{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# An Introduction to `pandas`\n", "\n", "Pandas! They are adorable animals. You might think they are [the worst animal ever](https://www.reddit.com/r/todayilearned/comments/3azkqx/til_naturalist_chris_packham_said_he_would_eat/cshqy9y) but that is not true. You might sometimes think `pandas` is the worst library every, and that is only *kind of* true.\n", "\n", "The important thing is **use the right tool for the job**. `pandas` is good for some stuff, SQL is good for some stuff, writing raw Python is good for some stuff. You'll figure it out as you go along.\n", "\n", "Now let's start coding. Hopefully you did `pip install pandas` before you started up this notebook." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# import pandas, but call it pd. Why? Because that's What People Do.\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When you import pandas, you use `import pandas as pd`. That means instead of typing `pandas` in your code you'll type `pd`.\n", "\n", "You don't *have* to, but every other person on the planet will be doing it, so you might as well." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we're going to read in a file. Our file is called `NBA-Census-10.14.2013.csv` because we're **sports moguls**. `pandas` can `read_` different types of files, so try to figure it out by typing `pd.read_` and hitting tab for autocomplete." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# We're going to call this df, which means \"data frame\"\n", "# It isn't in UTF-8 (I saved it from my mac!) so we need to set the encoding\n", "df = pd.read_csv(\"NBA-Census-10.14.2013.csv\", encoding='mac_roman')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**A dataframe is basically a spreadsheet**, except it lives in the world of Python or the statistical programming language R. They can't call it a spreadsheet because then people would think those programmers used Excel, which would make them boring and normal and they'd have to wear a tie every day.\n", "\n", "# Selecting rows\n", "\n", "Now let's look at our data, since that's what data is for" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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NameAgeTeamPOS#2013 $Ht (In.)WTEXP1st YearDOBSchoolCityState (Province, Territory, Etc..)CountryRaceHS Only
0Gee, Alonzo26CavaliersF33$3,250,00078219420095/29/1987AlabamaRiviera Beach, FLFloridaUSBlackNo
1Wallace, Gerald31CelticsF45$10,105,855792201220017/23/1982AlabamaSylacauga, ALAlabamaUSBlackNo
2Williams, Mo30Trail BlazersG25$2,652,0007319510200312/19/1982AlabamaJackson, MSMississippiUSBlackNo
3Gladness, Mickell27MagicC40$762,19583220220117/26/1986Alabama A&MBirmingham, ALAlabamaUSBlackNo
4Jefferson, Richard33JazzF44$11,046,000792301220016/21/1980ArizonaLos Angeles, CACaliforniaUSBlackNo
5Hill, Solomon22PacersF9$1,246,68079220020133/18/1991ArizonaLos Angeles, CACaliforniaUSBlackNo
6Budinger, Chase25TimberwolvesF10$5,000,00079218420095/22/1988ArizonaEncinitas, CACaliforniaUSWhiteNo
7Williams, Derrick22TimberwolvesF7$5,016,96080241220115/25/1991ArizonaLa Mirada, CACaliforniaUSBlackNo
8Hill, Jordan26LakersF/C27$3,563,60082235120127/27/1987ArizonaNewberry, SCSouth CarolinaUSBlackNo
9Frye, Channing30SunsF/C8$6,500,00083245820055/17/1983ArizonaWhite Plains, NYNew YorkUSBlackNo
10Bayless, Jerryd25GrizzliesG7$3,135,00075200520088/20/1988ArizonaPhoenix, AZArizonaUSBlackNo
11Terry, Jason36NetsG31$5,625,313741801419999/15/1977ArizonaSeattle, WAWashingtonUSBlackNo
12Fogg, Kyle23NuggetsG6n/a75183020131/27/1990ArizonaBrea, CACaliforniaUSBlackNo
13Iguodala, Andre29WarriorsG/F9$12,868,63278207920041/28/1984ArizonaSpringfield, ILIllinoisUSBlackNo
14Boateng, Eric27LakersC12n/a8225717199611/20/1985Arizona StateLondon, ENGn/aEnglandBlackNo
15Diogu, Ike29KnicksF/C50$792,377802558200511/9/1983Arizona StateBuffalo, NYNew YorkUSBlackNo
16Ayres, Jeff26SpursF/C11$1,750,00081250420094/29/1987Arizona StateOntario, CACaliforniaUSBlackNo
17Harden, James24RocketsG13$13,701,25077220420098/26/1989Arizona StateLos Angeles, CACaliforniaUSBlackNo
18Felix, Carrick23CavaliersG/F30$510,00078210020138/17/1990Arizona StateGoodyear, AZArizonaUSBlackNo
19Pargo, Jannero33BobcatsG5$884,2937318511200210/22/1979ArkansasChicago, ILIllinoisUSBlackNo
20Beverley, Patrick25RocketsG2$788,87273185520087/12/1988ArkansasChicago, ILIllinoisUSBlackNo
21Johnson, Joe32NetsG/F7$21,466,718792401220016/29/1981ArkansasLittle Rock, ARArkansasUSBlackNo
22Brewer, Ronnie28RocketsG/F10$1,186,45979235720063/20/1985ArkansasPortland, OROregonUSBlackNo
23Fisher, Derek39ThunderG6$884,293732101719968/9/1974Arkansas-Little RockLittle Rock, ARArkansasUSBlackNo
24Miller, Quincy20NuggetsF30$788,872812101201211/18/1992BaylorNorth Carolina, ILIllinoisUSBlackNo
25Acy, Quincy23RaptorsF4$788,872792251201210/6/1990BaylorTyler, TXTexasUSBlackNo
26Jones, Perry22ThunderF3$1,082,52083235120129/24/1991BaylorWinnsboro, LALouisianaUSBlackNo
27Udoh, Ekpe26BucksF/C5$4,469,54882245320105/20/1987BaylorEdmond, OKOklahomaUSBlackNo
28Clark, Ian22JazzG21$490,18075175020133/7/1991BelmontMemphis, TNTennesseeUSBlackNo
29Andersen, Chris35HeatF/C11$1,399,507822281220017/7/1978Blinn CollegeLong Beach, CACaliforniaUSWhiteNo
......................................................
498Paul, Chris28ClippersG3$18,668,43172175820055/6/1985Wake ForestForsyth County, NCNorth CarolinaUSBlackNo
499Teague, Jeff25HawksG0$8,000,00074181420096/10/1988Wake ForestIndianapolis, INIndianaUSBlackNo
500Smith, Ish25SunsG30$951,46372175320107/5/1988Wake ForestCharlotte, NCNorth CarolinaUSBlackNo
501Duncan, Tim37SpursF/C21$10,361,446832551619974/25/1976Wake ForestChristiansted, VIVirgin IslandsVirgin IslandsBlackNo
502Hawes, Spencer2576ersC0$6,500,00085245620074/28/1988WashingtonSeattle, WAWashingtonUSWhiteNo
503Wroten, Tony2076ersG8$1,160,04078205120124/13/1993WashingtonRenton, WAWashingtonUSBlackNo
504Gaddy, Abdul21BobcatsG10n/a75185020131/26/1992WashingtonTacoma, WAWashingtonUSBlackNo
505Thomas, Isaiah24KingsG22$884,29369185220112/7/1989WashingtonTacoma, WAWashingtonUSBlackNo
506Robinson, Nate29NuggetsG10$2,016,00069180820055/31/1984WashingtonSeattle, WAWashingtonUSBlackNo
507Ross, Terrence22RaptorsG31$2,678,64078195120122/5/1991WashingtonPortland, OROregonUSBlackNo
508Pondexter, Quincy25GrizzliesG/F20$225,47978225320103/10/1988WashingtonFresno, CACaliforniaUSBlackNo
509Holiday, Justin24JazzG/F22$788,87278185020134/5/1989WashingtonMission Hills, CACaliforniaUSBlackNo
510Baynes, Aron26SpursF/C16$788,872822600201312/9/1986Washington StateGisborne, NZn/aNew ZealandWhiteNo
511Thompson, Klay23WarriorsG/F11$2,317,92079205220112/8/1990Washington StateLos Angeles, CACaliforniaUSMixedNo
512Lillard, Damian23Trail BlazersG0$3,202,92075195120127/15/1990Weber StateOakland, CACaliforniaUSBlackNo
513Alexander, Joe26WarriorsF25$854,389802305200812/26/1986West VirginiaKaohsiung, TAn/aTaiwanWhiteNo
514Fischer, D'or32WizardsC21n/a832550201310/12/1981West VirginiaPhiladelphia, PAPennsylvaniaUSBlackNo
515Ebanks, Devin23MavericksF37$884,293812153201010/28/1989West VirginiaNew York City, NYNew YorkUSBlackNo
516Johnson, Amir26RaptorsF/C15$6,500,00081210820055/1/1987Westchester HS (CA)Los Angeles, CACaliforniaUSBlackYes
517Martin, Kevin30TimberwolvesG23$6,500,00079185920042/1/1983Western CarolinaZanesville, OHOhioUSMixedNo
518Evans, Jeremy25JazzF40$1,660,257811943201010/24/1987Western KentuckyCrossett, ARArkansasUSBlackNo
519Lee, Courtney28CelticsG/F11$5,225,000772005200810/3/1985Western KentuckyIndianapolis, INIndianaUSBlackNo
520Mekel, Gal25MavericksG33$490,18075191520083/4/1988Wichita StatePetah Tikvan/aIsraelWhiteNo
521Murry, Toure'23KnicksG/F23$490,180771950201311/8/1989Wichita StateHouston, TXTexasUSBlackNo
522Stiemsma, Greg28PelicansC34$2,676,00083260220119/26/1985WisconsinRandolph, WIWisconsinUSWhiteNo
523Leuer, Jon24GrizzliesF30$900,00082228220115/14/1989WisconsinLong Lake, MNMinnesotaUSWhiteNo
524Landry, Marcus27LakersF14$788,8727922517199611/1/1985WisconsinMilwaukee, WIWisconsinUSBlackNo
525Harris, Devin30MavericksG20$854,38975192920042/27/1983WisconsinMilwaukee, WIWisconsinUSBlackNo
526West, David33PacersF21$12,000,000812501020038/29/1980XavierTeaneck, NJNew JerseyUSBlackNo
527Crawford, Jordan24CelticsG27$2,162,419761953201010/23/1988XavierDetroit, MIMichiganUSBlackNo
\n", "

528 rows × 17 columns

\n", "
" ], "text/plain": [ " 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", "10 Bayless, Jerryd 25 Grizzlies G 7 $3,135,000 75 \n", "11 Terry, Jason 36 Nets G 31 $5,625,313 74 \n", "12 Fogg, Kyle 23 Nuggets G 6 n/a 75 \n", "13 Iguodala, Andre 29 Warriors G/F 9 $12,868,632 78 \n", "14 Boateng, Eric 27 Lakers C 12 n/a 82 \n", "15 Diogu, Ike 29 Knicks F/C 50 $792,377 80 \n", "16 Ayres, Jeff 26 Spurs F/C 11 $1,750,000 81 \n", "17 Harden, James 24 Rockets G 13 $13,701,250 77 \n", "18 Felix, Carrick 23 Cavaliers G/F 30 $510,000 78 \n", "19 Pargo, Jannero 33 Bobcats G 5 $884,293 73 \n", "20 Beverley, Patrick 25 Rockets G 2 $788,872 73 \n", "21 Johnson, Joe 32 Nets G/F 7 $21,466,718 79 \n", "22 Brewer, Ronnie 28 Rockets G/F 10 $1,186,459 79 \n", "23 Fisher, Derek 39 Thunder G 6 $884,293 73 \n", "24 Miller, Quincy 20 Nuggets F 30 $788,872 81 \n", "25 Acy, Quincy 23 Raptors F 4 $788,872 79 \n", "26 Jones, Perry 22 Thunder F 3 $1,082,520 83 \n", "27 Udoh, Ekpe 26 Bucks F/C 5 $4,469,548 82 \n", "28 Clark, Ian 22 Jazz G 21 $490,180 75 \n", "29 Andersen, Chris 35 Heat F/C 11 $1,399,507 82 \n", ".. ... ... ... ... .. ... ... \n", "498 Paul, Chris 28 Clippers G 3 $18,668,431 72 \n", "499 Teague, Jeff 25 Hawks G 0 $8,000,000 74 \n", "500 Smith, Ish 25 Suns G 30 $951,463 72 \n", "501 Duncan, Tim 37 Spurs F/C 21 $10,361,446 83 \n", "502 Hawes, Spencer 25 76ers C 0 $6,500,000 85 \n", "503 Wroten, Tony 20 76ers G 8 $1,160,040 78 \n", "504 Gaddy, Abdul 21 Bobcats G 10 n/a 75 \n", "505 Thomas, Isaiah 24 Kings G 22 $884,293 69 \n", "506 Robinson, Nate 29 Nuggets G 10 $2,016,000 69 \n", "507 Ross, Terrence 22 Raptors G 31 $2,678,640 78 \n", "508 Pondexter, Quincy 25 Grizzlies G/F 20 $225,479 78 \n", "509 Holiday, Justin 24 Jazz G/F 22 $788,872 78 \n", "510 Baynes, Aron 26 Spurs F/C 16 $788,872 82 \n", "511 Thompson, Klay 23 Warriors G/F 11 $2,317,920 79 \n", "512 Lillard, Damian 23 Trail Blazers G 0 $3,202,920 75 \n", "513 Alexander, Joe 26 Warriors F 25 $854,389 80 \n", "514 Fischer, D'or 32 Wizards C 21 n/a 83 \n", "515 Ebanks, Devin 23 Mavericks F 37 $884,293 81 \n", "516 Johnson, Amir 26 Raptors F/C 15 $6,500,000 81 \n", "517 Martin, Kevin 30 Timberwolves G 23 $6,500,000 79 \n", "518 Evans, Jeremy 25 Jazz F 40 $1,660,257 81 \n", "519 Lee, Courtney 28 Celtics G/F 11 $5,225,000 77 \n", "520 Mekel, Gal 25 Mavericks G 33 $490,180 75 \n", "521 Murry, Toure' 23 Knicks G/F 23 $490,180 77 \n", "522 Stiemsma, Greg 28 Pelicans C 34 $2,676,000 83 \n", "523 Leuer, Jon 24 Grizzlies F 30 $900,000 82 \n", "524 Landry, Marcus 27 Lakers F 14 $788,872 79 \n", "525 Harris, Devin 30 Mavericks G 20 $854,389 75 \n", "526 West, David 33 Pacers F 21 $12,000,000 81 \n", "527 Crawford, Jordan 24 Celtics G 27 $2,162,419 76 \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", "10 200 5 2008 8/20/1988 Arizona Phoenix, AZ \n", "11 180 14 1999 9/15/1977 Arizona Seattle, WA \n", "12 183 0 2013 1/27/1990 Arizona Brea, CA \n", "13 207 9 2004 1/28/1984 Arizona Springfield, IL \n", "14 257 17 1996 11/20/1985 Arizona State London, ENG \n", "15 255 8 2005 11/9/1983 Arizona State Buffalo, NY \n", "16 250 4 2009 4/29/1987 Arizona State Ontario, CA \n", "17 220 4 2009 8/26/1989 Arizona State Los Angeles, CA \n", "18 210 0 2013 8/17/1990 Arizona State Goodyear, AZ \n", "19 185 11 2002 10/22/1979 Arkansas Chicago, IL \n", "20 185 5 2008 7/12/1988 Arkansas Chicago, IL \n", "21 240 12 2001 6/29/1981 Arkansas Little Rock, AR \n", "22 235 7 2006 3/20/1985 Arkansas Portland, OR \n", "23 210 17 1996 8/9/1974 Arkansas-Little Rock Little Rock, AR \n", "24 210 1 2012 11/18/1992 Baylor North Carolina, IL \n", "25 225 1 2012 10/6/1990 Baylor Tyler, TX \n", "26 235 1 2012 9/24/1991 Baylor Winnsboro, LA \n", "27 245 3 2010 5/20/1987 Baylor Edmond, OK \n", "28 175 0 2013 3/7/1991 Belmont Memphis, TN \n", "29 228 12 2001 7/7/1978 Blinn College Long Beach, CA \n", ".. ... ... ... ... ... ... \n", "498 175 8 2005 5/6/1985 Wake Forest Forsyth County, NC \n", "499 181 4 2009 6/10/1988 Wake Forest Indianapolis, IN \n", "500 175 3 2010 7/5/1988 Wake Forest Charlotte, NC \n", "501 255 16 1997 4/25/1976 Wake Forest Christiansted, VI \n", "502 245 6 2007 4/28/1988 Washington Seattle, WA \n", "503 205 1 2012 4/13/1993 Washington Renton, WA \n", "504 185 0 2013 1/26/1992 Washington Tacoma, WA \n", "505 185 2 2011 2/7/1989 Washington Tacoma, WA \n", "506 180 8 2005 5/31/1984 Washington Seattle, WA \n", "507 195 1 2012 2/5/1991 Washington Portland, OR \n", "508 225 3 2010 3/10/1988 Washington Fresno, CA \n", "509 185 0 2013 4/5/1989 Washington Mission Hills, CA \n", "510 260 0 2013 12/9/1986 Washington State Gisborne, NZ \n", "511 205 2 2011 2/8/1990 Washington State Los Angeles, CA \n", "512 195 1 2012 7/15/1990 Weber State Oakland, CA \n", "513 230 5 2008 12/26/1986 West Virginia Kaohsiung, TA \n", "514 255 0 2013 10/12/1981 West Virginia Philadelphia, PA \n", "515 215 3 2010 10/28/1989 West Virginia New York City, NY \n", "516 210 8 2005 5/1/1987 Westchester HS (CA) Los Angeles, CA \n", "517 185 9 2004 2/1/1983 Western Carolina Zanesville, OH \n", "518 194 3 2010 10/24/1987 Western Kentucky Crossett, AR \n", "519 200 5 2008 10/3/1985 Western Kentucky Indianapolis, IN \n", "520 191 5 2008 3/4/1988 Wichita State Petah Tikva \n", "521 195 0 2013 11/8/1989 Wichita State Houston, TX \n", "522 260 2 2011 9/26/1985 Wisconsin Randolph, WI \n", "523 228 2 2011 5/14/1989 Wisconsin Long Lake, MN \n", "524 225 17 1996 11/1/1985 Wisconsin Milwaukee, WI \n", "525 192 9 2004 2/27/1983 Wisconsin Milwaukee, WI \n", "526 250 10 2003 8/29/1980 Xavier Teaneck, NJ \n", "527 195 3 2010 10/23/1988 Xavier Detroit, MI \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 \n", "10 Arizona US Black No \n", "11 Washington US Black No \n", "12 California US Black No \n", "13 Illinois US Black No \n", "14 n/a England Black No \n", "15 New York US Black No \n", "16 California US Black No \n", "17 California US Black No \n", "18 Arizona US Black No \n", "19 Illinois US Black No \n", "20 Illinois US Black No \n", "21 Arkansas US Black No \n", "22 Oregon US Black No \n", "23 Arkansas US Black No \n", "24 Illinois US Black No \n", "25 Texas US Black No \n", "26 Louisiana US Black No \n", "27 Oklahoma US Black No \n", "28 Tennessee US Black No \n", "29 California US White No \n", ".. ... ... ... ... \n", "498 North Carolina US Black No \n", "499 Indiana US Black No \n", "500 North Carolina US Black No \n", "501 Virgin Islands Virgin Islands Black No \n", "502 Washington US White No \n", "503 Washington US Black No \n", "504 Washington US Black No \n", "505 Washington US Black No \n", "506 Washington US Black No \n", "507 Oregon US Black No \n", "508 California US Black No \n", "509 California US Black No \n", "510 n/a New Zealand White No \n", "511 California US Mixed No \n", "512 California US Black No \n", "513 n/a Taiwan White No \n", "514 Pennsylvania US Black No \n", "515 New York US Black No \n", "516 California US Black Yes \n", "517 Ohio US Mixed No \n", "518 Arkansas US Black No \n", "519 Indiana US Black No \n", "520 n/a Israel White No \n", "521 Texas US Black No \n", "522 Wisconsin US White No \n", "523 Minnesota US White No \n", "524 Wisconsin US Black No \n", "525 Wisconsin US Black No \n", "526 New Jersey US Black No \n", "527 Michigan US Black No \n", "\n", "[528 rows x 17 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's look at all of it\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we scroll we can see all of it. But maybe we don't want to see all of it. Maybe we hate scrolling?" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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NameAgeTeamPOS#2013 $Ht (In.)WTEXP1st YearDOBSchoolCityState (Province, Territory, Etc..)CountryRaceHS Only
0Gee, Alonzo26CavaliersF33$3,250,00078219420095/29/1987AlabamaRiviera Beach, FLFloridaUSBlackNo
1Wallace, Gerald31CelticsF45$10,105,855792201220017/23/1982AlabamaSylacauga, ALAlabamaUSBlackNo
2Williams, Mo30Trail BlazersG25$2,652,0007319510200312/19/1982AlabamaJackson, MSMississippiUSBlackNo
3Gladness, Mickell27MagicC40$762,19583220220117/26/1986Alabama A&MBirmingham, ALAlabamaUSBlackNo
4Jefferson, Richard33JazzF44$11,046,000792301220016/21/1980ArizonaLos Angeles, CACaliforniaUSBlackNo
\n", "
" ], "text/plain": [ " 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 " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Look at the first few rows\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "...but maybe we want to see more than a measly five results?" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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NameAgeTeamPOS#2013 $Ht (In.)WTEXP1st YearDOBSchoolCityState (Province, Territory, Etc..)CountryRaceHS Only
0Gee, Alonzo26CavaliersF33$3,250,00078219420095/29/1987AlabamaRiviera Beach, FLFloridaUSBlackNo
1Wallace, Gerald31CelticsF45$10,105,855792201220017/23/1982AlabamaSylacauga, ALAlabamaUSBlackNo
2Williams, Mo30Trail BlazersG25$2,652,0007319510200312/19/1982AlabamaJackson, MSMississippiUSBlackNo
3Gladness, Mickell27MagicC40$762,19583220220117/26/1986Alabama A&MBirmingham, ALAlabamaUSBlackNo
4Jefferson, Richard33JazzF44$11,046,000792301220016/21/1980ArizonaLos Angeles, CACaliforniaUSBlackNo
5Hill, Solomon22PacersF9$1,246,68079220020133/18/1991ArizonaLos Angeles, CACaliforniaUSBlackNo
6Budinger, Chase25TimberwolvesF10$5,000,00079218420095/22/1988ArizonaEncinitas, CACaliforniaUSWhiteNo
7Williams, Derrick22TimberwolvesF7$5,016,96080241220115/25/1991ArizonaLa Mirada, CACaliforniaUSBlackNo
8Hill, Jordan26LakersF/C27$3,563,60082235120127/27/1987ArizonaNewberry, SCSouth CarolinaUSBlackNo
9Frye, Channing30SunsF/C8$6,500,00083245820055/17/1983ArizonaWhite Plains, NYNew YorkUSBlackNo
\n", "
" ], "text/plain": [ " 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 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's look at MORE of the first few rows\n", "df.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But maybe we want to make a basketball joke and see the **final four?**" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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NameAgeTeamPOS#2013 $Ht (In.)WTEXP1st YearDOBSchoolCityState (Province, Territory, Etc..)CountryRaceHS Only
524Landry, Marcus27LakersF14$788,8727922517199611/1/1985WisconsinMilwaukee, WIWisconsinUSBlackNo
525Harris, Devin30MavericksG20$854,38975192920042/27/1983WisconsinMilwaukee, WIWisconsinUSBlackNo
526West, David33PacersF21$12,000,000812501020038/29/1980XavierTeaneck, NJNew JerseyUSBlackNo
527Crawford, Jordan24CelticsG27$2,162,419761953201010/23/1988XavierDetroit, MIMichiganUSBlackNo
\n", "
" ], "text/plain": [ " Name Age Team POS # 2013 $ Ht (In.) WT \\\n", "524 Landry, Marcus 27 Lakers F 14 $788,872 79 225 \n", "525 Harris, Devin 30 Mavericks G 20 $854,389 75 192 \n", "526 West, David 33 Pacers F 21 $12,000,000 81 250 \n", "527 Crawford, Jordan 24 Celtics G 27 $2,162,419 76 195 \n", "\n", " EXP 1st Year DOB School City \\\n", "524 17 1996 11/1/1985 Wisconsin Milwaukee, WI \n", "525 9 2004 2/27/1983 Wisconsin Milwaukee, WI \n", "526 10 2003 8/29/1980 Xavier Teaneck, NJ \n", "527 3 2010 10/23/1988 Xavier Detroit, MI \n", "\n", " State (Province, Territory, Etc..) Country Race HS Only \n", "524 Wisconsin US Black No \n", "525 Wisconsin US Black No \n", "526 New Jersey US Black No \n", "527 Michigan US Black No " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's look at the final few rows\n", "df.tail(4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So yes, `head` and `tail` work kind of like the terminal commands. That's nice, I guess.\n", "\n", "But maybe we're incredibly demanding (which we are) and we want, say, **the 6th through the 8th row** (which we do). Don't worry (which I know you were), we can do that, too." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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NameAgeTeamPOS#2013 $Ht (In.)WTEXP1st YearDOBSchoolCityState (Province, Territory, Etc..)CountryRaceHS Only
5Hill, Solomon22PacersF9$1,246,68079220020133/18/1991ArizonaLos Angeles, CACaliforniaUSBlackNo
6Budinger, Chase25TimberwolvesF10$5,000,00079218420095/22/1988ArizonaEncinitas, CACaliforniaUSWhiteNo
7Williams, Derrick22TimberwolvesF7$5,016,96080241220115/25/1991ArizonaLa Mirada, CACaliforniaUSBlackNo
\n", "
" ], "text/plain": [ " Name Age Team POS # 2013 $ Ht (In.) WT \\\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", "7 Williams, Derrick 22 Timberwolves F 7 $5,016,960 80 241 \n", "\n", " EXP 1st Year DOB School City \\\n", "5 0 2013 3/18/1991 Arizona Los Angeles, CA \n", "6 4 2009 5/22/1988 Arizona Encinitas, CA \n", "7 2 2011 5/25/1991 Arizona La Mirada, CA \n", "\n", " State (Province, Territory, Etc..) Country Race HS Only \n", "5 California US Black No \n", "6 California US White No \n", "7 California US Black No " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Show the 6th through the 8th rows\n", "df[5:8]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's kind of like an array, right? Except where in an array we'd say `df[0]` this time we need to give it two numbers, the start and the end.\n", "\n", "# Selecting columns\n", "\n", "But jeez, my eyes don't want to go that far over the data. I only want to see, uh, name and age." ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index(['Name', 'Age', 'Team', 'POS', '#', '2013 $', 'Ht (In.)', 'WT', 'EXP',\n", " '1st Year', 'DOB', 'School', 'City',\n", " 'State (Province, Territory, Etc..)', 'Country', 'Race', 'HS Only'],\n", " dtype='object')" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get the names of the columns, just because\n", "df.columns" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(['Name', 'Age', 'Team', 'POS', '#', '2013 $', 'Ht (In.)', 'WT',\n", " 'EXP', '1st Year', 'DOB', 'School', 'City',\n", " 'State (Province, Territory, Etc..)', 'Country', 'Race', 'HS Only'], dtype=object)" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# If we want to be \"correct\" we add .values on the end of it\n", "df.columns.values" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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NameAge
0Gee, Alonzo26
1Wallace, Gerald31
2Williams, Mo30
3Gladness, Mickell27
4Jefferson, Richard33
5Hill, Solomon22
6Budinger, Chase25
7Williams, Derrick22
8Hill, Jordan26
9Frye, Channing30
10Bayless, Jerryd25
11Terry, Jason36
12Fogg, Kyle23
13Iguodala, Andre29
14Boateng, Eric27
15Diogu, Ike29
16Ayres, Jeff26
17Harden, James24
18Felix, Carrick23
19Pargo, Jannero33
20Beverley, Patrick25
21Johnson, Joe32
22Brewer, Ronnie28
23Fisher, Derek39
24Miller, Quincy20
25Acy, Quincy23
26Jones, Perry22
27Udoh, Ekpe26
28Clark, Ian22
29Andersen, Chris35
.........
498Paul, Chris28
499Teague, Jeff25
500Smith, Ish25
501Duncan, Tim37
502Hawes, Spencer25
503Wroten, Tony20
504Gaddy, Abdul21
505Thomas, Isaiah24
506Robinson, Nate29
507Ross, Terrence22
508Pondexter, Quincy25
509Holiday, Justin24
510Baynes, Aron26
511Thompson, Klay23
512Lillard, Damian23
513Alexander, Joe26
514Fischer, D'or32
515Ebanks, Devin23
516Johnson, Amir26
517Martin, Kevin30
518Evans, Jeremy25
519Lee, Courtney28
520Mekel, Gal25
521Murry, Toure'23
522Stiemsma, Greg28
523Leuer, Jon24
524Landry, Marcus27
525Harris, Devin30
526West, David33
527Crawford, Jordan24
\n", "

528 rows × 2 columns

\n", "
" ], "text/plain": [ " Name Age\n", "0 Gee, Alonzo 26\n", "1 Wallace, Gerald 31\n", "2 Williams, Mo 30\n", "3 Gladness, Mickell 27\n", "4 Jefferson, Richard 33\n", "5 Hill, Solomon 22\n", "6 Budinger, Chase 25\n", "7 Williams, Derrick 22\n", "8 Hill, Jordan 26\n", "9 Frye, Channing 30\n", "10 Bayless, Jerryd 25\n", "11 Terry, Jason 36\n", "12 Fogg, Kyle 23\n", "13 Iguodala, Andre 29\n", "14 Boateng, Eric 27\n", "15 Diogu, Ike 29\n", "16 Ayres, Jeff 26\n", "17 Harden, James 24\n", "18 Felix, Carrick 23\n", "19 Pargo, Jannero 33\n", "20 Beverley, Patrick 25\n", "21 Johnson, Joe 32\n", "22 Brewer, Ronnie 28\n", "23 Fisher, Derek 39\n", "24 Miller, Quincy 20\n", "25 Acy, Quincy 23\n", "26 Jones, Perry 22\n", "27 Udoh, Ekpe 26\n", "28 Clark, Ian 22\n", "29 Andersen, Chris 35\n", ".. ... ...\n", "498 Paul, Chris 28\n", "499 Teague, Jeff 25\n", "500 Smith, Ish 25\n", "501 Duncan, Tim 37\n", "502 Hawes, Spencer 25\n", "503 Wroten, Tony 20\n", "504 Gaddy, Abdul 21\n", "505 Thomas, Isaiah 24\n", "506 Robinson, Nate 29\n", "507 Ross, Terrence 22\n", "508 Pondexter, Quincy 25\n", "509 Holiday, Justin 24\n", "510 Baynes, Aron 26\n", "511 Thompson, Klay 23\n", "512 Lillard, Damian 23\n", "513 Alexander, Joe 26\n", "514 Fischer, D'or 32\n", "515 Ebanks, Devin 23\n", "516 Johnson, Amir 26\n", "517 Martin, Kevin 30\n", "518 Evans, Jeremy 25\n", "519 Lee, Courtney 28\n", "520 Mekel, Gal 25\n", "521 Murry, Toure' 23\n", "522 Stiemsma, Greg 28\n", "523 Leuer, Jon 24\n", "524 Landry, Marcus 27\n", "525 Harris, Devin 30\n", "526 West, David 33\n", "527 Crawford, Jordan 24\n", "\n", "[528 rows x 2 columns]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Select only name and age\n", "columns_to_show = ['Name', 'Age']\n", "df[columns_to_show]" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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NameAge
0Gee, Alonzo26
1Wallace, Gerald31
2Williams, Mo30
3Gladness, Mickell27
4Jefferson, Richard33
\n", "
" ], "text/plain": [ " Name Age\n", "0 Gee, Alonzo 26\n", "1 Wallace, Gerald 31\n", "2 Williams, Mo 30\n", "3 Gladness, Mickell 27\n", "4 Jefferson, Richard 33" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Combing that with .head() to see not-so-many rows\n", "columns_to_show = ['Name', 'Age']\n", "df[columns_to_show].head()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/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", "
NameAge
0Gee, Alonzo26
1Wallace, Gerald31
2Williams, Mo30
3Gladness, Mickell27
4Jefferson, Richard33
\n", "
" ], "text/plain": [ " Name Age\n", "0 Gee, Alonzo 26\n", "1 Wallace, Gerald 31\n", "2 Williams, Mo 30\n", "3 Gladness, Mickell 27\n", "4 Jefferson, Richard 33" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We can also do this all in one line, even though it starts looking ugly\n", "# (unlike the cute bears pandas looks ugly pretty often)\n", "df[['Name', 'Age']].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**NOTE:** That was not `df['Name', 'Age']`, it was `df[['Name', 'Age]]`. You'll definitely type it wrong all of the time. When things break with pandas it's probably because you forgot to put in a million brackets." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Describing your data\n", "\n", "A powerful tool of pandas is being able to select a portion of your data, *because who ordered all that data anyway*." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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NameAgeTeamPOS#2013 $Ht (In.)WTEXP1st YearDOBSchoolCityState (Province, Territory, Etc..)CountryRaceHS Only
0Gee, Alonzo26CavaliersF33$3,250,00078219420095/29/1987AlabamaRiviera Beach, FLFloridaUSBlackNo
1Wallace, Gerald31CelticsF45$10,105,855792201220017/23/1982AlabamaSylacauga, ALAlabamaUSBlackNo
2Williams, Mo30Trail BlazersG25$2,652,0007319510200312/19/1982AlabamaJackson, MSMississippiUSBlackNo
3Gladness, Mickell27MagicC40$762,19583220220117/26/1986Alabama A&MBirmingham, ALAlabamaUSBlackNo
4Jefferson, Richard33JazzF44$11,046,000792301220016/21/1980ArizonaLos Angeles, CACaliforniaUSBlackNo
\n", "
" ], "text/plain": [ " 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 " ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I want to know how **many people are in each position**. Luckily, pandas can tell me!" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "G 175\n", "F 142\n", "F/C 74\n", "G/F 70\n", "C 67\n", "Name: POS, dtype: int64" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Grab the POS column, and count the different values in it.\n", "df['POS'].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Now that was a little weird, yes** - we used `df['POS']` instead of `df[['POS']]` when viewing the data's details.\n", "\n", "But now I'm curious about numbers: **how old is everyone?** Maybe we could, I don't know, get some statistics about age? Some statistics to **describe** age?" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "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", "Name: Age, dtype: float64" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Summary statistics for Age\n", "df['Age'].describe()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "count 528\n", "unique 308\n", "top n/a\n", "freq 43\n", "Name: 2013 $, dtype: object" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# That's pretty good. Does it work for everything? How about the money?\n", "df['2013 $'].describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unfortunately because that has dollar signs and commas it's thought of as a string. **We'll fix it in a second,** but let's try describing one more thing." ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "count 528.000000\n", "mean 79.119318\n", "std 3.431488\n", "min 69.000000\n", "25% 77.000000\n", "50% 80.000000\n", "75% 82.000000\n", "max 87.000000\n", "Name: Ht (In.), dtype: float64" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Doing more describing\n", "df['Ht (In.)'].describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's stupid, though, what's an inch even look like? What's 80 inches? I don't have a clue. If only there were some wa to manipulate our data.\n", "\n", "# Manipulating data\n", "\n", "Oh wait there is, HA HA HA." ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 78\n", "1 79\n", "2 73\n", "3 83\n", "4 79\n", "Name: Ht (In.), dtype: int64" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Take another look at our inches, but only the first few\n", "df['Ht (In.)'].head()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 6.500000\n", "1 6.583333\n", "2 6.083333\n", "3 6.916667\n", "4 6.583333\n", "Name: Ht (In.), dtype: float64" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Divide those inches by 12\n", "df['Ht (In.)'].head() / 12" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 6.500000\n", "1 6.583333\n", "2 6.083333\n", "3 6.916667\n", "4 6.583333\n", "5 6.583333\n", "6 6.583333\n", "7 6.666667\n", "8 6.833333\n", "9 6.916667\n", "10 6.250000\n", "11 6.166667\n", "12 6.250000\n", "13 6.500000\n", "14 6.833333\n", "15 6.666667\n", "16 6.750000\n", "17 6.416667\n", "18 6.500000\n", "19 6.083333\n", "20 6.083333\n", "21 6.583333\n", "22 6.583333\n", "23 6.083333\n", "24 6.750000\n", "25 6.583333\n", "26 6.916667\n", "27 6.833333\n", "28 6.250000\n", "29 6.833333\n", " ... \n", "498 6.000000\n", "499 6.166667\n", "500 6.000000\n", "501 6.916667\n", "502 7.083333\n", "503 6.500000\n", "504 6.250000\n", "505 5.750000\n", "506 5.750000\n", "507 6.500000\n", "508 6.500000\n", "509 6.500000\n", "510 6.833333\n", "511 6.583333\n", "512 6.250000\n", "513 6.666667\n", "514 6.916667\n", "515 6.750000\n", "516 6.750000\n", "517 6.583333\n", "518 6.750000\n", "519 6.416667\n", "520 6.250000\n", "521 6.416667\n", "522 6.916667\n", "523 6.833333\n", "524 6.583333\n", "525 6.250000\n", "526 6.750000\n", "527 6.333333\n", "Name: Ht (In.), dtype: float64" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's divide ALL of them by 12\n", "feet = df['Ht (In.)'] / 12\n", "feet" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "count 528.000000\n", "mean 6.593277\n", "std 0.285957\n", "min 5.750000\n", "25% 6.416667\n", "50% 6.666667\n", "75% 6.833333\n", "max 7.250000\n", "Name: Ht (In.), dtype: float64" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Can we get statistics on those?\n", "feet.describe()" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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NameAgeTeamPOS#2013 $Ht (In.)WTEXP1st YearDOBSchoolCityState (Province, Territory, Etc..)CountryRaceHS Only
0Gee, Alonzo26CavaliersF33$3,250,00078219420095/29/1987AlabamaRiviera Beach, FLFloridaUSBlackNo
1Wallace, Gerald31CelticsF45$10,105,855792201220017/23/1982AlabamaSylacauga, ALAlabamaUSBlackNo
\n", "
" ], "text/plain": [ " 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 City \\\n", "0 2009 5/29/1987 Alabama Riviera Beach, FL \n", "1 2001 7/23/1982 Alabama Sylacauga, AL \n", "\n", " State (Province, Territory, Etc..) Country Race HS Only \n", "0 Florida US Black No \n", "1 Alabama US Black No " ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's look at our original data again\n", "df.head(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Okay that was nice but unfortunately we can't do anything with it. It's just sitting there, separate from our data. If this were normal code we could do `blahblah['feet'] = blahblah['Ht (In.)'] / 12`, but since this is pandas, we can't. Right? **Right?**" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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NameAgeTeamPOS#2013 $Ht (In.)WTEXP1st YearDOBSchoolCityState (Province, Territory, Etc..)CountryRaceHS Onlyfeet
0Gee, Alonzo26CavaliersF33$3,250,00078219420095/29/1987AlabamaRiviera Beach, FLFloridaUSBlackNo6.500000
1Wallace, Gerald31CelticsF45$10,105,855792201220017/23/1982AlabamaSylacauga, ALAlabamaUSBlackNo6.583333
2Williams, Mo30Trail BlazersG25$2,652,0007319510200312/19/1982AlabamaJackson, MSMississippiUSBlackNo6.083333
3Gladness, Mickell27MagicC40$762,19583220220117/26/1986Alabama A&MBirmingham, ALAlabamaUSBlackNo6.916667
4Jefferson, Richard33JazzF44$11,046,000792301220016/21/1980ArizonaLos Angeles, CACaliforniaUSBlackNo6.583333
\n", "
" ], "text/plain": [ " 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 feet \n", "0 Florida US Black No 6.500000 \n", "1 Alabama US Black No 6.583333 \n", "2 Mississippi US Black No 6.083333 \n", "3 Alabama US Black No 6.916667 \n", "4 California US Black No 6.583333 " ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Store a new column\n", "df['feet'] = df['Ht (In.)'] / 12\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's cool, maybe we could do the same thing with their salary? Take out the $ and the , and convert it to an integer?" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 $3,250,000\n", "1 $10,105,855\n", "2 $2,652,000\n", "3 $762,195\n", "4 $11,046,000\n", "Name: 2013 $, dtype: object" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Can't just use .replace\n", "df['2013 $'].head().replace(\"$\",\"\")" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 3,250,000\n", "1 10,105,855\n", "2 2,652,000\n", "3 762,195\n", "4 11,046,000\n", "Name: 2013 $, dtype: object" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Need to use this weird .str thing\n", "df['2013 $'].head().str.replace(\"$\",\"\")" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 3,250,000\n", "1 10,105,855\n", "2 2,652,000\n", "3 762,195\n", "4 11,046,000\n", "Name: 2013 $, dtype: object" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Can't just immediately replace the , either\n", "df['2013 $'].head().str.replace(\"$\",\"\").replace(\",\",\"\")" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 3250000\n", "1 10105855\n", "2 2652000\n", "3 762195\n", "4 11046000\n", "Name: 2013 $, dtype: object" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Need to use the .str thing before EVERY string method\n", "df['2013 $'].head().str.replace(\"$\",\"\").str.replace(\",\",\"\")" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "count 5\n", "unique 5\n", "top 2652000\n", "freq 1\n", "Name: 2013 $, dtype: object" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Describe still doesn't work.\n", "df['2013 $'].head().str.replace(\"$\",\"\").str.replace(\",\",\"\").describe()" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "count 5.000000e+00\n", "mean 5.563210e+06\n", "std 4.679007e+06\n", "min 7.621950e+05\n", "25% 2.652000e+06\n", "50% 3.250000e+06\n", "75% 1.010586e+07\n", "max 1.104600e+07\n", "Name: 2013 $, dtype: float64" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's convert it to an integer using .astype(int) before we describe it\n", "df['2013 $'].head().str.replace(\"$\",\"\").str.replace(\",\",\"\").astype(int).describe()" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 3250000\n", "1 10105855\n", "2 2652000\n", "3 762195\n", "4 11046000\n", "Name: 2013 $, dtype: int64" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['2013 $'].head().str.replace(\"$\",\"\").str.replace(\",\",\"\").astype(int)" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 3.250000\n", "1 10.105855\n", "2 2.652000\n", "3 0.762195\n", "4 11.046000\n", "Name: 2013 $, dtype: float64" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Maybe we can just make them millions?\n", "df['2013 $'].head().str.replace(\"$\",\"\").str.replace(\",\",\"\").astype(int) / 1000000" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 3.250000\n", "1 10.105855\n", "2 2.652000\n", "3 0.762195\n", "4 11.046000\n", "5 1.246680\n", "6 5.000000\n", "7 5.016960\n", "8 3.563600\n", "9 6.500000\n", "10 3.135000\n", "11 5.625313\n", "12 0.000000\n", "13 12.868632\n", "14 0.000000\n", "15 0.792377\n", "16 1.750000\n", "17 13.701250\n", "18 0.510000\n", "19 0.884293\n", "20 0.788872\n", "21 21.466718\n", "22 1.186459\n", "23 0.884293\n", "24 0.788872\n", "25 0.788872\n", "26 1.082520\n", "27 4.469548\n", "28 0.490180\n", "29 1.399507\n", " ... \n", "498 18.668431\n", "499 8.000000\n", "500 0.951463\n", "501 10.361446\n", "502 6.500000\n", "503 1.160040\n", "504 0.000000\n", "505 0.884293\n", "506 2.016000\n", "507 2.678640\n", "508 0.225479\n", "509 0.788872\n", "510 0.788872\n", "511 2.317920\n", "512 3.202920\n", "513 0.854389\n", "514 0.000000\n", "515 0.884293\n", "516 6.500000\n", "517 6.500000\n", "518 1.660257\n", "519 5.225000\n", "520 0.490180\n", "521 0.490180\n", "522 2.676000\n", "523 0.900000\n", "524 0.788872\n", "525 0.854389\n", "526 12.000000\n", "527 2.162419\n", "Name: 2013 $, dtype: float64" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Unfortunately one is \"n/a\" which is going to break our code, so we can make n/a be 0\n", "df['2013 $'].str.replace(\"$\",\"\").str.replace(\",\",\"\").str.replace(\"n/a\", \"0\").astype(int) / 1000000" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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NameAgeTeamPOS#2013 $Ht (In.)WTEXP1st YearDOBSchoolCityState (Province, Territory, Etc..)CountryRaceHS Onlyfeetmillions
0Gee, Alonzo26CavaliersF33$3,250,00078219420095/29/1987AlabamaRiviera Beach, FLFloridaUSBlackNo6.5000003.250000
1Wallace, Gerald31CelticsF45$10,105,855792201220017/23/1982AlabamaSylacauga, ALAlabamaUSBlackNo6.58333310.105855
2Williams, Mo30Trail BlazersG25$2,652,0007319510200312/19/1982AlabamaJackson, MSMississippiUSBlackNo6.0833332.652000
3Gladness, Mickell27MagicC40$762,19583220220117/26/1986Alabama A&MBirmingham, ALAlabamaUSBlackNo6.9166670.762195
4Jefferson, Richard33JazzF44$11,046,000792301220016/21/1980ArizonaLos Angeles, CACaliforniaUSBlackNo6.58333311.046000
\n", "
" ], "text/plain": [ " 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 feet \\\n", "0 Florida US Black No 6.500000 \n", "1 Alabama US Black No 6.583333 \n", "2 Mississippi US Black No 6.083333 \n", "3 Alabama US Black No 6.916667 \n", "4 California US Black No 6.583333 \n", "\n", " millions \n", "0 3.250000 \n", "1 10.105855 \n", "2 2.652000 \n", "3 0.762195 \n", "4 11.046000 " ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Remove the .head() piece and save it back into the dataframe\n", "df['millions'] = df['2013 $'].str.replace(\"$\",\"\").str.replace(\",\",\"\").str.replace(\"n/a\",\"0\").astype(int) / 1000000\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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AgeHt (In.)WTEXP1st Yearfeetmillions
count528.000000528.000000528.000000528.000000528.000000528.000000528.000000
mean26.24242479.119318221.2064394.7727272008.2272736.5932773.818379
std4.1788683.43148827.9431694.3256284.3256280.2859574.728437
min18.00000069.00000020.0000000.0000001995.0000005.7500000.000000
25%23.00000077.000000200.0000001.0000002005.0000006.4166670.816844
50%25.00000080.000000220.0000004.0000002009.0000006.6666671.711620
75%29.00000082.000000240.0000008.0000002012.0000006.8333335.000000
max39.00000087.000000290.00000018.0000002013.0000007.25000030.453805
\n", "
" ], "text/plain": [ " Age Ht (In.) WT EXP 1st Year \\\n", "count 528.000000 528.000000 528.000000 528.000000 528.000000 \n", "mean 26.242424 79.119318 221.206439 4.772727 2008.227273 \n", "std 4.178868 3.431488 27.943169 4.325628 4.325628 \n", "min 18.000000 69.000000 20.000000 0.000000 1995.000000 \n", "25% 23.000000 77.000000 200.000000 1.000000 2005.000000 \n", "50% 25.000000 80.000000 220.000000 4.000000 2009.000000 \n", "75% 29.000000 82.000000 240.000000 8.000000 2012.000000 \n", "max 39.000000 87.000000 290.000000 18.000000 2013.000000 \n", "\n", " feet millions \n", "count 528.000000 528.000000 \n", "mean 6.593277 3.818379 \n", "std 0.285957 4.728437 \n", "min 5.750000 0.000000 \n", "25% 6.416667 0.816844 \n", "50% 6.666667 1.711620 \n", "75% 6.833333 5.000000 \n", "max 7.250000 30.453805 " ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The average basketball player makes 3.8 million dollars and is a little over six and a half feet tall.\n", "\n", "But who cares about those guys? I don't care about those guys. They're boring. I want the real rich guys!\n", "\n", "# Sorting and sub-selecting" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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NameAgeTeamPOS#2013 $Ht (In.)WTEXP1st YearDOBSchoolCityState (Province, Territory, Etc..)CountryRaceHS Onlyfeetmillions
0Gee, Alonzo26CavaliersF33$3,250,00078219420095/29/1987AlabamaRiviera Beach, FLFloridaUSBlackNo6.5000003.250000
1Wallace, Gerald31CelticsF45$10,105,855792201220017/23/1982AlabamaSylacauga, ALAlabamaUSBlackNo6.58333310.105855
2Williams, Mo30Trail BlazersG25$2,652,0007319510200312/19/1982AlabamaJackson, MSMississippiUSBlackNo6.0833332.652000
\n", "
" ], "text/plain": [ " 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", "\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", "\n", " State (Province, Territory, Etc..) Country Race HS Only feet \\\n", "0 Florida US Black No 6.500000 \n", "1 Alabama US Black No 6.583333 \n", "2 Mississippi US Black No 6.083333 \n", "\n", " millions \n", "0 3.250000 \n", "1 10.105855 \n", "2 2.652000 " ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# This is just the first few guys in the dataset. Can we order it?\n", "df.head(3)" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/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", "
NameAgeTeamPOS#2013 $Ht (In.)WTEXP1st YearDOBSchoolCityState (Province, Territory, Etc..)CountryRaceHS Onlyfeetmillions
496Johnson, James26HawksF13n/a81248420092/20/1987Wake ForestCheyene, WYWyomingUSBlackNo6.7500000.0
33Davies, Brandon22ClippersF23n/a81235020137/25/1991Brigham YoungProvo, UTUtahUSBlackNo6.7500000.0
465Drew, Larry23HeatG0n/a74180020133/5/1990UCLAEncino, CACaliforniaUSBlackNo6.1666670.0
\n", "
" ], "text/plain": [ " Name Age Team POS # 2013 $ Ht (In.) WT EXP \\\n", "496 Johnson, James 26 Hawks F 13 n/a 81 248 4 \n", "33 Davies, Brandon 22 Clippers F 23 n/a 81 235 0 \n", "465 Drew, Larry 23 Heat G 0 n/a 74 180 0 \n", "\n", " 1st Year DOB School City \\\n", "496 2009 2/20/1987 Wake Forest Cheyene, WY \n", "33 2013 7/25/1991 Brigham Young Provo, UT \n", "465 2013 3/5/1990 UCLA Encino, CA \n", "\n", " State (Province, Territory, Etc..) Country Race HS Only feet \\\n", "496 Wyoming US Black No 6.750000 \n", "33 Utah US Black No 6.750000 \n", "465 California US Black No 6.166667 \n", "\n", " millions \n", "496 0.0 \n", "33 0.0 \n", "465 0.0 " ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's try to sort them\n", "df.sort_values(by='millions').head(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Those guys are making nothing! If only there were a way to sort from high to low, a.k.a. descending instead of ascending." ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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NameAgeTeamPOS#2013 $Ht (In.)WTEXP1st YearDOBSchoolCityState (Province, Territory, Etc..)CountryRaceHS Onlyfeetmillions
203Bryant, Kobe35LakersG24$30,453,80578205720068/23/1978Lower Merion HS (PA)Philadelphia, PAPennsylvaniaUSBlackYes6.50000030.453805
282Nowitzki, Dirk35MavericksF41$22,721,381842451519986/19/1978n/aWurzburg, BABavariaGermanyWhiteNo7.00000022.721381
68Stoudemire, Amar'e†30KnicksF/C1$21,679,8938324511200211/16/1982Cypress Creek HS (FL)Lake Wales, FLFloridaUSBlackYes6.91666721.679893
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" ], "text/plain": [ " Name Age Team POS # 2013 $ Ht (In.) WT \\\n", "203 Bryant, Kobe 35 Lakers G 24 $30,453,805 78 205 \n", "282 Nowitzki, Dirk 35 Mavericks F 41 $22,721,381 84 245 \n", "68 Stoudemire, Amar'e† 30 Knicks F/C 1 $21,679,893 83 245 \n", "\n", " EXP 1st Year DOB School City \\\n", "203 7 2006 8/23/1978 Lower Merion HS (PA) Philadelphia, PA \n", "282 15 1998 6/19/1978 n/a Wurzburg, BA \n", "68 11 2002 11/16/1982 Cypress Creek HS (FL) Lake Wales, FL \n", "\n", " State (Province, Territory, Etc..) Country Race HS Only feet \\\n", "203 Pennsylvania US Black Yes 6.500000 \n", "282 Bavaria Germany White No 7.000000 \n", "68 Florida US Black Yes 6.916667 \n", "\n", " millions \n", "203 30.453805 \n", "282 22.721381 \n", "68 21.679893 " ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# It isn't descending = True, unfortunately\n", "df.sort_values(by='millions', ascending=False).head(3)" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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NameAgeTeamPOS#2013 $Ht (In.)WTEXP1st YearDOBSchoolCityState (Province, Territory, Etc..)CountryRaceHS Onlyfeetmillions
392Nash, Steve39LakersG10$9,300,50075178720062/7/1974Santa ClaraJohannesburg, SAn/aSouth AfricaWhiteNo6.2500009.300500
225Camby, Marcus39RocketsF/C21$884,293832401719963/22/1974MassachusettsHartford, CTConnecticutUSBlackNo6.9166670.884293
23Fisher, Derek39ThunderG6$884,293732101719968/9/1974Arkansas-Little RockLittle Rock, ARArkansasUSBlackNo6.0833330.884293
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" ], "text/plain": [ " Name Age Team POS # 2013 $ Ht (In.) WT EXP \\\n", "392 Nash, Steve 39 Lakers G 10 $9,300,500 75 178 7 \n", "225 Camby, Marcus 39 Rockets F/C 21 $884,293 83 240 17 \n", "23 Fisher, Derek 39 Thunder G 6 $884,293 73 210 17 \n", "\n", " 1st Year DOB School City \\\n", "392 2006 2/7/1974 Santa Clara Johannesburg, SA \n", "225 1996 3/22/1974 Massachusetts Hartford, CT \n", "23 1996 8/9/1974 Arkansas-Little Rock Little Rock, AR \n", "\n", " State (Province, Territory, Etc..) Country Race HS Only feet \\\n", "392 n/a South Africa White No 6.250000 \n", "225 Connecticut US Black No 6.916667 \n", "23 Arkansas US Black No 6.083333 \n", "\n", " millions \n", "392 9.300500 \n", "225 0.884293 \n", "23 0.884293 " ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We can use this to find the oldest guys in the league\n", "df.sort_values(by='Age', ascending=False).head(3)" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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NameAgeTeamPOS#2013 $Ht (In.)WTEXP1st YearDOBSchoolCityState (Province, Territory, Etc..)CountryRaceHS Onlyfeetmillions
285Antetokounmpo, Giannis18BucksG/F34$1,792,560812051201212/16/1994n/aAthensn/aGreeceBlackNo6.7500001.79256
174Noel, Nerlens1976ersC4$3,171,32083228020134/10/1994KentuckyMalden, MAMassachussettsUSBlackNo6.9166673.17132
191Goodwin, Archie19SunsG20$1,064,40077198020138/17/1994KentuckyLittle Rock, ARArkansasUSBlackNo6.4166671.06440
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" ], "text/plain": [ " Name Age Team POS # 2013 $ Ht (In.) WT \\\n", "285 Antetokounmpo, Giannis 18 Bucks G/F 34 $1,792,560 81 205 \n", "174 Noel, Nerlens 19 76ers C 4 $3,171,320 83 228 \n", "191 Goodwin, Archie 19 Suns G 20 $1,064,400 77 198 \n", "\n", " EXP 1st Year DOB School City \\\n", "285 1 2012 12/16/1994 n/a Athens \n", "174 0 2013 4/10/1994 Kentucky Malden, MA \n", "191 0 2013 8/17/1994 Kentucky Little Rock, AR \n", "\n", " State (Province, Territory, Etc..) Country Race HS Only feet \\\n", "285 n/a Greece Black No 6.750000 \n", "174 Massachussetts US Black No 6.916667 \n", "191 Arkansas US Black No 6.416667 \n", "\n", " millions \n", "285 1.79256 \n", "174 3.17132 \n", "191 1.06440 " ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Or the youngest, by taking out 'ascending=False'\n", "df.sort_values(by='Age').head(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But sometimes instead of just looking at them, I want to do stuff with them. Play some games with them! Dunk on them~ `describe` them! And we don't want to dunk on everyone, only the players above 7 feet tall.\n", "\n", "First, we need to check out **boolean things.**" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 False\n", "1 False\n", "2 False\n", "3 False\n", "4 False\n", "5 False\n", "6 False\n", "7 False\n", "8 False\n", "9 False\n", "10 False\n", "11 False\n", "12 False\n", "13 False\n", "14 False\n", "15 False\n", "16 False\n", "17 False\n", "18 False\n", "19 False\n", "20 False\n", "21 False\n", "22 False\n", "23 False\n", "24 False\n", "25 False\n", "26 False\n", "27 False\n", "28 False\n", "29 False\n", " ... \n", "498 False\n", "499 False\n", "500 False\n", "501 False\n", "502 True\n", "503 False\n", "504 False\n", "505 False\n", "506 False\n", "507 False\n", "508 False\n", "509 False\n", "510 False\n", "511 False\n", "512 False\n", "513 False\n", "514 False\n", "515 False\n", "516 False\n", "517 False\n", "518 False\n", "519 False\n", "520 False\n", "521 False\n", "522 False\n", "523 False\n", "524 False\n", "525 False\n", "526 False\n", "527 False\n", "Name: feet, dtype: bool" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get a big long list of True and False for every single row.\n", "df['feet'] > 7" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False 518\n", "True 10\n", "Name: feet, dtype: int64" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We could use value counts if we wanted\n", "above_seven_feet = df['feet'] > 7\n", "above_seven_feet.value_counts()" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 False\n", "1 False\n", "2 False\n", "3 False\n", "4 False\n", "Name: feet, dtype: bool" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# But we can also apply this to every single row to say whether YES we want it or NO we don't\n", "df['feet'].head() > 7" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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NameAgeTeamPOS#2013 $Ht (In.)WTEXP1st YearDOBSchoolCityState (Province, Territory, Etc..)CountryRaceHS Onlyfeetmillions
54Thabeet, Hasheem26ThunderC34$1,200,00087263420092/16/1987ConnecticutDar es Salaamn/aTanzaniaBlackNo7.2500001.200000
76Chandler, Tyson31KnicksC6$14,100,5388524012200110/2/1982Dominguez HS (CA)Hanford, CACaliforniaUSBlackYes7.08333314.100538
120Hibbert, Roy26PacersC55$14,283,844862805200812/11/1986GeorgetownNew York City, NYNew YorkUSBlackNo7.16666714.283844
145Leonard, Meyers21Trail BlazersC11$2,222,16085245120122/27/1992IllinoisRobinson, IILIllinoisUSWhiteNo7.0833332.222160
221Len, Alex20SunsC21$3,492,72085255020136/16/1993MarylandAntratsyn/aUkraineWhiteNo7.0833333.492720
274Gobert, Rudy21JazzC27$1,078,80085235020136/26/1992n/aSaint-QuentinAisneFranceMixedNo7.0833331.078800
297Mozgov, Timofey27NuggetsC25$4,400,00085250320107/16/1986n/aSt. Petersburgn/aRussiaWhiteNo7.0833334.400000
303Gasol, Marc28GrizzliesC33$14,860,52485265520081/29/1985n/aBarcelonan/aSpainHispanicNo7.08333314.860524
316Kuzmi?, Ognjen23WarriorsC1$490,18085231020135/16/1990n/aDobojn/aYugoslaviaWhiteNo7.0833330.490180
502Hawes, Spencer2576ersC0$6,500,00085245620074/28/1988WashingtonSeattle, WAWashingtonUSWhiteNo7.0833336.500000
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" ], "text/plain": [ " Name Age Team POS # 2013 $ Ht (In.) WT \\\n", "54 Thabeet, Hasheem 26 Thunder C 34 $1,200,000 87 263 \n", "76 Chandler, Tyson 31 Knicks C 6 $14,100,538 85 240 \n", "120 Hibbert, Roy 26 Pacers C 55 $14,283,844 86 280 \n", "145 Leonard, Meyers 21 Trail Blazers C 11 $2,222,160 85 245 \n", "221 Len, Alex 20 Suns C 21 $3,492,720 85 255 \n", "274 Gobert, Rudy 21 Jazz C 27 $1,078,800 85 235 \n", "297 Mozgov, Timofey 27 Nuggets C 25 $4,400,000 85 250 \n", "303 Gasol, Marc 28 Grizzlies C 33 $14,860,524 85 265 \n", "316 Kuzmi?, Ognjen 23 Warriors C 1 $490,180 85 231 \n", "502 Hawes, Spencer 25 76ers C 0 $6,500,000 85 245 \n", "\n", " EXP 1st Year DOB School City \\\n", "54 4 2009 2/16/1987 Connecticut Dar es Salaam \n", "76 12 2001 10/2/1982 Dominguez HS (CA) Hanford, CA \n", "120 5 2008 12/11/1986 Georgetown New York City, NY \n", "145 1 2012 2/27/1992 Illinois Robinson, IIL \n", "221 0 2013 6/16/1993 Maryland Antratsy \n", "274 0 2013 6/26/1992 n/a Saint-Quentin \n", "297 3 2010 7/16/1986 n/a St. Petersburg \n", "303 5 2008 1/29/1985 n/a Barcelona \n", "316 0 2013 5/16/1990 n/a Doboj \n", "502 6 2007 4/28/1988 Washington Seattle, WA \n", "\n", " State (Province, Territory, Etc..) Country Race HS Only \\\n", "54 n/a Tanzania Black No \n", "76 California US Black Yes \n", "120 New York US Black No \n", "145 Illinois US White No \n", "221 n/a Ukraine White No \n", "274 Aisne France Mixed No \n", "297 n/a Russia White No \n", "303 n/a Spain Hispanic No \n", "316 n/a Yugoslavia White No \n", "502 Washington US White No \n", "\n", " feet millions \n", "54 7.250000 1.200000 \n", "76 7.083333 14.100538 \n", "120 7.166667 14.283844 \n", "145 7.083333 2.222160 \n", "221 7.083333 3.492720 \n", "274 7.083333 1.078800 \n", "297 7.083333 4.400000 \n", "303 7.083333 14.860524 \n", "316 7.083333 0.490180 \n", "502 7.083333 6.500000 " ] }, "execution_count": 96, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Instead of putting column names inside of the brackets, we instead\n", "# put the True/False statements. It will only return the players above \n", "# seven feet tall\n", "df[df['feet'] > 7]" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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NameAgeTeamPOS#2013 $Ht (In.)WTEXP1st YearDOBSchoolCityState (Province, Territory, Etc..)CountryRaceHS Onlyfeetmillions
2Williams, Mo30Trail BlazersG25$2,652,0007319510200312/19/1982AlabamaJackson, MSMississippiUSBlackNo6.0833332.652000
10Bayless, Jerryd25GrizzliesG7$3,135,00075200520088/20/1988ArizonaPhoenix, AZArizonaUSBlackNo6.2500003.135000
11Terry, Jason36NetsG31$5,625,313741801419999/15/1977ArizonaSeattle, WAWashingtonUSBlackNo6.1666675.625313
12Fogg, Kyle23NuggetsG6n/a75183020131/27/1990ArizonaBrea, CACaliforniaUSBlackNo6.2500000.000000
17Harden, James24RocketsG13$13,701,25077220420098/26/1989Arizona StateLos Angeles, CACaliforniaUSBlackNo6.41666713.701250
19Pargo, Jannero33BobcatsG5$884,2937318511200210/22/1979ArkansasChicago, ILIllinoisUSBlackNo6.0833330.884293
20Beverley, Patrick25RocketsG2$788,87273185520087/12/1988ArkansasChicago, ILIllinoisUSBlackNo6.0833330.788872
23Fisher, Derek39ThunderG6$884,293732101719968/9/1974Arkansas-Little RockLittle Rock, ARArkansasUSBlackNo6.0833330.884293
28Clark, Ian22JazzG21$490,18075175020133/7/1991BelmontMemphis, TNTennesseeUSBlackNo6.2500000.490180
30Jackson, Reggie23ThunderG15$1,260,36075208220114/16/1990Boston CollegePordenonen/aItalyBlackNo6.2500001.260360
34Fredette, Jimmer24KingsG7$2,439,84074195220112/25/1989Brigham YoungGlens Falls, NYNew YorkUSWhiteNo6.1666672.439840
35Mack, Shelvin23HawksG8$884,29375215220114/22/1990ButlerLexington, KYKentuckyUSBlackNo6.2500000.884293
38Crabbe, Allen21Trail BlazersG23$825,00078210020134/4/1992CaliforniaLos Angeles, CACaliforniaUSBlackNo6.5000000.825000
40Taylor, Jermaine26CavaliersG8$780,87177204200912/8/1986Central FloridaTavares, FLFloridaUSBlackNo6.4166670.780871
44Stephenson, Lance23PacersG1$1,005,00077228320109/5/1990CincinnatiNew York City, NYNew YorkUSBlackNo6.4166671.005000
46Cole, Norris25HeatG30$1,129,200741752201110/13/1988Cleveland StateDayton, OHOhioUSBlackNo6.1666671.129200
49Burks, Alec22JazzG10$2,202,00078205220117/20/1991ColoradoGrandview, MOMissouriUSBlackNo6.5000002.202000
50Billups, Chauncey37PistonsG1$2,500,000752101619979/25/1976ColoradoDenver, COColoradoUSBlackNo6.2500002.500000
53Gordon, Ben30BobcatsG8$13,200,00075200920044/4/1983ConnecticutLondon, ENGn/aEnglandBlackNo6.25000013.200000
62Walker, Kemba23BobcatsG15$2,568,36073184220115/8/1990ConnecticutNew York City, NYNew YorkUSBlackNo6.0833332.568360
63Allen, Ray38HeatG34$3,229,050772051719967/20/1975ConnecticutMerced, CACaliforniaUSBlackNo6.4166673.229050
64Price, A.J.27TimberwolvesG22n/a741854200910/7/1986ConnecticutOrange, NJNew JerseyUsBlackNo6.1666670.000000
69Curry, Stephen25WarriorsG30$9,887,64075185420093/14/1988DavidsonAkron, OHOhioUSMixedNo6.2500009.887640
71Roberts, Brian27PelicansG22$788,872731731201212/3/1985DaytonToledo, OHOhioUSBlackNo6.0833330.788872
74Green, Willie32ClippersG34$1,399,507752011020037/28/1981DetroitDetroit, MIMichiganUSBlackNo6.2500001.399507
75McCallum, Ray22KingsG3$524,61675190020136/12/1991DetroitDetroit, MIMichiganUSBlackNo6.2500000.524616
77Irving, Kyrie21CavaliersG2$5,607,24075191220113/23/1992DukeMelbourneVictoriaAustraliaBlackNo6.2500005.607240
88Redick, J. J.29ClippersG4$6,500,00076190720066/24/1984DukeCookeville, TNTennesseeUSWhiteNo6.3333336.500000
89Rivers, Austin21PelicansG25$2,339,04076200120128/1/1992DukeSanta Monica, CACaliforniaUSMixedNo6.3333332.339040
90Curry, Seth23WarriorsG3$490,18074185020138/23/1990DukeCharlotte, NCNorth CarolinaUSBlackNo6.1666670.490180
............................................................
457Johnson, Orlando24PacersG11$788,87277220120123/11/1989UC Santa BarbaraMonterey, CACaliforniaUSBlackNo6.4166670.788872
464Collison, Darren26ClippersG2$1,900,00072175420098/23/1987UCLARancho Cucamonga, CACaliforniaUSBlackNo6.0000001.900000
465Drew, Larry23HeatG0n/a74180020133/5/1990UCLAEncino, CACaliforniaUSBlackNo6.1666670.000000
466Farmar, Jordan26LakersG1$884,2937418012200111/30/1986UCLALos Angeles, CACaliforniaUSMixedNo6.1666670.884293
467Holiday, Jrue23PelicansG11$9,713,48476205420096/12/1990UCLAChatsworth, CACaliforniaUSBlackNo6.3333339.713484
468Lee, Malcolm23SunsG30$884,29377200220115/22/1990UCLARiverside, CACaliforniaUSBlackNo6.4166670.884293
469Westbrook, Russell24ThunderG0$14,693,906751875200811/12/1988UCLALong Beach, CACaliforniaUSBlackNo6.25000014.693906
470Watson, Earl34Trail BlazersG17$884,293731991220016/12/1979UCLAKansas City, KAKansasUSBlackNo6.0833330.884293
480Miller, Andre37NuggetsG24$5,000,000742001419993/19/1976UtahLos Angeles, CACaliforniaUSBlackNo6.1666675.000000
481Price, Ronnie30MagicG10$1,146,33774190820056/21/1983Utah ValleyFriendswood, TexasTexasUSBlackNo6.1666671.146337
485Jenkins, John22HawksG12$1,258,80076215120123/6/1991VanderbiltHendersonville, TNTennesseeUSBlackNo6.3333331.258800
487Wayns, Maalik22ClippersG5$788,87273195120125/2/1991VillanovaPhiladelphia, PAPennsylvaniaUSBlackNo6.0833330.788872
488Foye, Randy30NuggetsG4$3,000,00076213720069/24/1983VillanovaNewark, NJNew JerseyUSBlackNo6.3333333.000000
489Lowry, Kyle27RaptorsG7$6,210,00072205720063/25/1986VillanovaPhiladelphia, PAPennsylvaniaUSBlackNo6.0000006.210000
491Mason, Jr., Roger33HeatG21$854,389772051120029/10/1980VirginiaWashington, DCDCUSBlackNo6.4166670.854389
493Daniels, Troy22BobcatsG30n/a76200020137/15/1991Virginia CommonwealthRoanoke, VAVirginiaUSBlackNo6.3333330.000000
494Maynor, Eric26WizardsG6$13,000,00075175420096/11/1987Virginia CommonwealthRaeford, NCNorth CarolinaUSBlackNo6.25000013.000000
498Paul, Chris28ClippersG3$18,668,43172175820055/6/1985Wake ForestForsyth County, NCNorth CarolinaUSBlackNo6.00000018.668431
499Teague, Jeff25HawksG0$8,000,00074181420096/10/1988Wake ForestIndianapolis, INIndianaUSBlackNo6.1666678.000000
500Smith, Ish25SunsG30$951,46372175320107/5/1988Wake ForestCharlotte, NCNorth CarolinaUSBlackNo6.0000000.951463
503Wroten, Tony2076ersG8$1,160,04078205120124/13/1993WashingtonRenton, WAWashingtonUSBlackNo6.5000001.160040
504Gaddy, Abdul21BobcatsG10n/a75185020131/26/1992WashingtonTacoma, WAWashingtonUSBlackNo6.2500000.000000
505Thomas, Isaiah24KingsG22$884,29369185220112/7/1989WashingtonTacoma, WAWashingtonUSBlackNo5.7500000.884293
506Robinson, Nate29NuggetsG10$2,016,00069180820055/31/1984WashingtonSeattle, WAWashingtonUSBlackNo5.7500002.016000
507Ross, Terrence22RaptorsG31$2,678,64078195120122/5/1991WashingtonPortland, OROregonUSBlackNo6.5000002.678640
512Lillard, Damian23Trail BlazersG0$3,202,92075195120127/15/1990Weber StateOakland, CACaliforniaUSBlackNo6.2500003.202920
517Martin, Kevin30TimberwolvesG23$6,500,00079185920042/1/1983Western CarolinaZanesville, OHOhioUSMixedNo6.5833336.500000
520Mekel, Gal25MavericksG33$490,18075191520083/4/1988Wichita StatePetah Tikvan/aIsraelWhiteNo6.2500000.490180
525Harris, Devin30MavericksG20$854,38975192920042/27/1983WisconsinMilwaukee, WIWisconsinUSBlackNo6.2500000.854389
527Crawford, Jordan24CelticsG27$2,162,419761953201010/23/1988XavierDetroit, MIMichiganUSBlackNo6.3333332.162419
\n", "

175 rows × 19 columns

\n", "
" ], "text/plain": [ " Name Age Team POS # 2013 $ Ht (In.) \\\n", "2 Williams, Mo 30 Trail Blazers G 25 $2,652,000 73 \n", "10 Bayless, Jerryd 25 Grizzlies G 7 $3,135,000 75 \n", "11 Terry, Jason 36 Nets G 31 $5,625,313 74 \n", "12 Fogg, Kyle 23 Nuggets G 6 n/a 75 \n", "17 Harden, James 24 Rockets G 13 $13,701,250 77 \n", "19 Pargo, Jannero 33 Bobcats G 5 $884,293 73 \n", "20 Beverley, Patrick 25 Rockets G 2 $788,872 73 \n", "23 Fisher, Derek 39 Thunder G 6 $884,293 73 \n", "28 Clark, Ian 22 Jazz G 21 $490,180 75 \n", "30 Jackson, Reggie 23 Thunder G 15 $1,260,360 75 \n", "34 Fredette, Jimmer 24 Kings G 7 $2,439,840 74 \n", "35 Mack, Shelvin 23 Hawks G 8 $884,293 75 \n", "38 Crabbe, Allen 21 Trail Blazers G 23 $825,000 78 \n", "40 Taylor, Jermaine 26 Cavaliers G 8 $780,871 77 \n", "44 Stephenson, Lance 23 Pacers G 1 $1,005,000 77 \n", "46 Cole, Norris 25 Heat G 30 $1,129,200 74 \n", "49 Burks, Alec 22 Jazz G 10 $2,202,000 78 \n", "50 Billups, Chauncey 37 Pistons G 1 $2,500,000 75 \n", "53 Gordon, Ben 30 Bobcats G 8 $13,200,000 75 \n", "62 Walker, Kemba 23 Bobcats G 15 $2,568,360 73 \n", "63 Allen, Ray 38 Heat G 34 $3,229,050 77 \n", "64 Price, A.J. 27 Timberwolves G 22 n/a 74 \n", "69 Curry, Stephen 25 Warriors G 30 $9,887,640 75 \n", "71 Roberts, Brian 27 Pelicans G 22 $788,872 73 \n", "74 Green, Willie 32 Clippers G 34 $1,399,507 75 \n", "75 McCallum, Ray 22 Kings G 3 $524,616 75 \n", "77 Irving, Kyrie 21 Cavaliers G 2 $5,607,240 75 \n", "88 Redick, J. J. 29 Clippers G 4 $6,500,000 76 \n", "89 Rivers, Austin 21 Pelicans G 25 $2,339,040 76 \n", "90 Curry, Seth 23 Warriors G 3 $490,180 74 \n", ".. ... ... ... .. .. ... ... \n", "457 Johnson, Orlando 24 Pacers G 11 $788,872 77 \n", "464 Collison, Darren 26 Clippers G 2 $1,900,000 72 \n", "465 Drew, Larry 23 Heat G 0 n/a 74 \n", "466 Farmar, Jordan 26 Lakers G 1 $884,293 74 \n", "467 Holiday, Jrue 23 Pelicans G 11 $9,713,484 76 \n", "468 Lee, Malcolm 23 Suns G 30 $884,293 77 \n", "469 Westbrook, Russell 24 Thunder G 0 $14,693,906 75 \n", "470 Watson, Earl 34 Trail Blazers G 17 $884,293 73 \n", "480 Miller, Andre 37 Nuggets G 24 $5,000,000 74 \n", "481 Price, Ronnie 30 Magic G 10 $1,146,337 74 \n", "485 Jenkins, John 22 Hawks G 12 $1,258,800 76 \n", "487 Wayns, Maalik 22 Clippers G 5 $788,872 73 \n", "488 Foye, Randy 30 Nuggets G 4 $3,000,000 76 \n", "489 Lowry, Kyle 27 Raptors G 7 $6,210,000 72 \n", "491 Mason, Jr., Roger 33 Heat G 21 $854,389 77 \n", "493 Daniels, Troy 22 Bobcats G 30 n/a 76 \n", "494 Maynor, Eric 26 Wizards G 6 $13,000,000 75 \n", "498 Paul, Chris 28 Clippers G 3 $18,668,431 72 \n", "499 Teague, Jeff 25 Hawks G 0 $8,000,000 74 \n", "500 Smith, Ish 25 Suns G 30 $951,463 72 \n", "503 Wroten, Tony 20 76ers G 8 $1,160,040 78 \n", "504 Gaddy, Abdul 21 Bobcats G 10 n/a 75 \n", "505 Thomas, Isaiah 24 Kings G 22 $884,293 69 \n", "506 Robinson, Nate 29 Nuggets G 10 $2,016,000 69 \n", "507 Ross, Terrence 22 Raptors G 31 $2,678,640 78 \n", "512 Lillard, Damian 23 Trail Blazers G 0 $3,202,920 75 \n", "517 Martin, Kevin 30 Timberwolves G 23 $6,500,000 79 \n", "520 Mekel, Gal 25 Mavericks G 33 $490,180 75 \n", "525 Harris, Devin 30 Mavericks G 20 $854,389 75 \n", "527 Crawford, Jordan 24 Celtics G 27 $2,162,419 76 \n", "\n", " WT EXP 1st Year DOB School \\\n", "2 195 10 2003 12/19/1982 Alabama \n", "10 200 5 2008 8/20/1988 Arizona \n", "11 180 14 1999 9/15/1977 Arizona \n", "12 183 0 2013 1/27/1990 Arizona \n", "17 220 4 2009 8/26/1989 Arizona State \n", "19 185 11 2002 10/22/1979 Arkansas \n", "20 185 5 2008 7/12/1988 Arkansas \n", "23 210 17 1996 8/9/1974 Arkansas-Little Rock \n", "28 175 0 2013 3/7/1991 Belmont \n", "30 208 2 2011 4/16/1990 Boston College \n", "34 195 2 2011 2/25/1989 Brigham Young \n", "35 215 2 2011 4/22/1990 Butler \n", "38 210 0 2013 4/4/1992 California \n", "40 20 4 2009 12/8/1986 Central Florida \n", "44 228 3 2010 9/5/1990 Cincinnati \n", "46 175 2 2011 10/13/1988 Cleveland State \n", "49 205 2 2011 7/20/1991 Colorado \n", "50 210 16 1997 9/25/1976 Colorado \n", "53 200 9 2004 4/4/1983 Connecticut \n", "62 184 2 2011 5/8/1990 Connecticut \n", "63 205 17 1996 7/20/1975 Connecticut \n", "64 185 4 2009 10/7/1986 Connecticut \n", "69 185 4 2009 3/14/1988 Davidson \n", "71 173 1 2012 12/3/1985 Dayton \n", "74 201 10 2003 7/28/1981 Detroit \n", "75 190 0 2013 6/12/1991 Detroit \n", "77 191 2 2011 3/23/1992 Duke \n", "88 190 7 2006 6/24/1984 Duke \n", "89 200 1 2012 8/1/1992 Duke \n", "90 185 0 2013 8/23/1990 Duke \n", ".. ... ... ... ... ... \n", "457 220 1 2012 3/11/1989 UC Santa Barbara \n", "464 175 4 2009 8/23/1987 UCLA \n", "465 180 0 2013 3/5/1990 UCLA \n", "466 180 12 2001 11/30/1986 UCLA \n", "467 205 4 2009 6/12/1990 UCLA \n", "468 200 2 2011 5/22/1990 UCLA \n", "469 187 5 2008 11/12/1988 UCLA \n", "470 199 12 2001 6/12/1979 UCLA \n", "480 200 14 1999 3/19/1976 Utah \n", "481 190 8 2005 6/21/1983 Utah Valley \n", "485 215 1 2012 3/6/1991 Vanderbilt \n", "487 195 1 2012 5/2/1991 Villanova \n", "488 213 7 2006 9/24/1983 Villanova \n", "489 205 7 2006 3/25/1986 Villanova \n", "491 205 11 2002 9/10/1980 Virginia \n", "493 200 0 2013 7/15/1991 Virginia Commonwealth \n", "494 175 4 2009 6/11/1987 Virginia Commonwealth \n", "498 175 8 2005 5/6/1985 Wake Forest \n", "499 181 4 2009 6/10/1988 Wake Forest \n", "500 175 3 2010 7/5/1988 Wake Forest \n", "503 205 1 2012 4/13/1993 Washington \n", "504 185 0 2013 1/26/1992 Washington \n", "505 185 2 2011 2/7/1989 Washington \n", "506 180 8 2005 5/31/1984 Washington \n", "507 195 1 2012 2/5/1991 Washington \n", "512 195 1 2012 7/15/1990 Weber State \n", "517 185 9 2004 2/1/1983 Western Carolina \n", "520 191 5 2008 3/4/1988 Wichita State \n", "525 192 9 2004 2/27/1983 Wisconsin \n", "527 195 3 2010 10/23/1988 Xavier \n", "\n", " City State (Province, Territory, Etc..) Country \\\n", "2 Jackson, MS Mississippi US \n", "10 Phoenix, AZ Arizona US \n", "11 Seattle, WA Washington US \n", "12 Brea, CA California US \n", "17 Los Angeles, CA California US \n", "19 Chicago, IL Illinois US \n", "20 Chicago, IL Illinois US \n", "23 Little Rock, AR Arkansas US \n", "28 Memphis, TN Tennessee US \n", "30 Pordenone n/a Italy \n", "34 Glens Falls, NY New York US \n", "35 Lexington, KY Kentucky US \n", "38 Los Angeles, CA California US \n", "40 Tavares, FL Florida US \n", "44 New York City, NY New York US \n", "46 Dayton, OH Ohio US \n", "49 Grandview, MO Missouri US \n", "50 Denver, CO Colorado US \n", "53 London, ENG n/a England \n", "62 New York City, NY New York US \n", "63 Merced, CA California US \n", "64 Orange, NJ New Jersey Us \n", "69 Akron, OH Ohio US \n", "71 Toledo, OH Ohio US \n", "74 Detroit, MI Michigan US \n", "75 Detroit, MI Michigan US \n", "77 Melbourne Victoria Australia \n", "88 Cookeville, TN Tennessee US \n", "89 Santa Monica, CA California US \n", "90 Charlotte, NC North Carolina US \n", ".. ... ... ... \n", "457 Monterey, CA California US \n", "464 Rancho Cucamonga, CA California US \n", "465 Encino, CA California US \n", "466 Los Angeles, CA California US \n", "467 Chatsworth, CA California US \n", "468 Riverside, CA California US \n", "469 Long Beach, CA California US \n", "470 Kansas City, KA Kansas US \n", "480 Los Angeles, CA California US \n", "481 Friendswood, Texas Texas US \n", "485 Hendersonville, TN Tennessee US \n", "487 Philadelphia, PA Pennsylvania US \n", "488 Newark, NJ New Jersey US \n", "489 Philadelphia, PA Pennsylvania US \n", "491 Washington, DC DC US \n", "493 Roanoke, VA Virginia US \n", "494 Raeford, NC North Carolina US \n", "498 Forsyth County, NC North Carolina US \n", "499 Indianapolis, IN Indiana US \n", "500 Charlotte, NC North Carolina US \n", "503 Renton, WA Washington US \n", "504 Tacoma, WA Washington US \n", "505 Tacoma, WA Washington US \n", "506 Seattle, WA Washington US \n", "507 Portland, OR Oregon US \n", "512 Oakland, CA California US \n", "517 Zanesville, OH Ohio US \n", "520 Petah Tikva n/a Israel \n", "525 Milwaukee, WI Wisconsin US \n", "527 Detroit, MI Michigan US \n", "\n", " Race HS Only feet millions \n", "2 Black No 6.083333 2.652000 \n", "10 Black No 6.250000 3.135000 \n", "11 Black No 6.166667 5.625313 \n", "12 Black No 6.250000 0.000000 \n", "17 Black No 6.416667 13.701250 \n", "19 Black No 6.083333 0.884293 \n", "20 Black No 6.083333 0.788872 \n", "23 Black No 6.083333 0.884293 \n", "28 Black No 6.250000 0.490180 \n", "30 Black No 6.250000 1.260360 \n", "34 White No 6.166667 2.439840 \n", "35 Black No 6.250000 0.884293 \n", "38 Black No 6.500000 0.825000 \n", "40 Black No 6.416667 0.780871 \n", "44 Black No 6.416667 1.005000 \n", "46 Black No 6.166667 1.129200 \n", "49 Black No 6.500000 2.202000 \n", "50 Black No 6.250000 2.500000 \n", "53 Black No 6.250000 13.200000 \n", "62 Black No 6.083333 2.568360 \n", "63 Black No 6.416667 3.229050 \n", "64 Black No 6.166667 0.000000 \n", "69 Mixed No 6.250000 9.887640 \n", "71 Black No 6.083333 0.788872 \n", "74 Black No 6.250000 1.399507 \n", "75 Black No 6.250000 0.524616 \n", "77 Black No 6.250000 5.607240 \n", "88 White No 6.333333 6.500000 \n", "89 Mixed No 6.333333 2.339040 \n", "90 Black No 6.166667 0.490180 \n", ".. ... ... ... ... \n", "457 Black No 6.416667 0.788872 \n", "464 Black No 6.000000 1.900000 \n", "465 Black No 6.166667 0.000000 \n", "466 Mixed No 6.166667 0.884293 \n", "467 Black No 6.333333 9.713484 \n", "468 Black No 6.416667 0.884293 \n", "469 Black No 6.250000 14.693906 \n", "470 Black No 6.083333 0.884293 \n", "480 Black No 6.166667 5.000000 \n", "481 Black No 6.166667 1.146337 \n", "485 Black No 6.333333 1.258800 \n", "487 Black No 6.083333 0.788872 \n", "488 Black No 6.333333 3.000000 \n", "489 Black No 6.000000 6.210000 \n", "491 Black No 6.416667 0.854389 \n", "493 Black No 6.333333 0.000000 \n", "494 Black No 6.250000 13.000000 \n", "498 Black No 6.000000 18.668431 \n", "499 Black No 6.166667 8.000000 \n", "500 Black No 6.000000 0.951463 \n", "503 Black No 6.500000 1.160040 \n", "504 Black No 6.250000 0.000000 \n", "505 Black No 5.750000 0.884293 \n", "506 Black No 5.750000 2.016000 \n", "507 Black No 6.500000 2.678640 \n", "512 Black No 6.250000 3.202920 \n", "517 Mixed No 6.583333 6.500000 \n", "520 White No 6.250000 0.490180 \n", "525 Black No 6.250000 0.854389 \n", "527 Black No 6.333333 2.162419 \n", "\n", "[175 rows x 19 columns]" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Or only the guards\n", "df[df['POS'] == 'G']" ] }, { "cell_type": "code", "execution_count": 108, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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NameAgeTeamPOS#2013 $Ht (In.)WTEXP1st YearDOBSchoolCityState (Province, Territory, Etc..)CountryRaceHS Onlyfeetmillions
147Williams, Deron29NetsG8$18,466,13075209820056/26/1984IllinoisParkersburg, WVWest VirginiaUSBlackNo6.25000018.466130
203Bryant, Kobe35LakersG24$30,453,80578205720068/23/1978Lower Merion HS (PA)Philadelphia, PAPennsylvaniaUSBlackYes6.50000030.453805
214Wade, Dwyane31HeatG3$18,673,000762201020031/17/1982MarquetteChicago, ILIllinoisUSBlackNo6.33333318.673000
227Rose, Derrick25BullsG1$17,632,688751905200810/4/1988MemphisChicago, ILIllinoisUSBlackNo6.25000017.632688
498Paul, Chris28ClippersG3$18,668,43172175820055/6/1985Wake ForestForsyth County, NCNorth CarolinaUSBlackNo6.00000018.668431
\n", "
" ], "text/plain": [ " Name Age Team POS # 2013 $ Ht (In.) WT EXP \\\n", "147 Williams, Deron 29 Nets G 8 $18,466,130 75 209 8 \n", "203 Bryant, Kobe 35 Lakers G 24 $30,453,805 78 205 7 \n", "214 Wade, Dwyane 31 Heat G 3 $18,673,000 76 220 10 \n", "227 Rose, Derrick 25 Bulls G 1 $17,632,688 75 190 5 \n", "498 Paul, Chris 28 Clippers G 3 $18,668,431 72 175 8 \n", "\n", " 1st Year DOB School City \\\n", "147 2005 6/26/1984 Illinois Parkersburg, WV \n", "203 2006 8/23/1978 Lower Merion HS (PA) Philadelphia, PA \n", "214 2003 1/17/1982 Marquette Chicago, IL \n", "227 2008 10/4/1988 Memphis Chicago, IL \n", "498 2005 5/6/1985 Wake Forest Forsyth County, NC \n", "\n", " State (Province, Territory, Etc..) Country Race HS Only feet \\\n", "147 West Virginia US Black No 6.250000 \n", "203 Pennsylvania US Black Yes 6.500000 \n", "214 Illinois US Black No 6.333333 \n", "227 Illinois US Black No 6.250000 \n", "498 North Carolina US Black No 6.000000 \n", "\n", " millions \n", "147 18.466130 \n", "203 30.453805 \n", "214 18.673000 \n", "227 17.632688 \n", "498 18.668431 " ] }, "execution_count": 108, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Or only the guards who make more than 15 million\n", "df[(df['POS'] == 'G') & (df['millions'] > 15)]" ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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NameAgeTeamPOS#2013 $Ht (In.)WTEXP1st YearDOBSchoolCityState (Province, Territory, Etc..)CountryRaceHS Onlyfeetmillions
147Williams, Deron29NetsG8$18,466,13075209820056/26/1984IllinoisParkersburg, WVWest VirginiaUSBlackNo6.25000018.466130
203Bryant, Kobe35LakersG24$30,453,80578205720068/23/1978Lower Merion HS (PA)Philadelphia, PAPennsylvaniaUSBlackYes6.50000030.453805
214Wade, Dwyane31HeatG3$18,673,000762201020031/17/1982MarquetteChicago, ILIllinoisUSBlackNo6.33333318.673000
227Rose, Derrick25BullsG1$17,632,688751905200810/4/1988MemphisChicago, ILIllinoisUSBlackNo6.25000017.632688
498Paul, Chris28ClippersG3$18,668,43172175820055/6/1985Wake ForestForsyth County, NCNorth CarolinaUSBlackNo6.00000018.668431
\n", "
" ], "text/plain": [ " Name Age Team POS # 2013 $ Ht (In.) WT EXP \\\n", "147 Williams, Deron 29 Nets G 8 $18,466,130 75 209 8 \n", "203 Bryant, Kobe 35 Lakers G 24 $30,453,805 78 205 7 \n", "214 Wade, Dwyane 31 Heat G 3 $18,673,000 76 220 10 \n", "227 Rose, Derrick 25 Bulls G 1 $17,632,688 75 190 5 \n", "498 Paul, Chris 28 Clippers G 3 $18,668,431 72 175 8 \n", "\n", " 1st Year DOB School City \\\n", "147 2005 6/26/1984 Illinois Parkersburg, WV \n", "203 2006 8/23/1978 Lower Merion HS (PA) Philadelphia, PA \n", "214 2003 1/17/1982 Marquette Chicago, IL \n", "227 2008 10/4/1988 Memphis Chicago, IL \n", "498 2005 5/6/1985 Wake Forest Forsyth County, NC \n", "\n", " State (Province, Territory, Etc..) Country Race HS Only feet \\\n", "147 West Virginia US Black No 6.250000 \n", "203 Pennsylvania US Black Yes 6.500000 \n", "214 Illinois US Black No 6.333333 \n", "227 Illinois US Black No 6.250000 \n", "498 North Carolina US Black No 6.000000 \n", "\n", " millions \n", "147 18.466130 \n", "203 30.453805 \n", "214 18.673000 \n", "227 17.632688 \n", "498 18.668431 " ] }, "execution_count": 110, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# It might be easier to break down the booleans into separate variables\n", "is_guard = df['POS'] == 'G'\n", "more_than_fifteen_million = df['millions'] > 15\n", "df[is_guard & more_than_fifteen_million]" ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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NameAgeTeamPOS#2013 $Ht (In.)WTEXP1st YearDOBSchoolCityState (Province, Territory, Etc..)CountryRaceHS Onlyfeetmillions
2Williams, Mo30Trail BlazersG25$2,652,0007319510200312/19/1982AlabamaJackson, MSMississippiUSBlackNo6.0833332.652000
10Bayless, Jerryd25GrizzliesG7$3,135,00075200520088/20/1988ArizonaPhoenix, AZArizonaUSBlackNo6.2500003.135000
11Terry, Jason36NetsG31$5,625,313741801419999/15/1977ArizonaSeattle, WAWashingtonUSBlackNo6.1666675.625313
12Fogg, Kyle23NuggetsG6n/a75183020131/27/1990ArizonaBrea, CACaliforniaUSBlackNo6.2500000.000000
17Harden, James24RocketsG13$13,701,25077220420098/26/1989Arizona StateLos Angeles, CACaliforniaUSBlackNo6.41666713.701250
19Pargo, Jannero33BobcatsG5$884,2937318511200210/22/1979ArkansasChicago, ILIllinoisUSBlackNo6.0833330.884293
20Beverley, Patrick25RocketsG2$788,87273185520087/12/1988ArkansasChicago, ILIllinoisUSBlackNo6.0833330.788872
23Fisher, Derek39ThunderG6$884,293732101719968/9/1974Arkansas-Little RockLittle Rock, ARArkansasUSBlackNo6.0833330.884293
28Clark, Ian22JazzG21$490,18075175020133/7/1991BelmontMemphis, TNTennesseeUSBlackNo6.2500000.490180
30Jackson, Reggie23ThunderG15$1,260,36075208220114/16/1990Boston CollegePordenonen/aItalyBlackNo6.2500001.260360
34Fredette, Jimmer24KingsG7$2,439,84074195220112/25/1989Brigham YoungGlens Falls, NYNew YorkUSWhiteNo6.1666672.439840
35Mack, Shelvin23HawksG8$884,29375215220114/22/1990ButlerLexington, KYKentuckyUSBlackNo6.2500000.884293
40Taylor, Jermaine26CavaliersG8$780,87177204200912/8/1986Central FloridaTavares, FLFloridaUSBlackNo6.4166670.780871
44Stephenson, Lance23PacersG1$1,005,00077228320109/5/1990CincinnatiNew York City, NYNew YorkUSBlackNo6.4166671.005000
46Cole, Norris25HeatG30$1,129,200741752201110/13/1988Cleveland StateDayton, OHOhioUSBlackNo6.1666671.129200
50Billups, Chauncey37PistonsG1$2,500,000752101619979/25/1976ColoradoDenver, COColoradoUSBlackNo6.2500002.500000
53Gordon, Ben30BobcatsG8$13,200,00075200920044/4/1983ConnecticutLondon, ENGn/aEnglandBlackNo6.25000013.200000
62Walker, Kemba23BobcatsG15$2,568,36073184220115/8/1990ConnecticutNew York City, NYNew YorkUSBlackNo6.0833332.568360
63Allen, Ray38HeatG34$3,229,050772051719967/20/1975ConnecticutMerced, CACaliforniaUSBlackNo6.4166673.229050
64Price, A.J.27TimberwolvesG22n/a741854200910/7/1986ConnecticutOrange, NJNew JerseyUsBlackNo6.1666670.000000
65Lamb, Jeremy21ThunderG/F11$2,111,16077180120125/30/1992ConnecticutNorcross, GAGeorgiaUSBlackNo6.4166672.111160
69Curry, Stephen25WarriorsG30$9,887,64075185420093/14/1988DavidsonAkron, OHOhioUSMixedNo6.2500009.887640
71Roberts, Brian27PelicansG22$788,872731731201212/3/1985DaytonToledo, OHOhioUSBlackNo6.0833330.788872
74Green, Willie32ClippersG34$1,399,507752011020037/28/1981DetroitDetroit, MIMichiganUSBlackNo6.2500001.399507
75McCallum, Ray22KingsG3$524,61675190020136/12/1991DetroitDetroit, MIMichiganUSBlackNo6.2500000.524616
77Irving, Kyrie21CavaliersG2$5,607,24075191220113/23/1992DukeMelbourneVictoriaAustraliaBlackNo6.2500005.607240
88Redick, J. J.29ClippersG4$6,500,00076190720066/24/1984DukeCookeville, TNTennesseeUSWhiteNo6.3333336.500000
89Rivers, Austin21PelicansG25$2,339,04076200120128/1/1992DukeSanta Monica, CACaliforniaUSMixedNo6.3333332.339040
90Curry, Seth23WarriorsG3$490,18074185020138/23/1990DukeCharlotte, NCNorth CarolinaUSBlackNo6.1666670.490180
91Henderson, Gerald25BobcatsG/F9$6,000,000772154200912/9/1987DukeCaldwell, NJNew JerseyUSBlackNo6.4166676.000000
............................................................
464Collison, Darren26ClippersG2$1,900,00072175420098/23/1987UCLARancho Cucamonga, CACaliforniaUSBlackNo6.0000001.900000
465Drew, Larry23HeatG0n/a74180020133/5/1990UCLAEncino, CACaliforniaUSBlackNo6.1666670.000000
466Farmar, Jordan26LakersG1$884,2937418012200111/30/1986UCLALos Angeles, CACaliforniaUSMixedNo6.1666670.884293
467Holiday, Jrue23PelicansG11$9,713,48476205420096/12/1990UCLAChatsworth, CACaliforniaUSBlackNo6.3333339.713484
468Lee, Malcolm23SunsG30$884,29377200220115/22/1990UCLARiverside, CACaliforniaUSBlackNo6.4166670.884293
469Westbrook, Russell24ThunderG0$14,693,906751875200811/12/1988UCLALong Beach, CACaliforniaUSBlackNo6.25000014.693906
470Watson, Earl34Trail BlazersG17$884,293731991220016/12/1979UCLAKansas City, KAKansasUSBlackNo6.0833330.884293
471Afflalo, Arron27MagicG/F4$7,750,000772156200710/15/1985UCLALos Angeles, CACaliforniaUSBlackNo6.4166677.750000
480Miller, Andre37NuggetsG24$5,000,000742001419993/19/1976UtahLos Angeles, CACaliforniaUSBlackNo6.1666675.000000
481Price, Ronnie30MagicG10$1,146,33774190820056/21/1983Utah ValleyFriendswood, TexasTexasUSBlackNo6.1666671.146337
482Howard, Ron31PacersG/F19n/a77200020131/14/1982ValparaisoChicago, ILIllinoisUSBlackNo6.4166670.000000
485Jenkins, John22HawksG12$1,258,80076215120123/6/1991VanderbiltHendersonville, TNTennesseeUSBlackNo6.3333331.258800
487Wayns, Maalik22ClippersG5$788,87273195120125/2/1991VillanovaPhiladelphia, PAPennsylvaniaUSBlackNo6.0833330.788872
488Foye, Randy30NuggetsG4$3,000,00076213720069/24/1983VillanovaNewark, NJNew JerseyUSBlackNo6.3333333.000000
489Lowry, Kyle27RaptorsG7$6,210,00072205720063/25/1986VillanovaPhiladelphia, PAPennsylvaniaUSBlackNo6.0000006.210000
491Mason, Jr., Roger33HeatG21$854,389772051120029/10/1980VirginiaWashington, DCDCUSBlackNo6.4166670.854389
493Daniels, Troy22BobcatsG30n/a76200020137/15/1991Virginia CommonwealthRoanoke, VAVirginiaUSBlackNo6.3333330.000000
494Maynor, Eric26WizardsG6$13,000,00075175420096/11/1987Virginia CommonwealthRaeford, NCNorth CarolinaUSBlackNo6.25000013.000000
498Paul, Chris28ClippersG3$18,668,43172175820055/6/1985Wake ForestForsyth County, NCNorth CarolinaUSBlackNo6.00000018.668431
499Teague, Jeff25HawksG0$8,000,00074181420096/10/1988Wake ForestIndianapolis, INIndianaUSBlackNo6.1666678.000000
500Smith, Ish25SunsG30$951,46372175320107/5/1988Wake ForestCharlotte, NCNorth CarolinaUSBlackNo6.0000000.951463
504Gaddy, Abdul21BobcatsG10n/a75185020131/26/1992WashingtonTacoma, WAWashingtonUSBlackNo6.2500000.000000
505Thomas, Isaiah24KingsG22$884,29369185220112/7/1989WashingtonTacoma, WAWashingtonUSBlackNo5.7500000.884293
506Robinson, Nate29NuggetsG10$2,016,00069180820055/31/1984WashingtonSeattle, WAWashingtonUSBlackNo5.7500002.016000
512Lillard, Damian23Trail BlazersG0$3,202,92075195120127/15/1990Weber StateOakland, CACaliforniaUSBlackNo6.2500003.202920
519Lee, Courtney28CelticsG/F11$5,225,000772005200810/3/1985Western KentuckyIndianapolis, INIndianaUSBlackNo6.4166675.225000
520Mekel, Gal25MavericksG33$490,18075191520083/4/1988Wichita StatePetah Tikvan/aIsraelWhiteNo6.2500000.490180
521Murry, Toure'23KnicksG/F23$490,180771950201311/8/1989Wichita StateHouston, TXTexasUSBlackNo6.4166670.490180
525Harris, Devin30MavericksG20$854,38975192920042/27/1983WisconsinMilwaukee, WIWisconsinUSBlackNo6.2500000.854389
527Crawford, Jordan24CelticsG27$2,162,419761953201010/23/1988XavierDetroit, MIMichiganUSBlackNo6.3333332.162419
\n", "

166 rows × 19 columns

\n", "
" ], "text/plain": [ " Name Age Team POS # 2013 $ Ht (In.) \\\n", "2 Williams, Mo 30 Trail Blazers G 25 $2,652,000 73 \n", "10 Bayless, Jerryd 25 Grizzlies G 7 $3,135,000 75 \n", "11 Terry, Jason 36 Nets G 31 $5,625,313 74 \n", "12 Fogg, Kyle 23 Nuggets G 6 n/a 75 \n", "17 Harden, James 24 Rockets G 13 $13,701,250 77 \n", "19 Pargo, Jannero 33 Bobcats G 5 $884,293 73 \n", "20 Beverley, Patrick 25 Rockets G 2 $788,872 73 \n", "23 Fisher, Derek 39 Thunder G 6 $884,293 73 \n", "28 Clark, Ian 22 Jazz G 21 $490,180 75 \n", "30 Jackson, Reggie 23 Thunder G 15 $1,260,360 75 \n", "34 Fredette, Jimmer 24 Kings G 7 $2,439,840 74 \n", "35 Mack, Shelvin 23 Hawks G 8 $884,293 75 \n", "40 Taylor, Jermaine 26 Cavaliers G 8 $780,871 77 \n", "44 Stephenson, Lance 23 Pacers G 1 $1,005,000 77 \n", "46 Cole, Norris 25 Heat G 30 $1,129,200 74 \n", "50 Billups, Chauncey 37 Pistons G 1 $2,500,000 75 \n", "53 Gordon, Ben 30 Bobcats G 8 $13,200,000 75 \n", "62 Walker, Kemba 23 Bobcats G 15 $2,568,360 73 \n", "63 Allen, Ray 38 Heat G 34 $3,229,050 77 \n", "64 Price, A.J. 27 Timberwolves G 22 n/a 74 \n", "65 Lamb, Jeremy 21 Thunder G/F 11 $2,111,160 77 \n", "69 Curry, Stephen 25 Warriors G 30 $9,887,640 75 \n", "71 Roberts, Brian 27 Pelicans G 22 $788,872 73 \n", "74 Green, Willie 32 Clippers G 34 $1,399,507 75 \n", "75 McCallum, Ray 22 Kings G 3 $524,616 75 \n", "77 Irving, Kyrie 21 Cavaliers G 2 $5,607,240 75 \n", "88 Redick, J. J. 29 Clippers G 4 $6,500,000 76 \n", "89 Rivers, Austin 21 Pelicans G 25 $2,339,040 76 \n", "90 Curry, Seth 23 Warriors G 3 $490,180 74 \n", "91 Henderson, Gerald 25 Bobcats G/F 9 $6,000,000 77 \n", ".. ... ... ... ... .. ... ... \n", "464 Collison, Darren 26 Clippers G 2 $1,900,000 72 \n", "465 Drew, Larry 23 Heat G 0 n/a 74 \n", "466 Farmar, Jordan 26 Lakers G 1 $884,293 74 \n", "467 Holiday, Jrue 23 Pelicans G 11 $9,713,484 76 \n", "468 Lee, Malcolm 23 Suns G 30 $884,293 77 \n", "469 Westbrook, Russell 24 Thunder G 0 $14,693,906 75 \n", "470 Watson, Earl 34 Trail Blazers G 17 $884,293 73 \n", "471 Afflalo, Arron 27 Magic G/F 4 $7,750,000 77 \n", "480 Miller, Andre 37 Nuggets G 24 $5,000,000 74 \n", "481 Price, Ronnie 30 Magic G 10 $1,146,337 74 \n", "482 Howard, Ron 31 Pacers G/F 19 n/a 77 \n", "485 Jenkins, John 22 Hawks G 12 $1,258,800 76 \n", "487 Wayns, Maalik 22 Clippers G 5 $788,872 73 \n", "488 Foye, Randy 30 Nuggets G 4 $3,000,000 76 \n", "489 Lowry, Kyle 27 Raptors G 7 $6,210,000 72 \n", "491 Mason, Jr., Roger 33 Heat G 21 $854,389 77 \n", "493 Daniels, Troy 22 Bobcats G 30 n/a 76 \n", "494 Maynor, Eric 26 Wizards G 6 $13,000,000 75 \n", "498 Paul, Chris 28 Clippers G 3 $18,668,431 72 \n", "499 Teague, Jeff 25 Hawks G 0 $8,000,000 74 \n", "500 Smith, Ish 25 Suns G 30 $951,463 72 \n", "504 Gaddy, Abdul 21 Bobcats G 10 n/a 75 \n", "505 Thomas, Isaiah 24 Kings G 22 $884,293 69 \n", "506 Robinson, Nate 29 Nuggets G 10 $2,016,000 69 \n", "512 Lillard, Damian 23 Trail Blazers G 0 $3,202,920 75 \n", "519 Lee, Courtney 28 Celtics G/F 11 $5,225,000 77 \n", "520 Mekel, Gal 25 Mavericks G 33 $490,180 75 \n", "521 Murry, Toure' 23 Knicks G/F 23 $490,180 77 \n", "525 Harris, Devin 30 Mavericks G 20 $854,389 75 \n", "527 Crawford, Jordan 24 Celtics G 27 $2,162,419 76 \n", "\n", " WT EXP 1st Year DOB School \\\n", "2 195 10 2003 12/19/1982 Alabama \n", "10 200 5 2008 8/20/1988 Arizona \n", "11 180 14 1999 9/15/1977 Arizona \n", "12 183 0 2013 1/27/1990 Arizona \n", "17 220 4 2009 8/26/1989 Arizona State \n", "19 185 11 2002 10/22/1979 Arkansas \n", "20 185 5 2008 7/12/1988 Arkansas \n", "23 210 17 1996 8/9/1974 Arkansas-Little Rock \n", "28 175 0 2013 3/7/1991 Belmont \n", "30 208 2 2011 4/16/1990 Boston College \n", "34 195 2 2011 2/25/1989 Brigham Young \n", "35 215 2 2011 4/22/1990 Butler \n", "40 20 4 2009 12/8/1986 Central Florida \n", "44 228 3 2010 9/5/1990 Cincinnati \n", "46 175 2 2011 10/13/1988 Cleveland State \n", "50 210 16 1997 9/25/1976 Colorado \n", "53 200 9 2004 4/4/1983 Connecticut \n", "62 184 2 2011 5/8/1990 Connecticut \n", "63 205 17 1996 7/20/1975 Connecticut \n", "64 185 4 2009 10/7/1986 Connecticut \n", "65 180 1 2012 5/30/1992 Connecticut \n", "69 185 4 2009 3/14/1988 Davidson \n", "71 173 1 2012 12/3/1985 Dayton \n", "74 201 10 2003 7/28/1981 Detroit \n", "75 190 0 2013 6/12/1991 Detroit \n", "77 191 2 2011 3/23/1992 Duke \n", "88 190 7 2006 6/24/1984 Duke \n", "89 200 1 2012 8/1/1992 Duke \n", "90 185 0 2013 8/23/1990 Duke \n", "91 215 4 2009 12/9/1987 Duke \n", ".. ... ... ... ... ... \n", "464 175 4 2009 8/23/1987 UCLA \n", "465 180 0 2013 3/5/1990 UCLA \n", "466 180 12 2001 11/30/1986 UCLA \n", "467 205 4 2009 6/12/1990 UCLA \n", "468 200 2 2011 5/22/1990 UCLA \n", "469 187 5 2008 11/12/1988 UCLA \n", "470 199 12 2001 6/12/1979 UCLA \n", "471 215 6 2007 10/15/1985 UCLA \n", "480 200 14 1999 3/19/1976 Utah \n", "481 190 8 2005 6/21/1983 Utah Valley \n", "482 200 0 2013 1/14/1982 Valparaiso \n", "485 215 1 2012 3/6/1991 Vanderbilt \n", "487 195 1 2012 5/2/1991 Villanova \n", "488 213 7 2006 9/24/1983 Villanova \n", "489 205 7 2006 3/25/1986 Villanova \n", "491 205 11 2002 9/10/1980 Virginia \n", "493 200 0 2013 7/15/1991 Virginia Commonwealth \n", "494 175 4 2009 6/11/1987 Virginia Commonwealth \n", "498 175 8 2005 5/6/1985 Wake Forest \n", "499 181 4 2009 6/10/1988 Wake Forest \n", "500 175 3 2010 7/5/1988 Wake Forest \n", "504 185 0 2013 1/26/1992 Washington \n", "505 185 2 2011 2/7/1989 Washington \n", "506 180 8 2005 5/31/1984 Washington \n", "512 195 1 2012 7/15/1990 Weber State \n", "519 200 5 2008 10/3/1985 Western Kentucky \n", "520 191 5 2008 3/4/1988 Wichita State \n", "521 195 0 2013 11/8/1989 Wichita State \n", "525 192 9 2004 2/27/1983 Wisconsin \n", "527 195 3 2010 10/23/1988 Xavier \n", "\n", " City State (Province, Territory, Etc..) Country \\\n", "2 Jackson, MS Mississippi US \n", "10 Phoenix, AZ Arizona US \n", "11 Seattle, WA Washington US \n", "12 Brea, CA California US \n", "17 Los Angeles, CA California US \n", "19 Chicago, IL Illinois US \n", "20 Chicago, IL Illinois US \n", "23 Little Rock, AR Arkansas US \n", "28 Memphis, TN Tennessee US \n", "30 Pordenone n/a Italy \n", "34 Glens Falls, NY New York US \n", "35 Lexington, KY Kentucky US \n", "40 Tavares, FL Florida US \n", "44 New York City, NY New York US \n", "46 Dayton, OH Ohio US \n", "50 Denver, CO Colorado US \n", "53 London, ENG n/a England \n", "62 New York City, NY New York US \n", "63 Merced, CA California US \n", "64 Orange, NJ New Jersey Us \n", "65 Norcross, GA Georgia US \n", "69 Akron, OH Ohio US \n", "71 Toledo, OH Ohio US \n", "74 Detroit, MI Michigan US \n", "75 Detroit, MI Michigan US \n", "77 Melbourne Victoria Australia \n", "88 Cookeville, TN Tennessee US \n", "89 Santa Monica, CA California US \n", "90 Charlotte, NC North Carolina US \n", "91 Caldwell, NJ New Jersey US \n", ".. ... ... ... \n", "464 Rancho Cucamonga, CA California US \n", "465 Encino, CA California US \n", "466 Los Angeles, CA California US \n", "467 Chatsworth, CA California US \n", "468 Riverside, CA California US \n", "469 Long Beach, CA California US \n", "470 Kansas City, KA Kansas US \n", "471 Los Angeles, CA California US \n", "480 Los Angeles, CA California US \n", "481 Friendswood, Texas Texas US \n", "482 Chicago, IL Illinois US \n", "485 Hendersonville, TN Tennessee US \n", "487 Philadelphia, PA Pennsylvania US \n", "488 Newark, NJ New Jersey US \n", "489 Philadelphia, PA Pennsylvania US \n", "491 Washington, DC DC US \n", "493 Roanoke, VA Virginia US \n", "494 Raeford, NC North Carolina US \n", "498 Forsyth County, NC North Carolina US \n", "499 Indianapolis, IN Indiana US \n", "500 Charlotte, NC North Carolina US \n", "504 Tacoma, WA Washington US \n", "505 Tacoma, WA Washington US \n", "506 Seattle, WA Washington US \n", "512 Oakland, CA California US \n", "519 Indianapolis, IN Indiana US \n", "520 Petah Tikva n/a Israel \n", "521 Houston, TX Texas US \n", "525 Milwaukee, WI Wisconsin US \n", "527 Detroit, MI Michigan US \n", "\n", " Race HS Only feet millions \n", "2 Black No 6.083333 2.652000 \n", "10 Black No 6.250000 3.135000 \n", "11 Black No 6.166667 5.625313 \n", "12 Black No 6.250000 0.000000 \n", "17 Black No 6.416667 13.701250 \n", "19 Black No 6.083333 0.884293 \n", "20 Black No 6.083333 0.788872 \n", "23 Black No 6.083333 0.884293 \n", "28 Black No 6.250000 0.490180 \n", "30 Black No 6.250000 1.260360 \n", "34 White No 6.166667 2.439840 \n", "35 Black No 6.250000 0.884293 \n", "40 Black No 6.416667 0.780871 \n", "44 Black No 6.416667 1.005000 \n", "46 Black No 6.166667 1.129200 \n", "50 Black No 6.250000 2.500000 \n", "53 Black No 6.250000 13.200000 \n", "62 Black No 6.083333 2.568360 \n", "63 Black No 6.416667 3.229050 \n", "64 Black No 6.166667 0.000000 \n", "65 Black No 6.416667 2.111160 \n", "69 Mixed No 6.250000 9.887640 \n", "71 Black No 6.083333 0.788872 \n", "74 Black No 6.250000 1.399507 \n", "75 Black No 6.250000 0.524616 \n", "77 Black No 6.250000 5.607240 \n", "88 White No 6.333333 6.500000 \n", "89 Mixed No 6.333333 2.339040 \n", "90 Black No 6.166667 0.490180 \n", "91 Black No 6.416667 6.000000 \n", ".. ... ... ... ... \n", "464 Black No 6.000000 1.900000 \n", "465 Black No 6.166667 0.000000 \n", "466 Mixed No 6.166667 0.884293 \n", "467 Black No 6.333333 9.713484 \n", "468 Black No 6.416667 0.884293 \n", "469 Black No 6.250000 14.693906 \n", "470 Black No 6.083333 0.884293 \n", "471 Black No 6.416667 7.750000 \n", "480 Black No 6.166667 5.000000 \n", "481 Black No 6.166667 1.146337 \n", "482 Black No 6.416667 0.000000 \n", "485 Black No 6.333333 1.258800 \n", "487 Black No 6.083333 0.788872 \n", "488 Black No 6.333333 3.000000 \n", "489 Black No 6.000000 6.210000 \n", "491 Black No 6.416667 0.854389 \n", "493 Black No 6.333333 0.000000 \n", "494 Black No 6.250000 13.000000 \n", "498 Black No 6.000000 18.668431 \n", "499 Black No 6.166667 8.000000 \n", "500 Black No 6.000000 0.951463 \n", "504 Black No 6.250000 0.000000 \n", "505 Black No 5.750000 0.884293 \n", "506 Black No 5.750000 2.016000 \n", "512 Black No 6.250000 3.202920 \n", "519 Black No 6.416667 5.225000 \n", "520 White No 6.250000 0.490180 \n", "521 Black No 6.416667 0.490180 \n", "525 Black No 6.250000 0.854389 \n", "527 Black No 6.333333 2.162419 \n", "\n", "[166 rows x 19 columns]" ] }, "execution_count": 118, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We can save this stuff\n", "short_players = df[df['feet'] < 6.5]\n", "short_players" ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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AgeHt (In.)WTEXP1st Yearfeetmillions
count166.000000166.000000166.000000166.000000166.000000166.000000166.000000
mean25.93373574.909639193.5301204.1686752008.8313256.2424703.423839
std4.2868871.77805619.0856684.0596144.0596140.1481714.122675
min19.00000069.00000020.0000000.0000001996.0000005.7500000.000000
25%23.00000074.000000185.0000001.0000002006.0000006.1666670.788872
50%25.00000075.000000195.0000003.0000002010.0000006.2500001.595675
75%28.00000076.000000205.0000007.0000002012.0000006.3333334.940940
max39.00000077.000000228.00000017.0000002013.0000006.41666718.673000
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" ], "text/plain": [ " Age Ht (In.) WT EXP 1st Year \\\n", "count 166.000000 166.000000 166.000000 166.000000 166.000000 \n", "mean 25.933735 74.909639 193.530120 4.168675 2008.831325 \n", "std 4.286887 1.778056 19.085668 4.059614 4.059614 \n", "min 19.000000 69.000000 20.000000 0.000000 1996.000000 \n", "25% 23.000000 74.000000 185.000000 1.000000 2006.000000 \n", "50% 25.000000 75.000000 195.000000 3.000000 2010.000000 \n", "75% 28.000000 76.000000 205.000000 7.000000 2012.000000 \n", "max 39.000000 77.000000 228.000000 17.000000 2013.000000 \n", "\n", " feet millions \n", "count 166.000000 166.000000 \n", "mean 6.242470 3.423839 \n", "std 0.148171 4.122675 \n", "min 5.750000 0.000000 \n", "25% 6.166667 0.788872 \n", "50% 6.250000 1.595675 \n", "75% 6.333333 4.940940 \n", "max 6.416667 18.673000 " ] }, "execution_count": 119, "metadata": {}, "output_type": "execute_result" } ], "source": [ "short_players.describe()" ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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AgeHt (In.)WTEXP1st Yearfeetmillions
count362.000000362.000000362.000000362.000000362.000000362.000000362.000000
mean26.38397881.049724233.8977905.0497242007.9502766.7541443.999301
std4.1266741.96443821.4391634.4201464.4201460.1637034.976573
min18.00000078.000000155.0000000.0000001995.0000006.5000000.000000
25%23.00000079.000000220.0000001.0000002005.0000006.5833330.854389
50%26.00000081.000000235.0000004.0000002009.0000006.7500001.750000
75%29.00000083.000000250.0000008.0000002012.0000006.9166675.012720
max39.00000087.000000290.00000018.0000002013.0000007.25000030.453805
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" ], "text/plain": [ " Age Ht (In.) WT EXP 1st Year \\\n", "count 362.000000 362.000000 362.000000 362.000000 362.000000 \n", "mean 26.383978 81.049724 233.897790 5.049724 2007.950276 \n", "std 4.126674 1.964438 21.439163 4.420146 4.420146 \n", "min 18.000000 78.000000 155.000000 0.000000 1995.000000 \n", "25% 23.000000 79.000000 220.000000 1.000000 2005.000000 \n", "50% 26.000000 81.000000 235.000000 4.000000 2009.000000 \n", "75% 29.000000 83.000000 250.000000 8.000000 2012.000000 \n", "max 39.000000 87.000000 290.000000 18.000000 2013.000000 \n", "\n", " feet millions \n", "count 362.000000 362.000000 \n", "mean 6.754144 3.999301 \n", "std 0.163703 4.976573 \n", "min 6.500000 0.000000 \n", "25% 6.583333 0.854389 \n", "50% 6.750000 1.750000 \n", "75% 6.916667 5.012720 \n", "max 7.250000 30.453805 " ] }, "execution_count": 121, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Maybe we can compare them to taller players?\n", "df[df['feet'] >= 6.5].describe()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Drawing pictures\n", "\n", "Okay okay enough code and enough stupid numbers. I'm visual. I want graphics. **Okay?????** Okay." ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 26\n", "1 31\n", "2 30\n", "3 27\n", "4 33\n", "Name: Age, dtype: int64" ] }, "execution_count": 123, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Age'].head()" ] }, { "cell_type": "code", "execution_count": 124, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ImportError", "evalue": "No module named 'matplotlib'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Age'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/Users/soma/.virtualenvs/pandas-intro/lib/python3.4/site-packages/pandas/tools/plotting.py\u001b[0m in \u001b[0;36mhist_series\u001b[0;34m(self, by, ax, grid, xlabelsize, xrot, ylabelsize, yrot, figsize, bins, **kwds)\u001b[0m\n\u001b[1;32m 2941\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2942\u001b[0m \"\"\"\n\u001b[0;32m-> 2943\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2944\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2945\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mby\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mImportError\u001b[0m: No module named 'matplotlib'" ] } ], "source": [ "# This will scream we don't have matplotlib.\n", "df['Age'].hist()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`matplotlib` is a graphing library. It's the Python way to make graphs!" ] }, { "cell_type": "code", "execution_count": 126, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting matplotlib\n", " Using cached matplotlib-1.5.1-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl\n", "Collecting cycler (from matplotlib)\n", " Using cached cycler-0.10.0-py2.py3-none-any.whl\n", "Requirement already satisfied (use --upgrade to upgrade): numpy>=1.6 in /Users/soma/.virtualenvs/pandas-intro/lib/python3.4/site-packages (from matplotlib)\n", "Requirement already satisfied (use --upgrade to upgrade): pytz in /Users/soma/.virtualenvs/pandas-intro/lib/python3.4/site-packages (from matplotlib)\n", "Requirement already satisfied (use --upgrade to upgrade): python-dateutil in /Users/soma/.virtualenvs/pandas-intro/lib/python3.4/site-packages (from matplotlib)\n", "Collecting pyparsing!=2.0.0,!=2.0.4,>=1.5.6 (from matplotlib)\n", " Using cached pyparsing-2.1.4-py2.py3-none-any.whl\n", "Requirement already satisfied (use --upgrade to upgrade): six in /Users/soma/.virtualenvs/pandas-intro/lib/python3.4/site-packages (from cycler->matplotlib)\n", "Installing collected packages: cycler, pyparsing, matplotlib\n", "Successfully installed cycler-0.10.0 matplotlib-1.5.1 pyparsing-2.1.4\n" ] } ], "source": [ "!pip install matplotlib" ] }, { "cell_type": "code", "execution_count": 127, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 127, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# this will open up a weird window that won't do anything\n", "df['Age'].hist()" ] }, { "cell_type": "code", "execution_count": 128, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# So instead you run this code\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 129, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 129, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df['Age'].hist()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But that's ugly. There's a thing called ``ggplot`` for R that looks nice. We want to look nice. We want to look like ``ggplot``." ] }, { "cell_type": "code", "execution_count": 130, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['grayscale',\n", " 'seaborn-muted',\n", " 'seaborn-paper',\n", " 'classic',\n", " 'seaborn-notebook',\n", " 'seaborn-white',\n", " 'seaborn-pastel',\n", " 'fivethirtyeight',\n", " 'seaborn-dark-palette',\n", " 'seaborn-ticks',\n", " 'seaborn-poster',\n", " 'seaborn-talk',\n", " 'seaborn-whitegrid',\n", " 'seaborn-deep',\n", " 'ggplot',\n", " 'dark_background',\n", " 'seaborn-bright',\n", " 'bmh',\n", " 'seaborn-darkgrid',\n", " 'seaborn-dark',\n", " 'seaborn-colorblind']" ] }, "execution_count": 130, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import matplotlib.pyplot as plt\n", "plt.style.available" ] }, { "cell_type": "code", "execution_count": 131, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.style.use('ggplot')" ] }, { "cell_type": "code", "execution_count": 132, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 132, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df['Age'].hist()" ] }, { "cell_type": "code", "execution_count": 133, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 133, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.style.use('seaborn-deep')\n", "df['Age'].hist()" ] }, { "cell_type": "code", "execution_count": 134, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 134, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.style.use('fivethirtyeight')\n", "df['Age'].hist()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That might look better with a little more customization. So let's customize it." ] }, { "cell_type": "code", "execution_count": 143, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 143, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Pass in all sorts of stuff!\n", "# Most from http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.hist.html\n", "# .range() is a matplotlib thing\n", "df['Age'].hist(bins=20, xlabelsize=10, ylabelsize=10, range=(0,40))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I want more graphics! **Do tall people make more money?!?!**" ] }, { "cell_type": "code", "execution_count": 149, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 149, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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RtYOTrU48/eEx/8+rvjcaY9JCU6MV0Rwrah5gMAaNKHk/ohI6++8iGxMgDQs1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\no15ILLYuH1YPsZzWLt+QY+l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NameAgeTeamPOS#2013 $Ht (In.)WTEXP1st YearDOBSchoolCityState (Province, Territory, Etc..)CountryRaceHS Onlyfeetmillions
0Gee, Alonzo26CavaliersF33$3,250,00078219420095/29/1987AlabamaRiviera Beach, FLFloridaUSBlackNo6.5000003.250000
1Wallace, Gerald31CelticsF45$10,105,855792201220017/23/1982AlabamaSylacauga, ALAlabamaUSBlackNo6.58333310.105855
2Williams, Mo30Trail BlazersG25$2,652,0007319510200312/19/1982AlabamaJackson, MSMississippiUSBlackNo6.0833332.652000
3Gladness, Mickell27MagicC40$762,19583220220117/26/1986Alabama A&MBirmingham, ALAlabamaUSBlackNo6.9166670.762195
4Jefferson, Richard33JazzF44$11,046,000792301220016/21/1980ArizonaLos Angeles, CACaliforniaUSBlackNo6.58333311.046000
\n", "
" ], "text/plain": [ " 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 feet \\\n", "0 Florida US Black No 6.500000 \n", "1 Alabama US Black No 6.583333 \n", "2 Mississippi US Black No 6.083333 \n", "3 Alabama US Black No 6.916667 \n", "4 California US Black No 6.583333 \n", "\n", " millions \n", "0 3.250000 \n", "1 10.105855 \n", "2 2.652000 \n", "3 0.762195 \n", "4 11.046000 " ] }, "execution_count": 150, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 152, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 152, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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NxUCRZPratDgyU+QhgUgOJj7SHJEbssjyXyLnFemXVGtytFo7HRQ4hzVHOzkV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\nUF1drXJ02uOxW7HhYHI1L4/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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# How does experience relate with the amount of money they're making?\n", "df.plot(kind='scatter', x='EXP', y='millions')" ] }, { "cell_type": "code", "execution_count": 153, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 153, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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yTavV4sYbb8RHH310RQdIREQ0FoZVEN9++23Mnj0bX/jCFyQ3xgeZTKaot2QQ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\n7Nq1C3l5eaivr8fcuXPFPja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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# At least we can assume height and weight are related\n", "df.plot(kind='scatter', x='WT', y='feet')" ] }, { "cell_type": "code", "execution_count": 157, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 157, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# At least we can assume height and weight are related\n", "# http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html\n", "df.plot(kind='scatter', x='WT', y='feet', xlim=(100,300), ylim=(5.5, 8))" ] }, { "cell_type": "code", "execution_count": 160, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.style.use('ggplot')" ] }, { "cell_type": "code", "execution_count": 161, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 161, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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2WNSIiMg2WNSIiMg2WNSIiMg2WNSIiMg2WNSIiMg2WNSIiMg2WNSI\niMg2WNSIiMg2/j8yoPiptsQ8ZwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.plot(kind='scatter', x='WT', y='feet', xlim=(100,300), ylim=(5.5, 8))" ] }, { "cell_type": "code", "execution_count": 177, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(5.5, 8)" ] }, "execution_count": 177, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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uKmlG5V/G1hY6CZttHhTFDqvVrXGfJ9HuFGy2i6AoYsb9oER+qKMj+zpF8XbR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\nvPXWW1ixYgUAYMWKFThw4AB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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# We can also use plt separately\n", "# It's SIMILAR but TOTALLY DIFFERENT\n", "centers = df[df['POS'] == 'C']\n", "guards = df[df['POS'] == 'G']\n", "forwards = df[df['POS'] == 'F']\n", "plt.scatter(y=centers[\"feet\"], x=centers[\"WT\"], c='c', alpha=0.75, marker='x')\n", "plt.scatter(y=guards[\"feet\"], x=guards[\"WT\"], c='y', alpha=0.75, marker='o')\n", "plt.scatter(y=forwards[\"feet\"], x=forwards[\"WT\"], c='m', alpha=0.75, marker='v')\n", "plt.xlim(100,300)\n", "plt.ylim(5.5,8)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.2" } }, "nbformat": 4, "nbformat_minor": 0 }