spidezad/PercentStackPlot as Mosaic - example.ipynb

## PercentStackPlot as Mosaic - example.ipynb
{
  "cells": [
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import pandas as pd\nimport numpy as np\nimport os, sys, re, time, random\nimport matplotlib.pyplot as plt",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "#  Sample Data\nimport random\nr = [0,1,2,3,4]\nraw_data = {'g': random.sample(range(1,100),5), 'r': random.sample(range(1,100),5),'b': random.sample(range(1,100),5)}\ndf = pd.DataFrame(raw_data)\n\ndf.head()",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": "    b   g   r\n0  48  32   4\n1  29  70   5\n2  39  94  16\n3  77  44  36\n4  99  19  87",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>b</th>\n      <th>g</th>\n      <th>r</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>48</td>\n      <td>32</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>29</td>\n      <td>70</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>39</td>\n      <td>94</td>\n      <td>16</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>77</td>\n      <td>44</td>\n      <td>36</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>99</td>\n      <td>19</td>\n      <td>87</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "def mosaicplot(df, color_list,  x_label_suffix = None, figsize=(5,5), barwidth = 1,\n              title = \"Simple Mosaic Plot\", xlabel = 'cat'):\n    \"\"\" \n        Create mosaic plot based on stacked bar charts.\n        x axis will be taken from the dateframe index.\n        the category will be based on all the columns.        \n        \n        Args:\n            df (dataframe): Dataframe object with index (x) and the columns as the categories.\n            color_list (list): Color list for the different categories.\n            x_label_suffix (str): additional string to added to x axis beside the df index.\n            figsize (tuple): size of plots\n            barwidth(int): width of bar (relative to 1)\n    \"\"\"\n    \n    plt.figure(figsize =figsize)\n    \n    # Convert to ratio of each row sum and do a cumsum along row\n    p_cnt_ratio_df = df.div(df.sum(1), axis =0)\n    ratio_cum_df = p_cnt_ratio_df.cumsum(axis=1)\n    \n    # Get column order.\n    col_order = ratio_cum_df.columns.tolist()\n    \n    if x_label_suffix is None:\n        x_label_suffix = ''\n        \n    x = [x_label_suffix + str(n) for n in ratio_cum_df.index.tolist()]\n    \n    # Do the actual plots\n    for col, colour in zip(col_order[::-1], color_list):\n        plt.bar(x, ratio_cum_df[col], color=colour, edgecolor='white', width=barwidth, label=col)\n        \n    # Add in legend\n    plt.legend(loc='upper left', bbox_to_anchor=(1,1), ncol=1)\n\n    # Configure title and axis\n    plt.title(title)\n    plt.xlabel(xlabel)\n\n        \n        \ncolor_list = ['#f9bc86',  '#b5ffb9', '#a3acff' ]\nmosaicplot(df, color_list,  x_label_suffix = 'd', figsize=(5,5), barwidth = 1 )\nplt.title",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "<function matplotlib.pyplot.title(s, *args, **kwargs)>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 360x360 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.4",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "gist": {
      "id": "",
      "data": {
        "description": "PercentStackPlot as Mosaic - example.ipynb",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}
	{
	"cells": [
	{
	"metadata": {
	"trusted": true
	},
	"cell_type": "code",
