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parulnith/soccer.ipynb

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soccer.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Access a Database with Python using sqlite3 Package - European Soccer Dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"---\n",
"We will be working with the soccer database from kaggle.: https://www.kaggle.com/hugomathien/soccer.\n",
"It is the ultimate Soccer database for data analysis and machine learning and the entire details can be accessed from kaggle. \n",
"\n",
"The database contains 8 tables further containing columns.\n",
" \n",
"Download the dataset in SQLite format from Kaggle and save it in the same directory as your jupyter notebook.\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Importing basic libraries"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"import sqlite3\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Accessing the Database with the sqlite3 Package"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Creating the connection to database\n",
"con = sqlite3.connect('soccer.sqlite')\n",
"cursor = con.cursor()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Accessing all the TABLES"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('sqlite_sequence',)\n",
"('Player_Attributes',)\n",
"('Player',)\n",
"('Match',)\n",
"('League',)\n",
"('Country',)\n",
"('Team',)\n",
"('Team_Attributes',)\n"
]
}
],
"source": [
"for row in cursor.execute(\"SELECT name FROM sqlite_master WHERE type='table';\"):\n",
" print(row)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reading all the TABLES"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"\n",
"\n",
"country_table = pd.read_sql_query(\"SELECT * FROM Country\", con)\n",
"league_table = pd.read_sql_query(\"SELECT * FROM League\", con)\n",
"match_table = pd.read_sql_query(\"SELECT * FROM Match\", con)\n",
"player_table = pd.read_sql_query(\"SELECT * FROM Player\", con)\n",
"player_att_table = pd.read_sql_query(\"SELECT * FROM Player_Attributes\", con)\n",
"team_table = pd.read_sql_query(\"SELECT * FROM Team\", con)\n",
"team_att_table = pd.read_sql_query(\"SELECT * FROM Team_Attributes\", con)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exploratory Data Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Country Table"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dimension of Country Table is: (11, 2)\n",
" \n",
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 11 entries, 0 to 10\n",
"Data columns (total 2 columns):\n",
"id 11 non-null int64\n",
"name 11 non-null object\n",
"dtypes: int64(1), object(1)\n",
"memory usage: 256.0+ bytes\n",
"None\n",
" \n",
" name\n",
"count 11\n",
"unique 11\n",
"top Poland\n",
"freq 1\n",
" \n",
" id\n",
"count 11.000000\n",
"mean 12452.090909\n",
"std 8215.308472\n",
"min 1.000000\n",
"25% 6289.000000\n",
"50% 13274.000000\n",
"75% 18668.000000\n",
"max 24558.000000\n",
" \n",
"id 0\n",
"name 0\n",
"dtype: int64\n"
]
}
],
"source": [
"print(\"Dimension of Country Table is: {}\".format(country_table.shape))\n",
"print(100*\" \")\n",
"print(country_table.info())\n",
"print(100*\" \")\n",
"print(country_table.select_dtypes(exclude=['float64','int64']).describe())\n",
"print(100*\" \")\n",
"print(country_table.describe())\n",
"print(100*\" \")\n",
"print(country_table.isnull().sum(axis=0))"
]
},
{
"cell_type": "code",
"execution_count": 27,
"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>name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Belgium</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1729</td>\n",
" <td>England</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4769</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>7809</td>\n",
" <td>Germany</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>10257</td>\n",
" <td>Italy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>13274</td>\n",
" <td>Netherlands</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>15722</td>\n",
