Titanic-Part1.ipynb

{"nbformat_minor": 0, "cells": [{"source": "# TITANIC: Wrangling the Passenger Manifest\n\n## Exploratory Analysis with Pandas\n\nThis tutorial is based on the Kaggle Competition,\n\"Predicting Survival Aboard the Titanic\"\nhttps://www.kaggle.com/c/titanic\n\n___Be sure to read the README before you begin!___\n\nSee also:    \nhttp://www.analyticsvidhya.com/blog/2014/08/baby-steps-python-performing-exploratory-analysis-python/    \nhttp://www.analyticsvidhya.com/blog/2014/09/data-munging-python-using-pandas-baby-steps-python/", "cell_type": "markdown", "metadata": {}}, {"execution_count": 1, "cell_type": "code", "source": "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas.io.sql as pd_sql\nimport sqlite3 as sql\n\n%matplotlib inline", "outputs": [], "metadata": {"collapsed": true, "trusted": false}}, {"source": "Here's a ```sqlite``` database for you to store the data once it's ready:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 2, "cell_type": "code", "source": "con = sql.connect(\"titanic.db\") ", "outputs": [], "metadata": {"collapsed": true, "trusted": false}}, {"source": "__=>YOUR  TURN!__\n\nUse ```pandas``` to open up the csv.\n\nRead the documentation to find out how:    \nhttp://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html", "cell_type": "markdown", "metadata": {}}, {"execution_count": 3, "cell_type": "code", "source": "df = pd.read_csv(\"../titanic/data/train.csv\") ", "outputs": [], "metadata": {"collapsed": true, "trusted": false}}, {"source": "### Exploring the Tabular Data", "cell_type": "markdown", "metadata": {}}, {"source": "The file we'll be exploring today, ```train.csv```, is the training set -- it represents\na subset of the full passenger manifest dataset. The rest of the data is in another\nfile called ```test.csv``` - we'll use that later (when we get to Machine Learning).\nLet's take a look...", "cell_type": "markdown", "metadata": {}}, {"source": "__=>YOUR  TURN!__\n\nUse ```pandas``` to view the \"head\" of the file with the first 10 rows.\n\nRead the documentation to find out how:    \nhttp://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.head.html", "cell_type": "markdown", "metadata": {}}, {"execution_count": 4, "cell_type": "code", "source": "df.head(10)", "outputs": [{"execution_count": 4, "output_type": "execute_result", "data": {"text/plain": "   PassengerId  Survived  Pclass  \\\n0            1         0       3   \n1            2         1       1   \n2            3         1       3   \n3            4         1       1   \n4            5         0       3   \n5            6         0       3   \n6            7         0       1   \n7            8         0       3   \n8            9         1       3   \n9           10         1       2   \n\n                                                Name     Sex  Age  SibSp  \\\n0                            Braund, Mr. Owen Harris    male   22      1   \n1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n2                             Heikkinen, Miss. Laina  female   26      0   \n3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n4                           Allen, Mr. William Henry    male   35      0   \n5                                   Moran, Mr. James    male  NaN      0   \n6                            McCarthy, Mr. Timothy J    male   54      0   \n7                     Palsson, Master. Gosta Leonard    male    2      3   \n8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female   27      0   \n9                Nasser, Mrs. Nicholas (Adele Achem)  female   14      1   \n\n   Parch            Ticket     Fare Cabin Embarked  \n0      0         A/5 21171   7.2500   NaN        S  \n1      0          PC 17599  71.2833   C85        C  \n2      0  STON/O2. 3101282   7.9250   NaN        S  \n3      0            113803  53.1000  C123        S  \n4      0            373450   8.0500   NaN        S  \n5      0            330877   8.4583   NaN        Q  \n6      0             17463  51.8625   E46        S  \n7      1            349909  21.0750   NaN        S  \n8      2            347742  11.1333   NaN        S  \n9      0            237736  30.0708   NaN        C  ", "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PassengerId</th>\n      <th>Survived</th>\n      <th>Pclass</th>\n      <th>Name</th>\n      <th>Sex</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Ticket</th>\n      <th>Fare</th>\n      <th>Cabin</th>\n      <th>Embarked</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Braund, Mr. Owen Harris</td>\n      <td>male</td>\n      <td>22</td>\n      <td>1</td>\n      <td>0</td>\n      <td>A/5 21171</td>\n      <td>7.2500</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n      <td>female</td>\n      <td>38</td>\n      <td>1</td>\n      <td>0</td>\n      <td>PC 17599</td>\n      <td>71.2833</td>\n      <td>C85</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>1</td>\n      <td>3</td>\n      <td>Heikkinen, Miss. Laina</td>\n      <td>female</td>\n      <td>26</td>\n      <td>0</td>\n      <td>0</td>\n      <td>STON/O2. 