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GettingStartedWithPandas.ipynb
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| { | |
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| "metadata": { | |
| "colab": { | |
| "name": "GettingStartedWithPandas.ipynb", | |
| "provenance": [], | |
| "authorship_tag": "ABX9TyOq/Ss//oJ9oZSb4YDMT8Sy", | |
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| "name": "python" | |
| } | |
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| { | |
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| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/stjohn/ca92cb126f7cb6ae33a389f6b5cdf410/gettingstartedwithpandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Reading Structured Data: Parking Tickets\n", | |
| "\n", | |
| "In this section, we will \n", | |
| "\n", | |
| "* run Python in a notebook, which has code intermixed with explanation.\n", | |
| "* explore the NYC OpenData data for parking tickets given in the city,\n", | |
| "* in particular, who got the most for 20th District?\n", | |
| "and \n", | |
| "* which color car gets the most?\n" | |
| ], | |
| "metadata": { | |
| "id": "_vFscggs8QaR" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### Running Commands:\n", | |
| "\n", | |
| "To get started, let's first execute a simple command that will print \"Hello World\" to the screen. To run the command, click to the left side of the \"cell\" (it will appear as brackets or a \"Play\" symbol, depending on your system). It may take a little bit of time to load, but you will see the message on the next line:" | |
| ], | |
| "metadata": { | |
| "id": "6PAZ5YxR9KCQ" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "1MXnLUHj791B", | |
| "outputId": "a385f8a9-3ecb-4cdf-a540-2c528e492a5d" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Hello World\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print(\"Hello World\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "##Binning Data: Parking Tickets\n", | |
| "\n", | |
| "\n", | |
| "Via the NYC Open Data project, you can access data from almost every city agency. Today, we will look at the parking tickets issues by New York City. We will use a small version (1000 lines), but you are welcome to also use any neighborhood in the city. To download data for a given neighborhood (and restricted to just the current fiscal year, since the data sets can be quite large):\n", | |
| "\n", | |
| "* Instead of neighborhood name or zipcode, parking tickets are classified by the police precinct that issued the ticket. First, figure out the precinct (here's a [useful tool](https://www1.nyc.gov/site/nypd/bureaus/patrol/find-your-precinct.page)).\n", | |
| "* [Fiscal year parking tickets for your precinct](https://data.cityofnewyork.us/City-Government/Parking-Violations-Issued-Fiscal-Year-2022/pvqr-7yc4/data): download the data set as a CSV, filtering by \"Violation Precinct\". For example, Hunter College is located in the 19th Precinct, so, you would enter 19 on the filter.\n", | |
| "\n", | |
| "A simple, but very powerful, technique is *binning data*-- that is grouping data into the number of occurrences for each categories. The category values can often show patterns that individual data points do not. For example, binning population by zipcode can show patterns in density that's difficult to see with individual data points." | |
| ], | |
| "metadata": { | |
| "id": "RfwWzY0j8MAq" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "##CSV Data Files\n", | |
| "\n", | |
| "To make reading files easier, we will use the Pandas library that lets you read in structured data files very efficiently. [Pandas](https://pandas.pydata.org), Python Data Analysis Library, is an elegant, open-source package for extracting, manipulating, and analyzing data, especially those stored in 2D arrays (like spreadsheets).\n", | |
