Day11 plotting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x= np.arange(0,10)\n",
    "y= np.array(random.sample(range(1, 50), 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20, 38, 11, 26, 17, 47, 39, 30, 36, 23])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "execution_count": 9,
     "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": [
    "fig= plt.figure()\n",
    "ax= fig.add_axes([0,0,1,1])\n",
    "ax.bar(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "execution_count": 11,
     "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": [
    "plt.bar(x,y, width=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[73 78 70 99 42 87 18 20  7 33 19 46  6 69 97 31 52 40 71 49 22 55 26 91\n",
      " 81  2 96 17 50 41 28 39 59 83 34 85 79 23  5 80 53 58  1 43 47 68 16 60\n",
      " 56 36]\n"
     ]
    }
   ],
   "source": [
    "data1= np.array(random.sample(range(1,100), 50))\n",
    "print(data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig, ax= plt.subplots()\n",
    "ax.hist(data1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18,  7,  6,  2, 17,  5,  1, 16])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1[data1<19]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([10., 10., 13.,  8.,  9.]),\n",
       " array([ 1. , 20.6, 40.2, 59.8, 79.4, 99. ]),\n",
       " <a list of 5 Patch objects>)"
      ]
     },
     "execution_count": 15,
     "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": [
    "plt.hist(data1, bins=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "group= ['A','B','C']\n",
    "students=[20,30,25]\n",
    "fig, ax= plt.subplots()\n",
    "_=ax.pie(students, labels=group, autopct='%1.2f%%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2289b5a8048>"
      ]
     },
     "execution_count": 19,
     "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": [
    "x= np.arange(0,50)\n",
    "fig, ax= plt.subplots()\n",
    "ax.scatter(x,x, color='b')\n",
    "ax.scatter(x,data1, color='g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x2289b6064e0>,\n",
       "  <matplotlib.lines.Line2D at 0x2289b6067b8>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x2289b606b00>,\n",
       "  <matplotlib.lines.Line2D at 0x2289b606e48>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x2289b606128>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x2289b606f28>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x2289b611518>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 20,
     "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": [
    "data2=[]\n",
    "data2.extend(data1)\n",
    "data2.extend([-10,-49,-150,150,200,155])\n",
    "plt.boxplot(data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Id</th>\n",
       "      <th>EmployeeName</th>\n",
       "      <th>JobTitle</th>\n",
       "      <th>BasePay</th>\n",
       "      <th>OvertimePay</th>\n",
       "      <th>OtherPay</th>\n",
       "      <th>Benefits</th>\n",
       "      <th>TotalPay</th>\n",
       "      <th>TotalPayBenefits</th>\n",
       "      <th>Year</th>\n",
       "      <th>Notes</th>\n",
       "      <th>Agency</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>NATHANIEL FORD</td>\n",
       "      <td>GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY</td>\n",
       "      <td>167411.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>400184.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>567595.43</td>\n",
       "      <td>567595.43</td>\n",
       "      <td>2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>GARY JIMENEZ</td>\n",
       "      <td>CAPTAIN III (POLICE DEPARTMENT)</td>\n",
       "      <td>155966.02</td>\n",
       "      <td>245131.88</td>\n",
       "      <td>137811.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>538909.28</td>\n",
       "      <td>538909.28</td>\n",
       "      <td>2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>ALBERT PARDINI</td>\n",
       "      <td>CAPTAIN III (POLICE DEPARTMENT)</td>\n",
       "      <td>212739.13</td>\n",
       "      <td>106088.18</td>\n",
       "      <td>16452.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>335279.91</td>\n",
       "      <td>335279.91</td>\n",
       "      <td>2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>CHRISTOPHER CHONG</td>\n",
       "      <td>WIRE ROPE CABLE MAINTENANCE MECHANIC</td>\n",
       "      <td>77916.00</td>\n",
       "      <td>56120.71</td>\n",
       "      <td>198306.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>332343.61</td>\n",
       "      <td>332343.61</td>\n",
       "      <td>2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>PATRICK GARDNER</td>\n",
       "      <td>DEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT)</td>\n",
       "      <td>134401.60</td>\n",
       "      <td>9737.00</td>\n",