3101282</td>\n      <td>7.9250</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n      <td>female</td>\n      <td>35</td>\n      <td>1</td>\n      <td>0</td>\n      <td>113803</td>\n      <td>53.1000</td>\n      <td>C123</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Allen, Mr. William Henry</td>\n      <td>male</td>\n      <td>35</td>\n      <td>0</td>\n      <td>0</td>\n      <td>373450</td>\n      <td>8.0500</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>6</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Moran, Mr. James</td>\n      <td>male</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>0</td>\n      <td>330877</td>\n      <td>8.4583</td>\n      <td>NaN</td>\n      <td>Q</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>7</td>\n      <td>0</td>\n      <td>1</td>\n      <td>McCarthy, Mr. Timothy J</td>\n      <td>male</td>\n      <td>54</td>\n      <td>0</td>\n      <td>0</td>\n      <td>17463</td>\n      <td>51.8625</td>\n      <td>E46</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>8</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Palsson, Master. Gosta Leonard</td>\n      <td>male</td>\n      <td>2</td>\n      <td>3</td>\n      <td>1</td>\n      <td>349909</td>\n      <td>21.0750</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>9</td>\n      <td>1</td>\n      <td>3</td>\n      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n      <td>female</td>\n      <td>27</td>\n      <td>0</td>\n      <td>2</td>\n      <td>347742</td>\n      <td>11.1333</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>10</td>\n      <td>1</td>\n      <td>2</td>\n      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n      <td>female</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0</td>\n      <td>237736</td>\n      <td>30.0708</td>\n      <td>NaN</td>\n      <td>C</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "__What do you see?__\n    - Are there any missing values?\n    - What kinds of values/numbers/text are there?\n    - Are the values continuous or categorical?\n    - Are some variables more sparse than others?\n    - Are there multiple values in a single column?", "cell_type": "markdown", "metadata": {}}, {"source": "__=>YOUR  TURN!__\n\nUse ```pandas``` to run summary statistics on the data.\n\nRead the documentation to find out how:    \nhttp://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.describe.html", "cell_type": "markdown", "metadata": {}}, {"execution_count": 5, "cell_type": "code", "source": "df.describe()", "outputs": [{"execution_count": 5, "output_type": "execute_result", "data": {"text/plain": "       PassengerId    Survived      Pclass         Age       SibSp  \\\ncount   891.000000  891.000000  891.000000  714.000000  891.000000   \nmean    446.000000    0.383838    2.308642   29.699118    0.523008   \nstd     257.353842    0.486592    0.836071   14.526497    1.102743   \nmin       1.000000    0.000000    1.000000    0.420000    0.000000   \n25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n75%     668.500000    1.000000    3.000000   38.000000    1.000000   \nmax     891.000000    1.000000    3.000000   80.000000    8.000000   \n\n            Parch        Fare  \ncount  891.000000  891.000000  \nmean     0.381594   32.204208  \nstd      0.806057   49.693429  \nmin      0.000000    0.000000  \n25%      0.000000    7.910400  \n50%      0.000000   14.454200  \n75%      0.000000   31.000000  \nmax      6.000000  512.329200  ", "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PassengerId</th>\n      <th>Survived</th>\n      <th>Pclass</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Fare</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>714.