udemy-ML/05-Seaborn/.ipynb_checkpoints/01-Distribution-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": [
    "# Distributions\n",
    "\n",
    "There are many ways to display the distributions of a feature. In this notebook we explore 3 related plots for displaying a distribution,  the rugplot , the distplot (histogram), and kdeplot.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----\n",
    "\n",
    "### IMPORTANT NOTE!\n",
    "\n",
    "**DO NOT WORRY IF YOUR PLOTS STYLING APPEARS SLIGHTLY DIFFERENT, WE WILL SHOW YOU HOW TO EDIT THE COLOR AND STYLE OF THE PLOTS LATER ON!**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Data\n",
    "\n",
    "We'll use some generated data from: http://roycekimmons.com/tools/generated_data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"dm_office_sales.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>division</th>\n",
       "      <th>level of education</th>\n",
       "      <th>training level</th>\n",
       "      <th>work experience</th>\n",
       "      <th>salary</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>printers</td>\n",
       "      <td>some college</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>91684</td>\n",
       "      <td>372302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>printers</td>\n",
       "      <td>associate's degree</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>119679</td>\n",
       "      <td>495660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>peripherals</td>\n",
       "      <td>high school</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>82045</td>\n",
       "      <td>320453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>office supplies</td>\n",
       "      <td>associate's degree</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>92949</td>\n",
       "      <td>377148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>office supplies</td>\n",
       "      <td>high school</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>71280</td>\n",
       "      <td>312802</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          division  level of education  training level  work experience  \\\n",
       "0         printers        some college               2                6   \n",
       "1         printers  associate's degree               2               10   \n",
       "2      peripherals         high school               0                9   \n",
       "3  office supplies  associate's degree               2                5   \n",
       "4  office supplies         high school               1                5   \n",
       "\n",
       "   salary   sales  \n",
       "0   91684  372302  \n",
       "1  119679  495660  \n",
       "2   82045  320453  \n",
       "3   92949  377148  \n",
       "4   71280  312802  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 6 columns):\n",
      " #   Column              Non-Null Count  Dtype \n",
      "---  ------              --------------  ----- \n",
      " 0   division            1000 non-null   object\n",
      " 1   level of education  1000 non-null   object\n",
      " 2   training level      1000 non-null   int64 \n",
      " 3   work experience     1000 non-null   int64 \n",
      " 4   salary              1000 non-null   int64 \n",
      " 5   sales               1000 non-null   int64 \n",
      "dtypes: int64(4), object(2)\n",
      "memory usage: 47.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rugplot\n",
    "\n",
    "Very simple plot that puts down one mark per data point. This plot needs the single array passed in directly. We won't use it too much since its not very clarifying for large data sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='salary'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The y axis doesn't really represent anything\n",
    "# X axis is just a stick per data point\n",