" <td>Poland</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>17642</td>\n",
" <td>Portugal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>19694</td>\n",
" <td>Scotland</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>21518</td>\n",
" <td>Spain</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>24558</td>\n",
" <td>Switzerland</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id name\n",
"0 1 Belgium\n",
"1 1729 England\n",
"2 4769 France\n",
"3 7809 Germany\n",
"4 10257 Italy\n",
"5 13274 Netherlands\n",
"6 15722 Poland\n",
"7 17642 Portugal\n",
"8 19694 Scotland\n",
"9 21518 Spain\n",
"10 24558 Switzerland"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"country_table"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Player Table"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dimension of Player Table is: (11060, 7)\n",
" \n",
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 11060 entries, 0 to 11059\n",
"Data columns (total 7 columns):\n",
"id 11060 non-null int64\n",
"player_api_id 11060 non-null int64\n",
"player_name 11060 non-null object\n",
"player_fifa_api_id 11060 non-null int64\n",
"birthday 11060 non-null object\n",
"height 11060 non-null float64\n",
"weight 11060 non-null int64\n",
"dtypes: float64(1), int64(4), object(2)\n",
"memory usage: 604.9+ KB\n",
"None\n",
" \n",
" player_name birthday\n",
"count 11060 11060\n",
"unique 10848 5762\n",
"top Danilo 1989-03-02 00:00:00\n",
"freq 7 10\n",
" \n",
" id player_api_id player_fifa_api_id height \\\n",
"count 11060.000000 11060.000000 11060.000000 11060.000000 \n",
"mean 5537.511392 156582.427215 165664.910488 181.867445 \n",
"std 3197.692647 160713.700624 58649.928360 6.369201 \n",
"min 1.000000 2625.000000 2.000000 157.480000 \n",
"25% 2767.750000 35555.500000 151889.500000 177.800000 \n",
"50% 5536.500000 96619.500000 184671.000000 182.880000 \n",
"75% 8306.250000 212470.500000 203883.250000 185.420000 \n",
"max 11075.000000 750584.000000 234141.000000 208.280000 \n",
"\n",
" weight \n",
"count 11060.000000 \n",
"mean 168.380289 \n",
"std 14.990217 \n",
"min 117.000000 \n",
"25% 159.000000 \n",
"50% 168.000000 \n",
"75% 179.000000 \n",
"max 243.000000 \n",
" \n",
"id 0\n",
"player_api_id 0\n",
"player_name 0\n",
"player_fifa_api_id 0\n",
"birthday 0\n",
"height 0\n",
"weight 0\n",
"dtype: int64\n"
]
}
],
"source": [
"print(\"Dimension of Player Table is: {}\".format(player_table.shape))\n",
"print(100*\" \")\n",
"print(player_table.info())\n",
"print(100*\" \")\n",
"print(player_table.select_dtypes(exclude=['float64','int64']).describe())\n",
"print(100*\" \")\n",
"print(player_table.describe())\n",
"print(100*\" \")\n",
"print(player_table.isnull().sum(axis=0))\n"
]
},
{
"cell_type": "code",
"execution_count": 27,
"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>player_fifa_api_id</th>\n",
" <th>player_api_id</th>\n",
" <th>date</th>\n",
" <th>overall_rating</th>\n",
" <th>potential</th>\n",
" <th>preferred_foot</th>\n",
" <th>attacking_work_rate</th>\n",
" <th>defensive_work_rate</th>\n",
" <th>crossing</th>\n",
" <th>...</th>\n",
" <th>vision</th>\n",
" <th>penalties</th>\n",
" <th>marking</th>\n",
" <th>standing_tackle</th>\n",
" <th>sliding_tackle</th>\n",
" <th>gk_diving</th>\n",
" <th>gk_handling</th>\n",
" <th>gk_kicking</th>\n",
" <th>gk_positioning</th>\n",
" <th>gk_reflexes</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>218353</td>\n",
" <td>505942</td>\n",
" <td>2016-02-18 00:00:00</td>\n",
" <td>67.0</td>\n",
" <td>71.0</td>\n",
" <td>right</td>\n",
" <td>medium</td>\n",
" <td>medium</td>\n",
" <td>49.0</td>\n",
" <td>...</td>\n",
" <td>54.0</td>\n",
" <td>48.0</td>\n",
" <td>65.0</td>\n",
" <td>69.0</td>\n",
" <td>69.0</td>\n",
" <td>6.0</td>\n",
" <td>11.0</td>\n",
" <td>10.0</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>218353</td>\n",
" <td>505942</td>\n",
" <td>2015-11-19 00:00:00</td>\n",
" <td>67.0</td>\n",
" <td>71.0</td>\n",
" <td>right</td>\n",
" <td>medium</td>\n",
" <td>medium</td>\n",
" <td>49.0</td>\n",
" <td>...</td>\n",
" <td>54.0</td>\n",
" <td>48.0</td>\n",
" <td>65.0</td>\n",
" <td>69.0</td>\n",
" <td>69.0</td>\n",
" <td>6.0</td>\n",
" <td>11.0</td>\n",
" <td>10.0</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>218353</td>\n",
" <td>505942</td>\n",
" <td>2015-09-21 00:00:00</td>\n",
" <td>62.0</td>\n",
" <td>66.0</td>\n",
" <td>right</td>\n",
" <td>medium</td>\n",
" <td>medium</td>\n",
" <td>49.0</td>\n",
" <td>...</td>\n",
" <td>54.0</td>\n",
" <td>48.0</td>\n",
" <td>65.0</td>\n",
" <td>66.0</td>\n",
" <td>69.0</td>\n",
" <td>6.0</td>\n",
" <td>11.0</td>\n",
" <td>10.0</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>218353</td>\n",
" <td>505942</td>\n",
" <td>2015-03-20 00:00:00</td>\n",
" <td>61.0</td>\n",
" <td>65.0</td>\n",
" <td>right</td>\n",
" <td>medium</td>\n",
" <td>medium</td>\n",
" <td>48.0</td>\n",
" <td>...</td>\n",
" <td>53.0</td>\n",
" <td>47.0</td>\n",
" <td>62.0</td>\n",
" <td>63.0</td>\n",
" <td>66.0</td>\n",
" <td>5.0</td>\n",
" <td>10.0</td>\n",
" <td>9.0</td>\n",
" <td>7.0</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>218353</td>\n",
" <td>505942</td>\n",
" <td>2007-02-22 00:00:00</td>\n",
" <td>61.0</td>\n",
" <td>65.0</td>\n",
" <td>right</td>\n",
" <td>medium</td>\n",
" <td>medium</td>\n",
" <td>48.0</td>\n",
" <td>...</td>\n",
" <td>53.0</td>\n",
" <td>47.0</td>\n",
" <td>62.0</td>\n",
" <td>63.0</td>\n",
" <td>66.0</td>\n",
" <td>5.0</td>\n",
" <td>10.0</td>\n",
" <td>9.0</td>\n",
" <td>7.0</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 42 columns</p>\n",
"</div>"
],
"text/plain": [
" id player_fifa_api_id player_api_id date overall_rating \\\n",
"0 1 218353 505942 2016-02-18 00:00:00 67.0 \n",
"1 2 218353 505942 2015-11-19 00:00:00 67.0 \n",
"2 3 218353 505942 2015-09-21 00:00:00 62.0 \n",
"3 4 218353 505942 2015-03-20 00:00:00 61.0 \n",
"4 5 218353 505942 2007-02-22 00:00:00 61.0 \n",
"\n",
" potential preferred_foot attacking_work_rate defensive_work_rate crossing \\\n",
"0 71.0 right medium medium 49.0 \n",
"1 71.0 right medium medium 49.0 \n",
"2 66.0 right medium medium 49.0 \n",
"3 65.0 right medium medium 48.0 \n",
"4 65.0 right medium medium 48.0 \n",
"\n",
" ... vision penalties marking standing_tackle sliding_tackle \\\n",
"0 ... 54.0 48.0 65.0 69.0 69.0 \n",
"1 ... 54.0 48.0 65.0 69.0 69.0 \n",
"2 ... 54.0 48.0 65.0 66.0 69.0 \n",
"3 ... 53.0 47.0 62.0 63.0 66.0 \n",
"4 ... 53.0 47.0 62.0 63.0 66.0 \n",
"\n",
" gk_diving gk_handling gk_kicking gk_positioning gk_reflexes \n",
"0 6.0 11.0 10.0 8.0 8.0 \n",
"1 6.0 11.0 10.0 8.0 8.0 \n",
"2 6.0 11.0 10.0 8.0 8.0 \n",
"3 5.0 10.0 9.0 7.0 7.0 \n",
"4 5.0 10.0 9.0 7.0 7.0 \n",
"\n",
"[5 rows x 42 columns]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"player_att_table.head()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" id player_api_id player_name player_fifa_api_id \\\n",
"0 1 505942 Aaron Appindangoye 218353 \n",
"1 2 155782 Aaron Cresswell 189615 \n",
"2 3 162549 Aaron Doran 186170 \n",
"3 4 30572 Aaron Galindo 140161 \n",
"4 5 23780 Aaron Hughes 17725 \n",
"5 6 27316 Aaron Hunt 158138 \n",
"6 7 564793 Aaron Kuhl 221280 \n",
"7 8 30895 Aaron Lennon 152747 \n",
"8 9 528212 Aaron Lennox 206592 \n",
"9 10 101042 Aaron Meijers 188621 \n",
"10 11 23889 Aaron Mokoena 47189 \n",
"11 12 231592 Aaron Mooy 194958 \n",
"12 13 163222 Aaron Muirhead 213568 \n",
"13 14 40719 Aaron Niguez 183853 \n",
"14 15 75489 Aaron Ramsey 186561 \n",
"15 16 597948 Aaron Splaine 226014 \n",
"16 17 161644 Aaron Taylor-Sinclair 213569 \n",
"17 18 23499 Aaron Wilbraham 2335 \n",
"18 19 120919 Aatif Chahechouhe 187939 \n",
"19 20 46447 Abasse Ba 156626 \n",
"20 21 167027 Abdelaziz Barrada 192274 \n",
"21 22 245653 Abdelfettah Boukhriss 202425 \n",
"22 23 128456 Abdelhamid El Kaoutari 188145 \n",
"23 24 42664 Abdelkader Ghezzal 178063 \n",
"24 25 425950 Abdellah Zoubir 212934 \n",
"25 26 38423 Abdelmajid Oulmers 52782 \n",
"26 27 3264 Abdelmalek Cherrad 51868 \n",
"27 28 467485 Abdelmalek El Hasnaoui 209399 \n",
