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import pandas as pd
s=pd.read_csv("C:/Users/nEW u/Documents/NBA2.csv")print(s) Name Overall Weight_lbs Tier Speed Midrange \
0 Luke Babbitt 76.0 225.0 Silver 69.0 83.0
1 Kent Bazemore 78.0 201.0 Gold 77.0 73.0
2 Marco Belinelli 76.0 210.0 Silver 78.0 77.0
3 Marco Belinelli 79.0 210.0 Gold 78.0 84.0
4 Deandre' Bembry 66.0 205.0 Bronze 86.0 56.0
5 Nicolas Brussino 68.0 216.0 Bronze 80.0 65.0
6 John Collins 73.0 235.0 Silver 80.0 42.0
7 John Collins 76.0 235.0 Silver 80.0 42.0
8 Dewayne Dedmon 77.0 255.0 Gold 73.0 43.0
9 Malcolm Delaney 74.0 190.0 Silver 83.0 79.0
10 Malcolm Delaney 77.0 190.0 Gold 83.0 84.0
11 Tyler Dorsey 73.0 185.0 Silver 84.0 68.0
12 Mike Dunleavy 72.0 230.0 Silver 75.0 74.0
13 Ersan Ilyasova 76.0 235.0 Silver 69.0 75.0
14 Ersan Ilyasova 79.0 235.0 Gold 69.0 84.0
15 Mike Muscala 72.0 239.0 Silver 61.0 69.0
16 Miles Plumlee 68.0 255.0 Bronze 74.0 45.0
17 Taurean Prince 71.0 220.0 Silver 84.0 68.0
18 Dennis Schroder 79.0 165.0 Gold 84.0 77.0
19 Dennis Schroder 79.0 165.0 Gold 84.0 77.0
20 Dennis Schroder 81.0 165.0 Gold 86.0 79.0
21 Jaylen Brown 74.0 225.0 Silver 88.0 72.0
22 Jaylen Brown 77.0 225.0 Gold 88.0 80.0
23 Gordon Hayward 81.0 220.0 Gold 81.0 79.0
24 Al Horford 79.0 245.0 Gold 70.0 71.0
25 Al Horford 79.0 245.0 Gold 70.0 71.0
26 Al Horford 81.0 245.0 Gold 72.0 73.0
27 Kyrie Irving 85.0 191.0 Elite 87.0 81.0
28 Marcus Morris 77.0 235.0 Gold 78.0 82.0
29 Abdel Nader 71.0 230.0 Silver 67.0 67.0
.. ... ... ... ... ... ...
698 Tiago Splitter 70.0 245.0 Bronze 68.0 45.0
699 Jarrod Uthoff 69.0 221.0 Bronze 79.0 68.0
700 Sasha Vujacic 70.0 193.0 Bronze 79.0 73.0
701 Dwyane Wade 82.0 220.0 Gold 82.0 74.0
702 Dwyane Wade 82.0 220.0 Gold 82.0 79.0
703 Dwyane Wade 82.0 220.0 Gold 82.0 75.0
704 Elliot Williams 68.0 180.0 Bronze 83.0 62.0
705 James Young 68.0 215.0 Bronze 82.0 68.0
706 Muggsy Bogues 89.0 136.0 Elite 95.0 69.0
707 Larry Johnson 90.0 250.0 Elite 84.0 81.0
708 Alonzo Mourning 88.0 240.0 Elite 72.0 41.0
709 Glen Rice 89.0 228.0 Elite 90.0 95.0
710 Jason Kidd 87.0 212.0 Elite 91.0 76.0
711 Alex Enlgish 88.0 190.0 Elite 88.0 75.0
712 Dikembe Mutumbo 86.0 245.0 Elite 79.0 42.0
713 David Thompson 87.0 195.0 Elite 87.0 86.0
714 Jalen Rose 89.0 210.0 Elite 85.0 89.0
715 Rik Smits 87.0 250.0 Elite 71.0 44.0
716 Magic Johnson 90.0 220.0 Elite 83.0 78.0
717 Tom Chambers 88.0 220.0 Elite 82.0 78.0
718 Jeff Hornacek 90.0 190.0 Elite 85.0 91.0
719 Kevin Johnson 88.0 180.0 Elite 94.0 71.0
720 Larry Nance 88.0 205.0 Elite 81.0 46.0
721 Steve Nash 92.0 178.0 Elite 94.0 93.0
722 Amar'e Stoudemire 89.0 245.0 Elite 84.0 67.0
723 Gary Payton 90.0 190.0 Elite 90.0 80.0
724 Manute Bol 87.0 200.0 Elite 69.0 61.0
725 Elvin Hayes 88.0 235.0 Elite 82.0 52.0
726 Bernard King 88.0 235.0 Elite 81.0 74.0
727 Wes Unseld 87.0 245.0 Elite 64.0 45.0
Finishing Three_Pt Dribble Post_scoring Rebound Defense \
0 74.0 86.0 39.0 70.0 74.0 70.0
1 79.0 74.0 72.0 49.0 72.0 78.0
2 71.0 77.0 75.0 50.0 70.0 68.0
3 71.0 84.0 75.0 51.0 70.0 68.0
4 70.0 60.0 78.0 48.0 73.0 60.0
5 61.0 62.0 77.0 66.0 70.0 60.0
6 68.0 28.0 40.0 70.0 79.0 75.0
7 69.0 28.0 40.0 70.0 84.0 82.0
8 79.0 25.0 38.0 68.0 80.0 86.0
9 69.0 60.0 78.0 62.0 69.0 64.0
10 75.0 60.0 86.0 62.0 69.0 64.0
11 68.0 72.0 68.0 55.0 72.0 82.0
12 74.0 80.0 69.0 56.0 72.0 71.0
13 76.0 75.0 53.0 69.0 74.0 71.0
14 76.0 85.0 53.0 70.0 78.0 71.0
15 75.0 60.0 40.0 69.0 69.0 71.0
16 84.0 28.0 52.0 72.0 77.0 66.0
17 72.0 57.0 66.0 56.0 75.0 75.0
18 74.0 71.0 79.0 47.0 70.0 77.0
19 74.0 71.0 79.0 47.0 70.0 77.0
20 76.0 73.0 81.0 47.0 71.0 79.0
21 82.0 60.0 71.0 72.0 76.0 76.0
22 84.0 61.0 81.0 73.0 77.0 76.0
23 83.0 82.0 81.0 67.0 75.0 76.0
24 78.0 71.0 56.0 83.0 77.0 72.0
25 78.0 71.0 56.0 83.0 77.0 72.0
26 80.0 72.0 56.0 85.0 78.0 74.0
27 86.0 79.0 90.0 64.0 70.0 71.0
28 80.0 75.0 65.0 81.0 74.0 63.0
29 66.0 67.0 67.0 67.0 69.0 67.0
.. ... ... ... ... ... ...
698 75.0 29.0 42.0 68.0 68.0 73.0
699 69.0 42.0 67.0 70.0 70.0 66.0
700 64.0 57.0 71.0 56.0 69.0 64.0
701 86.0 66.0 89.0 72.0 73.0 88.0
702 92.0 66.0 88.0 73.0 73.0 80.0
703 92.0 67.0 91.0 72.0 73.0 80.0
704 71.0 68.0 78.0 49.0 66.0 57.0
705 70.0 57.0 72.0 55.0 71.0 61.0
706 69.0 69.0 95.0 53.0 62.0 93.0
707 93.0 76.0 74.0 92.0 89.0 76.0
708 94.0 37.0 40.0 87.0 86.0 95.0
709 91.0 95.0 79.0 77.0 76.0 83.0
710 78.0 69.0 95.0 49.0 67.0 81.0
711 91.0 77.0 90.0 86.0 79.0 80.0
712 84.0 41.0 30.0 81.0 87.0 97.0
713 96.0 78.0 95.0 78.0 81.0 80.0
714 87.0 86.0 73.0 84.0 87.0 81.0
715 92.0 39.0 30.0 79.0 86.0 95.0
716 90.0 75.0 89.0 88.0 81.0 83.0
717 89.0 78.0 50.0 86.0 90.0 76.0
718 76.0 93.0 82.0 62.0 72.0 75.0
719 77.0 75.0 95.0 51.0 70.0 93.0
720 95.0 43.0 70.0 87.0 90.0 96.0
721 81.0 88.0 96.0 58.0 59.0 58.0
722 90.0 40.0 60.0 82.0 89.0 94.0
723 81.0 80.0 86.0 58.0 62.0 90.0
724 93.0 68.0 39.0 81.0 84.0 98.0
725 92.0 41.0 73.0 90.0 89.0 93.0
726 93.0 71.0 91.0 88.0 85.0 76.0
727 90.0 35.0 37.0 88.0 85.0 90.0
Team Position
0 Atlanta Hawks PF
1 Atlanta Hawks SG
2 Atlanta Hawks SG
3 Atlanta Hawks SG
4 Atlanta Hawks SG
5 Atlanta Hawks SF
6 Atlanta Hawks C
7 Atlanta Hawks C
8 Atlanta Hawks C
9 Atlanta Hawks PG
10 Atlanta Hawks PG
11 Atlanta Hawks SG
12 Atlanta Hawks SF
13 Atlanta Hawks PF
14 Atlanta Hawks PF
15 Atlanta Hawks C
16 Atlanta Hawks C
17 Atlanta Hawks SF
18 Atlanta Hawks PG
19 Atlanta Hawks PG
20 Atlanta Hawks PG
21 Boston Celtics SF
22 Boston Celtics SF
23 Boston Celtics SF
24 Boston Celtics C
25 Boston Celtics C
26 Boston Celtics C
27 Boston Celtics PG
28 Boston Celtics SF
29 Boston Celtics SF
.. ... ...
698 Free Agents C
699 Free Agents PF
700 Free Agents SG
701 Free Agents SG
702 Free Agents SG
703 Free Agents SG
704 Free Agents SG
705 Free Agents SG
706 Charlotte Hornets PG
707 Charolotte Hornets PF
708 Charlotte Hornets C
709 Charlotte Hornets SF
710 Dallas Mavericks PG
711 Denver Nuggets SF
712 Denver Nuggets C
713 Denver Nuggets SF
714 Indiana Pacers SF
715 Indiana Pacers C
716 Los Angeles Lakers PG
717 Phoenix Suns PF
718 Phoenix Suns SG
719 Phoenix Suns PG
720 Phoenix Suns PF
721 Phoenix Suns PG
722 Phoenix Suns C
723 Seattle Supersonics PG
724 Washington Bullets C
725 Washington Bullets PF
726 Washington Bullets SF
727 Washington Bullets C
[728 rows x 14 columns]
pandas.core.frame.DataFrames.loc[:,'Defense']0 70.0
1 78.0
2 68.0
3 68.0
4 60.0
5 60.0
6 75.0
7 82.0
8 86.0
9 64.0
10 64.0
11 82.0
12 71.0
13 71.0
14 71.0
15 71.0
16 66.0
17 75.0
18 77.0
19 77.0
20 79.0
21 76.0
22 76.0
23 76.0
24 72.0
25 72.0
26 74.0
27 71.0
28 63.0
29 67.0
...
698 73.0
699 66.0
700 64.0
701 88.0
702 80.0
703 80.0
704 57.0
705 61.0
706 93.0
707 76.0
708 95.0
709 83.0
710 81.0
711 80.0
712 97.0
713 80.0
714 81.0
715 95.0
716 83.0
717 76.0
718 75.0
719 93.0
720 96.0
721 58.0
722 94.0
723 90.0
724 98.0
725 93.0
726 76.0
727 90.0
Name: Defense, Length: 728, dtype: float64import jovian as j
j.commit()[jovian] Saving notebook..