{ "metadata": { "name": "", "signature": "sha256:26c57d9c88cdcc2542e706964424520429f837201a92a986f79b328e19587ef2" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "

[Data, the Humanist's New Best Friend](index.ipynb)
*Assignment 1*
Titanic: Women and Children First

\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "\n", "*Yep, no room for more people, sorry, mate*\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From [Wikipedia](http://en.wikipedia.org/wiki/Passengers_of_the_RMS_Titanic), \"the passengers of the **RMS Titanic** were among the estimated 2,223 people who sailed on the maiden voyage of the second of the White Star Line's Olympic class ocean liners, from Southampton to New York City. Halfway through the voyage, the ship struck an iceberg and sank in the early morning of 15 April 1912, resulting in the deaths of over 1,500 people, including approximately 703 of the passengers.\"" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.display import YouTubeVideo; YouTubeVideo(\"9xoqXVjBEF8\")" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", " \n", " " ], "metadata": {}, "output_type": "pyout", "prompt_number": 1, "text": [ "" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The goal will be to analyze the passengers list and look for some patterns and, ultimately, find whether woman and children really went first.\n", "\n", "Assignment\n", "----------\n", "\n", "**Your mission will be to complete the Python code in the cells below and execute it until the output looks similar or identical to the output shown.** I would recommend to use a temporary notebook to work with the dataset, and when the code is ready and producing the expected output, copypaste it to this notebook. Once is done and validated, just copy the file elsewhere, as the notebook will be the only file to be sent for evaluation. Of course, everything starts by downloading this [notebook](https://raw.githubusercontent.com/versae/DH2304/master/assignment1.ipynb).\n", "\n", "
\n", "\n", "*No worries, there is no test in this class, just... assignments!*\n", "
\n", "\n", "Deadline\n", "--------\n", "**February $24^{th}$.**\n", "\n", "Data\n", "----\n", "The Titanic's passengers were divided into three separate classes, determined not only by the price of their ticket but by wealth and social class: those travelling in first class, the wealthiest passengers on board, were prominent members of the upper class and included businessmen, politicians, high-ranking military personnel, industrialists, bankers and professional athletes. Second class passengers were middle class travellers and included professors, authors, clergymen and tourists. Third class or steerage passengers were primarily immigrants moving to the United States and Canada.\n", "\n", "In the file [titanic.xls](data/titanic.xls) you will find part of the original list of passengers. The variables or columns are described below:\n", "- `survival`: Survival (0 = No; 1 = Yes)\n", "- `pclass`: Passenger Class (1 = 1st; 2 = 2nd; 3 = 3rd)\n", "- `name`: Name\n", "- `sex`: Sex\n", "- `age`: Age\n", "- `sibsp`: Number of Siblings/Spouses Aboard\n", "- `parch`: Number of Parents/Children Aboard\n", "- `ticket`: Ticket Number\n", "- `fare`: Passenger Fare in pound sterling (\u00a3)\n", "- `cabin`: Cabin\n", "- `embarked`: Port of Embarkation (C = Cherbourg; Q = Queenstown; S = Southampton)\n", "- `boat`: Boat number used for survival\n", "- `home.dest`: Home / Final destination\n", "\n", "Consider that `pclass` is a proxy for socio-economic status:\n", "- 1st ~ Upper\n", "- 2nd ~ Middle\n", "- 3rd ~ Lower\n", "\n", "And that age is given in years, with a couple of exceptions\n", "- If `age` less than 1, is given as a fraction\n", "- If the `age` is an estimation, it is in the form `xx.5`\n", "\n", "With respect to the family relation variables (i.e. `sibsp` and `parch`) some relations were ignored. The following are the definitions used for `sibsp` and `parch`.\n", "- Sibling: Brother, Sister, Stepbrother, or Stepsister of Passenger Aboard Titanic\n", "- Spouse: Husband or Wife of Passenger Aboard Titanic (Mistresses and Fiances Ignored)\n", "- Parent: Mother or Father of Passenger Aboard Titanic\n", "- Child: Son, Daughter, Stepson, or Stepdaughter of Passenger Aboard Titanic\n", "\n", "Other family relatives excluded from this study include cousins, nephews/nieces, aunts/uncles, and in-laws. Some children travelled only with a nanny, therefore `parch=0` for them. As well, some travelled with very close friends or neighbors in a village, however, the definitions do not support such relations.\n", "\n", "When loaded into a `DataFrame`, the dataset looks like this:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# Set some Pandas options\n", "pd.set_option('display.max_columns', 20)\n", "pd.set_option('display.max_rows', 25)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "titanic = pd.read_excel(\"data/titanic.xls\")\n", "titanic.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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pclasssurvivednamesexagesibspparchticketfarecabinembarkedboathome.dest
0 1 1 Allen, Miss. Elisabeth Walton female 29.0000 0 0 24160 211.3375 B5 S 2 St Louis, MO
1 1 1 Allison, Master. Hudson Trevor male 0.9167 1 2 113781 151.5500 C22 C26 S 11 Montreal, PQ / Chesterville, ON
2 1 0 Allison, Miss. Helen Loraine female 2.0000 1 2 113781 151.5500 C22 C26 S NaN Montreal, PQ / Chesterville, ON
3 1 0 Allison, Mr. Hudson Joshua Creighton male 30.0000 1 2 113781 151.5500 C22 C26 S NaN Montreal, PQ / Chesterville, ON
4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female 25.0000 1 2 113781 151.5500 C22 C26 S NaN Montreal, PQ / Chesterville, ON
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ " pclass survived name sex \\\n", "0 1 1 Allen, Miss. Elisabeth Walton female \n", "1 1 1 Allison, Master. Hudson Trevor male \n", "2 1 0 Allison, Miss. Helen Loraine female \n", "3 1 0 Allison, Mr. Hudson Joshua Creighton male \n", "4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female \n", "\n", " age sibsp parch ticket fare cabin embarked boat \\\n", "0 29.0000 0 0 24160 211.3375 B5 S 2 \n", "1 0.9167 1 2 113781 151.5500 C22 C26 S 11 \n", "2 2.0000 1 2 113781 151.5500 C22 C26 S NaN \n", "3 30.0000 1 2 113781 151.5500 C22 C26 S NaN \n", "4 25.0000 1 2 113781 151.5500 C22 C26 S NaN \n", "\n", " home.dest \n", "0 St Louis, MO \n", "1 Montreal, PQ / Chesterville, ON \n", "2 Montreal, PQ / Chesterville, ON \n", "3 Montreal, PQ / Chesterville, ON \n", "4 Montreal, PQ / Chesterville, ON " ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Preparation\n", "-----------\n", "\n", "Before we can actually start playing with the data, we need to add some stuff. First, create a new column, `cabin_type`, with the letter of the cabin if known. For example, if the cabin is `'C22 C26'`, `cabin_type` would be `C`; for something like `'A90 B11'` (which never happens), only the first code is used, being `A` the `cabin_type`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "titanic[\"cabin_type\"] = titanic[\"cabin\"].\n", "titanic.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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pclasssurvivednamesexagesibspparchticketfarecabinembarkedboathome.destcabin_type