| "\n", | |
| "In Pandas, the basic structure is a DataFrame which stored data in rectangular grids. The Comma-Separated-Values (CSV) files, we saw in the previous section, store tabular information in readable text files. The files downloaded above have information separated by commas (using tabs as delimiters is also common). \n", | |
| "\n", | |
| "Let's use Pandas to examine a small subset of parking tickets given every year. This set is 1000 tickets from the Upper West Side (District 20) in 2016:" | |
| ], | |
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| "id": "7bHvHmBs8N0q" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import pandas as pd\n", | |
| "url = 'https://raw.githubusercontent.com/stjohn/datasci/main/openDataWeek/tickets.csv'\n", | |
| "tickets = pd.read_csv(url)" | |
| ], | |
| "metadata": { | |
| "id": "P1Lwe3VMPhuF" | |
| }, | |
| "execution_count": 3, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "The commands above load the Pandas library, and alias its name to the common abbreviation: pd. The small CSV files of tickets is then read and stored in a variable, called tickets:" | |
| ], | |
| "metadata": { | |
| "id": "uaKXu_DlQlp9" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "tickets" | |
| ], | |
| "metadata": { | |
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| "base_uri": "https://localhost:8080/", | |
| "height": 681 | |
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| "id": "RXqTit5kQUeW", | |
| "outputId": "bc30bb11-36a3-4d30-aa2e-5d7e5735242f" | |
| }, | |
| "execution_count": 4, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/html": [ | |
| "\n", | |
| " <div id=\"df-719d706d-1894-4614-9443-17cdb22b935f\">\n", | |
| " <div class=\"colab-df-container\">\n", | |
| " <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", | |
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| "\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></th>\n", | |
| " <th>Summons Number</th>\n", | |
| " <th>Plate ID</th>\n", | |
| " <th>Registration State</th>\n", | |
| " <th>Plate Type</th>\n", | |
| " <th>Issue Date</th>\n", | |
| " <th>Violation Code</th>\n", | |
| " <th>Vehicle Body Type</th>\n", | |
| " <th>Vehicle Make</th>\n", | |
| " <th>Issuing Agency</th>\n", | |
| " <th>Street Code1</th>\n", | |
| " <th>...</th>\n", | |
| " <th>Vehicle Color</th>\n", | |
| " <th>Unregistered Vehicle?</th>\n", | |
| " <th>Vehicle Year</th>\n", | |
| " <th>Meter Number</th>\n", | |
| " <th>Feet From Curb</th>\n", | |
| " <th>Violation Post Code</th>\n", | |
| " <th>Violation Description</th>\n", | |
| " <th>No Standing or Stopping Violation</th>\n", | |
| " <th>Hydrant Violation</th>\n", | |
| " <th>Double Parking Violation</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>1335632335</td>\n", | |
| " <td>L040HZ</td>\n", | |
| " <td>FL</td>\n", | |
| " <td>PAS</td>\n", | |
| " <td>06/09/2015</td>\n", | |
| " <td>46</td>\n", | |
| " <td>SUBN</td>\n", | |
| " <td>NISSA</td>\n", | |
| " <td>X</td>\n", | |
| " <td>35430</td>\n", | |
| " <td>...</td>\n", | |
| " <td>RED</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>-</td>\n", | |
| " <td>0</td>\n", | |
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| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>1351345497</td>\n", | |
| " <td>GYA2946</td>\n", | |
| " <td>NY</td>\n", | |
| " <td>PAS</td>\n", | |
| " <td>06/25/2015</td>\n", | |
| " <td>21</td>\n", | |
| " <td>SDN</td>\n", | |
| " <td>FORD</td>\n", | |
| " <td>S</td>\n", | |
| " <td>35550</td>\n", | |
| " <td>...</td>\n", | |
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| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>1355390564</td>\n", | |
| " <td>T512895C</td>\n", | |
| " <td>NY</td>\n", | |
| " <td>OMT</td>\n", | |
| " <td>07/06/2015</td>\n", | |
| " <td>20</td>\n", | |
| " <td>SUBN</td>\n", | |
| " <td>LINCO</td>\n", | |