"28 29 306735 Abdelouahed Chakhsi 210504 \n",
"29 30 41659 Abderrazak Jadid 149241 \n",
"... ... ... ... ... \n",
"11030 11046 244155 Zezinho 212639 \n",
"11031 11047 19696 Zhi Zheng 158293 \n",
"11032 11048 290868 Zhi-Gin Lam 206209 \n",
"11033 11049 169594 Zie Diabate 207780 \n",
"11034 11050 244953 Ziggy Gordon 223263 \n",
"11035 11051 200607 Ziguy Badibanga 195862 \n",
"11036 11052 642618 Zinedine Machach 228821 \n",
"11037 11053 277772 Zinho Gano 204227 \n",
"11038 11054 246332 Ziri Hammar 202751 \n",
"11039 11055 532766 Zizo 222537 \n",
"11040 11056 27305 Zlatan Bajramovic 31667 \n",
"11041 11057 35724 Zlatan Ibrahimovic 41236 \n",
"11042 11058 12574 Zlatan Ljubijankic 176103 \n",
"11043 11059 39494 Zlatko Dedic 141111 \n",
"11044 11060 17233 Zlatko Janjic 188405 \n",
"11045 11061 42860 Zlatko Junuzovic 173474 \n",
"11046 11062 37128 Zola Matumona 158142 \n",
"11047 11063 24737 Zoltan Gera 137089 \n",
"11048 11064 107281 Zoltan Stieber 179712 \n",
"11049 11065 39801 Zoltan Szelesi 157283 \n",
"11050 11066 491794 Zoran Josipovic 219943 \n",
"11051 11067 56929 Zoran Rendulic 188593 \n",
"11052 11068 99031 Zoran Tosic 105454 \n",
"11053 11069 192132 Zouhaier Dhaouadhi 195840 \n",
"11054 11070 282473 Zouhair Feddal 205705 \n",
"11055 11071 26357 Zoumana Camara 2488 \n",
"11056 11072 111182 Zsolt Laczko 164680 \n",
"11057 11073 36491 Zsolt Low 111191 \n",
"11058 11074 35506 Zurab Khizanishvili 47058 \n",
"11059 11075 39902 Zvjezdan Misimovic 102359 \n",
"\n",
" birthday height weight \n",
"0 1992-02-29 00:00:00 182.88 187 \n",
"1 1989-12-15 00:00:00 170.18 146 \n",
"2 1991-05-13 00:00:00 170.18 163 \n",
"3 1982-05-08 00:00:00 182.88 198 \n",
"4 1979-11-08 00:00:00 182.88 154 \n",
"5 1986-09-04 00:00:00 182.88 161 \n",
"6 1996-01-30 00:00:00 172.72 146 \n",
"7 1987-04-16 00:00:00 165.10 139 \n",
"8 1993-02-19 00:00:00 190.50 181 \n",
"9 1987-10-28 00:00:00 175.26 170 \n",
"10 1980-11-25 00:00:00 182.88 181 \n",
"11 1990-09-15 00:00:00 175.26 150 \n",
"12 1990-08-30 00:00:00 187.96 168 \n",
"13 1989-04-26 00:00:00 170.18 143 \n",
"14 1990-12-26 00:00:00 177.80 154 \n",
"15 1996-10-13 00:00:00 172.72 163 \n",
"16 1991-04-08 00:00:00 182.88 176 \n",
"17 1979-10-21 00:00:00 190.50 159 \n",
"18 1986-07-02 00:00:00 175.26 150 \n",
"19 1976-07-12 00:00:00 187.96 185 \n",
"20 1989-06-19 00:00:00 177.80 161 \n",
"21 1986-10-22 00:00:00 185.42 161 \n",
"22 1990-03-17 00:00:00 180.34 161 \n",
"23 1984-12-05 00:00:00 182.88 172 \n",
"24 1991-12-05 00:00:00 180.34 161 \n",
"25 1978-09-12 00:00:00 172.72 143 \n",
"26 1981-01-14 00:00:00 185.42 165 \n",
"27 1994-02-09 00:00:00 180.34 159 \n",
"28 1986-10-01 00:00:00 182.88 170 \n",
"29 1983-06-01 00:00:00 177.80 157 \n",
"... ... ... ... \n",
"11030 1992-09-23 00:00:00 175.26 161 \n",
"11031 1980-08-20 00:00:00 180.34 165 \n",
"11032 1991-06-04 00:00:00 175.26 146 \n",
"11033 1989-03-02 00:00:00 177.80 143 \n",
"11034 1993-04-23 00:00:00 180.34 170 \n",
"11035 1991-11-26 00:00:00 172.72 154 \n",
"11036 1996-01-05 00:00:00 185.42 163 \n",
"11037 1993-10-13 00:00:00 198.12 205 \n",
"11038 1992-07-25 00:00:00 180.34 163 \n",
"11039 1996-01-10 00:00:00 175.26 148 \n",
"11040 1979-08-12 00:00:00 182.88 174 \n",
"11041 1981-10-03 00:00:00 195.58 209 \n",
"11042 1983-12-15 00:00:00 185.42 176 \n",
"11043 1984-10-05 00:00:00 182.88 170 \n",
"11044 1986-05-07 00:00:00 187.96 183 \n",
"11045 1987-09-26 00:00:00 172.72 152 \n",
"11046 1981-11-26 00:00:00 165.10 143 \n",
"11047 1979-04-22 00:00:00 182.88 165 \n",
"11048 1988-10-16 00:00:00 175.26 148 \n",
"11049 1981-11-22 00:00:00 182.88 176 \n",
"11050 1995-08-25 00:00:00 187.96 165 \n",
"11051 1984-05-22 00:00:00 190.50 179 \n",
"11052 1987-04-28 00:00:00 170.18 157 \n",
"11053 1988-01-01 00:00:00 180.34 159 \n",
"11054 1989-01-01 00:00:00 190.50 172 \n",
"11055 1979-04-03 00:00:00 182.88 168 \n",
"11056 1986-12-18 00:00:00 182.88 176 \n",
"11057 1979-04-29 00:00:00 180.34 154 \n",
"11058 1981-10-06 00:00:00 185.42 172 \n",
"11059 1982-06-05 00:00:00 180.34 176 \n",
"\n",
"[11060 rows x 7 columns]\n"
