We Keep Coding

Is there a way to iterate through a column in pandas if it is an index

I have a pandas DataFrame which looks like this
Region Sub Region Country Size Plants Birds Mammals
Africa Northern Africa Algeria 2380000 22 41 15
Egypt 1000000 8 58 14
Libya 1760000 7 32 8
Sub-Saharan Africa Angola 1250000 34 53 32
Benin 115000 20 40 12
Western Africa Cape Verde 4030 51 35 7
Americas Latin America Antigua 440 4 31 3
Argentina 2780000 70 42 52
Bolivia 1100000 106 8 55
Northern America Canada 9980000 18 44 24
Grenada 340 3 29 2
USA 9830000 510 251 91
Asia Central Asia Kazakhstan 2720000 14 14 27
Kyrgyz 200000 13 3 15
Uzbekistan 447000 16 7 19
Eastern Asia China 9560000 593 136 96
Japan 378000 50 77 49
South Korea 100000 31 28 33
So I am trying to prompt the user to input a value and if the input exists within the Sub Region column, perform a particular task.
I tried turning the 'Sub region' column to a list and iterate through it if it matches the user input
sub_region_list=[]
for i in world_data.index.values:
sub_region_list.append(i[1])
print(sub_region_list[0])
That is not the output I had in mind.
I believe there is an easier way to do this but can not seem to figure it out

You can use get_level_values to filter.
sub_region = input("Enter a sub region:")
if sub_region not in df.index.get_level_values('Sub Region'):
raise ValueError("You must enter a valid sub-region")
If you want to save the column values in a list, try:
df.index.get_level_values("Sub Region").unique().to_list()

How to sort dataframe with values

I want to sort my dataframe in decending order with "Total Confirmed cases"
My Code
high_cases_sorted_df = df.sort_values(by='Total Confirmed cases',ascending=False)
print(high_cases_sorted_df)
Output
state Total Confirmed cases
19 Maharashtra 8590
14 Jharkhand 82
24 Puducherry 8
9 Goa 7
32 West Bengal 697
13 Jammu and Kashmir 546
15 Karnataka 512
30 Uttarakhand 51
16 Kerala 481
6 Chandigarh 40
12 Himachal Pradesh 40
7 Chhattisgarh 37
4 Assam 36
10 Gujarat 3548
5 Bihar 345
1 Andaman and Nicobar Islands 33
25 Punjab 313
8 Delhi 3108
11 Haryana 296
26 Rajasthan 2262
18 Madhya Pradesh 2168
17 Ladakh 20
20 Manipur 2
29 Tripura 2
31 Uttar Pradesh 1955
I don't know why it shows like this it should be
(1.Maharashtra, 2.Gujarat, 3.Delhi, etc)
complete script Here

simple by converting that column into integer
df['Total_Confirmed_cases'] = df['Total_Confirmed_cases'].astype(int)

Pybaseball: Extract standings data and save to disk using pandas

What I am trying to do is take this output from pybaseball which is set in as a list.
[ Tm W L W-L% GB 1 Boston Red Sox 94 44 .681 -- 2 New York Yankees 86 51 .628]
and put it into a csv file using pandas. So far these are the are the queries I have tried I have the information for this output set as data. Whenever I try to import it from pd.DataFrame() it tells me that:
AttributeError: 'list' object has no attribute 'to_csv'.
So I add a dataframe to that using df = pd.Dataframe(data) and that prints out just the headers
0 Teams W L W-L% GB
0 Tm Tm
1 W W
2 L L
3 W-L% W-L%
4 GB GB
How would I get this to import all of the information in the list to csv?
from pybaseball import standings
import pandas as pd
data = standings()
data.to_csv('file.csv', header = True, sep = ',')