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>446.000000</td>\n      <td>0.383838</td>\n      <td>2.308642</td>\n      <td>29.699118</td>\n      <td>0.523008</td>\n      <td>0.381594</td>\n      <td>32.204208</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>257.353842</td>\n      <td>0.486592</td>\n      <td>0.836071</td>\n      <td>14.526497</td>\n      <td>1.102743</td>\n      <td>0.806057</td>\n      <td>49.693429</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.420000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>223.500000</td>\n      <td>0.000000</td>\n      <td>2.000000</td>\n      <td>20.125000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>7.910400</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>446.000000</td>\n      <td>0.000000</td>\n      <td>3.000000</td>\n      <td>28.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>14.454200</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>668.500000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>38.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>31.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>891.000000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>80.000000</td>\n      <td>8.000000</td>\n      <td>6.000000</td>\n      <td>512.329200</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "__What can we infer from the summary statistics?__\n    - How many missing values does the 'Age' column have?\n    - What's the age distribution?\n    - What percent of the passengers survived?\n    - How many passengers belonged to Class 3?\n    - Are there any outliers in the 'Fare' column?", "cell_type": "markdown", "metadata": {}}, {"source": "__=>YOUR  TURN!__\n\nUse ```pandas``` to get the median for the Age column.\n\nRead the documentation to find out how:    \nhttp://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.median.html", "cell_type": "markdown", "metadata": {}}, {"execution_count": 6, "cell_type": "code", "source": "df['Age'].median()", "outputs": [{"execution_count": 6, "output_type": "execute_result", "data": {"text/plain": "28.0"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "__=>YOUR  TURN!__\n\nUse ```pandas``` to find the number of unique values in the Ticket column.\n\nRead the documentation to find out how:    \nhttp://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.nunique.html", "cell_type": "markdown", "metadata": {}}, {"execution_count": 7, "cell_type": "code", "source": "df['Ticket'].nunique()", "outputs": [{"execution_count": 7, "output_type": "execute_result", "data": {"text/plain": "681"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "### Visually Exploring the Data", "cell_type": "markdown", "metadata": {}}, {"source": "Let's look at a histogram of the age distribution.\nWhat can you tell from the graph?", "cell_type": "markdown", "metadata": {}}, {"execution_count": 8, "cell_type": "code", "source": "fig = plt.figure()\nax = fig.add_subplot(111)\nax.hist(df['Age'], bins = 10, range = (df['Age'].min(),df['Age'].max()))\nplt.title('Age distribution')\nplt.xlabel('Age')\nplt.ylabel('Count of Passengers')\nplt.show()", "outputs": [{"output_type": "display_data", "data": {"image/png": 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9zRkRlX8ABwM3NCzPBGaWnashzyTg3oblpcCE9Hx7YGnZGVtkvgY4uqpZgc2A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\nC2qRw1kMzQAAAABJRU5ErkJggg==\n", "text/plain": "<matplotlib.figure.Figure at 0x852e810>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Now let's look at a histogram of the fares.\nWhat does it tell you?", "cell_type": "markdown", "metadata": {}}, {"execution_count": 9, "cell_type": "code", "source": "fig = plt.figure()\nax = fig.add_subplot(111)\nax.hist(df['Fare'], bins = 10, range = (df['Fare'].min(),df['Fare'].max()))\nplt.title('Fare distribution')\nplt.xlabel('Fare')\nplt.ylabel('Count of Passengers')\nplt.show()", "outputs": [{"output_type": "display_data", "data": {"image/png": 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gEWB5+wZ7076JlCjuBua1Lc/j5zPiRNUvaRaApKOB7aV+cHvnlrqeJukAqiRx\noe1LS/WkaiNA6dZ/EXgOk6N9JwFLJN0BrAVeKulCJkfbALD9w/LnvcAlVJdaJkv7tgJbbX+rLH+O\nKnFsG4v2TaREcT1wvKRjJU0FzgIuG+eYxsJlwLJSXgZc2lZ/tqSpkuYDx1PdgNizVD1b4ePARtsf\nals1Kdoo6ciBWSOSDgJ+E7iBSdA+2++2Pc/2fOBs4Gu2X88kaBuApIMlHVrK04DFwM1MkvbZ3gbc\nJWlBqToVuAW4nLFo33iP1O/hqP7LqGbSbAZWjHc8o4h/LdUd5z+jGm/5XWAGcAVwG7AemN62/btL\nWzcBvzXe8XfQvhdRXd++keoL9AaqR8ZPijYCzwC+U9p3E/DOUj8p2tcW84vZPetpUrSN6hr+jeXz\nvYHvj8nSvhLvrwHfAr4LXEw162lM2pcb7iIiotZEuvQUERHjIIkiIiJqJVFEREStJIqIiKiVRBER\nEbWSKCIiolZPvbgooldJepzq3okBS23fOV7xRHRT7qOI6ICkh20fuof7CHY9Yydiwsqlp4hRkDRN\n0hWSvl1ehrOk1B8r6T8lraF6RMQ8Se+UdJ2k70paOa6BR4xCLj1FdOYgSTeU8n8BZwK/bfthSUcC\n32T3s8eOA15v+7ry5rDjbC+SNAX4N0mnuHo3ScSEkEQR0ZmfunpENbDrKbnvK4+qfgKYLenJZfUP\nbA88YG0xsLgtyUyjSiRJFDFhJFFEjM5rgSOBZ9t+vDye+8Cy7pFB277P9se6Gl3EGMoYRcToHEb1\nop/Hy8uLnjLMdl8F3lgebY2kOZKO6laQEWMhPYqIzgyeuXQRcLmkm6jelXLrUNva3iDpBOCbZRLU\nw8DrgHubDTdi7GR6bERE1Mqlp4iIqJVEERERtZIoIiKiVhJFRETUSqKIiIhaSRQREVEriSIiImol\nUURERK3/D7JXM1Ml5KC+AAAAAElFTkSuQmCC\n", "text/plain": "<matplotlib.figure.Figure at 0x8692c70>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "### Dealing with Missing Values\n\nPart of data wrangling is figuring out how to deal with missing values.\nBut before you decide, think about which variables are likely to be predictive\nof survival. Which ones do you think will be the best predictors?\n\n__Age__\nAge is likely to play a role, so we'll probably want to estimate or 'impute'\nthe missing values in some way.\n\n__Fare__\nThere are a lot of extremes on the high end and low end for ticket fares.\nHow should we handle them?\n\n__Other Variables__\nWhat do YOU think??", "cell_type": "markdown", "metadata": {}}, {"source": "__=>YOUR  TURN!__\n\nUse ```pandas``` to get the sum of all the null values in the Cabin column.\n\nRead the documentation to find out how:    \nhttp://pandas.pydata.org/pandas-docs/stable/generated/pandas.isnull.html    \nhttp://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sum.html", "cell_type": "markdown", "metadata": {}}, {"execution_count": 10, "cell_type": "code", "source": "sum(df['Cabin'].isnull()) ", "outputs": [{"execution_count": 10, "output_type": "execute_result", "data": {"text/plain": "687"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "__=>YOUR  TURN!__\n\nUse ```pandas``` to drop the Ticket column.\n\nRead the documentation to find out how:    \nhttp://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.drop.html", "cell_type": "markdown", "metadata": {}}, {"execution_count": 14, "cell_type": "code", "source": "df = df.drop(['Ticket'], axis=1)\ndf.head(20)", "outputs": [{"ename": "ValueError", "evalue": "labels ['Ticket'] not contained in axis", "traceback": ["\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)", "\u001b[1;32m<ipython-input-14-9b9b556b42cc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Ticket'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mP:\\Continuum\\Anaconda\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, level, inplace, errors)\u001b[0m\n\u001b[0;32m   1595\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1596\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1597\u001b[1;33m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1598\u001b[0m             \u001b[0mdropped\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1599\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mP:\\Continuum\\Anaconda\\lib\\site-packages\\pandas\\core\\index.pyc\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m   2568\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2569\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m'ignore'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2570\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'labels %s not contained in axis'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2571\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2572\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mValueError\u001b[0m: labels ['Ticket'] not contained in axis"], "output_type": "error"}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "__=>YOUR  TURN!__\n\nUse ```pandas``` to calculate the mean age and fill all the null values in the Age column with that number..\n\nRead the documentation to find out how:    \nhttp://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.mean.html     \nhttp://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.fillna.html", "cell_type": "markdown", "metadata": {}}, {"execution_count": 13, "cell_type": "code", "source": "mean_age = np.mean(df.Age)\ndf.Age = df.Age.fillna(mean_age)\ndf.head(10)", "outputs": [{"execution_count": 13, "output_type": "execute_result", "data": {"text/plain": "   PassengerId  Survived  Pclass  \\\n0            1         0       3   \n1            2         1       1   \n2            3         1       3   \n3            4         1       1   \n4            5         0       3   \n5            6         0       3   \n6            7         0       1   \n7            8         0       3   \n8            9         1       3   \n9           10         1       2   \n\n                                                Name     Sex        Age  \\\n0                            Braund, Mr. Owen Harris    male  22.000000   \n1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.000000   \n2                             Heikkinen, Miss. Laina  female  26.000000   \n3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.000000   \n4                           Allen, Mr. William Henry    male  35.000000   \n5                                   Moran, Mr. James    male  29.699118   \n6                            McCarthy, Mr. Timothy J    male  54.000000   \n7                     Palsson, Master. Gosta Leonard    male   2.000000   \n8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.000000   \n9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.000000   \n\n   SibSp  Parch     Fare Cabin Embarked  \n0      1      0   7.2500   NaN        S  \n1      1      0  71.2833   C85        C  \n2      0      0   7.9250   NaN        S  \n3      1      0  53.1000  C123        S  \n4      0      0   8.0500   NaN        S  \n5      0      0   8.4583   NaN        Q  \n6      0      0  51.8625   E46        S  \n7      3      1  21.0750   NaN        S  \n8      0      2  11.1333   NaN        S  \n9      1      0  30.0708   NaN        C  ", "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PassengerId</th>\n      <th>Survived</th>\n      <th>Pclass</th>\n      <th>Name</th>\n      <th>Sex</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Fare</th>\n      <th>Cabin</th>\n      <th>Embarked</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Braund, Mr. Owen Harris</td>\n      <td>male</td>\n      <td>22.000000</td>\n      <td>1</td>\n      <td>0</td>\n      <td>7.2500</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n      <td>female</td>\n      <td>38.000000</td>\n      <td>1</td>\n      <td>0</td>\n      <td>71.2833</td>\n      <td>C85</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>1</td>\n      <td>3</td>\n      <td>Heikkinen, Miss. Laina</td>\n      <td>female</td>\n      <td>26.000000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>7.9250</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n      <td>female</td>\n      <td>35.000000</td>\n      <td>1</td>\n      <td>0</td>\n      <td>53.1000</td>\n      <td>C123</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Allen, Mr. William Henry</td>\n      <td>male</td>\n      <td>35.000000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>8.0500</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>6</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Moran, Mr. James</td>\n      <td>male</td>\n      <td>29.699118</td>\n      <td>0</td>\n      <td>0</td>\n      <td>8.4583</td>\n      <td>NaN</td>\n      <td>Q</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>7</td>\n      <td>0</td>\n      <td>1</td>\n      <td>McCarthy, Mr. Timothy J</td>\n      <td>male</td>\n      <td>54.000000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>51.8625</td>\n      <td>E46</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>8</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Palsson, Master. Gosta Leonard</td>\n      <td>male</td>\n      <td>2.000000</td>\n      <td>3</td>\n      <td>1</td>\n      <td>21.0750</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>9</td>\n      <td>1</td>\n      <td>3</td>\n      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n      <td>female</td>\n      <td>27.000000</td>\n      <td>0</td>\n      <td>2</td>\n      <td>11.1333</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>10</td>\n      <td>1</td>\n      <td>2</td>\n      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n      <td>female</td>\n      <td>14.000000</td>\n      <td>1</td>\n      <td>0</td>\n      <td>30.0708</td>\n      <td>NaN</td>\n      <td>C</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "### Save Your Work\n...you will need it in a few weeks!", "cell_type": "markdown", "metadata": {}}, {"source": "__=>YOUR  TURN!__\n\nUse ```pandas``` to write your dataframe to our sqlite database.\n\nRead the documentation to find out how:   \nhttp://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_sql.html", "cell_type": "markdown", "metadata": {}}, {"execution_count": 17, "cell_type": "code", "source": "pd_sql.to_sql(df, \"training_data\", con) ", "outputs": [], "metadata": {"collapsed": false, "trusted": false}}, {"execution_count": null, "cell_type": "code", "source": "", "outputs": [], "metadata": {"collapsed": true, "trusted": false}}], "nbformat": 4, "metadata": {"kernelspec": {"display_name": "Python 2", "name": "python2", "language": "python"}, "language_info": {"mimetype": "text/x-python", "nbconvert_exporter": "python", "version": "2.7.10", "name": "python", "file_extension": ".py", "pygments_lexer": "ipython2", "codemirror_mode": {"version": 2, "name": "ipython"}}}}