{ "metadata": { "name": "", "signature": "sha256:aae9f7e5626efa14f35db5cf0a45dbaf0f5f38bc58a600b456c6c2c9b2c4adcb" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "nba_df = pd.read_csv(\"NBA-Census-10.14.2013.csv\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "# Look at the first few parts of the dataframe\n", "nba_df.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
NameAgeTeamPOS#2013 $Ht (In.)WTEXP1st YearDOBSchoolCityState (Province, Territory, Etc..)CountryRaceHS Only
0 Gee, Alonzo 26 Cavaliers F 33 $3,250,000 78 219 4 2009 5/29/1987 Alabama Riviera Beach, FL Florida US Black No
1 Wallace, Gerald 31 Celtics F 45 $10,105,855 79 220 12 2001 7/23/1982 Alabama Sylacauga, AL Alabama US Black No
2 Williams, Mo 30 Trail Blazers G 25 $2,652,000 73 195 10 2003 12/19/1982 Alabama Jackson, MS Mississippi US Black No
3 Gladness, Mickell 27 Magic C 40 $762,195 83 220 2 2011 7/26/1986 Alabama A&M Birmingham, AL Alabama US Black No
4 Jefferson, Richard 33 Jazz F 44 $11,046,000 79 230 12 2001 6/21/1980 Arizona Los Angeles, CA California US Black No
5 Hill, Solomon 22 Pacers F 9 $1,246,680 79 220 0 2013 3/18/1991 Arizona Los Angeles, CA California US Black No
6 Budinger, Chase 25 Timberwolves F 10 $5,000,000 79 218 4 2009 5/22/1988 Arizona Encinitas, CA California US White No
7 Williams, Derrick 22 Timberwolves F 7 $5,016,960 80 241 2 2011 5/25/1991 Arizona La Mirada, CA California US Black No
8 Hill, Jordan 26 Lakers F/C 27 $3,563,600 82 235 1 2012 7/27/1987 Arizona Newberry, SC South Carolina US Black No
9 Frye, Channing 30 Suns F/C 8 $6,500,000 83 245 8 2005 5/17/1983 Arizona White Plains, NY New York US Black No
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ " Name Age Team POS # 2013 $ Ht (In.) \\\n", "0 Gee, Alonzo 26 Cavaliers F 33 $3,250,000 78 \n", "1 Wallace, Gerald 31 Celtics F 45 $10,105,855 79 \n", "2 Williams, Mo 30 Trail Blazers G 25 $2,652,000 73 \n", "3 Gladness, Mickell 27 Magic C 40 $762,195 83 \n", "4 Jefferson, Richard 33 Jazz F 44 $11,046,000 79 \n", "5 Hill, Solomon 22 Pacers F 9 $1,246,680 79 \n", "6 Budinger, Chase 25 Timberwolves F 10 $5,000,000 79 \n", "7 Williams, Derrick 22 Timberwolves F 7 $5,016,960 80 \n", "8 Hill, Jordan 26 Lakers F/C 27 $3,563,600 82 \n", "9 Frye, Channing 30 Suns F/C 8 $6,500,000 83 \n", "\n", " WT EXP 1st Year DOB School City \\\n", "0 219 4 2009 5/29/1987 Alabama Riviera Beach, FL \n", "1 220 12 2001 7/23/1982 Alabama Sylacauga, AL \n", "2 195 10 2003 12/19/1982 Alabama Jackson, MS \n", "3 220 2 2011 7/26/1986 Alabama A&M Birmingham, AL \n", "4 230 12 2001 6/21/1980 Arizona Los Angeles, CA \n", "5 220 0 2013 3/18/1991 Arizona Los Angeles, CA \n", "6 218 4 2009 5/22/1988 Arizona Encinitas, CA \n", "7 241 2 2011 5/25/1991 Arizona La Mirada, CA \n", "8 235 1 2012 7/27/1987 Arizona Newberry, SC \n", "9 245 8 2005 5/17/1983 Arizona White Plains, NY \n", "\n", " State (Province, Territory, Etc..) Country Race HS Only \n", "0 Florida US Black No \n", "1 Alabama US Black No \n", "2 Mississippi US Black No \n", "3 Alabama US Black No \n", "4 California US Black No \n", "5 California US Black No \n", "6 California US White No \n", "7 California US Black No \n", "8 South Carolina US Black No \n", "9 New York US Black No " ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "# ....or\n", "nba_df[:10]" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
NameAgeTeamPOS#2013 $Ht (In.)WTEXP1st YearDOBSchoolCityState (Province, Territory, Etc..)CountryRaceHS Only
0 Gee, Alonzo 26 Cavaliers F 33 $3,250,000 78 219 4 2009 5/29/1987 Alabama Riviera Beach, FL Florida US Black No
1 Wallace, Gerald 31 Celtics F 45 $10,105,855 79 220 12 2001 7/23/1982 Alabama Sylacauga, AL Alabama US Black No
2 Williams, Mo 30 Trail Blazers G 25 $2,652,000 73 195 10 2003 12/19/1982 Alabama Jackson, MS Mississippi US Black No
3 Gladness, Mickell 27 Magic C 40 $762,195 83 220 2 2011 7/26/1986 Alabama A&M Birmingham, AL Alabama US Black No
4 Jefferson, Richard 33 Jazz F 44 $11,046,000 79 230 12 2001 6/21/1980 Arizona Los Angeles, CA California US Black No
5 Hill, Solomon 22 Pacers F 9 $1,246,680 79 220 0 2013 3/18/1991 Arizona Los Angeles, CA California US Black No
6 Budinger, Chase 25 Timberwolves F 10 $5,000,000 79 218 4 2009 5/22/1988 Arizona Encinitas, CA California US White No
7 Williams, Derrick 22 Timberwolves F 7 $5,016,960 80 241 2 2011 5/25/1991 Arizona La Mirada, CA California US Black No
8 Hill, Jordan 26 Lakers F/C 27 $3,563,600 82 235 1 2012 7/27/1987 Arizona Newberry, SC South Carolina US Black No
9 Frye, Channing 30 Suns F/C 8 $6,500,000 83 245 8 2005 5/17/1983 Arizona White Plains, NY New York US Black No
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ " Name Age Team POS # 2013 $ Ht (In.) \\\n", "0 Gee, Alonzo 26 Cavaliers F 33 $3,250,000 78 \n", "1 Wallace, Gerald 31 Celtics F 45 $10,105,855 79 \n", "2 Williams, Mo 30 Trail Blazers G 25 $2,652,000 73 \n", "3 Gladness, Mickell 27 Magic C 40 $762,195 83 \n", "4 Jefferson, Richard 33 Jazz F 44 $11,046,000 79 \n", "5 Hill, Solomon 22 Pacers F 9 $1,246,680 79 \n", "6 Budinger, Chase 25 Timberwolves F 10 $5,000,000 79 \n", "7 Williams, Derrick 22 Timberwolves F 7 $5,016,960 80 \n", "8 Hill, Jordan 26 Lakers F/C 27 $3,563,600 82 \n", "9 Frye, Channing 30 Suns F/C 8 $6,500,000 83 \n", "\n", " WT EXP 1st Year DOB School City \\\n", "0 219 4 2009 5/29/1987 Alabama Riviera Beach, FL \n", "1 220 12 2001 7/23/1982 Alabama Sylacauga, AL \n", "2 195 10 2003 12/19/1982 Alabama Jackson, MS \n", "3 220 2 2011 7/26/1986 Alabama A&M Birmingham, AL \n", "4 230 12 2001 6/21/1980 Arizona Los Angeles, CA \n", "5 220 0 2013 3/18/1991 Arizona Los Angeles, CA \n", "6 218 4 2009 5/22/1988 Arizona Encinitas, CA \n", "7 241 2 2011 5/25/1991 Arizona La Mirada, CA \n", "8 235 1 2012 7/27/1987 Arizona Newberry, SC \n", "9 245 8 2005 5/17/1983 Arizona White Plains, NY \n", "\n", " State (Province, Territory, Etc..) Country Race HS Only \n", "0 Florida US Black No \n", "1 Alabama US Black No \n", "2 Mississippi US Black No \n", "3 Alabama US Black No \n", "4 California US Black No \n", "5 California US Black No \n", "6 California US White No \n", "7 California US Black No \n", "8 South Carolina US Black No \n", "9 New York US Black No " ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "# Find out how many people are in each category\n", "# If you're dealing with numerical data, use .describe()\n", "nba_df[\"POS\"].value_counts()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "G 175\n", "F 142\n", "F/C 74\n", "G/F 70\n", "C 67\n", "dtype: int64" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "# Get all of the people who match a certain characteristic\n", "nba_df[nba_df[\"POS\"] == \"F\"].head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
NameAgeTeamPOS#2013 $Ht (In.)WTEXP1st YearDOBSchoolCityState (Province, Territory, Etc..)CountryRaceHS Only
0 Gee, Alonzo 26 Cavaliers F 33 $3,250,000 78 219 4 2009 5/29/1987 Alabama Riviera Beach, FL Florida US Black No
1 Wallace, Gerald 31 Celtics F 45 $10,105,855 79 220 12 2001 7/23/1982 Alabama Sylacauga, AL Alabama US Black No
4 Jefferson, Richard 33 Jazz F 44 $11,046,000 79 230 12 2001 6/21/1980 Arizona Los Angeles, CA California US Black No
5 Hill, Solomon 22 Pacers F 9 $1,246,680 79 220 0 2013 3/18/1991 Arizona Los Angeles, CA California US Black No
6 Budinger, Chase 25 Timberwolves F 10 $5,000,000 79 218 4 2009 5/22/1988 Arizona Encinitas, CA California US White No
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ " Name Age Team POS # 2013 $ Ht (In.) WT \\\n", "0 Gee, Alonzo 26 Cavaliers F 33 $3,250,000 78 219 \n", "1 Wallace, Gerald 31 Celtics F 45 $10,105,855 79 220 \n", "4 Jefferson, Richard 33 Jazz F 44 $11,046,000 79 230 \n", "5 Hill, Solomon 22 Pacers F 9 $1,246,680 79 220 \n", "6 Budinger, Chase 25 Timberwolves F 10 $5,000,000 79 218 \n", "\n", " EXP 1st Year DOB School City \\\n", "0 4 2009 5/29/1987 Alabama Riviera Beach, FL \n", "1 12 2001 7/23/1982 Alabama Sylacauga, AL \n", "4 12 2001 6/21/1980 Arizona Los Angeles, CA \n", "5 0 2013 3/18/1991 Arizona Los Angeles, CA \n", "6 4 2009 5/22/1988 Arizona Encinitas, CA \n", "\n", " State (Province, Territory, Etc..) Country Race HS Only \n", "0 Florida US Black No \n", "1 Alabama US Black No \n", "4 California US Black No \n", "5 California US Black No \n", "6 California US White No " ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "# Get all of the people who match a certain characteristic\n", "nba_df[(nba_df[\"POS\"] == \"F\") & (nba_df[\"HS Only\"] == \"No\") ].head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
NameAgeTeamPOS#2013 $Ht (In.)WTEXP1st YearDOBSchoolCityState (Province, Territory, Etc..)CountryRaceHS Only
0 Gee, Alonzo 26 Cavaliers F 33 $3,250,000 78 219 4 2009 5/29/1987 Alabama Riviera Beach, FL Florida US Black No
1 Wallace, Gerald 31 Celtics F 45 $10,105,855 79 220 12 2001 7/23/1982 Alabama Sylacauga, AL Alabama US Black No
4 Jefferson, Richard 33 Jazz F 44 $11,046,000 79 230 12 2001 6/21/1980 Arizona Los Angeles, CA California US Black No
5 Hill, Solomon 22 Pacers F 9 $1,246,680 79 220 0 2013 3/18/1991 Arizona Los Angeles, CA California US Black No
6 Budinger, Chase 25 Timberwolves F 10 $5,000,000 79 218 4 2009 5/22/1988 Arizona Encinitas, CA California US White No
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ " Name Age Team POS # 2013 $ Ht (In.) WT \\\n", "0 Gee, Alonzo 26 Cavaliers F 33 $3,250,000 78 219 \n", "1 Wallace, Gerald 31 Celtics F 45 $10,105,855 79 220 \n", "4 Jefferson, Richard 33 Jazz F 44 $11,046,000 79 230 \n", "5 Hill, Solomon 22 Pacers F 9 $1,246,680 79 220 \n", "6 Budinger, Chase 25 Timberwolves F 10 $5,000,000 79 218 \n", "\n", " EXP 1st Year DOB School City \\\n", "0 4 2009 5/29/1987 Alabama Riviera Beach, FL \n", "1 12 2001 7/23/1982 Alabama Sylacauga, AL \n", "4 12 2001 6/21/1980 Arizona Los Angeles, CA \n", "5 0 2013 3/18/1991 Arizona Los Angeles, CA \n", "6 4 2009 5/22/1988 Arizona Encinitas, CA \n", "\n", " State (Province, Territory, Etc..) Country Race HS Only \n", "0 Florida US Black No \n", "1 Alabama US Black No \n", "4 California US Black No \n", "5 California US Black No \n", "6 California US White No " ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "# Get all of the people who match one of any X characteristics\n", "nba_df[(nba_df[\"POS\"] == \"F\") | (nba_df[\"POS\"] == \"G\") ].head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