Looks like standings() returns a list of dataframes:
from pybaseball import standings
import pandas as pd
data = standings()
print type(data)
print type(data[0])
Output:
<type 'list'>
<class 'pandas.core.frame.DataFrame'>
To write it to file, you need to concatenate the list of dataframes into a single dataframe before writing:
all_data = pd.concat(data)
print all_data
all_data.to_csv("baseball_data.csv", sep=",", index=False)
Output:
Tm W L W-L% GB
1 Boston Red Sox 95 44 .683 --
2 New York Yankees 86 52 .623 8.5
3 Tampa Bay Rays 74 63 .540 20.0
4 Toronto Blue Jays 62 75 .453 32.0
5 Baltimore Orioles 40 98 .290 54.5
1 Cleveland Indians 77 60 .562 --
2 Minnesota Twins 63 74 .460 14.0
3 Chicago White Sox 56 82 .406 21.5
4 Detroit Tigers 55 83 .399 22.5
5 Kansas City Royals 46 91 .336 31.0
1 Houston Astros 85 53 .616 --
2 Oakland Athletics 83 56 .597 2.5
3 Seattle Mariners 77 61 .558 8.0
4 Los Angeles Angels 67 71 .486 18.0
5 Texas Rangers 60 78 .435 25.0
1 Atlanta Braves 76 61 .555 --
2 Philadelphia Phillies 72 65 .526 4.0
3 Washington Nationals 69 69 .500 7.5
4 New York Mets 62 75 .453 14.0
5 Miami Marlins 55 83 .399 21.5
1 Chicago Cubs 81 56 .591 --
2 Milwaukee Brewers 78 61 .561 4.0
3 St. Louis Cardinals 76 62 .551 5.5
4 Pittsburgh Pirates 67 71 .486 14.5
5 Cincinnati Reds 59 79 .428 22.5
1 Colorado Rockies 75 62 .547 --
2 Los Angeles Dodgers 75 63 .543 0.5
3 Arizona Diamondbacks 74 64 .536 1.5
4 San Francisco Giants 68 71 .489 8.0
5 San Diego Padres 55 85 .393 21.5
And you'll have a file baseball_data.csv which is a comma-separated representation of the dataframe above.

Combine certain rows values of duplicate rows Pandas

I have a dataframe based on football players. I am finding duplicate rows for when a player has transferred mid-season. My aim is to add the points the accumalted in both leagues and add them together to make just one row.
Here is a sample of the data:
name full_name club Points Start Sub
84 S. Mustafi Shkodran Mustafi Arsenal 76 26 1
85 S. Mustafi Shkodran Mustafi Arsenal -2 0 1
89 Bruno Bruno Soriano Llido Villarreal CF 43 15 16
90 Bruno Bruno Gonzalez Cabrera Getafe CF 43 15 16
119 Oscar Oscar dos Santos Emboaba NaN 16 5 8
120 Oscar Oscar dos Santos Emboaba NaN 1 0 2
121 Oscar Oscar Rodriguez Arnaiz Real Madrid CF 16 5 8
122 Oscar Oscar Rodriguez Arnaiz Real Madrid CF 1 0 2
188 C. Bravo Claudio Bravo Manchester City 61 22 8
189 C. Bravo Claudio Bravo Manchester City 1 1 0
193 Naldo Ronaldo Aparecido Rodrigues FC Schalke 04 58 19 1
194 Naldo Edinaldo Gomes Pereira RCD Espanyol 58 19 1
200 G. Castro Gonzalo Castro Borussia Dortmund 79 23 6
201 G. Castro Gonzalo Castro Malaga CF 79 23 6
209 Juanfran Juan Francisco Torres Belen Atletico Madrid 86 21 8
210 Juanfran Juan Francisco Torres Belen Atletico Madrid 74 34 2
211 Juanfran Juan Francisco Moreno Fuertes RC Coruna 86 21 8
212 Juanfran Juan Francisco Moreno Fuertes RC Coruna 74 34 2
My goal dataframe would have players like for example Mustafi's Points Start and Sum values added together to give just one player.
Players like Bruno are clearly not the same person so I don't want to add the two brunos together.
name full_name club Points Start Sub
84 S. Mustafi Shkodran Mustafi Arsenal 74 26 2
89 Bruno Bruno Soriano Llido Villarreal CF 43 15 16
90 Bruno Bruno Gonzalez Cabrera Getafe CF 43 15 16
119 Oscar Oscar dos Santos Emboaba NaN 17 5 10
121 Oscar Oscar Rodriguez Arnaiz Real Madrid CF 17 5 10
188 C. Bravo Claudio Bravo Manchester City 62 23 8
193 Naldo Ronaldo Aparecido Rodrigues FC Schalke 04 58 19 1
194 Naldo Edinaldo Gomes Pereira RCD Espanyol 58 19 1
200 G. Castro Gonzalo Castro Borussia Dortmund 158 46 12
209 Juanfran Juan Francisco Torres Belen Atletico Madrid 86 21 8
212 Juanfran Juan Francisco Moreno Fuertes RC Coruna 74 34 2
Any help would be great!

