{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Basics of using generalized code to make a donut plot with subgroups\n", "\n", "This just covers the basics to remake [the example from The Python Graph Gallery](https://python-graph-gallery.com/163-donut-plot-with-subgroups/) shown below. Let's make something very similar to this:\n", "\n", "\n", "![plot example](https://python-graph-gallery.com/wp-content/uploads/163_Double_Donut_Chart.png)\n", "\n", "\n", "A more full-featured script that makes a similar plot from the same data **without further need for coding** that allows you to plug in your own data input is demonstrated in [this notebook](index.ipynb). Click back to [that page](index.ipynb) if you want the fastest route to a donut plot that you can easily customize. Or go to the following in the series if you want related full-featured scripts and aren't interesting in the coding behind it.\n", "\n", "- [Demonstrate a full-featured script that plots a summary for the subgroups in addition to the donut plot with subgroups](demo_summary_subgroups.ipynb)\n", "- [Demonstrate a full-featured script that plots a summary for binary data in addition to the donut plot with the binary group broken down by a group](demo_summary_binary.ipynb)\n", "\n", "----" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preparation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A dataframe will be used for input data. The following code will define it." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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groupsubgroupsub-subgroup
0A1frizzled
1A1lethargic
2A1polythene
3A1epic
4A2frizzled
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" ], "text/plain": [ " group subgroup sub-subgroup\n", "0 A 1 frizzled\n", "1 A 1 lethargic\n", "2 A 1 polythene\n", "3 A 1 epic\n", "4 A 2 frizzled" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "obs = [('A', 1, \"frizzled\"), \n", " ('A', 1, \"lethargic\"), \n", " ('A', 1, \"polythene\"), \n", " ('A', 1, \"epic\"),\n", " ('A', 2, \"frizzled\"), \n", " ('A', 2, \"lethargic\"), \n", " ('A', 2, \"epic\"),\n", " ('A', 3, \"frizzled\"), \n", " ('A', 3, \"lethargic\"),\n", " ('A', 3, \"polythene\"),\n", " ('A', 3, \"epic\"),\n", " ('A', 3, \"bedraggled\"),\n", " ('B', 1, \"frizzled\"), \n", " ('B', 1, \"lethargic\"),\n", " ('B', 1, \"polythene\"),\n", " ('B', 1, \"epic\"),\n", " ('B', 1, \"bedraggled\"),\n", " ('B', 1, \"moombahcored\"),\n", " ('B', 2, \"frizzled\"), \n", " ('B', 2, \"lethargic\"),\n", " ('B', 2, \"polythene\"),\n", " ('B', 2, \"epic\"),\n", " ('B', 2, \"bedraggled\"),\n", " ('C', 1, \"frizzled\"), \n", " ('C', 1, \"lethargic\"),\n", " ('C', 1, \"polythene\"),\n", " ('C', 1, \"epic\"),\n", " ('C', 1, \"bedraggled\"),\n", " ('C', 1, \"moombahcored\"),\n", " ('C', 1, \"zoned\"),\n", " ('C', 1, \"erstaz\"),\n", " ('C', 1, \"mined\"),\n", " ('C', 1, \"liberated\"),\n", " ('C', 2, \"frizzled\"), \n", " ('C', 2, \"lethargic\"),\n", " ('C', 2, \"polythene\"),\n", " ('C', 2, \"epic\"),\n", " ('C', 2, \"bedraggled\"),\n", " ('C', 3, \"frizzled\"), \n", " ('C', 3, \"lethargic\"),\n", " ('C', 3, \"polythene\"),\n", " ('C', 3, \"epic\"),\n", " ('C', 3, \"bedraggled\"),\n", " ('C', 4, \"bedraggled\"),\n", " ('C', 4, \"frizzled\"), \n", " ('C', 4, \"lethargic\"),\n", " ('C', 4, \"polythene\"),\n", " ('C', 4, \"epic\"),\n", " ('C', 5, \"frizzled\"), \n", " ('C', 5, \"lethargic\"),\n", " ('C', 5, \"polythene\"),\n", " ('C', 5, \"epic\"),\n", " ('C', 5, \"bedraggled\"),\n", " ('C', 5, \"moombahcored\")]\n", "labels = ['group', 'subgroup', 'sub-subgroup']\n", "df = pd.DataFrame.from_records(obs, columns=labels)\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To illustrate that this can work with tabular text data, let's save that as tab-separated text, like something you can easily generate from data in Excel or Google sheets, too." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "df.to_csv('data.tsv', sep='\\t',index = False) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To verify this is just text:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "group\tsubgroup\tsub-subgroup\r\n", "A\t1\tfrizzled\r\n", "A\t1\tlethargic\r\n", "A\t1\tpolythene\r\n", "A\t1\tepic\r\n", "A\t2\tfrizzled\r\n", "A\t2\tlethargic\r\n", "A\t2\tepic\r\n", "A\t3\tfrizzled\r\n", "A\t3\tlethargic\r\n" ] } ], "source": [ "!head data.tsv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's use that data to make something very similar to [the example from The Python Graph Gallery](https://python-graph-gallery.com/163-donut-plot-with-subgroups/) shown above.\n", "\n", "First we need to define some helper functions that the original one didn't have in order to help generalize thus current version." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "## helper functions and settings\n", "import sys\n", "import os\n", "try:\n", " from pathlib import Path\n", "except ImportError:\n", " from pathlib2 import Path\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import numpy as np\n", "plot_figure_size = (7,8) # width by height written as `(width,height)`; \n", "# If you change this to substantial degree, you may also want to \n", "# adjust text size settings below and possibly turn off plot titles using \n", "# `include_title=False`in favor of adding your own in post-processing.\n", "outer_ring_radius = 1.3 # radius of the outer ring of the donut plot\n", "inner_ring_radius = outer_ring_radius-0.3 # radius of the inner ring of donut\n", "outer_ring_width=0.3\n", "inner_ring_width=0.4\n", "include_title = True\n", "plot_title = \"BREAKDOWN\"\n", "title_text_size = 20 # font size for title above plot\n", "plot_text_size = 14 # font size for text in the plot\n", "large_img_size = (14,15) # size to be used with `--large_image` `flag. Width \n", "# by height written as `(width,height)`\n", "light_color_for_last_in_subgroup = True # Set this to False to reverse the \n", "# order of the subgroup coloring.\n", "save_plot_name_prefix = \"donut_plot\"\n", "def sequential_color_maps_generator():\n", " '''\n", " generator to yield a never-ending supply of sequential color palettes/ \n", " color maps.\n", " '''\n", " color_brewer_seq_names = [\"Blues\", \"Reds\",\"Greens\",\"Oranges\",\n", " \"Purples\"] #\"Greys\" looks bad because white is least\n", " list_of_other_good_sequences = [\"teal\", \"fuchsia\", \"darkslateblue\", \"sage\", \n", " \"darkviolet\", \"crimson\", \"darkgoldenrod\", \n", " \"dodgerblue\", \"maroon\", \"darkolivegreen\", \n", " \"darkturquoise\", \"royalblue\", \"chocolate\"]\n", " np.random.seed(42)\n", " for col_name in color_brewer_seq_names:\n", " yield plt.get_cmap(col_name) #`plt.get_cmap` use based on\n", " # https://matplotlib.org/tutorials/colors/colormaps.html\n", " for col_name in list_of_other_good_sequences:\n", " try:\n", " yield sns.light_palette(col_name, as_cmap=True)\n", " except ValueError:\n", " yield sns.light_palette(col_name, as_cmap=True,input=\"xkcd\")\n", " while True:\n", " rgb = tuple((np.random.random(size=3) * 1)) # based on \n", " # https://stackoverflow.com/a/48793922/8508004\n", " yield sns.light_palette(rgb, input=\"rgb\", as_cmap=True)\n", "def extract_dataframe(file_name):\n", " '''\n", " Takes a file name and using the extension determines how to extract the\n", " dataframe recorded in it. \n", " Returns a pandas dataframe object.\n", " '''\n", " extension = Path(file_name).suffix\n", " if extension.lower() == \".pkl\":\n", " return pd.read_pickle(file_name)\n", " elif extension.lower() == \".tsv\":\n", " return pd.read_csv(file_name, sep='\\t')\n", " elif extension.lower() == \".csv\":\n", " return pd.read_csv(file_name)\n", " else:\n", " sys.stderr.write(\"\\n**ERROR** Cannot determine how dataframe is stored \"\n", " \"in '{}'.\\nChange the file name extension in the input file to be \"\n", " \"`.pkl`, `.tsv`, or `.csv` to indicate\\nif dataframe stored \"\n", " \"pickled, stored as tab-separated text, or stored as\\n\"\n", " \"comma-separated text.\"\n", " \".\\n**EXITING !!**.\\n\".format(file_name))\n", " sys.exit(1)\n", "def is_number(s):\n", " '''\n", " check if a string can be cast to a float or numeric (integer).\n", " fixed from https://www.pythoncentral.io/how-to-check-if-a-string-is-a-number-in-python-including-unicode/\n", " later noted similar code is at https://code-maven.com/slides/python-programming/is-number\n", " '''\n", " try:\n", " float(s)\n", " return True\n", " except ValueError:\n", " pass\n", " try:\n", " import unicodedata\n", " unicodedata.numeric(s)\n", " return True\n", " except (TypeError, ValueError):\n", " pass\n", " return False\n", "\n", "def cast_to_number(s):\n", " '''\n", " Cast a string to a float or integer. \n", " based on fixed code from https://www.pythoncentral.io/how-to-check-if-a-string-is-a-number-in-python-including-unicode/\n", " '''\n", " try:\n", " number = float(s)\n", " try:\n", " number = int(s)\n", " return number\n", " except ValueError:\n", " pass\n", " return number\n", " except ValueError:\n", " pass\n", " try:\n", " import unicodedata\n", " num = unicodedata.numeric(s)\n", " return num\n", " except (TypeError, ValueError):\n", " pass\n", " return False\n", "\n", "def f7(seq):\n", " '''\n", " remove duplicates from a list whilst preserving order.\n", " from https://stackoverflow.com/a/480227/8508004\n", " '''\n", " seen = set()\n", " seen_add = seen.add\n", " return [x for x in seq if not (x in seen or seen_add(x))]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now to convert the core of the example script:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def donut_plot_with_subgroups_from_dataframe(\n", " df_file=None, df=None, groups_col=None, subgroups_col=None,\n", " hilolist = None, \n", " sort_on_subgroup_name=False, advance_color_increments=0):\n", " '''\n", " Takes a dataframe either as a file or passed directly along some information \n", " about columns in the dataframe and makes a donut plot. The plot is a \n", " breakdown of the main groups to subgroups with the main groups in an outer\n", " ring of the dount plot and the subgroups on the inner ring. The style sought \n", " is seen at https://python-graph-gallery.com/163-donut-plot-with-subgroups/ .\n", " '''\n", " if df is None:\n", " # use file extension to decide how to parse dataframe file.\n", " df = extract_dataframe(df_file)\n", "\n", " # Prepare derivatives of the dataframe that may be needed for delineating \n", " # the plotting data\n", " tc = df[subgroups_col].value_counts()\n", " total_state_names = tc.index.tolist()\n", " total_state_size = tc.tolist()\n", " grouped = df.groupby(groups_col)\n", " # use `value_counts()` on each group to get the count and name of each state\n", " list_o_subgroup_names_l = []\n", " list_o_subgroup_size_l = []\n", " subgroups_per_group_l = []\n", " for name,group in grouped:\n", " dfc = group[subgroups_col].value_counts()\n", " if sort_on_subgroup_name:\n", " dfc = group[subgroups_col].value_counts().sort_index()\n", " #list_o_subgroup_names_l.append(dfc.index.tolist())\n", " # to make the subgroup names like in the example, incorporate\n", " # group name to each as well\n", " list_o_subgroup_names_l.append([\"{}.{}\".format(name,x) for x in dfc.index.tolist()])\n", " list_o_subgroup_size_l.append(dfc.tolist())\n", " \n", " # Delineate data for the plot:\n", " group_names= grouped.size().index.tolist()\n", " group_size= grouped.size().tolist() #len of each groupby grouping\n", " # flatten each list of lists made above to get the list needed\n", " subgroup_names=[i for sublt in list_o_subgroup_names_l for i in sublt]\n", " subgroup_size=[i for sublt in list_o_subgroup_size_l for i in sublt]\n", " assert len(subgroup_size) == len(subgroup_names)\n", "\n", " # Create colors generator and colors\n", " colormp = sequential_color_maps_generator()\n", " [next(colormp) for g in range(advance_color_increments)]#advance prior to \n", " # use, if initial skips specified\n", " colorm_per_grp=[next(colormp) for g in group_names]\n", "\n", " #Set up for plot.\n", " fig, ax = plt.subplots(figsize=plot_figure_size)\n", " ax.axis('equal')\n", "\n", " ### First Ring (outside)\n", " ### This will be the main groups\n", " labels_with_grp_sz = [\"group{}\".format(\n", " x) for x, y in zip(group_names, group_size)]\n", " mypie, _ = plt.pie(\n", " group_size, radius=outer_ring_radius, labels=labels_with_grp_sz, \n", " textprops={'fontsize': plot_text_size},\n", " colors=[colormp(0.63) for colormp in colorm_per_grp] )\n", " plt.setp( mypie, width=outer_ring_width, edgecolor='white')\n", " \n", " ### Second Ring (Inside)\n", " ### This will be the subgroup counting for each group\n", " list_sub_grp_colors_l = []\n", " subgroups_represented = f7(df[subgroups_col].tolist())\n", " #int_degree = [0.6,0.2]\n", " if hilolist:\n", " assert len(hilolist) == len(subgroups_represented), \"The list provided \"\n", " \"to specify the intensity degree must include all subgroups. Subgroups \"\n", " \"are: '{}'.format(subgroups_represented)\"\n", " subgroups_represented = hilolist\n", " else:\n", " # Provide feedback on what is being used as high to low intensity list \n", " # so user can adjust; using `if __name__ == \"__main__\"` to customize \n", " # note depending if script called from command line.\n", " sys.stderr.write(\"Note: No list to specify high to low intensity coloring \"\n", " \"provided, and so using '{}',\\nwhere leftmost identifer corresponds \"\n", " \"to most intense and rightmost is least.\\n\".format(\n", " \",\".join(str(i) for i in subgroups_represented))) # because subgroups \n", " # could be integers as in example from \n", " # https://python-graph-gallery.com/163-donut-plot-with-subgroups/, best \n", " # to have conversion to string,\n", " if __name__ == \"__main__\":\n", " sys.stderr.write(\"Look into adding use of the `--hilolist` opition \"\n", " \"to specify the order.\\n\\n\")\n", " else:\n", " sys.stderr.write(\"Provide a Python list as `hilolist` when calling \"\n", " \"the function to specify the order.\\n\\n\")\n", " # assign intensity degree settings for each subgroup so consistent among \n", " # other groups\n", " int_degree = np.linspace(0.6, 0.2, num=len(subgroups_represented))\n", " if not light_color_for_last_in_subgroup:\n", " int_degree.reverse()\n", " # determine colors for each subgroup before `plt.pie` step\n", " for idx,subgroups_l in enumerate(list_o_subgroup_names_l):\n", " cm = colorm_per_grp[idx]\n", " grp_colors = [cm(int_degree[subgroups_represented.index(\n", " int(sgrp.split(\".\")[1]))]) for sgrp in subgroups_l] # `int(sgrp.split(\".\")[1])` is\n", " # to get the subgroup name back to an integer, e.g., `C.3` becomes `3`.\n", " list_sub_grp_colors_l.append(grp_colors)\n", " # flatten that list\n", " sub_grp_colors = [i for sublt in list_sub_grp_colors_l for i in sublt]\n", " mypie2, _ = plt.pie(\n", " subgroup_size, radius=inner_ring_radius, labels=subgroup_names, \n", " textprops={'fontsize': plot_text_size}, labeldistance=0.7, \n", " colors=sub_grp_colors)\n", " plt.setp( mypie2, width=inner_ring_width, edgecolor='white')\n", " plt.margins(0,0)\n", "\n", " return ax" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Note: No list to specify high to low intensity coloring provided, and so using '1,2,3,4,5',\n", "where leftmost identifer corresponds to most intense and rightmost is least.\n", "Look into adding use of the `--hilolist` opition to specify the order.\n", "\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a = donut_plot_with_subgroups_from_dataframe(df_file=\"data.tsv\", groups_col=\"group\",subgroups_col =\"subgroup\",sort_on_subgroup_name=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note we could have also used the dataframe object made in the first cell as the object to act on. The file form of tabular text was used originally as it would be something more people may have and can make.\n", "\n", "So instead of `df_file`, now we provide `df` and indicate the dataframe to use." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Note: No list to specify high to low intensity coloring provided, and so using '1,2,3,4,5',\n", "where leftmost identifer corresponds to most intense and rightmost is least.\n", "Look into adding use of the `--hilolist` opition to specify the order.\n", "\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "b = donut_plot_with_subgroups_from_dataframe(df=df, groups_col=\"group\",subgroups_col =\"subgroup\",sort_on_subgroup_name=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**NOTE:** \n", "In [the example from The Python Graph Gallery](https://python-graph-gallery.com/163-donut-plot-with-subgroups/) shown below the ordering of the subgroups is based on the name of the subgroups.\n", "\n", "----\n", "\n", "![plot example](https://python-graph-gallery.com/wp-content/uploads/163_Double_Donut_Chart.png)\n", "\n", "----\n", "\n", "To get ordering like in the example, the function is called with the `sort_on_subgroup_name` set to `True`. That isn't the default.\n", "\n", "This is the default where the subgroups are ordered by size:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Note: No list to specify high to low intensity coloring provided, and so using '1,2,3,4,5',\n", "where leftmost identifer corresponds to most intense and rightmost is least.\n", "Look into adding use of the `--hilolist` opition to specify the order.\n", "\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "c = donut_plot_with_subgroups_from_dataframe(df=df, groups_col=\"group\",subgroups_col =\"subgroup\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "\n", "----" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Go to the following pages in the series if you want related full-featured scripts that add summary subplots on the left, along with the donut plots with subgroups on the right.\n", "\n", "- [Demonstrate a full-featured script that plots a summary for the subgroups in addition to the donut plot with subgroups](demo_summary_subgroups.ipynb)\n", "- [Demonstrate a full-featured script that plots a summary for binary data in addition to the donut plot with the binary group broken down by a group](demo_summary_binary.ipynb)\n", "- [Demonstrate using a custom color list](demo_custom_color_list.ipynb)\n", "\n", "Go back to [the first page in the series](index.ipynb) if you want a full-featured script that provides the fastest route to a donut plot that you can easily customize.\n", "\n", "----\n", "\n", "\n", "----" ] } ], "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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }