Group Boxplots in Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating Group Boxplots in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Follow me on [Twitter](https://twitter.com/javedali99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "\n",
    "import random\n",
    "from datetime import date\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Using colors to differentiate between categories and spacing to separate in groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3000x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create dummy data\n",
    "data_a = [[1, 2, 5], [5, 7, 2, 2, 5], [7, 2, 5]]\n",
    "data_b = [[6, 4, 2], [1, 2, 5, 3, 2], [2, 3, 5, 1]]\n",
    "\n",
    "ticks = ['A', 'B', 'C']\n",
    "\n",
    "\n",
    "def set_box_color(bp, color):\n",
    "    plt.setp(bp['boxes'], color=color)\n",
    "    plt.setp(bp['whiskers'], color=color)\n",
    "    plt.setp(bp['caps'], color=color)\n",
    "    plt.setp(bp['medians'], color=color)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10, 6), dpi=300)\n",
    "\n",
    "bpl = plt.boxplot(data_a, positions=np.array(range(len(data_a)))*2.0-0.4, sym='', widths=0.6)\n",
    "bpr = plt.boxplot(data_b, positions=np.array(range(len(data_b)))*2.0+0.4, sym='', widths=0.6)\n",
    "\n",
    "set_box_color(bpl, '#D7191C')  # colors are from http://colorbrewer2.org/\n",
    "set_box_color(bpr, '#2C7BB6')\n",
    "\n",
    "# draw temporary red and blue lines and use them to create a legend\n",
    "plt.plot([], c='#D7191C', label='Apples')\n",
    "plt.plot([], c='#2C7BB6', label='Oranges')\n",
    "plt.legend(loc='best', fontsize=12)\n",
    "\n",
    "plt.xticks(range(0, len(ticks) * 2, 2), ticks, fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.xlim(-2, len(ticks)*2)\n",
    "plt.ylim(0, 8)\n",
    "plt.tight_layout()\n",
    "# plt.savefig('boxcompare.png') # uncomment for saving the figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. A simple way to plot group boxplots by using `pandas`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.rand(12,2), columns=['Apples', 'Oranges'] )\n",
    "\n",
    "df['Categories'] = pd.Series(list('AAAABBBBCCCC'))\n",
    "\n",
    "df.boxplot(by='Categories', figsize=(12, 8))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "col1 = np.random.random(10)\n",
    "col2 = np.random.random(10)\n",
    "\n",
    "DF = pd.DataFrame({'col1': col1, 'col2': col2})\n",
    "\n",
    "ax = DF[['col1', 'col2']].plot(kind='box', title='boxplot', showmeans=True, figsize=(10, 6))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. We can also use the `seaborn` library for boxplots. First melt the dataframe to format data and then create the boxplot of your choice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data\n",
    "df = pd.DataFrame({'Group': ['A', 'A', 'A', 'B', 'C', 'B', 'B', 'C', 'A', 'C'],\n",
    "                   'Apple': np.random.rand(10), 'Orange': np.random.rand(10)})\n",
    "df = df[['Group', 'Apple', 'Orange']]\n",
    "\n",
    "dd = pd.melt(df, id_vars=['Group'], value_vars=[\n",
    "             'Apple', 'Orange'], var_name='fruits')\n",
    "\n",
    "# viz\n",
    "plt.figure(figsize=(10, 6), dpi=300)\n",
    "\n",
    "sns.boxplot(x='Group', y='value', data=dd, hue='fruits')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(\n",
    "    [\n",
    "        [2, 4, 5, 6, 1],\n",
    "        [4, 5, 6, 7, 2],\n",
    "        [5, 4, 5, 5, 1],\n",
    "        [10, 4, 7, 8, 2],\n",
    "        [9, 3, 4, 6, 2],\n",
    "        [3, 3, 4, 4, 1]\n",
    "    ], columns=['a1', 'a2', 'a3', 'a4', 'b'])\n",
    "\n",
    "df_long = pd.melt(df, \"b\", var_name=\"a\", value_name=\"c\")\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(x=\"a\", hue=\"b\", y=\"c\", data=df_long)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1509ec21ca0>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(\"a\", hue=\"b\", y=\"c\", data=df_long, kind=\"box\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, sharey=True)\n",