NameAgeTeamPOS#2013 $Ht (In.)WTEXP1st YearDOBSchoolCityState (Province, Territory, Etc..)CountryRaceHS Only
0 Gee, Alonzo 26 Cavaliers F 33 $3,250,000 78 219 4 2009 5/29/1987 Alabama Riviera Beach, FL Florida US Black No
1 Wallace, Gerald 31 Celtics F 45 $10,105,855 79 220 12 2001 7/23/1982 Alabama Sylacauga, AL Alabama US Black No
2 Williams, Mo 30 Trail Blazers G 25 $2,652,000 73 195 10 2003 12/19/1982 Alabama Jackson, MS Mississippi US Black No
4 Jefferson, Richard 33 Jazz F 44 $11,046,000 79 230 12 2001 6/21/1980 Arizona Los Angeles, CA California US Black No
5 Hill, Solomon 22 Pacers F 9 $1,246,680 79 220 0 2013 3/18/1991 Arizona Los Angeles, CA California US Black No
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ " Name Age Team POS # 2013 $ Ht (In.) WT \\\n", "0 Gee, Alonzo 26 Cavaliers F 33 $3,250,000 78 219 \n", "1 Wallace, Gerald 31 Celtics F 45 $10,105,855 79 220 \n", "2 Williams, Mo 30 Trail Blazers G 25 $2,652,000 73 195 \n", "4 Jefferson, Richard 33 Jazz F 44 $11,046,000 79 230 \n", "5 Hill, Solomon 22 Pacers F 9 $1,246,680 79 220 \n", "\n", " EXP 1st Year DOB School City \\\n", "0 4 2009 5/29/1987 Alabama Riviera Beach, FL \n", "1 12 2001 7/23/1982 Alabama Sylacauga, AL \n", "2 10 2003 12/19/1982 Alabama Jackson, MS \n", "4 12 2001 6/21/1980 Arizona Los Angeles, CA \n", "5 0 2013 3/18/1991 Arizona Los Angeles, CA \n", "\n", " State (Province, Territory, Etc..) Country Race HS Only \n", "0 Florida US Black No \n", "1 Alabama US Black No \n", "2 Mississippi US Black No \n", "4 California US Black No \n", "5 California US Black No " ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "# Retrieve what's nan/null/etc\n", "nba_df[pd.isnull(nba_df[\"Race\"])].head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
NameAgeTeamPOS#2013 $Ht (In.)WTEXP1st YearDOBSchoolCityState (Province, Territory, Etc..)CountryRaceHS Only
300 Karasev, Sergey 19 Cavaliers G/F 10 $1,467,840 79 203 0 2013 10/26/1993 n/a Saint Petersburg n/a Russia NaN No
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ " Name Age Team POS # 2013 $ Ht (In.) WT EXP \\\n", "300 Karasev, Sergey 19 Cavaliers G/F 10 $1,467,840 79 203 0 \n", "\n", " 1st Year DOB School City \\\n", "300 2013 10/26/1993 n/a Saint Petersburg \n", "\n", " State (Province, Territory, Etc..) Country Race HS Only \n", "300 n/a Russia NaN No " ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "# Retrieve what's NOT nan/null/etc\n", "nba_df[~pd.isnull(nba_df[\"Race\"])].head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
NameAgeTeamPOS#2013 $Ht (In.)WTEXP1st YearDOBSchoolCityState (Province, Territory, Etc..)CountryRaceHS Only
0 Gee, Alonzo 26 Cavaliers F 33 $3,250,000 78 219 4 2009 5/29/1987 Alabama Riviera Beach, FL Florida US Black No
1 Wallace, Gerald 31 Celtics F 45 $10,105,855 79 220 12 2001 7/23/1982 Alabama Sylacauga, AL Alabama US Black No
2 Williams, Mo 30 Trail Blazers G 25 $2,652,000 73 195 10 2003 12/19/1982 Alabama Jackson, MS Mississippi US Black No
3 Gladness, Mickell 27 Magic C 40 $762,195 83 220 2 2011 7/26/1986 Alabama A&M Birmingham, AL Alabama US Black No
4 Jefferson, Richard 33 Jazz F 44 $11,046,000 79 230 12 2001 6/21/1980 Arizona Los Angeles, CA California US Black No
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ " Name Age Team POS # 2013 $ Ht (In.) WT \\\n", "0 Gee, Alonzo 26 Cavaliers F 33 $3,250,000 78 219 \n", "1 Wallace, Gerald 31 Celtics F 45 $10,105,855 79 220 \n", "2 Williams, Mo 30 Trail Blazers G 25 $2,652,000 73 195 \n", "3 Gladness, Mickell 27 Magic C 40 $762,195 83 220 \n", "4 Jefferson, Richard 33 Jazz F 44 $11,046,000 79 230 \n", "\n", " EXP 1st Year DOB School City \\\n", "0 4 2009 5/29/1987 Alabama Riviera Beach, FL \n", "1 12 2001 7/23/1982 Alabama Sylacauga, AL \n", "2 10 2003 12/19/1982 Alabama Jackson, MS \n", "3 2 2011 7/26/1986 Alabama A&M Birmingham, AL \n", "4 12 2001 6/21/1980 Arizona Los Angeles, CA \n", "\n", " State (Province, Territory, Etc..) Country Race HS Only \n", "0 Florida US Black No \n", "1 Alabama US Black No \n", "2 Mississippi US Black No \n", "3 Alabama US Black No \n", "4 California US Black No " ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "# or this\n", "nba_df[pd.notnull(nba_df[\"Race\"])].head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
NameAgeTeamPOS#2013 $Ht (In.)WTEXP1st YearDOBSchoolCityState (Province, Territory, Etc..)CountryRaceHS Only
0 Gee, Alonzo 26 Cavaliers F 33 $3,250,000 78 219 4 2009 5/29/1987 Alabama Riviera Beach, FL Florida US Black No
1 Wallace, Gerald 31 Celtics F 45 $10,105,855 79 220 12 2001 7/23/1982 Alabama Sylacauga, AL Alabama US Black No
2 Williams, Mo 30 Trail Blazers G 25 $2,652,000 73 195 10 2003 12/19/1982 Alabama Jackson, MS Mississippi US Black No
3 Gladness, Mickell 27 Magic C 40 $762,195 83 220 2 2011 7/26/1986 Alabama A&M Birmingham, AL Alabama US Black No
4 Jefferson, Richard 33 Jazz F 44 $11,046,000 79 230 12 2001 6/21/1980 Arizona Los Angeles, CA California US Black No
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ " Name Age Team POS # 2013 $ Ht (In.) WT \\\n", "0 Gee, Alonzo 26 Cavaliers F 33 $3,250,000 78 219 \n", "1 Wallace, Gerald 31 Celtics F 45 $10,105,855 79 220 \n", "2 Williams, Mo 30 Trail Blazers G 25 $2,652,000 73 195 \n", "3 Gladness, Mickell 27 Magic C 40 $762,195 83 220 \n", "4 Jefferson, Richard 33 Jazz F 44 $11,046,000 79 230 \n", "\n", " EXP 1st Year DOB School City \\\n", "0 4 2009 5/29/1987 Alabama Riviera Beach, FL \n", "1 12 2001 7/23/1982 Alabama Sylacauga, AL \n", "2 10 2003 12/19/1982 Alabama Jackson, MS \n", "3 2 2011 7/26/1986 Alabama A&M Birmingham, AL \n", "4 12 2001 6/21/1980 Arizona Los Angeles, CA \n", "\n", " State (Province, Territory, Etc..) Country Race HS Only \n", "0 Florida US Black No \n", "1 Alabama US Black No \n", "2 Mississippi US Black No \n", "3 Alabama US Black No \n", "4 California US Black No " ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "# Retrieve everyone who is not a guard\n", "nba_df[~(nba_df[\"POS\"] == \"G\")].head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
NameAgeTeamPOS#2013 $Ht (In.)WTEXP1st YearDOBSchoolCityState (Province, Territory, Etc..)CountryRaceHS Only
0 Gee, Alonzo 26 Cavaliers F 33 $3,250,000 78 219 4 2009 5/29/1987 Alabama Riviera Beach, FL Florida US Black No
1 Wallace, Gerald 31 Celtics F 45 $10,105,855 79 220 12 2001 7/23/1982 Alabama Sylacauga, AL Alabama US Black No
3 Gladness, Mickell 27 Magic C 40 $762,195 83 220 2 2011 7/26/1986 Alabama A&M Birmingham, AL Alabama US Black No
4 Jefferson, Richard 33 Jazz F 44 $11,046,000 79 230 12 2001 6/21/1980 Arizona Los Angeles, CA California US Black No
5 Hill, Solomon 22 Pacers F 9 $1,246,680 79 220 0 2013 3/18/1991 Arizona Los Angeles, CA California US Black No
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ " Name Age Team POS # 2013 $ Ht (In.) WT \\\n", "0 Gee, Alonzo 26 Cavaliers F 33 $3,250,000 78 219 \n", "1 Wallace, Gerald 31 Celtics F 45 $10,105,855 79 220 \n", "3 Gladness, Mickell 27 Magic C 40 $762,195 83 220 \n", "4 Jefferson, Richard 33 Jazz F 44 $11,046,000 79 230 \n", "5 Hill, Solomon 22 Pacers F 9 $1,246,680 79 220 \n", "\n", " EXP 1st Year DOB School City \\\n", "0 4 2009 5/29/1987 Alabama Riviera Beach, FL \n", "1 12 2001 7/23/1982 Alabama Sylacauga, AL \n", "3 2 2011 7/26/1986 Alabama A&M Birmingham, AL \n", "4 12 2001 6/21/1980 Arizona Los Angeles, CA \n", "5 0 2013 3/18/1991 Arizona Los Angeles, CA \n", "\n", " State (Province, Territory, Etc..) Country Race HS Only \n", "0 Florida US Black No \n", "1 Alabama US Black No \n", "3 Alabama US Black No \n", "4 California US Black No \n", "5 California US Black No " ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "# Get numerical data on a column\n", "# If you're dealing with labels or groups, use .value_counts()\n", "nba_df[\"Age\"].describe()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ "count 528.000000\n", "mean 26.242424\n", "std 4.178868\n", "min 18.000000\n", "25% 23.000000\n", "50% 25.000000\n", "75% 29.000000\n", "max 39.000000\n", "dtype: float64" ] } ], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "# Get numerical data on grouped data\n", "nba_df.groupby(\"POS\")[\"Age\"].describe()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 20, "text": [ "POS \n", "C count 67.000000\n", " mean 26.208955\n", " std 3.800069\n", " min 19.000000\n", " 25% 23.500000\n", " 50% 26.000000\n", " 75% 28.000000\n", " max 36.000000\n", "F count 142.000000\n", " mean 26.352113\n", " std 4.122585\n", " min 20.000000\n", " 25% 23.000000\n", " 50% 25.500000\n", " 75% 29.000000\n", " max 37.000000\n", "F/C count 74.000000\n", " mean 27.175676\n", " std 4.142523\n", " min 20.000000\n", " 25% 24.000000\n", " 50% 26.000000\n", " 75% 30.000000\n", " max 39.000000\n", "G count 175.000000\n", " mean 25.725714\n", " std 4.364719\n", " min 19.000000\n", " 25% 22.000000\n", " 50% 25.000000\n", " 75% 28.000000\n", " max 39.000000\n", "G/F count 70.000000\n", " mean 26.357143\n", " std 4.121473\n", " min 18.000000\n", " 25% 23.000000\n", " 50% 26.000000\n", " 75% 28.750000\n", " max 36.000000\n", "dtype: float64" ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "# Remove columns that you HATE with .drop\n", "# Need to save it as a new (or the same) variable\n", "nba_df = nba_df.drop([\"City\"], axis=1)\n", "nba_df.columns" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 24, "text": [ "Index([u'Name', u'Age', u'Team', u'POS', u'#', u'2013 $', u'Ht (In.)', u'WT', u'EXP', u'1st Year', u'DOB', u'School', u'State (Province, Territory, Etc..)', u'Country', u'Race', u'HS Only'], dtype='object')" ] } ], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "# Calculate a new column from an existing column\n", "nba_df[\"Ht (Cm.)\"] = nba_df[\"Ht (In.)\"] * 2.54\n", "nba_df[:2]" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
NameAgeTeamPOS#2013 $Ht (In.)WTEXP1st YearDOBSchoolState (Province, Territory, Etc..)CountryRaceHS OnlyHt (Cm.)