0 1 1 Allen, Miss. Elisabeth Walton female 29.0000 0 0 24160 211.3375 B5 S 2 St Louis, MO B
1 1 1 Allison, Master. Hudson Trevor male 0.9167 1 2 113781 151.5500 C22 C26 S 11 Montreal, PQ / Chesterville, ON C
2 1 0 Allison, Miss. Helen Loraine female 2.0000 1 2 113781 151.5500 C22 C26 S NaN Montreal, PQ / Chesterville, ON C
3 1 0 Allison, Mr. Hudson Joshua Creighton male 30.0000 1 2 113781 151.5500 C22 C26 S NaN Montreal, PQ / Chesterville, ON C
4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female 25.0000 1 2 113781 151.5500 C22 C26 S NaN Montreal, PQ / Chesterville, ON C
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ " pclass survived name sex \\\n", "0 1 1 Allen, Miss. Elisabeth Walton female \n", "1 1 1 Allison, Master. Hudson Trevor male \n", "2 1 0 Allison, Miss. Helen Loraine female \n", "3 1 0 Allison, Mr. Hudson Joshua Creighton male \n", "4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female \n", "\n", " age sibsp parch ticket fare cabin embarked boat \\\n", "0 29.0000 0 0 24160 211.3375 B5 S 2 \n", "1 0.9167 1 2 113781 151.5500 C22 C26 S 11 \n", "2 2.0000 1 2 113781 151.5500 C22 C26 S NaN \n", "3 30.0000 1 2 113781 151.5500 C22 C26 S NaN \n", "4 25.0000 1 2 113781 151.5500 C22 C26 S NaN \n", "\n", " home.dest cabin_type \n", "0 St Louis, MO B \n", "1 Montreal, PQ / Chesterville, ON C \n", "2 Montreal, PQ / Chesterville, ON C \n", "3 Montreal, PQ / Chesterville, ON C \n", "4 Montreal, PQ / Chesterville, ON C " ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also need a column, `name_title`, with the title included in the name but in lower case. For example, if the name is `Allison, Mrs. Hudson J C (Bessie Waldo Daniels)`, `name_title` would be `mrs`. Note the dot is excluded. If the title is none of `Master`, `Miss`, `Ms` or `Mrs`, it will be classified as `other`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def assign_name(name):\n", " pass\n", "\n", "titanic[\"name_title\"] = titanic[\"name\"].apply(assign_name)\n", "titanic.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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pclasssurvivednamesexagesibspparchticketfarecabinembarkedboathome.destcabin_typename_title
0 1 1 Allen, Miss. Elisabeth Walton female 29.0000 0 0 24160 211.3375 B5 S 2 St Louis, MO B miss
1 1 1 Allison, Master. Hudson Trevor male 0.9167 1 2 113781 151.5500 C22 C26 S 11 Montreal, PQ / Chesterville, ON C master
2 1 0 Allison, Miss. Helen Loraine female 2.0000 1 2 113781 151.5500 C22 C26 S NaN Montreal, PQ / Chesterville, ON C miss
3 1 0 Allison, Mr. Hudson Joshua Creighton male 30.0000 1 2 113781 151.5500 C22 C26 S NaN Montreal, PQ / Chesterville, ON C mr
4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female 25.0000 1 2 113781 151.5500 C22 C26 S NaN Montreal, PQ / Chesterville, ON C mrs
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ " pclass survived name sex \\\n", "0 1 1 Allen, Miss. Elisabeth Walton female \n", "1 1 1 Allison, Master. Hudson Trevor male \n", "2 1 0 Allison, Miss. Helen Loraine female \n", "3 1 0 Allison, Mr. Hudson Joshua Creighton male \n", "4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female \n", "\n", " age sibsp parch ticket fare cabin embarked boat \\\n", "0 29.0000 0 0 24160 211.3375 B5 S 2 \n", "1 0.9167 1 2 113781 151.5500 C22 C26 S 11 \n", "2 2.0000 1 2 113781 151.5500 C22 C26 S NaN \n", "3 30.0000 1 2 113781 151.5500 C22 C26 S NaN \n", "4 25.0000 1 2 113781 151.5500 C22 C26 S NaN \n", "\n", " home.dest cabin_type name_title \n", "0 St Louis, MO B miss \n", "1 Montreal, PQ / Chesterville, ON C master \n", "2 Montreal, PQ / Chesterville, ON C miss \n", "3 Montreal, PQ / Chesterville, ON C mr \n", "4 Montreal, PQ / Chesterville, ON C mrs " ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, a third new columnd `age_cat` that maps the `age` to one of the next values: `children` (under 14 years), `adolescents` (14-20), `adult` (21-64), and `senior` (65+)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def assign_age(age):\n", " pass\n", "\n", "titanic[\"age_cat\"] = titanic[\"age\"].apply(assign_age)\n", "titanic.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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pclasssurvivednamesexagesibspparchticketfarecabinembarkedboathome.destcabin_typename_titleage_cat
0 1 1 Allen, Miss. Elisabeth Walton female 29.0000 0 0 24160 211.3375 B5 S 2 St Louis, MO B miss adult
1 1 1 Allison, Master. Hudson Trevor male 0.9167 1 2 113781 151.5500 C22 C26 S 11 Montreal, PQ / Chesterville, ON C master children
2 1 0 Allison, Miss. Helen Loraine female 2.0000 1 2 113781 151.5500 C22 C26 S NaN Montreal, PQ / Chesterville, ON C miss children
3 1 0 Allison, Mr. Hudson Joshua Creighton male 30.0000 1 2 113781 151.5500 C22 C26 S NaN Montreal, PQ / Chesterville, ON C mr adult
4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female 25.0000 1 2 113781 151.5500 C22 C26 S NaN Montreal, PQ / Chesterville, ON C mrs adult
