part-to-whole.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part-to-whole\n",
    "Show how a single entity can be broken down into its component elements. If the reader's interest is solely in the size of the components, consider a magnitude-type chart instead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "#ggplot equivalent: plotnine\n",
    "from plotnine import *\n",
    "#scales package equivalent: mizani\n",
    "from mizani.breaks import *\n",
    "from mizani.formatters import *\n",
    "import utils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stacked column\n",
    "\n",
    "A simple way of showing part-to-whole relationships but can be difficult to read with more than a few components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Region</th>\n",
       "      <th>Year</th>\n",
       "      <th>Sales_x</th>\n",
       "      <th>Sales_y</th>\n",
       "      <th>per</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>West</td>\n",
       "      <td>2018</td>\n",
       "      <td>250137.86</td>\n",
       "      <td>725385.27</td>\n",
       "      <td>0.344834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>West</td>\n",
       "      <td>2017</td>\n",
       "      <td>187458.30</td>\n",
       "      <td>725385.27</td>\n",
       "      <td>0.258426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>West</td>\n",
       "      <td>2016</td>\n",
       "      <td>139940.81</td>\n",
       "      <td>725385.27</td>\n",
       "      <td>0.192919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>West</td>\n",
       "      <td>2015</td>\n",
       "      <td>147848.30</td>\n",
       "      <td>725385.27</td>\n",
       "      <td>0.203820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>East</td>\n",
       "      <td>2018</td>\n",
       "      <td>213069.43</td>\n",
       "      <td>678781.24</td>\n",
       "      <td>0.313900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Region  Year    Sales_x    Sales_y       per\n",
       "0   West  2018  250137.86  725385.27  0.344834\n",
       "1   West  2017  187458.30  725385.27  0.258426\n",
       "2   West  2016  139940.81  725385.27  0.192919\n",
       "3   West  2015  147848.30  725385.27  0.203820\n",
       "4   East  2018  213069.43  678781.24  0.313900"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/marimekko.csv')\n",
    "df_agg = df[['Region','Sales']].groupby('Region').sum().reset_index()\n",
    "df =df.merge(df_agg,on='Region')\n",
    "df['per'] = df['Sales_x']/df['Sales_y']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223363305206076686)>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = (ggplot(df,aes(x='Region',y='per',fill='Year')) +\n",
    "     geom_col() + \n",
    "     scale_fill_gradient(low='blue',high='lightblue') +\n",
    "     theme(legend_title=element_blank(),axis_text_x=element_blank()) +\n",
    "     ylab('')\n",
    "    )\n",
    "g"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Marimekko\n",
