2015-04-06 20:51:28 +08:00
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"metadata": {
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"name": "",
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"signature": "sha256:08003effc72ed7242f0098f883755ff31312100e58bf63705b8a1b8c45afc8a4"
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"nbformat": 3,
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"nbformat_minor": 0,
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"worksheets": [
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# matplotlib"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"* Setting Global Parameters\n",
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"* Bar Plots, Histograms, subplot2grid\n",
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"* Normalized Plots\n",
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"* Scatter Plots, subplots\n",
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"* Kernel Density Estimation Plots"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import pylab as plt"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 1
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Prepare data to plot:"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"df_train = pd.read_csv('../data/titanic/train.csv')\n",
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"\n",
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"def clean_data(df):\n",
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" \n",
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" # Get the unique values of Sex\n",
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" sexes = sort(df['Sex'].unique())\n",
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" \n",
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" # Generate a mapping of Sex from a string to a number representation \n",
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" genders_mapping = dict(zip(sexes, range(0, len(sexes) + 1)))\n",
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"\n",
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" # Transform Sex from a string to a number representation\n",
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" df['Sex_Val'] = df['Sex'].map(genders_mapping).astype(int)\n",
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" \n",
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" # Get the unique values of Embarked\n",
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" embarked_locs = sort(df['Embarked'].unique())\n",
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"\n",
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" # Generate a mapping of Embarked from a string to a number representation \n",
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" embarked_locs_mapping = dict(zip(embarked_locs, \n",
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" range(0, len(embarked_locs) + 1)))\n",
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" \n",
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" # Transform Embarked from a string to dummy variables\n",
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" df = pd.concat([df, pd.get_dummies(df['Embarked'], prefix='Embarked_Val')], axis=1)\n",
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" \n",
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" # Fill in missing values of Embarked\n",
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" # Since the vast majority of passengers embarked in 'S': 3, \n",
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" # we assign the missing values in Embarked to 'S':\n",
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" if len(df[df['Embarked'].isnull()] > 0):\n",
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" df.replace({'Embarked_Val' : \n",
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" { embarked_locs_mapping[nan] : embarked_locs_mapping['S'] \n",
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" }\n",
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" }, \n",
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" inplace=True)\n",
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" \n",
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" # Fill in missing values of Fare with the average Fare\n",
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" if len(df[df['Fare'].isnull()] > 0):\n",
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" avg_fare = df['Fare'].mean()\n",
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" df.replace({ None: avg_fare }, inplace=True)\n",
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" \n",
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" # To keep Age in tact, make a copy of it called AgeFill \n",
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" # that we will use to fill in the missing ages:\n",
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" df['AgeFill'] = df['Age']\n",
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"\n",
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" # Determine the Age typical for each passenger class by Sex_Val. \n",
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" # We'll use the median instead of the mean because the Age \n",
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" # histogram seems to be right skewed.\n",
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" df['AgeFill'] = df['AgeFill'] \\\n",
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" .groupby([df['Sex_Val'], df['Pclass']]) \\\n",
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" .apply(lambda x: x.fillna(x.median()))\n",
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" \n",
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" # Define a new feature FamilySize that is the sum of \n",
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" # Parch (number of parents or children on board) and \n",
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" # SibSp (number of siblings or spouses):\n",
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" df['FamilySize'] = df['SibSp'] + df['Parch']\n",
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" \n",
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" return df\n",
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"\n",
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"df_train = clean_data(df_train)"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 2
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Setting Global Parameters"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"# Set the global default size of matplotlib figures\n",
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"plt.rc('figure', figsize=(10, 5))"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 3
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2015-04-06 20:52:43 +08:00
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Bar Plots, Histograms, subplot2grid"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"# Size of matplotlib figures that contain subplots\n",
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"figsize_with_subplots = (10, 10)\n",
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"\n",
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"# Set up a grid of plots\n",
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"fig = plt.figure(figsize=figsize_with_subplots) \n",
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"fig_dims = (3, 2)\n",
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"\n",
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"# Plot death and survival counts\n",
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"plt.subplot2grid(fig_dims, (0, 0))\n",
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"df_train['Survived'].value_counts().plot(kind='bar', \n",
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" title='Death and Survival Counts',\n",
