWcdrcKWlpTAwMAAAREZGwsnJCW5ubqisrOTiOkBzoap9u8bGxujfvz/Ky8sV5kPXrl1jmEw6r3fory72dHR0GmWHfj5bCP1LH330EaysrNC0aVNxrFOnTlws4YuJiRF/3r59u8J7PB2ka2JigoKCAtYxCJFcZWWl2IH2woUL+Pjjj8X35HI5q1iSerX76vLlyxXeCwkJkTgNYcnIyAifffYZLCws8NtvvyEtLY11JMldvHgR5ubmAMBNR0qaCwELFiwQfx43bpzCe99++63UcZiYMmUKEhMT8fXXX+PkyZNITExEYmIiIiMjG+VNP3qyR+rk1dW+r6/85WklcE5ODmxsbPDhhx9yu0ad8GnkyJGYNGkSdHV1oampib59+wIAHjx4AB0dHcbppEHXwSrBwcFwcnKCtrY2vL29kZKSAk9PTy6aFFU/0Tt58iTatWuHESNGYNasWdwcJj1gwAB4eHhAX18fz58/F2/6ZGdnc9Goia4B9B28ytXVFX/88Qfu3LmD8vJycbyxHT9BxR6pk1db677eZpentrvVHVgJv65duwZ/f3/cu3cPFRUVkMvl0NLSUvrmHLwvZQfoOlgtJCQEU6ZMwZkzZ5Cbm4uVK1fCy8uLi2LPysoK3bp1w2effQYdHR08fvwY+/fvF9+fNm0aw3QNz9vbGzExMXj69CkOHDggFnjPnj3DV199xThdw6NrAH0HrwoMDMSlS5dw9+5dDBkyBKdPn4aZmRkVe+TdlJqaij59+kAQBJSVlaFPnz4Aqu7ivHo3Q9n1798fmZmZePDgAT755BOUlJRws4SNVFm2bBk2btwIDw8PhISEIDw8HOnp6axjSeKjjz6qMdapUycGSdi4d+8e7OzsAADp6eniz0DVEx9eVN+9T0pKgpOTE0xMTLi5oz9nzhxxQltcXMw4jfRkMhlGjhxZY/w///kPgzTSo7kQ8OTJEyxfvhyCIIg/A1XfQVZWFuN00oqPj0dERATs7e2xcuVKPHv2DEuWLGEdqwYq9kidpKSksI7QKBw+fBiHDh3C8+fPkZCQgKysLPj6+ja6NrukYXXs2BFyuRyqqqpwcnKCi4sL60hEAq/u1+GZqakp3Nzc8PDhQ3h6eqKwsJCbPVu0uoNvNBcCFi1aJP5samqq8N7rr5WdhoYGVFRUoKamhsLCQrRq1apR3vijYo+Qeti3bx+OHDmCsWPHAgDef/995ObmMk5FpKSlpYXy8nJ0794da9asQZs2bbi8w88jQ0ND1hEahYCAAKSkpMDIyAhaWlrIz8/HihUrWMcihEjAwcGBdYRGw9TUFC9evICzszMcHR3RtGlT9OzZk3WsGqjYI6Qe1NXVFdoMV3cnJPxYs2YNBEGAj48Pdu3ahcePHyMwMJB1LEIkk5ycjO7du6Np06aIiIjA7du3MXnyZNaxCCFEUn5+fgCA8ePHY9CgQSgsLISJiQnbULXgY90FIW9Jv379EBQUhNLSUpw7dw4eHh6wtLRkHYtIKCEhARoaGtDR0cHcuXPh5eWFX375hXUsyVy7dg1OTk7o3bs3TE1N0b17d3HfCuGDn58ftLS0kJqaip9++gnt27fH119/zTqWpGpbqtUYl281lCtXrmDatGmwtrbGZ599BktLS3z22WesYxEiqSlTpog/d+jQASYmJgpjjQUVe6TeMjMzcf78eQBVB6wWFhYyTiSdBQsWQE9PD127dsWhQ4cwZMgQzJs3j3UsIqHw8PAaY2FhYQySsLFs2TJs2LABHTt2xPXr17F8+XK4urqyjkUkpKamBplMhoSEBEyePBlTpkxBUVER61iScnd3rzHm4eHBIAkb3t7emDp1Kvbv34+jR48iJCQER48eZR2LEEmUlZUhPz8feXl5eP78OfLz85Gfn4+HDx8iOzubdbwaaBknqZfXG5Q8efKEqwYlKioqGDt2rLhnj/AjOjoa0dHRePjwIb744gtxvKioCC1btmSYTHq8N6h5tQtntWbNmsHU1BSzZs2Crq4ug1TS0dbWxrZt2xAZGYl9+/ZBLpdzs6T97t27uHPnDgoKCnD8+HFxvLCwEGVlZQyTSatZs2YYMmQI6xjM8HoEz6vS0tLg5+eHnJwcREdHIzU1FYmJiZg9ezbraA3u4MGDCA4ORnZ2NhwdHcVxbW1tTJw4kWGy2lGxR+qFGpQQXvXu3Rv6+vrIy8uDm5ubOK6trY1u3boxTCYtalADDBo0CKqqqrC1tQXwV5dObW1teHl5ISgoiGW8Brdx40ZER0djxYoV0NfXx6NHjzB9+nTWsSSRlpaGU6dOoaCgQGH5tra2Nvz9/Rkmk8atW7cAVB2uvnr1agwbNkxhH3uPHj1YRZMUz0fwVFu6dCkWLVoEHx8fAICJiQkWLFjARbHXu3dv2NjYID4+Hq6urggLC0N8fDw6dOgg/l5oTKjYI/VCDUoIrwwNDWFoaIhDhw6xjsIUNagBrl69ioMHD4qvu3XrBhcXFxw8eLDWp37KRl9fH8OGDcODBw8AALq6uhg6dCjjVNIYOnQohg4diuTkZPTu3Zt1HMmtWrVK4fXNmzfFn2UyGXbv3i11JGZ4X+FQUlJSo/OkqqoqozTS8vX1xc6dO+Hq6opff/0V69evx9KlS5GSkgIfHx9s3ryZdUQFVOyRenm9Qcn+/fu5alASGxsLGxubfxwjyov35TsJCQmYMmUKNDQ0MHfuXABAcHBwo9yU3lCKi4tx/fp19OrVCwBw48YN8ekmD5MdOm+0ahmXsbExmjdvDgB4/vw5Vq1ahZUrVzJO1rD27NnDOkKjQCscqm7ypKenQyaTAQDi4uKgr6/POJU05HK5uH0jJiYG48aNg7W1NaytrTF69GjG6WqiBi2kXhYuXMh1g5Lt27fXaYwoL94blPDeoAYAli9fjiVLlsDS0hKWlpbw9vbG8uXLUVxcjP/973+s4zW4ffv24cCBA9DR0QHA53L+33//XSz0AKBFixZcHbi9YcMGvHjxQnz9/PlzbNy4kWEiab26wqFp06ZcrnDw9fWFj48P7t27h0GDBiE4OFg8ikDZVVZWiivbLly4gI8//lh8Ty6Xs4r1RvRkj9RZZWUlRo0ahejoaO4alCQlJeH06dPIysrC8uXLxfHCwkIu7uQTRTwu36EGNX/p2bMnoqKiUFBQAEEQFCb9I0aMYJhMGrScv+r34fPnz9GiRQsAQH5+fqOc5DWU06dPY/78+eLrFi1a4PTp0/jqq68YppIOrXAAjIyMsGvXLhQXF6OyslK8+cODkSNHYtKkSdDV1YWmpib69u0LAHjw4EGj/B6o2CN1pqKigm7duuHRo0do37496ziSMjAwgKmpKRITExU2oFc3ZCD84HX5DjWo+Ut5eTni4+ORmZmpUOhUT/qUHe/L+QHAzc0NLi4usLa2hkwmQ2xsrMJNEGUnl8tRXl4uFv2lpaUoLy9nnEo64eHhNQq7sLAwroq94OBgODk5QVtbG0uWLMHt27fh6emJgQMHso7W4GbNmgVzc3M8ffoUFhYW4lLWyspKLF26lHG6mmSCIAisQ5B3x+TJk/Hbb7+hZ8+e0NLSEseVvftctYqKCgiCgPv37wMAOnXqhCZNmrANRSSVmZmJ1q1bo6KiArt27UJBQQEmTJiAjh07so5GJDJ9+nQ0a9YMPXr0UHiy/2oRrMwqKytx9OhRnD17FgAwcOBAODs7ixMeXty5cwcXL16EIAgwNzfHBx98wDqSZH788UckJibC0dERMpkMISEhsLS0xIwZM1hHa1DVKxyuXLkCMzMzcbyoqAiqqqrYtWsXu3ASGzVqFCIjI3HmzBns27cP8+bNg5eXF3fL+t8F9GSP1Asvd67fJDk5GV9//TUMDQ0hCAIeP36M1atXo1+/fqyjEYkYGhqK+5N4/PfAe4MaAMjKysKOHTtYx2CGzhutkp+fDy0tLTg5OSE3NxcZGRkwMjJiHavBCYIAW1tbdOvWDRcuXIAgCJg9ezYGDRrEOlqDoxUOf6l+VpSUlAQnJyeYmJiAnh81TlTskXrp378/6whMrVq1Cjt27EDnzp0BVJ255OnpidDQUMbJSEMTBAGBgYHYu3cvgKqnG6qqqpg0aRJXRR+dL1U14fv999+5m9xVu3LlCgIDA/Ho0SO8fPkSgiBAJpPh5MmTrKNJJjAwEDdv3kRaWhqcnJxQUVGBhQsXKhzJoaxkMhnmzJmD0NBQDB48mHUcSdERPH8xNTWFm5sbHj58CE9PTxQWFkJFhfo+NkZU7JF64f2ufkVFhVjoAVXLOCsqKhgmIlIJDg7G1atXcfToUfHufUZGBvz8/LBr1y5MnTqVbUAJ8dig5lVXrlxBWFgYDA0NFRqVREVFMUwlHW9vb3h5ecHU1JTbyd2JEycQHh4OBwcHAFX7uouKihinkk6vXr1w48aNGues8YL3uRAABAQEICUlBUZGRtDS0kJeXh5WrFjBOhapBRV7pF5qu6tffbAuD0xNTbF48WLxHJWoqCiYmpoyTkWkEB4ejp9//hl6enrimJGREdauXQs3Nzduij1eG9S86scff2QdgalmzZphyJAhrGMw1aRJE8hkMnGfIm//Bi5duoRDhw6hffv2Cvv3ebnhQSscqpZzd+jQAffv30dZWRnrOORvULFH6o3nu/rffvst9u3bhz179kAQBPTr1w8TJkxgHYtI4OXLlwqFXjU9PT2uWs+/er7Url278PjxY2zZsoV1LEkUFhZCR0cH2trarKMwNWDAAKxevRrDhg1TeLL5aqdiZWdjYwMfHx+8ePEChw8fRkhICFd7GHm/4QHwPRcCgCNHjmD37t148uQJTExMcP36dXz00UfYvXs362jkNVTskXrh/a6+uro6pk2bhmnTprGOQiT2d11XeerIynODGk9PT2zbtk3sQPhqMwKe9qxdv34dAHDz5k1xTCaTcTXJmz59Os6dOwdtbW2kpaXB3d0dFhYWrGNJxtDQEACQk5PD5VMd3udCALB7924cPXoUY8eOxZ49e3D37l1ubvy9a+joBVIvvLedf70xQTVeJnk86969u8JypWqCIKC8vBy3bt1ikEo61KCGEFLt5MmTWL16NbKzs6Gnp4dHjx7B2NgYx44dYx1NErzPhQDAyckJISEhGD16NI4cOQJ1dXWMHj0aERERrKOR19CTPVIv6enp0NPTg46ODpcTPGpMwK+UlBTWEZiiBjWKsrKykJmZCblcLo4p+xEsERERGD16NHbu3Fnr+zyseBg/fjwOHDiA3r1713quYMuWLTF9+nRMnDiRQTrpbNq0CYcOHcK0adMQHh6OixcvclPoAXyvcKjWtm1bvHjxAkOHDsW0adPQvHlztGnThnUsUgsq9ki9hIWFwc/PDy1btoSZmRn69u0LMzMztGjRgnU0SVBjAsIralDzl7Vr1yI2NhbGxsYKh6ore7FXUlICAFx1nXzdgQMHAFSduVqbvLw8uLi4KH2xp6amBl1dXVRWVqKyshIff/wx1q1bxzpWg6MVDn/ZunUrAODLL7/EgAEDUFBQwMVZi+8iKvZIvaxZswZA1V3t+Ph4LFu2DNnZ2bh9+zbjZNKgxgSEV9Sg5i8JCQmIi4tTuAbwoLoBBW+T2jdJTU3F5cuXAQB9+/aFiYkJdHV1sWfPHsbJGl7z5s1RVFSEvn37YsGCBdDT04OamvJPKWmFg6Jbt27hypUrkMlk6NOnD3fXxHeF8v/LJG9VREQErly5gj/++AMtW7bExIkT0bdvX9axJEONCQivqEHNX4yMjFBRUcHtxGbNmjWYPXs2NDQ08PnnnyM1NVXhSBoeBAcH48iRI7CysgIALFy4EGPHjoWrqysXS9m+//57aGpqYvHixYiKikJBQQHmzJnDOlaDoxUOfwkMDER8fLz4b8DLywvDhw/H7NmzGScjr6MGLaReBgwYgPfeew8uLi4YMGAAOnTowDoSIUQCvDeoedWXX36J1NRUmJubKxR8S5YsYZhKOtVNGE6cOIGEhAR4eXlh8uTJiIyMZB1NMnZ2djh06BCaNm0KoOqcvXHjxnFzzhxQ1aTkwYMH+OSTT1BSUgK5XA4dHR3WsRqUra0toqOj6/2eMrKxsUF4eDg0NDQAAKWlpXBwcEBsbCzjZOR19GSP1MulS5fw559/4tdff8XGjRvx4MEDdOrUCWvXrmUdjRDSgHhvUPMqS0tLWFpaso7BTPWy3aSkJIwcORItW7ZknIiNV/drvvozDw4fPoxDhw7h+fPnSEhIQFZWFnx9fREcHMw6WoOiFQ5/MTQ0RFlZmVjslZeX47333mOcitSGij1SL4WFhXj06BEePXqEzMxMFBQU1NqRjBBClJFcLkdERAR27drFOgozn376KYYPHw5NTU34+voiNzdXnPDxwsnJCc7OzuIStoSEBDg5OTFOJZ19+/bhyJEj4kHy77//vtidUpmlpqaiT58+NcarVzjwwN/fHzKZDOrq6hg5ciQsLP1v9vcAABm/SURBVCwgk8lw7tw5mJmZsY5HakHFHqmX8ePHw8zMDGZmZpg0aRLatm3LOhIhhEhGVVUVmpqaKCgoQLNmzVjHYWLBggWYMWMGdHR0oKqqCi0tLXz//fesY0mmsrISPXv2RL9+/XDlyhUIgoCVK1fiP//5D+toklFXV1dYwsxLkyZa4QCYmpoCqGpMV32zAwD69+/PKhL5B1TskXqZNWsWRowYoTAWGxsLGxsbRomk5ejoCCcnJ9ja2nJz3AQhRJGGhgbs7OzwySefiHu2AH727IWHh9c6bm9vL3ESNlRUVLB69WocOnSI207M/fr1Q1BQEEpLS3Hu3Dns37+f66XNPHFwcGAdgdQTNWgh9eLg4ICwsLB/HFNWDx48QGhoKGJiYmBqagpHR0cMHDiQlrISwpE3Xe94mQT5+/uLP5eVleHChQvo0aMHNm/ezDCVtDZv3oxu3bph2LBhXF7/KysrcfToUZw9exYAMHDgQDg7O3P5XfDq/v372LBhA+7cuYOysjJx/OTJkwxTkdpQsUfqJCkpCadPn0ZsbKzCk73CwkLcuXMHR48eZZhOepWVlfjll1/g5+cHFRUVODk5YfLkydw2KiCE8KugoAALFy5EUFAQ6yiS6d27N0pKSqCmpgZ1dXUIggCZTIarV6+yjia5/Px8PHnyBCYmJqyjEAmNHz8e7u7uWLFiBYKCghAaGgpBEODu7s46GnkNLeMkddKyZUuYmpoiMTFRYdmKtrY2vLy8GCaTXmpqKkJDQ5GUlARra2vY2dnhypUrmDJlCiIiIljHI4Q0MLqjrUhTUxMPHjxgHUNSycnJrCMw5erqih9++AEvX76Evb099PT00K9fP+7mAzwrKyuDubk5gKrOnF9++SUmTJhAxV4jRMUeqRM/Pz+EhYXh7Nmz3CxVqo2joyOaNWuGMWPGYMGCBeIG9V69enF5R5cQHnl5eYl3tHfv3i3e0ebFF198If4sCALu3LnDzb7t+/fvY/Xq1cjIyEC3bt2waNEiGBgYsI4luYKCAujo6ODIkSNwdHSEu7s77OzsWMciElJXV0dlZSU6duyIvXv3wsDAADk5OaxjkVpQsUfqpKKiAmFhYbh+/TqOHz9e4/1hw4YxSCW9TZs2wcjIqNb3AgMDJU5DCGGB9zvabm5u4s+qqqowNDTkpjPz4sWLYW9vj759+yIxMRH+/v5cXvvlcjmys7MRGxuLefPmsY5DGFi8eDFKSkqwZMkSbNq0CZcuXcKaNWtYxyK1oGKP1Imfnx+ioqJQUFCAX375pcb7yl7s7dy582/fnzZtmkRJCCGs8X5H+9UW67m5udDV1WWYRlpFRUXi2XKdO3fmdqXL7NmzMX36dPTp0wc9e/ZERkYG3n//fdaxiIQyMzPRs2dPaGtrY+XKlQCqurP36tWLcTLyOmrQQurlyJEjcHZ2Zh1Dcv9053bu3LkSJSGEsHbjxg0YGxujoKAAmzZtQmFhIaZPn46PPvqIdbQGde3aNaxfvx4tWrTA7NmzsWjRIuTl5aGyshKrV6/G4MGDWUdscMOHD8eGDRvEZbsLFizA+vXrxde8HsVA+MN7d/Z3CRV7pF7Ky8tx8OBBXL58GUDVWTsuLi5o0qQJ42TSuHLlCszMzP5xjBCi/IqLixXO2VN2jo6OmD9/PgoKCuDj44Mff/wRH330Ee7evQtPT883nr+nTFxdXd/4nkwmw+7duyVMw05aWhr8/PyQk5OD6OhopKamIjExEbNnz2YdjTQw6s7+7qFij9SLt7e32H0LACIjI6GiooKAgADGyaRBd7IIIcnJyfD29kZxcTFOnTqF1NRUHDx4EH5+fqyjNajRo0eLHYdtbGwQGxsrvmdvb89FsZednY02bdqwjsHcpEmTsGjRIvj4+Ij/v9va2iI6OppxMtLQUlNTkZKSgs2bNyvsU9bW1saAAQPQokULhulIbWjPHqmX3377DZGRkeJrc3NzjBo1imEiaSQnJyM5ORm5ubkK+/cKCwshl8sZJiOESG3FihXYsWMHZs2aBQAwMTERVzsoMxUVFfFnTU1Nhfd4OUx78eLFePHiBfr3749BgwbBzMwMamr8TaVKSkrQs2dPhTFVVVVGaYiUTExMYGJiAltbW25Wdb3r+LtCkf8nqqqqSE9Px3vvvQcAyMjI4OICX1FRgeLiYsjlchQVFYnjOjo62Lx5M8NkhBAW2rVrp/D61UJIWaWmpqJPnz4QBAFlZWXo06cPgKrjF8rLyxmnk8ZPP/2EsrIyXLp0CSdOnMDq1avRvn17DBo0CIMGDUL79u1ZR5SErq4u0tPTxSI/Li4O+vr6jFMRKWVmZtJ5o+8IKvZIvSxatAiTJ0+GkZERBEHAo0ePsGLFCtaxGlz//v3Rv39/ODg4wNDQkHUcQghD7dq1w9WrVyGTyVBeXo49e/bA2NiYdawGl5KSwjpCo6ChoYHBgweLDWkyMjJw+vRpLFu2DM+ePeNiz5Kvry+WLl2Ke/fuYdCgQejQoQPWrVvHOhaREO/njb5LaM8eqbfy8nLcu3cPQFXr6eqDxXmQlpaGn3/+GZmZmXj58qU4zsumfEJI1XEDAQEBuHDhAgRBgIWFBZYsWYKWLVuyjkYksnbtWixcuFBhbM2aNZg3bx5XvxOLi4tRWVkJTU1NxMTEcLGtg1RxdHREaGgo7OzsEBUVBQCYMGEC9u/fzzgZeR092SN1cuPGDbRr1w76+vpQV1dHamoq4uPjYWhoiLlz53IzyfHw8ICLiwucnZ25WLZFCKlJT08P69evVxhbvXo1vv76a0aJiNTOnz9fY+zMmTP/X3v3Ghxldcdx/PfkAglRILFYNUmVMoI0EYOFFowpJgptibkQlAEFh1IY7RTHIdZy74hxmlE02gYKnTahpMYwKDcTtNa7NhNESCTCoCWQBN3SFpJKF9DdTfbpC4YdlgTctWYP7n4/r/Y5Z/fJb/Nik/+em375y18aSBM6J06cUHV1tf71r3/p1ltv1U033aTq6mpVVlZqxIgRFHsRJNLPG/06YWQPAZkyZYrWrVunwYMH67333tOCBQu0fPly7d+/X4cOHYqYdWtnvskCgLPdcsstevPNN03HQB979tlnVVNT47d2XTp92Pro0aN7fAkQbn72s59p0KBBysjIUENDg/773//K4/Fo6dKlGjlypOl4CKFzzxt1Op2aO3du2J83+nVEsYeA5Ofn+3bhXLFihZKSknT//fdL8t+OO9yVl5crKSlJEydO9JuqEykjmwB6N2HCBL311lumY6CPOZ1OHT9+XGVlZXrwwQd97QkJCRHxd+DsKXvd3d0aN26c3njjDV1yySWGkwE4H6ZxIiBer1ddXV2KiYlRQ0ODSkpKfH2RdPTAmfP0KioqfG2WZbH7FBABPv30017bbdtmY4IIcemll+rSSy/VPffco0GDBvmKnBMnTmjPnj264YYbDCfsW2cfMxEdHa2UlBQKvQi0ZcsWVVVVqbW1VdLp/Rvuuece3xnMuLhQ7CEgubm5mjlzphITExUXF6cxY8ZIktrb2yPqg/711183HQGAIUVFRbIsq9fCjvOmIsvDDz/s+/JPkgYMGNCjLRydOX5Dkt8RHLZty7IsNTY2Gk6IvrZ161atX79eixYtUlpammzb1r59+7Ry5UpJouC7CDGNEwF7//33dfToUWVmZmrAgAGSTu9OeerUKaWlpRlOFxqfffaZ1q1bpyNHjqikpERtbW1qbW1Vdna26WgAgBDpbfnC2VMcgXA1bdo0lZWVKSUlxa/9k08+UXFxsTZu3GgoGc6H7QQRsIyMDE2cONFX6EnS0KFDI6bQk06fKxMbG6umpiZJ0hVXXKGnn37acCoAQCilpqaqqqpKHo9HHo9H69evV2pqqulYQJ87ceJEj0JPklJSUnTixAkDifBFKPaAIBw+fFjz5s3zrVuIi4tjrQ4ARJgVK1aoqalJP/jBDzRhwgQ1Nzf7rWUHwlVcXNyX6oM5rNkDgtCvXz99/vnnsixL0uniL5IO0AUASJdddpmeeuop0zGAkDt48KDy8vJ67fv4449DnAaBYM0eEIT6+nqtWbNGLS0tyszMVFNTk0pLS/X973/fdDQAIbRr1y61t7dr6tSp6uzs1MmTJ5nGF0EWL17ca3tpaWmIkwCh5XA4LtifnJwcoiQIFCN7QIC8Xq+OHz+u8vJy7dmzR7Zta+nSpUpKSjIdDUAIrVq1Snv37lVra6umTp0qj8ejhx56SBs2bDAdDSFyyy23+B67XC69+uqruvzyy80FAkLkV7/6lbKyspSVlaVhw4aZjoMAMLIHBOHuu+9WdXW16RgADCooKNDWrVs1ZcoUbd26VRI7MUY6r9er2bNnq6qqynQUoE8dPXpU77zzjt555x21tbVp1KhRysrK0vjx45WQkGA6HnrByB4QhJtuukkVFRWaPHmy4uPjfe2DBw82mApAKMXGxsqyLN/a3VOnThlOBNPa2tp05MgR0zGAPjdkyBAVFRWpqKhIXq9Xe/bs0dtvv60//vGPiouLU2ZmpubNm2c6Js7CyB4QhJycnB5tlmXptddeM5AGgAkVFRVqb29XfX297r33Xm3atEm33367Zs2aZToaQmT06NGyLMt3mPiQIUNUXFysH/7wh6ajAcY0Nzerra1N+fn5pqPgLBR7QIC8Xq/+8pe/aPLkyaajADCsvr5ef/vb3yRJN998szIzMw0nAoDQa2lp0fbt2/Xiiy8qISFBmzdvNh0J56DYA4LAmj0AiFz79u27YH9aWlqIkgDmOBwObd++XXV1dYqJiZHD4dCmTZt6PWwd5lHsAUFYvXq14uLiWLMHRLC//vWveuKJJ9TR0SHbtn1T+RobG01HQx87M1XX7XZr7969GjFihCTpo48+0qhRo1RTU2MyHtDnpk+fLqfTqcmTJys3N1fXXHONcnJy9Prrr5uOhvNggxYgCJs2bZIkv9E91uwBkWXlypVau3Yt245HoD//+c+SpAULFuiRRx7xFXt///vfVVlZaTIaEBKJiYn65z//qY6ODnV2duqaa67xbVaFixPFHhAEvrkCcNlll1HoRbhDhw75Cj1JGj58uPbv328wERAaa9askdPp1Msvv6zy8nK1t7fL6XSqublZo0aNMh0PvWAaJxCEM2dqnauwsDDESQCY8uijj+rYsWO67bbb1K9fP1/7pEmTDKZCKBUXFys+Pl75+fmyLEsvvPCCTp06pbKyMtPRgJDq6OjwbdBy5MgRvfXWW6Yj4RwUe0AQSkpKfI9dLpcaGhqUlpam3/72twZTAQilxYsX99peWloa4iQwxeVyqaamRu+9954kaezYsZoxY4b69+9vOBlgjsPhUHJysukYOAfFHvB/cDqdeuihh7R27VrTUQAAIeR2u9Xa2irLsjR06FDFxsaajgT0uc7OTj377LMaOHCgpk6dqscff1y7d+9WamqqFi1apKuvvtp0RJyDYg/4P3g8HuXn5+ull14yHQVAH/vDH/6gefPmqaSkpNcNCZYtW2YgFUx49913tWjRIiUnJ8u2bR05ckSPPfaYxo4dazoa0KfmzJmj9PR0nTx5Ug0NDSoqKlJ2drZ2796t2tpa3yZGuHiwQQsQhPvuu8/32LZttbS06Mc//rHBRABC5cymLOnp6YaTwLTHHntMFRUV+va3vy1Jam1t1YMPPsiB0gh7x44dU3FxsWzbVnZ2tubOnSvp9Ocj5xBfnCj2gCDMmTPH9zg6OlrJycm64oorDCYCECo5OTmSpClTphhOAtM8Ho+v0JOkoUOHyuPxGEwEhEZ0dLSk08dOJSYm+vVFRUWZiIQvQLEHBKC9vV3Hjh3T9773Pb/2Xbt2ye1261vf+pahZABCrbW1VZWVlXI4HOrq6vK1V1VVGUyFUEpPT9eSJUtUUFAgSaqtrWXEFxHh448/9s1yOvuxJH3yySemYuECWLMHBODee+/VggULdN111/m1f/DBB1q9ejUbtAARJD8/X9OnT1d6errfN9n8sx853G63qqurtXv3btm2rbFjx+quu+7yO4oDCEc7d+68YP+5X4rDPEb2gAA4HI4ehZ4kXX/99XI4HAYSATAlJiZGd911l+kYMMTr9aqoqEh1dXX6yU9+YjoOEFJnF3OdnZ2SpKSkJFNxEACKPSAALpfrvH2ff/55CJMAMOXTTz+VJGVnZ6u6uloTJ070G8kZPHiwqWgIoaioKI0YMUL/+Mc/dNVVV5mOA4SUbdtavXq1b9dNr9er6OhozZw5U/PnzzecDr2h2AMCcP3112vjxo2aNm2aX/tzzz2ntLQ0Q6kAhFJRUZEsy9KZ1Q8VFRW+Psuy9Nprr5mKhhA7evSocnNzNWrUKMXHx/vamdKPcLd+/Xrt3r1bzz//vFJTUyWdXrv38MMP609/+pNmz55tNiB6YM0eEIBjx45p/vz5io2N9RV3e/fulcfj0apVqzRkyBDDCQEAoXK+dUusV0K4KywsVGVlZY+pm52dnZozZ462bt1qKBnOh5E9IADf+MY3tGHDBu3YsUMHDhyQJE2YMEHjx483nAxAqFVXVysvL08DBw6UJB0/flx1dXW6++67DSdDX3O5XKqpqdHhw4c1fPhw3XHHHYqJ4V8pRI6urq5e1+glJSX57U6MiwefUEAQxo0bp3HjxpmOAcCgjRs3+hV2gwYN0nPPPUexFwEWLlyomJgYjRkzRm+//bZaWlq0bNky07GAkImNjf1SfTCHYg8AgCB4vV7Zti3LsiRJ3d3dHKgdIQ4ePKja2lpJ0h133KE777zTcCIgtD788EPdeOONPdpt25bb7TaQCF+EYg8AgCDcfPPNeuCBBzRjxgxJ0oYNG5SVlWU4FULh7CmbTN9EJNq/f7/pCAgSG7QAABAEr9frW8Nr27YyMzN15513Kjo62nQ09LGRI0f6dt+0bVsul0txcXG+kd7GxkbDCQHAH8UeAAAAAIQh5iAAABCEtrY2lZWVqaWlRS6Xy9fOOXsAgItNlOkAAAB8nSxevFgzZsxQdHS0qqqqVFhYqIKCAtOxAADogWIPAIAguFwu3xmbycnJuv/++7Vjxw7DqQAA6IlpnAAABKFfv37yer26+uqr9cwzz+ib3/ymOjo6TMcCAKAHNmgBACAIzc3NGjZsmJxOp37zm9/I6XRq7ty5ysjIMB0NAAA/FHsAAAAAEIaYxgkAQADuu+++C/avXbs2REkAAAgMxR4AAAF4//33deWVVyo3N1c33HCDmBgDALjYMY0TAIAAdHd3q76+Xtu3b9dHH32kCRMm6Pbbb9e1115rOhoAAL2i2AMAIEhut1t1dXV6/PHH9fOf/1yzZs0yHQkAgB6YxgkAQIDcbrfefPNN1dXVyeFwaNasWZo0aZLpWAAA9IqRPQAAArBw4UIdOHBAWVlZys3N1fDhw01HAgDggij2AAAIwHXXXaf4+HhJkmVZvnbbtmVZlhobG01FAwCgVxR7AAAAABCGokwHAAAAAAB89Sj2AAAAACAMUewBAAAAQBji6AUAQNg4evSofv3rX+uDDz5Qv379lJycrCVLlmjo0KEhy/Duu+8qNjZWN95445e+R3l5uQYMGKCf/vSnX2EyAECkodgDAIQF27Y1f/58FRYW6qmnnpIk7d+/Xx0dHQEXe93d3YqOjj7vdSB27typAQMGfGGx19XVpZgY/gwDAPoOf2UAAGFhx44diomJ0YwZM3xtI0eOlHR6tK2yslK///3vJUmPPPKI0tPTVVRUpJycHBUVFam+vl4zZ87Uk08+6Xc9aNAglZeXy+12KzU1VaWlpUpISFBOTo4KCwv1xhtvqKurS08//bT69++vDRs2KCoqSi+88IKWL1+uMWPG+PKUl5fr3//+txwOhxITE1VcXKwlS5aos7NTSUlJKi0t1VVXXeX3vg4fPqwVK1boP//5j+Li4lRSUqJhw4aF4DcKAPi6Y80eACAsHDhwQGlpaV/qtf3791dNTY1yc3P9rsePH681a9Zo3bp12rJli9LT07Vu3Trf6xITE7VlyxZNnz5dlZWVSklJ0fTp0zV79mxt27bNr9A7Y9++ffrd736nJ598UiUlJSosLFRtba3y8vL06KOP9nj+8uXLtXz5cm3evFkLFy7UihUrvtR7BABEHkb2AAARb/Lkyb1e79mzRy0tLb7RQo/Ho4yMDN/zJk2aJElKT0/XK6+8EtDPysnJUVxcnCSpqalJ5eXlkqSCggKtXLnS77knT55UU1OTHnjgAV+b2+0O5q0BACIYxR4AICxce+21evnll3vti46Oltfr9V27XC6//vj4+F6vbdtWZmamysrKer1vbGysJCkqKkrd3d0B5Tz3Z53Nsiy/a9u2NXDgQG3bti2gewMAcDamcQIAwsK4cePkdru1ceNGX1tzc7N27typ5ORkHTx4UG63W06nUw0NDQHdMyMjQ42NjWpvb5ckffbZZ2ptbb3gaxISEnTy5MmA7j969Ght375dklRbW6vvfve7fv2XXHKJUlJS9NJLL0k6Xfx9+OGHAd0bAACKPQBAWLAsS6tWrVJ9fb1uu+025ebmatWqVbr88st15ZVX6kc/+pHy8vL0i1/8Qt/5zncCuueZTVOKi4uVl5enadOm6dChQxd8TXZ2tl555RUVFBRo165dF3zusmXLtHnzZuXl5Wnbtm1aunRpj+esXLlSzz//vPLz85Wbm6tXX301oOwAAFi2bdumQwAAAAAAvlqM7AEAAABAGKLYAwAAAIAwRLEHAAAAAGGIYg8AAAAAwhDFHgAAAACEIYo9AAAAAAhDFHsAAAAAEIYo9gAAAAAgDP0POHoqNOowqnAAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 1080x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1,1, figsize=(15,6))\n",
"XP=MostXP.Q5.value_counts().sort_values(ascending=False).to_frame()\n",
"ax=sns.barplot(data=XP,x=XP.index,y='Q5',palette=\"viridis\")\n",
"ax.set_title('Current job with coding experience greater then 5 years',fontsize=21, fontweight='bold')\n",
"\n",
"ax.set_xlabel('Current role')\n",
"ax.set_ylabel('Number of Particpents')\n",
"ax.set_xticklabels(ax.get_xticklabels(),rotation=90)\n",
"for p in ax.patches:\n",
" ax.annotate(format(p.get_height(), '1.0f'), \n",
" (p.get_x() + p.get_width() / 2., p.get_height()), \n",
" ha = 'center', va = 'center', \n",
" xytext = (0, 9), \n",
" textcoords = 'offset points')\n",
"for s in ['top', 'left', 'right', 'bottom']:\n",
" ax.spines[s].set_visible(False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.13531,
"end_time": "2020-12-05T10:05:15.203551",
"exception": false,
"start_time": "2020-12-05T10:05:15.068241",
"status": "completed"
},
"tags": []
},
"source": [
"Participants without a degree who have more coding experience are most students and are transitioning into the field of machine learning and data science and the same goes for the software engineers. This trend is seen in the previous part too. We will be focusing more on jobs percentage by eliminating Unemployed, student and other from data to make some sense. "
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.134555,
"end_time": "2020-12-05T10:05:15.473402",
"exception": false,
"start_time": "2020-12-05T10:05:15.338847",
"status": "completed"
},
"tags": []
},
"source": [
"## Job titles with 5+ years of coding experience with and without degree"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"_kg_hide-input": true,
"execution": {
"iopub.execute_input": "2020-12-05T10:05:15.758348Z",
"iopub.status.busy": "2020-12-05T10:05:15.753356Z",
"iopub.status.idle": "2020-12-05T10:05:16.200492Z",
"shell.execute_reply": "2020-12-05T10:05:16.199665Z"
},
"papermill": {
"duration": 0.592067,
"end_time": "2020-12-05T10:05:16.200630",
"exception": false,
