data-science-ipython-notebooks/matplotlib/matplotlib.ipynb

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{
"metadata": {
"name": "",
"signature": "sha256:b9b2f9479b5476332be91a56ede4d0d4db39f54e70ab41f0b75e3698754f5d98"
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"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# matplotlib"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* 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",
"collapsed": false,
"input": [
"import pandas as pd\n",
"import numpy as np\n",
"import pylab as plt\n",
"import seaborn\n",
"\n",
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setting Global Parameters"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# 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()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Basic Plots"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"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()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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aRESkHTYUbuaFba8RFxnLtyfeSkxEjNMlSRuFeDwep2toD4/2GgKT9vgCm7Zf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\nRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10bbb9910>"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Histograms"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# 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()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(1000,)\n",
"float64\n",
"[ 1.44418744 0.46400374 1.25345292 1.97078609 0.80932915 0.09078554\n",
" 0.66967121 0.15271056 0.57636656 0.84077082 1.35157266 0.29905241\n",
" 1.38532784 1.80721741 0.4306797 0.43750271 0.6245115 1.06267641\n",
" 1.51031708 1.38542507 1.2802048 1.48447563 1.36070319 -0.0184091\n",
" 0.42067395 0.93246141 1.75653823 1.21452101 1.41877116 1.21999727]\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10bbccf50>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Two Histograms on the Same Plot"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"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()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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bYcLywLTz9u11cGsdBrUkaU2aGT2en59l18Bw3a0w1RyDWpK0Js2MHp8tHeYJ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\n",
"text": [
"<matplotlib.figure.Figure at 0x10bfb2390>"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Scatter Plots"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.scatter(samples_1, samples_2, alpha=0.1);\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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ZN1abaUOdjsqvftV96dS1et0mlwvI5YJ117oQb05awOKD8fyasWkrOCYt3J/g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\nHXQ9R6cDqdQOhcIa3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++QSIz5mZ+5S6fTxDQVarX8qUErm03SbnfR9RxRVCIIhjQaDaLI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\nIcQKSAAWQgghVkACsBBCCLEC/z9TMjwQmP3StgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10c222f90>"
]
}
],
"prompt_number": 9
},
{
"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",
"collapsed": false,
"input": [
"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 = 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 = 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[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)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bar Plots, Histograms, subplot2grid"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# 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')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"<matplotlib.text.Text at 0x10bf7ba50>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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2TEmSJNVgMCVJklSDwZQkSVINBlOSJEk1GExJkiTVYDAlSZJUgzOg97CIeCNl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\nkiRV8P8AUrk88f84hHIAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10be60210>"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Get the unique values of Embarked and its maximum\n",
"family_sizes = 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')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"<matplotlib.text.Text at 0x10bf0cb10>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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XM3zdq58ccpsX1jw7CpVs37Fzr7Ge9Q33+Lbh9h/fNtz+499zz49GoZJNdt11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\nJEkZMqAlScqQAS1JUoYMaEmSMvTfCghK2bXcxIIAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10bf60910>"
]
}
],
"prompt_number": 12
},
{
"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": 13,
"text": [
"<matplotlib.text.Text at 0x10d1a4a90>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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WJKliDG9JkirG8JYkqWIMb0mSKsbwliSpYgxvSZIqxvCWJKliDG9JkirG8JYk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\nAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10c536d90>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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pYgxvSZIqxvCWJKliDG9JkirG8JYkqWIMb0mSKsbwliSpYgxvSZIqxvCWJKli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\nJUmqGMNbkqSKMbwlSaoYw1uSpIr5/wDsnIBBppSpAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10c288f10>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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Mb0mSCmN4S5JUGMNbkqTCGN6SJBXG8JYkqTCGtyRJhTG8JUkqjOEtSVJhDG9J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\nvCVJKsz/BzBpRWazCFqKAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10ced51d0>"
]
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Scatter Plots, subplots"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# 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')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
"<matplotlib.text.Text at 0x10bf60690>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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Xqn29qhlJkoqrvn4R2dSY3fPbrXlMlVDxAi0iFkXE6ymlJrJi7ewuebwODKt0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\nTJ6DJkmSVBgLmiRJUmEsaJIkSYWxoEmSJBXGgiZ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"text": [
"<matplotlib.figure.Figure at 0x10c536250>"
]
}
],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Kernel Density Estimation Plots"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Get the unique values of Pclass:\n",
"passenger_classes = 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')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
"<matplotlib.legend.Legend at 0x10d4ad3d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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S9IVwQNeYvp0t/UxZqy+ESIwEfSEcEGpsxOXx2JKC19K16Y507wsh4iRBXwgH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\n9aQl2IgM8ogTcX8OXr0J+tIQaDvSqqp63pkm1MXFRRCq5fuyeCt/L/i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"text": [
"<matplotlib.figure.Figure at 0x10c2dae10>"
]
}
],
"prompt_number": 15
}
],
"metadata": {}
}
]
}