Plotnine/geom_col.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "execution": {
     "iopub.execute_input": "2021-03-04T13:02:10.366323Z",
     "iopub.status.busy": "2021-03-04T13:02:10.364198Z",
     "iopub.status.idle": "2021-03-04T13:02:15.343399Z",
     "shell.execute_reply": "2021-03-04T13:02:15.345889Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from plotnine import *\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Two Variable Bar Plot\n",
    "\n",
    "*Visualising on a single plot the values of a variable that has nested (and independent) variables*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-04T13:02:15.385129Z",
     "iopub.status.busy": "2021-03-04T13:02:15.382045Z",
     "iopub.status.idle": "2021-03-04T13:02:15.419365Z",
     "shell.execute_reply": "2021-03-04T13:02:15.420870Z"
    }
   },
   "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>category</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gender</td>\n",
       "      <td>Female</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>gender</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>age</td>\n",
       "      <td>1-24</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>age</td>\n",
       "      <td>25-54</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>age</td>\n",
       "      <td>55+</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>income</td>\n",
       "      <td>Lo</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>income</td>\n",
       "      <td>Lo-Med</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>income</td>\n",
       "      <td>Med</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>income</td>\n",
       "      <td>High</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  variable category  value\n",
       "0   gender   Female     60\n",
       "1   gender     Male     40\n",
       "2      age     1-24     50\n",
       "3      age    25-54     30\n",
       "4      age      55+     20\n",
       "5   income       Lo     10\n",
       "6   income   Lo-Med     25\n",
       "7   income      Med     25\n",
       "8   income     High     40"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    'variable': ['gender', 'gender', 'age', 'age', 'age', 'income', 'income', 'income', 'income'],\n",
    "    'category': ['Female', 'Male', '1-24', '25-54', '55+', 'Lo', 'Lo-Med', 'Med', 'High'],\n",
    "    'value': [60, 40, 50, 30, 20, 10, 25, 25, 40],\n",
    "})\n",
    "df['variable'] = pd.Categorical(df['variable'], categories=['gender', 'age', 'income'])\n",
    "df['category'] = pd.Categorical(df['category'], categories=df['category'])\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to visualise this data and at a galance get an idea to how the `value` breaks down along the `category`s for the different `variable`.\n",
    "Note that each `variable` has different `category`s.\n",
    "\n",
    "First we make a simple plot with all this information and see what to draw from it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-04T13:02:15.475700Z",
     "iopub.status.busy": "2021-03-04T13:02:15.440512Z",
     "iopub.status.idle": "2021-03-04T13:02:17.364990Z",
     "shell.execute_reply": "2021-03-04T13:02:17.366576Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (97654321012345679)>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ggplot(df, aes(x='variable', y='value', fill='category'))\n",
    " + geom_col()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the `value`s along each variable add up to 100, but stacked together the difference within and without the groups is not clear. \n",
    "The solution is to `dodge` the bars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-04T13:02:17.708414Z",
     "iopub.status.busy": "2021-03-04T13:02:17.500356Z",
     "iopub.status.idle": "2021-03-04T13:02:18.756331Z",
     "shell.execute_reply": "2021-03-04T13:02:18.757921Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (97654321012345679)>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ggplot(df, aes(x='variable', y='value', fill='category'))\n",
    " + geom_col(stat='identity', position='dodge'))                     # modified"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is good, it gives us the plot we want but the legend is not great. Each `variable` has a different set of `category`s, but the legend has them all clamped together. We cannot easily change the legend, but we can replicate it's purpose by labelling the individual bars.\n",
    "\n",
    "To do this, we create a `geom_text` with `position_dodge(width=0.9)` to match the ratio of the space taken up by each variable. If there was no spacing between the bars of different variables, we would have `width=1`.\n",
    "\n",
    "A minor quack, when text extends beyond the limits we have to manually make space or it would get clipped. Therefore we adjust the bottom `y` limits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-04T13:02:18.826395Z",
     "iopub.status.busy": "2021-03-04T13:02:18.778361Z",
     "iopub.status.idle": "2021-03-04T13:02:20.921301Z",
     "shell.execute_reply": "2021-03-04T13:02:20.922938Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (97654321012345679)>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dodge_text = position_dodge(width=0.9)                              # new\n",
    "\n",
    "(ggplot(df, aes(x='variable', y='value', fill='category'))\n",
    " + geom_col(stat='identity', position='dodge', show_legend=False)   # modified\n",
    " + geom_text(aes(y=-.5, label='category'),                          # new\n",
    "             position=dodge_text,\n",
    "             color='gray', size=8, angle=45, va='top')\n",
    " + lims(y=(-5, 60))                                                 # new\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Would it look too crowded if we add value labels on top of the bars?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-04T13:02:20.950758Z",
     "iopub.status.busy": "2021-03-04T13:02:20.940401Z",
     "iopub.status.idle": "2021-03-04T13:02:23.927314Z",
     "shell.execute_reply": "2021-03-04T13:02:23.928901Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (97654321012345679)>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dodge_text = position_dodge(width=0.9)\n",
    "\n",
    "(ggplot(df, aes(x='variable', y='value', fill='category'))\n",
    " + geom_col(stat='identity', position='dodge', show_legend=False)\n",
    " + geom_text(aes(y=-.5, label='category'),\n",
    "             position=dodge_text,\n",
    "             color='gray', size=8, angle=45, va='top')\n",
    " + geom_text(aes(label='value'),                                    # new\n",
    "             position=dodge_text,\n",
    "             size=8, va='bottom', format_string='{}%')\n",
    " + lims(y=(-5, 60))\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That looks okay. The `value`s line up with the `category`s because we used the same `dodge` parameters. For the final polish, we remove the y-axis, clear out the panel and make the `variable` and `category` labels have the same color."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-04T13:02:23.963189Z",
     "iopub.status.busy": "2021-03-04T13:02:23.957756Z",
     "iopub.status.idle": "2021-03-04T13:02:26.812730Z",
     "shell.execute_reply": "2021-03-04T13:02:26.810815Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (97654321012345679)>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dodge_text = position_dodge(width=0.9)\n",
    "ccolor = '#555555'\n",
    "\n",
    "# Gallery Plot\n",
    "\n",
    "(ggplot(df, aes(x='variable', y='value', fill='category'))\n",
    " + geom_col(stat='identity', position='dodge', show_legend=False)\n",
    " + geom_text(aes(y=-.5, label='category'),\n",
    "             position=dodge_text,\n",
    "             color=ccolor, size=8, angle=45, va='top')              # modified\n",
    " + geom_text(aes(label='value'),\n",
    "             position=dodge_text,\n",
    "             size=8, va='bottom', format_string='{}%')\n",
    " + lims(y=(-5, 60))\n",
    " + theme(panel_background=element_rect(fill='white'),               # new\n",
    "         axis_title_y=element_blank(),\n",
    "         axis_line_x=element_line(color='black'),\n",
    "         axis_line_y=element_blank(),\n",
    "         axis_text_y=element_blank(),\n",
    "         axis_text_x=element_text(color=ccolor),\n",
    "         axis_ticks_major_y=element_blank(),\n",
    "         panel_grid=element_blank(),\n",
    "         panel_border=element_blank())\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Credit**: I saved a plot this example is based on a while ago and forgot/misplaced the link to the source. The user considered it a minor coup."
   ]
  }
 ],
 "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": "3.8.3"
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700"
   },
   "moveMenuLeft": true,
   "nav_menu": {
    "height": "12px",
    "width": "252px"
   },
   "navigate_menu": true,
   "number_sections": false,
   "sideBar": true,
   "threshold": 4,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": true,
   "widenNotebook": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}