	"source": "import pandas as pd\nimport numpy as np\nimport os, sys, re, time, random\nimport matplotlib.pyplot as plt",
	"execution_count": 1,
	"outputs": []
	},
	{
	"metadata": {
	"trusted": true
	},
	"cell_type": "code",
	"source": "# Sample Data\nimport random\nr = [0,1,2,3,4]\nraw_data = {'g': random.sample(range(1,100),5), 'r': random.sample(range(1,100),5),'b': random.sample(range(1,100),5)}\ndf = pd.DataFrame(raw_data)\n\ndf.head()",
	"execution_count": 2,
	"outputs": [
	{
	"output_type": "execute_result",
	"execution_count": 2,
	"data": {
	"text/plain": " b g r\n0 48 32 4\n1 29 70 5\n2 39 94 16\n3 77 44 36\n4 99 19 87",
	"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>b</th>\n <th>g</th>\n <th>r</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>48</td>\n <td>32</td>\n <td>4</td>\n </tr>\n <tr>\n <th>1</th>\n <td>29</td>\n <td>70</td>\n <td>5</td>\n </tr>\n <tr>\n <th>2</th>\n <td>39</td>\n <td>94</td>\n <td>16</td>\n </tr>\n <tr>\n <th>3</th>\n <td>77</td>\n <td>44</td>\n <td>36</td>\n </tr>\n <tr>\n <th>4</th>\n <td>99</td>\n <td>19</td>\n <td>87</td>\n </tr>\n </tbody>\n</table>\n</div>"
	},
	"metadata": {}
	}
	]
	},
	{
	"metadata": {
	"trusted": true
	},
	"cell_type": "code",
	"source": "def mosaicplot(df, color_list, x_label_suffix = None, figsize=(5,5), barwidth = 1,\n title = \"Simple Mosaic Plot\", xlabel = 'cat'):\n \"\"\" \n Create mosaic plot based on stacked bar charts.\n x axis will be taken from the dateframe index.\n the category will be based on all the columns. \n \n Args:\n df (dataframe): Dataframe object with index (x) and the columns as the categories.\n color_list (list): Color list for the different categories.\n x_label_suffix (str): additional string to added to x axis beside the df index.\n figsize (tuple): size of plots\n barwidth(int): width of bar (relative to 1)\n \"\"\"\n \n plt.figure(figsize =figsize)\n \n # Convert to ratio of each row sum and do a cumsum along row\n p_cnt_ratio_df = df.div(df.sum(1), axis =0)\n ratio_cum_df = p_cnt_ratio_df.cumsum(axis=1)\n \n # Get column order.\n col_order = ratio_cum_df.columns.tolist()\n \n if x_label_suffix is None:\n x_label_suffix = ''\n \n x = [x_label_suffix + str(n) for n in ratio_cum_df.index.tolist()]\n \n # Do the actual plots\n for col, colour in zip(col_order[::-1], color_list):\n plt.bar(x, ratio_cum_df[col], color=colour, edgecolor='white', width=barwidth, label=col)\n \n # Add in legend\n plt.legend(loc='upper left', bbox_to_anchor=(1,1), ncol=1)\n\n # Configure title and axis\n plt.title(title)\n plt.xlabel(xlabel)\n\n \n \ncolor_list = ['#f9bc86', '#b5ffb9', '#a3acff' ]\nmosaicplot(df, color_list, x_label_suffix = 'd', figsize=(5,5), barwidth = 1 )\nplt.title",
	"execution_count": 3,
	"outputs": [
	{
	"output_type": "execute_result",
	"execution_count": 3,
	"data": {
	"text/plain": "<function matplotlib.pyplot.title(s, args, *kwargs)>"
	},
	"metadata": {}
	},
	{
	"output_type": "display_data",
	"data": {
	"text/plain": "<Figure size 360x360 with 1 Axes>",
	"image/png": 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\n"
	},
	"metadata": {}
	}
	]
	}
	],
	"metadata": {
	"kernelspec": {
	"name": "python3",
	"display_name": "Python 3",
	"language": "python"
	},
	"language_info": {
	"name": "python",
	"version": "3.6.4",
	"mimetype": "text/x-python",
	"codemirror_mode": {
	"name": "ipython",
	"version": 3
	},
	"pygments_lexer": "ipython3",
	"nbconvert_exporter": "python",
	"file_extension": ".py"
	},
	"gist": {
	"id": "",
	"data": {
	"description": "PercentStackPlot as Mosaic - example.ipynb",
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