0 Gee, Alonzo 26 Cavaliers F 33 $3,250,000 78 219 4 2009 5/29/1987 Alabama Florida US Black No 198.12
1 Wallace, Gerald 31 Celtics F 45 $10,105,855 79 220 12 2001 7/23/1982 Alabama Alabama US Black No 200.66
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 29, "text": [ " Name Age Team POS # 2013 $ Ht (In.) WT EXP \\\n", "0 Gee, Alonzo 26 Cavaliers F 33 $3,250,000 78 219 4 \n", "1 Wallace, Gerald 31 Celtics F 45 $10,105,855 79 220 12 \n", "\n", " 1st Year DOB School State (Province, Territory, Etc..) Country \\\n", "0 2009 5/29/1987 Alabama Florida US \n", "1 2001 7/23/1982 Alabama Alabama US \n", "\n", " Race HS Only Ht (Cm.) \n", "0 Black No 198.12 \n", "1 Black No 200.66 " ] } ], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [ "# String manipulation on an entire column\n", "# Need to use .str to treat it as a string\n", "nba_df[\"Name\"].str.lower()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 35, "text": [ "0 gee, alonzo\n", "1 wallace, gerald\n", "2 williams, mo\n", "3 gladness, mickell\n", "4 jefferson, richard\n", "5 hill, solomon\n", "6 budinger, chase\n", "7 williams, derrick\n", "8 hill, jordan\n", "9 frye, channing\n", "10 bayless, jerryd\n", "11 terry, jason\n", "12 fogg, kyle\n", "13 iguodala, andre\n", "14 boateng, eric\n", "...\n", "513 alexander, joe\n", "514 fischer, d'or\n", "515 ebanks, devin\n", "516 johnson, amir\n", "517 martin, kevin\n", "518 evans, jeremy\n", "519 lee, courtney\n", "520 mekel, gal\n", "521 murry, toure'\n", "522 stiemsma, greg\n", "523 leuer, jon\n", "524 landry, marcus\n", "525 harris, devin\n", "526 west, david\n", "527 crawford, jordan\n", "Name: Name, Length: 528, dtype: object" ] } ], "prompt_number": 35 }, { "cell_type": "code", "collapsed": false, "input": [ "# Do more intense manipulation with .apply + an external function\n", "# You will always forget to do axis=1, so remember it!\n", "# Just treat row like a dictionary, it goes one at a time\n", "def do_i_like_them(row):\n", " if row[\"Age\"] >= 31:\n", " return True\n", " else:\n", " return False\n", "\n", "nba_df[\"Liked\"] = nba_df.apply(do_i_like_them, axis=1)\n", "nba_df[\"Liked\"].value_counts()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 42, "text": [ "False 439\n", "True 89\n", "dtype: int64" ] } ], "prompt_number": 42 }, { "cell_type": "code", "collapsed": false, "input": [ "# OPEN QUESTION: HOW DO YOU ADD A ROW TO A DATAFRAME!!!!!!!" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 50 }, { "cell_type": "code", "collapsed": false, "input": [ "# Get one column of a dataframe\n", "nba_df.ix[0]\n", "# Maybe sometimes use .iloc" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 54, "text": [ "Name Gee, Alonzo\n", "Age 26\n", "Team Cavaliers\n", "POS F\n", "# 33\n", "2013 $ $3,250,000\n", "Ht (In.) 78\n", "WT 219\n", "EXP 4\n", "1st Year 2009\n", "DOB 5/29/1987\n", "School Alabama\n", "State (Province, Territory, Etc..) Florida\n", "Country US\n", "Race Black\n", "HS Only No\n", "Ht (Cm.) 198.12\n", "Liked False\n", "Name: 0, dtype: object" ] } ], "prompt_number": 54 }, { "cell_type": "code", "collapsed": false, "input": [ "# For loops with dataframes\n", "# Can't do for row in nba_df, gotta use iterrows()\n", "for index, row in nba_df.iterrows():\n", " print str(index) + \": \" + row[\"Name\"]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0: Gee, Alonzo\n", "1: Wallace, Gerald\n", "2: Williams, Mo\n", "3: Gladness, Mickell\n", "4: Jefferson, Richard\n", "5: Hill, Solomon\n", "6: Budinger, Chase\n", "7: Williams, Derrick\n", "8: Hill, Jordan\n", "9: Frye, Channing\n", "10: Bayless, Jerryd\n", "11: Terry, Jason\n", "12: Fogg, Kyle\n", "13: Iguodala, Andre\n", "14: Boateng, Eric\n", "15: Diogu, Ike\n", "16: Ayres, Jeff\n", "17: Harden, James\n", "18: Felix, Carrick\n", "19: Pargo, Jannero\n", "20: Beverley, Patrick\n", "21: Johnson, Joe\n", "22: Brewer, Ronnie\n", "23: Fisher, Derek\n", "24: Miller, Quincy\n", "25: Acy, Quincy\n", "26: Jones, Perry\n", "27: Udoh, Ekpe\n", "28: Clark, Ian\n", "29: Andersen, Chris\n", "30: Jackson, Reggie\n", "31: Dudley, Jared\n", "32: O'Bryant, Patrick\n", "33: Davies, Brandon\n", "34: Fredette, Jimmer\n", "35: Mack, Shelvin\n", "36: Hayward, Gordon\n", "37: Anderson, Ryan\n", "38: Crabbe, Allen\n", "39: Griffin, Eric\n", "40: Taylor, Jermaine\n", "41: Kaman, Chris\n", "42: Martin, Kenyon\n", "43: Maxiell, Jason\n", "44: Stephenson, Lance\n", "45: Booker, Trevor\n", "46: Cole, Norris\n", "47: Perkins, Kendrick\n", "48: Copeland, Chris\n", "49: Burks, Alec\n", "50: Billups, Chauncey\n", "51: Roberson, Andr\ufffd\n", "52: Smith, Jason\n", "53: Gordon, Ben\n", "54: Thabeet, Hasheem\n", "55: Drummond, Andre\n", "56: Adrien, Jeff\n", "57: Butler, Caron\n", "58: Villanueva, Charlie\n", "59: Gay, Rudy\n", "60: Armstrong, Hilton\n", "61: Okafor, Emeka\n", "62: Walker, Kemba\n", "63: Allen, Ray\n", "64: Price, A.J.\n", "65: Lamb, Jeremy\n", "66: Tolliver, Anthony\n", "67: Korver, Kyle\n", "68: Stoudemire, Amar'e\ufffd\n", "69: Curry, Stephen\n", "70: Wright, Chris\n", "71: Roberts, Brian\n", "72: Koshwal, Mac\n", "73: Chandler, Wilson\n", "74: Green, Willie\n", "75: McCallum, Ray\n", "76: Chandler, Tyson\n", "77: Irving, Kyrie\n", "78: Boozer, Carlos\n", "79: Deng, Luol\n", "80: Battier, Shane\n", "81: Kelly, Ryan\n", "82: Thomas, Lance\n", "83: Maggette, Corey\n", "84: McRoberts, Josh\n", "85: Brand, Elton\n", "86: Plumlee, Mason\n", "87: Plumlee, Miles\n", "88: Redick, J. J.\n", "89: Rivers, Austin\n", "90: Curry, Seth\n", "91: Henderson, Gerald\n", "92: Dunleavy, Mike\n", "93: Singler, Kyle\n", "94: James, Mike\n", "95: Stuckey, Rodney\n", "96: O'Neal, Jermaine\n", "97: Lewis, Rashard\n", "98: Garnett, Kevin\n", "99: Horford, Al\n", "100: Murphy, Erik\n", "101: Noah, Joakim\n", "102: Parsons, Chandler\n", "103: Haslem, Udonis\n", "104: Bonner, Matt\n", "105: Speights, Marreese\n", "106: Lee, David\n", "107: Calathes, Nick\n", "108: Beal, Bradley\n", "109: Miller, Mike\n", "110: Brewer, Corey\n", "111: James, Bernard\n", "112: Singleton, Chris\n", "113: Douglas, Toney\n", "114: McGuire, Dominic\n", "115: Ely, Melvin\n", "116: Smith, Greg\n", "117: George, Paul\n", "118: Mensah-Bonsu, Pops\n", "119: Sims, Henry\n", "120: Hibbert, Roy\n", "121: Green, Jeff\n", "122: Porter, Otto\n", "123: Monroe, Greg\n", "124: Thompson, Hollis\n", "125: Caldwell-Pope, Kentavious\n", "126: Wilkins, Damien\n", "127: Young, Thaddeus\n", "128: Lawal, Gani\n", "129: Bosh, Chris\n", "130: Favors, Derrick\n", "131: Jack, Jarrett\n", "132: Morrow, Anthony\n", "133: Bynum, Will\n", "134: Shumpert, Iman\n", "135: Rice, Glen\n", "136: Brown, Kwame\n", "137: Olynyk, Kelly\n", "138: Harris, Elias\n", "139: Turiaf, Ronny\n", "140: Sacre, Robert\n", "141: Daye, Austin\n", "142: Green, Gerald\n", "143: Lin, Jeremy\n", "144: Cousin, Marcus\n", "145: Leonard, Meyers\n", "146: Cook, Brian\n", "147: Williams, Deron\n", "148: White, DJ\n", "149: Zeller, Cody\n", "150: Oladipo, Victor\n", "151: Gordon, Eric\n", "152: Machado, Scott\n", "153: Evans, Reggie\n", "154: White, Royce\n", "155: Garrett, Diante\n", "156: Hill, George\n", "157: Henry, Xavier\n", "158: Aldrich, Cole\n", "159: Withey, Jeff\n", "160: Pierce, Paul\n", "161: Arthur, Darrell\n", "162: Jackson, Darnell\n", "163: Morris, Markieff\n", "164: Morris, Marcus\n", "165: Robinson, Thomas\n", "166: Collison, Nick\n", "167: Hinrich, Kirk\n", "168: Chalmers, Mario\n", "169: McLemore, Ben\n", "170: Taylor, Tyshawn\n", "171: Rush, Brandon\n", "172: Beasley, Michael\n", "173: McGruder, Rodney\n", "174: Noel, Nerlens\n", "175: Mohammed, Nazr\n", "176: Cousins, DeMarcus\n", "177: Harrellson, Josh\n", "178: Kidd-Gilchrist, Michael\n", "179: Prince, Tayshaun\n", "180: Miller, Darius\n", "181: Jones, Terrence\n", "182: Hayes, Chuck\n", "183: Patterson, Patrick\n", "184: Davis, Anthony\n", "185: Knight, Brandon\n", "186: Teague, Marquis\n", "187: Rondo, Rajon\n", "188: Meeks, Jodie\n", "189: Lamb, Doron\n", "190: Bledsoe, Eric\n", "191: Goodwin, Archie\n", "192: Wall, John\n", "193: Bogans, Keith\n", "194: Butler, Rasual\n", "195: Ellis, Monta\n", "196: McCollum, C. J.