    "sns.rugplot(x='salary',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='salary'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.rugplot(x='salary',data=df,height=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## displot and histplot()  \n",
    "The rugplot itself is not very informative for larger data sets distribution around the mean since so many ticks makes it hard to distinguish one tick from another. Instead we should count the number of tick marks per some segment of the x axis, then construct a histogram from this.\n",
    "\n",
    "The displot is a plot type that can show you the distribution of a single feature. It is a histogram with the option of adding a \"KDE\" plot (Kernel Density Estimation) on top of the histogram. Let's explore its use cases and syntax."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x21e6bf8a7c8>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=df,x='salary',kde=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Focusing on the Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x21e6c1f3108>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=df,x='salary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='salary', ylabel='Count'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=df,x='salary')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of Bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='salary', ylabel='Count'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=df,x='salary',bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='salary', ylabel='Count'>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=df,x='salary',bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding in a grid and styles\n",
    "\n",
    "You can reset to a different default style: one of {darkgrid, whitegrid, dark, white, ticks}.\n",
    "\n",
    "In a later notebook and lecture we will cover custom styling in a lot more detail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='salary', ylabel='Count'>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style='darkgrid')\n",
    "sns.histplot(data=df,x='salary',bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='salary', ylabel='Count'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style='white')\n",
    "sns.histplot(data=df,x='salary',bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding in keywords from matplotlib\n",
    "\n",
    "Seaborn plots can accept keyword arguments directly from the matplotlib code that seaborn uses. Keep in mind, not every seaborn plot can accept all matplotlib arguments, but the main styling parameters we've discussed are available. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x21e6eaaf508>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=df,x='salary',bins=20,kde=False,\n",
    "            color='red',edgecolor='black',lw=4,ls='--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Kernel Density Estimation Plot\n",
    "\n",
    "**Note: Review the video for full detailed explanation.**\n",
    "\n",
    "The KDE plot maps an estimate of a probability *density* function of a random variable. Kernel density estimation is a fundamental data smoothing problem where inferences about the population are made, based on a finite data sample.\n",
    "\n",
    "Let's build out a simple example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "# randint should be uniform, each age has the same chance of being chosen\n",
    "# note: in reality ages are almost never uniformally distributed, but this is just an example\n",
    "sample_ages = np.random.randint(0,100,200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([51, 92, 14, 71, 60, 20, 82, 86, 74, 74, 87, 99, 23,  2, 21, 52,  1,\n",
       "       87, 29, 37,  1, 63, 59, 20, 32, 75, 57, 21, 88, 48, 90, 58, 41, 91,\n",
       "       59, 79, 14, 61, 61, 46, 61, 50, 54, 63,  2, 50,  6, 20, 72, 38, 17,\n",
       "        3, 88, 59, 13,  8, 89, 52,  1, 83, 91, 59, 70, 43,  7, 46, 34, 77,\n",
       "       80, 35, 49,  3,  1,  5, 53,  3, 53, 92, 62, 17, 89, 43, 33, 73, 61,\n",