| " <td>P</td>\n", | |
| " <td>35210</td>\n", | |
| " <td>...</td>\n", | |
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| " <td>0.0</td>\n", | |
| " <td>2010</td>\n", | |
| " <td>-</td>\n", | |
| " <td>0</td>\n", | |
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| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>1355390825</td>\n", | |
| " <td>GVX5273</td>\n", | |
| " <td>NY</td>\n", | |
| " <td>PAS</td>\n", | |
| " <td>06/28/2015</td>\n", | |
| " <td>20</td>\n", | |
| " <td>SDN</td>\n", | |
| " <td>HONDA</td>\n", | |
| " <td>P</td>\n", | |
| " <td>35210</td>\n", | |
| " <td>...</td>\n", | |
| " <td>RED</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>1994</td>\n", | |
| " <td>-</td>\n", | |
| " <td>0</td>\n", | |
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| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>1355390862</td>\n", | |
| " <td>GOLPO</td>\n", | |
| " <td>NY</td>\n", | |
| " <td>OMT</td>\n", | |
| " <td>07/06/2015</td>\n", | |
| " <td>19</td>\n", | |
| " <td>VAN</td>\n", | |
| " <td>TOYOT</td>\n", | |
| " <td>P</td>\n", | |
| " <td>0</td>\n", | |
| " <td>...</td>\n", | |
| " <td>BLUE</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>2014</td>\n", | |
| " <td>-</td>\n", | |
| " <td>0</td>\n", | |
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| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>...</th>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>994</th>\n", | |
| " <td>7326771054</td>\n", | |
| " <td>XARL98</td>\n", | |
| " <td>NJ</td>\n", | |
| " <td>PAS</td>\n", | |
| " <td>07/24/2015</td>\n", | |
| " <td>14</td>\n", | |
| " <td>DELV</td>\n", | |
| " <td>ISUZU</td>\n", | |
| " <td>T</td>\n", | |
| " <td>15710</td>\n", | |
| " <td>...</td>\n", | |
| " <td>WHITE</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0</td>\n", | |
| " <td>40 7</td>\n", | |
| " <td>14-No Standing</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>995</th>\n", | |
| " <td>7326771121</td>\n", | |
| " <td>HXD2344</td>\n", | |
| " <td>PA</td>\n", | |
| " <td>PAS</td>\n", | |
| " <td>07/24/2015</td>\n", | |
| " <td>21</td>\n", | |
| " <td>4DSD</td>\n", | |
| " <td>TOYOT</td>\n", | |
| " <td>T</td>\n", | |
| " <td>35510</td>\n", | |
| " <td>...</td>\n", | |
| " <td>GREY</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0</td>\n", | |
| " <td>40 7</td>\n", | |
| " <td>21-No Parking (street clean)</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>996</th>\n", | |
| " <td>7326771182</td>\n", | |
| " <td>XR351G</td>\n", | |
| " <td>NJ</td>\n", | |
| " <td>PAS</td>\n", | |
| " <td>07/24/2015</td>\n", | |
| " <td>16</td>\n", | |
| " <td>VAN</td>\n", | |
| " <td>FORD</td>\n", | |
| " <td>T</td>\n", | |
| " <td>11710</td>\n", | |
| " <td>...</td>\n", | |
| " <td>WHITE</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0</td>\n", | |
| " <td>40 7</td>\n", | |
| " <td>16-No Std (Com Veh) Com Plate</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>997</th>\n", | |
| " <td>7326771236</td>\n", | |
| " <td>GVA4260</td>\n", | |
| " <td>NY</td>\n", | |
| " <td>PAS</td>\n", | |
| " <td>07/24/2015</td>\n", | |
| " <td>40</td>\n", | |
| " <td>SUBN</td>\n", | |
| " <td>NISSA</td>\n", | |
| " <td>T</td>\n", | |
| " <td>35570</td>\n", | |
| " <td>...</td>\n", | |
| " <td>GREY</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>2011</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0</td>\n", | |
| " <td>40 7</td>\n", | |
| " <td>40-Fire Hydrant</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>998</th>\n", | |
| " <td>7313165900</td>\n", | |
| " <td>T648267C</td>\n", | |
| " <td>NY</td>\n", | |
| " <td>OMT</td>\n", | |
| " <td>07/10/2015</td>\n", | |
| " <td>46</td>\n", | |
| " <td>SUBN</td>\n", | |
| " <td>CADIL</td>\n", | |
| " <td>T</td>\n", | |
| " <td>13610</td>\n", | |
| " <td>...</td>\n", | |