    "A good way of showing the size and proportion of data at the same time - as long as the data are not too complicated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Region</th>\n",
       "      <th>Year</th>\n",
       "      <th>Sales_x</th>\n",
       "      <th>Sales_y</th>\n",
       "      <th>per</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>West</td>\n",
       "      <td>2018</td>\n",
       "      <td>250137.86</td>\n",
       "      <td>725385.27</td>\n",
       "      <td>0.344834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>West</td>\n",
       "      <td>2017</td>\n",
       "      <td>187458.30</td>\n",
       "      <td>725385.27</td>\n",
       "      <td>0.258426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>West</td>\n",
       "      <td>2016</td>\n",
       "      <td>139940.81</td>\n",
       "      <td>725385.27</td>\n",
       "      <td>0.192919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>West</td>\n",
       "      <td>2015</td>\n",
       "      <td>147848.30</td>\n",
       "      <td>725385.27</td>\n",
       "      <td>0.203820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>East</td>\n",
       "      <td>2018</td>\n",
       "      <td>213069.43</td>\n",
       "      <td>678781.24</td>\n",
       "      <td>0.313900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Region  Year    Sales_x    Sales_y       per\n",
       "0   West  2018  250137.86  725385.27  0.344834\n",
       "1   West  2017  187458.30  725385.27  0.258426\n",
       "2   West  2016  139940.81  725385.27  0.192919\n",
       "3   West  2015  147848.30  725385.27  0.203820\n",
       "4   East  2018  213069.43  678781.24  0.313900"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/marimekko.csv')\n",
    "df_agg = df[['Region','Sales']].groupby('Region').sum().reset_index()\n",
    "df =df.merge(df_agg,on='Region')\n",
    "df['per'] = df['Sales_x']/df['Sales_y']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223363305207179846)>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = (ggplot(df,aes(x='Region',y='per',fill='Year')) +\n",
    "     geom_col(width=0.99) + coord_flip() +\n",
    "     scale_fill_gradient(low='blue',high='lightblue') +\n",
    "     theme(legend_title=element_blank(),axis_text_x=element_blank()) +\n",
    "     ylab('')\n",
    "    )\n",
    "g"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ~~Pie~~\n",
    "~~A common way of showing part-to-whole data - but be aware that it's difficult to accurately compare the size of the segments.~~ It has been widely debated why we should not use pie chart such as [this thread](https://stats.stackexchange.com/questions/8974/problems-with-pie-charts). Use gridplot instead."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ~~Donut~~\n",
    "\n",