\n", "197: Millsap, Paul\n", "198: Garc\ufffda, Francisco\n", "199: Dieng, Gorgui\n", "200: Clark, Earl\n", "201: Smith, Chris\n", "202: Siva, Peyton\n", "203: Bryant, Kobe\n", "204: Randolph, Anthony\n", "205: Hamilton, Justin\n", "206: Bass, Brandon\n", "207: Davis, Glen\n", "208: Johnson, Chris\n", "209: Thornton, Marcus\n", "210: Temple, Garrett\n", "211: Crowder, Jae\n", "212: Novak, Steve\n", "213: Blue, Vander\n", "214: Wade, Dwyane\n", "215: Lockett, Trent\n", "216: Johnson-Odom, Darius\n", "217: Buycks, Dwight\n", "218: Butler, Jimmy\n", "219: Hayward, Lazar\n", "220: Matthews, Wesley\n", "221: Len, Alex\n", "222: Blake, Steve\n", "223: V\ufffdsquez, Greivis\n", "224: Gaffney, Tony\n", "225: Camby, Marcus\n", "226: Williams, Shawne\n", "227: Rose, Derrick\n", "228: Williams, Elliot\n", "229: Evans, Tyreke\n", "230: Barton, Will\n", "231: Thomas, Adonis\n", "232: Douglas-Roberts, Chris\n", "233: Kadji, Kenny\n", "234: Jones, DeQuan\n", "235: Larkin, Shane\n", "236: Jones, James\n", "237: Salmons, John\n", "238: Morris, Darius\n", "239: Crawford, Jamal\n", "240: Burke, Trey\n", "241: Hardaway, Tim, Jr.\n", "242: Harris, Manny\n", "243: Randolph, Zach\n", "244: Green, Draymond\n", "245: Brown, Shannon\n", "246: Richardson, Jason\n", "247: Anderson, Alan\n", "248: Dawson, Eric\n", "249: Humphries, Kris\n", "250: Williams, Rodney\n", "251: Becton-Buckner, Reginald\n", "252: Moultrie, Arnett\n", "253: Varnado, Jarvis\n", "254: Bost, Dee\n", "255: Carroll, DeMarre\n", "256: Pressey, Phil\n", "257: Faried, Kenneth\n", "258: Canaan, Isaiah\n", "259: Morais, Carlos\n", "260: Scola, Luis\n", "261: Prigioni, Pablo\n", "262: Gin\ufffdbili, Manu\n", "263: Delfino, Carlos" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "264: Parker, Tony\n", "265: Teletovi?, Mirza\n", "266: Splitter, Tiago\n", "267: Varej\ufffdo, Anderson\n", "268: Faverani, V\ufffdtor\n", "269: Nen\ufffd\n", "270: Biyombo, Bismack\n", "271: Ibaka, Serge\n", "272: Vesel\ufffd, Jan\n", "273: Freeland, Joel\n", "274: Gobert, Rudy\n", "275: Mahinmi, Ian\n", "276: Diaw, Boris\n", "277: Fournier, Evan\n", "278: De Colo, Nando\n", "279: Batum, Nicolas\n", "280: Shengelia, Tornike\n", "281: Pachulia, Zaza\n", "282: Nowitzki, Dirk\n", "283: Ohlbrecht, Tim\n", "284: Schr\ufffdder, Dennis\n", "285: Antetokounmpo, Giannis\n", "286: Seraphin, Kevin\n", "287: Casspi, Omri\n", "288: Gallinari, Danilo\n", "289: Datome, Luigi\n", "290: Belinelli, Marco\n", "291: Valan?i?nas, Jonas\n", "292: Motiej?nas, Donatas\n", "293: Anti?, Pero\n", "294: Gortat, Marcin\n", "295: Bargnani, Andrea\n", "296: Biedri?\ufffd, Andris\n", "297: Mozgov, Timofey\n", "298: Kirilenko, Andrei\n", "299: Shved, Alexey\n", "300: Karasev, Sergey\n", "301: Raduljica, Miroslav\n", "302: Udrih, Beno\n", "303: Gasol, Marc\n", "304: Claver, V\ufffdctor\n", "305: Gasol, Pau\n", "306: Calder\ufffdn, Jos\ufffd\n", "307: Rubio, Ricky\n", "308: Jerebko, Jonas\n", "309: Kanter, Enes\n", "310: Sefolosha, Thabo\n", "311: A??k, \ufffdmer\n", "312: T\ufffdrko?lu, Hedo\n", "313: ?lyasova, Ersan\n", "314: Kravtsov, Viacheslav\n", "315: Pekovi?, Nikola\n", "316: Kuzmi?, Ognjen\n", "317: Dubljevic, Bojan\n", "318: Dragi?, Goran\n", "319: Nedovi?, Nemanja\n", "320: McGee, JaVale\n", "321: Sessions, Ramon\n", "322: Snell, Tony\n", "323: Granger, Danny\n", "324: O'Quinn, Kyle\n", "325: Haywood, Brendan\n", "326: Henson, John\n", "327: Jamison, Antawn\n", "328: Davis, Ed\n", "329: Williams, Marvin\n", "330: Barnes, Harrison\n", "331: Zeller, Tyler\n", "332: Wright, Brandan\n", "333: Hansbrough, Tyler\n", "334: Felton, Raymond\n", "335: Ellington, Wayne\n", "336: Lawson, Ty\n", "337: Marshall, Kendall\n", "338: Bullock, Reggie\n", "339: Carter, Vince\n", "340: Green, Danny\n", "341: Leslie, C. J.\n", "342: Powell, Josh\n", "343: Howell, Richard\n", "344: Hickson, J. J.\n", "345: Brown, Lorenzo\n", "346: Mitchell, Tony\n", "347: Barea, Jos\ufffd Juan\n", "348: Silas, Xavier\n", "349: Diop, DeSagana\n", "350: Smith, Josh\n", "351: Jennings, Brandon\n", "352: Koufos, Kosta\n", "353: Oden, Greg\n", "354: Sullinger, Jared\n", "355: Mullens, Byron\n", "356: Conley, Mike\n", "357: Turner, Evan\n", "358: Lighty, David\n", "359: Osby, Romero\n", "360: Griffin, Blake\n", "361: Anderson, James\n", "362: Lucas III, John\n", "363: Graham, Stephen\n", "364: Allen, Tony\n", "365: Bazemore, Kent\n", "366: Singler, E. J.\n", "367: Ridnour, Luke\n", "368: Brooks, Aaron\n", "369: Cunningham, Jared\n", "370: Livingston, Shaun\n", "371: Adams, Steven\n", "372: Gray, Aaron\n", "373: Blair, DeJuan\n", "374: Jefferson, Al\n", "375: Gomes, Ryan\n", "376: Brooks, MarShon\n", "377: Landry, Carl\n", "378: Moore, E'Twaun\n", "379: Hummel, Robbie\n", "380: Harris, Mike\n", "381: Thompson, Jason\n", "382: Jeffers, Othyus\n", "383: N'Diaye, Hamady\n", "384: Jones, Dwayne\n", "385: Nelson, Jameer\n", "386: Reed, Willie\n", "387: Mills, Patrick\n", "388: McConnell, Mickey\n", "389: Tyler, Jeremy\ufffd\n", "390: Franklin, Jamaal\n", "391: Leonard, Kawhi\n", "392: Nash, Steve\n", "393: Webster, Martell\n", "394: Dalembert, Samuel\n", "395: Miles, C. J.\n", "396: Balkman, Renaldo\n", "397: Westbrook, Charlie\n", "398: Wolters, Nate\n", "399: Jones, Solomon\n", "400: Williams, Louis\n", "401: Wright, Dorell\n", "402: Blatche, Andray\n", "403: Ledo, Ricky\n", "404: Vu?evi?, Nikola\n", "405: Dedmon, Dewayne\n", "406: Gibson, Taj\n", "407: Mayo, O. J.\n", "408: Young, Nick\n", "409: DeRozan, DeMar\ufffd\n", "410: Smith, J. R.\n", "411: Nicholson, Andrew\n", "412: Lee, Michael\n", "413: World Peace, Metta\n", "414: Harkless, Maurice\n", "415: Kennedy, D. J.\n", "416: Bynum, Andrew\n", "417: Dellavedova, Matthew\n", "418: Harrington, Al\n", "419: James, LeBron\n", "420: Lopez, Brook\n", "421: Lopez, Robin\n", "422: Childress, Josh\n", "423: Fields, Landry\n", "424: Outlaw, Travis\n", "425: Howard, Dwight\n", "426: Melo, Fab\n", "427: Joseph, Kris\n", "428: Southerland, James\n", "429: Anthony, Carmelo\n", "430: Onuaku, Arinze\n", "431: Carter-Williams, Michael\n", "432: Waiters, Dion\n", "433: Johnson, Wesley\n", "434: Allen, Lavoy\n", "435: Wyatt, Khalif\n", "436: Christmas, Dionte\n", "437: Harris, Tobias\n", "438: Watson, C. J.\n", "439: Covington, Robert\n", "440: Hudson, Lester\n", "441: Thompson, Tristan\n", "442: Joseph, Cory\n", "443: Pittman, Dexter\n", "444: James, Damion\n", "445: Tucker, P. J.\n", "446: Durant, Kevin\n", "447: Aldridge, LaMarcus\n", "448: Bradley, Avery\n", "449: Ivey, Royal\n", "450: Augustin, D. J.\n", "451: Hamilton, Jordan\n", "452: Jordan, DeAndre\n", "453: Middleton, Khris\n", "454: Sloan, Donald\n", "455: Stone, Julyan\n", "456: Neal, Gary\n", "457: Johnson, Orlando\n", "458: Nunnally, James\n", "459: Mbah a Moute, Luc\n", "460: Gadzuric, Dan\n", "461: Hollins, Ryan\n", "462: Barnes, Matt\n", "463: Love, Kevin\n", "464: Collison, Darren\n", "465: Drew, Larry\n", "466: Farmar, Jordan\n", "467: Holiday, Jrue\n", "468: Lee, Malcolm\n", "469: Westbrook, Russell\n", "470: Watson, Earl\n", "471: Afflalo, Arron\n", "472: Muhammad, Shabazz\n", "473: Ariza, Trevor\n", "474: Anthony, Joel\n", "475: Bennett, Anthony\n", "476: Amundson, Lou\n", "477: Marion, Shawn\n", "478: Ay\ufffdn, Gustavo\n", "479: Bogut, Andrew\n", "480: Miller, Andre\n", "481: Price, Ronnie\n", "482: Howard, Ron\n", "483: Ezeli, Festus\n", "484: Taylor, Jeffery\n", "485: Jenkins, John\n", "486: Cunningham, Dante\n", "487: Wayns, Maalik\n", "488: Foye, Randy\n", "489: Lowry, Kyle\n", "490: Scott, Mike\n", "491: Mason, Jr., Roger\n", "492: Sanders, Larry\n", "493: Daniels, Troy\n", "494: Maynor, Eric\n", "495: Williams, Reggie\n", "496: Johnson, James\n", "497: Aminu, Al-Farouq\n", "498: Paul, Chris\n", "499: Teague, Jeff\n", "500: Smith, Ish\n", "501: Duncan, Tim\n", "502: Hawes, Spencer\n", "503: Wroten, Tony\n", "504: Gaddy, Abdul\n", "505: Thomas, Isaiah\n", "506: Robinson, Nate\n", "507: Ross, Terrence\n", "508: Pondexter, Quincy\n", "509: Holiday, Justin\n", "510: Baynes, Aron\n", "511: Thompson, Klay\n", "512: Lillard, Damian\n", "513: Alexander, Joe\n", "514: Fischer, D'or\n", "515: Ebanks, Devin\n", "516: Johnson, Amir\n", "517: Martin, Kevin\n", "518: Evans, Jeremy\n", "519: Lee, Courtney\n", "520: Mekel, Gal\n", "521: Murry, Toure'\n", "522: Stiemsma, Greg\n", "523: Leuer, Jon\n", "524: Landry, Marcus\n", "525: Harris, Devin\n", "526: West, David\n", "527: Crawford, Jordan\n" ] } ], "prompt_number": 57 }, { "cell_type": "code", "collapsed": false, "input": [ "# Grouping by as many as you want\n", "# Be sure to put the groupby stuff in square brackets\n", "nba_df.groupby([\"POS\", \"Race\"])[\"Age\"].describe()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 60, "text": [ "POS Race \n", "C Black count 36.000000\n", " mean 26.972222\n", " std 3.974822\n", " min 19.000000\n", " 25% 24.750000\n", " 50% 26.000000\n", " 75% 30.250000\n", " max 36.000000\n", " Hispanic count 1.000000\n", " mean 28.000000\n", " std NaN\n", " min 28.000000\n", " 25% 28.000000\n", " 50% 28.000000\n", " 75% 28.000000\n", "...\n", "G/F Mixed mean 24.000000\n", " std 1.414214\n", " min 23.000000\n", " 25% 23.500000\n", " 50% 24.000000\n", " 75% 24.500000\n", " max 25.000000\n", " White count 6.000000\n", " mean 28.333333\n", " std 4.802777\n", " min 23.000000\n", " 25% 24.250000\n", " 50% 28.500000\n", " 75% 32.750000\n", " max 33.000000\n", "Length: 160, dtype: float64" ] } ], "prompt_number": 60 }, { "cell_type": "code", "collapsed": false, "input": [ "# Histograms\n", "# Shows you the spread of one numerical value\n", "nba_df[\"Age\"].hist()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEACAYAAAC57G0KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n", "text": [ "" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "# Cathy says there should always be 30 mins\n", "nba_df[\"Age\"].hist(bins=30)\n", "# Cathy is never wrong" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAEACAYAAACuzv3DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n", "text": [ "" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "# Scatterplots show you the relationship of two numerical values\n", "# If you have a line they're related, otherwise nopers\n", "nba_df.plot(\"Ht (In.)\",\"WT\", kind='scatter')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEPCAYAAACp/QjLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4lFX2xz8z6ZMKBBIgQJAAIbSgSEfpRRHrouAK2H6K\nXXQRWXVxdQXLyrIqrooC6irgWhARBZEg1diCSi+hGEIvSUifOb8/7iQThbyTBAYSOJ/n4ZF5M9+5\nd47h3vc959xzbCIiKIqiKEoZ7Gd7AoqiKEr1QzcHRVEU5QR0c1AURVFOQDcHRVEU5QR0c1AURVFO\nQDcHRVEU5QR8tjnk5+fTuXNnkpOTSUpK4tFHHwVg4sSJxMXF0aFDBzp06MDChQtLNZMmTaJ58+Yk\nJiayaNEiX01NURRF8YLNl+cccnNzcTgcFBcX06NHD1544QWWLFlCeHg4Y8eO/d17169fz4gRI/ju\nu+/IyMigX79+bN68GbtdH24URVHOND5deR0OBwCFhYU4nU5q1aoFwMn2o3nz5jF8+HACAgKIj48n\nISGB1NRUX05PURRFKQefbg4ul4vk5GRiYmLo3bs3rVu3BuCll16iffv23HrrrRw9ehSAPXv2EBcX\nV6qNi4sjIyPDl9NTFEVRysGnm4PdbictLY3ffvuNb775hpSUFMaMGUN6ejppaWnUr1+fhx56qFy9\nzWbz5fQURVGUcvA/E4NERkZy+eWX8/3339OrV6/S67fddhtXXHEFAA0bNmT37t2lP/vtt99o2LDh\nCZ/VsGFD9uzZ4/M5K4qinEs0a9aMrVu3Vvj9PntyOHjwYKnLKC8vj8WLF9OhQwf27t1b+p6PP/6Y\ntm3bAjB06FBmz55NYWEh6enpbNmyhU6dOp3wuXv27EFE9I8If/vb3876HKrLH7WF2kJtYf1n27Zt\nlVrDffbkkJmZyahRo3C5XLhcLm666Sb69u3LyJEjSUtLw2az0bRpU1577TUAkpKSGDZsGElJSfj7\n+zNt2jR1K3lhx44dZ3sK1Qa1hQe1hQe1RdXx2ebQtm1bfvzxxxOuv/322+VqJkyYwIQJE3w1JUVR\nFKWC6CGCGszo0aPP9hSqDWoLD2oLD2qLquPTQ3C+wGazUcOmrCiKctap7NqpTw41mJSUlLM9hWqD\n2sKD2sKD2qLq6OagKIqinIC6lRRFUc4D1K2kKIqinDK6OdRg1J/qQW3hQW3hQW1RdXRzUBRFUU5A\nYw6KoijnARpzUBRFUU4Z3RxqMOpP9aC28KC28KC2qDq6OSiKoignoDEHRVGU8wCNOSiKoiinjG4O\nNRj1p3pQW3hQW3hQW1Qd3RwURVGUE9CYg6IoynmAxhwURVGUU0Y3hxqM+lM9qC08qC08qC2qjm4O\niqIoyglozEFRFOU8QGMOiqIoyimjm0MNRv2pHtQWHtQWHtQWVcdnm0N+fj6dO3cmOTmZpKQkHn30\nUQAOHz5M//79adGiBQMGDODo0aOlmkmTJtG8eXMSExNZtGiRr6amKIqieMGnMYfc3FwcDgfFxcX0\n6NGDF154gU8//ZTo6GjGjRvHs88+y5EjR5g8eTLr169nxIgRfPfdd2RkZNCvXz82b96M3f77/Utj\nDoqiKJWnWsUcHA4HAIWFhTidTmrVqsWnn37KqFGjABg1ahSffPIJAPPmzWP48OEEBAQQHx9PQkIC\nqampvpyeoiiKUg4+3RxcLhfJycnExMTQu3dvWrduzb59+4iJiQEgJiaGffv2AbBnzx7i4uJKtXFx\ncWRkZPhyejUe9ad6UFt4UFt4UFtUHX9ffrjdbictLY1jx44xcOBAli5d+ruf22w2bDZbufryfjZ6\n9Gji4+MBiIqKIjk5mV69egGeXwZ9fX69LqG6zOdsvk5LS6tW8zmbr9PS0qrVfM7k65SUFGbOnAlQ\nul5WhjN2zuGpp54iJCSE6dOnk5KSQmxsLJmZmfTu3ZuNGzcyefJkAMaPHw/AoEGDePLJJ+ncufPv\nJ6wxB0VRlEpTbWIOBw8eLM1EysvLY/HixXTo0IGhQ4cya9YsAGbNmsVVV10FwNChQ5k9ezaFhYWk\np6ezZcsWOnXq5KvpKYqiKBb4bHPIzMykT58+JCcn07lzZ6644gr69u3L+PHjWbx4MS1atODrr78u\nfVJISkpi2LBhJCUlMXjwYKZNm2bpclLUn1oWtYUHtYUHtUXV8VnMoW3btvz4448nXK9duzZfffXV\nSTUTJkxgwoQJvpqSoih/QETYtGkT+fn5JCUlERgYWCHdwYMHSU9PJz4+nrp163p9f3FxMf37D2H5\n8u+x2/24++4bmTLlxVOdvuJDtLaSopynFBUVMXToDXzzTSp+fuHUrevPihVfUr9+fUvd7NlzueWW\nMQQENKGoaCfTp7/CiBE3WGr69bucJUt2AjOBI8D1PPvseMaNG3e6vo7ihcqunbo5KMp5ypQpU3ns\nsQXk5n4GBODv/1cGDtzGZ5/NKVdz8OBBGjduSV7eUqAd8AshIb3YuXOj5RNEYGAMRUUfAd3dV/5F\nQsJMtmxJO43fSLGi2gSkFd+j/lQPagsPFbVFWtoGcnOvAgIBG8XFf+LXXzdYanbs2EFAQBPMxgDQ\nloCAeNLT0y11fn5+wOfATcDtwFocjiCvc8zMzOTOO+9nyJDhvPrq65W+MdTfi6qjm4OinKckJ7fC\n4fgEKAQEf/8PaNOmlaUmPj6eoqKdwM/uK79SVLSDpk2bWuqGDr0E+A9wKdAcmMMDD9xpqTl8+DAd\nOnTjzTcDWbDgch5++DUeeeTxCn035TQgNYwaOGVFqZYUFhbKwIFXi8MRJ+HhraRp0zayZ88er7r3\n358jISG1JSKig4SE1Jb//vd9r5rWrbsJfC4gAiI220QZM+Z+S82MGTPE4bimVAMZEhgYKi6Xq8Lf\nUfFQ2bXTpyekFUWpvgQEBLBw4YeVzla64YZh9OvXp9LZShBS+lrEQWHhAa8akeAyV0JwuZxex6oq\n+/fvZ86cORQVFXHllVfSrFkzn41VI/DRJuUzauCUfcbSpUvP9hSqDWoLD9XRFi+//Ko4HIkCCwTe\nEYejrqxevdpSs2PHDvHzCxN4QeArge7So0f/So1bUVv89ttvEh3dSIKD/yyBgXdKWFhd+eGHHyo1\nVnWnsmunPjkoiuJz7rrrDgIC/Hn99RcJCQniySdn06VLF0vN6tWrCQpKJDd3DfAp0I6ffprrk/n9\n4x8vcOTIDTidzwFQWHghDz74BMuWfeaT8WoCujnUYEqKbSlqi7JUxhb5+fnMmDGDnJwcbrzxRho0\naFAh3fbt29m8eTPNmzevkPvFZrMxYsQNJCRcQFBQ0Ak1007GkSNHcLnaA3cD+4FW5OW9gcvlOqHP\nS3lU1Bb79h3G6bykzJWWHDx4uELacxYfPcH4jBo4ZUWplhw5ckRCQ+sLxAski90eLsuXL/eqe/XV\n1yUkJFoiI/tJSEhdefnl/3jVpKenS2zsBRIR0U3CwlpLp069JTc311Lz66+/ip9flEBDgd4CEdKu\nXecKf7/K8O67/3W7vTYI7BaH41KZMGGiT8Y6W1R27axxK61uDh6qo2/5bKG28FBRWwwceJnAZQLF\n7mygf0itWvGWmr1790pwcC2BrW7NNgkOruU1y6l//6vFz+9pt6ZYgoOvlqeeesZS8/nnn0tAQHOB\nHLfuM4mOblyh71ZCRW3hcrlk8uQXJDIyVkJDa8uddz4gRUVFlRqrulPZtVPPOShKNaSgoID9+/fj\ncrl8NsaWLRnAZYCf+8pgsrKyLTW7d+8mMLAJUOJKuoDAwKbs3r3bUrd163aczkHuV37k5/dnw4bt\nlppvvvmGoqJuGO/3fqA/Bw/u9olNbDYbjzzyEEePZpKTc4hXX52Cv//57XXXzaEGo352D+eSLaZP\nn0FkZDSNG7eiSZNWbNq0qVL6itqic+fWwAwgB3AB/yE2tp6lplmzZuTnbwdWuq+sJj9/KwkJCZa6\n5OQ22GyvusfJwd9/Fl26JFtqCgsLMYHoOkArIIGy6bAV4Vz6vTjT6OagKNWItWvXct99j1JQ8D0F\nBYfIyHiAwYOv88lYb789i/j4AqAeUJvg4I9Ztmy+pSY7O5vCwgJgCBAPDKawsIDsbOsnDnMXvgho\nAMQh8pvXO/Po6GjABvwCHALuxmbzr3AwWjk11Mo1GK0b4+FcscWPP/6I3T4AaAmAyJ3s3LmJvLy8\nCn9GRW3h7+/PihVfcNddt/DnP19JSsp8r5lH5rMbAXuAJUAm0ISvv/7aUvf992mIfAh8B2zG6fwr\nK1Z8b6kJDQ3Fbh8KlJTmGAscr5Rb6Vz5vTgb6OagKNWIxo0bYxbQ4+4rawgNjSI4ONhCVTV2795N\n27adeP11ePfdePr0uaLcXisltGnTBrMxHMTEHQ4Bv9G2bVtLXaNGDYARQGugJTbbCzRv3thS07Rp\nU+z2pUB7zBPHZURGxnh9cjhy5AhDhlxPVFQDbrzxdpYvX275fuXk6OZQg1F/qodzxRZ9+vThmmsu\nITS0PRERV+JwDOX992dUqitiRW0xdeorZGUNp7j4ZeBJcnNf5eGH/26pufDCC7nsskFAInAB0JKB\nA/vRsWNHS11W1lGgFvAD8CUiORQU5Fpq9u3bR3HxAeAfwLdAXbKzvT9BXXnlCBYvrsWxY9+yZ89z\nDB58DTt27PCqqwr5+fk8+eQ/uPbaUTz33D8pKiryyThng/M7HK8o1QybzcasWf9hzJg1ZGZmcuGF\nU4mPj/fJWEeP5uB0lg0kx5GdnWOpERHy84sICOhJUdEwAgI+oKCg2OvBtF9/3YoJLjd3X3mMuXPf\nZNKkSeVqpk2bBlyDiW8AvInTGUZhYWG5NaAKCwtZuXIJLtdxIADjAvuQlJQURo8ebfndKovT6aR3\n7yGkpUWQnz+EhQvnsGzZGj77bO450eJYnxxqMOpP9XAu2SI1NZUbbrid0aPHMnr0neTn53vVOJ1O\nJk/+J927X8allw5k+3brNFGA66+/koCAJzGB5SbY7VczYsTVlppNmzaxbNlqiooOA3+nqOgQy5d/\nx8aNGy11AQEBwHvAUGAYsILISOvMo4KCAmA9MBqTcvs3wN/9WSfH398ff/9A4Df3laXYbDuJiIiw\nHKsqrF27ll9+2UV+/gfALeTlfcrSpcvZuXPnaR/rbKCbg6JUI9LT0+natR+7dg0lO/slli3LJinp\nYq+6++8fx1NPfcSqVXeyfHkcHTv2ZN++fZaagwcPUlRUCIwHXsDlsrFzp/Wmsm3bNpzOQkxa6ctA\nC5zOArZt22aps9uzgQ+APwN9gc+IjLSOowwePBjYiNm87gS+AuyWDX/sdjuTJj2Dw9EH+BuBgRNI\nSBCGDBlSrqaqFBYWYreH4llGA7DZgtwpuOcAvjiJ50tq4JQVpcLcc889Av3K9DA4JuAnx48fL1fj\ncrkkIMAhcLfARQKDJDh4kLz22muWY7VufZHAU2XG+loCAupZaiZNmiQQI+B0a5wCsfL0009b6qCW\nwOIyYz0jNluYpea6664TGFJGs1/AX5xOp6VORGTRokUyYcJj8sorr0heXp7X91eFvLw8adIkSfz9\nxwusksDAu6Rt2y5SXFzsk/FOlcqunfrkoCjVCOOrLntnXLG2mMXFdmA75m7+GvLzV3L4sHXhODNW\nFjAJ47LZjDlXUD6xsbEnnVe9etaH507UuLz65cPCwv4wH/E6vxKcTidOpwun0+mznvPBwcGsWbOE\nIUN206LF/VxzTT4pKQvcLVFrPro51GDOJT/7qXKu2OLee+8F1gDjgI+BAdSp0wCHw+FFWYDx6XfB\nBH0H43RaN8YZPvxqzGayHSgCHqZ9+yaWGrPw5WHSUucBNwK5XpsE2e1ZGJfS+8A04BlatGhoqbnh\nhhswZykep8QW/v7BXlNZn39+CtdeezfPPhvEww/PpkuXvu74xeknNjaWjz9+l02bUnn//TepXbu2\nT8Y5G/hsc9i9eze9e/emdevWtGnThn//+98ATJw4kbi4ODp06ECHDh1YuHBhqWbSpEk0b96cxMRE\nFi1a5KupKUq1Zd26dYSEJAJHgLeAPhw7dsDrwS8RG3C0zJWjpKenW2pmzPgvcDvwBvAMMIOffrKO\nOSxbtgyzbOwCHgDSAT+WLFliqXO5IjAbygfAcmAoGzfusNQ8/PDDmLIZGRhbXEZxcYG7q1x547h4\n7LHHyc1dAjxBYeHTbN/uz4IFCyzHUk7EZ6msAQEBTJkyheTkZHJycrjooovo378/NpuNsWPHMnbs\n2N+9f/369cyZM4f169eTkZFBv3792Lx5sx6Vt+Bcye0/HVRXWxw9epSUlBQCAgLo06cPISHWGTpZ\nWVn4+TXHLNgAxYi8SFFREUFBQRZKP6AP0A/YC6xCZJjlWDt37sWkei4E8oFInM7yF17APQcXJoso\nG3MIzub1exmuxzzZALwEWDfSOXbsGHApZmMAyAWeo7i4uNzSG8XFxRQXFwKFmI0oFperEVlZWRWY\nn1IWn628sbGxJCebwlphYWG0atWKjIwMgJP6AOfNm8fw4cMJCAggPj6ehIQEUlNTfTU9RfE5O3bs\noEWLZEaOfJXhwyfTrl1Xjh49aqkxm9wSYC6wg8DAu+nWrY+XjQGMq2cf5k5+LRBMTEyMpUIkH3gK\nE294DbjW/Tnlc/fddwPFmKVjIOb+soi77rrLy/zyMU17NgKrgL9jt1un6F544YXAR8AnmO91GxBq\naYvAwECSktoDFwP/BUZTUPB5tb15qM6ckdvyHTt28NNPP5W2BXzppZdo3749t956a+k/lj179hAX\nF1eqiYuLK91MlJNzrvjZTwfV0Rb33juew4dvJzv7S7Kzv2HXrot56qnJlprGjRuzePGnJCa+QK1a\nPRk4MIt5896z1JjAbigmBrAY89QR7dWtVFxchEkr/Rb4ApgIWKeXvvXWW5jA8C+YJ45fABszZ860\n1Jknm9+ATsBgTGDZ2nFh/p8GYM45tMWkshZYBphFxH0a+gvMpjKNwMBYNm/e7GV+yh/x+QnpnJwc\nrrvuOqZOnUpYWBhjxozhiSeeAODxxx/noYce4s033zyptrxshtGjR5eeGo2KiiI5Obn0zqBkkdDX\n59frEqrLfHr16sX27btwOrsCKUAvCgt78O2375KSkuJVv2rVlxw8eJD09HTWrl1r+X4Tj8gFerrH\nWg90p6joqJf5BmLcSsuAXu4/T1jO74svvsCUwSi5kdsCRLF69WrL72OWmgWY8uAAG3C5HrWcnzn8\n1xt4xD23QiCIL7/80n0G4sTxFi9ezPHjh4EL3XP7AZEmpf0mqtPvh69fp6SklG7aVTpl74N02lIK\nCwtlwIABMmXKlJP+PD09Xdq0aSMiJn960qRJpT8bOHCgrFmz5gSNj6esKKeNMWMelODgPwkUCBwT\nh6OHTJnyb6+6F1/8twQGhktYWFOJjm4kaWlpXjWBgdECTwu4BLYJ1JY33njDUmOzBQu0EzgkUCQw\nUiDCUvP6668LhAh86T57sFggRF555RVLHUQI3CBQKHBYoI0EBIRYarp37+4+H7HV/b0meZ2fiEhM\nTLxAXYGmAmFit0dWyIbnOpVdO3220rpcLrnpppvkgQce+N31su0EX3zxRRk+fLiIiKxbt07at28v\nBQUFsn37drngggvE5XKdOGHdHJQawvHjx2XAgKvE398h/v7BMnr0nV4PcH3//fcSFBQrsNO9+M6S\n+vWbeR2rYcMEgUiBYIEAsdsjJCUlxVLj7x8skOTWhLo3ikhLzaJFiwQCBaIFotz/DZCFCxda6iDc\nPb9AAX+BKAkJCbfUfPPNNwJBAgHuOYZJRES0pUZExG6PEJjhtt8ugdoyc+ZMr7pzncqunT6LOaxc\nuZJ3332XpUuX/i5t9ZFHHqFdu3a0b9+eZcuWMWXKFACSkpIYNmwYSUlJDB48mGnTpp0Txat8yR9d\nKucz1dEWDoeDL7/8mEOHMjl27BAzZrzqNfsuJSWFgoJuQEk565vIzNxh2c9BRMjMTMdkKf0KfEpA\nwAjWrl1rOZZIMCZQ3AG4xK23PhvxwQcfYFxR+zBum31AE+bOnWupMwQDXYFkoDYFBdaHxdauXYuf\nXxsgwj1mMFlZhyzTerOysnC5coBR7ivbgP4sXbq0AvNTyuKzmEOPHj1O+j+xxFd4MiZMmMCECRN8\nNSVFOStUpuibOdW8BnNmIQr4Ggi0vFGy2WwEBUWRl3cBps+CHwUFQcTGvm45VlCQk9zcCzBZRIWY\noO9rlppu3brxxhvvYhbd5sBWIIOuXbt6+WZOTFxkNSY47SQyMspSkZWVhdNZEkjeAUQDDssNNiws\nDJstBJGvgP6YGMdykpP/4mV+yh/RQwQ1mJIglHLu2MJUHM3C9Eu4FJNeKpYnkEWEvLwiTMXTHZhM\nHWe5iR4lREbWAdKA+4GngVfx9uRg5idAZ0ywuBMglpVSDX6YIPEu4EegHsXF1mmz5mlQMOchDgBX\nAX6WTw52u52GDWPd7+0IjMRmy+byyy/3Mj/lj+jmoCjViF27dmHSS7/GlI3YCBRZHuIyJ4azMY10\nLsD0P0hmxYoVlmMdOnQE0470UeAWzAlma1fPnDlzgPrAT8AEzELfwO1ussIPeA6IwWx848nOtnYb\n//TTT5jN8RIgEpgC5FhWPS0oKGDPnl2Yp69hwAwcjqu82kI5Ed0cajDV0c9+tqiutrj55ttwOOII\nC2vMxIkTvb6/YcOGmAJ4LTGnnbMAu6VrypwWDsP0PMgDXgHWeT04Fxzsj7mTfwxTPuNjzOnn8jHt\nQA9gFuv+mLTWA17bhJongFcxKamDMGckrEtbm1PXv5aZ0wYg0OshuICAIEzNqE+BfyKSdk7VPDpT\n6OagKD7i5ptvY+bML8jLe53jx1/kySf/xT//+U9LzbXXXos5LNYD4+7pTnBweAXKyBzH3MnbMMHs\nq2jXrp2lIifnOKaG00vAu5iDdNZtLrt37+7+W0dMbaWOgNC5c2dLnZ9fPrAU85QyGlhG27bNLTXG\nFpsxtrgHs7FYu71sNhutWydjYiL/AJIoKNjBxRd774mh/AGf5Ez5kBo4ZeU8JSSkocDnZfoRvCKN\nGrW21IwZM0agi0BLgToCAwXskp2dXa6mqKjIffYgxT1OoUA7iY62Tvs0msll5veNQC1LTadOnQTq\nCXQVqO+ea4x07NjRy1i1ysxPBJ6XgADrsUw/h6sEbhe4XOBlAX8pKioqV1NYWCh+foECGQJrBNIl\nNPQ6efvtty3HOh+o7NqpTw6K4iPsdhvmjr6EHPz9rf/JGX/6z5iyETGYAHNQuYXmPBRhylK0ABoC\nByuQJSV4Tizjnqt174OoqChM1tFAzJPAZcDxCmZklbVFNk6n9VNKZGQkphT565ig9FWAzfIpyvxM\nMOm5dwMXU1j4awUC5sof0c2hBlNd/exngzNhiy1btjBv3jzWrVtXofdfe20fTLG4aZhg7JOMGXOT\npaZJkyaY4G0s0A0TQxDLVFazcfgBtYG7MOcIjrndMuXj51cM/BPjfnkN05vBujCgKbxXC1NhdSPw\nJ6C2uw+FFccx/Rxew8Q3nsdut3YR/fWvf8VuXwHcB8wE+tKlS3fLzcHPz4/AwEhMfanvgekUFx/R\nmEMV0M1BUSrAG2+8Rfv23Rk58g0uvrgfTz/9nFfNli37MIv195jzAKNYv966X8K8efMw5xvM+QGz\nqBZSVFT+XbapQQSmRtIDmNhD19IDpuURFRUDNMNsXI9igtrWi+h3332H2UAuxSzAvYAjfPvtt5Y6\nE8+4B5NFtAsY7u5eVz5NmzZl7dpVtGmzhtjYZxk5shsrV1r3jSgoKKCg4ChwhftKJCEhA91ZYEpl\n0M2hBnOu5PafDnxpiyNHjnDffWPJy1tJVtZn5OX9yDPPvMC2bdssdbm5eUA85q4+AGhCTo51bv+R\nI0cw2Tk3AHUxi30gx48ft9SZMtrR7r/3AmK9doI7fjwPUwr7CuAazEZkXUbbM9a3GFdPKlBcwWoG\nA4EZwH+AthXq1dKmTRt++SWVzMwNzJr1lldNUFAQ9eo1Bj50X2kFpNC6desKzE8pi24OiuKFzMxM\nAgJiMCeCAeoTGNiytNJnefj752MW972YNMynqFvXut1ndHQ0ZoH+CBNHeA4osPSZmwNyDmA45lDb\nO8CHXvs6FxXlY55S9uOJN1ifc2jUqBHmnEO8+0pjoIH7evnY7YUYF9u3mDTWJ2ncONZSU1Xmz59D\nVNR9RES0IyioFX/5yx0VOMGt/BHdHGowGnPw4EtbxMfHY7Mdw5w8BviW4uKNJCYmWup++GETppnO\nfMyhtpt49dVZlhrzNFIL0yfhPWAFEGC5OZi76ULMxtAbE4gN8/q0YZ4segOLML2dn8Hb5mCeEDIx\npcEBvgH2eL2jj4qqh6nWczkmlbU+LVu2sNSAcZlNnz6dZ555hpUrV3p9P0DHjh3ZvXszKSmzeOed\n15g4UUvyVAWf93NQlOqGiLB161ZycnJISkryeljM4XDw2WcfMGjQEPLyCvD3tzN79lxiY73d+fph\nzgGU0AWYbakwC3Z7TK8FgDaAk4yMjHI3I5PhVARsxxS3SwFmUVxsPZb551/2fMLFeMtW2rt3L+Zp\n4zpKaiRBLfbs2WOpCw8P5/DhVzGxhyBgCdHRaZaagoICunbtx+bNYRQUtCcoaBgvv/wMN988ylIH\npsZShw4d3K1GlSrhm4xa31EDp6xUI4qLi+Xqq2+UkJD6Eh6eJI0aJcrOnTu96lq0SHSXjU4QCJG6\ndRt61UCYQD+BbIH97vLYgZaaO++80637TsAp8JSAdWlrM1aEwDiBYoGfBSKka9eulpqQEIe750GG\nQK7AEIFQS82mTZvcZbQjBFq7/xskmzZtstRde+0Id4+FlgINBaK99pt4//33JSysl5heDiLws4SF\n1bHUKOVT2bVT3UrKecVbb73Fl1/uIi9vG9nZ69iz50ZGjbrbUrN06VI2b96BcaFsAX7iwIEjPP/8\n815GK8YEbKOABsBOzAnm8snOznbremKC2C/ircyE+XefC7zp1lwE1OOmm6zTZjt27IQpz9EECAe+\nxeEIq8AB0xOuAAAgAElEQVRYNowtfsW4vWxeg9/Ll6cCN2FiL9uBJD75ZL6l5siRIzidCXhs1py8\nvCzLwnvK6UM3hxrMuRhzsErZtNJUtF7/2rUbyM29AuO2ycHp/BPr12+w1Lz//vuYFM+LMe6blkAL\nPvnkEy+jhQBPYM4C3AD8BW89mo1ffQgmQHwMOAQUs3XrVktdYGAwZhHtA9TBz0/ctYnKJyPjCPAk\npoTGLuBLCgqKLTUmZTUO4/oCU+a7MWvWrLHUHT58FFPYz4ax/XB+/TXdUtOrVy9stk8wvaMPEBj4\nAD17DqhQllMJ5+K/kTOFbg5KteDzzz+nVq36BAWFkJjY0WuaKMCBAwfo2rU/wcEOBg26gunT3/Kq\nad++FaY8dQimeFwXGjSwzuoZPnw4pk9CM8zi3gjYwFVXXeVltCLM4a2PgP8B/8VbqmidOnUwMYOD\nmHMH84Eg4uLiytXYbDZstkBMmuhXwAycTsHhsM6MatEiDhiPCYA3BnoRHl5+aXDAXUMpA3OKG8zT\nw266dOni5XvVwsRbBPMkNIc2bZpaalq1asWHH75Dw4b34HC0pHfvA3z44duWGuU04hPnlg+pgVNW\nvLB9+3ZxOKIFlgs4xWb7pzRt2uakbWLL0qvXEAkIuM9dS2i9OBwNZOXKlZaali1bun3fAwX6CnQU\nb32JMzMz3XWIZrrjAJ8JhMi3335rqQsMrCXQWyBLYJ9AosTFxVtq2rRpI6ZlZy2BDu7/OuT48ePl\nalwul4BdIL9M7aKbpX379pZj1apVyx1z+E3guMAVXm0hInLvvQ+47dFCIETuuus+r5q1a9eKv38t\ngcYC0VK7drzld1JOP5VdO2vcSqubw7nHnDlzJDz8mjILm0sCAyPk4MGDlrqgoHCBrwWeE3hD/Pzu\nk8mTJ1tqbLZgMb2MXxeY7w4SWze6nzZtmrvInJT5007uvvtuSx3UFlhWRjNT7HbrYnixsbEC14np\nIZ0qcETAT7Zs2VKuprCw0L2h/Ms9zm6BWHE4HF7mFyHwUpn5/SAQZakpYf369fLWW2/Jr7/+WqH3\ni4hkZ2fL7NmzZf78+V57aSunn8qunepWqsFUR3+qiDBt2ms0b96RFi0uZsYM67x+gJiYGFyudXhc\nLpsBp9dibsHBDkwxtp3Ayzid73itoSPixJwDuB3j25+JJ2305HTo0AE4DJSkax4Fdlagh4ELUzqj\nhG/dh8HKp0WLFpjzCrGYGMcuwI+mTct3wZgzEEUYd1ltTDMdl7twXfnYbAUYd1dXTKG6Z/CWygrm\nUODDD/+Nv/3tnzz00N/IyMjwqhERpk6dxqOPTmbcuH+4y4T4nur4b6TG4JMtyofUwCn7jKVLl57t\nKZzAm2/OEIejhfuO+WtxOOJl7twPLDUul0uuu26khIW1FYfjZnE4YuWNN97yOpbNFiGwwn3X+7XA\npV7v5iMjaws8VOZueYVXV0pubq77zjxWYLRAE4EwrymwEOB2v1wh0EcgVAIDrZ9SbrvtNjGprG0E\nRglECQRLbm5uuRrjVgoVGCDwo8B4AYf06tXLcqyBAwe6n6IWiClvnSghIZGWmoKCAmnatI34+08Q\nSBN//8ckPr615OfnW+qeeeZ5CQ29UGC1wOficNSXJUuWWGpOB9Xx38jZorJrZ41baXVzqN507TpI\n4OMyi+87MmjQn7zqXC6XfP755/L666/Ljz/+WKGxIFDgYJmx7pUGDRpYagICQt0L4iSBtwXiBazd\nL9OnTxeIFlgp8Jp7I0qUBx980Mv8ags8K3CJmPjG37y6bRo3biymd8F8t+vrOwE/2bVrV7ka41by\nEzhaxhYjJDTU+sxCREQDgRfKaFaJt34OP/30k4SHJ4rn7IFLwsOTvP4/S0i4qMxGLgIvys03j7HU\nKKeXyq6dekJaKZfi4mKmTPk3K1f+SGJiUx577BHCwqzz4ENDQzCuisfdV9q4r1ljs9kYPHhwJWcY\nCFyNOUMAsJDCQmsXkXH19MCUpigCOmHKR5SPp4fB3zEurPrAEa+posZFsxaTjuqHyfG3ThWNjo5m\n167dmDLaR4EkQKhVq5aXsfwwGU4lrqT9Xv9fmayhfWVeH/DyftO6s7g4y60NAgpxOrO82sLhCPnd\n59tsByv0e6GcPTTmUIPxtT91xIhbmThxAfPm9edf/0qnW7f+ls3dARITGwLzMD79m4FPadWqsY9m\nWIzxyQ/ALPrFdOrUyVLRpEljTJ2jq4EHMQ1rrEssmDiAH6bxzHggAsglOTnZy/yOAguAMZgc/88w\n/RnK57HHHsO0uGwLPIKJvwRbLvQm5mDD1En6N6ZH82puu+02y7FiYmKB6e7v9DxwG3a79ebVokUL\n+vTpjsNxOfAyISFDuOSSTrRs2dJSN2nSozgcdwLPYbc/SljYdO6/f4yl5nSgMYdTwEdPMLJr1y7p\n1auXJCUlSevWrWXq1KkiInLo0CHp16+fNG/eXPr37y9Hjhwp1TzzzDOSkJAgLVu2lC+//PKkn+vD\nKdc4fOlPPXDggAQGRohJcSxxH3SQlJQUS11UVFMxKZ8l7oOXpX79RJ/M0d8/QmCje5ylAtfJo48+\naqkJCQkXeLDM/JZ5dfXccccd7syeklTRYoE4ufDCCy11xq30QZmxnhVvpTCSk5MFepTRHBTws2wT\n6nQ63fGNfgIN3LGRHjJ8+HDLsYKDowT+LKbsxn1ut5e1W0nEtCX9979fklGj7pSpU/9t2bazLMuX\nL5c77rhPHnxwnGzdurVCmlNFYw4eKrt2+sytFBAQwJQpU0hOTiYnJ4eLLrqI/v37M2PGDPr378+4\nceN49tlnmTx5MpMnT2b9+vXMmTOH9evXk5GRQb9+/di8eXOlTkOeb/iyh0FhYSF2ewCeTB4bNpvD\n65OD0+nCHN4qIZTiYuvSCiXjPfLII2zdupXBgwdz1113edUEBPhRXBzqftULu/1dd8nr8rHZ/DDf\naTrmFHIc3jJ0TLkGf8zhtF/xHIarCKFl/h6Gt4f1w4cPY9xWJRjXS3Z2drlPD55eCuMxbqy6wDzq\n169/0veXEBAQRH7+F5isrTrASxhXmzX+/v7ce+89Xt/3R+rXr09SUjOCg4PPWGc27XlyCvhokzqB\nK6+8UhYvXiwtW7aUvXv3iog5XNSyZUsRMU8NZXPUBw4cKKtXrz7hc87glM9rXC6XdO3aT4KCRgp8\nI35+j0uDBgmWd7AiIvfee5874Nva/SdMHn/8cUtNUVGRREc3FWgvcLdAtFx1lfcgtjlkdrGYxvXT\nBELk5ZdfttSYDJ1QMUXm7hAIFbs9yFKzb98+t6axwL1iiseFyVdffeVlfqFiiswtFPifQKQ0btzY\nUnP33XeLyXB6zv1UM1AgXPLy8ix1F13UVUxm090CPcRmi5Bt27ZZasaNGyfgELhRTBZWnLRunWyp\nqSqpqakSGhotQUH/Jw7Hn6R+/Qtk//79PhlLOTmVXTvPyEqbnp4ujRs3lqysLImK8jzCu1yu0tf3\n3HOPvPvuu6U/u/XWW+V///vfCZ+lm4MHXz8yZ2Vlyc033yWtWnWRoUOHy+7du71qZs58W/z9Gwss\nEVgk/v715X//+9BS88ILLwhcIOakswikC/h73YjMwnarQDeBLgIdvS6+fn6BAjeUcduYRduKRx55\nRExF1gNuTY5AbenWrZulLioq3j2vhgJxAl0kKekiS83LL7/sdgvVFajj1vl7PS0eFhYt8L17fkvE\n37+zvPfee5YaEZM6a7dHi80WLe3bXyzFxcVeNVWhc+d+AjNK7R4QcJc8/LC1C/B0oG4lD5VdO32e\nrZSTk8O1117L1KlTCQ8P/93PTE2Y8qtUlvez0aNHEx8fD5hMkuTk5NLHx5IAlL7+/etLL72Un376\niW+++YaEhASGDBniVR8eHs7IkX9i5Mg/VXi8559/meLi0ZhMFhvFxTfx7LMvce2115SrN0XbmmMq\niqZg3Dz+7Nu3j++//95ivGJMELY1pqpoGtnZ75GSklLu/JxOwbh3vse4lQ5S1pVysvmtWLECU1k1\nD/gnxu3TkMzMTEt75OUVYALfwZisqHXs3fuk5fx2795NQEAriopexnSQO4rNdj0FBQUEBwefdDwR\nISfnCCazKQX4meLiRJYtW1bqWipvvBtvvJEbb7zR579/Bw4cKjM/KCpKYt++n33++5+WlubTz6/O\nr1NSUpg5cyZA6XpZKXyzRxkKCwtlwIABMmXKlNJrLVu2lMzMTBER2bNnT6lbadKkSTJp0qTS9w0c\nOFDWrFlzwmf6eMrnJMXFxTJkyDAJDW0qkZHdpU6dOFm3bp1PxurYsZeYej3JAm0FmknfvkMtNWvW\nrHE/BXwupg7RE+LvX8triQXj6okU6O6+w46QQYMGWWrq1q3rdr9c4L6rjxJv5xw+//xz95NDpJgz\nC7UEQuSJJ56w1CUnd3K/90KBRIFaMnz4ny01W7ZsEZstXCBGoKdAhDRsmGCp8RyCu1vgmJgaVZFy\n2WWXWepERDZs2CC3336PjBhxmyxatMjr+6vKffeNk5CQQWL6WmwUh6O5fPCB9eFI5fRS2bXTZ9Fe\nEeHWW28lKSmJBx54oPT60KFDmTXLlFSYNWtWaWXLoUOHMnv2bAoLC0lPT2fLli1e0xKVivH222+z\ndOkejh/fwLFjKzh8+DFuvPFOn4y1bt06oD/wIyY42pnvv//OUuPn5+dOx7wBU/7hP/j7O929AyyV\nmJLT9wKvAA2oW7eupeLYsRxMmuhGYDXwV8wTS/mYJ17B9C5YBvwE2L32aN637xgmnfcHYB1wKenp\n1n2nFy5ciEgo5kzEN8CH7NmzpwI9DAT4DvNUcyMQ5PVucfPmzVx88SVMn16X995L5qqrRvPRRx95\nGadqPP/8U1x3XWOCgxMID+/JxIljuO6663wylnKa8MkWJSZtzWazSfv27SU5OVmSk5Nl4cKFcujQ\nIenbt+9JU1n/8Y9/SLNmzaRly5byxRdfnPRzfTjlGkdF/amPPvpXgYll/Oy7JDKyfoW0LpdLDh48\nWGFftN1e1x2ALRnrA/H3r2epmT17toSHXyumyNwigSIJDIyUAwcOWOrA3303P0igkcAFFSiGF+wO\n9pbMb6N4Sy996aWXxKSIli28117GjLE+4RsYGCvwTRnNLImJaWmpufnmmwX+VEbjEvCTQ4cOWeq6\nd+8nJt22k0Ck+PlFeu3Odu+9D4nN9liZsRZIUpJ197iahsYcPFR27axxK61uDh4q+os/d+5cCQ1t\n7158XeLn93fp0cPa/SIi8uOPP0pUVIz4+QVLYGCozJs3z6umdu3GYqqKFgkUCAySBg2s3SK//vqr\nmPIPgWIydUIlODisAm6lcDFlJkTgK4EOFQgSR4nJospyL7wPCYRZarZs2eLeVFLcY6UJOGT+/PmW\numbNkgVGiinznSfQS/r3t3b1zJo1S8z5iF3usf4rdrv3MtrZ2dlyzTU3SmRkrMTGxsuyZcu8av7v\n/+79w0a5XBISrAPmNQ3dHDzo5qCcgMvlkjFjHpSgoEgJDW0s8fGtLWv1iJh4UUhISbbMPQJtxc8v\n0muxua1bt4rdHuW+ow8Xf/9apTGm8pg0aZKYYnNp7kXqDYFQr4erzGZyuMzidr/4+flZasLCIsRk\nAkW5nzbqe31y2LZtm/j5RYqpr9RMIErs9gayatUqS93OnTvF4Yhxxx3CpF69CyrUw+Dyy68V06c5\nRmy2UJk9e7ZXTVVYsWKFBAXVEXNQ72sJCEiU559/0SdjKWefyq6desLsPMBmszFt2ovs2rWZtLQl\nbNmSRqNGjSw1GzduJC8vG1iDORz1LU5nCO+9956lrlmzZvz66yqGDRvI9ddfzoYNqcTGxlpq/vvf\n/2J6Jpe0nrwNcFWg9WcQpuyDYGoezfHayzgnpwjTrnMDpnTGYrz1dbbZbAQGOjBlLRYAO3E4altm\n2gE0btyYY8d+Y/nyT0lN/ZrMzC1eu7MBfPbZ/9i+fQNffDGLo0f3cP3113vVVAWzXjgxpbofBHJw\nubzFeZTzBt/sUb6jBk65XH7++WdJTu4ptWrFSb9+V3m9w/4jvnxkXrZsmfvuuqyfvZPcc889lrrU\n1FS3ayjC/fQQJD///LOlpkePHu47+KwybpvACpTEDnG7YBxiykcEe31yCAkJdcconGWeUqzPObhc\nLvchvaECnwiMEocjRgoKCix1RUVF0rNnP7HZIsVuryUjRtxk+f4SVq9eLa1adZLatRvJlVeOkMOH\nD1dIV0JFfy9uv/2eP7iVVqhb6RymsmunPjmcJQ4dOsQllwwkLW0kR44sJyWlBX37Dq1AVsqZwTSX\nKQSuxWTc3AT8TJMmTSx1PXr0x5Sk+AZYAsTQteullhrTfzgLk0V0DXApYPNaCsPc8TfBnCFoC/h7\nLRmRmroGWOl+f1/gPoYPv8xSs2vXLg4e3AckAG8AEeTmOvnuO+ssrCFDrmX58kxEVuByLeS9977i\noYf+4nWs/v2HsmHDWA4fXsbChaFceeUIS01VCQkJwmYrW3TwKEFB3qraKucNvtmjfEcNnPJJWbBg\ngURE9P1dVkpwcF3JyMg421MTEZH58+e74wAXCfxHTMOa2nLjjTda6syp3k/LfK/3BGpbakyxuT7u\nce4X+EIgSJYvX+5lrCAxvZlLxrrL65ODiDlf065dO2natKm8+eabXt9vzjnEiKeHgQi0kZdeeslS\nFxTUwB0oL9FMlwYNWllqZs2aJWFhZU9wF4qfX6DXZjpVYfPmzRIWVldsticEporDUV8++uij0z6O\nUj2o7Nqp/RzOEuHh4Tidu4F7MGWnW1FcfJzQ0FAvyjODKfJWjDnRGoYpwd2C4mLrks6eMtol7MRb\nD4M6depgWmOuB9oAbwIFxMXFeRnLH9gNlJw32F6hQo379++nWbN2ZGUdx9/f+51yUlIS5jT1U8Ao\nYD6ww90+tHxMS9Ky5xp2UFxcYKkxZyp+w8RRbMBe7PaScyCnl+bNm/PDDyt48cVXyM3dy6hR79C3\nb9/TPo5SQylv1+jfv/8p71S+wGLKNYqcnBwJDo4W+D+BjwT6SXx8m0p9hi/9qRkZGWJSRYvL3MW2\nlwULFljqkpLau2MBD7mfAkKkS5celppp06a578xLYg4/CQRWIFvJ5o45TBRzcjlUbrnlFkvNhg0b\nJDQ0WmCKwGxxOFrISy9Ns9Q4nU7x948UqOceL0ZstjDJycmx1DVocIE7HjJO4C6BEOnevZ+lJj8/\nX9q37yYhIUMFnhKHI0EmTXreUvNH1M/uQW3hobJrZ7m3WQcOeO8KpVSd1atXExDQDPgPpv7OZ+zZ\ns9undj9w4ADbt2/3mtEDprxyx44XYrPdhjl5+xT16h2jZ8+elrpvv11BgwYNME1npnHBBU1ZunSx\npeZf//oXpsF9Se2tZMCPVatWWeoGDLgcyAZexdRKgrFjx1pqZs58h9zc24EHgOvJzZ3J889Ps9Ss\nWLEC88D0AuYJ6mlE6vD+++9b6lq1aotp8vMtsBG7/Uratm1tqQkKCmL16q+YPLkvf/nLcebO/Rfj\nxz9sqVEUX1CuW+nYsWN89NFHJy1hYLPZuOaaa3w6sXMdY9eye7MpQngye5dHSbGtiox1xx338tZb\n07Hbg6lbN5pvv02xdNvYbDYWLvwfiYlJHDr0DiEhDr788psTiif+kbCwMJYu/ZzXXnsNm83OPffc\nTXCwde8Dk965EvgZaAe8BdiIjIy01K1duwGzYB8GoggK+pjU1FRaty5/ATb2LZuCavdqc5MkYAeG\nY/7JCDDJa/LAlClP0bVrHwoKBmOz5RARsZLHH19jqQGw2+0EBgYSHBxYJXdSRX8vzgfUFlXHcnOY\nP39+uULdHE6N7t27U6dOFvn5Yykq6kdIyJv07NnLa22gqjBr1iymT38XkWtwOuPZs+c1Bgy4kvXr\nf7DUNWnSmtzcUOBO8vK+4MILe5Gbu9dysV+7di09evQnL2844OSNN7qQmrrMso3kAw88wOjRdwBd\nMb+SdkBo27at5fz27/8NmAzcAqykoGAL27Zts9SMGvVnXnnlUo4fbwA0wOH4K2PHWrervOSSSwgP\nDyE7+wZM5tYnBAYeY8QI6yyivLw8XC4nTmcGNlsxRUWFFBRYxxwKCwvp3n0AGzaEkpvbEYfjTv7+\n9/t56KH7LXWKctopz9+UnOybph+nisWUaxz79++XkSPvkC5dBsrDD0/w2tDlj1TUn9qnT1+B68vE\nDpaIzWad2798+XIxJ5BLehgUCMR5zVbq1+8qgallxnparrvOOr//0ksvFdPzIMad7RQvYJP169db\n6kw21bfiaRPaXwYOHGipERH5/vvv5bLLhkmPHpfL9Olvee2VIGIa/rRv31XCwhpLy5YXyvbt271q\nLrnkcoHppbaw2x+T22+3Pify8ccfS1hY1zLnMNIlMNDhtZRIWdTP7kFt4aGya2e5Tw7bt28nNTWV\nCy+8EH9/TWryBXXr1mXWrP/4fByTAdWszJWm2O3WrpRNmzZhWlTWcV8JBBqyd+9eS93ateuBW8tc\naUZq6lxLjYmBtMKcVgZwAQ5yc3MtdaYPQ9Myr5vjcm3xooGLLrqIBQvmeH1fWerVq0damnUM5I8c\nOXLsd/NzuZpy8GCKpSYrKwuIx+NyjMPpLKaoqIigoKBKja8op0R5u8Y111wjXbt2laioKOnZs6eM\nHz9e5s+f77U6pK+xmLJSDsuXLxe7vZaYwnHpAgNk6NDrLTV5eXnuO/MJAr8JvCXg8Frzv1GjVgJt\nBNYJrBVoLomJ1qduFy1a5M5wel9gt8ADAt6LzdlsEQLDBHaKqeYaITfffLNX3Zni6aefFYeji5jK\nrz+Iw5Egc+bMtdTs2LHDnU31kcBuCQi4S7p1q56Zg0rNorJrp9d35+fny4oVK+T555+Xq6++WmJj\nYyUxMbHKEzxVdHOoGh988IHUq3eBhIXVkxtuuFlyc3O9akyF0Cj3wh0p999/v1fNoEFD3SmwMWLa\nXYbKDTdYN7gREXnsscfElNtwiJ9fnQpVFQ0OjnK7y+oLtBa7/TJ54YUXvOrOFE6nU8aNe0zq1Gks\n9epdIFOnWve3LmH58uWSkNBBIiJiZfDg6+TgwYM+nqlyPlDZtdPmFpXL0aNHWb16NatWrWLVqlUc\nPXqUdu3aMWPGjDPxYHMClc3oOZcp226yIhw+fJjs7GwaNWpUocNiVaFHj8tYufJGTMMZgNe4/PKl\nfPbZ7NM+1i233M3s2VvJy/sH8DGhoa/zww8rLIPf5wOV/b04l1FbeKjs2lluMOH2229n/fr1hIeH\n06lTJ7p168bYsWOpVavWaZmocuYQEcaOfZRp06bh5xdGXFwMS5d+RsOGDX00Ytn0S3+fVfr8z3+m\nEB7+OPPn30