tonyfast · September 20, 2021 17:02
diff --git a/2021-09-20-pandas-one-liner.ipynb b/2021-09-20-pandas-one-liner.ipynb
 {
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4a4e4198-ac5a-41a6-9cd9-f35bc42112c1",
   "metadata": {},
   "source": [
    "a one liner to demonstrate using `pandas.Series.apply` to widen dataframes with elements of containers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1cf2472b-28ca-4976-9ed9-9796ceab6baa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='language'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "    import pandas\n",
    "\n",
    "    pandas.read_json(\"https://api.github.com/users/tonyfast/gists\").set_index(\n",
    "        \"id\"\n",
    "    )[\"files\"].apply(\n",
    "        dict.values\n",
    "    ).apply(\n",
    "        list\n",
    "    ).apply(\n",
    "        pandas.Series # widen a dataframe by a list\n",
    "    ).stack(\n",
    "    ).apply(\n",
    "        pandas.Series # widen a dataframe by a dictionary\n",
    "    )[\"language\"].value_counts().plot.pie()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
 }
	{
	"cells": [
	{
	"cell_type": "markdown",
	"id": "4a4e4198-ac5a-41a6-9cd9-f35bc42112c1",
	"metadata": {},
	"source": [
	"a one liner to demonstrate using `pandas.Series.apply` to widen dataframes with elements of containers."
	]
	},
	{
	"cell_type": "code",
	"execution_count": 18,
	"id": "1cf2472b-28ca-4976-9ed9-9796ceab6baa",
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"<AxesSubplot:ylabel='language'>"
	]
	},
	"execution_count": 18,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"data": {
	"image/png": 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\n",
	"text/plain": [
	"<Figure size 432x288 with 1 Axes>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	" import pandas\n",
	"\n",
	" pandas.read_json(\"https://api.github.com/users/tonyfast/gists\").set_index(\n",
	" \"id\"\n",
	" )[\"files\"].apply(\n",
	" dict.values\n",
	" ).apply(\n",
	" list\n",
	" ).apply(\n",
	" pandas.Series # widen a dataframe by a list\n",
	" ).stack(\n",
	" ).apply(\n",
	" pandas.Series # widen a dataframe by a dictionary\n",
	" )[\"language\"].value_counts().plot.pie()"
	]
	}
	],
	"metadata": {
	"kernelspec": {
	"display_name": "Python 3 (ipykernel)",
	"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": "3.9.5"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 5
	}