{
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"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",
" Name | \n",
" Age | \n",
" Team | \n",
" POS | \n",
" # | \n",
" 2013 $ | \n",
" Ht (In.) | \n",
" WT | \n",
" EXP | \n",
" 1st Year | \n",
" DOB | \n",
" School | \n",
" City | \n",
" State (Province, Territory, Etc..) | \n",
" Country | \n",
" Race | \n",
" HS Only | \n",
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\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Gee, Alonzo | \n",
" 26 | \n",
" Cavaliers | \n",
" F | \n",
" 33 | \n",
" $3,250,000 | \n",
" 78 | \n",
" 219 | \n",
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" US | \n",
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" No | \n",
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" 1 | \n",
" Wallace, Gerald | \n",
" 31 | \n",
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" F | \n",
" 45 | \n",
" $10,105,855 | \n",
" 79 | \n",
" 220 | \n",
" 12 | \n",
" 2001 | \n",
" 7/23/1982 | \n",
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" 2 | \n",
" Williams, Mo | \n",
" 30 | \n",
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" G | \n",
" 25 | \n",
" $2,652,000 | \n",
" 73 | \n",
" 195 | \n",
" 10 | \n",
" 2003 | \n",
" 12/19/1982 | \n",
" Alabama | \n",
" Jackson, MS | \n",
" Mississippi | \n",
" US | \n",
" Black | \n",
" No | \n",
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" \n",
" 3 | \n",
" Gladness, Mickell | \n",
" 27 | \n",
" Magic | \n",
" C | \n",
" 40 | \n",
" $762,195 | \n",
" 83 | \n",
" 220 | \n",
" 2 | \n",
" 2011 | \n",
" 7/26/1986 | \n",
" Alabama A&M | \n",
" Birmingham, AL | \n",
" Alabama | \n",
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" No | \n",
"
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" \n",
" 4 | \n",
" Jefferson, Richard | \n",
" 33 | \n",
" Jazz | \n",
" F | \n",
" 44 | \n",
" $11,046,000 | \n",
" 79 | \n",
" 230 | \n",
" 12 | \n",
" 2001 | \n",
" 6/21/1980 | \n",
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" \n",
" 5 | \n",
" Hill, Solomon | \n",
" 22 | \n",
" Pacers | \n",
" F | \n",
" 9 | \n",
" $1,246,680 | \n",
" 79 | \n",
" 220 | \n",
" 0 | \n",
" 2013 | \n",
" 3/18/1991 | \n",
" Arizona | \n",
" Los Angeles, CA | \n",
" California | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
" 6 | \n",
" Budinger, Chase | \n",
" 25 | \n",
" Timberwolves | \n",
" F | \n",
" 10 | \n",
" $5,000,000 | \n",
" 79 | \n",
" 218 | \n",
" 4 | \n",
" 2009 | \n",
" 5/22/1988 | \n",
" Arizona | \n",
" Encinitas, CA | \n",
" California | \n",
" US | \n",
" White | \n",
" No | \n",
"
\n",
" \n",
" 7 | \n",
" Williams, Derrick | \n",
" 22 | \n",
" Timberwolves | \n",
" F | \n",
" 7 | \n",
" $5,016,960 | \n",
" 80 | \n",
" 241 | \n",
" 2 | \n",
" 2011 | \n",
" 5/25/1991 | \n",
" Arizona | \n",
" La Mirada, CA | \n",
" California | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
" 8 | \n",
" Hill, Jordan | \n",
" 26 | \n",
" Lakers | \n",
" F/C | \n",
" 27 | \n",
" $3,563,600 | \n",
" 82 | \n",
" 235 | \n",
" 1 | \n",
" 2012 | \n",
" 7/27/1987 | \n",
" Arizona | \n",
" Newberry, SC | \n",
" South Carolina | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
" 9 | \n",
" Frye, Channing | \n",
" 30 | \n",
" Suns | \n",
" F/C | \n",
" 8 | \n",
" $6,500,000 | \n",
" 83 | \n",
" 245 | \n",
" 8 | \n",
" 2005 | \n",
" 5/17/1983 | \n",
" Arizona | \n",
" White Plains, NY | \n",
" New York | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
"
\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",
" Name | \n",
" Age | \n",
" Team | \n",
" POS | \n",
" # | \n",
" 2013 $ | \n",
" Ht (In.) | \n",
" WT | \n",
" EXP | \n",
" 1st Year | \n",
" DOB | \n",
" School | \n",
" City | \n",
" State (Province, Territory, Etc..) | \n",
" Country | \n",
" Race | \n",
" HS Only | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Gee, Alonzo | \n",
" 26 | \n",
" Cavaliers | \n",
" F | \n",
" 33 | \n",
" $3,250,000 | \n",
" 78 | \n",
" 219 | \n",
" 4 | \n",
" 2009 | \n",
" 5/29/1987 | \n",
" Alabama | \n",
" Riviera Beach, FL | \n",
" Florida | \n",
" US | \n",
" Black | \n",
" No | \n",
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" \n",
" 1 | \n",
" Wallace, Gerald | \n",
" 31 | \n",
" Celtics | \n",
" F | \n",
" 45 | \n",
" $10,105,855 | \n",
" 79 | \n",
" 220 | \n",
" 12 | \n",
" 2001 | \n",
" 7/23/1982 | \n",
" Alabama | \n",
" Sylacauga, AL | \n",
" Alabama | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
" 2 | \n",
" Williams, Mo | \n",
" 30 | \n",
" Trail Blazers | \n",
" G | \n",
" 25 | \n",
" $2,652,000 | \n",
" 73 | \n",
" 195 | \n",
" 10 | \n",
" 2003 | \n",
" 12/19/1982 | \n",
" Alabama | \n",
" Jackson, MS | \n",
" Mississippi | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
" 3 | \n",
" Gladness, Mickell | \n",
" 27 | \n",
" Magic | \n",
" C | \n",
" 40 | \n",
" $762,195 | \n",
" 83 | \n",
" 220 | \n",
" 2 | \n",
" 2011 | \n",
" 7/26/1986 | \n",
" Alabama A&M | \n",
" Birmingham, AL | \n",
" Alabama | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
" 4 | \n",
" Jefferson, Richard | \n",
" 33 | \n",
" Jazz | \n",
" F | \n",
" 44 | \n",
" $11,046,000 | \n",
" 79 | \n",
" 230 | \n",
" 12 | \n",
" 2001 | \n",
" 6/21/1980 | \n",
" Arizona | \n",
" Los Angeles, CA | \n",
" California | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
" 5 | \n",
" Hill, Solomon | \n",
" 22 | \n",
" Pacers | \n",
" F | \n",
" 9 | \n",
" $1,246,680 | \n",
" 79 | \n",
" 220 | \n",
" 0 | \n",
" 2013 | \n",
" 3/18/1991 | \n",
" Arizona | \n",
" Los Angeles, CA | \n",
" California | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
" 6 | \n",
" Budinger, Chase | \n",
" 25 | \n",
" Timberwolves | \n",
" F | \n",
" 10 | \n",
" $5,000,000 | \n",
" 79 | \n",
" 218 | \n",
" 4 | \n",
" 2009 | \n",
" 5/22/1988 | \n",
" Arizona | \n",
" Encinitas, CA | \n",
" California | \n",
" US | \n",
" White | \n",
" No | \n",
"
\n",
" \n",
" 7 | \n",
" Williams, Derrick | \n",
" 22 | \n",
" Timberwolves | \n",
" F | \n",
" 7 | \n",
" $5,016,960 | \n",
" 80 | \n",
" 241 | \n",
" 2 | \n",
" 2011 | \n",
" 5/25/1991 | \n",
" Arizona | \n",
" La Mirada, CA | \n",
" California | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
" 8 | \n",
" Hill, Jordan | \n",
" 26 | \n",
" Lakers | \n",
" F/C | \n",
" 27 | \n",
" $3,563,600 | \n",
" 82 | \n",
" 235 | \n",
" 1 | \n",
" 2012 | \n",
" 7/27/1987 | \n",
" Arizona | \n",
" Newberry, SC | \n",
" South Carolina | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
" 9 | \n",
" Frye, Channing | \n",
" 30 | \n",
" Suns | \n",
" F/C | \n",
" 8 | \n",
" $6,500,000 | \n",
" 83 | \n",
" 245 | \n",
" 8 | \n",
" 2005 | \n",
" 5/17/1983 | \n",
" Arizona | \n",
" White Plains, NY | \n",
" New York | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
"
\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",
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\n",
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" \n",
" | \n",
" Name | \n",
" Age | \n",
" Team | \n",
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" # | \n",
" 2013 $ | \n",
" Ht (In.) | \n",
" WT | \n",
" EXP | \n",
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" No | \n",
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\n",
" \n",
" 4 | \n",
" Jefferson, Richard | \n",
" 33 | \n",
" Jazz | \n",
" F | \n",
" 44 | \n",
" $11,046,000 | \n",
" 79 | \n",
" 230 | \n",
" 12 | \n",
" 2001 | \n",
" 6/21/1980 | \n",
" Arizona | \n",
" Los Angeles, CA | \n",
" California | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
" 5 | \n",
" Hill, Solomon | \n",
" 22 | \n",
" Pacers | \n",
" F | \n",
" 9 | \n",
" $1,246,680 | \n",
" 79 | \n",
" 220 | \n",
" 0 | \n",
" 2013 | \n",
" 3/18/1991 | \n",
" Arizona | \n",
" Los Angeles, CA | \n",
" California | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
" 6 | \n",
" Budinger, Chase | \n",
" 25 | \n",
" Timberwolves | \n",
" F | \n",
" 10 | \n",
" $5,000,000 | \n",
" 79 | \n",
" 218 | \n",
" 4 | \n",
" 2009 | \n",
" 5/22/1988 | \n",
" Arizona | \n",
" Encinitas, CA | \n",
" California | \n",
" US | \n",
" White | \n",
" No | \n",
"
\n",
" \n",
"
\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": [
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" \n",
" | \n",
" Name | \n",
" Age | \n",
" Team | \n",
" POS | \n",
" # | \n",
" 2013 $ | \n",
" Ht (In.) | \n",
" WT | \n",
" EXP | \n",
" 1st Year | \n",
" DOB | \n",
" School | \n",
" City | \n",
" State (Province, Territory, Etc..) | \n",
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" 0 | \n",
" Gee, Alonzo | \n",
" 26 | \n",
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" 78 | \n",
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" Alabama | \n",
" Sylacauga, AL | \n",
" Alabama | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
" 4 | \n",
" Jefferson, Richard | \n",
" 33 | \n",
" Jazz | \n",
" F | \n",
" 44 | \n",
" $11,046,000 | \n",
" 79 | \n",
" 230 | \n",
" 12 | \n",
" 2001 | \n",
" 6/21/1980 | \n",
" Arizona | \n",
" Los Angeles, CA | \n",
" California | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
" 5 | \n",
" Hill, Solomon | \n",
" 22 | \n",
" Pacers | \n",
" F | \n",
" 9 | \n",
" $1,246,680 | \n",
" 79 | \n",
" 220 | \n",
" 0 | \n",
" 2013 | \n",
" 3/18/1991 | \n",
" Arizona | \n",
" Los Angeles, CA | \n",
" California | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
" 6 | \n",
" Budinger, Chase | \n",
" 25 | \n",
" Timberwolves | \n",
" F | \n",
" 10 | \n",
" $5,000,000 | \n",
" 79 | \n",
" 218 | \n",
" 4 | \n",
" 2009 | \n",
" 5/22/1988 | \n",
" Arizona | \n",
" Encinitas, CA | \n",
" California | \n",
" US | \n",
" White | \n",
" No | \n",
"
\n",
" \n",
"
\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",
" Name | \n",
" Age | \n",
" Team | \n",
" POS | \n",
" # | \n",
" 2013 $ | \n",
" Ht (In.) | \n",
" WT | \n",
" EXP | \n",
" 1st Year | \n",
" DOB | \n",
" School | \n",
" City | \n",
" State (Province, Territory, Etc..) | \n",
" Country | \n",
" Race | \n",
" HS Only | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Gee, Alonzo | \n",
" 26 | \n",
" Cavaliers | \n",
" F | \n",
" 33 | \n",
" $3,250,000 | \n",
" 78 | \n",
" 219 | \n",
" 4 | \n",
" 2009 | \n",
" 5/29/1987 | \n",
" Alabama | \n",
" Riviera Beach, FL | \n",
" Florida | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
" 1 | \n",
" Wallace, Gerald | \n",
" 31 | \n",
" Celtics | \n",
" F | \n",
" 45 | \n",
" $10,105,855 | \n",
" 79 | \n",
" 220 | \n",
" 12 | \n",
" 2001 | \n",
" 7/23/1982 | \n",
" Alabama | \n",
" Sylacauga, AL | \n",
" Alabama | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
" 2 | \n",
" Williams, Mo | \n",
" 30 | \n",
" Trail Blazers | \n",
" G | \n",
" 25 | \n",
" $2,652,000 | \n",
" 73 | \n",
" 195 | \n",
" 10 | \n",
" 2003 | \n",
" 12/19/1982 | \n",
" Alabama | \n",
" Jackson, MS | \n",
" Mississippi | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
" 4 | \n",
" Jefferson, Richard | \n",
" 33 | \n",
" Jazz | \n",
" F | \n",
" 44 | \n",
" $11,046,000 | \n",
" 79 | \n",
" 230 | \n",
" 12 | \n",
" 2001 | \n",
" 6/21/1980 | \n",
" Arizona | \n",
" Los Angeles, CA | \n",
" California | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
" 5 | \n",
" Hill, Solomon | \n",
" 22 | \n",
" Pacers | \n",
" F | \n",
" 9 | \n",
" $1,246,680 | \n",
" 79 | \n",
" 220 | \n",
" 0 | \n",
" 2013 | \n",
" 3/18/1991 | \n",
" Arizona | \n",
" Los Angeles, CA | \n",
" California | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
"
\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",
" Name | \n",
" Age | \n",
" Team | \n",
" POS | \n",
" # | \n",
" 2013 $ | \n",
" Ht (In.) | \n",
" WT | \n",
" EXP | \n",
" 1st Year | \n",
" DOB | \n",
" School | \n",
" City | \n",
" State (Province, Territory, Etc..) | \n",
" Country | \n",
" Race | \n",
" HS Only | \n",
"
\n",
" \n",
" \n",
" \n",
" 300 | \n",
" Karasev, Sergey | \n",
" 19 | \n",
" Cavaliers | \n",
" G/F | \n",
" 10 | \n",
" $1,467,840 | \n",
" 79 | \n",
" 203 | \n",
" 0 | \n",
" 2013 | \n",
" 10/26/1993 | \n",
" n/a | \n",
" Saint Petersburg | \n",
" n/a | \n",
" Russia | \n",
" NaN | \n",
" No | \n",
"
\n",
" \n",
"
\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",
" Name | \n",
" Age | \n",
" Team | \n",
" POS | \n",
" # | \n",
" 2013 $ | \n",
" Ht (In.) | \n",
" WT | \n",
" EXP | \n",
" 1st Year | \n",
" DOB | \n",
" School | \n",
" City | \n",
" State (Province, Territory, Etc..) | \n",
" Country | \n",
" Race | \n",
" HS Only | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Gee, Alonzo | \n",
" 26 | \n",
" Cavaliers | \n",
" F | \n",
" 33 | \n",
" $3,250,000 | \n",
" 78 | \n",
" 219 | \n",
" 4 | \n",
" 2009 | \n",
" 5/29/1987 | \n",
" Alabama | \n",
" Riviera Beach, FL | \n",
" Florida | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
" 1 | \n",
" Wallace, Gerald | \n",
" 31 | \n",
" Celtics | \n",
" F | \n",
" 45 | \n",
" $10,105,855 | \n",
" 79 | \n",
" 220 | \n",
" 12 | \n",
" 2001 | \n",
" 7/23/1982 | \n",
" Alabama | \n",
" Sylacauga, AL | \n",
" Alabama | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
" 2 | \n",
" Williams, Mo | \n",
" 30 | \n",
" Trail Blazers | \n",
" G | \n",
" 25 | \n",
" $2,652,000 | \n",
" 73 | \n",
" 195 | \n",
" 10 | \n",
" 2003 | \n",
" 12/19/1982 | \n",
" Alabama | \n",
" Jackson, MS | \n",
" Mississippi | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
" 3 | \n",
" Gladness, Mickell | \n",
" 27 | \n",
" Magic | \n",
" C | \n",
" 40 | \n",
" $762,195 | \n",
" 83 | \n",
" 220 | \n",
" 2 | \n",
" 2011 | \n",
" 7/26/1986 | \n",
" Alabama A&M | \n",
" Birmingham, AL | \n",
" Alabama | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
" 4 | \n",
" Jefferson, Richard | \n",
" 33 | \n",
" Jazz | \n",
" F | \n",
" 44 | \n",
" $11,046,000 | \n",
" 79 | \n",
" 230 | \n",
" 12 | \n",
" 2001 | \n",
" 6/21/1980 | \n",
" Arizona | \n",
" Los Angeles, CA | \n",
" California | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
"
\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",
" Name | \n",
" Age | \n",
" Team | \n",
" POS | \n",
" # | \n",
" 2013 $ | \n",
" Ht (In.) | \n",
" WT | \n",
" EXP | \n",
" 1st Year | \n",
" DOB | \n",
" School | \n",
" City | \n",
" State (Province, Territory, Etc..) | \n",
" Country | \n",
" Race | \n",
" HS Only | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Gee, Alonzo | \n",
" 26 | \n",
" Cavaliers | \n",
" F | \n",
" 33 | \n",
" $3,250,000 | \n",
" 78 | \n",
" 219 | \n",
" 4 | \n",
" 2009 | \n",
" 5/29/1987 | \n",
" Alabama | \n",
" Riviera Beach, FL | \n",
" Florida | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
" 1 | \n",
" Wallace, Gerald | \n",
" 31 | \n",
" Celtics | \n",
" F | \n",
" 45 | \n",
" $10,105,855 | \n",
" 79 | \n",
" 220 | \n",
" 12 | \n",
" 2001 | \n",
" 7/23/1982 | \n",
" Alabama | \n",
" Sylacauga, AL | \n",
" Alabama | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
" 2 | \n",
" Williams, Mo | \n",
" 30 | \n",
" Trail Blazers | \n",
" G | \n",
" 25 | \n",
" $2,652,000 | \n",
" 73 | \n",
" 195 | \n",
" 10 | \n",
" 2003 | \n",
" 12/19/1982 | \n",
" Alabama | \n",
" Jackson, MS | \n",
" Mississippi | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
" 3 | \n",
" Gladness, Mickell | \n",
" 27 | \n",
" Magic | \n",
" C | \n",
" 40 | \n",
" $762,195 | \n",
" 83 | \n",
" 220 | \n",
" 2 | \n",
" 2011 | \n",
" 7/26/1986 | \n",
" Alabama A&M | \n",
" Birmingham, AL | \n",
" Alabama | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
" 4 | \n",
" Jefferson, Richard | \n",
" 33 | \n",
" Jazz | \n",
" F | \n",
" 44 | \n",
" $11,046,000 | \n",
" 79 | \n",
" 230 | \n",
" 12 | \n",
" 2001 | \n",
" 6/21/1980 | \n",
" Arizona | \n",
" Los Angeles, CA | \n",
" California | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
"
\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",
" Name | \n",
" Age | \n",
" Team | \n",
" POS | \n",
" # | \n",
" 2013 $ | \n",
" Ht (In.) | \n",
" WT | \n",
" EXP | \n",
" 1st Year | \n",
" DOB | \n",
" School | \n",
" City | \n",
" State (Province, Territory, Etc..) | \n",
" Country | \n",
" Race | \n",
" HS Only | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Gee, Alonzo | \n",
" 26 | \n",
" Cavaliers | \n",
" F | \n",
" 33 | \n",
" $3,250,000 | \n",
" 78 | \n",
" 219 | \n",
" 4 | \n",
" 2009 | \n",
" 5/29/1987 | \n",
" Alabama | \n",
" Riviera Beach, FL | \n",
" Florida | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
" 1 | \n",
" Wallace, Gerald | \n",
" 31 | \n",
" Celtics | \n",
" F | \n",
" 45 | \n",
" $10,105,855 | \n",
" 79 | \n",
" 220 | \n",
" 12 | \n",
" 2001 | \n",
" 7/23/1982 | \n",
" Alabama | \n",
" Sylacauga, AL | \n",
" Alabama | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
" 3 | \n",
" Gladness, Mickell | \n",
" 27 | \n",
" Magic | \n",
" C | \n",
" 40 | \n",
" $762,195 | \n",
" 83 | \n",
" 220 | \n",
" 2 | \n",
" 2011 | \n",
" 7/26/1986 | \n",
" Alabama A&M | \n",
" Birmingham, AL | \n",
" Alabama | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
" 4 | \n",
" Jefferson, Richard | \n",
" 33 | \n",
" Jazz | \n",
" F | \n",
" 44 | \n",
" $11,046,000 | \n",
" 79 | \n",
" 230 | \n",
" 12 | \n",
" 2001 | \n",
" 6/21/1980 | \n",
" Arizona | \n",
" Los Angeles, CA | \n",
" California | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
" 5 | \n",
" Hill, Solomon | \n",
" 22 | \n",
" Pacers | \n",
" F | \n",
" 9 | \n",
" $1,246,680 | \n",
" 79 | \n",
" 220 | \n",
" 0 | \n",
" 2013 | \n",
" 3/18/1991 | \n",
" Arizona | \n",
" Los Angeles, CA | \n",
" California | \n",
" US | \n",
" Black | \n",
" No | \n",
"
\n",
" \n",
"
\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",
" Name | \n",
" Age | \n",
" Team | \n",
" POS | \n",
" # | \n",
" 2013 $ | \n",
" Ht (In.) | \n",
" WT | \n",
" EXP | \n",
" 1st Year | \n",
" DOB | \n",
" School | \n",
" State (Province, Territory, Etc..) | \n",
" Country | \n",
" Race | \n",
" HS Only | \n",
" Ht (Cm.) | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Gee, Alonzo | \n",
" 26 | \n",
" Cavaliers | \n",
" F | \n",
" 33 | \n",
" $3,250,000 | \n",
" 78 | \n",
" 219 | \n",
" 4 | \n",
" 2009 | \n",
" 5/29/1987 | \n",
" Alabama | \n",
" Florida | \n",
" US | \n",
" Black | \n",
" No | \n",
" 198.12 | \n",
"
\n",
" \n",
" 1 | \n",
" Wallace, Gerald | \n",
" 31 | \n",
" Celtics | \n",
" F | \n",
" 45 | \n",
" $10,105,855 | \n",
" 79 | \n",
" 220 | \n",
" 12 | \n",
" 2001 | \n",
" 7/23/1982 | \n",
" Alabama | \n",
" Alabama | \n",
" US | \n",
" Black | \n",
" No | \n",
" 200.66 | \n",
"
\n",
" \n",
"
\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",
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"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": 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"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": 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eVz5NmzZl7dpVtGmzhtjYZxk5shsrV1r3jSgoKKCg4ChwhftKJCEhA91ZYEpl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\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": 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C41lPYmKiZdy3327AtN+9y//IOF54YS4zZsyoMMZ8KojB9FkPwdxwPc8ywZtZ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\nadOmlT3Wvn17ycnJERGR3bt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fOlEURWUp+pd9FEVRWYoWPEVRVJaiBU9RFJWl\naMFTFEVlKVrwFEVRWYoWPEVRVJaiBU9RFJWlaMFTFEVlqX8AfeM+4iUuk5kAAAAASUVORK5CYII=\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": 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"text": [
""
]
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}