plot.ipynb

plot.ipynb

{
  "cells": [
    {
      "metadata": {
        "toc": "true"
      },
      "cell_type": "markdown",
      "source": "# Table of Contents\n <p><div class=\"lev1 toc-item\"><a href=\"#Plot\" data-toc-modified-id=\"Plot-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Plot</a></div>"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Plot\n\nhttps://drive.google.com/file/d/0B6g9zQtBhHH4WFJPQnBNbTFLREU/preview"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "%matplotlib inline\n%load_ext watermark\n%config InlineBackend.figure_format = 'retina'\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n%watermark -a 'Ethen' -d -t -v -p pandas,matplotlib ",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Ethen 2017-10-25 21:38:59 \n\nCPython 3.5.2\nIPython 6.2.1\n\npandas 0.20.3\nmatplotlib 2.1.0\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# stacked bar chart requires the differences\n# between the values, e.g. For Monday, B's value\n# is around 11, but the value for it will be\n# 11 - 3 (A's value)\nA_values = 3, 10, 21, 2, 5\nB_values = 8, 5, 4, 0.5, 2\nC_values = 12, 16, 1, 19.5, 5\nD_values = 15, 5, 5, 15, 6\nE_values = 1, 12, 1.5, 15, 6\ndf = pd.DataFrame({\n    'A': A_values,\n    'B': B_values,\n    'C': C_values,\n    'D': D_values,\n    'E': E_values})\n\nfontsize = 12\nticklabels = 'M', 'T', 'W', 'Th', 'F'\ncolor = '#3D50BD', '#8EB2FB', '#DDDCDC', '#F2987D', '#B20B2B'\nax = df.plot(\n    kind = 'bar', stacked = True,\n    fontsize = fontsize, figsize = (10, 8),\n    title = 'user activity', color = color,\n    edgecolor = ['black'] * len(color))\nax.grid(linestyle = 'dashed')\nax.set_yticks([0, 10, 20, 30, 40, 50, 60])\nax.set_xticklabels(ticklabels, rotation = 'horizontal')\nax.legend(fontsize = fontsize)\nplt.show()",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {
            "image/png": {
              "height": 483,
              "width": 596
            }
          },
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x111d5a080>",
            "image/png": 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FnxheuCxJMjTmtqTwZlJ79KqmaaojzKq3ve1t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