    "~~Similar to a pie chart - but the centre can be a good way of making space to include more information about the data (eg. total).~~ It has been widely debated why we should not use pie chart such as [this thread](https://stats.stackexchange.com/questions/8974/problems-with-pie-charts). Use gridplot instead."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Treemap\n",
    "\n",
    "Use for hierarchical part-to-whole relationships; can be difficult to read when there are many small segments. We use `squarify` instead of `plotnine`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting squarify\n",
      "  Downloading https://files.pythonhosted.org/packages/4e/f2/1f47d7f8c34e3c5f45de8b7fad06947558964e05ce6cccc94cf2066bd22b/squarify-0.4.2-py3-none-any.whl\n",
      "Installing collected packages: squarify\n",
      "Successfully installed squarify-0.4.2\n",
      "\u001b[33mYou are using pip version 19.0.1, however version 19.1.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# !pip install squarify\n",
    "import squarify"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Sales</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Technology</td>\n",
       "      <td>Phones</td>\n",
       "      <td>330007.05</td>\n",
       "      <td>Phones</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>328449.10</td>\n",
       "      <td>Chairs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>223843.61</td>\n",
       "      <td>Storage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>206965.53</td>\n",
       "      <td>Tables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>203412.73</td>\n",
       "      <td>Binders</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Category Sub-Category      Sales    label\n",
       "0        Technology       Phones  330007.05   Phones\n",
       "15        Furniture       Chairs  328449.10   Chairs\n",
       "5   Office Supplies      Storage  223843.61  Storage\n",
       "13        Furniture       Tables  206965.53   Tables\n",
       "10  Office Supplies      Binders  203412.73  Binders"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/treemap.csv').sort_values('Sales',ascending=False)\n",
    "df['label'] = None\n",
    "df.iloc[:10,3] = df.iloc[:10,1]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0fe4fc9a20>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(12,6))\n",
    "squarify.plot(label=df['label'],sizes=df.Sales,alpha=.6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Voronoi\n",
    "\n",
    "A way of turning points into areas - any point within the area is closer to the central point than any other point"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Arc\n",
    "\n",
    "A hemicycle, often used for visualising political results. But really, you do not need to curve it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Region</th>\n",
       "      <th>Sales</th>\n",
       "      <th>per</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>South</td>\n",
       "      <td>391721.90</td>\n",
       "      <td>0.170521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Central</td>\n",