ZAgI2ZMz897zcGRTldlHv7uGvXLgoKCoiNjaVhw4Y0bNiQqKioMzk3xQsVvSP66KOP\neOONBRQW7iAvL4Pt269gxIj/88mcoqKCMCVB5gLvAX+hVi3rcw5VJTAwkKlTn2X79jQ2bfqJrl27\n+mScmobeKXtQW1Sdcp8cvvzyS1wuF+vWrWP16tW8+OKL/PLLL9SpU4cuXbrw97///UzOUzkFfvjh\nJ44fvw7TnxmczttZu3a6V53L5WLmzJmsXPkDrVpdUKEDbfv35wB3Am9jDpuNYe/e9af6FRRFOcNY\nOp7tdjtt27Zl8ODBDB48mO7du7N161amTp16puanWJCSklKh9yUkXIDDsRRTghtgEU2aXOBVd/vt\n93Lvva/z1lstefzxb+jV63KvhfcSEy8gICAHU5zuUwIDD9CqlfexTpWK2uJ8QG3hQW1Rdcp9cpg6\ndSqrVq1i9erV+Pv7061bN7p3786tt95KmzZtzuQclVNk5MiRzJ37GStWtMXPrwF+fpt5990vLDWH\nDx/m3XffobDwNyCC/Py7WbcumdWrV1vWV3rhhadYsaIPBw92AYpp0ACefnrJ6f1CiqL4nHKzlR58\n8EF69OhB165d3YXUqgearVQ1XC4XqampZGVl0bFjR2rXrm35/oyMDBISOpCfv5eSB8yIiF588MEE\nBgwYYKnNy8tjzZo12Gw2unbtqoe3FKUaUNm102sqa3VDN4czg4hw4YU9WbeuA0VF/4fdvpjataey\ndevPXgviKcrpYtOmTTz44ONkZu7nsst6M3HiBJ/0tjgfqOzaqW1CazC+9KfabDa++moel19+hIYN\nr6dHjyWsXr2k2m4M6lv2cK7YIjMzk86de/HFF11IS3ucf/1rOXfcUbkChOeKLc4GPt0cbrnlFmJi\nYn5XXXPixInExcXRoUMHOnTowMKFC0t/NmnSJJo3b05iYiKLFi3y5dSUClCnTh0+/vhdfvttPcuW\nLSAhIeFsT0k5j1iwYAFFRX0QGQv0JTd3Lu+8M0M9B2cIn7qVli9fTlhYGCNHjuSXX34B4MknnyQ8\nPPyEpizr169nxIgRfPfdd2RkZNCvXz82b958wkledSspyvnBjBkzuOeez8jN/dB9JZPAwObk52dj\ns9kstcqJVCu3Us+ePU96ovpkE5w3bx7Dhw8nICCA+Ph4EhISSE1N9eX0FEWpxlx55ZWEhf2Iv/84\n4L84HFdw333368ZwhjgrMYeXXnqJ9u3bc+utt3L06FEA9uzZ87vOZHFxcWRkZJyN6dUY1J/qQW3h\n4VyxRe3atfnpp5XcfHM+l102n+efv53nnnu6Up9xrtjibHDGGzWMGTOGJ554AoDHH3+chx56iDff\nfPOk7y3vDmH06NHEx8cDEBUVRXJycukx+ZJfBn19fr0uobrM52y+TktLq1bzOZXXmzdvZsSIa6qs\nT0tLq1bf50y+TklJYebMmQCl62Vl8Hkq644dO7jiiitKYw7l/Wzy5MkAjB8/HoBBgwbx5JNP0rlz\n599PWGMOiqIolaZaxRxORmZmZunfP/7449JMpqFDhzJ79mwKCwtJT09ny5YtdOrU6UxPT1EURcHH\nm5EtMUAAAA84SURBVMPw4cPp1q0bmzZtolGjRrz11ls88sgjtGvXjvbt27Ns2TKmTJkCQFJSEsOG\nDSMpKYnBgwczbdo0DTx54Y8ulfMZtYUHtYUHtUXV8WnM4f333z/h2i233FLu+ydMmMCECRN8OSVF\nURSlAmj5DEVRlPOAah9zUBRFUao/ujnUYNSf6kFt4UFt4UFtUXV0c1AURVFOQGMOiqIo5wEac1AU\nRVFOGd0cajDqT/WgtvCgtvCgtqg6ujkoiqIoJ6AxB0VRlPMAjTkoiqIop4xuDjUY9ad6UFt4UFt4\nUFtUHd0cFEVRlBPQmIOiKMp5gMYcFEVRlFNGN4cajPpTPagtPKgtPKgtqo5uDoqiKMoJaMxBURTl\nPEBjDoqiKMopo5tDDUb9qR7UFh7UFh7UFlVHNwdFURTlBDTmoCiKch6gMQdFURTllNHNoQaj/lQP\nagsPagsPaouq49PN4ZZbbiEmJoa2bduWXjt8+DD9+/enRYsWDBgwgKNHj5b+bNKkSTRv3pzExEQW\nLVrky6kpiqIoFvg05rB8+XLCwsIYOXIkv/zyCwDjxo0jOjqacePG8eyzz3LkyBEmT57M+vXrGTFi\nBN999x0ZGRn069ePzZs3Y7f/fv/SmIOiKErlqVYxh549e1KrVq3fXfv0008ZNWoUAKNGjeKTTz4B\nYN68eQwfPpyAgADi4+NJSEggNTXVl9NTFEVRyuGMxxz27dtHTEwMADExMezbtw+APXv2EBcXV/q+\nuLg4MjIyzvT0ahTqT/WgtvCgtvCgtqg6/mdzcJvNhs1ms/z5yRg9ejTx8fEAREVFkZycTK9evQDP\nL4O+Pr9el1Bd5nM2X6elpVWr+ZzN12lpadVqPmfydUpKCjNnzgQoXS8rg8/POezYsYMrrriiNOaQ\nmJhISkoKsbGxZGZm0rt3bzZu3MjkyZMBGD9+PACDBg3iySefpHPnzr+fsMYcFEVRKk21ijmcjKFD\nhzJr1iwAZs2axVVXXVV6ffbs2RQWFpKens6WLVvo1KnTmZ6eoiiKgo83h+HDh9OtWzc2bdpEo0aN\nmDFjBuPHj2fx4sW0aNGCr7/+uvRJISkpiWHDhpGUlMTgwYOZNm2apctJUX9qWdQWHtQWHtQWVcen\nMYf333//pNe/+uqrk16fMGECEyZM8OWUFEVRlAqgtZUURVHOA6p9zEFRFEWp/ujmUINRf6oHtYUH\ntYUHtUXV0c1BURRFOQGNOSiKopwHaMxBURRFOWV0c6jBqD/Vg9rCg9rCg9qi6ujmoCiKopyAxhwU\nRVHOAzTmoCiKopwyujnUYNSf6kFt4UFt4UFtUXV0c1AURVFOQGMOiqIo5wEac1AURVFOGd0cajDq\nT/WgtvCgtvCgtqg6ujkoiqIoJ6AxB0VRlPMAjTkoiqIop4xuDjUY9ad6UFt4UFt4UFtUHd0cFEVR\nlBPQmIOiKMp5gMYcFEVRlFNGN4cajPpTPagtPKgtPKgtqo7/2Ro4Pj6eiIgI/Pz8CAgIIDU1lcOH\nD3P99dezc+dO4uPjmTt3LlFRUWdrioqiKOctZy3m0LRpU3744Qdq165dem3cuHFER0czbtw4nn32\nWY78f3v3H1NV/cdx/HkHSC3MX8HlxyVxIuEVkB8m31pmPwTTJf6YfVP7gWH8kat/NMutP7L23YTV\nmqRbNVZf2HL0j0X6XbEivzY1jYjYdyXaVt5hhtcWECEMB57vH+o9woXiXpRz2Hk9NjcP95x73/e1\nz8575/O599yODsrKygYdpzUHEZHQTag1h6GF7t+/n+LiYgCKi4upra21oiwREcezrDm4XC6WLFnC\nggULqKysBMDv9+N2uwFwu934/X6rypsQNJ9qUhYmZWFSFuGzbM3h6NGjJCQk8Ntvv1FQUEB6evqg\nx10uFy6Xa9hjN27cSEpKCgBTp04lOzub++67DzAHg7adtX2VXeqxcru5udlW9Vi53dzcbKt6xnP7\n0KFDVFVVAQTOl6GwxfccXnnlFWJiYqisrOTQoUPEx8fT1tbG/fffz8mTJwftqzUHEZHQTYg1h56e\nHv78808ALly4wGeffUZmZiZFRUVUV1cDUF1dzapVq6woT0TE8SxpDn6/n0WLFpGdnU1+fj4PP/ww\nhYWFbN++nc8//5y0tDQOHjzI9u3brShvwhg6peJkysKkLEzKInyWrDnMmjUrMBd4renTp1NfX29B\nRSIici1brDmEQmsOIiKhmxBrDiIiYm9qDhOY5lNNysKkLEzKInxqDiIiEkRrDiIiDqA1BxERGTM1\nhwlM86kmZWFSFiZlET41BxERCaI1BxERB9Cag4iIjJmawwSm+VSTsjApC5OyCJ+ag8go9PX18fLL\n/2LZsn+ybdtLdHd3W12SyA2lNQeRv2EYBkuXrubIkUv09q4nOvo/eL2tNDT8l8hIy34vSyQkoZ47\n1RxE/obP58Pr/Qe9va3AJOASMTEZ1Nf/m/z8fKvLExkVLUg7iOZTTTcyi4GBAVyuSCDiyl9cuFyT\nGBgYuGGvORYaFyZlET41B5G/MWvWLLzeVKKjS4GDREVtIS7OIC8vz+rSRG4YTSuJjEJXVxdbtrxE\nY+P/8HrnUFGxk9jYWKvLEhk1rTmIiEgQrTk4iOZTTcrCpCxMyiJ8ag4iIhJE00oiIg6gaSURERkz\n2zWHuro60tPTmTNnDuXl5VaXY2uaTzUpC5OyMCmL8NmqOQwMDPDss89SV1fHiRMnqKmpoaWlxeqy\nbKu5udnqEmxDWZiUhUlZhM9WzaGhoYHU1FRSUlKIiopi3bp1fPzxx1aXZVudnZ1Wl2AbysKkLEzK\nIny2ag5nz54lOTk5sO3xeDh79qyFFYmIOJOtmoPL5bK6hAnF5/NZXYJtKAuTsjApi/DZ6n7DSUlJ\nnDlzJrB95swZPB7PoH1mz56tJnKN6upqq0uwDWVhUhYmZXHZ7NmzQ9rfVt9z6O/v54477uCLL74g\nMTGRhQsXUlNTw9y5c60uTUTEUWx15RAZGcmePXtYunQpAwMDbNq0SY1BRMQCtrpyEBERe7DVgvRQ\nnZ2drF27lrlz5+L1ejl+/Dg7duzA4/GQk5NDTk4OdXV1Vpd5w506dSrwfnNycpgyZQpvvvkm7e3t\nFBQUkJaWRmFhoSM+tjdcFhUVFY4cFwA7d+5k3rx5ZGZmsmHDBvr6+hw5LmD4LJw6LioqKsjMzCQj\nI4OKigqAkMeFra8ciouLWbx4MSUlJfT393PhwgV27drF5MmT2bJli9XlWeLSpUskJSXR0NDA7t27\nue2223jhhRcoLy+no6ODsrIyq0scN9dm8d577zluXPh8Ph544AFaWlqIjo7m0UcfZfny5fzwww+O\nGxcjZeHz+Rw3Lr7//nvWr1/PN998Q1RUFA899BBvv/0277zzTkjjwrZXDn/88QeHDx+mpKQEuLwe\nMWXKFABH33ivvr6e1NRUkpOT2b9/P8XFxcDlRlpbW2txdePr2iwMw3DcuLj11luJioqip6eH/v5+\nenp6SExMdOS4GC6LpKQkwHnni5MnT5Kfn89NN91EREQEixcvZt++fSGPC9s2h9OnTxMbG8tTTz1F\nbm4upaWl9PT0ALB7927mz5/Ppk2bHHPJfNUHH3zA+vXrAfD7/bjdbgDcbjd+v9/K0sbdtVm4XC7H\njYvp06ezdetWbr/9dhITE5k6dSoFBQWOHBfDZbFkyRLAeeeLjIwMDh8+THt7Oz09PXzyySf88ssv\nIY8L2zaH/v5+mpqa2Lx5M01NTdxyyy2UlZWxefNmTp8+TXNzMwkJCWzdutXqUsfNxYsXOXDgAI88\n8kjQYy6Xy1Hf/xiaxTPPPOO4cfHTTz+xa9cufD4fv/76K93d3bz//vuD9nHKuBgui7179zpyXKSn\np/Piiy9SWFjIsmXLyM7OJiIiYtA+oxkXtm0OHo8Hj8fDnXfeCcDatWtpamoiNjY28MaefvppGhoa\nLK50/Hz66afk5eUFfrvY7XZz7tw5ANra2oiLi7OyvHE1NIu4uDjHjYvGxkbuvvtuZsyYQWRkJGvW\nrOHYsWPEx8c7blwMl8VXX33lyHEBUFJSQmNjI19++SXTpk0jLS0t5POFbZtDfHw8ycnJ/Pjjj8Dl\n+eV58+YF3hzARx99RGZmplUljruamprANApAUVFR4Nuf1dXVrFq1yqrSxt3QLNra2gL/d8q4SE9P\n5/jx4/T29mIYBvX19Xi9XlasWOG4cTFSFk49X5w/fx6A1tZWPvzwQzZs2BD6+cKwsebmZmPBggVG\nVlaWsXr1aqOjo8N44oknjMzMTCMrK8tYuXKlce7cOavLHBfd3d3GjBkzjK6ursDffv/9d+PBBx80\n5syZYxQUFBgdHR0WVjh+hsvCqeOivLzc8Hq9RkZGhvHkk08aFy9edOy4GJpFX1+fY8fFokWLDK/X\na8yfP984ePCgYRihny9s/VFWERGxhm2nlURExDpqDiIiEkTNQUREgqg5iIhIEDUHEREJouYgIiJB\n1BxEroiJiRm0XVVVxXPPPQdAbW0tLS0tIx67Z88eqqqqANi4cSP79u0b9ev6/X6WL18eesEiN5Ca\ng8gVQ+81c+12bW0tJ06cGPY4wzB49913efzxxwPHhXI/I7fbzbRp02hqagqjapEbQ81BZARXvx96\n7NgxDhw4wLZt28jJyeHnn38etN/Ro0dJT08nMjL4V3dTUlLYsWMHeXl5ZGVlcerUqWFfq6ioiJqa\nmuv/JkTCZKvfkBaxUm9vLzk5OYHt9vZ2Vq5cyV133UVRURErVqxgzZo1QccdOXIkcIPIoVwuF7Gx\nsXz77be89dZbvP7661RWVgbtt3DhQt54443r92ZExkhXDiJX3HzzzXz33XeBf6+++uqgH4oZ6U4z\nra2txMfHj/i8VxtKbm4uPp9v2H0SEhJGfEzECmoOIiMY2gz+ah3hr25RFh0dDUBERAT9/f0jHu+E\n312QiUPNQWQUJk+eTFdX17CPzZw5c9CtocPR1tbGzJkzx/QcIteTmoPIFcN9Wunq39atW8drr71G\nXl5e0IL0PffcQ2Nj46ie/+rzNTY2UlpaGnisoaGBe++9d6xvQeS60S27RcbIMAxyc3P5+uuvmTRp\nUljP8dhjj/H8888PWhAXsZKuHETGyOVyUVpayt69e8M6/vz583R2dqoxiK3oykFERILoykFERIKo\nOYiISBA1BxERCaLmICIiQdQcREQkiJqDiIgE+T/KnJhCa8nrYwAAAABJRU5ErkJggg==\n", "text": [ "" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "# You can also scatterplot like this\n", "plt.scatter(nba_df[\"Ht (In.)\"], nba_df[\"WT\"])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEACAYAAAC57G0KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text": [ "" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "# Bar Chart\n", "pos_size = nba_df.groupby(\"POS\").size()\n", "print pos_size\n", "pos_size.plot(kind='bar', title=\"Position\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "POS\n", "C 67\n", "F 142\n", "F/C 74\n", "G 175\n", "G/F 70\n", "dtype: int64\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 21, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAEfCAYAAABcTk2NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text": [ "" ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }