Data Formatting Test Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "import names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.randint(low=0, high=100, size=300)\n",
    "y = np.random.randint(low=0, high=100, size=300)\n",
    "\n",
    "types = [80,60,34,49,3,5,9,6]\n",
    "p_dist = []\n",
    "for i in types:\n",
    "    p_dist.append(i/sum(types))\n",
    "\n",
    "from numpy.random import choice\n",
    "draw = choice(['a','b','c','d','e','f','g','h'],300,\n",
    "              p= p_dist)\n",
    "\n",
    "raw_data = pd.DataFrame({'x':x,'y':y,'type':draw})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    109\n",
      "b     66\n",
      "d     58\n",
      "c     37\n",
      "g     14\n",
      "f      9\n",
      "e      5\n",
      "h      2\n",
      "Name: type, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(raw_data['type'].value_counts())\n",
    "\n",
    "sizes = list(raw_data['type'].value_counts())\n",
    "labels = list(raw_data['type'].value_counts().index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes,labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dataGrouping(df,groups=4):\n",
    "    sizes = list(df['type'].value_counts())\n",
    "    labels = list(df['type'].value_counts().index)    \n",
    "    new_df = df.copy()\n",
    "    \n",
    "    for i in labels[groups:]:\n",
    "        new_df['type'] = new_df['type'].replace(i,'other')\n",
    "    return new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a        109\n",
       "other     67\n",
       "b         66\n",
       "d         58\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = dataGrouping(raw_data,3)\n",
    "new_df['type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "new_sizes = list(new_df['type'].value_counts())\n",
    "new_labels = list(new_df['type'].value_counts().index)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(new_sizes,labels=new_labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "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",
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       "      x   y type\n",
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       "4    82  61    b\n",
       "..   ..  ..  ...\n",
       "295  35  59    g\n",
       "296  50  48    d\n",
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       "298   2  56    a\n",
       "299  44  54    c\n",
       "\n",
       "[300 rows x 3 columns]"
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       "      x   y   type\n",
       "0    88  52      b\n",
       "1    87  69      b\n",
       "2     9  15      b\n",
       "3    82  16      b\n",
       "4    82  61      b\n",
       "..   ..  ..    ...\n",
       "295  35  59  other\n",
       "296  50  48      d\n",
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       "298   2  56      a\n",
       "299  44  54  other\n",
       "\n",
       "[300 rows x 3 columns]"
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