       "      <td>501239.89</td>\n",
       "      <td>0.218196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>East</td>\n",
       "      <td>678781.24</td>\n",
       "      <td>0.295482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>West</td>\n",
       "      <td>725457.82</td>\n",
       "      <td>0.315801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Region      Sales       per\n",
       "0    South  391721.90  0.170521\n",
       "1  Central  501239.89  0.218196\n",
       "2     East  678781.24  0.295482\n",
       "3     West  725457.82  0.315801"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/ordered-bar.csv')\n",
    "df['per'] = df['Sales'] / df['Sales'].sum()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YQ9kf/+kxhxy3425tGudS76uSnDjG+sAIvOvYh2bL3GyS5BHP+p3c71FHb7PfNRsHYW16/wNu1n70Lz0zH33/u/P+U0/Os174siTJx04fnPjx6Cfd/LIxB95v8PVvP77+utE9AWjXuI6ZWMwLbh47hprX5KavV+MWxrnU+6YMvtB41P/uM8YxQ1Pe/av/LXObrk+SPPiXnp+HHPPc7fbd9OPB11HOX5pl3ot+7/VJkg+/551b2977jrcmSV7xprffrO/BD3xokuTGGxx7DSNwn4znfZZVbGwzftPT0zckuWHUdccxrQ0tOu3XHpkbr78mSfJfj35mHnnsS2+z/403DP6cD/mvP3ur+/bYa+9cu2D5duNVG3LXPW59VMYBBw1m/GZnZ3d63MBW1yzmsiyrg5M7oEHvec6jcsO1VydJ7vfzXY447tU7fMzmzXNJsvXSLAv9+osG1/I75aRX5LRT/jBJ8svPecF2a23ZvPkOjxmAO0/wg8a87wWPz4+vGpyosf+DH5XHv+TNt+txtxXWnvqswRLxpz/+kXzsA4Pj++aP99tmrbrl9g4XYGRKKUeUUs4spXyulHJ2KeVJd7LePUop+yzYPqmU8og7P9LxcR0/aMgn3vjCXHPZD5Mkdz/wkDz5Ne+63Y+tw7N+t2efe+6bKy67NEmybp977KjY7f65AKNQStkzye8nOabWelUpZU2S3e9k2Rcl+WSSK+7s+BaL4AcN+cH5X9x6+8dXXZm/Pu7Rt9n/Q5MlL3zl63PkUU/eYe2XvO5Ned2Ln50kecErT9puv1LKDkMkwBjcL8nZtdarkqTWujlJc8dICn7QkPnLtiTJdVf+aIf9r0syvf9Pbt0uZftXeTjssUclpSS15nG/+NTt9ttllzVbjxcEWEQXJDm8lLJPrXXrDF0ZvLC9NkmXZC7JpUleVGv9YSnlJ5O8stb6ggX9T8pglu+ZSZ6cpCulbEwyf4bcgaWU1yXZM4OZwOcs/HlLTfCDhrzoo/9+h/o/9ZB1W2+f+c1Ldtj/zH/f8Vn3//SNH9yhMQCMQq312lLKS5OcXkr5fJJTa60bkhyf5C5JHlZrraWUhyX5q1LKE3dQ77dKKVck+WSt9ctJUkr570meleTptdaNpZSnJ3l1ku0f9LzInNwBADSh1vq1WusTk3w1yQdKKYcl+aUkr6nDY1BqreckOTfJQ3fyx5xaa904rPWhJIfe+ZGPjhk/AKAptdZ/LqV8MclHk8wNj/db6JtJfjLJZbnj32Ry2S1/3E4NckzM+AEAzam1Xj+8OTE8w3ehQ5JclOSqJHe/xX33XFhmPKMbH8EPAFj1Sik/XUq5z4Ltx2TwfccfSfKG4UkeGR7j9+Ak/1JrvTrJ/qWUfYf33T/J4zI4JjBJfpTkvov3LO48S70AQAvukuSUUspUkh8n+c8MzsS9LoOzes8ppcxlEOaeU2+67tQrknyilHJdkv/I4Np98wHyw0n+tpTy3CRPWbRncicIfgDAqldr/VqSo7dz9+uH/7b1uM9mOyd61FqvTPLYBU0nbaPPUXdooGNmqRcAoBGCHwBAIwQ/AIBGCH4AAI0Q/AAAGiH4AQA0wuVcAIAl8afHHHJHvw6NO8mMHwBAIwQ/AIBGWOoFAJbEgS/7PxeOoey1F5589APGUHdVEPwAgKVywBhqXjOGmquGpV4AgEYIfgAAjRD8AAAaIfgBAKtaKeWcUsouC7Z3L6VcX0q5y4K2NaWUc3ey/lQp5T6jGOu4CX4AwGp3XpKFZ/o+PMmXkzxkQduDk3x1J+s/LcnhO/nYRSX4AQCr3aeTHLlg+5FJTh7+P+/IYb9VTfADAFa7zyR51ILtg5L8w/D/eYcP+6WUcmAp5YxSyudKKV8spWydzSulvKWU8tlSyldKKQ8tpbw6ySuTvG7Y/6jxP52d5zp+AMCqVmu9spSyRylll1rrlkFTraWUkgyO70uyW631qlLK2iTvSfLsWut3Syn3SPKRUsrjkhycZM9a66NLKRNJ1tRa/6WU8sMkm2qtH1yip3i7CX4AQAvOTfKAUso1Sb4zbPteKWX/JPdK8pVh2y8k+VCt9btJUmtdX0r5uwyOC/zPJAeWUu5Wa70yydyiPoMREPwAgBbMH+d3eZIvDdu+lMFxfvvnpuP7firJsaWUpy947F2TfLvW+qNSysuSvLGUsiHJ79dab1iMwY+KY/wAgBacneRhw3/zZ+9+NYOZvIdkcJZvkswk+cNa65EL/j281vqPSVJr/bda