udemy-ML/05-Seaborn/.ipynb_checkpoints/04-Comparison-Plots-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a href='http://www.pieriandata.com'><img src='../Pierian_Data_Logo.png'/></a>\n",
    "___\n",
    "<center><em>Copyright by Pierian Data Inc.</em></center>\n",
    "<center><em>For more information, visit us at <a href='http://www.pieriandata.com'>www.pieriandata.com</a></em></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparison Plots with pairplot() and jointplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"StudentsPerformance.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": 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>gender</th>\n",
       "      <th>race/ethnicity</th>\n",
       "      <th>parental level of education</th>\n",
       "      <th>lunch</th>\n",
       "      <th>test preparation course</th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>completed</td>\n",
       "      <td>69</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>master's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>90</td>\n",
       "      <td>95</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>group A</td>\n",
       "      <td>associate's degree</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>47</td>\n",
       "      <td>57</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>76</td>\n",
       "      <td>78</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender race/ethnicity parental level of education         lunch  \\\n",
       "0  female        group B           bachelor's degree      standard   \n",
       "1  female        group C                some college      standard   \n",
       "2  female        group B             master's degree      standard   \n",
       "3    male        group A          associate's degree  free/reduced   \n",
       "4    male        group C                some college      standard   \n",
       "\n",
       "  test preparation course  math score  reading score  writing score  \n",
       "0                    none          72             72             74  \n",
       "1               completed          69             90             88  \n",
       "2                    none          90             95             93  \n",
       "3                    none          47             57             44  \n",
       "4                    none          76             78             75  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## jointplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1ec7d365b08>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='math score',y='reading score',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1ec7dde1188>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='math score',y='reading score',data=df,kind='hex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1ec7ef41b88>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='math score',y='reading score',data=df,kind='kde')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pairplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1ec7f071348>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1ec01c711c8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 610.5x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df,hue='gender',palette='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1ec7fbd0608>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 610.5x540 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df,hue='gender',palette='viridis',corner=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1ec02dccf88>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 610.5x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df,hue='gender',palette='viridis',diag_kind='hist')"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}