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" color='r',\n",
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" align='center')\n",
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"\n",
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"# Plot Pclass counts\n",
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"plt.subplot2grid(fig_dims, (0, 1))\n",
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"df_train['Pclass'].value_counts().plot(kind='bar', \n",
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" title='Passenger Class Counts')\n",
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"\n",
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"# Plot Sex counts\n",
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"plt.subplot2grid(fig_dims, (1, 0))\n",
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"df_train['Sex'].value_counts().plot(kind='bar', \n",
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" title='Gender Counts')\n",
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"plt.xticks(rotation=0)\n",
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"\n",
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"# Plot Embarked counts\n",
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"plt.subplot2grid(fig_dims, (1, 1))\n",
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"df_train['Embarked'].value_counts().plot(kind='bar', \n",
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" title='Ports of Embarkation Counts')\n",
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"\n",
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"# Plot the Age histogram\n",
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"plt.subplot2grid(fig_dims, (2, 0))\n",
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"df_train['Age'].hist()\n",
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"plt.title('Age Histogram')"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 4,
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"text": [
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"<matplotlib.text.Text at 0x10a5bfe10>"
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]
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},
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{
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"metadata": {},
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"output_type": "display_data",
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"text": [
|
|
|
|
|
"<matplotlib.figure.Figure at 0x1057ff910>"
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|
]
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}
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],
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"prompt_number": 4
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"# Get the unique values of Embarked and its maximum\n",
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|
|
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|
"family_sizes = sort(df_train['FamilySize'].unique())\n",
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|
|
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"family_size_max = max(family_sizes)\n",
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"\n",
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"df1 = df_train[df_train['Survived'] == 0]['FamilySize']\n",
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"df2 = df_train[df_train['Survived'] == 1]['FamilySize']\n",
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"plt.hist([df1, df2], \n",
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" bins=family_size_max + 1, \n",
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" range=(0, family_size_max), \n",
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" stacked=True)\n",
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"plt.legend(('Died', 'Survived'), loc='best')\n",
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"plt.title('Survivors by Family Size')"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 5,
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"text": [
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"<matplotlib.text.Text at 0x10a896c50>"
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]
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},
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{
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"metadata": {},
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"output_type": "display_data",
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"png": "iVBORw0KGgoAAAANSUhEUgAAAlcAAAFCCAYAAADcyPgxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n9957L5nJH/7wB2655RbWrVvH
|
|
|
|
|
"text": [
|
|
|
|
|
"<matplotlib.figure.Figure at 0x1057fcf90>"
|
|
|
|
|
]
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"prompt_number": 5
|
2015-04-06 20:54:12 +08:00
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"source": [
|
|
|
|
|
"## Normalized Plots"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"collapsed": false,
|
|
|
|
|
"input": [
|
|
|
|
|
"pclass_xt = pd.crosstab(df_train['Pclass'], df_train['Survived'])\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"# Normalize the cross tab to sum to 1:\n",
|
|
|
|
|
"pclass_xt_pct = pclass_xt.div(pclass_xt.sum(1).astype(float), axis=0)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"pclass_xt_pct.plot(kind='bar', \n",
|
|
|
|
|
" stacked=True, \n",
|
|
|
|
|
" title='Survival Rate by Passenger Classes')\n",
|
|
|
|
|
"plt.xlabel('Passenger Class')\n",
|
|
|
|
|
"plt.ylabel('Survival Rate')\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"# Plot survival rate by Sex\n",
|
|
|
|
|
"females_df = df_train[df_train['Sex'] == 'female']\n",
|
|
|
|
|
"females_xt = pd.crosstab(females_df['Pclass'], df_train['Survived'])\n",
|
|
|
|
|
"females_xt_pct = females_xt.div(females_xt.sum(1).astype(float), axis=0)\n",
|
|
|
|
|
"females_xt_pct.plot(kind='bar', \n",
|
|
|
|
|
" stacked=True, \n",
|
|
|
|
|
" title='Female Survival Rate by Passenger Class')\n",
|
|
|
|
|
"plt.xlabel('Passenger Class')\n",
|
|
|
|
|
"plt.ylabel('Survival Rate')\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"# Plot survival rate by Pclass\n",
|
|
|
|
|
"males_df = df_train[df_train['Sex'] == 'male']\n",
|
|
|
|
|
"males_xt = pd.crosstab(males_df['Pclass'], df_train['Survived'])\n",
|
|
|
|
|
"males_xt_pct = males_xt.div(males_xt.sum(1).astype(float), axis=0)\n",
|
|
|
|
|
"males_xt_pct.plot(kind='bar', \n",
|
|
|
|
|
" stacked=True, \n",
|
|
|
|
|
" title='Male Survival Rate by Passenger Class')\n",
|
|
|
|
|
"plt.xlabel('Passenger Class')\n",
|
|
|
|
|
"plt.ylabel('Survival Rate')"
|
|
|
|
|
],
|
|
|
|
|
"language": "python",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "pyout",
|
|
|
|
|
"prompt_number": 6,
|
|
|
|
|
"text": [
|
|
|
|
|
"<matplotlib.text.Text at 0x10aea5350>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data",
|
|
|
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{
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"text": [
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"<matplotlib.figure.Figure at 0x10ae36ad0>"
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]
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}
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],
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"prompt_number": 6
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2015-04-06 20:51:28 +08:00
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}
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],
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"metadata": {}
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}
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]
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}
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