]
}
],
"source": [
"height_more_than_150 = pd.read_sql_query(\"SELECT * FROM Player WHERE height >= 150 \", con)\n",
"print(height_more_than_150)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x27829655da0>"
]
},
"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": {},
"output_type": "display_data"
}
],
"source": [
"# Relationship between Player's heights and weights in a boxplot\n",
"sns.boxplot(data = player_table.loc[:,[\"height\",'weight']])"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Parul\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
" warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
"C:\\Users\\Parul\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
" warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
]
},
{
"data": {
"text/plain": [
"<seaborn.axisgrid.JointGrid at 0x27825fbf6d8>"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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6gp5ApJ75LpuUlONEsoXWDeice9s592z69i7gZWCIc+43zrm96YetBprSty8CFjvn9jjn3gReA04Pq30iSee7T1RSjpOetn/wcbWbUDWRjFmZ2TBgPLAm565vAP+Vvj0E2JR1X3s6lvuzrjaztWa2duvWrZVvrEhC+C6blJTjJCX7M2/Xju3Vbk7VhJ6szOxQ4N+A7zjn3suKzyHVVbgwE8pzuOsRcO5251yzc6550KBBYTRZJBF8l01KynGSkv2Z13/AwGo3p2pCTVZm1pdUolronFuSFb8c+AIwwzmXSUjtwNCsw5uAzWG2TyQufPaX8l02qTfH9T2g+9+UfQ+w0J4Pot13S+ItzGpAA+4AXnbO3ZoVPxe4HjjLOfdh1iEPAr80s1tJFViMBJ4Oq30iceFbgOC7bFKvllvK7f/I1x9SoedTYYZks08ubCr8g83OBJ4A2kiVrgP8LfBT4CBgWzq22jn3zfQxc0iNY+0l1W34XxTQ3Nzs1q5dG0LrRaLTMu9ROvKM3wwZ0MhTsyZXoUX5Rd3OpPxeKqSEtA8nnDzWvfHyC2G3JWolnXtoV1bOuScDGrGswDE3ATeF1SaROEpKAULU7UzK70WioYVsRaps8IDGvFcQYRYgzG1tY9GaTXQ5R4MZ0ycO5cZpYwoe05t2+kwK7s3z+ZyfxJuWWxKpsqj3l5rb2sa9qzfSlR4C6HKOe1dvZG5rWyjtzIw9dezYjeOTsadixRK+z+d7fhJvSlYiVTZt/BBuvngMQwY0YqTGZG6+eExoRQSL1mwqK57h207fScG+z+d7fhJv6gYUiYEo95fqCiiqCopn82lnb8aefJ6vN+cn8aUrK5E602D5i6+C4r0V9aTgqM9PoqFkJVJBUU9i9Xm+6ROHlhXv7fP5Tib21Zvzk/hSN6BIhUQ9idX3+d7c+n5Z8d4+H+A1mdhXpupP1YC1RclKpEIKFRKEkax8n++p1/MvhhoU7+3zzV++ns6u7uNFnV0utN8LpBKWklNtUTegSIXU+qRZrbou1aQrK6l5UU0QjXpyb1KeL+rJxAAzFqzqdqXYcuJAFl51RtHjJL50ZSU1LcoJolFP7t3blX8T7aB4xjH9DywrnnHwgfk/LoLivT3OdzJxbqKCVBfnjAWrCh4n8aZkJTUtygmiUU/ufWdX/l1jg+IZ777fWVY849UtH5QV7+1xvpOJfcfkJN7UDSg1LeoJolFO7vWVlEmzGuuSbLqykpqmCaI9JeV3oh2GJZuSldS0Wp4gOvLoQ8qKZ/j+TlpOzL+lelC8t8f5jgH6Pp/Em5KV1LQbp43hsknH779qaDDjsknH18QcnBXXnd0jMY08+hBWXHd2weN8fycLrzqjxwd+KVV2vsf5jgH6Pp/Em8aspOZFOUHUt9Ta97h3dn5U8Psga97Y1q1Ccs0b24ockdLWvrPg98HPt73g90F8xwCHDzqU1W/8cf90heGDDi37Z8TVL9ds5C8mHl/tZkROV1YiFeJbau173NgfPMR7e7pXy723p4uxP3io4HFTbl3ZoxLv1S0fMOXWlaE834jZS9mbU7ux16XiYdB+VrVJyUqkQnxLrX2Py00cxeIZvqXkvs+Xm6iKxXtL+1nVJiUrkQrRckTxkJTSfCmPkpVIhfiWWqtEu7KSUpov5VGyEqkQ31LrYUfmT0pBcSmslqcr1DNVA4pUSKZqrdyqvtVv/LGsuBRW6/tZ1WMlIChZiVSUT6m1xlgqT/tZ1R51A4pUmcZYRIpTshKpoLmtbZw4exnDZi3lxNnLSprb4zvG4rvVR1AKLJYa+zXkf0RQPEPLH0klKFmJVIjvZNQVL/2/suIZ297PvxVIUDwjqHOxWKfjgX3yf1wExTO27NpTVlwkHyUrkQrxnYzquy9V1JNto56ELPn9cs3G/V/1RMlKpEJUKCESHlUDijffxVejFlU7G8zyJiYVSkQvKe9NKZ2SlXjJLL6aWdMus/gqEKsPhSjbecKgg/N2bZ0w6OCKPo8UlpT3ppRH3YDixXfx1ahF2c43tn5YVlzCkZT3ppRHyUq8JGXx1SjbqTGreEjKe1PKo2QlXpKy+GqU7dTk3nhIynuzt+pt2SUlK/Hiu2hr1HzbGeXk3qTwnYR82EENZcV7KynvTSmPkpV4mTZ+CDdfPIYhAxoxYMiARm6+eEzsBrB92hn15F7flSGi5jsfbEzT4WXFeysp700pT2jVgGY2FLgHOBbYB9zunPuJmQ0E7gOGARuArzjn/mhmBvwEOB/4ELjCOfdsWO2T3vNZtLUaym1nocm9hRZH9f0w/6gr/5hWUDxpnnp9e1nxSkjKe1NKF+aV1V7gr51zJwOTgGvM7FPALOAR59xI4JH09wDnASPTX1cDvwixbSKBVCghEj+hJSvn3NuZKyPn3C7gZWAIcBFwd/phdwPT0rcvAu5xKauBAWZ2XFjtEwmiQgmR+IlkzMrMhgHjgTXAMc65tyGV0ICj0w8bAmT3v7SnY7k/62ozW2tma7du3Rpms6VORb0KelLGrHxp1fXeyf7M27Xjk67TelsjMPRkZWaHAv8GfMc5916hh+aJ9eh3cc7d7pxrds41Dxo0qFLNFNnvxmljuGzS8fuvpBrMuGzS8UU381szZ0qPxHRM/wNZM2dKweNeuen8HompX4Pxyk3nFzxuw7wLyopX67iFV53RIzG1nDiQhVedUfA4Scn+zOs/oH4TfKjJysz6kkpUC51zS9LhdzLde+l/t6Tj7UD2n65NwOYw2ycSJF81YCl2fthZ8PsgucUUtVJckfHalvcLfh+kdV0HLfMeZfispbTMe5TWdR1hNE8SILRkla7uuwN42Tl3a9ZdDwKXp29fDjyQFf+6pUwCdma6C0WiNGzW0rLiGaPnLMubdEbPWRbK8yXluIk3rehREfnOro+ZeNOKgsdl1vjr2LEbxydr/Clh1acwr6xagK8Bk83sufTX+cA8YIqZvQpMSX8PsAx4A3gNWAD8VYhtE6m4Wi9B9+Vb0q81/iRbaPOsnHNPErxT9v/I83gHXBNWe0QkWbTGX2nqZdklbREikYt6ryHtbZRMgwc00pEnMdXaGn9SGi23JJGKehxC4x7VF9S9UqwwX2v8STYlK4lU1OMQGveovqARu2IjeVrjT7KpG1AiFfU4hMY9kk1r/EmGrqwkUlHvNVQvexuJ1DolK4lUb8YhfPaYmjl1FAfkDI4cYIQy7lHryyb5Gnn0IWXFK6GeJhPXy5JLSlYSKd9xCN89pta+tZ19OYMj+1wqHsR3s8Ban2eVlGSsoprapDEriZzPOITvHlM+x723p6useL3wTcavbvmgrHhvFSqq0fhXcunKShLBd48p7U1Vf1RUU5t0ZSWJ0GCWN8EU22PK9ziJB58J3ZpMXJt0ZSWJ4LvH1AmDDi4rLpXnu8+X79jTOaPzbx0UFJdkULKSRPDdY+qNrR+WFZfK69OQvzAlKJ7hO6H7sVfyb8oaFJdkUDegJMaN08YUTU65NGZVfb5jSFEfJ/GmKyupaUFjUxqzio7vxOyoj5N4U7KSyEU5YdN3rEt68p3cGzQBu9jEbN8J5DOnjqJvzkzwvgeYFsBNOCUriVTUEzYfDPi5QXEJ9ubW/POiguIZtz32alnxjF4tZJt74awL6cTTmJVEKuoJm5rgWzl7A4b5guIZvZkU7DOBfP7y9XTmTFTu7HI1Pyk4s+RSrW7GqCsriZQGvyVseo/VJiUriZQGvyVseo/VJiUriVTUu7/6LkorPfUJGPcJimdEveq6dhiuTUpWEqneDJpPuXUlw2Yt3f815daVRY954e/PLSsOsGHeBWXF6+W4127Of39QPGPFdWf3SGh9LBUvxqdyVDsM1yZzCZ4c2dzc7NauXVvtZkgEpty6Mu+A/MijDyn4oTds1tLA+4I+nH2O0XHBx/m+dpnK0eyCnMa+DbWaeEqqVzzh5LHuxrv+s+BjElhgUdK5qxpQEiHqbSakcnxfO2314Sd3I8YEJq+81A0oIrGkqj7JpmQlIrGkqj7JVlKyMrNvlxKT+uK7bJLPcVFXlEnl9GaZJt9lk6Jc0kuiUeqV1eV5YldUsB2SML7LJvke987Oj8qKZ/RryD92GxSXYL77Uk084ciy4t14LJsU9ZJeEo2CycrMppvZfwDDzezBrK/HgG3RNFHiyHevId/jfJdN+qgrf7VrUFyCvbPr47LiGYvWbCornlFo2aRix/m8xyTeilUD/g54GzgK+Mes+C7ghbAaJfGnvYakVL57iuk9Vhm51YG9Uc3KwoLJyjn3FvAWcEY0zZGkGDygkY48//lL2WvI5zhJrgazvImp2J5ieo9JtlILLC42s1fNbKeZvWdmu8zsvbAbJ/F1zuhBZcUz9nbl77YLikt8BKWWYsNIk044oqx4hu97TMst1aZSJwXfAnzROfdymI2R5Hjsla1lxTN8xz2k+oI67YqN/m3Ylr/7LSie4fsey0wYnr98PZt37GbwgEZmTh2licQJV2qyekeJSrJpXEBKVY2xJ599sCTeCiYrM7s4fXOtmd0HtAJ7Mvc755aE2DaJMY0LSKk09hRPSVuGqdiY1RfTX4cBHwKfz4p9IdymSZzNnDqKhpwJmw0lTNjU5N7k6s3kXp8xJI09SbZi1YB/GVVDJFnWvrWdrn3dRyu69jnWvrW9YPfLNeeM5Dv3PZc3LvHmOzHbdwxJY0+SraQxKzP7aZ7wTmCtc+6BgGPuJHX1tcU5d2o6Ng74F6AfsBf4K+fc02ZmwE+A80ldwV3hnHu23JOR6BSa6HnjtDGBxwVNzNRK2vHnOzEb/MeQNPYkGaUut9QPGAe8mv4aCwwErjSzfwo45i4gd4e7W4C/d86NA76f/h7gPGBk+utq4BcltkuqJOqJniJS30qtBhwBTHbO7QUws18AvwGmAG35DnDOPW5mw3LDpMa/AA4HNqdvXwTc41I7Qa42swFmdpxz7u1ST0SiFfVETxGpb6UmqyHAIaS6/kjfHuyc6zKzPcGH9fAdYLmZ/YjUVd2fZv387H6l9nSsR7Iys6tJXX1x/PHJqmapJdMnDuXe1T2XcZk+cWjB47btyj++ERSX+DDyz6nSksDhyv7MO+rY3nWJJq0CMFup3YC3AM+Z2b+a2V3AOuBHZnYI8HAZz/ct4LvOuaHAd4E70vF87/e8/UnOududc83OueZBgwrPZJfw3DhtDJdNOn7/lVSDGZdNOr7geBVoYdkk850ULL2T/ZnXf8DAajenakq6snLO3WFmy4DTSSWWv3XOZbrwZpbxfJcDmX2wfg383/TtdiD7T/ImPukilJi6cdqYoslJRKQSim0RMjr976eB40h11W0Ejk3HyrUZOCt9ezKpYg2AB4GvW8okYKfGq0REJKPYldV1pPpK/zHPfY5UwsnLzBYBZwNHmVk78APgKuAnZtYH+Cj9swGWkSpbf41U6brmd9Woww5qyFvqfNhBDXkeLWHo12B5u12LbUjZcuJAnnp9e964SNjMFSk1jrPm5ma3du3aajdDyjT2Bw91S1iHHdTAC3+fO8uhp2GzlvaIbZh3QSjHRflc1Thu9Jxl3RJWvwbjlZvOL3rcjAWruiWslhMHsvAq7SBUASXVqZxw8lh3413/6f0kMS2wKOncS50UfDCpq6zjnXNXm9lIYJRzzv+3Jok3t7WNRWs20eUcDWZMnzi0pDGs3CurUiaVSmXlXlmVWuCyZdeegt8HaV3XoZUoYsBnI8a4JLhSqwH/FfiYT0rN24EbQ2mRJMLc1jbuXb1x/1yrLue4d/VG5rbmnXa3X74rgULxahyXhDZW47gpt67k1S0fdIu9uuUDpty6suBxres6mL2kjY4du3FAx47dzF7SRuu6joLHiWQrNVmd6Jy7BegEcM7tRtMr6lqh5ZakNuUmqmLxjPnL17O7s/vV8+7OrsClt0TyKTVZfWxmjaSnVJjZiWRtFSL1x3e5Jak/WmJLKqHUZPUD4CFgqJktBB4B/ia0VknsBS2rVGy5Jak/QUtpaYktKUepyy19HVgK3A+8AXzbOfduaK2SSPkMfvsutyTJNfLoQ/J2+ZWyn9XM+5+nM6uIo29D8b3PJBpxKaAoppwCi37AhcBPgf9jZt8ufIgkge/gt+9yS5J4ztFIAAAY8UlEQVRc15wzssdAtVHiXmS5vcPqLZYylbrc0qNm9ltgAnAO8E3gFFJ7UEmCFRr8LnZ1peWW6sv85evz5pxi75X5y9fTmbNRZ+c+pz3MpCylzrN6hNRK66uAJ4AJzrktYTZMoqHBbymV73tF7zGphFLHrF4APgOcSmqbkB1mtipdwi4x4TP21Jv9pYbPWtrtL20D3ixh9QRJJt/3ivYwk0ooaczKOfdd59zngC8B20iNYe0Is2FSHt+xp5lTR9HYt/u6fI19G4oOfucmKkh1CQ0vMrFUkuuc0fm35AmK9/Y4kWyldgP+T+CzpK6u3gLuJNUdKDHhO/aUua/cKzLtbVR/Hntla1nx3h4n0fBZgqkSyq1CLLUbsBG4FXgms7W9xEtvxgWmjR+igW4pSmNWUk2ldgPOd86tUaKKL028lLD5vsf03pRKKHWelcSc79gTpBYoHTZr6f6vYguTQvDCkFq/onbNnDqKvgd0f4X7HlB8cm9v3put6zpomfcow2ctpWXeo1r8to4pWdWIaeOHcPPFYxgyoBEDhgxo5OaLxxTt3vNdSXtEwKoFQXGJj6CNLkvaADPfrOAifN+bWq1dspU6ZiUJ4DP25LuStu9xUn1B+4cV21ds/vL13ZZMAujsKm1yr897szcT1qX2KFmJSEmiLpRQYUZ1xHWtQCUrESlJbyb3Rj1hXWqPxqzqXL+G/IMOQXGpX76FEr5jT5pMLNmUrOrcR135p/EGxaV++RZK+O4UrMnEkk3dgCJSMp9CCU0mlkpQshKRUGkB3GQpZfmlahRhqBuwzrWcOLCseIbGupIr6gndvmNdvZlMLLVHyarOLbzqjB6JqeXEgSy86oyCx71y0/k9ElO/BuOVm84veNyGgC1EguLVOC4JbezNcW/OuyDv3N6wtnfxHevyPU5qk7oBa8jc1jYWrdlEl3M0mDF94tBQd/LNLcJQUUb0+hjsdd2/L8XR/Q/knV0fd/u+FL7vMd/FkrXIsmToyqpGzG1t497VG+lyqU+uLue4d/VG5ra2FTxuxoJVPPX69m6xp17fzowFqwoeNyxg36qgeJKOS0IbAUbMXtotUUEqcY2YXfi4iTet6JaoAN7Z9TETb1pR8Djf95hIJShZ1YhFazaVFc/ITVTF4hIfuYmqWDwjN1EVi2f4vsdEKkHdgDUi89duqXGRcuk9VvviutQS6MqqZjRY/sGKoLhIufQek2pSsqoRk044oqx4hvalqj++0w6mTxxaVlykktQNWCM2bMs/qz8onhHUgaOOndrVua+8eEam6i/KilORDCWrGqGlaaRUvRl7unHaGCUnqQp1A9aIoCVotDSN5NLYkySRklUMta7roGXeowyftZSWeY+WtI130BI0xZam6dUW51JVQROAi00M1tiTJJGSVcz47v1z87LflxXP8N3iXKrv0oAy46B4RvOfDOSAnIR2gKXiInEVWrIyszvNbIuZvZgTv9bM1pvZS2Z2S1Z8tpm9lr5valjtijvfvX98J3pKcvlO0p2/fD37coan9jmKvsdEqinMAou7gJ8B92QCZnYOcBEw1jm3x8yOTsc/BVwKnAIMBh42s5Occ3X3570KJaRUvoUSeo9JEoV2ZeWcexzIXbPnW8A859ye9GO2pOMXAYudc3ucc28CrwGnh9W2OFOhhJTKt1BC7zFJoqjHrE4CPmtma8zst2Y2IR0fAmT3XbSnYz2Y2dVmttbM1m7dWnvbW/vu4eM72C7J5TsRXPtEJUv2Z96uHeGu2VnKxovVEnWy6gMcAUwCZgK/MjMj/4IJefsynHO3O+eanXPNgwYNCq+lVeK7h4/voqaSXL4TwbVPVLJkf+b1H1C/RTBRTwpuB5Y45xzwtJntA45Kx7PrZpuAzRG3LTa0h4+UojdjT3qPSdJEfWXVCkwGMLOTgAOBd4EHgUvN7CAzGw6MBJ6OuG0iiaKxJ6knYZauLwJWAaPMrN3MrgTuBE5Il7MvBi53KS8BvwJ+DzwEXFOPlYAZPpOCc7emLxaX5Js5dRR9cyZM9T3ANPYkNSm0bkDn3PSAuy4LePxNwE1htScpMpOCM3OtMpOCAXXb1Kh+DcZHXT0HF4utgg70HO1VQY3UKC1kGzOFJgUXSlba8Te58iWqQvGM+cvX05nzmM4uV/S9IvUtzhssFqLllmJGEzalVHqvSD1RsooZDZpLqfRekXqiZBUzvhM2VWCRXL4792pyr9QTJauYmTZ+CE1H9OsWazqiX9ExiIVXnVFWPGPDvAvKitfDcVG38ZWbzi8rnjFt/BA+ffzh3WKfPv7wksarZixYxbBZS/d/zViwqugxItWkZBUzMxas4tUtH3SLvbrlg6IfJqPnLCsrnjFs1tKy4vVwXBLaCDC3ta1HAc1Tr29nbmtbweNmLFiV9zglLIkzVQPGjG9Vn29FmSRXoS1CCm09r8rR+lbO+n9xqhzUlZVIQvluESKSREpWIgnlu0WISBIpWcWMb1Wfb0WZJNf0iUPLimeoclSSSMkqZhZedUaPD42WEwcWrer7zLD8exgFxaXygv4sKPbngm8V4Y3TxnDZpOP3X0k1mHHZpOMLjleB/3tMpJpUYBFDPh8aGjSvvqCRolJGkIolpiA3ThtTNDnlo8QkQeJUVJFNV1YiIhJ7urKKodFzlnUrOe/XYEUniEqy5c59UrecSHe6soqZ3EQFqblSxSb3SnJpkq5IcUpWMaPJvfVH440ixSlZiYhI7GnMSkSkxsS1oq83dGUVM76Te/sE3B0Ul8o77KCGsuIZmqQrUpySVcz4Tu7dGzCkFRSXYL5JZ0zT4WXFM77cnP+v4KC4SD1SsooZDbZX33t7usqKZ/i+dvOXry8rLlKPlKxEqmzzjt1lxUXqkQosQjS3tY1FazbR5RwNZkyfONRraRypbYMHNNKRJzENHtBYhdaIxJOurEIyt7WNe1dv3L+3UJdz3Lt6Y9FdXI/pf2BZcak83wVpfQslZk4dRWPf7uNhjX0bmDl1VJFnFOnuLyYeX5OVgKBkFZpCu7gW8u77nWXFpfJ8F6T1Xc182vgh3HzxGIYMaMSAIQMaufniMUwbP6TkNovUOnUDhsR3F1ft/ppsvuv5TRs/RMlJpABdWYXEdxdX7f4qItKTklVIfHdx9T1OOwVXjibpisSPklVIfHdx9T3ulZvO75GYStlaxHeX2lo+TjvpSlL9cs3GajchNBqzCpH3Lq6rN+4fzO9yjoWrN5b0c/JtLSJ+fpczkTf3+0prXdfB/OXr2bxjN4MHNDJz6iiNYYlk0ZVVzAyftbRH1ZlLxwsZFnB/UFzHBR/n+xr4al3XwewlbXTs2I0DOnbsZvaSNlrXdYTyfCJJpGQVM75l01I5Ub8G85evZ3dn96Wcdnd2abklkSxKViJVpuWWRIpTshKpsqBllbTcksgnVGARIp+1AY383U0qQI9Ob14Dn0KJmVNHMXtJW7euQC23JOWo1SWWsunKKiS+awMeFDAvKiguhfmstfinAfOpguIZvoUSWm5JpLjQrqzM7E7gC8AW59ypOfd9D5gPDHLOvWtmBvwEOB/4ELjCOfdsWG2LQqG1AQtdXQWVm6sM3c87uz4uKw6925cqqFCiWOLRcksihYV5ZXUXcG5u0MyGAlOA7Nlr5wEj019XA78IsV2R0Bp/9UeFEiLhCS1ZOeceB/L9Kfpj4G/oPixwEXCPS1kNDDCz48JqWxS0xl/9UaGESHgiHbMyswuBDufc8zl3DQGy+83a07F8P+NqM1trZmu3bt0aUkt7b9IJR5QVl3AcdlBDWXHQvlQSL9mfebt2hLuSSpxFVg1oZgcDc4DP57s7Tyxvf5lz7nbgdoDm5ubY9qlt2Ja/6yco3lsNZnm7GOv9Sq5/44G8t6fn77x/Y3CBxcKrzmDGglXdxqhK3ZcK0LJJUlHZn3knnDx2/3/yeqgAzBZl6fqJwHDg+VQ9BU3As2Z2OqkrqexlxZuAzRG2reKiHr/QGFl+vq+D9qUSiZfIugGdc23OuaOdc8Occ8NIJahPO+f+H/Ag8HVLmQTsdM69HVXbwhD1+IXGyPLTOJJIbQgtWZnZImAVMMrM2s3sygIPXwa8AbwGLAD+Kqx2RcV3/MJ3XyrffbCSok/A6QfFM2ZOHdXjTX5AOh4nres6aJn3KMNnLaVl3qNaxFYkR5jVgNOdc8c55/o655qcc3fk3D/MOfdu+rZzzl3jnDvROTfGObc2rHZFxXei5959+bvtguIZ967Ov49NULy3fJOH73HDBx1SVjxj7Vvb2ZcT25eOx4VWXRcpTssthchn/GJvQE4KileLbzt9j3t1ywdlxTN8J2dHqTeTiUXqhZZbkpqWhMITTSYWKU7JSmpaEgpPVAQiUpySVcz4julELeoxq5FH5x+bCopnJKHwRJOJRYpTsgqRT4XXazdf0GOGtKXjhWyYl//+oHhvjwtqT7F2+h634rqzeyS0PpaKF3LjtDE9Vp5oOXFgbMarQKuui5RCySokvhVeres66JfzV3a/vg1Fj5t404qy4hnDZi0tK54xYnb++4PiGaPnLCsrnjHl1pU9ijD2ulS8kNZ1HTy7cWe32LMbd8au0m7a+CE8NWsyb867gKdmTVaiEsmhZBWSQhVeYRznsxVGb/hW9flugeJbDej7+xSJs3pbagmUrELjW+GlyrDK0u9TpDYoWYXEt8JLlWGVpd+nSG1QsgrJzKmj6HtA94qAvgdY0QqvoPuLHeezfXtv+Fb1+S4n5VsN6Pv7FJF4UbIKU76yviK+39pWVjxj54edZcV768hD8yfBoHiG73JSvn69Nv9yU0FxEYknJauQzF++ns6cooHOLld0YP+9PV1lxTN8Cxd8+RZ0RL3cUvaeVKXERSSelKxCooF9EQnLL9fUX8+AklVINLAvIlI5SlYhOWf0oLLiGUlZbsm3oCPq5ZZyV68oFheReFKyCsljr2wtK57hAqowguK95bvQ65