Can help how to write Python code to answer those questions?

1. In below two datasets "Athletes" (contains details about the participating Athletes) and "Coaches" (details about coaches, countries and disciplines along with event), how many athletics (Discipline = "Athletics") where the NOC of athletics and coaches are different.

Data Set: Athletes

Name	NOC	Discipline
AALERUD Katrine	Norway	Cycling Road
ABAD Nestor	Spain	Artistic Gymnastics
ABAGNALE Giovanni	Italy	Rowing
ABALDE Alberto	Spain	Basketball
ABALDE Tamara	Spain	Basketball
ABALO Luc	France	Handball
ABAROA Cesar	Chile	Rowing
ABASS Abobakr	Sudan	Swimming
ABBASALI Hamideh	Islamic Republic of Iran	Karate
ABBASOV Islam	Azerbaijan	Wrestling
ABBINGH Lois	Netherlands	Handball
ABBOT Emily	Australia	Rhythmic Gymnastics
ABBOTT Monica	United States of America	Baseball/Softball
ABDALLA Abubaker Haydar	Qatar	Athletics
ABDALLA Maryam	Egypt	Artistic Swimming
ABDALLAH Shahd	Egypt	Artistic Swimming
ABDALRASOOL Mohamed	Sudan	Judo
ABDEL LATIF Radwa	Egypt	Shooting
ABDEL RAZEK Samy	Egypt	Shooting

Data Set: Coaches

Name	NOC	Discipline
ABDELMAGID Wael	Egypt	Football
ABE Junya	Japan	Volleyball
ABE Katsuhiko	Japan	Basketball
ADAMA Cherif	Cte d'Ivoire	Football
AGEBA Yuya	Japan	Volleyball
AIKMAN Siegfried Gottlieb	Japan	Hockey
AL SAADI Kais	Germany	Hockey
ALAMEDA Lonni	Canada	Baseball/Softball
ALEKNO Vladimir	Islamic Republic of Iran	Volleyball
ALEKSEEV Alexey	ROC	Handball
ALLER CARBALLO Manuel Angel	Spain	Basketball
ALSHEHRI Saad	Saudi Arabia	Football
ALY Kamal	Egypt	Football
AMAYA GAITAN Fabian	Puerto Rico	Basketball
AMO AGUADO Pablo	Spain	Football
ANDONOVSKI Vlatko	United States of America	Football
ANNAN Alyson	Netherlands	Hockey
ARNAU CREUS Xavier	Japan	Hockey
ARNOLD Graham	Australia	Football

2. In below data set "EntriesGender" (details about the Discipline and the number of females and males participating), please find the top 5 disciplines having highest proportions of females.

Data Set: EntriesGende

Discipline	Female	Male	Total
3x3 Basketball	32	32	64
Archery	64	64	128
Artistic Gymnastics	98	98	196
Artistic Swimming	105	0	105
Athletics	969	1072	2041
Badminton	86	87	173
Baseball/Softball	90	144	234
Basketball	144	144	288
Beach Volleyball	48	48	96
Boxing	102	187	289
Canoe Slalom	41	41	82
Canoe Sprint	123	126	249
Cycling BMX Freestyle	10	9	19
Cycling BMX Racing	24	24	48
Cycling Mountain Bike	38	38	76
Cycling Road	70	131	201
Cycling Track	90	99	189
Diving	72	71	143
Equestrian	73	125	198

3. In below data set "Medals" (contains the Medals and Scoreboard of countries that participated in Tokyo Olympics), please draw a stacked bar chart, medals no. against countries, having appropriate colors for various medal types, ordering countries by rank in the dataset. Show top 30 countries only.

Data Set: Medals

Rank	Team/NOC	Gold	Silver	Bronze	Total	Rank by Total
1	United States of America	39	41	33	113	1
2	People's Republic of China	38	32	18	88	2
3	Japan	27	14	17	58	5
4	Great Britain	22	21	22	65	4
5	ROC	20	28	23	71	3
6	Australia	17	7	22	46	6
7	Netherlands	10	12	14	36	9
8	France	10	12	11	33	10
9	Germany	10	11	16	37	8
10	Italy	10	10	20	40	7
11	Canada	7	6	11	24	11
12	Brazil	7	6	8	21	12
13	New Zealand	7	6	7	20	13
14	Cuba	7	3	5	15	18
15	Hungary	6	7	7	20	13
16	Republic of Korea	6	4	10	20	13
17	Poland	4	5	5	14	19
18	Czech Republic	4	4	3	11	23
19	Kenya	4	4	2	10	25
20	Norway	4	2	2	8	29
21	Jamaica	4	1	4	9	26
22	Spain	3	8	6	17	17
23	Sweden	3	6	0	9	26
24	Switzerland	3	4	6	13	20
25	Denmark	3	4	4	11	23
26	Croatia	3	3	2	8	29
27	Islamic Republic of Iran	3	2	2	7	33
28	Serbia	3	1	5	9	26
29	Belgium	3	1	3	7	33
30	Bulgaria	3	1	2	6	39
31	Slovenia	3	1	1	5	42
32	Uzbekistan	3	0	2	5	42
33	Georgia	2	5	1	8	29
34	Chinese Taipei	2	4	6	12	22
35	Turkey	2	2	9	13	20
36	Greece	2	1	1	4	47
36	Uganda	2	1	1	4	47
38	Ecuador	2	1	0	3	60
39	Ireland	2	0	2	4	47
39	Israel	2	0	2	4	47
41	Qatar	2	0	1	3	60
42	Bahamas	2	0	0	2	66
42	Kosovo	2	0	0	2	66
44	Ukraine	1	6	12	19	16
45	Belarus	1	3	3	7	33
46	Romania	1	3	0	4	47
46	Venezuela	1	3	0	4	47
48	India	1	2	4	7	33
49	Hong Kong, China	1	2	3	6	39
50	Philippines	1	2	1	4	47
50	Slovakia	1	2	1	4	47
52	South Africa	1	2	0	3	60
53	Austria	1	1	5	7	33
54	Egypt	1	1	4	6	39
55	Indonesia	1	1	3	5	42
56	Ethiopia	1	1	2	4	47
56	Portugal	1	1	2	4	47
58	Tunisia	1	1	0	2	66
59	Estonia	1	0	1	2	66
59	Fiji	1	0	1	2	66
59	Latvia	1	0	1	2	66
59	Thailand	1	0	1	2	66
63	Bermuda	1	0	0	1	77
63	Morocco	1	0	0	1	77
63	Puerto Rico	1	0	0	1	77
66	Colombia	0	4	1	5	42
67	Azerbaijan	0	3	4	7	33
68	Dominican Republic	0	3	2	5	42
69	Armenia	0	2	2	4	47
70	Kyrgyzstan	0	2	1	3	60
71	Mongolia	0	1	3	4	47
72	Argentina	0	1	2	3	60
72	San Marino	0	1	2	3	60
74	Jordan	0	1	1	2	66
74	Malaysia	0	1	1	2	66
74	Nigeria	0	1	1	2	66
77	Bahrain	0	1	0	1	77
77	Saudi Arabia	0	1	0	1	77
77	Lithuania	0	1	0	1	77
77	North Macedonia	0	1	0	1	77
77	Namibia	0	1	0	1	77
77	Turkmenistan	0	1	0	1	77
83	Kazakhstan	0	0	8	8	29
84	Mexico	0	0	4	4	47
85	Finland	0	0	2	2	66
86	Botswana	0	0	1	1	77
86	Burkina Faso	0	0	1	1	77
86	Cte d'Ivoire	0	0	1	1	77
86	Ghana	0	0	1	1	77
86	Grenada	0	0	1	1	77
86	Kuwait	0	0	1	1	77
86	Republic of Moldova	0	0	1	1	77
86	Syrian Arab Republic	0	0	1	1	77

4. In below data set "Teams" (details about the Teams, discipline, Name of Country and the event), how many countries where the names are different from NOC ? please show results with Python code.

Data Set: Teams

Name	Discipline	NOC	Event
Belgium	3x3 Basketball	Belgium	Men
China	3x3 Basketball	People's Republic of China	Men
China	3x3 Basketball	People's Republic of China	Women
France	3x3 Basketball	France	Women
Italy	3x3 Basketball	Italy	Women
Japan	3x3 Basketball	Japan	Men
Japan	3x3 Basketball	Japan	Women
Latvia	3x3 Basketball	Latvia	Men
Mongolia	3x3 Basketball	Mongolia	Women
Netherlands	3x3 Basketball	Netherlands	Men
Poland	3x3 Basketball	Poland	Men
ROC	3x3 Basketball	ROC	Men
ROC	3x3 Basketball	ROC	Women
Romania	3x3 Basketball	Romania	Women
Serbia	3x3 Basketball	Serbia	Men
United States	3x3 Basketball	United States of America	Women
Australia	Archery	Australia	Men's Team
Australia	Archery	Australia	Mixed Team
Bangladesh	Archery	Bangladesh	Mixed Team