{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Functions & Methods\n", "## Dataframe/Series.head() method\n", "**Example #1**" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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NameTeamNumberPositionAgeHeightWeightCollegeSalary
0Avery BradleyBoston Celtics0.0PG25.06-2180.0Texas7730337.0
1Jae CrowderBoston Celtics99.0SF25.06-6235.0Marquette6796117.0
2John HollandBoston Celtics30.0SG27.06-5205.0Boston UniversityNaN
3R.J. HunterBoston Celtics28.0SG22.06-5185.0Georgia State1148640.0
4Jonas JerebkoBoston Celtics8.0PF29.06-10231.0NaN5000000.0
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" ], "text/plain": [ " Name Team Number Position Age Height Weight \\\n", "0 Avery Bradley Boston Celtics 0.0 PG 25.0 6-2 180.0 \n", "1 Jae Crowder Boston Celtics 99.0 SF 25.0 6-6 235.0 \n", "2 John Holland Boston Celtics 30.0 SG 27.0 6-5 205.0 \n", "3 R.J. Hunter Boston Celtics 28.0 SG 22.0 6-5 185.0 \n", "4 Jonas Jerebko Boston Celtics 8.0 PF 29.0 6-10 231.0 \n", "\n", " College Salary \n", "0 Texas 7730337.0 \n", "1 Marquette 6796117.0 \n", "2 Boston University NaN \n", "3 Georgia State 1148640.0 \n", "4 NaN 5000000.0 " ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# importing pandas module \n", "import pandas as pd \n", " \n", "# making data frame \n", "data = pd.read_csv(\"https://media.geeksforgeeks.org/wp-content/uploads/nba.csv\") \n", " \n", "# calling head() method \n", "# storing in new variable \n", "data_top = data.head() \n", " \n", "# display \n", "data_top " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Example #2**" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 Avery Bradley\n", "1 Jae Crowder\n", "2 John Holland\n", "3 R.J. Hunter\n", "4 Jonas Jerebko\n", "5 Amir Johnson\n", "6 Jordan Mickey\n", "7 Kelly Olynyk\n", "8 Terry Rozier\n", "Name: Name, dtype: object" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# importing pandas module \n", "import pandas as pd \n", " \n", "# making data frame \n", "data = pd.read_csv(\"https://media.geeksforgeeks.org/wp-content/uploads/nba.csv\") \n", " \n", "# number of rows to return \n", "n = 9\n", " \n", "# creating series \n", "series = data[\"Name\"] \n", " \n", "# returning top n rows \n", "top = series.head(n = n) \n", " \n", "# display \n", "top " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dataframe/Series.describe() method\n", "**Example #1**" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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NameTeamNumberPositionAgeHeightWeightCollegeSalary
count364364364.000000364364.000000364364.0000003643.640000e+02
unique36430NaN5NaN17NaN115NaN
topCharlie VillanuevaNew Orleans PelicansNaNSGNaN6-9NaNKentuckyNaN
freq116NaN87NaN49NaN22NaN
meanNaNNaN16.829670NaN26.615385NaN219.785714NaN4.620311e+06
stdNaNNaN14.994162NaN4.233591NaN24.793099NaN5.119716e+06
minNaNNaN0.000000NaN19.000000NaN161.000000NaN5.572200e+04
20%NaNNaN4.000000NaN23.000000NaN195.000000NaN9.472760e+05
40%NaNNaN9.000000NaN25.000000NaN212.000000NaN1.638754e+06
50%NaNNaN12.000000NaN26.000000NaN220.000000NaN2.515440e+06
60%NaNNaN17.000000NaN27.000000NaN228.000000NaN3.429934e+06
80%NaNNaN30.000000NaN30.000000NaN242.400000NaN7.838202e+06
maxNaNNaN99.000000NaN40.000000NaN279.000000NaN2.287500e+07
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" ], "text/plain": [ " Name Team Number Position \\\n", "count 364 364 364.000000 364 \n", "unique 364 30 NaN 5 \n", "top Charlie Villanueva New Orleans Pelicans NaN SG \n", "freq 1 16 NaN 87 \n", "mean NaN NaN 16.829670 NaN \n", "std NaN NaN 14.994162 NaN \n", "min NaN NaN 0.000000 NaN \n", "20% NaN NaN 4.000000 NaN \n", "40% NaN NaN 9.000000 NaN \n", "50% NaN NaN 12.000000 NaN \n", "60% NaN NaN 17.000000 NaN \n", "80% NaN NaN 30.000000 NaN \n", "max NaN NaN 99.000000 NaN \n", "\n", " Age Height Weight College Salary \n", "count 364.000000 364 364.000000 364 3.640000e+02 \n", "unique NaN 17 NaN 115 NaN \n", "top NaN 6-9 NaN Kentucky NaN \n", "freq NaN 49 NaN 22 NaN \n", "mean 26.615385 NaN 219.785714 NaN 4.620311e+06 \n", "std 4.233591 NaN 24.793099 NaN 5.119716e+06 \n", "min 19.000000 NaN 161.000000 NaN 5.572200e+04 \n", "20% 23.000000 NaN 195.000000 NaN 9.472760e+05 \n", "40% 25.000000 NaN 212.000000 NaN 1.638754e+06 \n", "50% 26.000000 NaN 220.000000 NaN 2.515440e+06 \n", "60% 27.000000 NaN 228.000000 NaN 3.429934e+06 \n", "80% 30.000000 NaN 242.400000 NaN 7.838202e+06 \n", "max 40.000000 NaN 279.000000 NaN 2.287500e+07 " ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# importing pandas module \n", "import pandas as pd \n", " \n", "# importing regex module \n", "import re \n", " \n", "# making data frame \n", "data = pd.read_csv(\"https://media.geeksforgeeks.org/wp-content/uploads/nba.csv\") \n", " \n", "# removing null values to avoid errors \n", "data.dropna(inplace = True) \n", " \n", "# percentile list \n", "perc =[.20, .40, .60, .80] \n", " \n", "# list of dtypes to include \n", "include =['object', 'float', 'int'] \n", " \n", "# calling describe method \n", "desc = data.describe(percentiles = perc, include = include) \n", " \n", "# display \n", "desc " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Example #2**" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 364\n", "unique 364\n", "top Charlie Villanueva\n", "freq 1\n", "Name: Name, dtype: object" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# importing pandas module \n", "import pandas as pd \n", " \n", "# importing regex module \n", "import re \n", " \n", "# making data frame \n", "data = pd.read_csv(\"https://media.geeksforgeeks.org/wp-content/uploads/nba.csv\") \n", " \n", "# removing null values to avoid errors \n", "data.dropna(inplace = True) \n", " \n", "# calling describe method \n", "desc = data[\"Name\"].describe() \n", " \n", "# display \n", "desc " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dataframe/Series.loc[] method\n", "**Example #1**" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Team Boston Celtics\n", "Number 0\n", "Position PG\n", "Age 25\n", "Height 6-2\n", "Weight 180\n", "College Texas\n", "Salary 7.73034e+06\n", "Name: Avery Bradley, dtype: object \n", "\n", "\n", " Team Boston Celtics\n", "Number 28\n", "Position SG\n", "Age 22\n", "Height 6-5\n", "Weight 185\n", "College Georgia State\n", "Salary 1.14864e+06\n", "Name: R.J. Hunter, dtype: object\n" ] } ], "source": [ "# importing pandas package \n", "import pandas as pd \n", " \n", "# making data frame from csv file \n", "data = pd.read_csv(\"nba.csv\", index_col =\"Name\") \n", " \n", "# retrieving row by loc method \n", "first = data.loc[\"Avery Bradley\"] \n", "second = data.loc[\"R.J. Hunter\"] \n", " \n", "print(first, \"\\n\\n\\n\", second) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Example #2**" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "text/html": [ "
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TeamNumberPositionAgeHeightWeightCollegeSalary
Name
Avery BradleyBoston Celtics0.0PG25.06-2180.0Texas7730337.0
R.J. HunterBoston Celtics28.0SG22.06-5185.0Georgia State1148640.0
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" ], "text/plain": [ " Team Number Position Age Height Weight \\\n", "Name \n", "Avery Bradley Boston Celtics 0.0 PG 25.0 6-2 180.0 \n", "R.J. Hunter Boston Celtics 28.0 SG 22.0 6-5 185.0 \n", "\n", " College Salary \n", "Name \n", "Avery Bradley Texas 7730337.0 \n", "R.J. Hunter Georgia State 1148640.0 " ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# importing pandas package \n", "import pandas as pd \n", " \n", "# making data frame from csv file \n", "data = pd.read_csv(\"nba.csv\", index_col =\"Name\") \n", " \n", "# retrieving rows by loc method \n", "rows = data.loc[[\"Avery Bradley\", \"R.J. Hunter\"]] \n", " \n", "# checking data type of rows \n", "print(type(rows)) \n", " \n", "# display \n", "rows " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dataframe/Series.iloc[] method\n", "**Example #1**" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Name True\n", "Team True\n", "Number True\n", "Position True\n", "Age True\n", "Height True\n", "Weight True\n", "College True\n", "Salary True\n", "Name: 3, dtype: bool" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# importing pandas package \n", "import pandas as pd \n", " \n", "# making data frame from csv file \n", "data = pd.read_csv(\"nba.csv\") \n", " \n", "# retrieving rows by loc method \n", "row1 = data.loc[3] \n", " \n", "# retrieving rows by iloc method \n", "row2 = data.iloc[3] \n", " \n", "# checking if values are equal \n", "row1 == row2 " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Example #2**" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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NameTeamNumberPositionAgeHeightWeightCollegeSalary
4TrueTrueTrueTrueTrueTrueTrueFalseTrue
5TrueTrueTrueTrueTrueTrueTrueFalseTrue
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" ], "text/plain": [ " Name Team Number Position Age Height Weight College Salary\n", "4 True True True True True True True False True\n", "5 True True True True True True True False True\n", "6 True True True True True True True True True\n", "7 True True True True True True True True True" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# importing pandas package \n", "import pandas as pd \n", " \n", "# making data frame from csv file \n", "data = pd.read_csv(\"nba.csv\") \n", " \n", "# retrieving rows by loc method \n", "row1 = data.iloc[[4, 5, 6, 7]] \n", " \n", "# retrieving rows by loc method \n", "row2 = data.iloc[4:8] \n", " \n", "# comparing values \n", "row1 == row2 " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pandas.read_csv() method\n", "**Example #1**" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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NameTeamNumberPositionAgeHeightWeightCollegeSalary
0Avery BradleyBoston Celtics0.0PG25.06-2180.0Texas7730337.0
1Jae CrowderBoston Celtics99.0SF25.06-6235.0Marquette6796117.0
2John HollandBoston Celtics30.0SG27.06-5205.0Boston UniversityNaN
3R.J. HunterBoston Celtics28.0SG22.06-5185.0Georgia State1148640.0
4Jonas JerebkoBoston Celtics8.0PF29.06-10231.0NaN5000000.0
5Amir JohnsonBoston Celtics90.0PF29.06-9240.0NaN12000000.0
6Jordan MickeyBoston Celtics55.0PF21.06-8235.0LSU1170960.0
7Kelly OlynykBoston Celtics41.0C25.07-0238.0Gonzaga2165160.0
8Terry RozierBoston Celtics12.0PG22.06-2190.0Louisville1824360.0
9Marcus SmartBoston Celtics36.0PG22.06-4220.0Oklahoma State3431040.0
10Jared SullingerBoston Celtics7.0C24.06-9260.0Ohio State2569260.0
11Isaiah ThomasBoston Celtics4.0PG27.05-9185.0Washington6912869.0
12Evan TurnerBoston Celtics11.0SG27.06-7220.0Ohio State3425510.0
13James YoungBoston Celtics13.0SG20.06-6215.0Kentucky1749840.0
14Tyler ZellerBoston Celtics44.0C26.07-0253.0North Carolina2616975.0
15Bojan BogdanovicBrooklyn Nets44.0SG27.06-8216.0NaN3425510.0
16Markel BrownBrooklyn Nets22.0SG24.06-3190.0Oklahoma State845059.0
17Wayne EllingtonBrooklyn Nets21.0SG28.06-4200.0North Carolina1500000.0
18Rondae Hollis-JeffersonBrooklyn Nets24.0SG21.06-7220.0Arizona1335480.0
19Jarrett JackBrooklyn Nets2.0PG32.06-3200.0Georgia Tech6300000.0
20Sergey KarasevBrooklyn Nets10.0SG22.06-7208.0NaN1599840.0
21Sean KilpatrickBrooklyn Nets6.0SG26.06-4219.0Cincinnati134215.0
22Shane LarkinBrooklyn Nets0.0PG23.05-11175.0Miami (FL)1500000.0
23Brook LopezBrooklyn Nets11.0C28.07-0275.0Stanford19689000.0
24Chris McCulloughBrooklyn Nets1.0PF21.06-11200.0Syracuse1140240.0
25Willie ReedBrooklyn Nets33.0PF26.06-10220.0Saint Louis947276.0
26Thomas RobinsonBrooklyn Nets41.0PF25.06-10237.0Kansas981348.0
27Henry SimsBrooklyn Nets14.0C26.06-10248.0Georgetown947276.0
28Donald SloanBrooklyn Nets15.0PG28.06-3205.0Texas A&M947276.0
29Thaddeus YoungBrooklyn Nets30.0PF27.06-8221.0Georgia Tech11235955.0
..............................