       "       99, 13, 94, 47, 14, 71, 77, 86, 61, 39, 84, 79, 81, 52, 23, 25, 88,\n",
       "       59, 40, 28, 14, 44, 64, 88, 70,  8, 87,  0,  7, 87, 62, 10, 80,  7,\n",
       "       34, 34, 32,  4, 40, 27,  6, 72, 71, 11, 33, 32, 47, 22, 61, 87, 36,\n",
       "       98, 43, 85, 90, 34, 64, 98, 46, 77,  2,  0,  4, 89, 13, 26,  8, 78,\n",
       "       14, 89, 41, 76, 50, 62, 95, 51, 95,  3, 93, 22, 14, 42, 28, 35, 12,\n",
       "       31, 70, 58, 85, 27, 65, 41, 44, 61, 56,  5, 27, 27, 43, 83, 29, 61,\n",
       "       74, 91, 88, 61, 96,  0, 26, 61, 76,  2, 69, 71, 26])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_ages = pd.DataFrame(sample_ages,columns=[\"age\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age\n",
       "0   51\n",
       "1   92\n",
       "2   14\n",
       "3   71\n",
       "4   60"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_ages.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age'>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.rugplot(data=sample_ages,x='age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data does not contain 9\n",
      "Data does not contain 15\n",
      "Data does not contain 16\n",
      "Data does not contain 18\n",
      "Data does not contain 19\n",
      "Data does not contain 24\n",
      "Data does not contain 30\n",
      "Data does not contain 45\n",
      "Data does not contain 55\n",
      "Data does not contain 66\n",
      "Data does not contain 67\n",
      "Data does not contain 68\n",
      "Data does not contain 97\n"
     ]
    }
   ],
   "source": [
    "for age in range(0,100):\n",
    "    if age not in sample_ages:\n",
    "        print(f\"Data does not contain {age}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x21e6f883e08>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.displot(data=sample_ages,x='age',bins=10,rug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x21e6f9d82c8>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.displot(data=sample_ages,x='age',bins=10,rug=True,kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='Density'>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data=sample_ages,x='age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cut Off KDE\n",
    "\n",
    "We could cut off the KDE if we know our data has hard limits (no one can be a negative age and no one in the population can be older than 100 for some reason)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='Density'>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.figure(figsize=(12,8))\n",
    "sns.kdeplot(data=sample_ages,x='age',clip=[0,100])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binwidth\n",
    "\n",
    "As explained in the video, the KDE is constructed through the summation of the kernel (most commonly Gaussian), we can effect the bandwith of this kernel to make the KDE more \"sensitive\" to the data. Notice how with a smaller bandwith, the kernels don't stretch so wide, meaning we don't need the cut-off anymore. This is analagous to increasing the number of bins in a histogram (making the actual bins narrower)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='Density'>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data=sample_ages,x='age',bw_adjust=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='Density'>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data=sample_ages,x='age',bw_adjust=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='Density'>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data=sample_ages,x='age',bw_adjust=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic Styling\n",
    "\n",
    "There are a few basic styling calls directly availble in a KDE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='Density'>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data=sample_ages,x='age',bw_adjust=0.5,shade=True,color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 Dimensional KDE Plots\n",
