00_readme.md

Random Utilities

cat_scatter.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6dd85844",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import pyspark\n",
    "from pyspark.sql import Window\n",
    "from pyspark.sql.functions import *\n",
    "import plotly.express as px\n",
    "\n",
    "class dotdict(dict):\n",
    "    __getattr__ = dict.__getitem__\n",
    "    __setattr__ = dict.__setitem__\n",
    "    __delattr__ = dict.__delitem__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "43ecb66c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def despine(ax):\n",
    "    for side in ['top', 'bottom', 'left', 'right']:\n",
    "        ax.spines[side].set_visible(False)\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "19d5379b",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rc('font', family='Open Sans')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a21dc520",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: An illegal reflective access operation has occurred\n",
      "WARNING: Illegal reflective access by org.apache.spark.unsafe.Platform (file:/opt/homebrew/anaconda3/lib/python3.9/site-packages/pyspark/jars/spark-unsafe_2.12-3.2.1.jar) to constructor java.nio.DirectByteBuffer(long,int)\n",
      "WARNING: Please consider reporting this to the maintainers of org.apache.spark.unsafe.Platform\n",
      "WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations\n",
      "WARNING: All illegal access operations will be denied in a future release\n",
      "Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties\n",
      "Setting default log level to \"WARN\".\n",
      "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n",
      "22/07/19 21:25:14 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n"
     ]
    }
   ],
   "source": [
    "spark = pyspark.sql.SparkSession.builder.getOrCreate()\n",
    "tips = spark.createDataFrame(px.data.tips())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4d9d53b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "t = (\n",
    "    tips.rollup('day', 'smoker')\n",
    "    .agg(mean('tip').alias('avg_tip'))\n",
    "    .dropna(subset='day')\n",
    "    .fillna(subset='smoker', value='Overall')\n",
    "    .groupby('day')\n",
    "    .pivot('smoker')\n",
    "    .agg(mean('avg_tip').alias('avg_tip'))\n",
    "    .toPandas()\n",
    ")\n",
    "\n",
    "t.day = pd.Categorical(t.day, categories=['Thur', 'Fri', 'Sat', 'Sun'], ordered=True)\n",
    "t = t.sort_values('day', ascending=False).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ae60e1a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rc('axes.spines', top=False, bottom=False, left=False, right=False)\n",
    "plt.rc('font', family='Open Sans')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a2576f9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.0, 1.0, 'Average Tip Amount By Day for Smokers and Non Smokers')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 3315x1785 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(13, 7), dpi=255)\n",
    "ax.scatter(t.No, t.day, label='Non Smoker', marker='o', color='white', ec='green', s=250)\n",
    "ax.scatter(t.Yes, t.day, label='Smoker', marker='o', color='white', ec='red', s=250)\n",
    "ax.scatter(t.Overall, t.day, label='Overall', marker='|', color='gray', s=250)\n",
    "ax.grid(axis='y')\n",
    "ax.set(ylim=(-0.5, 3.5), xticks=[])\n",
    "ax.xaxis.set_major_formatter('${:.2f}'.format)\n",
    "ax.legend(loc='upper right', bbox_to_anchor=(1, .85), frameon=False, labelspacing=1)\n",
    "for y, row in t.iterrows():\n",
    "    ax.text(row.No, y + .2, f'${row.No:.2f}', ha='center', color='darkgreen')\n",
    "    ax.text(row.Yes, y + .2, f'${row.Yes:.2f}', ha='center', color='firebrick')\n",
    "    ax.text(row.Overall, y - .2, f'${row.Overall:.2f}', ha='center', color='#222')\n",
    "ax.set_title('Average Tip Amount By Day for Smokers and Non Smokers', loc='left', size=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "824df7f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "22/07/19 21:25:18 WARN WindowExec: No Partition Defined for Window operation! Moving all data to a single partition, this can cause serious performance degradation.\n",
      "22/07/19 21:25:19 WARN WindowExec: No Partition Defined for Window operation! Moving all data to a single partition, this can cause serious performance degradation.\n"
     ]
    }
   ],
   "source": [
    "t = (\n",
    "    tips.withColumn('tip_average', mean('tip').over(Window.orderBy()))\n",
    "    .withColumn('tip', expr('tip - tip_average'))\n",
    "    .rollup('day', 'smoker')\n",
    "    .agg(mean('tip').alias('avg_tip'))\n",
    "    .dropna(subset='day')\n",
    "    .fillna(subset='smoker', value='Overall')\n",
    "    .groupby('day')\n",
    "    .pivot('smoker')\n",
    "    .agg(mean('avg_tip').alias('avg_tip'))\n",
    "    .toPandas()\n",
    ")\n",
    "\n",
    "t.day = pd.Categorical(t.day, categories=['Thur', 'Fri', 'Sat', 'Sun'], ordered=True)\n",
    "t = t.sort_values('day', ascending=False).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "86648122",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters\n",
    "df = t\n",
    "y = 'day'\n",
    "# TODO: is this much flexibility good? maybe just allow for specifying a baseline and compare?\n",
    "series = [\n",
    "    dotdict(\n",
    "        col='Yes',\n",
    "        label='Smokers',\n",
    "        color='blue',\n",
    "        textcolor='navy',\n",
    "        marker='o',\n",
    "        textoffset=.2,\n",
    "    ),\n",
    "    dotdict(\n",
    "        col='Overall',\n",
    "        label='Daily Average',\n",
    "        color='gray',\n",
    "        textcolor='#333',\n",
    "        marker='|',\n",
    "        textoffset=-.2,\n",
    "    ),\n",
    "]\n",
    "number_fmt = '${:.2f}'.format\n",
    "size = 250\n",
    "figsize = (13, 7)\n",
    "dpi = 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "00f6862a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cat_scatter(\n",
    "    df: pd.DataFrame,\n",
    "    y: str,\n",
    "    series: list,\n",
    "    number_fmt: str,\n",
    "    title: str,\n",
    "    size=250,\n",
    "    figsize=(13, 7),\n",
    "    dpi=255,\n",
    "    title_font_size=14,\n",
    "    draw_zero_line=False,\n",
    "    zero_line_label='',\n",
    "    legend_y_pos=.85,\n",
    "    legend_inside_or_outside='inside',\n",
    "):\n",
    "    fig, ax = plt.subplots(figsize=figsize, dpi=dpi)\n",
    "    \n",
    "    for data in series:\n",
    "        ax.scatter(\n",
    "            x=t[data.col],\n",
    "            y=t[y],\n",
    "            label=data.label,\n",
    "            marker=data.marker,\n",
    "            color='white',\n",
    "            ec=data.color,\n",
    "            s=size,\n",
    "        )\n",
    "\n",
    "    for y, row in t.iterrows():\n",
    "        for data in series:\n",
    "            ax.text(\n",
    "                row[data.col],\n",
    "                y + data.textoffset,\n",
    "                number_fmt(row[data.col]),\n",
    "                ha='center',\n",
    "                color=data.textcolor,\n",
    "            )\n",
    "\n",
    "    ax.grid(axis='y')\n",
    "    print(ax.get_ylim())\n",
    "    # TODO: programmatic y limits        \n",
    "    ax.set(ylim=(-0.5, 3.5), xticks=[])\n",
    "    ax.xaxis.set_major_formatter(number_fmt)\n",
    "\n",
    "    legend_loc = 'upper right' if legend_inside_or_outside == 'inside' else 'upper left'\n",
    "    ax.legend(loc=legend_loc, bbox_to_anchor=(1, legend_y_pos), frameon=False, labelspacing=1)\n",
    "\n",
    "    ax.set_title(\n",
    "        title, loc='left', size=title_font_size\n",
    "    )\n",
    "    \n",
    "    if draw_zero_line:\n",
    "        ax.vlines(0, *ax.get_ylim(), color='gray', ls=':')\n",
    "        if zero_line_label:\n",
    "            ax.text(0, -.6, zero_line_label, ha='center', va='top')\n",
    "        \n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "352f46ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-0.15000000000000002, 3.15)\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'number_format' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/5m/hv7mb0z95vnfhz0xb0s_rxym0000gn/T/ipykernel_40984/2437512902.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     17\u001b[0m     ),\n\u001b[1;32m     18\u001b[0m ]\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0mcat_scatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'day'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'${:.2}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Demo'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdraw_zero_line\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/var/folders/5m/hv7mb0z95vnfhz0xb0s_rxym0000gn/T/ipykernel_40984/1625694140.py\u001b[0m in \u001b[0;36mcat_scatter\u001b[0;34m(df, y, series, number_fmt, title, size, figsize, dpi, title_font_size, draw_zero_line, zero_line_label, legend_y_pos, legend_inside_or_outside)\u001b[0m\n\u001b[1;32m     41\u001b[0m     \u001b[0;31m# TODO: programmatic y limits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mylim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxticks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m     \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_major_formatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumber_format\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m     \u001b[0mlegend_loc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'upper right'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlegend_inside_or_outside\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'inside'\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'upper left'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'number_format' is not defined"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 3315x1785 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "series = [\n",
    "    dotdict(\n",
    "        col='Yes',\n",
    "        label='Smokers',\n",
    "        color='blue',\n",
    "        textcolor='navy',\n",
    "        marker='o',\n",
    "        textoffset=.2,\n",
    "    ),\n",
    "    dotdict(\n",
    "        col='Overall',\n",
    "        label='Daily Average',\n",
    "        color='gray',\n",
    "        textcolor='#333',\n",
    "        marker='|',\n",
    "        textoffset=-.2,\n",
    "    ),\n",
    "]\n",
    "cat_scatter(t, 'day', series, '${:.2}'.format, 'Demo', draw_zero_line=True)"
   ]
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}

market_basket_analysis.ipynb

pptx.ipynb