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ " pclass survived name sex \\\n", "0 1 1 Allen, Miss. Elisabeth Walton female \n", "1 1 1 Allison, Master. Hudson Trevor male \n", "2 1 0 Allison, Miss. Helen Loraine female \n", "3 1 0 Allison, Mr. Hudson Joshua Creighton male \n", "4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female \n", "\n", " age sibsp parch ticket fare cabin embarked boat \\\n", "0 29.0000 0 0 24160 211.3375 B5 S 2 \n", "1 0.9167 1 2 113781 151.5500 C22 C26 S 11 \n", "2 2.0000 1 2 113781 151.5500 C22 C26 S NaN \n", "3 30.0000 1 2 113781 151.5500 C22 C26 S NaN \n", "4 25.0000 1 2 113781 151.5500 C22 C26 S NaN \n", "\n", " home.dest cabin_type name_title age_cat \n", "0 St Louis, MO B miss adult \n", "1 Montreal, PQ / Chesterville, ON C master children \n", "2 Montreal, PQ / Chesterville, ON C miss children \n", "3 Montreal, PQ / Chesterville, ON C mr adult \n", "4 Montreal, PQ / Chesterville, ON C mrs adult " ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The last cosmetic change will be to replace the letters in the variable `embarked` for the actual names of the cities:\n", "- `C`: Cherbourg\n", "- `Q`: Queenstown\n", "- `S`: Southampton\n", "\n", "Note that in this case we won't create a new column but replace the original one." ] }, { "cell_type": "code", "collapsed": false, "input": [ "titanic[\"embarked\"] = titanic[\"embarked\"].\n", "titanic.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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pclasssurvivednamesexagesibspparchticketfarecabinembarkedboathome.destcabin_typename_titleage_cat
0 1 1 Allen, Miss. Elisabeth Walton female 29.0000 0 0 24160 211.3375 B5 Southampton 2 St Louis, MO B miss adult
1 1 1 Allison, Master. Hudson Trevor male 0.9167 1 2 113781 151.5500 C22 C26 Southampton 11 Montreal, PQ / Chesterville, ON C master children
2 1 0 Allison, Miss. Helen Loraine female 2.0000 1 2 113781 151.5500 C22 C26 Southampton NaN Montreal, PQ / Chesterville, ON C miss children
3 1 0 Allison, Mr. Hudson Joshua Creighton male 30.0000 1 2 113781 151.5500 C22 C26 Southampton NaN Montreal, PQ / Chesterville, ON C mr adult
4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female 25.0000 1 2 113781 151.5500 C22 C26 Southampton NaN Montreal, PQ / Chesterville, ON C mrs adult
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ " pclass survived name sex \\\n", "0 1 1 Allen, Miss. Elisabeth Walton female \n", "1 1 1 Allison, Master. Hudson Trevor male \n", "2 1 0 Allison, Miss. Helen Loraine female \n", "3 1 0 Allison, Mr. Hudson Joshua Creighton male \n", "4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female \n", "\n", " age sibsp parch ticket fare cabin embarked boat \\\n", "0 29.0000 0 0 24160 211.3375 B5 Southampton 2 \n", "1 0.9167 1 2 113781 151.5500 C22 C26 Southampton 11 \n", "2 2.0000 1 2 113781 151.5500 C22 C26 Southampton NaN \n", "3 30.0000 1 2 113781 151.5500 C22 C26 Southampton NaN \n", "4 25.0000 1 2 113781 151.5500 C22 C26 Southampton NaN \n", "\n", " home.dest cabin_type name_title age_cat \n", "0 St Louis, MO B miss adult \n", "1 Montreal, PQ / Chesterville, ON C master children \n", "2 Montreal, PQ / Chesterville, ON C miss children \n", "3 Montreal, PQ / Chesterville, ON C mr adult \n", "4 Montreal, PQ / Chesterville, ON C mrs adult " ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Analysis\n", "--------\n", "\n", "
\n", "\n", "*Rose: \"I'l never let go Jack...\"*\n", "
\n", "\n", "Where did the richer people embark from? And what about the destination? Where did the 10 poorest people want to go/come from? To solve this, take a look on the fare they paid, and return the average per city." ] }, { "cell_type": "code", "collapsed": false, "input": [ "titanic.groupby()[[\"fare\"]].aggregate().sort(\"fare\")" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
fare
embarked
Queenstown 12.409012
Southampton 27.418824
Cherbourg 62.336267
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ " fare\n", "embarked \n", "Queenstown 12.409012\n", "Southampton 27.418824\n", "Cherbourg 62.336267" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "titanic.groupby()[[]].aggregate().sort()[:10]" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
fare
home.dest
Liverpool, England / Belfast 0.000000
Belfast, NI 0.000000
Belfast 0.000000
Rotterdam, Netherlands 0.000000
Syria 6.155567
Liverpool 6.500000
Co Cork, Ireland Charlestown, MA 6.750000
Effington Rut, SD 6.975000
Portugal 7.050000
Argentina 7.050000
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ " fare\n", "home.dest \n", "Liverpool, England / Belfast 0.000000\n", "Belfast, NI 0.000000\n", "Belfast 0.000000\n", "Rotterdam, Netherlands 0.000000\n", "Syria 6.155567\n", "Liverpool 6.500000\n", "Co Cork, Ireland Charlestown, MA 6.750000\n", "Effington Rut, SD 6.975000\n", "Portugal 7.050000\n", "Argentina 7.050000" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's consider now proportions (ratio) of survival. Calculate the proportion of passengers that survived by sex. And the same proportion by age category." ] }, { "cell_type": "code", "collapsed": false, "input": [ "survived = titanic.groupby()[[]].aggregate()\n", "total = titanic.groupby()[[]].aggregate()\n", "ratio = 100 * survived / total\n", "ratio" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
survived
sex
female 72.746781
male 19.098458
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ " survived\n", "sex \n", "female 72.746781\n", "male 19.098458" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "survived = titanic.groupby()[[]].aggregate()\n", "total = titanic.groupby()[[]].aggregate()\n", "ratio = 100 * survived / total\n", "ratio" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
survived
age_cat
adolescents 38.255034
adult 39.617834
children 35.911602
senior 15.384615
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ " survived\n", "age_cat \n", "adolescents 38.255034\n", "adult 39.617834\n", "children 35.911602\n", "senior 15.384615" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate the same proportion, but by sex and class." ] }, { "cell_type": "code", "collapsed": false, "input": [ "survived = titanic.groupby()[[]].aggregate()\n", "total = titanic.groupby()[[]].aggregate()\n", "ratio = 100 * survived / total\n", "ratio" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
survived
sexpclass
female1 96.527778
2 88.679245
3 49.074074
male1 34.078212
2 14.619883
3 15.212982
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ " survived\n", "sex pclass \n", "female 1 96.527778\n", " 2 88.679245\n", " 3 49.074074\n", "male 1 34.078212\n", " 2 14.619883\n", " 3 15.212982" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate survival proportions by age category, class and sex." ] }, { "cell_type": "code", "collapsed": false, "input": [ "survived = titanic.groupby()[[]].aggregate()\n", "total = titanic.groupby()[[]].aggregate()\n", "ratio = 100 * survived / total\n", "ratio.unstack()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
survived
sexfemalemale
age_catpclass
adolescents1 100.000000 20.000000
2 92.307692 11.764706
3 54.285714 12.500000
adult1 96.551724 34.328358
2 86.842105 7.812500
3 44.186047 15.918367
children1 91.666667 39.393939
2 94.117647 54.166667
3 51.578947 15.469613
senior1 100.000000 14.285714
2 NaN 0.000000
3 NaN 0.000000
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ " survived \n", "sex female male\n", "age_cat pclass \n", "adolescents 1 100.000000 20.000000\n", " 2 92.307692 11.764706\n", " 3 54.285714 12.500000\n", "adult 1 96.551724 34.328358\n", " 2 86.842105 7.812500\n", " 3 44.186047 15.918367\n", "children 1 91.666667 39.393939\n", " 2 94.117647 54.166667\n", " 3 51.578947 15.469613\n", "senior 1 100.000000 14.285714\n", " 2 NaN 0.000000\n", " 3 NaN 0.000000" ] } ], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate survival proportions by age category and sex." ] }, { "cell_type": "code", "collapsed": false, "input": [ "survived = titanic.groupby()[[]].aggregate()\n", "total = titanic.groupby()[[]].aggregate()\n", "ratio = 100 * survived / total\n", "ratio.unstack()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
survived
sexfemalemale
age_cat
adolescents 73.015873 12.790698
adult 77.697842 18.737673
children 61.290323 22.689076
senior 100.000000 8.333333
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ " survived \n", "sex female male\n", "age_cat \n", "adolescents 73.015873 12.790698\n", "adult 77.697842 18.737673\n", "children 61.290323 22.689076\n", "senior 100.000000 8.333333" ] } ], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, women and children first?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "*[Mrs. Charlotte Collyer and her daughter Marjorie](http://en.wikipedia.org/wiki/Passengers_of_the_RMS_Titanic)*\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's now see price. In order to see distributions of price, the first thing we need to do is to calculate the average fare per class, as well as the average age, and the number of people in each class." ] }, { "cell_type": "code", "collapsed": false, "input": [ "pd.pivot_table(titanic,\n", " index=[],\n", " values=[],\n", " aggfunc={\"fare\": , \"age\": , \"name\": },\n", " margins=True)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
agefarename
pclass
1 39.159918 87.508992 323
2 29.506705 21.179196 277
3 24.816367 13.302889 709
All 29.881135 33.295479 1309
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ " age fare name\n", "pclass \n", "1 39.159918 87.508992 323\n", "2 29.506705 21.179196 277\n", "3 24.816367 13.302889 709\n", "All 29.881135 33.295479 1309" ] } ], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "And even split the latter per cabin type and adding an aggregate counting the number of survivors." ] }, { "cell_type": "code", "collapsed": false, "input": [ "pd.pivot_table(titanic,\n", " index=[],\n", " values=[],\n", " aggfunc={\"fare\": , \"age\": , \"name\": , \"survived\": },\n", " margins=)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
agefarenamesurvived
pclasscabin_type
1A 44.157895 41.244314 22 11
B 36.476190 122.383078 65 47
C 38.382752 107.926598 94 57
D 41.040541 58.919065 40 28
E 39.593750 63.464706 34 24
T 45.000000 35.500000 1 0
2D 29.800000 13.595833 6 4
E 38.833333 11.587500 4 3
F 19.076923 23.423077 13 10
3E 21.666667 11.000000 3 3
F 27.200000 9.395838 8 3
G 12.000000 14.205000 5 3
All 29.881135 33.295479 1309 500
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ " age fare name survived\n", "pclass cabin_type \n", "1 A 44.157895 41.244314 22 11\n", " B 36.476190 122.383078 65 47\n", " C 38.382752 107.926598 94 57\n", " D 41.040541 58.919065 40 28\n", " E 39.593750 63.464706 34 24\n", " T 45.000000 35.500000 1 0\n", "2 D 29.800000 13.595833 6 4\n", " E 38.833333 11.587500 4 3\n", " F 19.076923 23.423077 13 10\n", "3 E 21.666667 11.000000 3 3\n", " F 27.200000 9.395838 8 3\n", " G 12.000000 14.205000 5 3\n", "All 29.881135 33.295479 1309 500" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Well, it starts to look like the fare maybe, and only maybe, had something to do with the probability of survival, isn't?\n", "\n", "Visualizations\n", "--------------\n", "\n", "Before getting deeper into that relationship, let's just explore some basic plots. For example, let's plot the proportion of survived by name title." ] }, { "cell_type": "code", "collapsed": false, "input": [ "ax = titanic.boxplot(column=, by=, grid=False)\n", "ax.set_title(\"\")\n", "ax.set_xlabel(\"\")\n", "ax.set_ylabel(\"\")\n", "ax.get_figure().suptitle(\"\") # Do not change this statement" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "And also, if we define a new column `alone` that is `True` if the passenger was travelling withouh family, and `False` if with family, let's plot that into a box plot by age." ] }, { "cell_type": "code", "collapsed": false, "input": [ "titanic[\"alone\"] = \n", "ax = titanic.boxplot(column=\"\", by=\"\", grid=False)\n", "ax.set_title(\"\")\n", "ax.set_xlabel(\"\")\n", "ax.set_ylabel(\"\")\n", "ax.get_figure().suptitle(\"\") # Do not change this statement" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 18, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": {}, "source": [ "On the other hand, to better see the relationship between proportion of survived and fare, we are going to create a table that has, in rows, two indices, sex and age category, and as the values, the average age, the average fare, and the proportion of passengers that survived. Define and use the function `ratio` as an aggreate to calculate the proportion of people that survived." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def ratio(values):\n", " pass" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "pt_titanic = pd.pivot_table(titanic,\n", " index=[],\n", " values=[],\n", " aggfunc={\n", " \"fare\": ,\n", " \"age\": ,\n", " \"survived\": \n", " },\n", " margins=)\n", "pt_titanic" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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agefaresurvived
sexage_cat
femaleadolescents 17.428571 34.826592 73.015873
adult 34.953237 57.661422 77.697842
children 5.208335 26.012198 61.290323
senior 76.000000 78.850000 100.000000
maleadolescents 17.970930 21.331057 12.790698
adult 34.433925 28.636848 18.737673
children 5.416666 21.671076 22.689076
senior 69.541667 44.978483 8.333333
All 29.881135 33.295479 38.197097
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 20, "text": [ " age fare survived\n", "sex age_cat \n", "female adolescents 17.428571 34.826592 73.015873\n", " adult 34.953237 57.661422 77.697842\n", " children 5.208335 26.012198 61.290323\n", " senior 76.000000 78.850000 100.000000\n", "male adolescents 17.970930 21.331057 12.790698\n", " adult 34.433925 28.636848 18.737673\n", " children 5.416666 21.671076 22.689076\n", " senior 69.541667 44.978483 8.333333\n", "All 29.881135 33.295479 38.197097" ] } ], "prompt_number": 20 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's create two plots, one for the female and other for male, comparing the series of average fare and survival proportion, and sorted by fare." ] }, { "cell_type": "code", "collapsed": false, "input": [ "female = pt_titanic.query(\"sex == 'female'\").sort()[[]]\n", "male = pt_titanic.query().sort()[[]]\n", "\n", "fig = plt.figure(figsize=(10, 8))\n", "ax1 = fig.add_subplot\n", "ax2 = fig.add_subplot\n", "\n", "female.plot(ax=)\n", "ax1.set_ylabel()\n", "\n", "male.plot(ax=)\n", "ax2.set_xlabel()\n", "ax2.set_ylabel()\n", "\n", "ax2.set_xticklabels([\"children\", \"\", \"adolescent\", \"\", \"adult\", \"\", \"senior\"])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 21, "text": [ "[,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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msSUFWpfmXdSPJiISgLZuLS3WPvwQevUqLdguvlija4HOs+KtaPP4nwIdrLW/\nN8ZEA62ttWuqO5hTUfF2okJbSHpWeqWL2AKV7tfZPrI9NUyN03p99Ui4Tzl3n3LuPuW8crm5TpFW\nXLBlZTkXGQwf7iyYey6bvCvn7vOy5+1ZoBAYCPweyCl67NLqDkaqVlk/2qb9m0g9kFquH+2SNpdw\nc/eb1Y8mIhIkduwo3eR99Wro1s0p1l57DXr21CbvcqLTGXlba63tWfyz6LF11toerkRYPpaQH3k7\nlHvImeosU6Bt3r+Z9Kx02ke2P2EUrUvzLjSqo0YHEZFgkZcHn35aOrr2/fcwZIhTsA0Zok3eQ4mX\nI2/HjTElaz4YY1rgjMTJWSruR6tsqjPzWKazPlpRkTah+wS6Nu/KhU0vVD+aiEiQ2rMH3nnHKdbe\nfRcuuMAp1l54AXr3hgitrCRn4HRG3m7G2arqEuBV4EbgIWvtPP+Hd0IsQTXyVmgLSctKY9O+Mnt1\nFhVpxpgTrug80340N6hHwn3KufuUc/eFes4DcZP3UM95IPJs5M1a+x9jzP+Aa4oeGmmt3VTdgQSz\n3PxcthzccsIoWuqBVJrVb1ZSpF163qXOSFqLrrSo30L9aCIiIeTAAWdz9yVLtMm7+FeVI2/GmKYV\nHyr6aQGstQf9GFelvB55q6wfbdO+TWzP3k5MZEzJ6Jn60UREQl/xJu/FFxts2AADBpRuQ6VN3sX1\npUKMMWkUFWqVsdZ2qO5gTsWN4s1aS8aRjEq3gso6lkVss9gTlt+4sOmF1I6o7de4RETEexU3eY+M\nLF13rV8/bfIu5WmRXqq3eCsoLCA9O73SIq2GqVHpfp3RTaIDqh/NDeqRcJ9y7j7l3H3BknNr4Ztv\nSnvXvvqq/CbvF1zgdYSnL1hyHko83dvUGBMFdALqFj9mrf2guoPxh9z8XFIPpJYsuVG2H615/eYl\nxVmf8/swMW4iXZp3UT+aiEgYy8kpv8l7zZrOJu/TpjmbvNev73WEEu5O52rT24F7gHbAWuBy4FNr\n7UD/h3dCLFWOvB3KPVTpKNqO7B3ERMZUul9nw9oNXf4EIiISaKyF1NTSYu2zz+Cyy0qnQ2NjtQ2V\nnB0vt8f6BuiNU7DFGWO6AI9Za39c3cGcijHG7j68u9IiLftYNrHNY0svGlA/moiIVOGHH8pv8n78\neOk2VNdco03epXp4OW16zFr7gzEGY0xda+1mY0zsubypMSYSeAm4COeiiJ8BW4A3gPZAGnCTtTar\n4nMvfvYL6mzyAAAgAElEQVTicqNo13W+jq7Nu9KuSbuw60dzg3ok3Kecu085d58XOd+2rfwm7z17\nOsXaggXhscm7vueh43SKtx1FPW8LgHeNMZk4xdW5+Cuw1Fp7ozGmJtAAeBB411o7wxgzDbi/6FbO\n/l/vP8e3FhGRcJCbCx99VFqwZWY6o2uJic6+oeeyybuIl87oalNjTDzQGHjHWnv8rN7QmCbAWmtt\nxwqPbwb6W2szjDGtAZ+1tkuFc4JqhwUREXHXzp2l666tWlW6yfvw4drkXdzn6VIhRSNv7XBG6gxg\nrbVfndUbGhMHPA9sBHoA/wOmAjuttVFF5xjgYPFxmeeqeBMRkRL5+eU3ed+1S5u8S+DwrOfNGPMH\nIBHYSvkN6Qecw3v2AiZba78wxsyiwvSotdYaYyqt0hITE4mJiQEgMjKSuLi4kjl8n88HoONqPE5O\nTmbq1KkBE084HBc/FijxhMNxxdx7HU84HM+aNeus/37v2QOzZvn47DNYty6ejh2hWzcfd9wBd90V\nT0SEc/433wTO5w2EY/09d+fvt8/nIy0tDX86natNU4GLz3aatJLXa41z5WqHouOrgAeAjsAAa+0e\nY0wbYLWmTb3n8/lKvpziDuXcfcq5+84k5wUF8MUXpaNr330H115busl7mzb+jTVU6HvuPi+XCnkb\nuMtam1Ftb2rMB8Bt1tpUY8wjQPGShwestU8YY+4HIq2191d4noo3EZEwULzJ+9Klzs/WrUt71668\nUpu8S3DwsnjrDSwEvgFyix621tqEs35TY3rgLBVSG/gOZ6mQCGAeEE0VS4WoeBMRCU2FhZCcXDq6\n9s035Td5j472OkKRM+dl8bYJeA6neCvuebPW2verO5hTUfHmPg2zu085d59y7j6fz0fPnvHlNnlv\n3Lh0dO3qq7XJe3XT99x9Xi7Sm2Otfbq631hERMLP1q3OorizZzv3r7rKKdYefDC4NnkX8dLpjLzN\nxJkuTaJ02pSzXSrkXGjkTUQkuFgLX33lFGwLF0JGBiQkwMiRMHCgNnmX0ObltKkPZwurcqy1Z7tU\nyFlT8SYiEvjy8uD9951ibeFCqFsXRo1ybpddBhERXkco4g5/FW81TnWCtTbeWjug4q26A5HAVHbt\nGnGHcu4+5fzcHT4M//0v3HwztGrlTIOed55zpWhKCsyY4VwlWly4KefuU85Dx+ks0tsa+BNwvrV2\nqDGmG3CFtfZlv0cnIiIBa88eWLTImRL98EOnOBs1yinUzjvP6+hEQtfpTJu+A/wTeNBa290YUwtn\nb9KL3QiwQiyaNhUR8VBKijMVumABbNrkLJI7apTzs0kTr6MTCSxe9rx9aa291Biz1lrbs+ixZGtt\nXHUHcyoq3kRE3FVY6OxusGCBczt0yLnYYNQoiI+H2rW9jlAkcHnW8wbkGGOalQnkciC7ugORwKQe\nCfcp5+5TzsvLzYV33oG77oK2beHWW6FGDXj1VdixA559FgYPPrfCTTl3n3IeOk5nnbdfAouAjsaY\nT4AWwI1+jUpERFyVne0slrtggXORwcUXO6Nr778PnTp5HZ2IlFXltKkxJtpau73ofk2gC2CAlOra\npP5MadpURKT67NwJSUlOwfbZZ9C/vzMlOmKEc8WoiJwb13veKvS4vWWtvaG63/xMqXgTETl71sLG\njaX9a1u3wnXXOSNsgwdDw4ZeRygSWrzseQPoWN1vLMFBPRLuU87dF8o5LyiAjz6C++5zpj+HD4e9\ne+GJJ5ylPmbPhp/8xP3CLZRzHqiU89BxOj1vIiISRH74AVaudEbXFi1y1lwbORLefBPi4sBU+ziA\niLjpZNOmBcDRosN6wA9lfm2ttY39HFtlMWnaVESkEgcOwJIlzhpsK1dCr15OwTZyJHTo4HV0IuHJ\ns3XeAomKNxGRUmlppfuH/u9/zkbvo0Y5fWzNm3sdnYh43fMmYUo9Eu5Tzt0XLDm3FpKT4ZFHoGdP\n6NMH1q2DqVNh9254+22YODE4CrdgyXkoUc5Dhyc9b8aYNOAQUADkWWv7GGOaAm8A7YE04CZrbZYX\n8YmIBIr8fGff0AULnBG2iAhndO3pp8tv9C4i4cOTaVNjzDbgEmvtwTKPzQD2W2tnGGOmAVHW2vsr\nPE/TpiIS8o4ccRbKXbjQ6WOLiXEKtlGj4KKLdMGBSLAIqZ63ouLtUmvtgTKPbQb6W2szjDGtAZ+1\ntkuF56l4E5GQtHcvLF7sjLD5fHDZZU6xlpAA7dp5HZ2InI1Q63mzwEpjzJfGmNuLHmtlrc0oup8B\naH3vAKAeCfcp5+7zKufffgtPPgn9+kHnzs5+omPHwvbt8O678H//F7qFm77n7lPOQ4dX67z1tdbu\nNsa0AN4tGnUrYa21xphKh9gSExOJiYkBIDIykri4OOLj44HSL6aOq+84OTk5oOIJh+NigRKPjqvv\n2Fpo2DCehQvhtdd8ZGfD6NHx/OY3EBHho3btwIrXn8fJyckBFU84HOvvuTt/v30+H2lpafiT50uF\nGGN+B+QAtwPx1to9xpg2wGpNm4pIsDt+3NncvfiCg4YNnenQkSOdqdEaNbyOUET8xV/Tpq6PvBlj\n6gMR1trDxpgGwGDgUSAJmAg8UfRzgduxiYhUh0OHnCnQBQtg2TLo0sUp2FaudO6LiJwLL/7N1wr4\n0BiTDHwOLLbWrgAeBwYZY1KBgUXH4rGyQ8HiDuXcfdWR89274fnnYdgwaNsW/vUviI93NoL/9FOY\nNk2FW1n6nrtPOQ8dro+8WWu3AXGVPH4QuNbteEREztbmzc7o2oIFkJLibPr+s5/BG29AY9c3EBSR\ncOF5z9uZUM+biHipsBA+/7y0fy0nx+ldGzUK+veH2rW9jlBEAklIrfN2tlS8iYjbjh2DVaucgm3R\nImfrqeKC7ZJLtGCuiFQt1NZ5kyChHgn3Kefuq5jzzEx47TUYPRpat4bHH4fYWGebqvXr4Y9/hEsv\nVeF2LvQ9d59yHjq8WudNRCSg7NjhTIUuXOhMjQ4Y4IywPfMMtGzpdXQiIqU0bSoiYcla+Oab0v61\ntDS47jpnOnTwYGjQwOsIRSTYqecNFW8icm4KCuDjj51ibcEC57h4w/erroKamosQkWqknjfxhHok\n3KecV6+jRyEpCW691elfmzLFWcZj/nzYtg1mzQLwqXBzmb7n7lPOQ4f+XIlIyNm/HxYvdkbY3nvP\nubhg1Cj47W+haGtkEZGgpWlTEQkJ27aVToeuXQvXXusUbNddB02beh2diIQj9byh4k1ESlnrFGnF\nBdvu3ZCQ4BRs11wD9ep5HaGIhDv1vIkn1CPhPuW8anl5zoK599zjTH/edJPT0/bss07x9tJLcP31\nZ164KefuU87dp5yHDvW8iUhAy8mB5cud0bWlS+GCC5zRtaVLoVs3LZQrIuFH06YiEnAyMpytqBYs\ngA8+gCuucBbMTUiAtm29jk5E5PSo5w0VbyKhbMsWp1hbsAA2bIChQ52CbdgwiIz0OjoRkTOnnjfx\nhHok3BcuOS8shDVr4De/caY/+/eHrVvh4YedkbfXX4dx49wp3MIl54FEOXefch461PMmIq45fhxW\nr3ZG15KSoEkTp3/tn/+E3r2hhv45KSJySp5NmxpjIoAvgZ3W2hHGmKbAG0B7IA24yVqbVeE5mjYV\nCTLZ2bBsmVOwLV/ujLKNHOncYmO9jk5ExH9CrufNGPML4BKgkbU2wRgzA9hvrZ1hjJkGRFlr76/w\nHBVvIkFg1y5nZG3hQvjkE+jXzxlhGzHC2aJKRCQchFTPmzGmLTAceAko/lAJwKtF918FRnkQmlSg\nHgn3BWPOrYWNG2H6dLjsMvjRj5wN4G+7zSnkliyB228P3MItGHMe7JRz9ynnocOrnrengF8Bjcs8\n1spam1F0PwNo5XpUInLaCgrgs8+c6dCFC+GHH5zRtenT4eqroVYtryMUEQlNrk+bGmOuB4ZZa//P\nGBMP/LKo5y3TWhtV5ryD1tqmFZ6raVMRDx07BitXOsVaUhK0auUUbKNGQc+eWjBXRKQsf02bejHy\ndiWQYIwZDtQFGhtj/g1kGGNaW2v3GGPaAHsre3JiYiIxMTEAREZGEhcXR3x8PFA6JKxjHeu4+o67\nd49nyRJ46SUfX34Jl14az6hRMGCAj/PO8z4+HetYxzoOlOPi+2lpafiTp4v0GmP6A/cVjbzNAA5Y\na58wxtwPROqCBe/5fL6SL6e4IxByvn176YbvX3wBAwc6o2vXXw/Nm3saml8EQs7DjXLuPuXcfaE0\n8lZRcTX2ODDPGDOJoqVCPItIJMxYC+vXl+5wsH27c2XoPffAoEFQv77XEYqISDFtjyUSpvLz4aOP\nSkfYjHFG10aOhL59oWYg/NNORCSIhfLIm4i45OhRWLHCKdYWL4b27Z2CbeFCZ3kPXXAgIhL4angd\ngAS2sk2Y4o7qzvm+fc72UyNHOuus/f3vcMkl8NVX8L//OXuJdu8e3oWbvufuU87dp5yHDo28iYSg\n774rnQ5dtw4GD4bRo+Ff/4KoqFM+XUREAph63kRCgLXOSFrxBQf79kFCgjPads01ULeu1xGKiISf\nkNvb9GyoeBMplZcH779fusNBvXqlC+ZedhlERHgdoYhIeAupvU0leKhHwn0ny/nhw/Dmm/DTnzq7\nGzz4IJx/vnMRQkoKzJgBV16pwu1M6XvuPuXcfcp56FDPm0iA27PH2YpqwQJnaY8rr3RG1/78Zzjv\nPK+jExERt2naVCQApaSUTodu2gRDhzoF29Ch0KSJ19GJiMjpUM8bKt4kdFgLe/dCerpz27699P6m\nTXDkiHOxwahREB8PtWt7HbGIiJwpFW+oePOC9sI7O3l5sHNn5cVZejrs2AENGjiL5BbfoqOdn5mZ\nPn72s3hqqCPVNfqeu085d59y7j7tsCASQHJyqi7Mtm+HjAxnQdyyhVnv3nDjjaXHDRpU/to+Hyrc\nRESkShp5E6nAWmedtMoKs+LHfvihdKSs+GfZ23nnQa1aXn8SERHxkqZNUfEm1SM/35nSrGzErPhn\nvXonTmeWPW7RIry3kxIRkVNT8YaKNy8EY4/EkSNVT2empztLb7RqVXVh1r49NGzoXfzBmPNgp5y7\nTzl3n3LuPvW8ieBMae7fX3Vhlp7uFG/R0eULsyFDSo/bttWUpoiIBC+NvElAyc+H77+vvM+s+Ged\nOpX3mRU/1rKlpjRFRMR7mjZFxVsoOHr05KNmu3c7xVdV05nR0dC4sdefQkRE5NRCpngzxtQF3gfq\nALWBhdbaB4wxTYE3gPZAGnCTtTarwnNVvLnsTHokrIWDB6suzNLTnb0527WrvDArntIM9wVp1Zfi\nPuXcfcq5+5Rz94VMz5u19pgxZoC19qgxpibwkTHmKiABeNdaO8MYMw24v+gmAaKgoPyUZmUjaDVr\nnliYXX556XHLllrDTERE5Fx4Om1qjKmPMwqXCLwF9LfWZhhjWgM+a22XCudr5M2PfvihfEFWsTj7\n/nto3rzqUbPoaO27KSIiUixkRt4AjDE1gK+AC4DnrLUbjDGtrLUZRadkAK28iC1UWQuZmScfNcvO\ndqYtyxZjAwaUn9KsU8frTyIiIhLePCnerLWFQJwxpgmw3BgzoMLvrTGm0iG2xMREYmJiAIiMjCQu\nLq5kDt/n8wGE5XFBAcyf72PPHmjePJ70dPjkEx8ZGZCTE8/27VBY6KNVK7jooniioyE/30e3bnDf\nfc7x5s0+atQo//rJyckMHDjV888XTsfFjwVKPOFwXDH3XscTDsezZs3S32+Xj5OTk5k6VX/P/Xlc\nfD8tLQ1/8vxqU2PMw8APwG1AvLV2jzGmDbBa06aljh1zRseq2q5p1y5o2vTkC89GRp75+/p8vpIv\np7hDOXefcu4+5dx9yrn7Qulq0+ZAvrU2yxhTD1gOPAoMAQ5Ya58wxtwPRFpr76/w3JAs3qyFrKyT\n7wqQmelMW1a1vlm7dlC3rtefRERERIqFUvH2I+BVoEbR7d/W2j8XLRUyD4gmxJYKKSx01i872fpm\n1p5YlJUdNWvdGiIivP4kIiIicrpCpng7F4FavOXmwo4dVRdmO3dCVFTV05nFU5qBuCuAhtndp5y7\nTzl3n3LuPuXcfSF1tWmwyc4++cKzBw7A+eeXL8z69oXx45377dpBvXpefwoREREJBWE/8lZYCBkZ\nJy/OCgqqHjFr3x7atNGUpoiIiJSnaVPOrng7frz8lGbFwmznTmevzJMVZ1FRgTmlKSIiIoFLxRuV\nF2+HDp181Gz/fjjvvKoLs3btoH59jz5QEFCPhPuUc/cp5+5Tzt2nnLtPPW9Ffv7z8oXa8eMnFmYj\nRpQen3ees9+miIiISCgIupG3p56y5Qq1Zs00pSkiIiKBR9OmBO5SISIiIiIV+at4q1HdLyihpex+\nbeIO5dx9yrn7lHP3KeehQ8WbiIiISBDRtKmIiIiIH2jaVERERERUvMnJqUfCfcq5+5Rz9ynn7lPO\nQ4eKNxEREZEgop43ERERET9Qz5uIiIiIqHiTk1OPhPuUc/cp5+5Tzt2nnIcO14s3Y0w7Y8xqY8wG\nY8w3xph7ih5vaox51xiTaoxZYYyJdDs2OVFycrLXIYQd5dx9yrn7lHP3Keehw4uRtzzgXmvtRcDl\nwP8ZY7oC9wPvWms7A+8VHYvHsrKyvA4h7Cjn7lPO3aecu085Dx2uF2/W2j3W2uSi+znAJuB8IAF4\ntei0V4FRbscmJ0pLS/M6hLCjnLtPOXefcu4+5Tx0eNrzZoyJAXoCnwOtrLUZRb/KAFp5FJaUoWF2\n9ynn7lPO3aecu085Dx01vXpjY0xD4C1girX2sDGlV9Jaa60xptI1QcqeJ+5Qzt2nnLtPOXefcu4+\n5Tw0eFK8GWNq4RRu/7bWLih6OMMY09pau8cY0wbYW/F5/lgrRURERCSYeHG1qQFeBjZaa2eV+VUS\nMLHo/kRgQcXnioiIiIQ713dYMMZcBXwAfA0Uv/kDwBpgHhANpAE3WWt1aYyIiIhIGUG1PZaIiIhI\nuNMOCyIiIiJBRMWbiIiISBBR8SYiIiISRFS8iYiIiAQRFW8iIiIiQUTFm4iIiEgQUfEmIiIiEkRU\nvImIiIgEERVvIiIiIkFExZuIiIhIEFHxJiIiIhJEVLyJiIiIBBEVbyIiIiJBRMWbiIiISBBR8SYi\nIiISRFS8iYiIiAQRFW8iIiIiQUTFm4iIiEgQUfEmIiIiEkRUvImIiIgEERVvIiIiIkFExZuIiIhI\nEFHxJiIiIhJEVLyJiIiIBBEVbyIiIiJBRMWbiIiISBBR8SYiIiISRFS8iYiIiAQRFW8iIiIiQUTF\nm4iIiEgQUfEmIiIiEkRUvImIiIgEEc+KN2NMhDFmrTFmUdHxI8aYnUWPrTXGDPUqNhEREZFAVdPD\n954CbAQaFR1bYKa1dqZ3IYmIiIgENk9G3owxbYHhwEuAKX64zH0RERERqYRX06ZPAb8CCss8ZoGf\nG2PWGWNeNsZEehOaiIiISOByfdrUGHM9sNdau9YYE1/mV88Bvy+6/wfgSWBShedaV4IUERERqQbW\n2mqfVfRi5O1KIMEYsw2YCww0xsy21u61RXCmU/tU9mRrrW4u3n73u995HkO43ZRz5Twcbsq5ch5K\nt+PHLXPnWnr3tnTqZHn2WUtOjv/Gm1wfebPW/gb4DYAxpj9wn7X2FmNMG2vt7qLTfgysdzs2OVFa\nWprXIYQd5dx9yrn7lHP3KefVLysLXnoJnn4aOnaEhx6C66+HGn4eGvPyalNwLlAoLk1nGGN6FB1v\nA+70LCoRERGRKmzbBn/9K8yeDcOHw9tvwyWXuPf+nhZv1lof4Cu6P8HLWKRyiYmJXocQdpRz9ynn\n7lPO3aecn7tPP4WZM2H1arjtNvj6a2jb1v04jLXBcw2AMcYGU7wiIiIS3PLznZG1mTNh7164915I\nTISGDU/9XGMMNkQuWKh2xhjdPLiJf/h8Pq9DCDvKufuUc/cp52fm0CF46im48EKnp+3Xv4bUVJg8\n+fQKN3/yuuet2mhEzl0q3kREJBSlpzvF2r/+BYMHw7x50KfS9S+8ExLTpkXDkh5EFL6UcxERCSWf\nf+5Mja5cCbfeCj//OURHn9tr+mvaNGRG3kRERETOREEBLFzoFG27dsHUqc7SH40anfq5XgqJnjeR\nUKK+FPcp5+5Tzt2nnJc6fNiZGu3UCf7yF+cihC1bYMqUwC/cQMWbK1JSUoiLi6Nx48b8/e9/9zoc\nERGRsLRjh3PhQYcO8NFH8Npr8MkncMMNUDOI5iLV8+aCSZMmERkZyZNPPul1KNUm0HMuIiJS7Msv\nnanR5cth4kS45x6IifH/+2qpkCCWnp5Ot27dzvh5+fn5fohGREQk9BUUwIIFcPXVzsjapZfC1q1O\nEedG4eZPKt78bODAgfh8PiZPnkyjRo14+umn6dmzJ02aNCE6OppHH3205Ny0tDRq1KjBK6+8Qvv2\n7bn22msBeOWVV+jWrRtNmzZl6NChbN+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zP0E9W8eOwdDoLbdAhw5xt0zaOtW8iYiI1LJt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\nrNxZFOYU0rNTz7ibmLQ0bIqSNxERkbiVvV+WSORe3PAiY84fk1iOJMp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"text": [ "" ] } ], "prompt_number": 21 }, { "cell_type": "markdown", "metadata": {}, "source": [ "At least for woman, it looks like the richer the woman, the greater her chances to survive. Men, on the other hand, does not look like that; if any, it would be the opposite.\n", "\n", "But let's take a closer look to the distribution of ages (ignoring the people with unknown age), by creating a histogram of the age distribution." ] }, { "cell_type": "code", "collapsed": false, "input": [ "ax = titanic[].dropna()...(bins=30, alpha=0.8, grid=False, figsize=(12, 4))\n", "ax.set_title()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 22, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 22 }, { "cell_type": "markdown", "metadata": {}, "source": [ "To further support this idea, let's create another pivot table, but instead of using the age categories, let's just calculate the corresponding values for fare and propotion of survival for each individual age." ] }, { "cell_type": "code", "collapsed": false, "input": [ "pt_titanic2 = pd.pivot_table(titanic,\n", " index=[],\n", " values=[],\n", " aggfunc={\n", " },\n", " margins=True)\n", "pt_titanic2[[]]" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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faresurvived
sexage
female0.1667 20.575000 100.000000
0.75 19.258300 100.000000
0.9167 27.750000 100.000000
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2.0 39.955357 28.571429
3.0 25.476400 33.333333
4.0 22.828340 100.000000
5.0 22.717700 100.000000
6.0 32.137500 50.000000
7.0 26.250000 100.000000
8.0 24.441667 66.666667
9.0 24.808320 20.000000
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male62.0 18.321875 25.000000
63.0 26.000000 0.000000
64.0 121.416667 0.000000
65.0 32.093067 0.000000
66.0 10.500000 0.000000