You need:
df[['name','full_name','club']] = df[['name','full_name','club']].fillna('')
d = {'Points':'sum', 'Start':'sum', 'Sub':'sum', 'club':'first'}
df = (df.groupby(['name','full_name'], sort=False, as_index=False)
.agg(d)
.reindex(columns=df.columns))
with pd.option_context('display.expand_frame_repr', False):
print (df)
name full_name club Points Start Sub
0 S. Mustafi Shkodran Mustafi Arsenal 74 26 2
1 Bruno Bruno SorianoLlido Villarreal CF 43 15 16
2 Bruno Bruno Gonzalez Cabrera Getafe CF 43 15 16
3 Oscar Oscar dos Santos Emboaba 17 5 10
4 Oscar Oscar Rodriguez Arnaiz Real Madrid CF 17 5 10
5 C. Bravo Claudio Bravo Manchester City 62 23 8
6 Naldo Ronaldo Aparecido Rodrigues FC Schalke 04 58 19 1
7 Naldo Edinaldo Gomes Pereira RCD Espanyol 58 19 1
8 G. Castro Gonzalo Castro Borussia Dortmund 158 46 12
9 Juanfran Juan Francisco Torres Belen Atletico Madrid 160 55 10
10 Juanfran Juan Francisco Moreno Fuertes RC Coruna 160 55 10
Explanation:
First replace NaNs to '' by fillna for avoid omit rows with them in groupby
Aggregate by groupby, agg with dictionary with specify columns and their aggregating functions
Last for display all rows together temporarly use with

Can't split web scraped table on rows

I pulled a table of Tour de France winners from wikipedia using BeautifulSoup, but its returning the table in what appears to be a dataset, but the rows are separable.
First, here is what I did to grab the page and table:
import requests
response = requests.get("Https://en.wikipedia.org/wiki/List_of_Tour_de_France_general_classification_winners")
content = response.content
from bs4 import BeatifulSoup
parser = BeautifulSoup(content, 'html.parser')
# I know its the second table on the page, so grab it as such
winners_table = parser.find_all('table')[1]
import pandas as pd
data = pd.read_html(str(winners_table), flavor = 'html5lib')
Note that I used html5lib here because pycharm was telling me that there is no lxml, despite it certainly being there. When I print out the table, it appears as a table with 116 rows and 9 columns, but it isn't appearing to split on rows. It looks like this:
[ 0 1 \
0 Year Country
1 1903 France
2 1904 France
3 1905 France
4 1906 France
5 1907 France
6 1908 France
7 1909 Luxembourg
8 1910 France
9 1911 France
10 1912 Belgium
11 1913 Belgium
12 1914 Belgium
13 1915 World War I
14 1916 NaN
15 1917 NaN
16 1918 NaN
17 1919 Belgium
18 1920 Belgium
19 1921 Belgium
20 1922 Belgium
21 1923 France
22 1924 Italy
23 1925 Italy
24 1926 Belgium
25 1927 Luxembourg
26 1928 Luxembourg
27 1929 Belgium
28 1930 France
29 1931 France
.. ... ...
86 1988 Spain
87 1989 United States
88 1990 United States
89 1991 Spain
90 1992 Spain
91 1993 Spain
92 1994 Spain
93 1995 Spain
94 1996 Denmark
95 1997 Germany
96 1998 Italy
97 1999[B] United States
98 2000[B] United States
99 2001[B] United States
100 2002[B] United States
101 2003[B] United States
102 2004[B] United States
103 2005[B] United States
104 2006 Spain
105 2007 Spain
106 2008 Spain
107 2009 Spain
108 2010 Luxembourg
109 2011 Australia
110 2012 Great Britain
111 2013 Great Britain
112 2014 Italy
113 2015 Great Britain
114 2016 Great Britain
115 2017 Great Britain
2 \
0 Cyclist
1 Garin, MauriceMaurice Garin
2 Garin, MauriceMaurice Garin Cornet, HenriHenri...
3 Trousselier, LouisLouis Trousselier
4 Pottier, RenéRené Pottier
5 Petit-Breton, LucienLucien Petit-Breton
6 Petit-Breton, LucienLucien Petit-Breton
7 Faber, FrançoisFrançois Faber
8 Lapize, OctaveOctave Lapize
9 Garrigou, GustaveGustave Garrigou
10 Defraye, OdileOdile Defraye
11 Thys, PhilippePhilippe Thys
12 Thys, PhilippePhilippe Thys
13 NaN
14 NaN
15 NaN
16 NaN
17 Lambot, FirminFirmin Lambot
18 Thys, PhilippePhilippe Thys
19 Scieur, LéonLéon Scieur
20 Lambot, FirminFirmin Lambot
21 Pélissier, HenriHenri Pélissier
22 Bottecchia, OttavioOttavio Bottecchia
23 Bottecchia, OttavioOttavio Bottecchia
24 Buysse, LucienLucien Buysse
25 Frantz, NicolasNicolas Frantz
26 Frantz, NicolasNicolas Frantz
27 De Waele, MauriceMaurice De Waele
28 Leducq, AndréAndré Leducq
29 Magne, AntoninAntonin Magne
.. ...
86 Delgado, PedroPedro Delgado
87 LeMond, GregGreg LeMond
88 LeMond, GregGreg LeMond
89 Indurain, MiguelMiguel Indurain
90 Indurain, MiguelMiguel Indurain
91 Indurain, MiguelMiguel Indurain
92 Indurain, MiguelMiguel Indurain
93 Indurain, MiguelMiguel Indurain
94 Riis, BjarneBjarne Riis[A]
95 Ullrich, JanJan Ullrich#
96 Pantani, MarcoMarco Pantani
97 Armstrong, LanceLance Armstrong
98 Armstrong, LanceLance Armstrong
99 Armstrong, LanceLance Armstrong
100 Armstrong, LanceLance Armstrong
101 Armstrong, LanceLance Armstrong
102 Armstrong, LanceLance Armstrong
103 Armstrong, LanceLance Armstrong
104 Landis, FloydFloyd Landis Pereiro, ÓscarÓscar ...
105 Contador, AlbertoAlberto Contador#
106 Sastre, CarlosCarlos Sastre*
107 Contador, AlbertoAlberto Contador
108 Contador, AlbertoAlberto Contador Schleck, And...
109 Evans, CadelCadel Evans
110 Wiggins, BradleyBradley Wiggins
111 Froome, ChrisChris Froome
112 Nibali, VincenzoVincenzo Nibali
113 Froome, ChrisChris Froome*
114 Froome, ChrisChris Froome
115 Froome, ChrisChris Froome
3 4 \
0 Sponsor/Team Distance
1 La Française 2,428 km (1,509 mi)
2 Conte 2,428 km (1,509 mi)
3 Peugeot–Wolber 2,994 km (1,860 mi)
4 Peugeot–Wolber 4,637 km (2,881 mi)
5 Peugeot–Wolber 4,488 km (2,789 mi)
6 Peugeot–Wolber 4,497 km (2,794 mi)
7 Alcyon–Dunlop 4,498 km (2,795 mi)
8 Alcyon–Dunlop 4,734 km (2,942 mi)
9 Alcyon–Dunlop 5,343 km (3,320 mi)
10 Alcyon–Dunlop 5,289 km (3,286 mi)
11 Peugeot–Wolber 5,287 km (3,285 mi)
12 Peugeot–Wolber 5,380 km (3,340 mi)
13 NaN NaN
14 NaN NaN
15 NaN NaN
16 NaN NaN
17 La Sportive 5,560 km (3,450 mi)
18 La Sportive 5,503 km (3,419 mi)
19 La Sportive 5,485 km (3,408 mi)
20 Peugeot–Wolber 5,375 km (3,340 mi)
21 Automoto–Hutchinson 5,386 km (3,347 mi)
22 Automoto 5,425 km (3,371 mi)
23 Automoto–Hutchinson 5,440 km (3,380 mi)
24 Automoto–Hutchinson 5,745 km (3,570 mi)
25 Alcyon–Dunlop 5,398 km (3,354 mi)
26 Alcyon–Dunlop 5,476 km (3,403 mi)
27 Alcyon–Dunlop 5,286 km (3,285 mi)
28 Alcyon–Dunlop 4,822 km (2,996 mi)
29 France 5,091 km (3,163 mi)
.. ... ...
86 Reynolds 3,286 km (2,042 mi)
87 AD Renting–W-Cup–Bottecchia 3,285 km (2,041 mi)
88 Z–Tomasso 3,504 km (2,177 mi)
89 Banesto 3,914 km (2,432 mi)
90 Banesto 3,983 km (2,475 mi)
91 Banesto 3,714 km (2,308 mi)
92 Banesto 3,978 km (2,472 mi)
93 Banesto 3,635 km (2,259 mi)
94 Team Telekom 3,765 km (2,339 mi)
95 Team Telekom 3,950 km (2,450 mi)
96 Mercatone Uno–Bianchi 3,875 km (2,408 mi)
97 U.S. Postal Service 3,687 km (2,291 mi)
98 U.S. Postal Service 3,662 km (2,275 mi)
99 U.S. Postal Service 3,458 km (2,149 mi)
100 U.S. Postal Service 3,272 km (2,033 mi)
101 U.S. Postal Service 3,427 km (2,129 mi)
102 U.S. Postal Service 3,391 km (2,107 mi)
103 Discovery Channel 3,593 km (2,233 mi)
104 Caisse d'Epargne–Illes Balears 3,657 km (2,272 mi)
105 Discovery Channel 3,570 km (2,220 mi)
106 Team CSC 3,559 km (2,211 mi)
107 Astana 3,459 km (2,149 mi)
108 Team Saxo Bank 3,642 km (2,263 mi)
109 BMC Racing Team 3,430 km (2,130 mi)
110 Team Sky 3,496 km (2,172 mi)
111 Team Sky 3,404 km (2,115 mi)
112 Astana 3,660.5 km (2,274.5 mi)
113 Team Sky 3,360.3 km (2,088.0 mi)
114 Team Sky 3,529 km (2,193 mi)
115 Team Sky 3,540 km (2,200 mi)
5 6 7 8
0 Time/Points Margin Stage wins Stages in lead
1 094 !94h 33' 14" 24921 !+ 2h 59' 21" 3 6
2 096 !96h 05' 55" 21614 !+ 2h 16' 14" 1 3
3 35 26 5 10
4 31 8 5 12
5 47 19 2 5
6 36 32 5 13
7 37 20 6 13
8 63 4 4 3
9 43 18 2 13
10 49 59 3 13
11 197 !197h 54' 00" 00837 !+ 8' 37" 1 8
12 200 !200h 28' 48" 00150 !+ 1' 50" 1 15
13 NaN NaN NaN NaN
14 NaN NaN NaN NaN
15 NaN NaN NaN NaN
16 NaN NaN NaN NaN
17 231 !231h 07' 15" 14254 !+ 1h 42' 54" 1 2
18 228 !228h 36' 13" 05721 !+ 57' 21" 4 14
19 221 !221h 50' 26" 01836 !+ 18' 36" 2 14
20 222 !222h 08' 06" 04115 !+ 41' 15" 0 3
21 222 !222h 15' 30" 03041 !+ 30 '41" 3 6
22 226 !226h 18' 21" 03536 !+ 35' 36" 4 15
23 219 !219h 10' 18" 05420 !+ 54' 20" 4 13
24 238 !238h 44' 25" 12225 !+ 1h 22' 25" 2 8
25 198 !198h 16' 42" 14841 !+ 1h 48' 41" 3 14
26 192 !192h 48' 58" 05007 !+ 50' 07" 5 22
27 186 !186h 39' 15" 04423 !+44' 23" 1 16
28 172 !172h 12' 16" 01413 !+ 14' 13" 2 13
29 177 !177h 10' 03" 01256 !+ 12' 56" 1 16
.. ... ... ... ...
86 084 !84h 27' 53" 00713 !+ 7' 13" 1 11
87 087 !87h 38' 35" 00008 !+ 8" 3 8
88 090 !90h 43' 20" 00216 !+ 2' 16" 0 2
89 101 !101h 01' 20" 00336 !+ 3' 36" 2 10
90 100 !100h 49' 30" 00435 !+ 4' 35" 3 10
91 095 !95h 57' 09" 00459 !+ 4' 59" 2 14
92 103 !103h 38' 38" 00539 !+ 5' 39" 1 13
93 092 !92h 44' 59" 00435 !+ 4' 35" 2 13
94 095 !95h 57' 16" 00141 !+ 1' 41" 2 13
95 100 !100h 30' 35" 00909 !+ 9' 09" 2 12
96 092 !92h 49' 46" 00321 !+ 3' 21" 2 7
97 091 !91h 32' 16" 00737 !+ 7' 37" 4 15
98 092 !92h 33' 08" 00602 !+ 6' 02" 1 12
99 086 !86h 17' 28" 00644 !+ 6' 44" 4 8
100 082 !82h 05' 12" 00717 !+ 7' 17" 4 11
101 083 !83h 41' 12" 00101 !+ 1' 01" 1 13
102 083 !83h 36' 02" 00619 !+ 6' 19" 5 7
103 086 !86h 15' 02" 00440 !+ 4' 40" 1 17
104 089 !89h 40' 27" 00032 !+ 32" 0 8
105 091 !91h 00' 26" 00023 !+ 23" 1 4
106 087 !87h 52' 52" 00058 !+ 58" 1 5
107 085 !85h 48' 35" 00411 !+ 4' 11" 2 7
108 091 !91h 59' 27" 00122 !+ 1' 22" 2 12
109 086 !86h 12' 22" 00134 !+ 1' 34" 1 2
110 087 !87h 34' 47" 00321 !+ 3' 21" 2 14
111 083 !83h 56' 20" 00420 !+ 4' 20" 3 14
112 089 !89h 59' 06" 00737 !+ 7' 37" 4 19
113 084 !84h 46' 14" 00112 !+ 1' 12" 1 16
114 089 !89h 04' 48" 00405 !+ 4' 05" 2 14
115 086 !86h 20' 55" 00054 !+ 54" 0 15
[116 rows x 9 columns]]
This is all well and good, but the problem is it doesn't seem to be differentiating by rows. For instance, when I try to print just the first row, it reprints the whole dataset. Here's an example of trying to just print the first row and second column (so should just be one value):
print(data[0][2])
0 Country
1 France
2 France
3 France
4 France
5 France
6 France
7 Luxembourg
8 France
9 France
10 Belgium
11 Belgium
12 Belgium
13 World War I
14 NaN
15 NaN
16 NaN
17 Belgium
18 Belgium
19 Belgium
20 Belgium
21 France
22 Italy
23 Italy
24 Belgium
25 Luxembourg
26 Luxembourg
27 Belgium
28 France
29 France
...
86 Spain
87 United States
88 United States
89 Spain
90 Spain
91 Spain
92 Spain
93 Spain
94 Denmark
95 Germany
96 Italy
97 United States
98 United States
99 United States
100 United States
101 United States
102 United States
103 United States
104 Spain
105 Spain
106 Spain
107 Spain
108 Luxembourg
109 Australia
110 Great Britain
111 Great Britain
112 Italy
113 Great Britain
114 Great Britain
115 Great Britain
Name: 1, Length: 116, dtype: object
All I want is for this to behave as a data frame, with 116 rows and 9 columns. Any idea how to fix this?

If we take a look at the documentation here we can see that read_html actually outputs a list of DataFrames and not a single DataFrame. We can confirm this when we run:
>> print(type(data))
<class 'list'>
The format of the list is such that the first element of the list is the actual DataFrame containing your values.
>> print(type(data[0]))
<class 'pandas.core.frame.DataFrame'>
The simple solution to this is to reassign data to data[0]. From this you can then call individual rows. Indexing of rows for DataFrames doesn't behave like normal lists so I would recommend looking into .iloc and .loc. This is a nice article I found on indexing of DataFrames.
An example of this solution:
>> data = data[0]
>> print(data.iloc[1])
0 1903
1 France
2 Garin, MauriceMaurice Garin
3 La Française
4 2,428 km (1,509 mi)
5 094 !94h 33' 14"
6 24921 !+ 2h 59' 21"
7 3
8 6
Name: 1, dtype: object

The pandas function read_html returns a list of dataframes. So in your case I believe you need to choose the first index of the returned list as done in the 8th line in the code below.
Also note the you have a typo in the import line of BeautifulSoup, please update your code accordingly in the question.
I hope my output is what you're looking for.
Code:
import requests
import pandas as pd
from bs4 import BeautifulSoup
response = requests.get("Https://en.wikipedia.org/wiki/List_of_Tour_de_France_general_classification_winners")
parser = BeautifulSoup(response.content, 'html.parser')
winners_table = parser.find_all('table')[1]
data = pd.read_html(str(winners_table), flavor = 'lxml')[0]
print("type of variable data: " + str(type(data)))
print(data[0][2])
Output:
type of variable data: <class 'pandas.core.frame.DataFrame'>
1904
Note I used lxml instead of html5lib

You could try this:
df = data[0]
# iterate through the data frame using iterrows()
for index, row in df.iterrows():
print ("Col1:", row[0], " Col2: ", row[1], "Col3:", row[2], "Col4:", row[3]) #etc for all cols
I hope this helps!