13.12 Histograms and Density Plots.ipynb

{
 "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": [
    "## 13.12 Histograms and Density Plots"
   ]
  },
  {
   "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()** and display its head using the **head()** function: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>age</th>\n",
       "      <th>wage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  wage\n",
       "0  39.0  48.0\n",
       "1  28.0  44.0\n",
       "2  28.0  49.0\n",
       "3  36.0  56.0\n",
       "4  30.0  24.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = pd.read_csv('data/ex13.csv')\n",
    "frame.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create a histogram for numerical columns using the function **plot.hist()**:\n",
    "\n",
    "**Notice**: that we need to determine the number of bins ( which is number of bars int he histogram) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 3,
     "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": [
    "frame['age'].plot.hist(bins = 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can enhance the look of the histogram, for example:\n",
    "\n",
    "We use the argument **color** to change the colour of the bars.\n",
    "\n",
    "We use the argument **edgecolor** to change border color of the bars.\n",
    "\n",
    "We use the argument **linewidth** to change the width of the borders lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 4,
     "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": [
    "frame['age'].plot.hist(bins = 12, color = 'red', edgecolor = 'black', linewidth = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another example, we create here a histogram for the wage column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 5,
     "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": [
    "frame['wage'].plot.hist(bins = 12, color = 'blue', edgecolor = 'black', linewidth = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see the effect of using larger bins, in this example we used **bins = 20**: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 6,
     "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": [