67.0 221.779200 0.000000
70.0 40.750000 0.000000
70.5 7.750000 0.000000
71.0 42.079200 0.000000
74.0 7.775000 0.000000
80.0 30.000000 100.000000
All 33.295479 38.197097
\n", "

167 rows \u00d7 2 columns

\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 23, "text": [ " fare survived\n", "sex age \n", "female 0.1667 20.575000 100.000000\n", " 0.75 19.258300 100.000000\n", " 0.9167 27.750000 100.000000\n", " 1.0 19.467500 80.000000\n", " 2.0 39.955357 28.571429\n", " 3.0 25.476400 33.333333\n", " 4.0 22.828340 100.000000\n", " 5.0 22.717700 100.000000\n", " 6.0 32.137500 50.000000\n", " 7.0 26.250000 100.000000\n", " 8.0 24.441667 66.666667\n", " 9.0 24.808320 20.000000\n", "... ... ...\n", "male 62.0 18.321875 25.000000\n", " 63.0 26.000000 0.000000\n", " 64.0 121.416667 0.000000\n", " 65.0 32.093067 0.000000\n", " 66.0 10.500000 0.000000\n", " 67.0 221.779200 0.000000\n", " 70.0 40.750000 0.000000\n", " 70.5 7.750000 0.000000\n", " 71.0 42.079200 0.000000\n", " 74.0 7.775000 0.000000\n", " 80.0 30.000000 100.000000\n", "All 33.295479 38.197097\n", "\n", "[167 rows x 2 columns]" ] } ], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now plot that information into a scatter plot, being the X-axis the average fare in pound sterling, and the Y-axis the average ratio of survival." ] }, { "cell_type": "code", "collapsed": false, "input": [ "female2 = pt_titanic2.query\n", "male2 = pt_titanic2.query\n", "fig = plt.figure(figsize=(10, 8))\n", "ax = fig.add_subplot(1, 1, 1)\n", "ax.scatter(, , color='m')\n", "ax.scatter(, , color='b')\n", "ax.set_title()\n", "ax.set_xlabel(\"... (\u00a3)\")\n", "ax.set_ylabel(\"\")\n", "ax.legend([\"female\", \"male\"])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 24, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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gdZX52ZY5NknN4QADSZKkEnOAgSRJ0jRmsiZJklRiJmuSJEklZrImSZJUYiZr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\nUA8ma5IkqVXUtc9aZm4Fbo2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"text": [ "" ] } ], "prompt_number": 24 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The pattern, if any, is hard to see using this visualization. However, we can use `panda.cut()` to create bigger age groups and try again." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# This cell is complete\n", "labels = [\"{}-{}\".format(i, i+9) for i in range(0, 71, 10)]\n", "titanic[\"age_group\"] = pd.cut(titanic[\"age\"].dropna(), range(0, 81, 10), right=False, labels=labels)\n", "titanic.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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pclasssurvivednamesexagesibspparchticketfarecabinembarkedboathome.destcabin_typename_titleage_cataloneage_group
0 1 1 Allen, Miss. Elisabeth Walton female 29.0000 0 0 24160 211.3375 B5 Southampton 2 St Louis, MO B miss adult True 20-29
1 1 1 Allison, Master. Hudson Trevor male 0.9167 1 2 113781 151.5500 C22 C26 Southampton 11 Montreal, PQ / Chesterville, ON C master children False 0-9
2 1 0 Allison, Miss. Helen Loraine female 2.0000 1 2 113781 151.5500 C22 C26 Southampton NaN Montreal, PQ / Chesterville, ON C miss children False 0-9
3 1 0 Allison, Mr. Hudson Joshua Creighton male 30.0000 1 2 113781 151.5500 C22 C26 Southampton NaN Montreal, PQ / Chesterville, ON C mr adult False 30-39
4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female 25.0000 1 2 113781 151.5500 C22 C26 Southampton NaN Montreal, PQ / Chesterville, ON C mrs adult False 20-29
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 25, "text": [ " pclass survived name sex \\\n", "0 1 1 Allen, Miss. Elisabeth Walton female \n", "1 1 1 Allison, Master. Hudson Trevor male \n", "2 1 0 Allison, Miss. Helen Loraine female \n", "3 1 0 Allison, Mr. Hudson Joshua Creighton male \n", "4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female \n", "\n", " age sibsp parch ticket fare cabin embarked boat \\\n", "0 29.0000 0 0 24160 211.3375 B5 Southampton 2 \n", "1 0.9167 1 2 113781 151.5500 C22 C26 Southampton 11 \n", "2 2.0000 1 2 113781 151.5500 C22 C26 Southampton NaN \n", "3 30.0000 1 2 113781 151.5500 C22 C26 Southampton NaN \n", "4 25.0000 1 2 113781 151.5500 C22 C26 Southampton NaN \n", "\n", " home.dest cabin_type name_title age_cat alone \\\n", "0 St Louis, MO B miss adult True \n", "1 Montreal, PQ / Chesterville, ON C master children False \n", "2 Montreal, PQ / Chesterville, ON C miss children False \n", "3 Montreal, PQ / Chesterville, ON C mr adult False \n", "4 Montreal, PQ / Chesterville, ON C mrs adult False \n", "\n", " age_group \n", "0 20-29 \n", "1 0-9 \n", "2 0-9 \n", "3 30-39 \n", "4 20-29 " ] } ], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "pt_titanic3 = pd.pivot_table(titanic,\n", " index=[],\n", " values=[],\n", " aggfunc={\n", " },\n", " margins=True)\n", "female3 = pt_titanic3.query\n", "male3 = pt_titanic3.query\n", "fig = plt.figure(figsize=(10, 8))\n", "ax = fig.add_subplot\n", "ax.scatter(, , color='m')\n", "ax.scatter(, , color='b')\n", "ax.set_title(\"\")\n", "ax.set_xlabel(\"... (\u00a3)\")\n", "ax.set_ylabel(\"\")\n", "ax.legend([\"female\", \"male\"])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 26, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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VEZGtWLckSVUSEfjv6dgZ6vtZ7h9xUrVnTZIkqcIMa5IkSRVmWJMkSaoww5okSVKFGdYk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\nzMyIGLQLbc6cOSt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"text": [ "" ] } ], "prompt_number": 26 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Effectively, once grouped in groups of 10 years, the more expensive the fare, the more likely you were to survive. But only if you were a woman, and actually the trend gets stronger for older woman rather than for children. And for the males, it doesn't matter; if any, the trend seems to be the poorer the better your chances for survival.\n", "\n", "And now for a video." ] }, { "cell_type": "code", "collapsed": false, "input": [ "YouTubeVideo(\"FHG2oizTlpY\")" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", " \n", " " ], "metadata": {}, "output_type": "pyout", "prompt_number": 27, "text": [ "" ] } ], "prompt_number": 27 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Statistcs\n", "---------\n", "\n", "By applying the simple ordinary least squares we can see if these trends are actually significant." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import statsmodels.api as sm" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 28 }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we calculate the ordinary least squares for both, male and female, using the fare as our independent variable (X)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "female_model3 = sm.OLS(, )\n", "female_results3 = female_model3.fit()\n", "print(female_results3.summary())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: survived R-squared: 0.609\n", "Model: OLS Adj. R-squared: 0.544\n", "Method: Least Squares F-statistic: 9.335\n", "Date: Tue, 10 Feb 2015 Prob (F-statistic): 0.0224\n", "Time: 16:45:22 Log-Likelihood: -26.637\n", "No. Observations: 8 AIC: 57.27\n", "Df Residuals: 6 BIC: 57.43\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "const 57.7050 7.754 7.442 0.000 38.731 76.679\n", "fare 0.3644 0.119 3.055 0.022 0.073 0.656\n", "==============================================================================\n", "Omnibus: 1.994 Durbin-Watson: 2.682\n", "Prob(Omnibus): 0.369 Jarque-Bera (JB): 0.165\n", "Skew: 0.325 Prob(JB): 0.921\n", "Kurtosis: 3.269 Cond. No. 183.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "/home/versae/.venvs/dh2304/lib/python3.4/site-packages/scipy/stats/stats.py:1205: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=8\n", " int(n))\n" ] } ], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [ "male_model3 = sm.OLS(, )\n", "male_results3 = male_model3.fit()\n", "print(male_results3.summary())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: survived R-squared: 0.034\n", "Model: OLS Adj. R-squared: -0.126\n", "Method: Least Squares F-statistic: 0.2142\n", "Date: Tue, 10 Feb 2015 Prob (F-statistic): 0.660\n", "Time: 16:45:23 Log-Likelihood: -33.415\n", "No. Observations: 8 AIC: 70.83\n", "Df Residuals: 6 BIC: 70.99\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "const 27.6788 19.551 1.416 0.207 -20.160 75.517\n", "fare -0.2435 0.526 -0.463 0.660 -1.531 1.044\n", "==============================================================================\n", "Omnibus: 11.693 Durbin-Watson: 1.310\n", "Prob(Omnibus): 0.003 Jarque-Bera (JB): 3.872\n", "Skew: 1.484 Prob(JB): 0.144\n", "Kurtosis: 4.676 Cond. No. 113.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "prompt_number": 30 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we plot everything together." ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig, ax = plt.subplots(figsize=(12, 6))\n", "ax.plot(, , 'mo', label=\"Female\")\n", "ax.plot(female3[\"fare\"], female_results3.fittedvalues, 'r--.', label='$R^2$: {:.2}'.format(female_results3.rsquared))\n", "ax.plot(, , 'bo', label=\"Male\")\n", "ax.plot(male3[\"fare\"], male_results3.fittedvalues, 'g--.', label='$R^2$: {:.2}'.format(male_results3.rsquared))\n", "ax.legend(loc='best')\n", "ax.set_xlabel(\"... (\u00a3)\")\n", "ax.set_ylabel(\"\")\n", "ax.set_title(\"\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 31, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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revXqRZcuXQA4/fTTufLKK5mTNyVsiE2bNvHmm2/y2GOPUadOHZo1a8avfvUr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\nERERkUqs2FFEzGyJc+704HlNYL5zrls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"text": [ "" ] } ], "prompt_number": 31 }, { "cell_type": "markdown", "metadata": {}, "source": [ "And repeat the whole process for age group and proportion of survived." ] }, { "cell_type": "code", "collapsed": false, "input": [ "pt_titanic4 = pd.pivot_table(titanic,\n", " index=,\n", " values=,\n", " aggfunc=,\n", " margins=True)\n", "female4 = pt_titanic4.query\n", "male4 = pt_titanic4.query\n", "\n", "female_model4 = sm.OLS(, )\n", "female_results4 = female_model4.\n", "print(female_results4.)\n", "\n", "male_model4 = sm.OLS(, )\n", "male_results4 = male_model4.\n", "print(male_results4.)\n", "\n", "fig, ax = plt.subplots(figsize=(12, 6))\n", "ax.plot(, , 'mo', label=\"Female\")\n", "ax.plot(, , 'r--.', label='$R^2$: {:.2}'.format(female_results4.))\n", "ax.plot(, , 'bo', label=\"Male\")\n", "ax.plot(, , 'g--.', label='$R^2$: {:.2}'.format(male_results4.))\n", "ax.legend(loc='best')\n", "ax.set_xlabel()\n", "ax.set_ylabel()\n", "ax.set_title()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: survived R-squared: 0.801\n", "Model: OLS Adj. R-squared: 0.767\n", "Method: Least Squares F-statistic: 24.09\n", "Date: Tue, 10 Feb 2015 Prob (F-statistic): 0.00269\n", "Time: 16:45:23 Log-Likelihood: -23.941\n", "No. Observations: 8 AIC: 51.88\n", "Df Residuals: 6 BIC: 52.04\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "const 63.0402 3.950 15.959 0.000 53.375 72.706\n", "age 0.4267 0.087 4.908 0.003 0.214 0.639\n", "==============================================================================\n", "Omnibus: 0.953 Durbin-Watson: 3.483\n", "Prob(Omnibus): 0.621 Jarque-Bera (JB): 0.488\n", "Skew: -0.542 Prob(JB): 0.783\n", "Kurtosis: 2.461 Cond. No. 91.2\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: survived R-squared: 0.569\n", "Model: OLS Adj. R-squared: 0.498\n", "Method: Least Squares F-statistic: 7.937\n", "Date: Tue, 10 Feb 2015 Prob (F-statistic): 0.0305\n", "Time: 16:45:23 Log-Likelihood: -30.185\n", "No. Observations: 8 AIC: 64.37\n", "Df Residuals: 6 BIC: 64.53\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "const 40.7309 8.789 4.635 0.004 19.226 62.236\n", "age -0.5572 0.198 -2.817 0.030 -1.041 -0.073\n", "==============================================================================\n", "Omnibus: 1.502 Durbin-Watson: 2.045\n", "Prob(Omnibus): 0.472 Jarque-Bera (JB): 0.019\n", "Skew: -0.073 Prob(JB): 0.991\n", "Kurtosis: 3.186 Cond. No. 90.9\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 32, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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Yc845h+OPP57BgwcX2teoUaNo0aIFGRkZ7N69m/PPP582bdowevToEsUiIlKp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\nDvDqh6/iy/aR1zuPBEvQTjQx0EJPEW+pqBYREU9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"text": [ "" ] } ], "prompt_number": 32 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Conclusions\n", "-----------\n", "\n", "Who survived at the end? What about the correlation between fare and ratio of survival for women And men? Is OLS the best approach for men?\n", "\n", "What about class? And cabins?\n", "\n", "Had any age category better chances at sruvival? Which one(s)?\n", "\n", "Why? Did the children survive more than the older people?\n", "\n", "\n", "
\n", "\n", "*My Heart Will Go On*\n", "
" ] } ], "metadata": {} } ] }