13.14 Factor Plots for Categorical Data.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](img/cover4.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><em>Copyright! This material is protected, please do not copy or distribute.    by:Taher Assaf</em></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "<h1 align=\"center\">Udemy course : Python Bootcamp for Data Science 2021 Numpy Pandas & Seaborn</h1> \n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 13.14 Factor Plots for Categorical Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we import pandas and seaborn libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we read a dataframe from a csv file using the function **pd.read_csv()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "credit = pd.read_csv('data/credit.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can display the head of this dataframe by using the function **head()**: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hide_input": false
   },
   "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>Income</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Cards</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Student</th>\n",
       "      <th>Married</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>Balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19.225</td>\n",
       "      <td>1433</td>\n",
       "      <td>122</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "      <td>14</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>43.540</td>\n",
       "      <td>2906</td>\n",
       "      <td>232</td>\n",
       "      <td>4</td>\n",
       "      <td>69</td>\n",
       "      <td>11</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55.367</td>\n",
       "      <td>6340</td>\n",
       "      <td>448</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>15</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.741</td>\n",
       "      <td>2271</td>\n",
       "      <td>182</td>\n",
       "      <td>4</td>\n",
       "      <td>59</td>\n",
       "      <td>12</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Asian</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15.560</td>\n",
       "      <td>4307</td>\n",
       "      <td>352</td>\n",
       "      <td>4</td>\n",
       "      <td>57</td>\n",
       "      <td>8</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>African American</td>\n",
       "      <td>579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Income  Limit  Rating  Cards  Age  Education  Gender Student Married  \\\n",
       "0  19.225   1433     122      3   38         14  Female      No      No   \n",
       "1  43.540   2906     232      4   69         11    Male      No      No   \n",
       "2  55.367   6340     448      1   33         15    Male      No     Yes   \n",
       "3  11.741   2271     182      4   59         12  Female      No      No   \n",
       "4  15.560   4307     352      4   57          8    Male      No     Yes   \n",
       "\n",
       "          Ethnicity  Balance  \n",
       "0         Caucasian        0  \n",
       "1         Caucasian        0  \n",
       "2         Caucasian      815  \n",
       "3             Asian        0  \n",
       "4  African American      579  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use seaborn to create a factor plot between 'gender' and 'income' columns using the function **sns.catplot()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x166b2ddda00>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x = 'Gender', y =  'Income', data = credit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In factor plots, we can use bars instead of dots by using the argument **kind = 'bar'**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x166b2f14b20>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x = 'Gender', y =  'Income', data = credit, kind = 'bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can remove confidence intervals ( the black lines on top of the bars) by using the argument **(ci = False)**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x166b2f148b0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x = 'Gender', y =  'Income', data = credit, kind = 'bar', ci  =False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use **box plots** for a categorical variable to provide additional statistics using the argument **(kind = 'box')**:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x166b2fdcbe0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x = 'Gender', y =  'Income', data = credit, kind = 'box')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additional, we can also add a hue variable using the argument **hue**, in this example to explore the relationship between ethnicity and income with respect to the hue variable 'Gender'. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x166ae17ea00>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x = 'Ethnicity', y =  'Income', hue = 'Gender', data = credit, kind = 'bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "<h1 align=\"center\">Thank You</h1> \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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}