| " <td>BLACK</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>2011</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0</td>\n", | |
| " <td>42 7</td>\n", | |
| " <td>46A-Double Parking (Non-COM)</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>999 rows × 43 columns</p>\n", | |
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| " [theme=dark] .colab-df-convert {\n", | |
| " background-color: #3B4455;\n", | |
| " fill: #D2E3FC;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert:hover {\n", | |
| " background-color: #434B5C;\n", | |
| " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", | |
| " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", | |
| " fill: #FFFFFF;\n", | |
| " }\n", | |
| " </style>\n", | |
| "\n", | |
| " <script>\n", | |
| " const buttonEl =\n", | |
| " document.querySelector('#df-719d706d-1894-4614-9443-17cdb22b935f button.colab-df-convert');\n", | |
| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| "\n", | |
| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-719d706d-1894-4614-9443-17cdb22b935f');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
| " [key], {});\n", | |
| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
| " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", | |
| " + ' to learn more about interactive tables.';\n", | |
| " element.innerHTML = '';\n", | |
| " dataTable['output_type'] = 'display_data';\n", | |
| " await google.colab.output.renderOutput(dataTable, element);\n", | |
| " const docLink = document.createElement('div');\n", | |
| " docLink.innerHTML = docLinkHtml;\n", | |
| " element.appendChild(docLink);\n", | |
| " }\n", | |
| " </script>\n", | |
| " </div>\n", | |
| " </div>\n", | |
| " " | |
| ], | |
| "text/plain": [ | |
| " Summons Number Plate ID Registration State Plate Type Issue Date \\\n", | |
| "0 1335632335 L040HZ FL PAS 06/09/2015 \n", | |
| "1 1351345497 GYA2946 NY PAS 06/25/2015 \n", | |
| "2 1355390564 T512895C NY OMT 07/06/2015 \n", | |
| "3 1355390825 GVX5273 NY PAS 06/28/2015 \n", | |
| "4 1355390862 GOLPO NY OMT 07/06/2015 \n", | |
| ".. ... ... ... ... ... \n", | |
| "994 7326771054 XARL98 NJ PAS 07/24/2015 \n", | |
| "995 7326771121 HXD2344 PA PAS 07/24/2015 \n", | |
| "996 7326771182 XR351G NJ PAS 07/24/2015 \n", | |
| "997 7326771236 GVA4260 NY PAS 07/24/2015 \n", | |
| "998 7313165900 T648267C NY OMT 07/10/2015 \n", | |
| "\n", | |
| " Violation Code Vehicle Body Type Vehicle Make Issuing Agency \\\n", | |
| "0 46 SUBN NISSA X \n", | |
| "1 21 SDN FORD S \n", | |
| "2 20 SUBN LINCO P \n", | |
| "3 20 SDN HONDA P \n", | |
| "4 19 VAN TOYOT P \n", | |
| ".. ... ... ... ... \n", | |
| "994 14 DELV ISUZU T \n", | |
| "995 21 4DSD TOYOT T \n", | |
| "996 16 VAN FORD T \n", | |
| "997 40 SUBN NISSA T \n", | |
| "998 46 SUBN CADIL T \n", | |
| "\n", | |
| " Street Code1 ... Vehicle Color Unregistered Vehicle? Vehicle Year \\\n", | |
| "0 35430 ... RED 0.0 0 \n", | |
| "1 35550 ... TAN 0.0 2011 \n", | |
| "2 35210 ... BLACK 0.0 2010 \n", | |
| "3 35210 ... RED 0.0 1994 \n", | |
| "4 0 ... BLUE 0.0 2014 \n", | |
| ".. ... ... ... ... ... \n", | |
| "994 15710 ... WHITE NaN 0 \n", | |
| "995 35510 ... GREY NaN 0 \n", | |
| "996 11710 ... WHITE NaN 0 \n", | |
| "997 35570 ... GREY NaN 2011 \n", | |
| "998 13610 ... BLACK NaN 2011 \n", | |
| "\n", | |
| " Meter Number Feet From Curb Violation Post Code \\\n", | |
| "0 - 0 NaN \n", | |
| "1 - 0 NaN \n", | |
| "2 - 0 NaN \n", | |
| "3 - 0 NaN \n", | |
| "4 - 0 NaN \n", | |
| ".. ... ... ... \n", | |
| "994 NaN 0 40 7 \n", | |
| "995 NaN 0 40 7 \n", | |
| "996 NaN 0 40 7 \n", | |
| "997 NaN 0 40 7 \n", | |
| "998 NaN 0 42 7 \n", | |
| "\n", | |
| " Violation Description No Standing or Stopping Violation \\\n", | |
| "0 NaN NaN \n", | |
| "1 NaN NaN \n", | |
| "2 NaN NaN \n", | |
| "3 NaN NaN \n", | |
| "4 NaN NaN \n", | |
| ".. ... ... \n", | |
| "994 14-No Standing NaN \n", | |
| "995 21-No Parking (street clean) NaN \n", | |
| "996 16-No Std (Com Veh) Com Plate NaN \n", | |
| "997 40-Fire Hydrant NaN \n", | |
| "998 46A-Double Parking (Non-COM) NaN \n", | |
| "\n", | |
| " Hydrant Violation Double Parking Violation \n", | |
| "0 NaN NaN \n", | |
| "1 NaN NaN \n", | |
| "2 NaN NaN \n", | |
| "3 NaN NaN \n", | |
| "4 NaN NaN \n", | |
| ".. ... ... \n", | |
| "994 NaN NaN \n", | |
| "995 NaN NaN \n", | |
| "996 NaN NaN \n", | |
| "997 NaN NaN \n", | |
| "998 NaN NaN \n", | |
| "\n", | |
| "[999 rows x 43 columns]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 4 | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Warning: Total number of columns (43) exceeds max_columns (20) limiting to first (20) columns.\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "If working through this in colab, you can click on the blue magic wand in the bottom left corner to explore the data. You can click on columns to sort the data by that column and page through the data. \n", | |
| "\n", | |
| "Using the interactive table viewing: \n", | |
| "* What is the earliest day a ticket was given?\n", | |
| "* How many tickets went to cars with Vermont (VT) license plates? " | |
| ], | |
| "metadata": { | |
| "id": "4arQRW3JQ9eV" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "We can use Python to explore the data further. To see all the license plates, we write the name of the DataFrame, followed by the column ('Plate ID') that contains licence plate information:" | |
| ], | |
| "metadata": { | |
| "id": "MomKSsK3R4y6" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "tickets['Plate ID']" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "KB7_Jio8Q2IG", | |
| "outputId": "9ff6748f-1942-49ae-933b-22cfc23a5096" | |
| }, | |
| "execution_count": 5, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "0 L040HZ\n", | |
| "1 GYA2946\n", | |
| "2 T512895C\n", | |
| "3 GVX5273\n", | |
| "4 GOLPO\n", | |
| " ... \n", | |
| "994 XARL98\n", | |
| "995 HXD2344\n", | |
| "996 XR351G\n", | |
| "997 GVA4260\n", | |
| "998 T648267C\n", | |
| "Name: Plate ID, Length: 999, dtype: object" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 5 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Pandas has a useful function for counting how many times a value occurs. To count how many tickets were given, grouped by licence plate ID:" | |
| ], | |
| "metadata": { | |
| "id": "zUV0iKrwSiV0" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "tickets['Plate ID'].value_counts()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "lGCgG5UtShza", | |
| "outputId": "a47e2c88-7494-43dd-9aed-9a1a329cc93d" | |
| }, | |
| "execution_count": 7, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "TOPHAT5 6\n", | |
| "GXP4564 4\n", | |
| "XBLD33 4\n", | |
| "T63FNW 4\n", | |
| "XANK43 3\n", | |
| " ..\n", | |
| "XBVR37 1\n", | |
| "GGF4081 1\n", | |
| "R89DYH 1\n", | |
| "S98DZD 1\n", | |
| "T648267C 1\n", | |
| "Name: Plate ID, Length: 916, dtype: int64" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 7 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "There are a lot of cars that got only a single ticket. If you scroll back up the Python shell, you will see the cars with the most tickets are listed first. Let's just print out the 10 cars that got the most tickets. We can do this by \"slicing\" to the first 10 in the list by appending [:10]:" | |
| ], | |
| "metadata": { | |
| "id": "R0FUcFc1TCOU" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "tickets['Plate ID'].value_counts()[:10]" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "orI1lKDxSgeV", | |
| "outputId": "aeae2e5c-06ee-4007-c206-b3e86c8a2f8b" | |
| }, | |
| "execution_count": 8, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "TOPHAT5 6\n", | |
| "GXP4564 4\n", | |
| "XBLD33 4\n", | |
| "T63FNW 4\n", | |
| "XANK43 3\n", | |
| "R12FGF 3\n", | |
| "TOPHAT9 3\n", | |
| "XP627S 3\n", | |
| "GN8063 3\n", | |
| "ZDH5639 3\n", | |
| "Name: Plate ID, dtype: int64" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 8 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Let's use a slightly larger dataset with all tickets for the 20th precinct for January 2016 (14,000 tickets):" | |
| ], | |
| "metadata": { | |
| "id": "F92OchsbSOuT" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "url = 'https://raw.githubusercontent.com/stjohn/datasci/main/openDataWeek/Parking_Violations_Jan_2016.csv'\n", | |
| "jan_tickets = pd.read_csv(url)\n", | |
| "jan_tickets" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 90 | |
| }, | |
| "id": "hXVkCv8oTlVQ", | |
| "outputId": "5c9b3789-6327-44a1-d181-82106ad31f6e" | |
| }, | |
| "execution_count": 9, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/html": [ | |
| "\n", | |
| " <div id=\"df-372b0d07-8382-48fa-8e7f-3b15786fa9a9\">\n", | |
| " <div class=\"colab-df-container\">\n", | |
| " <div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
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| "\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></th>\n", | |
| " <th>Summons Number</th>\n", | |
| " <th>Plate ID</th>\n", | |
| " <th>Registration State</th>\n", | |
| " <th>Plate Type</th>\n", | |
| " <th>Issue Date</th>\n", | |
| " <th>Violation Code</th>\n", | |
| " <th>Vehicle Body Type</th>\n", | |
| " <th>Vehicle Make</th>\n", | |
| " <th>Issuing Agency</th>\n", | |
| " <th>Street Code1</th>\n", | |
| " <th>...</th>\n", | |
| " <th>Hydrant Violation</th>\n", | |
| " <th>Double Parking Violation</th>\n", | |
| " <th>Latitude</th>\n", | |
| " <th>Longitude</th>\n", | |
| " <th>Community Board</th>\n", | |
| " <th>Community Council</th>\n", | |
| " <th>Census Tract</th>\n", | |
| " <th>BIN</th>\n", | |
| " <th>BBL</th>\n", | |
| " <th>NTA</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>1364085707</td>\n", | |
| " <td>GKC6197</td>\n", | |
| " <td>NY</td>\n", | |
| " <td>PAS</td>\n", | |
| " <td>01/09/2016</td>\n", | |
| " <td>19</td>\n", | |
| " <td>SDN</td>\n", | |
| " <td>KIA</td>\n", | |
| " <td>P</td>\n", | |
| " <td>29650</td>\n", | |
| " <td>...</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>1364085719</td>\n", | |
| " <td>H51FEZ</td>\n", | |
| " <td>NJ</td>\n", | |
| " <td>PAS</td>\n", | |
| " <td>01/16/2016</td>\n", | |
| " <td>19</td>\n", | |
| " <td>SUBN</td>\n", | |
| " <td>LEXUS</td>\n", | |
| " <td>P</td>\n", | |
| " <td>29650</td>\n", | |
| " <td>...</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>1399171320</td>\n", | |
| " <td>GPL4108</td>\n", | |
| " <td>NY</td>\n", | |
| " <td>PAS</td>\n", | |
| " <td>01/07/2016</td>\n", | |
| " <td>21</td>\n", | |
| " <td>SUBN</td>\n", | |
| " <td>SUBAR</td>\n", | |
| " <td>S</td>\n", | |
| " <td>35150</td>\n", | |
| " <td>...</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>1399171355</td>\n", | |
| " <td>GHU9871</td>\n", | |
| " <td>NY</td>\n", | |
| " <td>PAS</td>\n", | |
| " <td>01/07/2016</td>\n", | |
| " <td>21</td>\n", | |
| " <td>SDN</td>\n", | |
| " <td>TOYOT</td>\n", | |
| " <td>S</td>\n", | |
| " <td>35050</td>\n", | |
| " <td>...</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>1399171380</td>\n", | |
| " <td>G8166E</td>\n", | |
| " <td>FL</td>\n", | |
| " <td>PAS</td>\n", | |
| " <td>01/12/2016</td>\n", | |
| " <td>21</td>\n", | |
| " <td>SUBN</td>\n", | |
| " <td>DODGE</td>\n", | |
| " <td>A</td>\n", | |
| " <td>35350</td>\n", | |
| " <td>...</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>...</th>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>14042</th>\n", | |
| " <td>8077509150</td>\n", | |
| " <td>EPV1024</td>\n", | |
| " <td>NY</td>\n", | |
| " <td>PAS</td>\n", | |
| " <td>01/31/2016</td>\n", | |
| " <td>19</td>\n", | |
| " <td>4DSD</td>\n", | |
| " <td>TOYOT</td>\n", | |
| " <td>T</td>\n", | |
| " <td>35210</td>\n", | |
| " <td>...</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>14043</th>\n", | |
| " <td>8077509162</td>\n", | |
| " <td>FAF3562</td>\n", | |
| " <td>NY</td>\n", | |
| " <td>PAS</td>\n", | |
| " <td>01/31/2016</td>\n", | |
| " <td>20</td>\n", | |
| " <td>4DSD</td>\n", | |
| " <td>NISSA</td>\n", | |
| " <td>T</td>\n", | |
| " <td>11710</td>\n", | |
| " <td>...</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>14044</th>\n", | |
| " <td>8077509230</td>\n", | |
| " <td>FTW7908</td>\n", | |
| " <td>NY</td>\n", | |
| " <td>PAS</td>\n", | |
| " <td>01/31/2016</td>\n", | |
| " <td>71</td>\n", | |
| " <td>4DSD</td>\n", | |
| " <td>AUDI</td>\n", | |
| " <td>T</td>\n", | |
| " <td>35490</td>\n", | |
| " <td>...</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>14045</th>\n", | |
| " <td>8077509265</td>\n", | |
| " <td>DUX1095</td>\n", | |
| " <td>NY</td>\n", | |
| " <td>PAS</td>\n", | |
| " <td>01/31/2016</td>\n", | |
| " <td>40</td>\n", | |
| " <td>SUBN</td>\n", | |
| " <td>CHEVR</td>\n", | |
| " <td>T</td>\n", | |
| " <td>15710</td>\n", | |
| " <td>...</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>14046</th>\n", | |
| " <td>1403742285</td>\n", | |
| " <td>FZH8385</td>\n", | |
| " <td>NY</td>\n", | |
| " <td>PAS</td>\n", | |
| " <td>01/14/2016</td>\n", | |
| " <td>71</td>\n", | |
| " <td>SUBN</td>\n", | |
| " <td>AUDI</td>\n", | |
| " <td>P</td>\n", | |
| " <td>14510</td>\n", | |
| " <td>...</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>14047 rows × 51 columns</p>\n", | |
| "</div>\n", | |
| " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-372b0d07-8382-48fa-8e7f-3b15786fa9a9')\"\n", | |
| " title=\"Convert this dataframe to an interactive table.\"\n", | |
| " style=\"display:none;\">\n", | |
| " \n", | |
| " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", | |
| " width=\"24px\">\n", | |
| " <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n", | |
| " <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n", | |
| " </svg>\n", | |
| " </button>\n", | |
| " \n", | |
| " <style>\n", | |
| " .colab-df-container {\n", | |
| " display:flex;\n", | |
| " flex-wrap:wrap;\n", | |
| " gap: 12px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert {\n", | |
| " background-color: #E8F0FE;\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: #1967D2;\n", | |
| " height: 32px;\n", | |
| " padding: 0 0 0 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert:hover {\n", | |
| " background-color: #E2EBFA;\n", | |
| " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: #174EA6;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert {\n", | |
| " background-color: #3B4455;\n", | |
| " fill: #D2E3FC;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert:hover {\n", | |
| " background-color: #434B5C;\n", | |
| " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", | |
| " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", | |
| " fill: #FFFFFF;\n", | |
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| " </style>\n", | |
| "\n", | |
| " <script>\n", | |
| " const buttonEl =\n", | |
| " document.querySelector('#df-372b0d07-8382-48fa-8e7f-3b15786fa9a9 button.colab-df-convert');\n", | |
| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| "\n", | |
| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-372b0d07-8382-48fa-8e7f-3b15786fa9a9');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
| " [key], {});\n", | |
| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
| " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", | |
| " + ' to learn more about interactive tables.';\n", | |
| " element.innerHTML = '';\n", | |
| " dataTable['output_type'] = 'display_data';\n", | |
| " await google.colab.output.renderOutput(dataTable, element);\n", | |
| " const docLink = document.createElement('div');\n", | |
| " docLink.innerHTML = docLinkHtml;\n", | |
| " element.appendChild(docLink);\n", | |
| " }\n", | |
| " </script>\n", | |
| " </div>\n", | |
| " </div>\n", | |
| " " | |
| ], | |
| "text/plain": [ | |
| " Summons Number Plate ID Registration State Plate Type Issue Date \\\n", | |
| "0 1364085707 GKC6197 NY PAS 01/09/2016 \n", | |
| "1 1364085719 H51FEZ NJ PAS 01/16/2016 \n", | |
| "2 1399171320 GPL4108 NY PAS 01/07/2016 \n", | |
| "3 1399171355 GHU9871 NY PAS 01/07/2016 \n", | |
| "4 1399171380 G8166E FL PAS 01/12/2016 \n", | |
| "... ... ... ... ... ... \n", | |
| "14042 8077509150 EPV1024 NY PAS 01/31/2016 \n", | |
| "14043 8077509162 FAF3562 NY PAS 01/31/2016 \n", | |
| "14044 8077509230 FTW7908 NY PAS 01/31/2016 \n", | |
| "14045 8077509265 DUX1095 NY PAS 01/31/2016 \n", | |
| "14046 1403742285 FZH8385 NY PAS 01/14/2016 \n", | |
| "\n", | |
| " Violation Code Vehicle Body Type Vehicle Make Issuing Agency \\\n", | |
| "0 19 SDN KIA P \n", | |
| "1 19 SUBN LEXUS P \n", | |
| "2 21 SUBN SUBAR S \n", | |
| "3 21 SDN TOYOT S \n", | |
| "4 21 SUBN DODGE A \n", | |
| "... ... ... ... ... \n", | |
| "14042 19 4DSD TOYOT T \n", | |
| "14043 20 4DSD NISSA T \n", | |
| "14044 71 4DSD AUDI T \n", | |
| "14045 40 SUBN CHEVR T \n", | |
| "14046 71 SUBN AUDI P \n", | |
| "\n", | |
| " Street Code1 ... Hydrant Violation Double Parking Violation \\\n", | |
| "0 29650 ... NaN NaN \n", | |
| "1 29650 ... NaN NaN \n", | |
| "2 35150 ... NaN NaN \n", | |
| "3 35050 ... NaN NaN \n", | |
| "4 35350 ... NaN NaN \n", | |
| "... ... ... ... ... \n", | |
| "14042 35210 ... NaN NaN \n", | |
| "14043 11710 ... NaN NaN \n", | |
| "14044 35490 ... NaN NaN \n", | |
| "14045 15710 ... NaN NaN \n", | |
| "14046 14510 ... NaN NaN \n", | |
| "\n", | |
| " Latitude Longitude Community Board Community Council Census Tract \\\n", | |
| "0 NaN NaN NaN NaN NaN \n", | |
| "1 NaN NaN NaN NaN NaN \n", | |
| "2 NaN NaN NaN NaN NaN \n", | |
| "3 NaN NaN NaN NaN NaN \n", | |
| "4 NaN NaN NaN NaN NaN \n", | |
| "... ... ... ... ... ... \n", | |
| "14042 NaN NaN NaN NaN NaN \n", | |
| "14043 NaN NaN NaN NaN NaN \n", | |
| "14044 NaN NaN NaN NaN NaN \n", | |
| "14045 NaN NaN NaN NaN NaN \n", | |
| "14046 NaN NaN NaN NaN NaN \n", | |
| "\n", | |
| " BIN BBL NTA \n", | |
| "0 NaN NaN NaN \n", | |
| "1 NaN NaN NaN \n", | |
| "2 NaN NaN NaN \n", | |
| "3 NaN NaN NaN \n", | |
| "4 NaN NaN NaN \n", | |
| "... .. .. .. \n", | |
| "14042 NaN NaN NaN \n", | |
| "14043 NaN NaN NaN \n", | |
| "14044 NaN NaN NaN \n", | |
| "14045 NaN NaN NaN \n", | |
| "14046 NaN NaN NaN \n", | |
| "\n", | |
| "[14047 rows x 51 columns]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 9 | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Warning: Total number of columns (51) exceeds max_columns (20) limiting to first (20) columns.\n", | |
| "Warning: Total number of columns (51) exceeds max_columns (20) limiting to first (20) columns.\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "##Challenges:\n", | |
| "\n", | |
| "Using the tools from above, for the month of January:\n", | |
| "\n", | |
| " * How many cars that got more than a ticket a day\n", | |
| " * What color of car is most likely to get a ticket?\n", | |
| " * What type of registration (i.e. passenger, commercial) gets the most tickets?\n", | |
| " * Which location yields the most tickets?\n", | |
| " * On what date were the most tickets issued?\n", | |
| "\n", | |
| "Try your code below:" | |
| ], | |
| "metadata": { | |
| "id": "PNZXcB68T-_x" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "" | |
| ], | |
| "metadata": { | |
| "id": "Rgq4QZZ1VBBG" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## What's Next\n", | |
| "\n", | |
| "Next, we'll combine pandas with folium to make HTML maps of NYC OpenData." | |
| ], | |
| "metadata": { | |
| "id": "LUaM1oxpVJcH" | |
| } | |
| } | |
| ] | |
| } |
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