o5U3okpmP6H8iaOVMKHvfazRfkXTaplOWWchPTyKMPKbrc0sKrzsi73JLvtvUiUh3azyokvmNWUW9p0ZvnK5aYghRLTEGKJaYgSkwiyadkFZLBAxrpyJOYio1ZNZjlTRRhbWkR9fOJSO/U41JLoG7A0Phu++C7pYXvGFJvttDwWVW+N6J+PhGJDyWrkPhu+9D8JwN7vCgHpONx4ruqfFKeT0TiRd2AIZo2fkjZWz3MX76efTmxfel4oZ/lO0l30ZpNgfFCez4VWs08jO0ton4+EYkXXVnFTNSTiX0LLKJupyZZi9Q3XVmVoHVdB/OXr2fzjt0MHtDIzKmjSvpr3uc438IMX74FFlG3M+rnE5F40ZVVEb3Z8dfnuGFH5v/wDYpnHHZQQ1nxDN8CC98CEl9RP5+IxIuSVRFR7/i7+o0/lhXP6N+Yv+ovKJ5x47QxXDbp+P1XUg1mXDbp+ILjVeBfQOIr6ucTkXhRN2ARUe/4W40xpBunjSmanPLxKSDpjaifT0TiQ1dWRUS946/v8kdaOFdEapmSVRG+YyVRTwrWmI6I1DJ1AxaR6XYqt6rP97hMd9yiNZvoco4GM6ZPHFrSGJLP84mIJIGSVYh8x1ia/2Qgj72ylc07dnPs4f1KXr1CYzoitW3gIYULpmqZklURmRL0TGVfpgQdCCUxRP18IiJJoDGrInxL0JPyfCIiSaBkVYSWFRIRqT4lqyKiLglXCbqISE9KVkVUY1mhvgd0n1PV9wCrqRJ07UslIuVSgUURVSkJz53/W0Ob9qqARER8hJaszOxO4AvAFufcqenYfOCLwMfA68BfOud2pO+bDVwJdAH/yzm3PKy2lSvKkvD5y9fT2dV9aaXOLlcz+zZpXyoR8RFmN+BdwLk5sRXAqc65scAfgNkAZvYp4FLglPQxPzezwsuF16haL7Co9fMTkXCElqycc48D23Niv3HO7U1/uxpoSt++CFjsnNvjnHsTeA04Pay2xVmtF1jU+vmJSDiqWWDxDeC/0reHANn7q7enYz2Y2dVmttbM1m7dujXkJkav1tf4q/XzE6m0Wv/MK1VVkpWZzQH2AgszoTwPy7snhnPududcs3OuedCgQWE1sWpqfd+mWj8/kUqr9c+8UkVeDWhml5MqvPgfzu3fpKkdyF5WvAnYHHXb4qLW1/ir9fMTkcqL9MrKzM4FrgcudM59mHXXg8ClZnaQmQ0HRgJPR9k2ERGJrzBL1xcBZwNHmVk78ANS1X8HASsstZngaufcN51zL5nZr4Dfk+oevMY515X/J/trXdehLTRERBIotGTlnJueJ3xHgcffBNwUVns0GVVEJLnqZrklrWYuIpJcdZOsNBlVRCS56iZZaTKqiEhy1U2y0mRUEZHkqptV16uyerqIiFRE3SQr0GRUEZGkqptuQBERSS4lKxERiT0lKxERiT0lKxERiT0lKxERiT0lKxERiT0lKxERiT0lKxERiT0lKxERiT37ZGf55DGzrcBbET7lUcC7ET5flHRuyVSr51ar5wX5z+1d59y5xQ40s4dKeVwtSnSyipqZrXXONVe7HWHQuSVTrZ5brZ4X1Pa5hUndgCIiEntKViIiEntKVuW5vdoNCJHOLZlq9dxq9bygts8tNBqzEhGR2NOVlYiIxJ6SlYiIxJ6SVRYzu9PMtpjZiznxa81svZm9ZGa3ZMVnm9lr6fumRt/i0uQ7LzO7z8yeS39tMLPnsu5LxHlB4LmNM7PV6XNba2anp+NmZj9Nn9sLZvbp6rW8uIBzO83MVplZm5n9h5kdlnVfkl63oWb2mJm9nP5/9e10fKCZrTCzV9P/HpGOJ+K1K3BeX05/v8/MmnOOSczrVlXOOX2lv4DPAZ8GXsyKnQM8DByU/v7o9L+fAp4HDgKGA68DDdU+h1LPK+f+fwS+n7TzKvCa/QY4L337fGBl1u3/AgyYBKypdvs9zu2/gbPSt78B/ENCX7fjgE+nb/cH/pA+h1uAWen4LOB/J+m1K3BeJwOjgJVAc9bjE/W6VfNLV1ZZnHOPA9tzwt8C5jnn9qQfsyUdvwhY7Jzb45x7E3gNOD2yxpYh4LyA1F+swFeARelQYs4LAs/NAZkrjsOBzenbFwH3uJTVwAAzOy6alpYv4NxGAY+nb68A/jx9O2mv29vOuWfTt3cBLwNDSJ3H3emH3Q1MS99OxGsXdF7OuZedc+vzHJKo162alKyKOwn4rJmtMbPfmtmEdHwIsCnrce3pWNJ8FnjHOfdq+vtaOK/vAPPNbBPwI2B2Ol4L5/YicGH69peBoenbiT03MxsGjAfWAMc4596G1Ac/cHT6YYk7v5zzCpK486oWJavi+gBHkOp6mAn8Kn01Ynkem8R5ANP55KoKauO8vgV81zk3FPgucEc6Xgvn9g3gGjN7hlQ308fpeCLPzcwOBf4N+I5z7r1CD80Ti+351ep5VZOSVXHtwJJ098PTwD5SC1G288lftQBNfNLdlAhm1ge4GLgvK5z48wIuB5akb/+aT7pVEn9uzrlXnHOfd859htQfGa+n70rcuZlZX1If6Audc5nX651M917630y3e2LOL+C8giTmvKpNyaq4VmAygJmdBBxIasXkB4FLzewgMxsOjASerlor/fwZ8Ipzrj0rVgvntRk4K317MpDp4nwQ+Hq6smwSsDPT5ZQUZnZ0+t8DgLnAv6TvStTrlu6duAN42Tl3a9ZdD5L6Y4P0vw9kxWP/2hU4ryCJet2qqtoVHnH6IvWX6ttAJ6m/eK4klZzuJTVW8CwwOevxc0j9ZbuedPVZHL/ynVc6fhfwzTyPT8R5FXjNzgSeIVVltQb4TPqxBtyWPrc2sqqy4vgVcG7fJlVh9gdgHulVaBL4up1JqrvrBeC59Nf5wJHAI6T+wHgEGJik167AeX0p/RruAd4Blifxdavml5ZbEhGR2FM3oIiIxJ6SlYiIxJ6SlYiIxJ6SlYiIxJ6SlYiIxJ6SldQ8MxuWu5J+kcd/08y+XuQxV5jZzwLu+9ty2ygihSlZieRwzv2Lc+6eXvwIJSuRClOyknrRYGYL0nsK/cbMGs3sRDN7yMyeMbMnzGw0gJndYGbfS9+ekN4/aZWZzc+5QhucPv5VS+9zZmbzgMb0XloLoz9NkdqkZCX1YiRwm3PuFGAHqa01bgeudal19r4H/DzPcf9KapWPM4CunPvGAV8FxgBfNbOhzrlZwG7n3Djn3IyQzkWk7vSpdgNEIvKmcy6zG/IzwDDgT4Ffp5ZzA1Ib4O1nZgOA/s6536VDvwS+kPWQR5xzO9OP/T3wJ3Tf7kFEKkTJSurFnqzbXcAxwA7n3LgCx+TbvqHQz9T/J5GQqBtQ6tV7wJtm9mVIrZZtZqdlP8A590dgV3qVb4BLS/zZneltIkSkQpSspJ7NAK40s+eBl0htMZ7rSuB2M1tF6kprZwk/93bgBRVYiFSOVl0XKcDMDnXOvZ++PQs4zjn37So3S6TuqI9dpLALzGw2qf8rbwFXVLc5IvVJV1YiIhJ7GrMSEZHYU7ISEZHYU7ISEZHYU7ISEZHYU7ISEZHY+//X4mcxS7HNigAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x432 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Relationship between Player's height and weight in a jointplot\n",
"sns.jointplot(x='height',y = 'weight',data = player_table,kind = 'scatter')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Please feel feel to explore other tables too"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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