Depending on your needs, you may want to add a few items into your plays dataframe. Doing so can help navigate your data more efficiently in the long run.
A simple data clean can be used with pyjanitor. Where we can remove all empty columns (if any exist).
match_id video_file_number video_time code team player_number player_name player_id skill evaluation_code setter_position attack_code set_code set_type start_zone end_zone end_subzone num_players_numeric \
4 017366f2-d6b1-4bed-ab7f-0d1dcc7b4097 1 494 *19SM+~~~78A~~~00 University of Louisville 19 Shannon Shields -296094 Serve + 1 NaN NaN NaN 7 8 A NaN
5 017366f2-d6b1-4bed-ab7f-0d1dcc7b4097 1 495 a02RM-~~~58AM~~00B University of Dayton 2 Maura Collins -230138 Reception - 6 NaN NaN NaN 5 8 A NaN
6 017366f2-d6b1-4bed-ab7f-0d1dcc7b4097 1 497 a08ET#~~~~8C~~~00 University of Dayton 8 Brooke Westbeld -232525 Set # 6 NaN NaN ~ NaN 8 C NaN
7 017366f2-d6b1-4bed-ab7f-0d1dcc7b4097 1 499 a10AT-X5~46CH2~00F University of Dayton 10 Jamie Peterson -11802 Attack - 6 X5 NaN NaN 4 6 C 2
8 017366f2-d6b1-4bed-ab7f-0d1dcc7b4097 1 499 *11BT+~~~~2C~~~00 University of Louisville 11 Anna Stevenson -278838 Block + 1 NaN NaN NaN NaN 2 C NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1476 017366f2-d6b1-4bed-ab7f-0d1dcc7b4097 1 5217 *08EH#~~~~9D~~~+6 University of Louisville 8 Lexi Hamilton -75970 Set # 4 NaN NaN ~ NaN 9 D NaN
1477 017366f2-d6b1-4bed-ab7f-0d1dcc7b4097 1 5218 *10AH=V5~44BH2~+6F University of Louisville 10 Mel McHenry -75967 Attack = 4 V5 NaN NaN 4 4 B 2
1478 017366f2-d6b1-4bed-ab7f-0d1dcc7b4097 1 5219 ap24:19 University of Dayton NaN NaN NaN Point NaN 3 NaN NaN NaN NaN NaN NaN NaN
1484 017366f2-d6b1-4bed-ab7f-0d1dcc7b4097 1 5252 a18SM=~~~71B~~~-5 University of Dayton 18 Grace Dynda -282421 Serve = 2 NaN NaN NaN 7 1 B NaN
1485 017366f2-d6b1-4bed-ab7f-0d1dcc7b4097 1 5253 *p25:19 University of Louisville NaN NaN NaN Point NaN 4 NaN NaN NaN NaN NaN NaN NaN
home_team_score visiting_team_score home_setter_position visiting_setter_position custom_code home_p1 home_p2 home_p3 home_p4 home_p5 home_p6 visiting_p1 visiting_p2 visiting_p3 visiting_p4 visiting_p5 visiting_p6 start_coordinate mid_coordinate end_coordinate point_phase attack_phase \
4 1 0 1 6 00 19 9 11 15 10 7 1 16 17 10 6 8 431 <NA> 7642 Serve nan
5 1 0 1 6 00B 19 9 11 15 10 7 1 16 17 10 6 8 431 <NA> 7642 Reception nan
6 1 0 1 6 00 19 9 11 15 10 7 1 16 17 10 6 8 3147 <NA> <NA> Reception nan
7 1 0 1 6 00F 19 9 11 15 10 7 1 16 17 10 6 8 4512 5522 8150 Reception Reception
8 1 0 1 6 00 19 9 11 15 10 7 1 16 17 10 6 8 4578 <NA> <NA> Serve nan
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1476 24 19 4 3 +6 17 10 7 19 9 11 10 15 8 1 16 3 2469 <NA> <NA> Serve nan
1477 24 19 4 3 +6F 17 10 7 19 9 11 10 15 8 1 16 3 4211 <NA> 5270 Serve BP-Transition
1478 24 19 4 3 None 17 10 7 19 9 11 10 15 8 1 16 3 4830 <NA> <NA> Reception nan
1484 25 19 4 2 -5 17 10 7 19 9 11 18 8 1 16 3 10 337 <NA> 8812 Serve nan
1485 25 19 4 2 None 17 10 7 19 9 11 18 8 1 16 3 10 1288 <NA> <NA> Reception nan
start_coordinate_x start_coordinate_y mid_coordinate_x mid_coordinate_y end_coordinate_x end_coordinate_y set_number home_team visiting_team home_team_id visiting_team_id point_won_by serving_team receiving_team rally_number \
4 1.26875 0.092596 <NA> <NA> 1.68125 5.425924 1 University of Louisville University of Dayton 17 42 University of Louisville University of Louisville University of Dayton 1
5 1.26875 0.092596 <NA> <NA> 1.68125 5.425924 1 University of Louisville University of Dayton 17 42 University of Louisville University of Louisville University of Dayton 1
6 1.86875 2.092594 <NA> <NA> <NA> <NA> 1 University of Louisville University of Dayton 17 42 University of Louisville University of Louisville University of Dayton 1
7 0.55625 3.12963 0.93125 3.87037 1.98125 5.796294 1 University of Louisville University of Dayton 17 42 University of Louisville University of Louisville University of Dayton 1
8 3.03125 3.12963 <NA> <NA> <NA> <NA> 1 University of Louisville University of Dayton 17 42 University of Louisville University of Louisville University of Dayton 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1476 2.69375 1.574076 <NA> <NA> <NA> <NA> 3 University of Louisville University of Dayton 17 42 University of Dayton University of Louisville University of Dayton 43
1477 0.51875 2.907408 <NA> <NA> 2.73125 3.648148 3 University of Louisville University of Dayton 17 42 University of Dayton University of Louisville University of Dayton 43
1478 1.23125 3.351852 <NA> <NA> <NA> <NA> 3 University of Louisville University of Dayton 17 42 University of Dayton University of Louisville University of Dayton 43
1484 1.49375 0.018522 <NA> <NA> 0.55625 6.314812 3 University of Louisville University of Dayton 17 42 University of Louisville University of Dayton University of Louisville 44
1485 3.40625 0.685188 <NA> <NA> <NA> <NA> 3 University of Louisville University of Dayton 17 42 University of Louisville University of Dayton University of Louisville 44
possesion_number
4 0
5 1
6 1
7 1
8 2
... ...
1476 2
1477 2
1478 3
1484 0
1485 1
[928 rows x 56 columns]
Perhaps you might want to change the match_id to the filename of the dvw.
match_id video_file_number video_time code team player_number player_name player_id skill evaluation_code setter_position attack_code set_code set_type start_zone end_zone end_subzone num_players_numeric home_team_score visiting_team_score \
4 example_data 1 494 *19SM+~~~78A~~~00 University of Louisville 19 Shannon Shields -296094 Serve + 1 NaN NaN NaN 7 8 A NaN 1 0
5 example_data 1 495 a02RM-~~~58AM~~00B University of Dayton 2 Maura Collins -230138 Reception - 6 NaN NaN NaN 5 8 A NaN 1 0
6 example_data 1 497 a08ET#~~~~8C~~~00 University of Dayton 8 Brooke Westbeld -232525 Set # 6 NaN NaN ~ NaN 8 C NaN 1 0
7 example_data 1 499 a10AT-X5~46CH2~00F University of Dayton 10 Jamie Peterson -11802 Attack - 6 X5 NaN NaN 4 6 C 2 1 0
8 example_data 1 499 *11BT+~~~~2C~~~00 University of Louisville 11 Anna Stevenson -278838 Block + 1 NaN NaN NaN NaN 2 C NaN 1 0
home_setter_position visiting_setter_position custom_code home_p1 home_p2 home_p3 home_p4 home_p5 home_p6 visiting_p1 visiting_p2 visiting_p3 visiting_p4 visiting_p5 visiting_p6 start_coordinate mid_coordinate end_coordinate point_phase attack_phase start_coordinate_x start_coordinate_y \
4 1 6 00 19 9 11 15 10 7 1 16 17 10 6 8 431 <NA> 7642 Serve nan 1.26875 0.092596
5 1 6 00B 19 9 11 15 10 7 1 16 17 10 6 8 431 <NA> 7642 Reception nan 1.26875 0.092596
6 1 6 00 19 9 11 15 10 7 1 16 17 10 6 8 3147 <NA> <NA> Reception nan 1.86875 2.092594
7 1 6 00F 19 9 11 15 10 7 1 16 17 10 6 8 4512 5522 8150 Reception Reception 0.55625 3.12963
8 1 6 00 19 9 11 15 10 7 1 16 17 10 6 8 4578 <NA> <NA> Serve nan 3.03125 3.12963
mid_coordinate_x mid_coordinate_y end_coordinate_x end_coordinate_y set_number home_team visiting_team home_team_id visiting_team_id point_won_by serving_team receiving_team rally_number possesion_number
4 <NA> <NA> 1.68125 5.425924 1 University of Louisville University of Dayton 17 42 University of Louisville University of Louisville University of Dayton 1 0
5 <NA> <NA> 1.68125 5.425924 1 University of Louisville University of Dayton 17 42 University of Louisville University of Louisville University of Dayton 1 1
6 <NA> <NA> <NA> <NA> 1 University of Louisville University of Dayton 17 42 University of Louisville University of Louisville University of Dayton 1 1
7 0.93125 3.87037 1.98125 5.796294 1 University of Louisville University of Dayton 17 42 University of Louisville University of Louisville University of Dayton 1 1
8 <NA> <NA> <NA> <NA> 1 University of Louisville University of Dayton 17 42 University of Louisville University of Louisville University of Dayton 1 2
Any data cleaning taken place can prove useful long term. Perhaps there is a nested folder which contains the week of the season, the league, the conference, maybe the file has the correct date. Parsing additional data into your dataset will give more tools in your data journey.