"start_time": "2020-12-05T10:05:15.608563",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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p9dZbL0OHDs2uu+6a6urq7LXXXivc75In9Iut6FqZ5Wnbtm2mTZuWuXPnZsqUKUudNK2sKVOmlB7X1dVl6tSppeetW7fO8OHDS2HpqKOOysCBA9OqVav89Kc/ze23377c/S4ZUJbXvk9r9//+3/87Rx99dN58881MmjQpzz77bH75y1/m+eefz1133ZVTTz11pdq4ONQt6dOmCbZp06b0+L333iuN9i3ZX0uWWWxZ7++S5e65557sueeeS5VZfG3Zsix5HdI3vvGN3Hbbbcvcfsn+fO655zJ8+PDS85kzZ+bKK6/MwIEDl/kan3YcLFmH/v37L3O63rLasKz+WJZWrVqlSZMmqa2tzTbbbLPU9U2L97+sY2bJ93JZ65es+1tvvVWaArm8Mivbx0V8Wl2X5z/+4z9KN0i5/fbb071796W2r6mpSUVFRb3jbcn3NUm9kaa2bdvW++fItGnT6pX99+cr69PauOQI8bRp09KxY8ckqXfMAWsvI1NAkn+doJeXl6dly5aZO3durrjiikL72njjjUuP33zzzZUavVnyLmg/+MEP8sYbb6S6ujoTJ07MXXfdtdKvPXLkyIwfPz5z5szJ4MGDSyc0O++8c1q3bl3vxKhZs2ZZf/31M2HChAwbNmylX2N5VtTuGTNm5PLLL8+ECRPStm3bHHzwwaUfC02WPklc1f7jP/6j9Pimm27KO++8kw8++KB0s4J/L7MiS06Ju/jiizNx4sTMmzcv77zzTn7/+9+Xri1bnm222aY0Pe6pp57KHXfckdmzZ2f27Nl5+eWXc/3119e74+Ts2bNzzjnnpLa2Nu3bt09lZWWSRbfJX9Yd+pJPPw4OOOCAUkC+7bbbMnbs2MydOzfTp0/PH/7wh1xwwQX17iL5WW2wwQb5+te/nmTRlLsrrrgiH374YebMmZOJEyfm9ttvL11j9lkt+Vm55ppr8swzz2Tu3LmZPHly6Tq6z9rHa9qJJ55YCiGvvPJKzjrrrPztb3/LggUL8v777+fBBx9M7969kyR77rlnWrVqlWTRlMxRo0blH//4R37961/n1VdfTbJo9GjrrbfOtttum0033TTJopvGDBs2LHPmzCk8xW9lLH6fk+TWW2/NtGnT8ve//32ZARZY+xiZApIsusbkvvvuS3V1dekOY8u7HuTTbL311mndunU++uijPProo3n00UeTJEOHDl3uD9lWVVXlD3/4Q95+++08++yz6d69e2ndvvvum+OOO26lXrtly5Y5+eST6y1r0qRJ6SYEnTt3zvrrr5958+blV7/6VX71q18lSen3bT6PFbV76623zm233bbcE6zF152tLkceeWQeeuihjB07Nq+88krpupPFOnbsWO/215+2r4cffjhPPvlkXnnllWVOtzvppJNWuI+f/OQn6dOnT+bNm5fLLrssl112Wb31++67b+nx+eefnylTpqS8vDyXX355dtppp7zwwgt55513MmDAgDz00EPZZJNN6m3/acfBFltskbPOOis/+9nPMmvWrGXeLXLJmzkU8cMf/jDHHntsZs6cmV/84hf5xS9+UW990emsRxxxRH73u9/lsccey9SpU3PCCSfUW3/GGWck+Wx9vKZtsskm+fnPf54zzjgj77//fh577LGlRu9atGiRZNG02oEDB+YHP/hBFixYkNNPP71eufXWW690M4ry8vL0798/AwYMSLLoHzOLl7dq1SqzZs1a5W3p0aNHhg4dmjfeeCN//vOf07lz5ySpdy3a5x0BBBovI1NAkkW3OD7mmGPStm3bbLjhhjn44INXOO1tRdZff/1cc8016dix41LX2yxPmzZtcv/99+f000/PV7/61ay//vpp1qxZtt9++9LJycr4P//n/2TAgAFp3759mjZtmg4dOuTGG2+sd5H+TTfdlJ133jnrr79+2rdvnx/96Ef1wltRK2p3y5Ytc8IJJ2TXXXdN69at06RJk2y00Ubp1KlTrrrqqqXCzarWpEmT3HTTTTn33HOz8847p1mzZllvvfWy/fbb5/TTT1/mbdGXp7y8PDfeeGMuuOCC7L777tlwww2z3nrrZcstt8yBBx6YCy64oDTVaXn23XffPPjgg+nZs2c222yzNG3aNBtvvHHpN40Wj5rcd999+d3vfpdk0U0E9t5772y00Ua5/PLLU15eng8//DADBgxYakrepx0HSXLKKafklltuSefOnbPxxhunoqIiX/rSl9KpU6f069evdPOCorbffvsMGzYsvXv3zlZbbZWmTZumRYsW+cpXvpJvfetb+fGPf1xov2VlZRk0aFAuvvji7LnnnmnevHmaNm2aLbbYIt26dSuVW9k+bii77757Hn744VRVVWWXXXbJRhttlKZNm2bLLbdMly5dSjd5SRZNyf3lL3+Zgw8+uPRebbLJJjniiCNy33331fsnzdFHH51LLrkkX/7yl9O0adPssMMOpd/GWh3WX3/93HbbbTnssMOy4YYbplWrVvn2t79dr3+XHLUG1i5ldSua2A7wBfDHP/6xdLe0T7s2jLWX44CG8vzzz2ebbbYpXas2ffr09OvXLy+88EKSRdP/Pss/hYAvDtP8AAA+hyFDhuSxxx7LxhtvnKZNm+bDDz8s/cDwEUccIUjBWkyYAgD4HDp37pypU6fm73//e2bMmJEWLVqkQ4cO6dGjR775zW82dPWA1cg0PwAAgALcgAIAAKAAYQoAAKAAYQoAAKAAYQoAAKAAYQoAAKAAYQoAAKAAYQoAAKAAYQoAAKAAYQoAAKAAYQoAAKAAYQoAAKAAYQoAAKAAYQoAAKAAYQoAAKAAYQoAAKAAYQoAAKAAYQoAAKAAYQoAAKAAYQoAAKAAYQoAAKAAYQoAAKAAYQoAAKAAYQoAAKAAYQoAAKAAYQoAAKAAYQoAAKAAYQoAAKAAYQoAAKCAioauAKvHvHnzctxxx2X+/Pmpra3NYYcdlqqqqvz3f/93Ro0alfLy8rRt2zaXXXZZ2rVrV2/bt956K/379y89f+edd1JVVZUTTjghV155ZcaOHZuddtopV1xxRZJk2LBhmTVrVr7zne+s0TYCAEBDKqurq6tr6Eqw6tXV1eWTTz7JRhttlAULFuTYY4/Neeedl6985Stp3rx5kmTo0KH529/+losuumi5+6mtrU3nzp3z61//Oi1btsypp56au+++O9/97nfzX//1X9l6661z6qmnZvDgwWnatOmaah4AADQ40/zWUmVlZdloo42SJDU1NampqUlZWVkpSCXJ3LlzU1ZWtsL9PPPMM9lqq62y5ZZbpqysLAsWLEhdXV3mzZuXioqKDB48OMcff7wgBQDAOkeYWovV1tamR48e2X///bP//vtn9913T5Jcc801Oeigg/Lwww/nrLPOWuE+RowYke7duydJmjdvnq5du6Znz55p3759WrRokb/85S855JBDVntbAACgsTHNbx0we/bsnHHGGTn//POzww47lJb//Oc/z7x581JVVbXM7ebPn58DDzwwI0aMyCabbLLU+vPOOy/HHXdcXnnllYwfPz4dOnTI6aefvtraAQAAjYmRqXVAy5Yts99++2XcuHH1lnfv3j2///3vl7vd2LFj07Fjx2UGqVdffTVJss0222TYsGEZNGhQ3njjjUyaNGmV1h0AABorYWotNWPGjMyePTtJUl1dnaeffjrbbbddvbAzevTobLfddsvdx4gRI9KtW7dlrhs0aFCqqqpSU1OT2traJEl5eXmqq6tXXSMAAKARc2v0tdT06dNz7rnnpra2NnV1dTn88MNz8MEHp1+/fnn77bdTVlaWLbfcMhdeeGGSZNq0aRk4cGBuvfXWJItuTvH0008v805/I0eOzK677lq6pXqnTp1SWVmZHXbYITvuuOOaayQAADQg10wBAAAUYJofAABAAcIUAABAAcJUI1K9oKahq9Bo6AsAABo710w1MpuceFlDV6FR+OD2AQ1dBQAAWCEjUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwDwTwMGDMjXv/71dO/efal1v/jFL9KhQ4fMmDFjmduOHTs2hx12WA499NDccsstpeVXXnllKisr8/3vf7+0bNiwYRkyZMiqbwBrLccmNE7CFAD809FHH53Bgwcvtfy9997L008/nS222GKZ29XW1uaiiy7K4MGDM2LEiDzyyCP529/+lo8//jgTJkzIww8/nNra2rz22muprq7Ob37zmxx77LGruzmsRRyb0DgJUwDwT/vss09atWq11PLLLrss55xzTsrKypa53UsvvZStt946W221VdZbb71069Yto0aNSllZWRYsWJC6urrMmzgIo5UAACAASURBVDcvFRUVGTx4cI4//vg0bdp0dTeHtYhjExonYQoAVmDUqFHZdNNNs+OOOy63zLRp07LZZpuVnrdr1y7Tpk1L8+bN07Vr1/Ts2TPt27dPixYt8pe//CWHHHLImqg6aznHJjS8ioauAAA0VnPnzs3NN9+c2267bYXl6urqllq2eKTglFNOySmnnJIkOe+881JVVZX77rsv48ePT4cOHXL66aev+oqz1nNsQuNgZAoAluN//ud/Mnny5PTo0SNdunTJ1KlTc/TRR+f999+vV26zzTbL1KlTS8+nTZuWTTfdtF6ZV199NUmyzTbbZNiwYRk0aFDeeOONTJo0abW3g7WPYxMaByNTALAcHTp0yDPPPFN63qVLl9x///1p06ZNvXK77rprJk2alHfeeSft2rXLiBEjctVVV9UrM2jQoFx00UWpqalJbW1tkqS8vDzV1dWrvyGsdRyb0DgYmQKAfzr77LNzzDHH5O23307nzp1z3333LbfstGnTSlOkKioqcsEFF6RPnz458sgjc8QRR+SrX/1qqezIkSOz6667pl27dmnZsmU6deqUysrKJFnh9S6wmGMTGqeyumVNpqXBbHLiZQ1dhUbhg9sHNHQVAABghYxMAQAAFCBMAfCFVr2gpqGr0Gjoi8bF+/Ev+oK1lRtQAPCFtkHTClOk/8kU6cbFsfkvjk3WVkamAAAAChCmAAAAChCmAAAAChCmAAAAChCmAAAAChCmAAAAChCmAAAAChCmAAAAChCmAAAAChCmAAAAChCmAAAAChCmAAAAChCmAAAAChCmAAAAChCmAAAAChCmAL7ABgwYkK9//evp3r17admjjz6abt26Zccdd8zLL7+83G3vuOOOdOvWLd27d8/ZZ5+defPmJUmuvPLKVFZW5vvf/36p7LBhwzJkyJDV1xAA+AISpgC+wI4++ugMHjy43rIddtgh1113XfbZZ5/lbjdt2rQMHTo0DzzwQB555JHU1tZmxIgR+fjjjzNhwoQ8/PDDqa2tzWuvvZbq6ur85je/ybHHHru6mwMAXygVDV0BAIrbZ599Mnny5HrLtt9++5Xatra2NtXV1amoqEh1dXU23XTTlJWVZcGCBamrq8u8efNSUVGRwYMH5/jjj0/Tpk1XRxMA4AvLyBTAOqhdu3Y56aSTcvDBB+eAAw5I8+bNS3+7du2anj17pn379mnRokX+8pe/5JBDDmnoKgNAoyNMAayDZs2alVGjRmXUqFEZN25c5s6dm+HDhydJTjnllAwfPjznnntuBg0alKqqqtx3330566yzcuONNzZwzQGg8RCmANZBTz/9dNq3b582bdqkadOm6dq1ayZMmFCvzKuvvpok2WabbTJs2LAMGjQob7zxRiZNmtQANQaAxkeYAlgHbbHFFnnxxRczd+7c1NXV5ZlnnlnqWqvFo1I1NTWpra1NkpSXl6e6urohqgwAjY4wBfAFdvbZZ+eYY47J22+/nc6dO+e+++7L448/ns6dO2fChAk59dRTc/LJJydZdAe/U045JUmy++6757DDDkuvXr1SWVmZhQsX5tvf/nZpvyNHjsyuu+6adu3apWXLlunUqVMqKyuTJDvuuOOabygANEJldXV1dQ1dCf5lkxMva+gqNAof3D6goasAfIH47lzEd2fj49hcxLHJ2srIFAAAQAHCFMAaVr2gpqGr0GjoCwC+yPxoL8AatkHTClN//snUHwC+yIxMAQAAFCBMAQAAFCBMAQAAFCBMAQAAFCBMAQAAFCBMAQAAFCBMAQAAFCBMAQAAFCBMAQAAFCBMAQAAFCBMAQAAFCBMAQAAFCBMAQAAFCBMAQAAFCBMAQAAFCBMAQAAFCBMAQAAFCBMAQAAFCBMAQAAFCBMAQAAFCBMAQAAFCBMAQAAFCBMAQAAFCBMAQAAFCBMAQAAFCBMAQAAFCBMAQAAFCBMAQAAFCBMAQAAq8Qdd9yRbt26pXv37jn77LMzb968euv/+Mc/Zq+99kqPHj3So0ePXH/99UmSGTNmpHfv3unevXtGjhxZKn/aaadl2rRpa7QNn4UwBQAAfG7Tpk3L0KFD88ADD