428Al-Farouq AminuPortland Trail Blazers8.0SF25.06-9215.0Wake Forest8042895.0
429Pat ConnaughtonPortland Trail Blazers5.0SG23.06-5206.0Notre Dame625093.0
430Allen CrabbePortland Trail Blazers23.0SG24.06-6210.0California947276.0
431Ed DavisPortland Trail Blazers17.0C27.06-10240.0North Carolina6980802.0
432Maurice HarklessPortland Trail Blazers4.0SF23.06-9215.0St. John's2894059.0
433Gerald HendersonPortland Trail Blazers9.0SG28.06-5215.0Duke6000000.0
434Chris KamanPortland Trail Blazers35.0C34.07-0265.0Central Michigan5016000.0
435Meyers LeonardPortland Trail Blazers11.0PF24.07-1245.0Illinois3075880.0
436Damian LillardPortland Trail Blazers0.0PG25.06-3195.0Weber State4236287.0
437C.J. McCollumPortland Trail Blazers3.0SG24.06-4200.0Lehigh2525160.0
438Luis MonteroPortland Trail Blazers44.0SG23.06-7185.0Westchester CC525093.0
439Mason PlumleePortland Trail Blazers24.0C26.06-11235.0Duke1415520.0
440Brian RobertsPortland Trail Blazers2.0PG30.06-1173.0Dayton2854940.0
441Noah VonlehPortland Trail Blazers21.0PF20.06-9240.0Indiana2637720.0
442Trevor BookerUtah Jazz33.0PF28.06-8228.0Clemson4775000.0
443Trey BurkeUtah Jazz3.0PG23.06-1191.0Michigan2658240.0
444Alec BurksUtah Jazz10.0SG24.06-6214.0Colorado9463484.0
445Dante ExumUtah Jazz11.0PG20.06-6190.0NaN3777720.0
446Derrick FavorsUtah Jazz15.0PF24.06-10265.0Georgia Tech12000000.0
447Rudy GobertUtah Jazz27.0C23.07-1245.0NaN1175880.0
448Gordon HaywardUtah Jazz20.0SF26.06-8226.0Butler15409570.0
449Rodney HoodUtah Jazz5.0SG23.06-8206.0Duke1348440.0
450Joe InglesUtah Jazz2.0SF28.06-8226.0NaN2050000.0
451Chris JohnsonUtah Jazz23.0SF26.06-6206.0Dayton981348.0
452Trey LylesUtah Jazz41.0PF20.06-10234.0Kentucky2239800.0
453Shelvin MackUtah Jazz8.0PG26.06-3203.0Butler2433333.0
454Raul NetoUtah Jazz25.0PG24.06-1179.0NaN900000.0
455Tibor PleissUtah Jazz21.0C26.07-3256.0NaN2900000.0
456Jeff WitheyUtah Jazz24.0C26.07-0231.0Kansas947276.0
457NaNNaNNaNNaNNaNNaNNaNNaNNaN
\n", "

458 rows × 9 columns

\n", "
" ], "text/plain": [ " Name Team Number Position Age \\\n", "0 Avery Bradley Boston Celtics 0.0 PG 25.0 \n", "1 Jae Crowder Boston Celtics 99.0 SF 25.0 \n", "2 John Holland Boston Celtics 30.0 SG 27.0 \n", "3 R.J. Hunter Boston Celtics 28.0 SG 22.0 \n", "4 Jonas Jerebko Boston Celtics 8.0 PF 29.0 \n", "5 Amir Johnson Boston Celtics 90.0 PF 29.0 \n", "6 Jordan Mickey Boston Celtics 55.0 PF 21.0 \n", "7 Kelly Olynyk Boston Celtics 41.0 C 25.0 \n", "8 Terry Rozier Boston Celtics 12.0 PG 22.0 \n", "9 Marcus Smart Boston Celtics 36.0 PG 22.0 \n", "10 Jared Sullinger Boston Celtics 7.0 C 24.0 \n", "11 Isaiah Thomas Boston Celtics 4.0 PG 27.0 \n", "12 Evan Turner Boston Celtics 11.0 SG 27.0 \n", "13 James Young Boston Celtics 13.0 SG 20.0 \n", "14 Tyler Zeller Boston Celtics 44.0 C 26.0 \n", "15 Bojan Bogdanovic Brooklyn Nets 44.0 SG 27.0 \n", "16 Markel Brown Brooklyn Nets 22.0 SG 24.0 \n", "17 Wayne Ellington Brooklyn Nets 21.0 SG 28.0 \n", "18 Rondae Hollis-Jefferson Brooklyn Nets 24.0 SG 21.0 \n", "19 Jarrett Jack Brooklyn Nets 2.0 PG 32.0 \n", "20 Sergey Karasev Brooklyn Nets 10.0 SG 22.0 \n", "21 Sean Kilpatrick Brooklyn Nets 6.0 SG 26.0 \n", "22 Shane Larkin Brooklyn Nets 0.0 PG 23.0 \n", "23 Brook Lopez Brooklyn Nets 11.0 C 28.0 \n", "24 Chris McCullough Brooklyn Nets 1.0 PF 21.0 \n", "25 Willie Reed Brooklyn Nets 33.0 PF 26.0 \n", "26 Thomas Robinson Brooklyn Nets 41.0 PF 25.0 \n", "27 Henry Sims Brooklyn Nets 14.0 C 26.0 \n", "28 Donald Sloan Brooklyn Nets 15.0 PG 28.0 \n", "29 Thaddeus Young Brooklyn Nets 30.0 PF 27.0 \n", ".. ... ... ... ... ... \n", "428 Al-Farouq Aminu Portland Trail Blazers 8.0 SF 25.0 \n", "429 Pat Connaughton Portland Trail Blazers 5.0 SG 23.0 \n", "430 Allen Crabbe Portland Trail Blazers 23.0 SG 24.0 \n", "431 Ed Davis Portland Trail Blazers 17.0 C 27.0 \n", "432 Maurice Harkless Portland Trail Blazers 4.0 SF 23.0 \n", "433 Gerald Henderson Portland Trail Blazers 9.0 SG 28.0 \n", "434 Chris Kaman Portland Trail Blazers 35.0 C 34.0 \n", "435 Meyers Leonard Portland Trail Blazers 11.0 PF 24.0 \n", "436 Damian Lillard Portland Trail Blazers 0.0 PG 25.0 \n", "437 C.J. McCollum Portland Trail Blazers 3.0 SG 24.0 \n", "438 Luis Montero Portland Trail Blazers 44.0 SG 23.0 \n", "439 Mason Plumlee Portland Trail Blazers 24.0 C 26.0 \n", "440 Brian Roberts Portland Trail Blazers 2.0 PG 30.0 \n", "441 Noah Vonleh Portland Trail Blazers 21.0 PF 20.0 \n", "442 Trevor Booker Utah Jazz 33.0 PF 28.0 \n", "443 Trey Burke Utah Jazz 3.0 PG 23.0 \n", "444 Alec Burks Utah Jazz 10.0 SG 24.0 \n", "445 Dante Exum Utah Jazz 11.0 PG 20.0 \n", "446 Derrick Favors Utah Jazz 15.0 PF 24.0 \n", "447 Rudy Gobert Utah Jazz 27.0 C 23.0 \n", "448 Gordon Hayward Utah Jazz 20.0 SF 26.0 \n", "449 Rodney Hood Utah Jazz 5.0 SG 23.0 \n", "450 Joe Ingles Utah Jazz 2.0 SF 28.0 \n", "451 Chris Johnson Utah Jazz 23.0 SF 26.0 \n", "452 Trey Lyles Utah Jazz 41.0 PF 20.0 \n", "453 Shelvin Mack Utah Jazz 8.0 PG 26.0 \n", "454 Raul Neto Utah Jazz 25.0 PG 24.0 \n", "455 Tibor Pleiss Utah Jazz 21.0 C 26.0 \n", "456 Jeff Withey Utah Jazz 24.0 C 26.0 \n", "457 NaN NaN NaN NaN NaN \n", "\n", " Height Weight College Salary \n", "0 6-2 180.0 Texas 7730337.0 \n", "1 6-6 235.0 Marquette 6796117.0 \n", "2 6-5 205.0 Boston University NaN \n", "3 6-5 185.0 Georgia State 1148640.0 \n", "4 6-10 231.0 NaN 5000000.0 \n", "5 6-9 240.0 NaN 12000000.0 \n", "6 6-8 235.0 LSU 1170960.0 \n", "7 7-0 238.0 Gonzaga 2165160.0 \n", "8 6-2 190.0 Louisville 1824360.0 \n", "9 6-4 220.0 Oklahoma State 3431040.0 \n", "10 6-9 260.0 Ohio State 2569260.0 \n", "11 5-9 185.0 Washington 6912869.0 \n", "12 6-7 220.0 Ohio State 3425510.0 \n", "13 6-6 215.0 Kentucky 1749840.0 \n", "14 7-0 253.0 North Carolina 2616975.0 \n", "15 6-8 216.0 NaN 3425510.0 \n", "16 6-3 190.0 Oklahoma State 845059.0 \n", "17 6-4 200.0 North Carolina 1500000.0 \n", "18 6-7 220.0 Arizona 1335480.0 \n", "19 6-3 200.0 Georgia Tech 6300000.0 \n", "20 6-7 208.0 NaN 1599840.0 \n", "21 6-4 219.0 Cincinnati 134215.0 \n", "22 5-11 175.0 Miami (FL) 1500000.0 \n", "23 7-0 275.0 Stanford 19689000.0 \n", "24 6-11 200.0 Syracuse 1140240.0 \n", "25 6-10 220.0 Saint Louis 947276.0 \n", "26 6-10 237.0 Kansas 981348.0 \n", "27 6-10 248.0 Georgetown 947276.0 \n", "28 6-3 205.0 Texas A&M 947276.0 \n", "29 6-8 221.0 Georgia Tech 11235955.0 \n", ".. ... ... ... ... \n", "428 6-9 215.0 Wake Forest 8042895.0 \n", "429 6-5 206.0 Notre Dame 625093.0 \n", "430 6-6 210.0 California 947276.0 \n", "431 6-10 240.0 North Carolina 6980802.0 \n", "432 6-9 215.0 St. John's 2894059.0 \n", "433 6-5 215.0 Duke 6000000.0 \n", "434 7-0 265.0 Central Michigan 5016000.0 \n", "435 7-1 245.0 Illinois 3075880.0 \n", "436 6-3 195.0 Weber State 4236287.0 \n", "437 6-4 200.0 Lehigh 2525160.0 \n", "438 6-7 185.0 Westchester CC 525093.0 \n", "439 6-11 235.0 Duke 1415520.0 \n", "440 6-1 173.0 Dayton 2854940.0 \n", "441 6-9 240.0 Indiana 2637720.0 \n", "442 6-8 228.0 Clemson 4775000.0 \n", "443 6-1 191.0 Michigan 2658240.0 \n", "444 6-6 214.0 Colorado 9463484.0 \n", "445 6-6 190.0 NaN 3777720.0 \n", "446 6-10 265.0 Georgia Tech 12000000.0 \n", "447 7-1 245.0 NaN 1175880.0 \n", "448 6-8 226.0 Butler 15409570.0 \n", "449 6-8 206.0 Duke 1348440.0 \n", "450 6-8 226.0 NaN 2050000.0 \n", "451 6-6 206.0 Dayton 981348.0 \n", "452 6-10 234.0 Kentucky 2239800.0 \n", "453 6-3 203.0 Butler 2433333.0 \n", "454 6-1 179.0 NaN 900000.0 \n", "455 7-3 256.0 NaN 2900000.0 \n", "456 7-0 231.0 Kansas 947276.0 \n", "457 NaN NaN NaN NaN \n", "\n", "[458 rows x 9 columns]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Import pandas \n", "import pandas as pd \n", " \n", "# reading csv file \n", "pd.read_csv(\"nba.csv\") " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Example #2**" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PokemonType
0CharmeleonFire
1CharizardFire
2SquirtleWater
3WartortleWater
4BlastoiseWater
5CaterpieBug
6MetapodBug
7ButterfreeBug
8WeedleBug
9KakunaBug
10BeedrillBug
11PidgeyNormal
12PidgeottoNormal
13PidgeotNormal
14RattataNormal
15RaticateNormal
16SpearowNormal
17FearowNormal
18EkansPoison
19ArbokPoison
20PikachuElectric
21RaichuElectric
22SandshrewGround
23SandslashGround
24NidoranPoison
25NidorinaPoison
26NidoqueenPoison
27Nidoran♂Poison
28NidorinoPoison
29NidokingPoison
.........
687ClauncherWater
688ClawitzerWater
689HelioptileElectric
690HelioliskElectric
691TyruntRock
692TyrantrumRock
693AmauraRock
694AurorusRock
695SylveonFairy
696HawluchaFighting
697DedenneElectric
698CarbinkRock
699GoomyDragon
700SliggooDragon
701GoodraDragon
702KlefkiSteel
703PhantumpGhost
704TrevenantGhost
705PumpkabooGhost
706GourgeistGhost
707BergmiteIce
708AvaluggIce
709NoibatFlying
710NoivernFlying
711XerneasFairy
712YveltalDark
713ZygardeDragon
714DiancieRock
715HoopaPsychic
716VolcanionFire
\n", "

717 rows × 2 columns

\n", "
" ], "text/plain": [ " Pokemon Type\n", "0 Charmeleon Fire\n", "1 Charizard Fire\n", "2 Squirtle Water\n", "3 Wartortle Water\n", "4 Blastoise Water\n", "5 Caterpie Bug\n", "6 Metapod Bug\n", "7 Butterfree Bug\n", "8 Weedle Bug\n", "9 Kakuna Bug\n", "10 Beedrill Bug\n", "11 Pidgey Normal\n", "12 Pidgeotto Normal\n", "13 Pidgeot Normal\n", "14 Rattata Normal\n", "15 Raticate Normal\n", "16 Spearow Normal\n", "17 Fearow Normal\n", "18 Ekans Poison\n", "19 Arbok Poison\n", "20 Pikachu Electric\n", "21 Raichu Electric\n", "22 Sandshrew Ground\n", "23 Sandslash Ground\n", "24 Nidoran Poison\n", "25 Nidorina Poison\n", "26 Nidoqueen Poison\n", "27 Nidoran♂ Poison\n", "28 Nidorino Poison\n", "29 Nidoking Poison\n", ".. ... ...\n", "687 Clauncher Water\n", "688 Clawitzer Water\n", "689 Helioptile Electric\n", "690 Heliolisk Electric\n", "691 Tyrunt Rock\n", "692 Tyrantrum Rock\n", "693 Amaura Rock\n", "694 Aurorus Rock\n", "695 Sylveon Fairy\n", "696 Hawlucha Fighting\n", "697 Dedenne Electric\n", "698 Carbink Rock\n", "699 Goomy Dragon\n", "700 Sliggoo Dragon\n", "701 Goodra Dragon\n", "702 Klefki Steel\n", "703 Phantump Ghost\n", "704 Trevenant Ghost\n", "705 Pumpkaboo Ghost\n", "706 Gourgeist Ghost\n", "707 Bergmite Ice\n", "708 Avalugg Ice\n", "709 Noibat Flying\n", "710 Noivern Flying\n", "711 Xerneas Fairy\n", "712 Yveltal Dark\n", "713 Zygarde Dragon\n", "714 Diancie Rock\n", "715 Hoopa Psychic\n", "716 Volcanion Fire\n", "\n", "[717 rows x 2 columns]" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# importing Pandas library \n", "import pandas as pd \n", " \n", "pd.read_csv(filepath_or_buffer = \"pokemon.csv\") \n", " \n", "# makes the passed rows header \n", "pd.read_csv(\"pokemon.csv\", header =[1, 2]) \n", " \n", "# make the passed column as index instead of 0, 1, 2, 3.... \n", "pd.read_csv(\"pokemon.csv\", index_col ='Type') \n", " \n", "# uses passed cols only for data frame \n", "pd.read_csv(\"pokemon.csv\", usecols =[\"Type\"]) \n", " \n", "# reutruns pandas series if there is only one colunmn \n", "pd.read_csv(\"pokemon.csv\", usecols =[\"Type\"], squeeze = True) \n", " \n", "# skips the passed rows in new series \n", "pd.read_csv(\"pokemon.csv\", skiprows = [1, 2, 3, 4]) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The data which is being used in the above examples are stored in two fies:**\n", "* nba.csv\n", "* pokemon.csv
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**However, you can choose data as per your choice**\n", "\n", "## Again you can change the code and play with it as much as you want !!!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 4 }