    "\n",
    "for i, grp in enumerate(df.filter(regex=\"a\").groupby(by=df.b)):\n",
    "    sns.boxplot(grp[1], ax=ax[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1509ecad970>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(pd.melt(df, id_vars='b'), col='b')\n",
    "g.map(sns.boxplot, 'value', 'variable')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame({'Group': [1, 1, 1, 2, 3, 2, 2, 3, 1, 3],\n",
    "                   'M': np.random.rand(10), 'F': np.random.rand(10)})\n",
    "df = df[['Group', 'M', 'F']]\n",
    "\n",
    "dd = pd.melt(df, id_vars=['Group'], value_vars=['M', 'F'], var_name='sex')\n",
    "\n",
    "sns.boxplot(y='Group', x='value', data=dd, orient=\"h\", hue='sex')\n",
    "\n",
    "plt.legend(loc='best')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a seaborn boxplot with month as x-axes with a daily dataset\n",
    "\n",
    "df = pd.DataFrame(np.random.randint(50, 1000, 365).reshape(-1, 1),\n",
    "                  index=pd.date_range('2018-01-01', '2018-12-31', freq='D'),\n",
    "                  columns=['sum'])\n",
    "df.reset_index(inplace=True)\n",
    "df.columns = ['Date', 'sum']\n",
    "\n",
    "# use dt.strftime('%b') element to create the month column\n",
    "df['month'] = df['Date'].dt.strftime('%b')\n",
    "\n",
    "# Use seaborn.boxplot\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "fig.set_size_inches((12, 4))\n",
    "sns.boxplot(x='month', y='sum', data=df, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. A more elegant way to change the color of the box plot by iterating over the dictionary of the object itself"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def color_box(bp, color):\n",
    "\n",
    "    # Define the elements to color. You can also add medians, fliers and means\n",
    "    elements = ['boxes', 'caps', 'whiskers']\n",
    "\n",
    "    # Iterate over each of the elements changing the color\n",
    "    for elem in elements:\n",
    "        [plt.setp(bp[elem][idx], color=color) for idx in range(len(bp[elem]))]\n",
    "    return\n",
    "\n",
    "\n",
    "a = np.random.uniform(0, 10, [100, 5])\n",
    "\n",
    "bp = plt.boxplot(a)\n",
    "color_box(bp, 'blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. A function to make slightly fancier grouped boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def custom_legend(colors, labels, linestyles=None):\n",
    "    \"\"\" Creates a list of matplotlib Patch objects that can be passed to the legend(...) function to create a custom\n",
    "        legend.\n",
    "\n",
    "    :param colors: A list of colors, one for each entry in the legend. You can also include a linestyle, for example: 'k--'\n",
    "    :param labels:  A list of labels, one for each entry in the legend.\n",
    "    \"\"\"\n",
    "\n",
    "    if linestyles is not None:\n",
    "        assert len(linestyles) == len(\n",
    "            colors), \"Length of linestyles must match length of colors.\"\n",
    "\n",
    "    h = list()\n",
    "    for k, (c, l) in enumerate(zip(colors, labels)):\n",
    "        clr = c\n",
    "        ls = 'solid'\n",
    "        if linestyles is not None:\n",
    "            ls = linestyles[k]\n",
    "        patch = patches.Patch(color=clr, label=l, linestyle=ls)\n",
    "        h.append(patch)\n",
    "    return h\n",
    "\n",
    "\n",
    "def grouped_boxplot(data, group_names=None, subgroup_names=None, ax=None, subgroup_colors=None,\n",
    "                    box_width=0.6, box_spacing=1.0):\n",
    "    \"\"\" Draws a grouped boxplot. The data should be organized in a hierarchy, where there are multiple\n",
    "        subgroups for each main group.\n",
    "\n",
    "    :param data: A dictionary of length equal to the number of the groups. The key should be the\n",
    "                group name, the value should be a list of arrays. The length of the list should be\n",
    "                equal to the number of subgroups.\n",
    "    :param group_names: (Optional) The group names, should be the same as data.keys(), but can be ordered.\n",
    "    :param subgroup_names: (Optional) Names of the subgroups.\n",
    "    :param subgroup_colors: A list specifying the plot color for each subgroup.\n",
    "    :param ax: (Optional) The axis to plot on.\n",
    "    \"\"\"\n",
    "\n",
    "    if group_names is None:\n",
    "        group_names = data.keys()\n",
    "\n",
    "    if ax is None:\n",
    "        ax = plt.gca()\n",
    "    plt.sca(ax)\n",
    "\n",
    "    nsubgroups = np.array([len(v) for v in data.values()])\n",
    "    assert len(np.unique(nsubgroups)\n",
    "               ) == 1, \"Number of subgroups for each property differ!\"\n",
    "    nsubgroups = nsubgroups[0]\n",
    "\n",
    "    if subgroup_colors is None:\n",
    "        subgroup_colors = list()\n",
    "        for k in range(nsubgroups):\n",
    "            subgroup_colors.append(np.random.rand(3))\n",
    "    else:\n",
    "        assert len(\n",
    "            subgroup_colors) == nsubgroups, \"subgroup_colors length must match number of subgroups (%d)\" % nsubgroups\n",
    "\n",
    "    def _decorate_box(_bp, _d):\n",
    "        plt.setp(_bp['boxes'], lw=0, color='k')\n",
    "        plt.setp(_bp['whiskers'], lw=3.0, color='k')\n",
    "\n",
    "        # fill in each box with a color\n",
    "        assert len(_bp['boxes']) == nsubgroups\n",
    "        for _k, _box in enumerate(_bp['boxes']):\n",
    "            _boxX = list()\n",
    "            _boxY = list()\n",
    "            for _j in range(5):\n",
    "                _boxX.append(_box.get_xdata()[_j])\n",
    "                _boxY.append(_box.get_ydata()[_j])\n",
    "            _boxCoords = zip(_boxX, _boxY)\n",
    "            _boxPolygon = plt.Polygon(\n",
    "                _boxCoords, facecolor=subgroup_colors[_k])\n",
    "            ax.add_patch(_boxPolygon)\n",
    "\n",
    "        # draw a black line for the median\n",
    "        for _k, _med in enumerate(_bp['medians']):\n",
    "            _medianX = list()\n",
    "            _medianY = list()\n",
    "            for _j in range(2):\n",
    "                _medianX.append(_med.get_xdata()[_j])\n",
    "                _medianY.append(_med.get_ydata()[_j])\n",
    "                plt.plot(_medianX, _medianY, 'k', linewidth=3.0)\n",
    "\n",
    "            # draw a black asterisk for the mean\n",
    "            plt.plot([np.mean(_med.get_xdata())], [np.mean(_d[_k])], color='w', marker='*',\n",
    "                     markeredgecolor='k', markersize=12)\n",
    "\n",
    "    cpos = 1\n",
    "    label_pos = list()\n",
    "    for k in group_names:\n",
    "        d = data[k]\n",
    "        nsubgroups = len(d)\n",
    "        pos = np.arange(nsubgroups) + cpos\n",
    "        label_pos.append(pos.mean())\n",
    "        bp = plt.boxplot(d, positions=pos, widths=box_width)\n",
    "        _decorate_box(bp, d)\n",
    "        cpos += nsubgroups + box_spacing\n",
    "\n",
    "    plt.xlim(0, cpos-1)\n",
    "    plt.xticks(label_pos, group_names)\n",
    "\n",
    "    if subgroup_names is not None:\n",
    "        leg = custom_legend(subgroup_colors, subgroup_names)\n",
    "        plt.legend(handles=leg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create random data\n",
    "data = {'A': [np.random.randn(100), np.random.randn(100) + 5],\n",
    "        'B': [np.random.randn(100)+1, np.random.randn(100) + 9],\n",
    "        'C': [np.random.randn(100)-3, np.random.randn(100) - 5]\n",
    "        }\n",
    "\n",
    "# data viz\n",
    "grouped_boxplot(data, group_names=['A', 'B', 'C'], subgroup_names=[\n",
    "                'Apples', 'Oranges'], subgroup_colors=['#D02D2E', '#D67700'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Grouped boxplots, towards subtle academic publication styling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data, e.g. results per algorithm:\n",
    "data1 = [5, 5, 4, 3, 3, 5]\n",
    "data2 = [6, 6, 4, 6, 8, 5]\n",
    "data3 = [7, 8, 4, 5, 8, 2]\n",
    "data4 = [6, 9, 3, 6, 8, 4]\n",
    "\n",
    "# Combining the data:\n",
    "data_group1 = [data1, data2]\n",
    "data_group2 = [data3, data4]\n",
    "\n",
    "# Labels for the data:\n",
    "labels_list = ['a', 'b']\n",
    "xlocations = range(len(data_group1))\n",
    "width = 0.3\n",
    "symbol = 'r+'\n",
    "ymin = 0\n",
    "ymax = 10\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "ax = plt.gca()\n",
    "ax.set_ylim(ymin, ymax)\n",
    "ax.set_xticklabels(labels_list, rotation=0)\n",
    "ax.grid(True, linestyle='dotted')\n",
    "ax.set_axisbelow(True)\n",
    "ax.set_xticks(xlocations)\n",
    "plt.xlabel('X axis label', fontsize=12)\n",
    "plt.ylabel('Y axis label', fontsize=12)\n",
    "plt.title('Title for Boxplot', fontsize=16, y=1.03)\n",
    "\n",
    "# Offset the positions per group:\n",
    "positions_group1 = [x-(width+0.01) for x in xlocations]\n",
    "positions_group2 = xlocations\n",
    "\n",
    "plt.boxplot(data_group1,\n",
    "            sym=symbol,\n",
    "            labels=['']*len(labels_list),\n",
    "            positions=positions_group1,\n",
    "            widths=width,\n",
    "            #           notch=False,\n",
    "            #           vert=True,\n",
    "            #           whis=1.5,\n",
    "            #           bootstrap=None,\n",
    "            #           usermedians=None,\n",
    "            #           conf_intervals=None,\n",
    "            #           patch_artist=False,\n",
    "            )\n",
    "\n",
    "plt.boxplot(data_group2,\n",
    "            labels=labels_list,\n",
    "            sym=symbol,\n",
    "            positions=positions_group2,\n",
    "            widths=width,\n",
    "            #           notch=False,\n",
    "            #           vert=True,\n",
    "            #           whis=1.5,\n",
    "            #           bootstrap=None,\n",
    "            #           usermedians=None,\n",
    "            #           conf_intervals=None,\n",
    "            #           patch_artist=False,\n",
    "            )\n",
    "\n",
    "# plt.savefig('boxplot_grouped.png', dpi=300)   # uncomment for saving the plots\n",
    "# plt.savefig('boxplot_grouped.pdf', dpi=300)    # when publishing, use high quality PDFs\n",
    "plt.show()                   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. A flexible generic solution with `matplotlib`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.0\n",
      "0.0\n",
      "1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data, e.g. results per algorithm:\n",
    "data1 = [5, 5, 4, 3, 3, 5]\n",
    "data2 = [6, 6, 4, 6, 8, 5]\n",
    "data3 = [7, 8, 4, 5, 8, 2]\n",
    "data4 = [6, 9, 3, 6, 8, 4]\n",
    "data6 = [17, 8, 4, 5, 8, 1]\n",
    "data7 = [6, 19, 3, 6, 1, 1]\n",
    "\n",
    "\n",
    "# Combining the data:\n",
    "data_group1 = [data1, data2, data6]\n",
    "data_group2 = [data3, data4, data7]\n",
    "data_group3 = [data1, data1, data1]\n",
    "data_group4 = [data2, data2, data2]\n",
    "data_group5 = [data2, data2, data2]\n",
    "\n",
    "# , data_group4] #, data_group5]\n",
    "data_groups = [data_group1, data_group2, data_group3]\n",
    "\n",
    "# Labels for the data:\n",
    "labels_list = ['a', 'b', 'c']\n",
    "width = 0.3\n",
    "xlocations = [x*((1 + len(data_groups))*width)\n",
    "              for x in range(len(data_group1))]\n",
    "\n",
    "symbol = 'r+'\n",
    "ymin = min([val for dg in data_groups for data in dg for val in data])\n",
    "ymax = max([val for dg in data_groups for data in dg for val in data])\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "ax = plt.gca()\n",
    "ax.set_ylim(ymin, ymax)\n",
    "\n",
    "ax.grid(True, linestyle='dotted')\n",
    "ax.set_axisbelow(True)\n",
    "\n",
    "plt.xlabel('X axis label', fontsize=12)\n",
    "plt.ylabel('Y axis label', fontsize=12)\n",
    "plt.title('title')\n",
    "\n",
    "space = len(data_groups)/2\n",
    "offset = len(data_groups)/2\n",
    "\n",
    "\n",
    "ax.set_xticks(xlocations)\n",
    "ax.set_xticklabels(labels_list, rotation=0)\n",
    "\n",
    "# Offset the positions per group:\n",
    "group_positions = []\n",
    "for num, dg in enumerate(data_groups):\n",
    "    _off = (0 - space + (0.5+num))\n",
    "    print(_off)\n",
    "    group_positions.append([x-_off*(width+0.01) for x in xlocations])\n",
    "\n",
    "for dg, pos in zip(data_groups, group_positions):\n",
    "    plt.boxplot(dg,\n",
    "               sym=symbol,\n",
    "               #            labels=['']*len(labels_list),\n",
    "               labels=['']*len(labels_list),\n",
    "               positions=pos,\n",
    "               widths=width,\n",
    "               #           notch=False,\n",
    "               #           vert=True,\n",
    "               #           whis=1.5,\n",
    "               #           bootstrap=None,\n",
    "               #           usermedians=None,\n",
    "               #           conf_intervals=None,\n",
    "               #           patch_artist=False,\n",
    "               )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. To plot multiple boxplots on one `matplotlib` graph, pass a list of data arrays to boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x1 = 10*np.random.random(100)\n",
    "x2 = 10*np.random.exponential(0.5, 100)\n",
    "x3 = 10*np.random.normal(0, 0.4, 100)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.boxplot([x1, x2, x3])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Groups = [1, 2, 3]\n",
    "Columns = df.columns.tolist()[1:]\n",
    "Mgroups = []\n",
    "Fgroups = []\n",
    "\n",
    "for g in Groups:\n",
    "    dfgc = df[df['Group'] == g]\n",
    "    m = dfgc['M'].dropna()\n",
    "    f = dfgc['F'].dropna()\n",
    "    Mgroups.append(m.tolist())\n",
    "    Fgroups.append(f.tolist())\n",
    "\n",
    "fig = plt.figure(figsize=(10, 6))\n",
    "ax = plt.axes()\n",
    "\n",
    "\n",
    "def setBoxColors(bp, cl):\n",
    "    plt.setp(bp['boxes'], color=cl, linewidth=2.)\n",
    "    plt.setp(bp['whiskers'], color=cl, linewidth=2.5)\n",
    "    plt.setp(bp['caps'], color=cl, linewidth=2)\n",
    "    plt.setp(bp['medians'], color=cl, linewidth=3.5)\n",
    "\n",
    "\n",
    "bpl = plt.boxplot(Mgroups, positions=np.array(range(len(Mgroups)))\n",
    "                  * 3.0-0.4, vert=False, whis='range', sym='', widths=0.6)\n",
    "bpr = plt.boxplot(Fgroups, positions=np.array(range(len(Fgroups)))\n",
    "                  * 3.0+0.4, vert=False, whis='range', sym='', widths=0.6)\n",
    "\n",
    "setBoxColors(bpr, '#D7191C')  # colors are from http://colorbrewer2.org/\n",
    "setBoxColors(bpl, '#2C7BB6')\n",
    "\n",
    "# draw temporary red and blue lines and use them to create a legend\n",
    "plt.plot([], c='#D7191C', label='F')\n",
    "plt.plot([], c='#2C7BB6', label='M')\n",
    "plt.legend()\n",
    "\n",
    "plt.yticks(range(0, len(Groups) * 3, 3), Groups)\n",
    "plt.ylim(-3, len(Groups)*3)\n",
    "#plt.xlim(0, 8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = {}\n",
    "data['dataset1'] = {}\n",
    "data['dataset2'] = {}\n",
    "data['dataset3'] = {}\n",
    "\n",
    "n = 500\n",
    "for k, v in data.items():\n",
    "    upper = random.randint(0, 1000)\n",
    "    v['A'] = np.random.uniform(0, upper, size=n)\n",
    "    v['B'] = np.random.uniform(0, upper, size=n)\n",
    "    v['C'] = np.random.uniform(0, upper, size=n)\n",
    "\n",
    "fig, axes = plt.subplots(ncols=3, sharey=True, figsize=(10, 6))\n",
    "fig.subplots_adjust(wspace=0)\n",
    "\n",
    "for ax, name in zip(axes, ['dataset1', 'dataset2', 'dataset3']):\n",
    "    ax.boxplot([data[name][item] for item in ['A', 'B', 'C']])\n",
    "    ax.set(xticklabels=['A', 'B', 'C'], xlabel=name)\n",
    "    ax.margins(0.05)  # Optional\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.8.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
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