+SRRx5JbW1tRowYsVS5vffeO8OHD8/w4cNz5plnJkkeeeSR9OrVK/fee28GDx6cJBk9enQ6duyYdu3ardF2fBbCFAAAsErU1tamuro6NTU1qa6uzqabbrpS21VUVKS6ujrz589PeXl5ampqMmTIkJx88smrucafjzAFAAB8bu3atctJJ52Ugw8+OAcccECaN2+eAw44YKlyL7zwQo466qj06dMnb7zxRpKksrIy48ePT58+fdKvX7/cfffd6dmzZ5o1a7amm/GZCFMAAMDnNmvWrIwaNSqjRo3KuHHjMnfu3AwfPrxemY4dO2b06NF56KGHcvzxx+eMM85IkrRo0SK33HJLHnzwwey888558skn07Vr1wwcODBVVVWZMGFCQzTpUwlTAADA5/b000+nffv2adOmTZo2bZquXbsuFYKaN2+ejTbaKEly0EEHpaamJjNmzKhX5oYbbkjfvn0zYsSIdOzYMZdeemmuvvrqNdaOz0KYAgAAPrctttgiL774YubOnZu6uro888wz2X777euVef/991NXV5ckeemll7Jw4cK0bt26tH7SpEmZPn169t1338ydOzfl5Yviyvz589dcQz6DioauAAAA8MW3++6757DDDkuvXr1SUVGRnXbaKd/+9rdzzz33JEl69+6dxx57LPfcc0+aNGmSDTbYIFdffXXKyspK+7jmmmvSv3//JEn37t1zxhlnZOjQoamqqmqQNn2asrrF0ZBGYZMTL2voKjQKH9w+oKGrAKuVz/oiq+qzrj8X8d3Z+Dg2F3FssrYyzQ8AAKAAYQoAAKAAYQoAANYx1QtqGroKjcbn6Qs3oAAAgHXMBk0rXNP3T5/nmj4jUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIUwAAAAUIU0CD6tKlSyorK9OjR48cffTRS61/6KGHUllZmcrKyhxzzDGZOHFikmTGjBnp3bt3unfvnpEjR5bKn3baaZk2bdoaqz8AsO6qaOgKAAwZMiRt2rRZ5rr27dvnzjvvTKtWrTJmzJicf/75ue+++/LII4+kV69eOfLII9OnT58ccsghGT16dDp27Jh27dqt4RYAAOsiYQpo1Pbcc8/S4z322CNTp05NklRUVKS6ujrz589PeXl5ampqMmTIkNx8880NVVUAYB1jmh/Q4E4++eQcffTR+dWvfrXCcvfff386d+6cJKmsrMz48ePTp0+f9OvXL3fffXd69uyZZs2arYkqAwAYmQIa1j333JN27drlww8/zIknnpjtttsu++yzz1Ll/vCHP+T+++/P3XffnSRp0aJFbrnlliTJrFmzcuutt+a6667LwIEDM3v27Jx44onp1KnTGm0LALBuMTIFNKjF1ze1bds2hx56aF566aWlykycODEDBw7MjTfemNatWy+1/oYbbkjfvn0zYsSIdOzYMZdeemmuvvrq1V53AGDdJkwBDeaTTz7JnDlzSo+feuqpfPWrX61XZsqUKenXr1+uuOKKbLvttkvtY9KkSZk+fXr23XffzJ07N+Xli77W5s+fv/obAACs00zzAxrMhx9+mDPOOCNJUltbm+7du6dz58655557kiS9e/fODTfckJkzZ+bCCy9MkjRp0iQPPvhgaR/XXHNN+vfvnyTp3r17zjjjjAwdOjRVVVVruDUAwLpGmAIazFZbbZWHHnpoqeW9e/cuPb7kkktyySWXLHcfgwYNKj1u27Zt7r333lVbSQCA5TDNDwAAoABhCgAAoABhClgp1QtqGroKjYa+AAAS10wBK2mDphXZ5MTLGroajcIHtw9o6CoAAI2AkSkAAIAChCkAAIAChCkAAIAChCkAAIAChCn4DObNm5dvfetbOeqoo9KtW7dce+21S5X5+OOP07dv31KZBx54IEkyY8aM9O7dO927d8/IkSNL5U877bRMmzZtjbUBYE3z3QmsrdzNDz6D9dZbL0OGDMlGG22UBQsW5Nhjj03nzp2zxx57lMrcdddd2X777XPzzTdnxowZOfzww1NZWZlHHnkkvXr1ypFHHpk+ffrkkEMOyejRo9OxY8e0a9euAVsFsHr57gTWVkam4DMoKyvLRhttlCSpqalJTU1NysrKlirzj3/8I3V1dfnHP/6RVq1apaKiIhUVFamurs78+fNTXl6empqaDBkyJCeffHJDNAVgjfHdCaythCn4jGpra9OjR4/sv//+2X///bP77rvXW3/cccflzTffzIEHHpijjjoq5513XsrLy1NZWZnx48enT58+6devX+6+++707NkzzZo1a6CWAKw5vjuBtZEwBZ9RkyZNMnz48IwZMyYvvfRSXn/99Xrrx48fn5122injxo3LsGHDctFFF2XOnDlp0aJFbrnlljz44IPZeeed8+STT6Zr164ZOHBgqqqqMmHChAZqEcDq57sTWBsJU1BQy5Yts99++2XcuHH1lj/44IPp2rVrysrKsvXWW6d9+/Z566236pW54YYb0rdv34wYMSIdO3bMpZdemquvvnpNVh+gQfjuBNYmwhR8BjNmzMjs2bOTJNXV1Xn66aez3Xbb1Suz+eab55lnnkmSfPDBB3n77bfTvn370vpJkyZl+vTp2XfffTN37tyUly/6GM6fP38NtQJgzfLdCayt3M0PPoPp06fn3HPPTW1tberq6nL44Yfn4IMPzj333JMk6d27d04//fQMGDAglZWVqaury/e+9720adOmtI9rrrkm/fv3T5J07949Z5xxRoYOHZqqqqoGaRPA6ua7E1hbldXV1dU1dCX4l01OvKyhq9AofHD7gIauAsvg+FxkVRyf+nKRVfVZ15+L+O5sfBybizg2GyfH5yKf5/g0zQ8AAKAAYQoAgHXWvHnz8q1vfStHHXVUunXrlmuvvXapMnV1dbn44otz6KGHprKyMq+88kqSRdcD9u7dO927d8/IkSNL5U877bRMmzZtjbWBhiNMsdaqXlDT0FVoNPQFsLJ8X/yLvlg3rLfeehkyZEgeeuihDBs2LOPGjcsLL7xQr8zYsWMzadKk/P73v89PfvKT/PjHP06SPPLII+nVq1fuvffeDB48OEkyevTodOzYMe3atVvTTaEBuAEFa60NmlaYC/xP5qoDK8t357/47lw3lJWVZaONNkqS1NTUpKamJmVlZfXKjBo1Kj179kxZWVn22GOPzJ49O9OnT09FRUWqq6szf/78lJeXp6amJkOGDMnNN9/cEE2hARiZAgBgnVZbW5sePXpk//33z/7775/dd9+93vpp06Zls802Kz3fbLPNMm3atFRWVmb8+PHp06dP+vXrl7vvvjs9e/ZMs2bN1nQTaCDCFAAA67QmTZpk+PDhGTNmTF566aW8/vrr9dYv6+bXZWVladGiRW655ZY8+OCD2XnnnfPkk0+ma9euGThwYKqqqjJhwoQ11QQaiDAFAABJWrZsmf322y/jxo2rt3yzzTbL1KlTS8+nTp2aTTfdtF6ZG264IX379s2IESPSsWPHXHrppbn66qvXSL1pOMIUAADrrBkzZmT27NlJkurq6jz99NPZbrvt6pXp0qVLhg0blrq6urzwwgtp0aJFvTA1adKkTJ8+Pfvuu2/mzp2b8vJFp9jz589fcw2hQbgBBQAA66zp06fn3HPPTW1tberq6nL44Yfn4IMPzj333JMk6d27dw466KCMGTMmhx56aJo1a5ZLL7203j6uueaa9O/fP0nSvXv3nHHGGRk6dGiqqqrWeHtYs4QpAADWWTvuuGOGDRu21PLevXuXHpeVleVHP/rRcvcxaNCg0uO2bdvm3nvvXbWVpNEyzQ8AAKCARhmm3nvvvRx//PE54ogj0q1btwwZMmSpMg899FAqKytTWVmZY445JhMnTkzil6gBAIA1o1FO82vSpEnOPffcdOzYMXPmzMk3v/nNfOMb38hXvvKVUpn27dvnzjvvTKtWrTJmzJicf/75ue+++0q/RH3kkUemT58+OeSQQ/wSNQDAF1z1gpps0LRRnrqucfqi8WiU78Kmm25aukNK8+bNs91222XatGn1wtSee+5ZerzHHnuUblfpl6gBANY+GzStyCYnXtbQ1WgUPrh9QENXgX9qlNP8ljR58uT89a9/XeqXqJd0//33p3Pnzknil6gBAIA1olGOTC32j3/8I1VVVfnhD3+Y5s2bL7PMH/7wh9x///25++67k6T0S9RJMmvWrNx666257rrrMnDgwMyePTsnnnhiOnXqtMbaAAAArJ0a7cjUggULUlVVlcrKynTt2nWZZSZOnJiBAwfmxhtvTOvWrZda75eoAQCA1aVRhqm6urqcd9552W677XLiiScus8yUKVPSr1+/XHHFFdl2222XWu+XqAEAgNWpUU7ze/755zN8+PDssMMO6dGjR5Lk7LPPzpQpU5Is+hG1G264ITNnzsyFF16YZNEdAB988MHSPvwSNQAAsDo1yjC1995757XXXlthmUsuuSSXXHLJctf7JWoAAGB1apTT/AAAABo7YQoAAKCAzx2mqhfUrIp6rBX0BQAArDs+9zVTfo36X/waNQAArDtM8wMAAChAmAIAAChAmAIAAChAmAIAAChAmAIAAChAmAIAAChAmAIAAChAmAIAAChAmAIAAChAmAIAAChAmAIAAChAmAIAAChAmAIAAChAmAIAAChAmAIAAChAmAIAAChAmAIAAChAmAIAAChAmAIAAChAmAIAAChAmAIAAChAmAIAAChAmAIAAChAmAIAACigrK6uru7z7GDhvPkpX3+9VVWfL7TP2xf68l9WRV/oz3/Rn6uWz/qq49hctfTnquWzvuo4Nlct/blqfZ6++NxhKkneq6z8vLtYK2z+8MOfex/6cpFV0ZeJ/lxMf65aPuurjmNz1dKfq5bP+qrj2Fy19Oeq9Xn60zQ/AACAAoQpAACAAoQpAACAAoQpAACAAoQpAACAAoQpAACAAoQpAACAAioaugIAANBQLp86Nc/84x/ZuEmT3LHNNkutn/DJJxk4ZUo2a9o0SdK5efN8p23bzKypycApUzJn4cKcvMkmObB58yTJee++m/7t2mWTCqfZ6wLvMgAA66zDW7ZMr403zqVTpy63zK7NmuWnW25Zb9mojz/O4S1bpkvLlvn+5Mk5sHnzPD1nTr66wQaC1DrEND8AANZZu2+4YVo0afKZt6soK8u8urrMr6tLWVlZaurqcv9HH+WY1q1XQy1prIQpAABYgVfnzs3Jkybl+5Mn5+1585Ik/6tFi/zpk0/y/cmTc0Lbthk+c2a6tmyZDcqdXq9LjEECAMBy7LD++rl3u+2yYXl5/jBnTgZOmZK7tt02zZs0KU39+7i2NvfMmJGLttgiV06dmo8XLsy3W7dOx2bNGrj2rG6iMwAALMdGTZpkw3+ONn2tefPU1NVlZm1tvTJDPvww/7dNm4yePTs7bLBBftCuXW794IOGqC5rmDAFAADL8WFNTerq6pIkf507N3VJWi0xlW/y/Pn5sKYme2y4Yarr6lKepKysLPP/uQ1rN9P8AABYZ1303nt54ZNPMqu2Nt96662c2LZtav4ZhHpsvHHGfPxxHpo1K02SrFdengs23zxlZWWl7Qd/8EH6bLJJkkXXUQ2cMiUPzJyZE9u2bYjmsIYJUwAArLMu2HzzFa4/unXrHL2CO/T9eIstSo9bV1Tkhi9/eZXVjcbPND8AAIACjEwBANDo1c2fn80ffrihq9Eo1M2fn7L11mvoahAjUwAAfAEID/+iLxqPRjkyNX3Bglw6dWpm1NamPEn3Vq3yrX+bqzrhk08ycMqUbNa0aZKkc/Pm+U7btplZU5OBU6ZkzsKFOXmTTfL/2bvz+Kjqe//j78lMJpNksieGhB0JilAEutz6E6zFHY3sChRUROtyFbjSqqwioii4VC8qWDdExV4FoaGIFNC24HJvJbQiIjshKxCyTpJZz+8PYCQCAoeByYTX8/Hg8cicOefwOSfJnLzPdzm9nU5J0qSiIv1XZqbSbU3ykAEAAABEmCaZLKwWi+7NyFAnh0N1gYB+u3u3fhYXp3YxMY3W+0lsbPBhaYetrqnRtYmJ6pOYqAcLC9Xb6dRntbXKcTgIUgAAAABCpkl280uz2dTJ4ZAkxUVFqa3drv0+30lta7NY5DYMeQxDFotFPsPQBxUVGvojs7AAAAAAwKlqkmHqSCVer7a63ep8KFwdaVN9vUbv2qUHCwu10+2WdHB+//+rq9ODhYW6LS1NSysrdXViohxRTf5QAQAAAESQJt3vrS4Q0CPFxbovI0PxVmuj9zrFxOi9Dh0UFxWlL2prNbm4WO+0by+n1Rrs+lfj92vhgQOanp2t2aWlqgkEdHNKirrExobjcAAAAAA0I022ucZnGHqkuFhXJibqsoSEo96Pt1oVd6i16ZdOp3yGoUq/v9E688vLNSI1VWuqq9XJ4dBDmZn64/79Z6V+AAAAAM1bkwxThmFoVmmp2tjtuuk4Y53KfT4ZhiFJ+ra+XoakpCO68hV6PCr3+dQ9Lk4NhqEoSRaLRZ5D2wAAAADA6WiS3fy+bmjQypoadbDbNXr3bknSnWlpKjs0CUW/5GT9raZGf66qklWSPSpKU7OyZLFYgvt4df9+3ZGeLungOKrJxcVaVFmpUWlpZ/14AAAAADQ/TTJMdYuN1aedOv3oOgNTUjTwR2bom5adHfw6xWbTi23ahKw+AAAAAGiS3fwAAAAAoKkjTAEAAACACafdzc/weJSVlxeKWiKe4fHIYreHuwwAAAAAZ8Fpt0wRHr7HuQAAAADOHXTzAwAAAAATCFMAAAAAYAJhCgAAAABMIEwBAAAAgAmEKQAAAAAwgTAFAAAAACYQpgAAAADABMIUAAAAAJhAmAIAAAAAEwhTAAAAAGACYQoAAABAyPgNQ3fs3q2Hi4qOu87mhgb12bJFn9bUSJIqfT7dV1Cg23bt0j9qa4PrTSoq0n6f74zXbBZhCgAAAEDILKqsVFu7/bjv+w1D8/bt08/j4oLLVtfU6NrERL3Upo3+dOCAJOmz2lrlOBxKt9nOeM1mEaYAAAAAhMRer1df1Nbq+qSk466zuLJSlyUkKPmIkGSzWOQ2DHkMQxaLRT7D0AcVFRqaknI2yjaNMAUAAAAgJObs26e7MjJkOc77+7xera2t1Y0/CFtXJCTo/+rq9GBhoW5LS9PSykpdnZgoR1TTjitNt80MAAAAQMT4rLZWKVarLnA4lF9Xd8x15uzbp9+mp8tqaRy3nFarnmzZUpJU4/dr4YEDmp6drdmlpaoJBHRzSoq6xMae8WM4VYQpAABwVvgNQ3cVFCjdZgv+0XRYrd+vx0tLtdfrlV/SzSkpui4pSZU+nyYXF6s2ENDo9HT1djolHRyU/l+ZmU16LAVwrtlYX691Lpe+2LFDHsNQXSCgGSUlmpyVFVznu4YGTS8pkSRV+f360uWS1WIJ/m5L0vzyco1ITdWa6mp1cjh0ZUKCJhUX6w+tW5/1YzoRPoEAAMBZcXhQuisQOOq9JZWVame3a2bLlqr0+TRy1y5dmZgYHJTeJzFRDxYWqrfTGRGD0oFz0W8zMvTbjAxJUn5dnf5UUdEoSEnSex06BL+eWVqqS+LjGwWpQo9H5T6fusfFaZvbrRiLRRaLRR7DODsHcYqadidEAADQLJxoULrFYlFdICDDMFRvGEqwWmVV5A5KB/C9pZWVWlpZeVLrvrp/v0anp0s6OI5qRXW17i0o0M1N9HeeWzoAAOCMOzwove4YrVKSNCA5WROLijRoxw7VBQJ6JCtLURaLrkhI0IzSUn1cXa27MjIiZlA6cK7rERenHoemPu+XnHzMdSa0aHHUsmnZ2cGvU2w2vdimzZkpMEQIUwAA4Iw6mUHp/+tyqWNMjJ5r1UpFXq9+V1iobrGxETsoHcC5gds6AADgjDo8KP3mHTs0vaRE+XV1mnFoAPphK6qrdZnTKYvFolZ2u7Kio1Xg8TRa54eD0h/KzNQf9+8/m4cCAI3QMgUAAM6okxmUfp7Npq/q6tQtLk4HfD7t8XiUZbcH34+0QelAU2d4PMrKywt3GU2C4fHIcsTnzakgTAEAgLA4PCC9X3KybklL05OlpRq1a5cMHQxgyVZrcN1X9+/XHUcMSp9cXKxFlZUalZYWjtKBiGc2PDRHp3MuCFMAAOCsOd6g9HSbTU+3anXc7SJtUDqAcwNjpgAAAADABFqmAABAEOMovnc64ygAnBtomQIAAEGEh+9xLgCcCGEKAAAAAEwgTAEAAACACYQpAAAAADCBMAUAAAAAJhCmAAAAAMAEwhQAAAAAmECYAgAAAAATCFMAAAAAYAJhCgAAAABMIEwBAAAAgAmEKQAAAAAwgTAFAAAAACYQpgAAAADABFu4CwAAAMCpqfH7NbusTDvdblksFj2UmakusbHB92v9fj1eWqq9Xq/8km5OSdF1SUmq9Pk0ubhYtYGARqenq7fTKUmaVFSk/8rMVLqNPw2BU8FvDAAAQISZs2+ffhEfr+nZ2fIahhoCgUbvL6msVDu7XTNbtlSlz6eRu3bpysREra6p0bWJieqTmKgHCwvV2+nUZ7W1ynE4CFKACXTzAwAAiCAuv1//qqvT9YmJkqRoi0UJVmujdSwWi+oCARmGoXrDUILVKqskm8Uit2HIYxiyWCzyGYY+qKjQ0JSUMBwJEPm4BQEAABBBir1eJVuterKsTNvdbnWKidH9552n2KjvnHYQEwAAIABJREFU75EPSE7WxKIiDdqxQ3WBgB7JylKUxaIrEhI0o7RUH1dX666MDC2trNTViYlyRHF/HTCDMAUgbAo8Hj1aUhJ8XeL1alRamoYccYf0r9XVWnjggCQpNipK/5WZqY4xMfT7B3DO8kva4nZrzHnn6aLYWP333r1698ABjU5PD67zvy6XOsbE6LlWrVTk9ep3hYXqFhsrp9WqJ1u2lHRw3NXCAwc0PTtbs0tLVRMI6OaUlEZjrwD8OG5DAAibNna7XmvbVq+1batX2rRRjMUSDEWHZUVH6/nWrfV6u3a6JS1Nz5SVSVKw3/9LbdroT4fCFv3+AZwLMmw2ZdhsuuhQ6PmV06mtbnejdVZUV+syp1MWi0Wt7HZlRUerwONptM788nKNSE3VmupqdXI49FBmpv64f/9ZOw6gOSBMAWgS1tfVqWV0tFpERzda3jU2NjgW4CKHQ/u8Xkn0+wdw7kqz2XTeEeHoq7o6tbXbG61zns2mr+rqJEkHfD7t8XiUdcQ6hR6Pyn0+dY+LU4NhKEoHx1l5DOOsHQfQHHD7FkCTsKamRn0SEn50nb9UVekX8fGSRL9/AOe0MRkZmlFSIp9hKCs6Wg+3aKGllZWSpH7JybolLU1PlpZq1K5dMiT9NiNDyUdMUvHq/v2641C3wCsSEjS5uFiLKis1Ki0tHIcDRCzCFICw8xqG1tXW6s4j+vv/UH5dnZZXV+u/W7eWJPr9Azin5TgceqVt20bL+iUnB79Ot9n0dKtWx91+WnZ28OsUm00vtmkT+iKBcwC3cAGE3Zculzo5HEo9zlin7W63ZpeV6fHsbCX9YPpfiX7/AAAgPGiZAhB2q2tqdMVxuviVeb2aUlysiS1aqPUPxgRIjfv9b3O7FWOx0O8fQJNgeDzKyssLdxlNguHxyHKMz3Ag0hGmAIRVQyCgr1wujT/vvOCyI/v9zy8vV7Xfr+f27pUkWaVGXVvo9w+gqSI8fI9zgeaKMAUgrBxRUfpzx46Nlh3Z7//BFi304I9sT79/AAAQLoyZAgAAAAATaJkCcFLo+/89+v4DAACJlikAJ4nw8D3OBQAAkAhTAAAAAGAKYQoAAAAATCBMAQAAAIAJhCkAAAAAMIEwBQAAAAAmMDU6ADQj71dU6C9VVZKkDjExeigzUzFRje+b5dfVac6+ffIbhpKsVj3furUqfT5NLi5WbSCg0enp6u10SpImFRXpvzIzlW7jcgEAwA9xdQSAZmKf16tFFRWa366dYqKiNK24WGtqanRdUlJwnRq/X3/Yu1ezWrZUZnS0Knw+SdLqmhpdm5ioPomJerCwUL2dTn1WW6sch4MgBQDAcdDNDwCaEb8kt2HIZxhqMIyjgtDqmhr1djqVGR0tSUo59L7NYpHbMOQxDFksFvkMQx9UVGhoSsrZPgQAACIGtxsBoJnIiI7WzSkpumnHDsVERenncXH6eXx8o3X2eDzyG4bG7tmj+kBAg1JSdE1ioq5ISNCM0lJ9XF2tuzIytLSyUlcnJsoRxT03AACOhzAFAM1Ejd+vdbW1eq99ezmtVj1SXKyV1dW6OjExuI7fMPSd261nW7WS2zD0nwUFusjhUGu7XU+2bBncz8IDBzQ9O1uzS0tVEwjo5pQUdYmNDdehAQDQJHHLEQCaia/q6pQVHa1km002i0WXJSTom/r6RutkREfrF3Fxio2KUrLVqotjY7Xd7W60zvzyco1ITdWa6mp1cjj0UGam/rh//9k8FAAAIgJhCgCaifNsNm1qaFBDICDDMLS+rk5t7fZG6/SKj9fX9fUHx1QFAtrU0KA2R6xT6PGo3OdT97g4NRiGoiRZLBZ5DOMsHw0AAE0f3fwAoJm4KDZWv3I6defu3bJaLMqJidENSUlaWlkpSeqXnKy2MTH6RXy8Ru/eLYuk65OS1CEmJriPV/fv1x3p6ZKkKxISNLm4WIsqKzUqLS0chwQAQJNGmAKAZmRUerpGHQpDh/VLTm70emhqqoamph5z+2nZ2cGvU2w2vdimTeiLBACgmaCbHwAAAACYQJgCAAAAABPo5gcAZ5nh8SgrLy/cZTQJhscjyw8myQAAIFLQMgUAZxnh4XucCwBAJCNMAQAAAIAJhCkAAAAAMIEwBQAAAAAmEKYAAAAAwATCFAAAAACYQJgCAAAAABMIUwAAAABgAmEKAAAAAEwgTAEAAACACYQpAAAAADCBMAUAAAAAJhCmAAAAAMAEwhQAAAAAmECYAgAAAAATCFMAAAAAYAJhCgAAAABMIEwBAAAAgAmEKQAAAAAwgTAFAAAAACYQpgAAAADABMIUAAAAAJhAmAIAAAAAEwhTAAAAAGACYQoAAAAATCBMAQAAAIAJhCkAAAAAMIEwBQAAAAAmEKYAAAAAwATCFAAAAACYQJgCAAAAABMIUwAAAABgAmEKAAAAAEwgTAEAAACACYQpAAAAADCBMAUAAAAAJhCmAAAAAMAEwhQAAAAAmECYAgAAAAATCFMAAAAAYAJhCgAAAABMIEwBAAAAgAmEKQAAAAAwgTAFAAAAACYQpgAAAADABMIUAAAAAJhAmAIAAAAAEwhTAAAAAGACYQoAAAAATCBMAQAAAIAJhCkAAAAAMIEwBQAAAAAmEKYAAAAAwATCFAAAAACYQJgCAAAAABMIUwAAAABgAmEKAAAAAEwgTAEAAACACYQpAAAAADCBMAUAAAAAJhCmAAAAAMAEwhQAAAAAmECYAgAAAAATCFMAAAAAYAJhCgAAAABMIEwBAAAAgAmEKQAAAAAwgTAFAAAAACbYwl0AEAmeKi3V5y6Xkq1WvdmunSTpjf379ZeqKiXZDv4a3ZmWpl86nSe1rSTN27dPX7pc6hgTo4lZWZKkldXVqvb7NTgl5YwfEwAAAE4PLVPASbg2MVGzWrY8avnglBS91ratXmvb9phB6njb1vr92tjQoNfbtVNA0g63W+5AQCuqqtQ/OflMHAIAAABCjDAFnISL4+KUYLWGbNsoi0U+w5BhGHIbhqwWi96rqNDAlBTZLJZQlAwAAIAzjDAFnIYPKyt1+65deqq0VDV+/0lvFxcVpcucTt1RUKCs6Gg5o6K0uaFBvY7TugUAAICmhzFTgEn9kpN1S1qaLJJeLy/XS/v26aEWLU56+2GpqRqWmipJmlVaqtvT0rSsqkr/dLnUISZGt6SlnaHKAQAAEAq0TAEmpdpsslosirJYdH1Skr5taDC1n62Htmtlt2tldbWmZWdrp8ejQo8nlOUCAAAgxAhTgEnlPl/w67W1tWofE2NqP6+Vl+v29HT5DEMBw5B08Bez4dDXAAAAaJro5gechOklJdpQV6cqv1+Dd+zQqLQ0bair0za3WxZJLaKjNT4zU5K03+fT7NJSPdWq1XG3vT4pSZL0j9paXehwKP3Q9OoXxcZq1K5dOj8mRh1NhjMAAACcHYQp4CRMPfQcqCMdDkQ/lG6zBYPU8bY9rLfTqd5HTDpxb0aGlJFxGpUCAADgbKGbHwAAAACYQJgCAAAAABPo5odmy/B4lJWXF+4ymgTD45HFbg93GQAAAM0KLVNotggP3+NcAAAAhB5hCgAAAABMIEwBAAAAgAmEKQAAAAAwgTAFAAAAACYQpgAAAADABMIUAAAAAJhAmAIAAAAAEwhTAAAAAGACYQoAAAAATCBMAQAAAIAJhCkAAAAAMIEwBQAAAAAmEKYAAAAAwATCFAAAAACYQJgCAAAAABMIUwAAAABgAmEKAAAAAEwgTAEAAACACYQpAAAAADCBMAUAAAAAJhCmAAAAAMAEwhQAAAAAmECYAgAAAAATCFMAAAAAYAJhCgAAAABMIEwBAAAAgAmEKQAAAAAwgTAFAAAAACYQpgAAAADABMIUAAAAAJhAmAIAAAAAEwhTAAAAAGACYQoAAAAATCBMAQAAAIAJhCkAAAAAMIEwBQAAAAAmEKYAAAAAwATCFAAAAACYQJgCAAAAABMIUwAAAABgAmEKAAAAAEwgTAEAAACACYQpAAAAADCBMAUAAAAAJhCmAAAAAMAEwhQAAAAAmECYAgAAAAATCFMAAAAAYAJhCgAAAABMIEwBAAAAgAmEKQAAAAAwgTAFAAAAACYQpgAAAADABMIUAAAAAJhAmAIAAAAAEwhTAAAAAGACYQoAAAAATCBMAQAAAIAJhCkAAAAAMIEwBQAAAAAmEKYAAAAAwATCFAAAAACYQJgCAAAAABMIUwAAAABgAmEKAAAAAEwgTAEAAACACYQpAAAAADCBMAUAAAAAJhCmAAAAAMAEwhQAAAAAmECYAgAAAAATCFMAAAAAYIIt3AXgzNjr9eqJ0lId8PsVJemGpCQNTknRy/v26bPaWkVbLMqOjtZDLVoowWo9avunSkv1uculZKtVb7ZrF1w+b98+felyqWNMjCZmZUmSVlZXq9rv1+CUlLN0dAAAAED40TLVTFktFt2bkaG32rXTS23aaEllpXa53fpZXJzeaNdOr7drp9Z2u949cOCY21+bmKhZLVs2Wlbr92tjQ4Neb9dOAUk73G65AwGtqKpS/+Tks3BUAAAAQNNBmGqm0mw2dXI4JElxUVFqa7drv8+nn8fHy2axSJIucji0z+c75vYXx8Ud1WIVZbHIZxgyDENuw5DVYtF7FRUamJIS3CcAAABwriBMnQNKvF5tdbvV+VC4Omx5dbV+ER9/0vuJi4rSZU6n7igoUFZ0tJxRUdrc0KBeTmeoSwYAAACaPMZMNXN1gYAeKS7WfRkZij+ipWlBebmskq5KSDil/Q1LTdWw1FRJ0qzSUt2elqZlVVX6p8ulDjExuiUtLZTlAwAAAE0WLVPNmM8w9Ehxsa5MTNRlR4SmFVVV+tzl0uSsLFlMds/b2tAgSWplt2tldbWmZWdrp8ejQo8nJLUDAAAATR1hqpkyDEOzSkvVxm7XTUfMsvely6WFFRV6Ijtbjijz3/7Xyst1e3q6fIahgGFIOvjD1HDoawAAAKC5o5tfM/V1Q4NW1tSog92u0bt3S5LuTEvTC/v2yWsYGl9UJOngJBTjMzO13+fT7NJSPdWqlSRpekmJNtTVqcrv1+AdOzQqLU3XJyVJkv5RW6sLHQ6l2w7++FwUG6tRu3bp/JgYdYyJCcPRAgAAAGefxTBoSmhKSnJzw11Ck5CVlxfuEgBEED47D+KzEwDOLrr5AQAAAIAJhCkAAAAAMIExU02I4fHQReMQw+ORxW4PdxkAAADAcdEy1YQQHr7HuQAAAEBTR5gCAAAAABMIUwAAAABgAmEKAAAAAEwgTAEAAACACYQpAAAAADCBMAUAAAAAJhCmAAAAAMAEwhQAAAAAmECYAgAAAAATCFMAAAAAYAJhCgAAAABMIEwBAAAAgAmEKQAAAAAwgTAFAAAAACYQpgAAAADABMIUAAAAAJhAmAIAAAAAEwhTAAAAAGACYQoAAAAATCBMAQAAAIAJhCkAAAAAMIEwBQAAAAAmEKYAAAAAwATCFAAAAACYQJgCAAAAABMIUwAAAABgAmEKAAAAAEwgTAEAAACACYQpAAAAADCBMAUAAAAAJhCmAAAAAMAEwhQAAAAAmECYAgAAAAATCFMAAAAAYAJhCgAAAABMIEwBAAAAgAmEKQAAAAAwgTAFAAAAACYQpgAAAADABMIUAAAAAJhAmAIAAAAAEwhTAAAAAGACYQoAAAAATCBMAQAAAIAJhCkAAAAAMIEwBQAAAAAm2MJdAAAATcFer1dPlJbqgN+vKEk3JCVpcEqKJGlxRYU+rKyU1WLRL+PjdXdGxlHbf1BRoWVVVZKk65OSNOTQtvP27dOXLpc6xsRoYlaWJGlldbWq/f7g/gEAkYkwBQCAJKvFonszMtTJ4VBdIKDf7t6tn8XFqcLv11qXS6+1bSt7VJQqfL6jtt3hdmtZVZXmtmkjm8WiB4uKdEl8vJKtVm1saNDr7dppRkmJdrjdahkdrRVVVZrVqlUYjhIAEEp08wMAQFKazaZODockKS4qSm3tdu33+bS0slLDU1Jkjzp4yUyxHX0fssDj0UUOhxxRUbJZLOoeG6t/1NYqymKRzzBkGIbchiGrxaL3Kio0MCVFNovlrB4fACD0CFMAAPxAiderrW63Ojsc2uP16uv6et1TUKCxe/Zoc0PDUeu3t9v17/p6Vfn9aggE9IXLpb0+n+KionSZ06k7CgqUFR0tZ1SUNjc0qJfTGYajAgCEGt38AAA4Ql0goEeKi3VfRobirVb5DUM1gYBeat1amxsaNK24WAvbt5fliJaltjExGpaaqt8VFio2Kkrnx8TIeui9YampGpaaKkmaVVqq29PStKyqSv90udQhJka3pKWF4SgBAKFAyxQAAIf4DEOPFBfrysREXZaQIEnKsNnU2+mUxWJR59hYRVksqvL7j9r2+qQk/bFtW73QurUSrVa1stsbvb/1UItWK7tdK6urNS07Wzs9HhV6PGf+wAAAZwRhCgAASYZhaFZpqdrY7brpiFn2ejmdyq+rkyTt8XjkNQwlWa1HbX94Yooyr1d/r6nRFYfC2GGvlZfr9vR0+QxDAcOQdPAi3HDoawBA5KGbHwAAkr5uaNDKmhp1sNs1evduSdKdaWnqm5Skp0pLdduuXYq2WDShRQtZLBbt9/k0u7RUTx2alW9qSYmq/X7ZJI3LzFTCEYHrH7W1utDhUPqhySsuio3VqF27dH5MjDrGxJz1YwUAhIbFMLglBgCIbCW5ueEuoUnIyssLdwkAcE6hZQoAAAA4TV6vV4WFhWo4xoyfiAwOh0OtWrVSdHT0SW9DyxQAIKIZHo8sP5js4VzFuQDCZ+fOnUpISFBaWlqj2T4RGQzDUHl5uWpqatS+ffuT3o4JKAAAEY3w8D3OBRA+DQ0NBKkIZrFYlJaWdsoti4QpAAAAIAQIUpHNzPePMAUAAAAAJhCmAAAAgBBr8PrO6v6eeOIJvfnmm8HXo0eP1qRJk4Kvn3zySb3xxhtavXq1XnnlFUnSqlWrtG3btuA6I0eO1Ndffx2SeufOnXvc9/r06aPc3Fzl5uaqb9++eu655+SJ0AeYM5sfAAAAEGKOaJvSR80M2f72vzHhR9/v0aOHVqxYodtuu02BQEAVFRWqra0Nvp+fn6+JEyfq4osv1hVXXCHpYJi6/PLL1bFjx5DVedi8efN09913H/f9+fPnKzU1VS6XS1OnTtWUKVP01FNPnfb/6/P5ZLOdvYhDyxQAAAAQ4Xr27Kn8/HxJ0tatW5WTk6P4+HhVVVXJ4/Fo+/bt6ty5sxYvXqzp06dr/fr1WrNmjWbNmqV+/fqpoKBAkrRixQoNHjxY11xzjf75z39KktxutyZMmKDc3Fz1799fX3zxhSQF93XYXXfdpS+//FJPP/20Ghoa1K9fP40fP/5H646Pj9ejjz6qVatWqbKyUpL06quvatCgQcrNzdULL7wQXPfFF1/Utddeq1GjRumBBx7Qa6+9Julgi9qzzz6rESNG6K233tLGjRs1YsQIDRw4UKNHj9bevXslSQUFBRo9erQGDhyo4cOHa/v27ad93mmZAgAAACJcZmambDabiouLlZ+fr+7du6usrEwbNmyQ0+nUBRdcIPsRM3727NlTffr00eWXX65rr702uNzv9+uDDz7Q3/72N82ZM0dvvvmm3nnnHUlSXl6etm/frtGjR+vjjz8+bi2/+93v9M4772jp0qUnVbvT6VSrVq20e/dubdy4Ubt379YHH3wgwzB0zz336P/+7//kcDi0cuVKLVmyRD6fTwMHDlSXLl2C+6iurtbbb78tr9erkSNH6qWXXlJqaqqWL1+u5557TjNnztSUKVP06KOPql27dvrXv/6lRx99VG+99dapnupGCFMAAABAM9CjRw/l5+crPz9fo0aNUllZmdavX6+EhAT16NHjpPZx1VVXSZK6dOmioqIiSdJXX32lESNGSJLOP/98ZWdna+fOnSGt/fCjb9etW6d169apf//+kqS6ujrt2rVLLpdLV1xxhRwOhyTp17/+daPt+/btK+ng8762bNmiUaNGSZICgYAyMjLkcrmUn5+vsWPHBrcJxTgtwhQAAADQDBzu6rdlyxbl5OSoRYsWev311+V0OjVo0KCT2sfh1quoqCj5/X5J3wedH7JarQoEAsHXbrfbVN21tbUqKipSu3btZBiGfvvb32ro0KGN1jlyco1jiY2NDdaak5OjP/3pT0f9H4mJiSfdWnayGDMFAAAANAM9e/bUJ598oqSkJFmtViUnJ6umpkYbNmxQ9+7dj1o/Pj5eLpfrhPv9+c9/rry8PEkHW35KSkrUoUMHtWzZUps3b1YgEFBJSYn+/e9/B7ex2Wzyer0n3LfL5dKjjz6qK6+8UklJSerVq5cWLVoUrKusrEzl5eXBY3O73XK5XPr000+Pub/27dvrwIEDwfFjXq9XW7duDXYl/OijjyQdDF2bN28+YX0nQssUAAAAEGINXt8JZ+A71f05on/8T/dOnTqpoqJCN9xwQ6NlLpdLqampR63ft29fTZkyRQsWLGg00cMPDR8+XI888ohyc3NltVo1c+ZM2e12/fSnP1XLli2Vm5urnJycRmOYbrrpJt1444266KKL9Mwzzxy1z1tvvVWGYSgQCOiqq67SvffeK0nq1auXtm/fHmyZiouL0+zZs9WtWzf16dNHN954o1q2bKmuXbsqISHhqP3a7Xa98MILmjFjhmpqauT3+3XrrbcqJydHs2fP1rRp0/Tyyy/L5/Opb9++uvDCC3/0nJ6IxTheux0AAACAk/Ltt9+qc+fO4S6jWXO5XIqPj1d9fb1+85vf6LHHHmsU4ELhVL+PtEwBAAAAaPKmTp2qbdu2ye12a8CAASEPUmYQpgAAAAA0ecfqLhhuTEABAAAAACYQpgAAAADABMIUAAAAAJhAmAIAAAAAEwhTAAAAQIgZHs9Z3d8TTzyhN998M/h69OjRmjRpUvD1k08+qTfeeEOrV6/WK6+8IklatWqVtm3bFlxn5MiR+vrrr3/0/yksLFS3bt3Uv39/XXfddRo8eLA+/PBDE0fUPDCbHwAAABBiFrtdJbm5IdtfVl7ej77fo0cPrVixQrfddpsCgYAqKipUW1sbfD8/P18TJ07UxRdfrCuuuELSwTB1+eWXq2PHjqdUS5s2bbRkyRJJ0p49e3TfffcpEAho0KBBp3hUR/P7/bJarae9n7OFlikAAAAgwvXs2VP5+fmSpK1btyonJ0fx8fGqqqqSx+PR9u3b1blzZy1evFjTp0/X+vXrtWbNGs2aNUv9+vVTQUGBJGnFihUaPHiwrrnmGv3zn/884f/bunVrPfzww1qwYIEkqa6uThMmTNCgQYPUv39/rVq1SpJUX1+vsWPHKjc3V+PGjdOQIUOCrWA9evTQ888/ryFDhig/P19Lly7V4MGD1a9fP02dOlV+v1+StHbtWt18880aMGCAxowZI5fLFfLzeKoIUwAAAECEy8zMlM1mU3FxsfLz89W9e3d169ZNGzZs0Ndff60LLrhAdrs9uH7Pnj3Vp08fPfjgg1q6dKnatGkj6WDL0AcffKCJEydqzpw5J/V/d+nSRTt27JAkzZ07V7/85S+1aNEivfXWW5o9e7bq6ur07rvvKikpSXl5ebr33nv1zTffBLevq6tTTk6O3n//faWkpOijjz7SwoULtXTpUkVFRSkvL08HDhzQyy+/rDfeeEMffvihunbtqjfeeCOEZ9AcuvkBAAAAzUCPHj2Un5+v/Px8jRo1SmVlZVq/fr0SEhLUo0ePk9rHVVddJelgQCoqKjqpbQzDCH69du1arVmzRq+//rokye12q6SkRF999ZVuueUWSVKnTp10wQUXBLexWq265pprJEmff/65Nm7cqMGDB0uSGhoalJaWpn/961/atm2bhg0bJknyer3q3r37SdV3JhGmAAAAgGbgcFe/LVu2KCcnRy1atNDrr78up9N50uOZDrdeRUVFBbvXncimTZt0/vnnB1+/8MIL6tChQ6N1jgxcPxQTExMcJ2UYhgYMGKDx48c3WmfNmjW69NJL9eyzz55UTWcL3fwAAACAZqBnz5765JNPlJSUJKvVquTkZNXU1GjDhg3HbMWJj48/7XFHhYWFmjVrlkaMGCFJ6tWrl95+++1geNq0aZMk6ac//ak++ugjSdK2bdu0ZcuWY+7vkksu0ccff6zy8nJJUmVlpYqKitS9e3etX79eu3fvlnRwDNbOnTtPq/ZQoGUKAAAACDHD4znhDHynuj/LEWOejqVTp06qqKjQDTfc0GiZy+VSamrqUev37dtXU6ZM0YIFC/TCCy+cdC0FBQXq37+/3G634uPjNWLEiGDL17333qsnnnhCN954owzDUMuWLTVv3jwNHz5cDz/8sHJzc3XRRRfpggsuUEJCwlH77tixo8aNG6fbb79dgUBA0dHRmjp1qrp3766ZM2fqgQcekOfQNPHjxo1T+/btT7ruM8Fi/FibGwAAAIAT+vbbb9W5c+dwl9Fk+f1++Xw+xcTEqKCgQLfddptWrFjRaFKMpuBUv4+0TAEAAAA4o+rr63XLLbfI5/PJMAw98sgjTS5ImUGYAgAAAHBGOZ1OLV68ONxlhBwTUAAAAAAhwOiZyGbm+0eYAgAAAE6Tw+FQeXk5gSpCGYah8vJyORyOU9qOCSgAAACA0+T1elVYWKiGhoZwlwKTHA6HWrVqpejo6JPehjAFAAAAACbQzQ8AAAAATCBMAQAAAIAJhCkAAAAAMIEwBQAAAAAmEKYAAAAAwATCFAAAAACYQJgCAAAAABMIUwAAAABgAmEKAAAAAEwgTAEAAACACYQpAAAAADCBMHWO8/v9euqpp8JdRrMxe/bsk1qGEwsEAlq+fHm4y2g2/H6/3nzzzXCXARyF61DoeTyek1qGEwsEAlq/fn24y2g25s+ff1LLIglh6hxntVr1zTffyDCMcJfSLHz22WdHLfv73/8ehkoiX1RlRdwTAAAgAElEQVRUlN55551wl9FsWK1WrV69OtxlNCu///3vT2oZfhzXodC7+eabT2oZTiwqKoqwH0JLliw5atmHH34YhkpCxxbuAsxauXKlnn76aZWXl8swDBmGIYvFwt0DEy666CLdc889uvbaaxUXFxdcfvXVV4exqsjy7rvvauHChdqzZ49yc3ODy10ul3r27BnGyiLb//t//0+vvfaa+vbtq9jY2ODy5OTkMFYVuXr27Knp06cfdT67dOkSxqoi17Zt2xq99vv9+uabb8JUTWTjOhQa+/btU1lZmRoaGrRp06ZgQK2trVV9fX2Yq4tcl156qT7++GNdffXVslgs4S4nIi1btkzLli1TYWGh7r777uByl8sV8dd0ixGht4KuuuoqzZ07V+eff364S4l4EyZMOObymTNnnuVKIldNTY2qqqr07LPPavz48cHl8fHxEf8hEU59+vQ5apnFYqGFxaSRI0cetcxiseitt94KQzWRa968eZo7d67cbrccDockyTAM2e123XTTTY0+A3ByuA6FxocffqjFixdr48aN+slPfhIMU/Hx8Ro4cCDh1KQePXqovr5eVqtVMTEx3MA3oaioSIWFhcf8O+mCCy6QzRax7TuRG6aGDh2q9957L9xlAI0UFBSoRYsWstvt+vLLL/Xdd9+pf//+SkxMDHdpAELsmWeeITihSfr44491zTXXhLsM4Ch1dXVyOByKiorSzp07tWPHDl122WWKjo4Od2mmReyYqa5du2rcuHFatmyZVq5cGfyHU7dz507deuutuuGGGyRJmzdv1ksvvRTmqiLT/fffr6ioKO3evVuTJk1SYWEhf2ydhvr6er300kuaMmWKJGnXrl365JNPwlxV5Nq/f78mTpyoO+64Q9LBbmrvv/9+mKuKXJdffrnq6uokSUuXLtXMmTNVVFQU5qoiE9eh0CotLVVtba0Mw9CkSZM0YMAArV27NtxlRSzDMLR06VK9+OKLkqSSkhL9+9//DnNVkWnEiBFyu90qKyvTbbfdpsWLF+vhhx8Od1mnJWLDlMvlUmxsrNatW6dPPvkk+A+nbsqUKRo/fnywifXCCy9kFjWToqKiZLPZtHLlSt16662aOHGi9u3bF+6yItaECRMUHR2t/Px8SVKLFi30hz/8IcxVRa6HH35YvXr10t69eyVJ7dq1o4vfaZg2bZpiY2O1efNmvfrqq8rOztZDDz0U7rIiEteh0Fq0aJGcTqfWrl2r8vJyzZw5U88880y4y4pY06ZN04YNG7Rs2TJJUlxcnB599NEwVxWZDMNQbGysVq5cqREjRujFF1/U9u3bw13WaYnYDor0ow6d+vp6devWrdEyq9Uapmoim81m07Jly7R06VK9/PLLkiSfzxfmqiJXQUGB/vCHP+gvf/mLJMnhcDDj12moqKhQ37599corr0g6+PMaFRWx99TCzmazyWKxaNWqVbrllls0ZMiQY85UhRPjOhRahz8n//a3v2nQoEG68MIL+ew8Df/+97/14Ycfqn///pKkpKQkeb3eMFcVmQzDUH5+vvLy8vT4449LOjh5TySL2DDldrv1wQcfaOvWrXK73cHlhKxTl5KSooKCguAMNStWrFBGRkaYq4pMM2fO1Hvvvae7775brVu31p49e3TjjTeGu6yIZbfb1dDQEPzZLCgokN1uD3NVkSsuLk4VFRXB87lhwwYlJCSEuarIFR8fr3nz5ikvL09vv/22/H4/N09M4joUWl27dtXtt98e7GpeW1vLjZPTYLPZ5Pf7gz+fBw4c4HyaNGnSJM2bN09XXnmlcnJytGfPHv3Hf/xHuMs6LRE7AcWYMWPUoUMHLVu2TP/5n/+pvLw8dejQQZMnTw53aRFnz549mjJlivLz85WYmKhWrVpp9uzZatWqVbhLwzlu3bp1evnll7Vt2zZdeumlys/P18yZMyP+gzdcvvnmGz322GPaunWrcnJyVFFRoeeff14XXnhhuEuLSPv27dOyZcv0k5/8RD/72c9UXFys//3f/w3evcbJ4zoUWoFAQN9++61at26txMREVVRUqKysjN91k/785z9r+fLl+uabbzRw4ECtWLFC48aN03XXXRfu0iLOli1b1KlTp3CXEVIRG6b69++vJUuWKDc3V3l5efJ6vRo9ejT9/09DXV2dAoGAnE5nuEuJOGPHjtXzzz/f6BlTR8rLyzvLFTUfFRUV+te//iXDMHTxxRcrNTU13CVFNJ/Pp507d8owDLVv3z6iZ1BC88N1KDTGjBmjgQMH6rLLLqMFJUS2b9+uL774QoZh6JJLLuHRPCYNGzZMXq9XAwYMUG5ubrOY7Thiu/kdHqSamJioLVu2KD09nVmUTNq/f7+effZZ7d27V6+++qq2bdum/Px8DRkyJNylRYxJkyZJkubOnRvmSpqXww9APdzdp6SkRLW1tcrOzo7oZ1KES319vd544w0VFxdrxowZ2rVrl3bu3Klf//rX4S4togwbNkwLFy5Ujx49Gj3Ak2fPmMd1KLSGDh2qRYsWacaMGbr22ms1YMAA/vg/TRUVFXI4HBo0aJAOHDigPXv2qHXr1uEuK+IsXLhQO3fu1OLFizVo0CB169ZNAwYMUK9evcJdmmkR2zL1/vvv6+qrr9Z3332nCRMmqK6uTmPGjNGwYcPCXVrEueOOOzRw4EDNnTtXf/7zn+Xz+TRgwABaU0yYPXu2fv/7359wGU7OTTfdpE2bNgW7BGzZskUXXHCBKisr9eijj0b0h284jBs3Tl26dNHSpUu1bNkyNTQ06Oabb9bSpUvDXRrOcVyHzoyamhotW7ZMc+fOVVZWloYMGaIbb7yRFulTNGfOHG3cuFE7d+7Uxx9/rLKyMo0dO5bnnZ4Gv9+vVatWacaMGXI6nTIMQw888EBEPlg6Ytt+hwwZoqSkJP3iF7/Q6tWr9fnnnxOkTDo8w9fhrgDM8GXeZ599dtSyv//972GopHlo2bKlPvzwQy1evFiLFy/WkiVL1KlTJ7355puaPXt2uMuLOAUFBbrzzjuDrXrMjnh6jnWThBsn5nAdCr2KigotXrxY77//vjp37qxbbrlFmzZt0u233x7u0iLOX//6V7388suKjY2VJGVmZsrlcoW5qsi0efNmPfHEE+rbt6+++OILzZ07Vx999JHmz58fsZPIRVw/maVLl6pfv3564403jvn+qFGjznJFkY8Zvk7fu+++q4ULF2rPnj2Nxk25XC717NkzjJVFth07dignJyf4umPHjtq0aRNdK0xidsTQ2rZtW6PXPp8v2DUVp4brUGjdd9992rFjh/r166e5c+fqvPPOkyT17dtXAwcODHN1kSc6OloWiyX483n4Yd04dY899piGDBmiBx54QA6HI7g8MzNTY8eODWNl5kVcmKqvr5ck7giE0MMPP6x77rlHBQUFGjp0aHCGL5y83NxcXXbZZXr22Wc1fvz44PL4+HglJyeHsbLI1r59ez3yyCO6/vrrJUnLly9Xu3bt5PF4GDNlwv3336877rhDJSUlGj9+fHB2RJyaefPmae7cuXK73cGbJYZhyG6366abbgpzdZGJ61Bo/eY3v9Ell1xyzPcWL158lquJfNddd52mTp2q6upq/c///I8WLVrE77pJ77zzznHfi9SZUCN2zBRCixm+Qsfv92v//v2NHkKXnZ0dxooiV0NDg95991199dVXMgxDP/3pTzV8+HDFxMSovr5e8fHx4S4x4jA7Yug888wzjW6e4PRwHTp9K1eu/NH3I3E8SlOxbt06rV27VpLUq1cvXXrppWGuKLIcb7bjwyJ5fGTEhqmHHnpIkyZNCk6pWFVVpSeffJK7rCatX79eRUVFjQJApN4hCKe3335b//3f/6309PRG/f0j+UMCzUtZWdlRv+s///nPw1hRZON8hg7XodM3YcKEH32fv5EQLieacbtly5ZnqZLQi9h+Mt99912juemTkpL07bffhrGiyPX73/9ee/bs0YUXXiir1SpJslgsXMRMmD9/vlasWKGUlJRwl9Is7Nq1S88++6y2bdsmt9sdXL569eowVhW5Zs+erY8++kgdO3ZsFPb549+cp59+WsuXL9f5558f/OyUOJ9mcB0KDcLSmbFy5Uo9/fTTKi8vl2EYPAbBhEgOSycSsWEqEAioqqpKSUlJkqTKyspGd7Nw8jZu3Kjly5c3el4KzGnRogWDpkNowoQJGjNmjJ544gm99dZbWrx4MbPPnYZVq1ZpxYoVTDoRIn/96185nyHCdSj0Pv30U23du
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