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

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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# matplotlib"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Setting Global Parameters\n",
      "* Bar Plots, Histograms, subplot2grid\n",
      "* Normalized Plots\n",
      "* Scatter Plots, subplots\n",
      "* Kernel Density Estimation Plots\n",
      "* Basic Plots\n",
      "* Histograms\n",
      "* Two Histograms on the Same Plot\n",
      "* Scatter 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\n",
      "\n",
      "seaborn.set()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Prepare 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": 2
    },
    {
     "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))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "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": 4,
       "text": [
        "<matplotlib.text.Text at 0x10bf0fed0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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        "<matplotlib.figure.Figure at 0x10bbc2b50>"
       ]
      }
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     "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": 5,
       "text": [
        "<matplotlib.text.Text at 0x10c679150>"
       ]
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       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAlgAAAFCCAYAAAAtw6O8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nD/+Q2267lba2V/jMZ67g0ksv
       "text": [
        "<matplotlib.figure.Figure at 0x10bbbb090>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "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 0x10ca22f50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAl8AAAFNCAYAAAA+SQoQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHeedN4t9990fgMmTt+IDH/gQ
       "text": [
        "<matplotlib.figure.Figure at 0x10c8a9f10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAl8AAAFVCAYAAADRzIrmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nO+yw44r9dt99T6677hoArr32
       "text": [
        "<matplotlib.figure.Figure at 0x10c928450>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAl8AAAFVCAYAAADRzIrmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOjo6uOyyH3DeeRfz1a+eVe7X
       "text": [
        "<matplotlib.figure.Figure at 0x10c9c2690>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "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": 7,
       "text": [
        "<matplotlib.text.Text at 0x10bfbe890>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAmgAAAJoCAYAAADS7x1JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXXdN4KGHDmP48OFVzqw22INW
       "text": [
        "<matplotlib.figure.Figure at 0x10c8a98d0>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "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": 8,
       "text": [
        "<matplotlib.legend.Legend at 0x10d0a7750>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAmwAAAFRCAYAAADJmfHLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfOnECV1eM2LESG6//W5uu+2W
       "text": [
        "<matplotlib.figure.Figure at 0x10bdc1ed0>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "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",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAmYAAAFRCAYAAADeu2ECAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n3hW+YcMMAL169ebgwYPs3r0L
       "text": [
        "<matplotlib.figure.Figure at 0x10c9ff410>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "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",
        "[ 0.78414546  0.7624507   0.79832022  1.43772119  0.6277384   0.95119108\n",
        "  0.86033247  2.05204185  1.34378449  0.51229619  2.08940893  1.96080879\n",
        "  0.90780563  0.82401283  0.63668015  1.50731702  1.52091633  0.59678776\n",
        "  0.5543333   0.67782493  0.64534513  0.87408671  0.99390343  1.81493002\n",
        "  0.46069816  1.31869943  1.26709913  1.96472827  0.97586513  0.96092536]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAlQAAAE5CAYAAABWGr4wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8eO3jKgyABgMgYqRmJ0/nAM3
       "text": [
        "<matplotlib.figure.Figure at 0x10d0b2d90>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "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": "iVBORw0KGgoAAAANSUhEUgAAAloAAAE5CAYAAABI046DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3X+UpFV95/F3d3V1T3d1dY8900iMgBLDFY9jBHWTGAN4\nMvJDyRETsierx0OQDOxEJRg2uBJPEl2GMYLZIwmbRNBFo5t1w4muhlVD1AUyG2VRohjw7uoCwjDM\nTP/ururu6q6q/aN7JiPzo57q6aequur9OodDd9W3n/72MzU9n7rPfe7tqlarSJIkaf11N7sBSZKk\ndmXQkiRJSolBS5IkKSUGLUmSpJQYtCRJklJi0JIkSUpJT62CEMLPAh+KMb4+hPAS4C6gAnwPeGeM\nsRpC2AFcDSwDN8UY7wkh9AOfBkaBWeCKGONYSj+HJElSyznhiFYI4QbgDqBv9aE/Bm6MMZ4HdAFv\nDiGcCrwbeC1wEbA7hNAL7AS+s1r7KeD96fwIkiRJranWpcMfAL/CSqgCODfGeP/qx18CtgOvAfbE\nGJdijDOrX/MK4BeAL6/Wfnm1VpIkqWOcMGjFGP+GlcuBh3Qd8fEsMAwMAdPHeXzmOY9JkiR1jJpz\ntJ6jcsTHQ8AUK2Eqf8Tj+WM8fuixE6pWq9Wurq5aZZKkDaRcLjM1VfOfgMM2b95MJpNJsSNp3dQM\nLfUGrYdDCOfHGO8DLgG+CjwI7Aoh9AGbgLNZmSi/B3gj8L9Xa+8/9iGP6Lari4MHZ+tsSSdjdDTv\nOW8wz3njec4b78hzPjExzt1//wgDg0M1v644N8Pl27cxMrIl7Rbbjq/zxhsdzdesSRq0Du08fT1w\nx+pk90eBu1fvOrwNeICVS5E3xhgXQwh/BnwyhPAAsAi8td4fQJLUHgYGhxjMb252G1LD1QxaMcYn\nWLmjkBjj/wUuOEbNncCdz3lsHvjX69GkJEnSRuSCpZIkSSkxaEmSJKXEoCVJkpQSg5YkSVJK6l3e\noanK5TLT08nXYklieNj1WiRJUjo2VNCanp5KvBZLEq7XIkn1S/Kmt7u7xMTEyppOk5OTVKvVE9ZL\n7WpDBS1oz7VY3vWuq7nhhhs5/fQXrfkYk5OT7Nx5FX/5l58lm82uX3OS9BxJ3vQODPRSLJYAGHv2\naQaHt5Jfn/fI0oay4YJWO1rZdmjtWw9985v/yJ//+Z8wNTWxfk1J0gnUetOby/XRnVkEoDA3fdw6\nqd0ZtGr40Y+eZPfuD5DJ9FCtVvmDP7iJrVtH+fCHd3HgwAHGx8d43evOY8eOneza9Yf09GTZv38f\npVKJ7dsvZM+eB9i//1l27/4I+/c/y2c/+xlKpRITExO85S2/ymWXXX74e83NzfGhD32QmZmVvbiv\nu+7fceaZL+Hmmz/A3r1Ps7i4yK/92q9z0UVv/LEeu7u7+ehH/4yrrnp7Q8+NJEk6MYNWDQ899CAv\ne9k2du58N9/97j8xNzdHpVLh5S/fxqWXXsbi4iK/+qtvYseOnXR1dfGCF7yA977397j11t3s27eP\nW275KB//+F+wZ88D/PRPn8X09DS3334HS0tLXHHFr3P++b+0+p2qfOpTn+DVr/5XXHbZ5Tz11I/Y\nvfuD3HrrbXznOw/zsY/dBcCDD37jqB5f85qfbdwJkSRJiRm0arj00jfzmc98kuuvv5bBwRzXXPNO\n8vk8jz32KN/+9rcYGMhRKi0drj/rrJcCMDiY54wzXgRAPj9EqbQyhP7KV55LJpMhk8lw5pk/xTPP\n7D38tY8//kMefvghvvrVewGYnZ1hYGCAa6+9nj/6o10UCgUuuuiSBv3kkiTpZG24oFWcm2nosR54\n4D5+5mfO4cord3DvvV/m05/+JGedFRgczPO7v3sjTz/9FF/84ucSf8/vf/9RABYWFnjiicc57bTT\nDj93+ukv4sILL+ENb7iYgwcPcO+9X2Z8fIwYH+Pmm29ZHT27lIsvfhPd3S6BJklSq9tQQWt4eDOX\nb9+27sc8kZe+9Gx27fpDstkslUqFa6/9HXp6snzgA+8nxsc49dSfIISzGRs7CBya2H60Q48XCgWu\nu+63mJ2d5corr2ZoaPhQBVdc8Q527/4PfOELn6NQKHDVVdewZctWJibG2bnzHXR3Z3jrW99+gpC1\n9gn1kiRp/XW12Nom1YMHZ5vdQ2q+/e2HuO++r/Ge99zQ7FYOGx3N087nvBV5zhvPc76+JibG+R/f\neLLmXYeFwsqUif37niST6WPrKafWPPbc7BRv/LkzXN9wDXydN97oaL7mCIfXnxqoq6vruCNekiSp\n/WyoS4cb3TnnvIpzznlVs9uQJEkN4oiWJElSSgxakiRJKdlQlw6TbGRar+HhzWQymXU9piRJEmyw\noDU9PcXnH/lbckOD63K8wswcl2271LtbJKlFVCplJicn6/oa3zCrlW2ooAWQGxpkcDjf7DbW1bve\ndTU33HAjp5/+ojV9/Wc/+5nDq8n//M//AldeuWMdu5OkxpkvznHPnnFGtp6SqL44N8Pl27f5hlkt\na8MFrXa0suTD2pZ92Lv3ae699yvccccn6erqYufOqzjvvNfzUz/1kvVtUpIaZCA3dMI1uqSNxKBV\nw49+9CS7d3+ATKaHarXKH/zBTWzdOsqHP7yLAwcOMD4+xutedx47duxk164/pKcny/79+yiVSmzf\nfiF79jzA/v3Psnv3R9i//1k++9nPUCqVmJiY4C1v+VUuu+zyw99rbm6OD33og8zMrGwNdN11/44z\nz3wJN9/8AfbufZrFxUV+7dd+nYsueuPhr3n+80/lj//4Tw6vz7W8vExfX19jT5IkSTomg1YNDz30\nIC972TZ27nw33/3uPzE3N0elUuHlL9/GpZdetrr/4JvYsWMnXV1dvOAFL+C97/09br11N/v27eOW\nWz7Kxz/+F+zZ8wA//dNnMT09ze2338HS0hJXXPHrnH/+L61+pyqf+tQnePWr/xWXXXY5Tz31I3bv\n/iC33nob3/nOw3zsY3cB8OCD3/ix/np6ehgaGqZarXL77R8lhJfywheehiRJaj6DVg2XXvpmPvOZ\nT3L99dcyOJjjmmveST6f57HHHuXb3/4WAwM5SqWlw/VnnfVSAAYH85xxxosAyOeHKJVWtqJ45SvP\nJZPJkMlkOPPMn+KZZ/Ye/trHH/8hDz/80OH5VrOzMwwMDHDttdfzR3+0i0KhwEUXXXJUj4uLi+ze\n/UEGBwe5/vp/n9apkCRJddpwQaswM9fQYz3wwH38zM+cw5VX7uDee7/Mpz/9Sc46KzA4mOd3f/dG\nnn76Kb74xc8l/p7f//6jACwsLPDEE49z2mn/Mvp0+ukv4sILL+ENb7iYgwcPcO+9X2Z8fIwYH+Pm\nm29ZHT27lIsvftPhjaWr1Srve9/1vOpVr+Ftb7uizjMgSSvqWT5ncnKSFtsnV2pZGypoDQ9v5rJt\nl677MU/kpS89m127/pBsNkulUuHaa3+Hnp4sH/jA+4nxMU499ScI4WzGxg4CHHcvw0OPFwoFrrvu\nt5idneXKK69maGj4UAVXXPEOdu/+D3zhC5+jUChw1VXXsGXLViYmxtm58x10d2d461vffjhkAdx/\n///kn/7pYZaXl/nGN/4XANdc8y5e/vJtJ3lmJHWS6ekp7v77RxgYHKpZO/bs0wwObyVfu1TqeF0t\n9q6k2s47j3/72w9x331f4z3vuaHZrRzmbu+N5zlvPM95bRMT4/yPbzyZ6G6//fueJJPpY+sppx63\nJpfro1BYTFxfz7GPNDc7xRt/7gyXd8DXeTOMjuZrLhngFjwN1NXVddwRL0mS1H421KXDje6cc17F\nOee8qtltSJKkBnFES5IkKSUGLUmSpJQYtCRJklJi0JIkSUqJQUuSJCklBi1JkqSUGLQkSZJSYtCS\nJElKiUFLkiQpJQYtSZKklBi0JEmSUmLQkiRJSolBS5IkKSUGLUmSpJQYtCRJklJi0JIkSUqJQUuS\nJCklBi1JkqSU9NT7BSGEbuBO
       "text": [
        "<matplotlib.figure.Figure at 0x10d500610>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "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": "iVBORw0KGgoAAAANSUhEUgAAAlQAAAE5CAYAAABWGr4wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nw9vu+Tvf01MuZ9D1MbZtkM2G
       "text": [
        "<matplotlib.figure.Figure at 0x10d20c2d0>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    }
   ],
   "metadata": {}
  }
 ]
}