01. Data Binning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b98fa997",
   "metadata": {},
   "source": [
    "# Data Binning\n",
    "* Data binning is a data pre-processing technique used to reduce the effects of minor observation errors. The original data values which fall into a given small interval, a bin, are replaced by a value representative of that interval, often a central value (mean or median) or categorical variables.\n",
    "\n",
    "* Histograms are an example of data binning used in order to observe underlying frequency distributions. They typically occur in one-dimensional space and in equal intervals for ease of visualization.\n",
    "\n",
    "* Binning is used in machine learning to speed up. The decision-tree boosting method for supervised classification and regression in algorithms such as Microsoft's LightGBM and scikit-learn's Histogram-based Gradient Boosting Classification Tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d55273a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Library\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#Loading data\n",
    "df=pd.read_csv(\"multiTimeline.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "706ad1d7",
   "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",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Cupcake</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2004-01</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2004-02</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004-03</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004-04</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004-05</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Month  Cupcake\n",
       "0  2004-01        4\n",
       "1  2004-02        7\n",
       "2  2004-03        5\n",
       "3  2004-04        6\n",
       "4  2004-05        5"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#top five rows\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d26a2a0c",
   "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",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Cupcake</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>2021-03</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>2021-05</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>2021-06</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>2021-07</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Month  Cupcake\n",
       "206  2021-03       47\n",
       "207  2021-04       43\n",
       "208  2021-05       43\n",
       "209  2021-06       40\n",
       "210  2021-07       38"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#last five rows\n",
    "df.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d2d18c20",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min_value : 4\n",
      "max_value : 100\n"
     ]
    }
   ],
   "source": [
    "#Getting minimum and maximum of the data column\n",
    "min_value=df['Cupcake'].min()\n",
    "max_value=df['Cupcake'].max()\n",
    "print('min_value :', min_value)\n",
    "print('max_value :', max_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6ff3f013",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  4.,  36.,  68., 100.])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating bins of equal width\n",
    "bins=np.linspace(min_value, max_value, 4)\n",
    "bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d90d3c03",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Labelling the bins\n",
    "#4-36 : small\n",
    "#36-68 : medium\n",
    "#68-100: big\n",
    "labels=['small','medium','big']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d26a1ab6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating new dataframe based on the bins\n",
    "data1=pd.cut(df['Cupcake'], bins=bins, labels=labels, include_lowest=True)\n",
    "#include_lowest=True includes lower limit of the bin while assigning categorical variable\n",
    "#if batch size was 36 it would be considerered as medium not small"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "82e07641",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    small\n",
       "1    small\n",
       "2    small\n",
       "3    small\n",
       "4    small\n",
       "Name: Cupcake, dtype: category\n",
       "Categories (3, object): ['small' < 'medium' < 'big']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4ffae3c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "206    medium\n",
       "207    medium\n",
       "208    medium\n",
       "209    medium\n",
       "210    medium\n",
       "Name: Cupcake, dtype: category\n",
       "Categories (3, object): ['small' < 'medium' < 'big']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de7e34e3",
   "metadata": {},
   "source": [
    "You can see that the new dataframe has categorical values in place of numerical values which are substituted based on binning,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f6362961",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "medium    83\n",
       "small     67\n",
       "big       61\n",
       "Name: Cupcake, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b406bb58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([67., 83., 61.]),\n",
       " array([0.        , 0.66666667, 1.33333333, 2.        ]),\n",
       " <BarContainer object of 3 artists>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#Plotting the histogram of bins\n",
    "plt.hist(data1,bins=3)\n",
    "#setting number of bins=3 since we have three categorical variables created based on binning\n",
    "#the same binning method is used by matplotlib for plotting the histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "23091d12",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Binning based on quantiles :\n",
    "#here we use quantiles such as quartiles, deciles etc for making bins\n",
    "labels=['small','medium','big','huge']\n",
    "data2=pd.qcut(df['Cupcake'], q=4, labels=labels)\n",
    "#precaution: number of labels should be equal to number of quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a377e4b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    small\n",
       "1    small\n",
       "2    small\n",
       "3    small\n",
       "4    small\n",
       "Name: Cupcake, dtype: category\n",
       "Categories (4, object): ['small' < 'medium' < 'big' < 'huge']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bf2db66e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "206    medium\n",
       "207    medium\n",
       "208    medium\n",
       "209    medium\n",
       "210    medium\n",
       "Name: Cupcake, dtype: category\n",
       "Categories (4, object): ['small' < 'medium' < 'big' < 'huge']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d24d15d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([55., 56., 47., 53.]),\n",
       " array([0.  , 0.75, 1.5 , 2.25, 3.  ]),\n",
       " <BarContainer object of 4 artists>)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#Plotting histograms with bins\n",
    "plt.hist(data2, bins=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23a8c9c1",
   "metadata": {},
   "source": [
    "The End"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}