    "\n",
    "We will cover these in more detail later, but just keep in mind you could compare two continuous features and create a 2d KDE plot showing there distributions with the same kdeplot() call. Don't worry about this now, since we will cover it in more detail later when we talk about comparing 2 features against each other in a plot call."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_data = pd.DataFrame(np.random.normal(0,1,size=(100,2)),columns=['x','y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.926178</td>\n",
       "      <td>1.909417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.398568</td>\n",
       "      <td>0.562969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.650643</td>\n",
       "      <td>-0.487125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.592394</td>\n",
       "      <td>-0.863991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.048522</td>\n",
       "      <td>-0.830950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>-0.360966</td>\n",
       "      <td>1.159330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>-1.081063</td>\n",
       "      <td>0.615936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.593101</td>\n",
       "      <td>-0.309546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.326133</td>\n",
       "      <td>-1.251114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.924027</td>\n",
       "      <td>-0.184902</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           x         y\n",
       "0   0.926178  1.909417\n",
       "1  -1.398568  0.562969\n",
       "2  -0.650643 -0.487125\n",
       "3  -0.592394 -0.863991\n",
       "4   0.048522 -0.830950\n",
       "..       ...       ...\n",
       "95 -0.360966  1.159330\n",
       "96 -1.081063  0.615936\n",
       "97  0.593101 -0.309546\n",
       "98  0.326133 -1.251114\n",
       "99  0.924027 -0.184902\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='x', ylabel='y'>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data=random_data,x='x',y='y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bonus Code for Visualizations from Video Lecture\n",
    "\n",
    "Below is the code used to create the visualizations shown in the video lecture for an explanation of a KDE plot. We will not cover this code in further detail, since it was only used for the creation of the slides shown in the video."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(101)\n",
    "x = np.random.normal(0, 1, size=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "bandwidth = x.std() * x.size ** (-0.001)\n",
    "support = np.linspace(-5, 5, 100)\n",
    "\n",
    "kernels = []\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "\n",
    "for x_i in x:\n",
    "\n",
    "    kernel = stats.norm(x_i, bandwidth).pdf(support)\n",
    "    kernels.append(kernel)\n",
    "    plt.plot(support, kernel, color=\"lightblue\")\n",
    "\n",
    "sns.rugplot(x, color=\"darkblue\", linewidth=4);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAFoCAYAAABOqt04AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAABGvElEQVR4nO3deVzVdeL98XO57CAgCIiioqKgIOK+pGapg/temZZt2r45k5PT1GTNNM30dbKpbLVyGpfU3NLMJcdS0zI3BHdcQ2QTRHYul/v7w+I3ThSW4Ocur+fj4UPv/dyr51699x4/972YbDabTQAAAAAu42Z0AAAAAMAeUZQBAACAGlCUAQAAgBpQlAEAAIAaUJQBAACAGrgbHaAmZWVlSk1NVWhoqMxms9FxAAAA4KSsVqtycnIUHx8vb2/vy47ZZVFOTU3VpEmTjI4BAAAAF7FgwQJ17dr1suvssiiHhoZKuhS4cePGBqcBAACAs8rMzNSkSZOq++d/s8ui/MNwi8aNGysyMtLgNAAAAHB2NQ33ZTIfAAAAUAOKMgAAAFADijIAAABQA4oyAAAAUAOKMgAAAFADijIAAABQA4oyAAAAUAOKMgAAAFADijIAAABQA4oyAAAAUAOKMgAAAFADijIAAABQA4oyAAAAUAOKMgAAAFADijIAAABQA4oyAAAAUAOKMgAAAFADijIAAABQA4oyAAAAUAOKMgAAAFADijIAAABQA4oyAAAAUAOKMgAAAFADd6MDAADsT1lFpbLySpR1vkS5BaXy8XJXgJ+nGvh6KsDv0g8fL3eZTCajowJAvaEoA4CLKymz6OvUTO07mq3M8yXKPF+s/MLyWu/n4e6mmBYN1SU2XF1iwxQVEUBxBuBUKMoA4ILKyiu182Cmtu47q92Hs2WprFLDBl5qFt5AXduFq3GInxqH+KpxiJ9CAr1VVmFVYXGFLhZX6GJxuS4WW5R3sUz703L0r08P6l+fHlRIoHd1ae4cGyZvTz5iADg23sUAwIUcOHFea7ad0M6DWaqwWBUc4KXBvaLUL7Gp2jZvKDe3nzkjHFrz1ecLSrXncLZ2Hc7StuSz2vDNaQX5e2n09a019LqW8vHiowaAY7qid6/Vq1frzTfflMVi0Z133qlJkyZddvzzzz/Xa6+9JpvNpsjISL344osKDAzUypUrNWvWLIWEhEiS+vfvr2nTptX9owAA/KwzmRf1r08PaefBTAX4eWpAt2bqm9hU7VuGyPxz5fgKhAT6aFCPFhrUo4UqrVVKPZ6r5ZvTNO/Tg1q2OU2jrm+l4de1kp+PRx09GgC4NmotyllZWZo9e7aWL18uT09PTZgwQT169FB0dLQkqaioSDNnztSyZcsUHh6uf/7zn3rttdf09NNPKyUlRTNmzNDw4cPr/YEAAH4s90KpFq4/rE3fnpG3l7smD22nEX1b1duwCHezmxLbhimxbZgOn87T4o1HNf+zw1rxxXGN7NtKI/u1lj+FGYCDqPWdcvv27erZs6eCgoIkSUlJSVq3bp0efvhhSZLFYtHMmTMVHh4uSYqJidHq1aslSSkpKTp9+rTeeecdtW3bVs8884wCAwPr6aEAAH5QVGrRx5uOavXWE6qySSP6ttbNA9sqwM/zmmWIbRGsZ6f0VNp3F/TRxiNatOGI1u04pUdv6aSu7cKvWQ4A+LVqXUc5OztboaH/f2BaWFiYsrKyqi83bNhQAwcOlCSVlZXpnXfeqb4cGhqqRx55RKtWrVJERISef/75us4PAPgfB06c1yP/9x8t/yJNvTs20VszBmjKqPhrWpL/W3SzID19dw/Nfvx6Bfp76bm5X+v1pftUWl5pSB4AuFK1nlG22Ww/uq6m5X8KCwv14IMPKjY2VmPGjJEkzZkzp/r4lClTqgs0AKDuWatsWrLxiD7aeEThIX6a9Wg/tW3e0OhY1aKbBenlx/tpwbrDWv5FmpKP5ejxCZ0V1yrE6GgAUKNazyiHh4crNze3+nJ2drbCwsIuu012drYmTpyo2NhYvfDCC5IuFed58+ZV38Zms8ndnZnPAFAfci+U6um3vtLCDUfUr3OkXpl2vV2V5B94uJt15/A4vfhgH0nSH97Ypg9WH1CFxWpwMgD4sVqLcu/evbVjxw7l5eWptLRUGzZsUL9+/aqPW61W3X///RoyZIj++Mc/Vp9t9vX11dy5c5WcnCxJmj9/vgYNGlRPDwMAXNfOA5l69B9fKO27C5p2ayf9bmIX+Xrb94S5uFYhevV3NyipZ5SWf5Gm6a9uVe6FUqNjAcBlaj3FGx4ermnTpmny5MmyWCwaP368EhISNHXqVD366KPKzMzUwYMHZbVatX79eklSfHy8XnjhBb3yyiuaOXOmysrKFBUVpZdeeqneHxAAuIqqKps+WHNAK788rlZNAjX99i6KDGtgdKwr5uPlrofGd1S39uGaNX+3nnh1i56d0lMtmzDpG4B9MNlqGoRssPT0dA0YMECbNm1SZGSk0XEAwO5YKqv0yqI92rLvrIb0jtLUUfHycDcbHetXO5lRoOfmfq2SskrNmNxNnWPDar8TANSBn+udtQ69AADYl9LySv35va+1Zd9Z3TmsvR4Ym+DQJVmSWjYJ1D8e66fGIb567r2vteGb00ZHAgCKMgA4koKicv3xza+UnJarx25J1Lgb29S4EpEjCgn00d8e6qPENqF6bck+/fuzQzWuvAQA1wpFGQAcRHZeiZ58fatOn7uoP97ZXQO7tzA6Up3z9fbQM/f00KDuzbXk86N6edEeWa1VRscC4KJYrw0AHMDpzIv609s7VG6x6vn7ejv12sPuZjc9cnOiwoN9NX/dYUnStAmd5ebmHGfOATgOijIA2LnT5y7qD29sk4e7WX97qI+iIgKMjlTvTCaTbhkUI5mk+Z8dlpeHWQ+N7+g0w0wAOAaKMgDYsczzxfrTO9vl4e6mvz/cR41D/IyOdE3dMjBG5RVWLd10TJ4eZk0dFU9ZBnDNUJQBwE7lXSzTM29vl6WySi8+5Hol+Qe3D2mncotVn2w5IS8PsyYPbUdZBnBNUJQBwA4VlVTo2Xd26EJhuf5yf2+1aOz8wy1+islk0pSR8aqwVOnj/xyTt6f50rAMAKhnFGUAsDNl5ZV6bu7XSs8u0rNTeiimRbDRkQxnMpn0wNgElVdUav66w/L0MGtM/2ijYwFwchRlALAjlkqr/jpvp46eydeTk7spsS071P3Azc2kx27ppIrKKr2/+oAaNvBS/y7NjI4FwIlRlAHATlRV2fSPhXu092iOHr05Ub0Tmhgdye6YzW763cQuKigq16tL9qlxiJ9iozjjDqB+sOEIANiJ+esO6avkDN01PE6DejjfZiJ1xcPdTX+4o7saBfrohQ92KjuvxOhIAJwURRkA7MCWvelauumYknq20Jj+rY2OY/cC/Dz1zD09ZKm06s/vf6OSMovRkQA4IYoyABgsLf2C/rl4n9q3DNZ9YxJY+uwKNQtvoCcnd9OZrEL9Y8EeWatsRkcC4GQoygBgoPzCMr3w/jcK8PPUH+7oLg933pZ/iU4xYbp3VLx2HszUh58eNDoOACfDZD4AMIil0qoX532riyUW/d8jfRXUwMvoSA5pWJ9W+i67SMu/SFOzcH8N7M74bgB1g1MXAGAAm82mN5ft16FTeXp8Qie1ahpodCSHNnVUvBLbhmrOx8k6ePK80XEAOAmKMgAYYM22k9q484xuGdhWfRObGh3H4ZnNbnpycjeFBvnqpX/vUkFRudGRADgBijIAXGMpx3M195NU9YhrrIlJsUbHcRr+Ph56cnJXXSyu0MuL9qiKyX0ArhJFGQCuoYKics2av1uNg33124md5ebGChd1qXVkkKaOiteew9latvmY0XEAODiKMgBcI1VVNr3y0V5dLK7Qk5O7ydfbw+hITmlwryj1S2yq+Z8dUurxXKPjAHBgFGUAuEZWbTmuXYeyNGVkHJP36pHJZNJDN3VURCM//d/8XbpQyHhlAL8ORRkAroGjZ/L1r08PqleHCA29rqXRcZyer7eHnpzcTUUlFv1j4W42IwHwq1CUAaCeFZda9NK/dykk0Fu
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.integrate import trapz\n",
    "plt.figure(figsize=(12,6))\n",
    "density = np.sum(kernels, axis=0)\n",
    "density /= trapz(density, support)\n",
    "plt.plot(support, density);\n",
    "sns.rugplot(x, color=\"darkblue\", linewidth=4);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "bandwidth = x.std() * x.size ** (-0.001)\n",
    "support = np.linspace(-5, 5, 100)\n",
    "\n",
    "kernels = []\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "\n",
    "for x_i in x:\n",
    "\n",
    "    kernel = stats.norm(x_i, bandwidth).pdf(support)\n",
    "    kernels.append(kernel)\n",
    "    plt.plot(support, kernel, color=\"lightblue\")\n",
    "\n",
    "sns.rugplot(x, color=\"darkblue\", linewidth=4);\n",
    "sns.kdeplot(x,linewidth=6,shade=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "---"
   ]
  }
 ],
 "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
}