seaborn_tutorial_02052020.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tutorial from https://elitedatascience.com/python-seaborn-tutorial\n",
    "# download the Pokemon dataset from the link"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pandas for managing datasets\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Matplotlib for additional customization\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seaborn for plotting and styling\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read dataset\n",
    "df = pd.read_csv('Pokemon.csv', encoding= 'unicode_escape')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>#</th>\n",
       "      <th>Name</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Stage</th>\n",
       "      <th>Legendary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>318</td>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>405</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>525</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>80</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>309</td>\n",
       "      <td>39</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Charmeleon</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>405</td>\n",
       "      <td>58</td>\n",
       "      <td>64</td>\n",
       "      <td>58</td>\n",
       "      <td>80</td>\n",
       "      <td>65</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   #        Name Type 1  Type 2  Total  HP  Attack  Defense  Sp. Atk  Sp. Def  \\\n",
       "0  1   Bulbasaur  Grass  Poison    318  45      49       49       65       65   \n",
       "1  2     Ivysaur  Grass  Poison    405  60      62       63       80       80   \n",
       "2  3    Venusaur  Grass  Poison    525  80      82       83      100      100   \n",
       "3  4  Charmander   Fire     NaN    309  39      52       43       60       50   \n",
       "4  5  Charmeleon   Fire     NaN    405  58      64       58       80       65   \n",
       "\n",
       "   Speed  Stage  Legendary  \n",
       "0     45      1      False  \n",
       "1     60      2      False  \n",
       "2     80      3      False  \n",
       "3     65      1      False  \n",
       "4     80      2      False  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display first 5 observations\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a21e86cc0>"
      ]
     },
     "execution_count": 8,
     "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": [
    "# Recommended way\n",
    "sns.lmplot(x='Attack', y='Defense', data=df)\n",
    " \n",
    "# Alternative way\n",
    "# sns.lmplot(x=df.Attack, y=df.Defense)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scatterplot arguments\n",
    "sns.lmplot(x='Attack', y='Defense', data=df,\n",
    "           fit_reg=False, # No regression line\n",
    "           hue='Stage')   # Color by evolution stage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot using Seaborn\n",
    "sns.lmplot(x='Attack', y='Defense', data=df,\n",
    "           fit_reg=False, \n",
    "           hue='Stage')\n",
    " \n",
    "# Tweak using Matplotlib\n",
    "plt.ylim(0, None)\n",
    "plt.xlim(0, None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Boxplot\n",
    "sns.boxplot(data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pre-format DataFrame\n",
    "stats_df = df.drop(['Total', 'Stage', 'Legendary'], axis=1)\n",
    " \n",
    "# New boxplot using stats_df\n",
    "sns.boxplot(data=stats_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set theme\n",
    "sns.set_style('whitegrid')\n",
    " \n",
    "# Violin plot\n",
    "sns.violinplot(x='Type 1', y='Attack', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pkmn_type_colors = ['#78C850',  # Grass\n",
    "                    '#F08030',  # Fire\n",
    "                    '#6890F0',  # Water\n",
    "                    '#A8B820',  # Bug\n",
    "                    '#A8A878',  # Normal\n",
    "                    '#A040A0',  # Poison\n",
    "                    '#F8D030',  # Electric\n",
    "                    '#E0C068',  # Ground\n",
    "                    '#EE99AC',  # Fairy\n",
    "                    '#C03028',  # Fighting\n",
    "                    '#F85888',  # Psychic\n",
    "                    '#B8A038',  # Rock\n",
    "                    '#705898',  # Ghost\n",
    "                    '#98D8D8',  # Ice\n",
    "                    '#7038F8',  # Dragon\n",
    "                   ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Violin plot with Pokemon color palette\n",
    "sns.violinplot(x='Type 1', y='Attack', data=df, \n",
    "               palette=pkmn_type_colors) # Set color palette"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Swarm plot with Pokemon color palette\n",
    "sns.swarmplot(x='Type 1', y='Attack', data=df, \n",
    "              palette=pkmn_type_colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set figure size with matplotlib\n",
    "plt.figure(figsize=(10,6))\n",
    " \n",
    "# Create plot\n",
    "sns.violinplot(x='Type 1',\n",
    "               y='Attack', \n",
    "               data=df, \n",
    "               inner=None, # Remove the bars inside the violins\n",
    "               palette=pkmn_type_colors)\n",
    " \n",
    "sns.swarmplot(x='Type 1', \n",
    "              y='Attack', \n",
    "              data=df, \n",
    "              color='k', # Make points black\n",
    "              alpha=0.7) # and slightly transparent\n",
    " \n",
    "# Set title with matplotlib\n",
    "plt.title('Attack by Type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Melt DataFrame\n",
    "melted_df = pd.melt(stats_df, \n",
    "                    id_vars=[\"Name\", \"Type 1\", \"Type 2\"], # Variables to keep\n",
    "                    var_name=\"Stat\") # Name of melted variable\n",
    "melted_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print( stats_df.shape )\n",
    "print( melted_df.shape )\n",
    "# (151, 9)\n",
    "# (906, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Swarmplot with melted_df\n",
    "sns.swarmplot(x='Stat', y='value', data=melted_df, \n",
    "              hue='Type 1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Enlarge the plot\n",
    "plt.figure(figsize=(10,6))\n",
    " \n",
    "sns.swarmplot(x='Stat', \n",
    "              y='value', \n",
    "              data=melted_df, \n",
    "              hue='Type 1', \n",
    "              split=True, # 2. Separate points by hue\n",
    "              palette=pkmn_type_colors) # 3. Use Pokemon palette\n",
    " \n",
    "# 4. Adjust the y-axis\n",
    "plt.ylim(0, 260)\n",
    " \n",
    "# 5. Place legend to the right\n",
    "plt.legend(bbox_to_anchor=(1, 1), loc=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate correlations\n",
    "corr = stats_df.corr()\n",
    " \n",
    "# Heatmap\n",
    "sns.heatmap(corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Distribution Plot (a.k.a. Histogram)\n",
    "sns.distplot(df.Attack)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count Plot (a.k.a. Bar Plot)\n",
    "sns.countplot(x='Type 1', data=df, palette=pkmn_type_colors)\n",
    " \n",
    "# Rotate x-labels\n",
    "plt.xticks(rotation=-45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Factor Plot\n",
    "g = sns.factorplot(x='Type 1', \n",
    "                   y='Attack', \n",
    "                   data=df, \n",
    "                   hue='Stage',  # Color by stage\n",
    "                   col='Stage',  # Separate by stage\n",
    "                   kind='swarm') # Swarmplot\n",
    " \n",
    "# Rotate x-axis labels\n",
    "g.set_xticklabels(rotation=-45)\n",
    " \n",
    "# Doesn't work because only rotates last plot\n",
    "# plt.xticks(rotation=-45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Density Plot\n",
    "sns.kdeplot(df.Attack, df.Defense)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Joint Distribution Plot\n",
    "sns.jointplot(x='Attack', y='Defense', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### End of tutorial. \n",
    "# You have learned plenty of Seaborn. Apply it to your data. \n",
    "# Feel free to email me at ndacayis-at-usc.edu if any questions. "
   ]
  }
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