data-science-ipython-notebooks/matplotlib/matplotlib.ipynb
2015-05-31 09:38:14 -04:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# matplotlib"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Credits: Some content forked from [Parallel Machine Learning with scikit-learn and IPython](https://github.com/ogrisel/parallel_ml_tutorial) by Olivier Grisel\n",
"\n",
"* Setting Global Parameters\n",
"* Basic Plots\n",
"* Histograms\n",
"* Two Histograms on the Same Plot\n",
"* Scatter Plots\n",
"* Applying Matplotlib Visualizations to Kaggle: Titanic\n",
"* Bar Plots, Histograms, subplot2grid\n",
"* Normalized Plots\n",
"* Scatter Plots, subplots\n",
"* Kernel Density Estimation Plots"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pandas as pd\n",
"import numpy as np\n",
"import pylab as plt\n",
"import seaborn"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setting Global Parameters"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Set the global default size of matplotlib figures\n",
"plt.rc('figure', figsize=(10, 5))\n",
"\n",
"# Set seaborn aesthetic parameters to defaults\n",
"seaborn.set()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Basic Plots"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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aRESkHTYUbuaFba8RFxnLtyfeSkxEjNMlSRuFeDwep2toD4/2GgKT9vgCm7Zf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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c45bad0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = np.linspace(0, 2, 10)\n",
"\n",
"plt.plot(x, x, 'o-', label='linear')\n",
"plt.plot(x, x ** 2, 'x-', label='quadratic')\n",
"\n",
"plt.legend(loc='best')\n",
"plt.title('Linear vs Quadratic progression')\n",
"plt.xlabel('Input')\n",
"plt.ylabel('Output');\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Histograms"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1000,)\n",
"float64\n",
"[ 0.6806888 0.72202042 1.40490113 1.13979846 0.5729488 1.32584077\n",
" 0.61635621 0.60340336 1.29453467 0.69841457 0.6975998 0.72315991\n",
" 0.66912189 1.03420801 0.62283168 0.38582511 0.89488414 1.4802518\n",
" 1.43819256 0.98605861 0.60402232 1.03820507 0.35598796 1.32901087\n",
" 1.03194436 1.3374366 1.82526334 1.26614489 1.20061661 0.86344001]\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x10c4c1510>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Gaussian, mean 1, stddev .5, 1000 elements\n",
"samples = np.random.normal(loc=1.0, scale=0.5, size=1000)\n",
"print(samples.shape)\n",
"print(samples.dtype)\n",
"print(samples[:30])\n",
"plt.hist(samples, bins=50);\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Two Histograms on the Same Plot"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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7DOdHGSns3u5mdDyDugsNjwx7tLxDrTXr2MmcdUzqLAa11GVOnnVsrecldQ6D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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c736a90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"samples_1 = np.random.normal(loc=1, scale=.5, size=10000)\n",
"samples_2 = np.random.standard_t(df=10, size=10000)\n",
"bins = np.linspace(-3, 3, 50)\n",
"\n",
"# Set an alpha and use the same bins since we are plotting two hists\n",
"plt.hist(samples_1, bins=bins, alpha=0.5, label='samples 1')\n",
"plt.hist(samples_2, bins=bins, alpha=0.5, label='samples 2')\n",
"plt.legend(loc='upper left');\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Scatter Plots"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Q9TKmWaBeLy03ulAJQ4+tLR3fDxgMJCqVFmmqkmUpo9GQIGiSJ4H1aTTqQEi/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\nxBL5/wH5igtfVA4yZQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c514950>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(samples_1, samples_2, alpha=0.1);\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Applying Matplotlib Visualizations to Kaggle: Titanic"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Prepare the titanic data to plot:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df_train = pd.read_csv('../data/titanic/train.csv')\n",
"\n",
"def clean_data(df):\n",
" \n",
" # Get the unique values of Sex\n",
" sexes = np.sort(df['Sex'].unique())\n",
" \n",
" # Generate a mapping of Sex from a string to a number representation \n",
" genders_mapping = dict(zip(sexes, range(0, len(sexes) + 1)))\n",
"\n",
" # Transform Sex from a string to a number representation\n",
" df['Sex_Val'] = df['Sex'].map(genders_mapping).astype(int)\n",
" \n",
" # Get the unique values of Embarked\n",
" embarked_locs = np.sort(df['Embarked'].unique())\n",
"\n",
" # Generate a mapping of Embarked from a string to a number representation \n",
" embarked_locs_mapping = dict(zip(embarked_locs, \n",
" range(0, len(embarked_locs) + 1)))\n",
" \n",
" # Transform Embarked from a string to dummy variables\n",
" df = pd.concat([df, pd.get_dummies(df['Embarked'], prefix='Embarked_Val')], axis=1)\n",
" \n",
" # Fill in missing values of Embarked\n",
" # Since the vast majority of passengers embarked in 'S': 3, \n",
" # we assign the missing values in Embarked to 'S':\n",
" if len(df[df['Embarked'].isnull()] > 0):\n",
" df.replace({'Embarked_Val' : \n",
" { embarked_locs_mapping[np.nan] : embarked_locs_mapping['S'] \n",
" }\n",
" }, \n",
" inplace=True)\n",
" \n",
" # Fill in missing values of Fare with the average Fare\n",
" if len(df[df['Fare'].isnull()] > 0):\n",
" avg_fare = df['Fare'].mean()\n",
" df.replace({ None: avg_fare }, inplace=True)\n",
" \n",
" # To keep Age in tact, make a copy of it called AgeFill \n",
" # that we will use to fill in the missing ages:\n",
" df['AgeFill'] = df['Age']\n",
"\n",
" # Determine the Age typical for each passenger class by Sex_Val. \n",
" # We'll use the median instead of the mean because the Age \n",
" # histogram seems to be right skewed.\n",
" df['AgeFill'] = df['AgeFill'] \\\n",
" .groupby([df['Sex_Val'], df['Pclass']]) \\\n",
" .apply(lambda x: x.fillna(x.median()))\n",
" \n",
" # Define a new feature FamilySize that is the sum of \n",
" # Parch (number of parents or children on board) and \n",
" # SibSp (number of siblings or spouses):\n",
" df['FamilySize'] = df['SibSp'] + df['Parch']\n",
" \n",
" return df\n",
"\n",
"df_train = clean_data(df_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bar Plots, Histograms, subplot2grid"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10d8a2d90>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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RnqlbgNsAMvPWiPgDsFfD6/OBu4A1QF/D9j5gcKxCFy6cy4wZ07e6wq0wODivI8edLBYt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\nkiRV8P8AUrk88f84hHIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10ce4af10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Size of matplotlib figures that contain subplots\n",
"figsize_with_subplots = (10, 10)\n",
"\n",
"# Set up a grid of plots\n",
"fig = plt.figure(figsize=figsize_with_subplots) \n",
"fig_dims = (3, 2)\n",
"\n",
"# Plot death and survival counts\n",
"plt.subplot2grid(fig_dims, (0, 0))\n",
"df_train['Survived'].value_counts().plot(kind='bar', \n",
" title='Death and Survival Counts',\n",
" color='r',\n",
" align='center')\n",
"\n",
"# Plot Pclass counts\n",
"plt.subplot2grid(fig_dims, (0, 1))\n",
"df_train['Pclass'].value_counts().plot(kind='bar', \n",
" title='Passenger Class Counts')\n",
"\n",
"# Plot Sex counts\n",
"plt.subplot2grid(fig_dims, (1, 0))\n",
"df_train['Sex'].value_counts().plot(kind='bar', \n",
" title='Gender Counts')\n",
"plt.xticks(rotation=0)\n",
"\n",
"# Plot Embarked counts\n",
"plt.subplot2grid(fig_dims, (1, 1))\n",
"df_train['Embarked'].value_counts().plot(kind='bar', \n",
" title='Ports of Embarkation Counts')\n",
"\n",
"# Plot the Age histogram\n",
"plt.subplot2grid(fig_dims, (2, 0))\n",
"df_train['Age'].hist()\n",
"plt.title('Age Histogram')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10dd37850>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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XM3zdq58ccpsX1jw7CpVs37Fzr7Ge9Q33+Lbh9h/fNtz+499zz49GoZJNdt11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\nJEkZMqAlScqQAS1JUoYMaEmSMvTfCghK2bXcxIIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10cb96650>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Get the unique values of Embarked and its maximum\n",