       "      <td>182234.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>326373.19</td>\n",
       "      <td>326373.19</td>\n",
       "      <td>2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id       EmployeeName                                        JobTitle  \\\n",
       "0   1     NATHANIEL FORD  GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY   \n",
       "1   2       GARY JIMENEZ                 CAPTAIN III (POLICE DEPARTMENT)   \n",
       "2   3     ALBERT PARDINI                 CAPTAIN III (POLICE DEPARTMENT)   \n",
       "3   4  CHRISTOPHER CHONG            WIRE ROPE CABLE MAINTENANCE MECHANIC   \n",
       "4   5    PATRICK GARDNER    DEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT)   \n",
       "\n",
       "     BasePay  OvertimePay   OtherPay  Benefits   TotalPay  TotalPayBenefits  \\\n",
       "0  167411.18         0.00  400184.25       NaN  567595.43         567595.43   \n",
       "1  155966.02    245131.88  137811.38       NaN  538909.28         538909.28   \n",
       "2  212739.13    106088.18   16452.60       NaN  335279.91         335279.91   \n",
       "3   77916.00     56120.71  198306.90       NaN  332343.61         332343.61   \n",
       "4  134401.60      9737.00  182234.59       NaN  326373.19         326373.19   \n",
       "\n",
       "   Year  Notes         Agency  Status  \n",
       "0  2011    NaN  San Francisco     NaN  \n",
       "1  2011    NaN  San Francisco     NaN  \n",
       "2  2011    NaN  San Francisco     NaN  \n",
       "3  2011    NaN  San Francisco     NaN  \n",
       "4  2011    NaN  San Francisco     NaN  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= pd.read_csv('Salaries.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                       0\n",
       "EmployeeName             0\n",
       "JobTitle                 0\n",
       "BasePay                609\n",
       "OvertimePay              4\n",
       "OtherPay                 4\n",
       "Benefits             36163\n",
       "TotalPay                 0\n",
       "TotalPayBenefits         0\n",
       "Year                     0\n",
       "Notes               148654\n",
       "Agency                   0\n",
       "Status              148654\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2= df.drop(['Notes','Status'], axis=1).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                  0\n",
       "EmployeeName        0\n",
       "JobTitle            0\n",
       "BasePay             0\n",
       "OvertimePay         0\n",
       "OtherPay            0\n",
       "Benefits            0\n",
       "TotalPay            0\n",
       "TotalPayBenefits    0\n",
       "Year                0\n",
       "Agency              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## distplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2289d07fb70>"
      ]
     },
     "execution_count": 27,
     "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": [
    "sns.distplot(df2['BasePay'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2289cc10b70>"
      ]
     },
     "execution_count": 30,
     "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": [
    "sns.distplot(df2['BasePay'], hist=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## countplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2012, 2013, 2014], dtype=int64)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['Year'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2289ccae780>"
      ]
     },
     "execution_count": 32,
     "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": [
    "sns.countplot(df2['Year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3= sns.load_dataset('tips')\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(244, 7)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2289cd1ccf8>"
      ]
     },
     "execution_count": 35,
     "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": [
    "sns.countplot('sex', data=df3, hue='smoker')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## heatmap()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>total_bill</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.675734</td>\n",
       "      <td>0.598315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tip</th>\n",
       "      <td>0.675734</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.489299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>size</th>\n",
       "      <td>0.598315</td>\n",
       "      <td>0.489299</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            total_bill       tip      size\n",
       "total_bill    1.000000  0.675734  0.598315\n",
       "tip           0.675734  1.000000  0.489299\n",
       "size          0.598315  0.489299  1.000000"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2289cd8e908>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df3.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}