    "frame['wage'].plot.hist(bins = 20, color = 'y', edgecolor = 'black', linewidth = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see the effect of using smaller bins, in this example we used **bins = 5**: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAD4CAYAAAAD6PrjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAATW0lEQVR4nO3dfZBldX3n8ffHQVFQCwjtMMuDAxbBoKUDtqy7RBchJsQyELKrQiUp1hhHq7BW12xtgE1FN1tWbe2qJFvZaCAS0QiKIspaJishWdmk1mAPEBwEVh4GHAZ6WtmIEQoy+N0/7unDlXTP3Jnpc0/fvu9X1a0+53fuw/dLQ384v/NwU1VIkgTwrL4LkCStHoaCJKllKEiSWoaCJKllKEiSWgf0XcD+OPzww2vjxo19lyFJE2XLli3fraqZpbZNdChs3LiRubm5vsuQpImS5P7ltjl9JElqGQqSpJahIElqGQqSpJahIElqGQqSpJahIElqGQqSpJahIElqTfQVzdKeHHHERubnl714c01av/7FPPzwtr7L0IQyFLSmDQJhur5dcH4+fZegCeb0kSSpZShIklqGgiSpZShIklqGgiSpZShIklqGgiSpZShIklqGgiSp1VkoJLk8yc4kW4fGPpvk1uaxLcmtzfjGJI8PbftYV3VJkpbX5W0uPgH8PvDJxYGqeuvicpIPA98fev49VbWpw3okSXvQWShU1Y1JNi61LUmAtwCnd/X5kqS919cxhdcC81X17aGxY5PckuRrSV673AuTbE4yl2RuYWGh+0olaYr0FQrnAVcNrT8EHFNVJwHvA65M8sKlXlhVl1bVbFXNzszMjKFUSZoeYw+FJAcAvwR8dnGsqp6oqu81y1uAe4CfHHdtkjTt+thT+BngzqravjiQZCbJumb5OOB44N4eapOkqdblKalXAf8HOCHJ9iRvbzady49PHQG8Drgtyd8CnwfeVVWPdFWbJGlpXZ59dN4y4/96ibFrgGu6qkWSNBqvaJYktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVKry+9ovjzJziRbh8Y+kOTBJLc2jzcObbsoyd1J7kryc13VJUlaXpd7Cp8Azlxi/JKq2tQ8vgKQ5ETgXOBlzWv+IMm6DmuTJC2hs1CoqhuBR0Z8+tnAZ6rqiaq6D7gbOKWr2iRJS+vjmMK7k9zWTC8d2owdCXxn6DnbmzFJ0hiNOxQ+CrwE2AQ8BHy4Gc8Sz62l3iDJ5iRzSeYWFhY6KVKSptVYQ6Gq5qvqqar6EXAZT08RbQeOHnrqUcCOZd7j0qqararZmZmZbguWpCkz1lBIsmFo9Rxg8cyk64BzkxyY5FjgeOCmcdYmSYIDunrjJFcBpwGHJ9kOvB84LckmBlND24B3AlTV7UmuBr4F7AIuqKqnuqpNkrS0VC05dT8RZmdna25uru8ytIolYZnDU2tYmOT/rtW9JFuqanapbV7RLElqGQqSpJahIElqGQqSpJahIElqGQqSpJahIElqGQqSpJahIElqGQqSpJahIElqGQqSpFZnd0nV6nPEERuZn7+/7zIkrWKGwhQZBMK03T1zqS/1k7Qcp48kSS1DQZLUMhQkSS1DQZLUMhQkSa3OQiHJ5Ul2Jtk6NPZfk9yZ5LYk1yY5pBnfmOTxJLc2j491VZckaXld7il8AjjzGWPXAy+vqlcA/xe4aGjbPVW1qXm8q8O6JEnL6CwUqupG4JFnjH21qnY1q18Hjurq8yVJe6/PYwq/Bvzp0PqxSW5J8rUkr13uRUk2J5lLMrewsNB9lZI0RXoJhST/AdgFfLoZegg4pqpOAt4HXJnkhUu9tqourarZqpqdmZkZT8GSNCXGHgpJzgfeBPxyVRVAVT1RVd9rlrcA9wA/Oe7aJGnajTUUkpwJ/CZwVlU9NjQ+k2Rds3wccDxw7zhrkyR1eEO8JFcBpwGHJ9kOvJ/B2UYHAtcnAfh6c6bR64DfSbILeAp4V1U9suQbS5I601koVNV5Swx/fJnnXgNc01UtkqTRjDR9lOTlXRciSerfqHsKH0vyHAYXpF1ZVX/XWUWS9tOBNNOzU2P9+hfz8MPb+i5jTRgpFKrqp5Mcz+DagrkkNwF/XFXXd1qdpH3wBNP2ZUrz89MVgl0a+eyjqvo28FsMzh76F8B/a+5j9EtdFSdJGq9Rjym8IsklwB3A6cAvVNVPNcuXdFifJGmMRj2m8PvAZcDFVfX44mBV7UjyW51UJkkau1FD4Y3A41X1FECSZwHPrarHqupTnVUnSRqrUY8p/DnwvKH1g5oxSdIaMmooPLeq/n5xpVk+qJuSJEl9GTUUfpjk5MWVJK8CHt/N8yVJE2jUYwrvBT6XZEezvgF4aycVSZJ6M+rFa99I8lLgBCDAnVX1D51WJkkau725Id6rgY3Na05KQlV9spOqJEm9GCkUknwKeAlwK4NbW8PgOnpDQZLWkFH3FGaBExe/KU2StDaNevbRVuCILguRJPVv1D2Fw4FvNXdHfWJxsKrO6qQqSVIvRg2FD3RZhCRpdRhp+qiqvgZsA57dLH8DuHl3r0lyeZKdSbYOjR2W5Pok325+Hjq07aIkdye5K8nP7VM3kqT9Muqts98BfB74w2boSOCLe3jZJ4AznzF2IXBDVR0P3NCsk+RE4FzgZc1r/iDJulFqkyStnFEPNF8AnAo8Cu0X7rxody+oqhuBR54xfDZwRbN8BfCLQ+Ofqaonquo+4G7glBFrkyStkFFD4YmqenJxJckB7Nv3/a2vqocAmp+LwXIk8J2h521vxiRJYzRqKHwtycXA85K8Afgc8D9WsI6lvmB1ydBJsjnJXJK5hYWFFSxBkjRqKFwILADfBN4JfIXB9zXvrfkkGwCanzub8e3A0UPPOwrYwRKq6tKqmq2q2ZmZmX0oQZK0nFHPPvpRVV1WVW+uqn/VLO/L9NF1wPnN8vnAl4bGz01yYJJjgeOBm/bh/SVJ+2HUex/dxxLTOVV13G5ecxVwGnB4ku3A+4H/DFyd5O3AA8Cbm/e5PcnVwLeAXcAFi1/9KUkan72599Gi5zL4Y37Y7l5QVects+mMZZ7/QeCDI9YjSerAqNNH3xt6PFhVvwuc3m1pkqRxG3X66OSh1Wcx2HN4QScVSZJ6M+r00YeHlncxuOXFW1a8GklSr0b9Os7Xd12IJKl/o04fvW9326vqIytTjiSpT3tz9tGrGVxPAPALwI38+K0pJEkTbm++ZOfkqvoBQJIPAJ+rql/vqjBJ0viNepuLY4Anh9afBDaueDWSpF6NuqfwKeCmJNcyuLL5HOCTnVUlSerFqGcffTDJnwKvbYbeVlW3dFeWJKkPo04fARwEPFpVvwdsb25cJ0laQ0b9Os73A78JXNQMPRv4k66KkiT1Y9Q9hXOAs4AfAlTVDrzNhSStOaOGwpPN9ycUQJKDuytJktSXUUPh6iR/CByS5B3AnwOXdVeWJKkPezz7KEmAzwIvBR4FTgB+u6qu77g2SdKY7TEUqqqSfLGqXgUYBJK0ho06ffT1JK/utBJJUu9GvaL59cC7kmxjcAZSGOxEvKKrwiRJ47fbUEhyTFU9APz8Sn1gkhMYHKNYdBzw28AhwDuAhWb84qr6ykp9riRpz/a0p/BFBndHvT/JNVX1L/f3A6vqLmATQJJ1wIPAtcDbgEuq6kP7+xmSpH2zp2MKGVo+roPPPwO4p6ru7+C9JUl7aU+hUMssr5RzgauG1t+d5LYklyc5dKkXJNmcZC7J3MLCwlJPkSTtowwuVF5mY/IUTx9Yfh7w2OImBgeaX7jPH5w8B9gBvKyq5pOsB77LIHz+E7Chqn5td+8xOztbc3Nz+1rC1BlcctJFtq9m9jwdwu7+lunHJdlSVbNLbdvtMYWqWtdNScDg4PXNVTXffNb84oYklwFf7vCzJUlL2JtbZ6+08xiaOkqyYWjbOcDWsVckSVNu1OsUVlSSg4A3AO8cGv4vSTYx2O/d9oxtkqQx6CUUquox4CeeMfarfdQiSXpan9NHkqRVxlCQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSq5fvaE6yDfgB8BSwq6pmkxwGfBbYCGwD3lJV/6+P+iRpWvW5p/D6qtpUVbPN+oXADVV1PHBDsy5JGqPVNH10NnBFs3wF8Iv9lSJJ06mvUCjgq0m2JNncjK2vqocAmp8vWuqFSTYnmUsyt7CwMKZyJWk69HJMATi1qnYkeRFwfZI7R31hVV0KXAowOztbXRUoSdOolz2FqtrR/NwJXAucAswn2QDQ/NzZR22SNM3GHgpJDk7ygsVl4GeBrcB1wPnN084HvjTu2iRp2vUxfbQeuDbJ4udfWVV/luQbwNVJ3g48ALy5h9okaaqNPRSq6l7glUuMfw84Y9z1SJKetppOSZUk9cxQkCS1DAVJUstQkCS1DAVJUstQkCS1DAVJUstQkCS1+rohniStoANp7pIwNdavfzEPP7xtxd/XUJC0BjzB4I7802N+vpsQdPpIktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJrbGHQpKjk/xlkjuS3J7kPc34B5I8mOTW5vHGcdcmSdOujyuadwG/UVU3J3kBsCXJ9c22S6rqQz3UJEmih1CoqoeAh5rlHyS5Azhy3HVIkv6xXo8pJNkInAT8TTP07iS3Jbk8yaHLvGZzkrkkcwsLC+MqVZKmQm+hkOT5wDXAe6vqUeCjwEuATQz2JD681Ouq6tKqmq2q2ZmZmXGVK0lToZdQSPJsBoHw6ar6AkBVzVfVU1X1I+Ay4JQ+apOkadbH2UcBPg7cUVUfGRrfMPS0c4Ct465NkqZdH2cfnQr8KvDNJLc2YxcD5yXZxOCm6NuAd3ZdyBFHbGR+/v6uP0aSJkYfZx/9FbDUt0N8Zdy1DAJhmr6YY7q+mUrS3vOKZklSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSa9WFQpIzk9yV5O4kF/ZdjyRNk1UVCknWAf8d+HngROC8JCf2W5UkTY9VFQrAKcDdVXVvVT0JfAY4u+eaJGlqHNB3Ac9wJPCdofXtwD8dfkKSzcDmZvXvk9w1wvseDnx36U3Z6yJXid30tDurtt997GcUvfXcYU970knPPfYzin3qeZX3tHvJP+p51H5evNyG1RYKS/1W68dWqi4FLt2rN03mqmp2fwpbbdZaT2utH1h7Pa21fmDt9bQS/ay26aPtwNFD60cBO3qqRZKmzmoLhW8Axyc5NslzgHOB63quSZKmxqqaPqqqXUneDfxPYB1weVXdvgJvvVfTTRNirfW01vqBtdfTWusH1l5P+91PqmrPz5IkTYXVNn0kSeqRoSBJaq25UEhydJK/THJHktuTvKcZPyzJ9Um+3fw8tO9aR5HkuUluSvK3TT//sRmfyH4WJVmX5JYkX27WJ72fbUm+meTWJHPN2KT3dEiSzye5s/nv6Z9Nak9JTmh+N4uPR5O8d1L7AUjyb5u/CVuTXNX8rdjvftZcKAC7gN+oqp8CXgNc0Nwq40Lghqo6HrihWZ8ETwCnV9UrgU3AmUlew+T2s+g9wB1D65PeD8Drq2rT0Hnik97T7wF/VlUvBV7J4Pc1kT1V1V3N72YT8CrgMeBaJrSfJEcC/waYraqXMzgx51xWop+qWtMP4EvAG4C7gA3N2Abgrr5r24deDgJuZnCV98T2w+D6kxuA04EvN2MT209T8zbg8GeMTWxPwAuB+2hORlkLPQ318LPAX09yPzx994fDGJxF+uWmr/3uZy3uKbSSbAROAv4GWF9VDwE0P1/UY2l7pZlquRXYCVxfVRPdD/C7wL8HfjQ0Nsn9wODK+68m2dLcigUmu6fjgAXgj5tpvj9KcjCT3dOic4GrmuWJ7KeqHgQ+BDwAPAR8v6q+ygr0s2ZDIcnzgWuA91bVo33Xsz+q6qka7PYeBZyS5OU9l7TPkrwJ2FlVW/quZYWdWlUnM7jD7wVJXtd3QfvpAOBk4KNVdRLwQyZkamV3motizwI+13ct+6M5VnA2cCzwT4CDk/zKSrz3mgyFJM9mEAifrqovNMPzSTY02zcw+L/uiVJVfwf8L+BMJrefU4GzkmxjcBfc05P8CZPbDwBVtaP5uZPBXPUpTHZP24HtzV4pwOcZhMQk9wSD0L65quab9Unt52eA+6pqoar+AfgC8M9ZgX7WXChkcNvAjwN3VNVHhjZdB5zfLJ/P4FjDqpdkJskhzfLzGPzLcCcT2k9VXVRVR1XVRga78X9RVb/ChPYDkOTgJC9YXGYwt7uVCe6pqh4GvpPkhGboDOBbTHBPjfN4euoIJrefB4DXJDmo+Zt3BoMTAfa7nzV3RXOSnwb+N/BNnp6zvpjBcYWrgWMY/AN9c1U90kuReyHJK4ArGJxd8Czg6qr6nSQ/wQT2MyzJacC/q6o3TXI/SY5jsHcAg2mXK6vqg5PcE0CSTcAfAc8B7gXeRvPvIBPYU5KDGBycPa6qvt+MTezvqDk9/a0Mzri8Bfh14PnsZz9rLhQkSftuzU0fSZL2naEgSWoZCpKklqEgSWoZCpKklqEgSWoZCpKk1v8Hxa7TvHHpXuMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "frame['wage'].plot.hist(bins = 5, color = 'blue', edgecolor = 'black', linewidth = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create a density plot, we use the function **plot.density()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 8,
     "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": [
    "frame['wage'].plot.density()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Seaborn** can also be used to create histograms by using the function **sns.displot()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2953f42bd00>"
      ]
     },
     "execution_count": 9,
     "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.displot(frame['wage'], bins=12, color='r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Seaborn** can also be used to create density plots by using the function **sns.displot()** and the argument **(kind = 'kde')**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2953f433fd0>"
      ]
     },
     "execution_count": 10,
     "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.displot(frame['wage'], kind = 'kde', color='b')"
   ]
  },
  {
   "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
}