"family_sizes = np.sort(df_train['FamilySize'].unique())\n",
"family_size_max = max(family_sizes)\n",
"\n",
"df1 = df_train[df_train['Survived'] == 0]['FamilySize']\n",
"df2 = df_train[df_train['Survived'] == 1]['FamilySize']\n",
"plt.hist([df1, df2], \n",
" bins=family_size_max + 1, \n",
" range=(0, family_size_max), \n",
" stacked=True)\n",
"plt.legend(('Died', 'Survived'), loc='best')\n",
"plt.title('Survivors by Family Size')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Normalized Plots"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10ca8e490>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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G2Xrrbfjc575ET0/POm3X8JYkqUH23fcvGTNmzDpv1/CWJKliDG9JkirGE9YkSRuMrvb+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\nliSpYgxvSZIqxvC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"text/plain": [
"<matplotlib.figure.Figure at 0x100532f90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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pYgxvSZIqxvCWJKliDG9JkirG8JYkqWIMb0mSKsbwliSpYgxvSZIqxvCWJKli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\nJUmqGMNbkqSKMbwlSaoYw1uSpIr5/wDsnIBBppSpAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10cded610>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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Mb0mSCmN4S5JUGMNbkqTCGN6SJBXG8JYkqTCGtyRJhTG8JUkqjOEtSVJhDG9J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\nvCVJKsz/BzBpRWazCFqKAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10ded4b50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"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')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Scatter Plots, subplots"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10c4cc150>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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H9uudl19+iS22uJHW1nEANDScx6OPHsI666xb5cxWPs3NTXXLf9bbuZNAjfCK0JKk3hg9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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10e046810>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Set up a grid of plots\n",
"fig, axes = plt.subplots(2, 1, figsize=figsize_with_subplots)\n",
"\n",
"# Histogram of AgeFill segmented by Survived\n",
"df1 = df_train[df_train['Survived'] == 0]['Age']\n",
"df2 = df_train[df_train['Survived'] == 1]['Age']\n",
"max_age = max(df_train['AgeFill'])\n",
"\n",
"axes[1].hist([df1, df2], \n",
" bins=max_age / 10, \n",
" range=(1, max_age), \n",
" stacked=True)\n",
"axes[1].legend(('Died', 'Survived'), loc='best')\n",
"axes[1].set_title('Survivors by Age Groups Histogram')\n",
"axes[1].set_xlabel('Age')\n",
"axes[1].set_ylabel('Count')\n",
"\n",
"# Scatter plot Survived and AgeFill\n",
"axes[0].scatter(df_train['Survived'], df_train['AgeFill'])\n",
"axes[0].set_title('Survivors by Age Plot')\n",
"axes[0].set_xlabel('Survived')\n",
"axes[0].set_ylabel('Age')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Kernel Density Estimation Plots"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x10c93f450>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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S9IVwQNeYvp0t/UxZqy+ESIwEfSEcEGpsxOXx2JKC19K16Y507wsh4iRBXwgH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"text/plain": [
"<matplotlib.figure.Figure at 0x10e0a96d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Get the unique values of Pclass:\n",
"passenger_classes = np.sort(df_train['Pclass'].unique())\n",
"\n",
"for pclass in passenger_classes:\n",
" df_train.AgeFill[df_train.Pclass == pclass].plot(kind='kde')\n",
"plt.title('Age Density Plot by Passenger Class')\n",
"plt.xlabel('Age')\n",
"plt.legend(('1st Class', '2nd Class', '3rd Class'), loc='best')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}