6wuT/N8kLxs+pi7Wk7izBD8AYNWrtd44vLlXrfXaYds1SfZOMldrnR3e//dJnl1K2W/+saWUXcvAVCllcth8ZZK9hrd/lOS+Y38SI2CpFwBoxbm5+Zm8STKb5Pz5jVrrxuFy7jtLKVMZHMe3PslzM7gW4B+VUm5IcnWS5w8fdmaSF5ZSzkzynFrr98b7NHae4AcANKHWevI22l6wjbYLkjxlGyXOTnLYNvrPbqf/smOpFwCgEYIfAEAjBD8AgEYIfgAAjRD8AAAaIfgBADRibJdzmZ2d3S/JfjvseAetW7du91HXBICVZGJiIlNTUw+cnZ29btS1Jycnzxt1ze258OSjy2L9LAbGeR2/45OcOOqiExMuPQhA29atW5ckZ42pvDC2io0zRb0rST/qonNzc7tnfL/sALDsbdiwIVNTU0dMTEyMfMaP1W1swW9ycvKSJJeMuu769ev3HHVNAFhJ5ubmsmHDhvOnp6c3LvVYWFmc3AEA0AjBDwCgEYIfAEAjBD8AgEYIfgAAjRD8AAAaIfgBADRC8AMAaITgBwDQCMEPAKARgh8AQCMEPwCARgh+AACNEPwAABoh+AEANELwAwBohOAHANAIwQ8AoBGCHwBAIwQ/AIBGCH4AAI0Q/AAAGiH4AQA0QvADAGiE4AcA0AjBDwCgEYIfAEAjBD8AgEYIfgAAjRD8AAAaIfgBADRC8AMAaITgBwDQCMEPAKARgh8AQCMEPwCARgh+AACNEPwAABoh+AEANELwAwBohOAHANAIwQ8AoBGCHwBAIybGVXhmZmZtkrVjKD01hpoAsNJMzczMjLzo9PT0xpEXZdkYW/BL8qokJ46xPgC07OIx1S1jqssyMM7g96YkJ4+h7lTG98sOACvFfZJcs9SDYGUZW/Cbnp6+IckNo647jmltAFiBrrEsyx3l5A4AgEYIfgAAjRD8AAAaIfgBADRC8AMAaITgBwDQCMEPAKARgh8AQCMEPwCARgh+AACNEPwAABoh+AEANELwAwBohOAHANAIwQ8AoBGCHwBAIwQ/AIBGCH4AAI0Q/AAAGiH4AQA0QvADAGiE4AcA0AjBDwCgEYIfAEAjBD8AgEYIfgAAjRD8AAAaIfgBADRC8AMAaITgBwDQCMEPAKARgh8AQCMEPwCARgh+AACNEPwAABoh+AEANELwAwBohOAHANAIwQ8AoBGCHwBAIwQ/AIBGCH4AAI2YGFfh2dnZ/ZLsN+q669at233UNQFgJZmYmMjU1NQDZ2dnrxt17cnJyfNGXZPlY2zBL8nxSU4cddGJiXEOGQCWv3Xr1iXJWWMqX8ZUl2VgnCnqXUn6URedm5vbPeP7ZQeAZW/Dhg2Zmpo6YmJiYuQzfqxuYwt+k5OTlyS5ZNR1169fv+eoawLASjI3N5cNGzacPz09vXGpx8LK4uQOAIBGCH4AAI0Q/AAAGiH4AQA0QvADAGiE4AcA0AjBDwCgEYIfAEAjBD8AgEYIfgAAjRD8AAAaIfgBADRC8AMAaITgBwDQCMEPAKARgh8AQCMEPwCARgh+AACNEPwAABoh+AEANELwAwBohOAHANAIwQ8AoBGCHwBAIwQ/AIBGlFrrUo/hDpmZmdkzydVJ9pqent641OMZtdnZ2QcnOTfJQyYnJ89b6vGM2mrff4l9uNKt9v2X2Icr3Wrff4yXGT8AgEYIfgAAjRD8AAAaIfgBADRC8AMAaITgBwDQCMEPAKARgh8AQCMEPwCARgh+AACNEPwAABoh+AEANELwAwBohOAHANAIwQ8AoBGCHwBAI0qtdanHAADAIjDjBwDQCMEPAKARgh8AQCMEPwCARgh+AACNEPwAABoh+AEANELwAwBohOAHANAIwQ8AoBGCHwBAIwQ/AIBGCH4AAI0Q/AAAGiH4AQA0QvADAGiE4AcA0AjBDwCgEYIfAEAjJpZ6ANvSdd3eSf46ybokFyV5XpK1SU5PsleSc/u+P2FB/3cmeWnf95sWfbBs1XXdTyX5swz21XVJXtz3/UVd130+SV3Q9di+72e6rntGkt9OsjnJC/q+/8awzsOTPKTv+1MX9xm0reu65yZ5WZL1w6b39X3/nq7rHp/kxCRrkryj7/vTh/0fneQPh31/r+/7zw7bfyLJcX3fn7ioT6BxXdf9lySn3aL5EUmmk7w9yYFJZoftL+v7/jz7cHnoum7fDP7enrCg7WeTnJLB6+nf9n1/8oL7fjXJC5OUJK/t+/7MYbvXVHZoWQa/JG9J8sa+78/puu7XkpyQ5NoMfvnf13Xdu7uuO7Tv+3/ruu7nk3xJ6FsW3p7kt/q+//bwjeNtSX45yea+7x+zsGPXdSWDF64jkuyXwT5/xvDu45K8aNFGzbypJK/q+76fb+i6bs8kv5PksUnmkny867qz+77/XpLfS/LEDEL93yb57PBhJyT5g8UcOEnf9/+Z5Mj57a7rDkvy/L7vr+y6birJk/q+33iLh9mHS6zruv2SvCfJPRe07ZLkj5Ic0/f9hq7r/rLrukf0ff/lrusOTNIleVSS3ZL8Q9d1X8ngw7bXVHZo2S31dl23Nsk+fd+fM2x6f5LHJPmZJGcN285Kcsjw9rOGfVh6n+j7/ttJ0vf9D5LsOWwv2+h79yQX9n0/2/f995PsniRd1x2d5NN9389u4zGM11QGH7AWenKS9/Z9f0Pf95szCPe/PLxvtu/7jX3fX5PkxiTpuu7+SS7u+37DYg2a7XpFklcOb++RW+/bxD5cDg5I8vwkly5oe2SSzyzYB2/O4L0uGYS5U/q+39L3/fUZrIQ9KV5TuZ2WXfBL8pAk/29+o+/7muTyDKau1wybJ5Js7rrufyQ5o+/7LYs+Sm6l7/t3zt8eLtfPL+9uK/gt3J/zj9klyVOTfGRcY+Q2TSW55hZtP5+bPnAlyReSHHYbNV6U5M9HPC7uoK7rHpbk+33fXzJsmrgDr5P24SLq+/6Lww/KC93s7274gfqnhpsPT/LVBX0/l8Hsn9dUbpfluNS7b27+ySdJLknynQxmH/44yS8keXUGS1Bv67rufcN+b+z7/j8Wa6Dcpj9KMh8ED+667tPD22/p+/6fh8tPPzlcgjowyRUZfKL9QJL/2XXdzyT5977v37zoI2/XVJJTuq7bksGbyeuT3CvJj+Y79H2/qeu6XYebpeu66eHtNcPDLr6S5Jld1x2Z5OIMjj+aW6Txc5NfT/KOBdvruq77VAbHi72r7/u/Gbbbh8vTtt4H54P7xHD2fd4lSe7lNZXbaznO+E1m8MllobkkX0xyaNd1ZyU5O4Pl37/MIGD8TgbHpPiFXga6rvuNJHft+/7jw6YX9n3/+CS/kuTlw4PQk+SkJH2SNyZ5UwYHos8lWdf3/a8lmeq67vDFHHvjPpbkCRnMNlyVwTLhmtz0hjNvfgb3lUneO/z3exm8yZyV5HF93z8ryYUZ7HMW3yG3+BB8Ut/3T8zgQ3PXdd0Rw3b7cHna1vvg/N/dLd+354b9E6+p3A7LMfhdksFZaAvdO8lM3/fP6/v+iCR/keTQvu+/kmRN3/fr+76/ItteUmQRDc8SfG4GZ2InSfq+P2P4/xUZzAI+drh9Zt/3j+77/hczeEM6NcnDknxq+NB/Gm6zCPq+/1Tf99f3fV+HZxAenuSyDGYfkmw9BvfGYf/z+75//DDUH5RBcHxQkvnZXftvCQwP/r9wYduCv8HrMviw/KThtn24PG3rfXD+/XpL13ULl3TvPezvNZXbZTkGv3MyOIYhydazP+/Z9/36BX1+O8mfDG8vfA43O76BxTU8KPytSZ7W9/2Pt9PtbkmuvMXj9k6yf9/3X89gH84fG1hjny6Jrut2S7Ipyeez4EzRDM4YPPsWfSeSPLHv+0/E/ltLKA0hAAADrElEQVQO7p/k/Nu4f1t/g/bh8nKzv7uu6w7K4HCnZHB8339b0Pcxw/5Z0N9rKtu17ILfMDCs77rukcOmZ+emT5/puu4eSfbu+/5bw6a1XdftNTyuYe3ijpZ5w0sSnJ7kGX3fX7ag/d4Lbh+U5Ncy+NS50AkZXK8qSb6ZwVJjMggZ3xzXmLlJ13X36Lpucnh7bQbXY/zrDGaAntt13W7DcHBCBvt5oeNy0/Xj7L+ld98Mju9Kcqu/wf0yWNL98C0eYx8uL/83yeO6rttnuP3qJH81vP3+DA6Z2aXrut2TPDPJP9zi8V5T2a7leHJHMrgMwXuHn1ouzIJlwwwuMHvKgu03ZjCNXTP442Bp/HIGF9z+i67rFrZ/p+u6B2Uww7AhyXP7vr96/s7hm9Iuw8sPJMk/Jjl2eCznxRkcp8L4PSDJm7uuuz6Di/ye1vf9x5Kk67q3JDkzg5mCU/q+v3j+QcM3nvvPn9Hd9/03uq67uuu6LyS5Osmxi/w8GJyk8+0F208fXvB3Q5Lrk7xieB3GJPbhUuu6bv8k70vygK7rPpfBSYqf7rrud5OcMfxA9sH5S5z1fX9h13UfzSAcJoMLb1+3oJ7XVG5TqbXuuBcAACveslvqBQBgPAQ/AIBGCH4AAI0Q/AAAGiH4AQA0QvADAGiE4AcA0AjBDwCgEYIfNK6Uckwp5ZxSytmllL8rpex1i/tPLqU8aqnGB8DoCH7QsFLKHknemuSoWuthSc7L4GsR5+9/YJKpWusXlmiIAIyQ4Adtu1+Sr9darxhufzzJQ5OklFIy+C7sVy3R2AAYMcEP2rY5ycSC7TXDtiR5fpIzaq2XL/qoABgLwQ/a9h9JDi6lHDDc/o0kXyil3DPJLyT5cinl70spZ5VS3lpK2XWpBgrAnSf4QcNqrZuS/HqSd5ZSzho2vyPJm5K8JskHk7ym1nrE8L6XLv4oARiVUmtd6jEAy0gp5cgkT0ryniR/Xmt97LD9gCTvXRACAVhhJnbcBWjFcCn3lUmeluTBSS5dcPePktxzKcYFwGhY6gUWenkGs3zXJbkyyT4L7rvbsA2AFUrwA5JsXco9tNbaD5u+leTAUsp9h9vPSvLZJRkcACNhqReY9+YMZvySJLXW2VLK85J8qJSyJckPkxy3VIMD4M5zcgcAQCMs9QIANELwAwBohOAHANAIwQ8AoBGCHwBAIwQ/AIBGCH4AAI0Q/AAAGiH4AQA0QvADAGiE4AcA0AjBDwCgEYIfAEAj/j+o++nse/wYeAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8731640306864)>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = (ggplot(df,aes(x=1,y='per',fill='Region')) +\n",
    "     geom_col() + coord_flip() +\n",
    "     scale_fill_brewer() +\n",
    "     geom_text(x=1,y=0.5,label='2M',size=20) +\n",
    "     theme(legend_title=element_blank(),\n",
    "           axis_text_y=element_blank()) +\n",
    "     xlab('') + ylab('%') +\n",
    "     scale_y_continuous(labels=percent_format())\n",
    "    )\n",
    "g"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gridplot\n",
    "\n",
    "Good for showing % information, they work best when used on whole numbers and work well in multiple layout form."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>filled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x  y  filled\n",
       "0  0  0       1\n",
       "1  0  1       1\n",
       "2  0  2       1\n",
       "3  0  3       1\n",
       "4  0  4       1"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = []\n",
    "for i in range(10):\n",
    "    for j in range(10):\n",
    "        df.append({'x':i,'y':j})\n",
    "df = pd.DataFrame(df)\n",
    "df['filled'] = 0\n",
    "df.iloc[:32,2] = 1\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223363305216057456)>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = (ggplot(df, aes(x='x',y='y',fill='factor(filled)')) +\n",
    "     geom_tile(alpha=0.5,color='black') + guides(fill=False) +\n",
    "     scale_fill_manual(['grey','blue']) + #manual fill\n",
    "     theme_void() + coord_flip() +#remove grid \n",
    "     geom_text(x=5,y=5,label=f'{int(100*df.filled.mean())}%',size=30,color='white') +\n",
    "     theme(axis_text=element_blank()) + xlab('') + ylab('') \n",
    "    )\n",
    "g"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Venn\n",
    "Generally only used for schematic representation. Not available in `ggplot` so we use `matplotlib_venn` instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install matplotlib-venn\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib_venn import venn2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First way to call the 2 group Venn diagram:\n",
    "venn2(subsets = (10, 5, 2), set_labels = ('Group A', 'Group B'))\n",
    "plt.show()\n",
    " \n",
    "# Second way\n",
    "venn2([set(['A', 'B', 'C', 'D']), set(['D', 'E', 'F'])])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Waterfall\n",
    "\n",
    "Can be useful for showing part-to-whole relationships where some of the components are negative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>variable</th>\n",
       "      <th>kind</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Revenue</td>\n",
       "      <td>net</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Other</td>\n",
       "      <td>out</td>\n",
       "      <td>-400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sales Expense</td>\n",
       "      <td>out</td>\n",
       "      <td>-2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>out</td>\n",
       "      <td>-600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Other Income</td>\n",
       "      <td>in</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Profit</td>\n",
       "      <td>net</td>\n",
       "      <td>1100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        variable kind  value\n",
       "0        Revenue  net   4000\n",
       "1          Other  out   -400\n",
       "2  Sales Expense  out  -2000\n",
       "3            R&D  out   -600\n",
       "4   Other Income   in    100\n",
       "5         Profit  net   1100"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'variable':['Revenue','Other','Sales Expense','R&D','Other Income','Profit'],\n",
    "                   'kind':['net','out','out','out','in','net'],\n",
    "                   'value':[4000,-400,-2000,-600,100,1100]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>variable</th>\n",
       "      <th>kind</th>\n",
       "      <th>value</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Revenue</td>\n",
       "      <td>net</td>\n",
       "      <td>4000</td>\n",
       "      <td>0</td>\n",
       "      <td>4000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Other</td>\n",
       "      <td>out</td>\n",
       "      <td>-400</td>\n",
       "      <td>4000</td>\n",
       "      <td>3600</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sales Expense</td>\n",
       "      <td>out</td>\n",
       "      <td>-2000</td>\n",
       "      <td>3600</td>\n",
       "      <td>1600</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>out</td>\n",
       "      <td>-600</td>\n",
       "      <td>1600</td>\n",
       "      <td>1000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Other Income</td>\n",
       "      <td>in</td>\n",
       "      <td>100</td>\n",
       "      <td>1000</td>\n",
       "      <td>1100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Profit</td>\n",
       "      <td>net</td>\n",
       "      <td>1100</td>\n",
       "      <td>0</td>\n",
       "      <td>1100</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        variable kind  value  start   end  id\n",
       "0        Revenue  net   4000      0  4000   1\n",
       "1          Other  out   -400   4000  3600   2\n",
       "2  Sales Expense  out  -2000   3600  1600   3\n",
       "3            R&D  out   -600   1600  1000   4\n",
       "4   Other Income   in    100   1000  1100   5\n",
       "5         Profit  net   1100      0  1100   6"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#order cats\n",
    "ordered_cats = df['variable']\n",
    "df['variable'] = pd.Categorical(df['variable'], categories=ordered_cats, ordered=True)\n",
    "#create the water fall\n",
    "df['start'] = 0\n",
    "df['end'] = 0\n",
    "df['id'] = range(1,df.shape[0]+1)\n",
    "for i in range(df.shape[0]):\n",
    "    if df.iloc[i,1]=='net':\n",
    "        df.iloc[i,4] = df.iloc[i,2] #set end\n",
    "    else:\n",
    "        df.iloc[i,3] = df.iloc[i-1,4] #set start equals to previous end\n",
    "        df.iloc[i,4] = df.iloc[i,3] + df.iloc[i,2] #set end equals to start + value\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8731640207210)>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = (ggplot(df,aes(fill='kind')) +\n",
    "     #draw rectangles\n",
    "     geom_rect(aes(x='variable',xmin='id-0.45',xmax='id+0.45',\n",
    "              ymin='end',ymax='start')) +\n",
    "     geom_text(aes(x='variable',y='start+100',label='value')) +\n",
    "     scale_fill_manual(values=['green','lightblue','red']) +\n",
    "     xlab('Things') + ylab('Amount') +\n",
    "     theme(legend_title=element_blank())\n",
    "    )\n",
    "g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}