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Is there a method in turning user input into csv format?

This is the example data that would be pasted into an input() prompt and ideally I would like it to be processed and made into a csv file through python:
,,,,,,Performance,Performance,Performance,Performance,Performance,Performance,Performance,Performance,Performance,Performance,Performance,Performance,Performance,Expected,Expected,Expected,SCA,SCA,Passes,Passes,Passes,Passes,Carries,Carries,Dribbles,Dribbles,-additional
Player,#,Nation,Pos,Age,Min,Gls,Ast,PK,PKatt,Sh,SoT,CrdY,CrdR,Touches,Press,Tkl,Int,Blocks,xG,npxG,xA,SCA,GCA,Cmp,Att,Cmp%,Prog,Carries,Prog,Succ,Att,-9999
Gabriel Jesus,9,br BRA,FW,25-124,82,0,0,0,0,1,0,0,0,40,13,1,1,0,0.1,0.1,0.0,4,0,20,27,74.1,2,33,1,4,5,b66315ae
Eddie Nketiah,14,eng ENG,FW,23-067,8,0,0,0,0,0,0,0,0,6,2,0,0,0,0.0,0.0,0.1,2,0,4,4,100.0,1,4,1,0,0,a53649b7
Martinelli,11,br BRA,LW,21-048,90,1,0,0,0,2,1,0,0,38,21,0,2,1,0.6,0.6,0.1,1,0,24,28,85.7,1,34,5,3,4,48a5a5d6
Bukayo Saka,7,eng ENG,RW,20-334,90,0,0,0,0,3,0,0,0,52,23,3,0,3,0.2,0.2,0.0,2,1,24,36,66.7,2,37,8,2,2,bc7dc64d
Martin Ødegaard,8,no NOR,AM,23-231,89,0,0,0,0,2,0,0,0,50,22,2,1,2,0.1,0.1,0.0,2,0,30,39,76.9,5,28,3,1,2,79300479
Albert Sambi Lokonga,23,be BEL,CM,22-287,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0.0,0.0,0.0,0,0,1,1,100.0,0,1,1,0,0,1b4f1169
Granit Xhaka,34,ch SUI,DM,29-312,90,0,0,0,0,0,0,1,0,60,5,0,2,3,0.0,0.0,0.0,4,0,42,49,85.7,6,32,2,0,0,e61b8aee
Thomas Partey,5,gh GHA,DM,29-053,90,0,0,0,0,1,0,0,0,62,25,7,1,2,0.1,0.1,0.0,0,0,40,47,85.1,5,26,4,0,1,529f49ab
Oleksandr Zinchenko,35,ua UKR,LB,25-233,82,0,1,0,0,1,1,0,0,64,16,3,3,1,0.0,0.0,0.3,2,1,44,54,81.5,6,36,5,0,0,51cf8561
Kieran Tierney,3,sct SCO,LBWB,25-061,8,0,0,0,0,0,0,0,0,6,1,0,0,0,0.0,0.0,0.0,0,0,2,4,50.0,0,1,0,0,0,fce2302c
Gabriel Dos Santos,6,br BRA,CB,24-229,90,0,0,0,0,0,0,0,0,67,5,1,1,2,0.0,0.0,0.0,0,0,52,58,89.7,1,48,3,0,0,67ac5bb8
William Saliba,12,fr FRA,CB,21-134,90,0,0,0,0,0,0,0,0,58,3,1,2,2,0.0,0.0,0.0,0,0,42,46,91.3,1,35,1,0,0,972aeb2a
Ben White,4,eng ENG,RB,24-301,90,0,0,0,0,0,0,1,0,61,22,7,4,5,0.0,0.0,0.1,1,0,29,40,72.5,5,25,2,1,1,35e413f1
Aaron Ramsdale,1,eng ENG,GK,24-083,90,0,0,0,0,0,0,0,0,33,0,0,0,0,0.0,0.0,0.0,0,0,24,32,75.0,0,21,0,0,0,466fb2c5
14 Players,,,,,990,1,1,0,0,10,2,2,0,599,158,25,17,21,1.1,1.1,0.5,18,2,378,465,81.3,35,361,36,11,15,-9999
The link to the table is: https://fbref.com/en/matches/e62f6e78/Crystal-Palace-Arsenal-August-5-2022-Premier-League#stats_18bb7c10_summary
I have attempted to use pandas dataframe but I am only able to export the first row of headers and nothing else (only the items before player).

Would have been nice for you to include your attempt.
Pandas works just fine:
import pandas as pd
url = 'https://fbref.com/en/matches/e62f6e78/Crystal-Palace-Arsenal-August-5-2022-Premier-League#stats_18bb7c10_summary'
df = pd.read_html(url)[10]
cols = [f'{each[0]}_{each[1]}' if 'Unnamed' not in each[0] else f'{each[1]}' for each in df.columns]
df.columns = cols
df.to_csv('output.csv', index=False)
Output:
print(df.to_markdown())
| | Player | # | Nation | Pos | Age | Min | Gls | Ast | PK | PKatt | Sh | SoT | CrdY | CrdR | Touches | Press | Tkl | Int | Blocks | xG | npxG | xA | SCA | GCA | Cmp | Att | Cmp% | Prog | Carries | Prog.1 | Succ | Att.1 |
|---:|:---------------------|----:|:---------|:------|:-------|------:|------:|------:|-----:|--------:|-----:|------:|-------:|-------:|----------:|--------:|------:|------:|---------:|-----:|-------:|-----:|------:|------:|------:|------:|-------:|-------:|----------:|---------:|-------:|--------:|
| 0 | Gabriel Jesus | 9 | br BRA | FW | 25-124 | 82 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 40 | 13 | 1 | 1 | 0 | 0.1 | 0.1 | 0 | 4 | 0 | 20 | 27 | 74.1 | 2 | 33 | 1 | 4 | 5 |
| 1 | Eddie Nketiah | 14 | eng ENG | FW | 23-067 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 2 | 0 | 0 | 0 | 0 | 0 | 0.1 | 2 | 0 | 4 | 4 | 100 | 1 | 4 | 1 | 0 | 0 |
| 2 | Martinelli | 11 | br BRA | LW | 21-048 | 90 | 1 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 38 | 21 | 0 | 2 | 1 | 0.6 | 0.6 | 0.1 | 1 | 0 | 24 | 28 | 85.7 | 1 | 34 | 5 | 3 | 4 |
| 3 | Bukayo Saka | 7 | eng ENG | RW | 20-334 | 90 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 52 | 23 | 3 | 0 | 3 | 0.2 | 0.2 | 0 | 2 | 1 | 24 | 36 | 66.7 | 2 | 37 | 8 | 2 | 2 |
| 4 | Martin Ødegaard | 8 | no NOR | AM | 23-231 | 89 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 50 | 22 | 2 | 1 | 2 | 0.1 | 0.1 | 0 | 2 | 0 | 30 | 39 | 76.9 | 5 | 28 | 3 | 1 | 2 |
| 5 | Albert Sambi Lokonga | 23 | be BEL | CM | 22-287 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 100 | 0 | 1 | 1 | 0 | 0 |
| 6 | Granit Xhaka | 34 | ch SUI | DM | 29-312 | 90 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 60 | 5 | 0 | 2 | 3 | 0 | 0 | 0 | 4 | 0 | 42 | 49 | 85.7 | 6 | 32 | 2 | 0 | 0 |
| 7 | Thomas Partey | 5 | gh GHA | DM | 29-053 | 90 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 62 | 25 | 7 | 1 | 2 | 0.1 | 0.1 | 0 | 0 | 0 | 40 | 47 | 85.1 | 5 | 26 | 4 | 0 | 1 |
| 8 | Oleksandr Zinchenko | 35 | ua UKR | LB | 25-233 | 82 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 64 | 16 | 3 | 3 | 1 | 0 | 0 | 0.3 | 2 | 1 | 44 | 54 | 81.5 | 6 | 36 | 5 | 0 | 0 |
| 9 | Kieran Tierney | 3 | sct SCO | LB,WB | 25-061 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 50 | 0 | 1 | 0 | 0 | 0 |
| 10 | Gabriel Dos Santos | 6 | br BRA | CB | 24-229 | 90 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 67 | 5 | 1 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 52 | 58 | 89.7 | 1 | 48 | 3 | 0 | 0 |
| 11 | William Saliba | 12 | fr FRA | CB | 21-134 | 90 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 58 | 3 | 1 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 42 | 46 | 91.3 | 1 | 35 | 1 | 0 | 0 |
| 12 | Ben White | 4 | eng ENG | RB | 24-301 | 90 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 61 | 22 | 7 | 4 | 5 | 0 | 0 | 0.1 | 1 | 0 | 29 | 40 | 72.5 | 5 | 25 | 2 | 1 | 1 |
| 13 | Aaron Ramsdale | 1 | eng ENG | GK | 24-083 | 90 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 33 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 24 | 32 | 75 | 0 | 21 | 0 | 0 | 0 |
| 14 | 14 Players | nan | nan | nan | nan | 990 | 1 | 1 | 0 | 0 | 10 | 2 | 2 | 0 | 599 | 158 | 25 | 17 | 21 | 1.1 | 1.1 | 0.5 | 18 | 2 | 378 | 465 | 81.3 | 35 | 361 | 36 | 11 | 15 |

could you elaborate more?
maybe you could split the raw text by comma and then convert it to a dataframe
like:
list_of_string = input.split(',')
df = pd.DataFrame(list_of_string)
df.to_csv('yourfile.csv')

The correct approach is as proposed by chitown88, however if you want to copy paste the data by hand into the terminal and get a csv you can do something like this:
import pandas as pd
from datetime import datetime
while True:
print("Enter/Paste your content. Ctrl-D or Ctrl-Z ( windows ) to save it.")
contents = []
while True:
try:
line = input()
except EOFError:
break
contents.append(line)
df = pd.DataFrame(contents)
df.to_csv(f"df_{int(datetime.now().timestamp())}.csv", index=None)
Start the Python script, paste the data into the terminal, press CTRL+D and press enter to export the data you pasted into the terminal into a csv file.

You can use user input controlled while loop to get user input. Finally, you may exit depending on the user’s choice. Look at the code below:
user_input = 'Y'
while user_input.lower() == 'y':
# Run your code here.
user_input = input('Do you want to add one more entry: Y or N?')

This is most intuitive and understandable solution I could come up with uses of basic linear algebra to solve the problem which I find pretty neat. I recommend you to find an another way to parse the data. Check out beautifulsoup and requests.
import pandas as pd#for dataframe
data = '''
,,,,,,Performance,Performance,Performance,Performance,Performance,Performance,Performance,Performance,Performance,Performance,Performance,Performance,Performance,Expected,Expected,Expected,SCA,SCA,Passes,Passes,Passes,Passes,Carries,Carries,Dribbles,Dribbles,-additional
Player,#,Nation,Pos,Age,Min,Gls,Ast,PK,PKatt,Sh,SoT,CrdY,CrdR,Touches,Press,Tkl,Int,Blocks,xG,npxG,xA,SCA,GCA,Cmp,Att,Cmp%,Prog,Carries,Prog,Succ,Att,-9999
Gabriel Jesus,9,br BRA,FW,25-124,82,0,0,0,0,1,0,0,0,40,13,1,1,0,0.1,0.1,0.0,4,0,20,27,74.1,2,33,1,4,5,b66315ae
Eddie Nketiah,14,eng ENG,FW,23-067,8,0,0,0,0,0,0,0,0,6,2,0,0,0,0.0,0.0,0.1,2,0,4,4,100.0,1,4,1,0,0,a53649b7
Martinelli,11,br BRA,LW,21-048,90,1,0,0,0,2,1,0,0,38,21,0,2,1,0.6,0.6,0.1,1,0,24,28,85.7,1,34,5,3,4,48a5a5d6
Bukayo Saka,7,eng ENG,RW,20-334,90,0,0,0,0,3,0,0,0,52,23,3,0,3,0.2,0.2,0.0,2,1,24,36,66.7,2,37,8,2,2,bc7dc64d
Martin Ødegaard,8,no NOR,AM,23-231,89,0,0,0,0,2,0,0,0,50,22,2,1,2,0.1,0.1,0.0,2,0,30,39,76.9,5,28,3,1,2,79300479
Albert Sambi Lokonga,23,be BEL,CM,22-287,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0.0,0.0,0.0,0,0,1,1,100.0,0,1,1,0,0,1b4f1169
Granit Xhaka,34,ch SUI,DM,29-312,90,0,0,0,0,0,0,1,0,60,5,0,2,3,0.0,0.0,0.0,4,0,42,49,85.7,6,32,2,0,0,e61b8aee
Thomas Partey,5,gh GHA,DM,29-053,90,0,0,0,0,1,0,0,0,62,25,7,1,2,0.1,0.1,0.0,0,0,40,47,85.1,5,26,4,0,1,529f49ab
Oleksandr Zinchenko,35,ua UKR,LB,25-233,82,0,1,0,0,1,1,0,0,64,16,3,3,1,0.0,0.0,0.3,2,1,44,54,81.5,6,36,5,0,0,51cf8561
Kieran Tierney,3,sct SCO,LBWB,25-061,8,0,0,0,0,0,0,0,0,6,1,0,0,0,0.0,0.0,0.0,0,0,2,4,50.0,0,1,0,0,0,fce2302c
Gabriel Dos Santos,6,br BRA,CB,24-229,90,0,0,0,0,0,0,0,0,67,5,1,1,2,0.0,0.0,0.0,0,0,52,58,89.7,1,48,3,0,0,67ac5bb8
William Saliba,12,fr FRA,CB,21-134,90,0,0,0,0,0,0,0,0,58,3,1,2,2,0.0,0.0,0.0,0,0,42,46,91.3,1,35,1,0,0,972aeb2a
Ben White,4,eng ENG,RB,24-301,90,0,0,0,0,0,0,1,0,61,22,7,4,5,0.0,0.0,0.1,1,0,29,40,72.5,5,25,2,1,1,35e413f1
Aaron Ramsdale,1,eng ENG,GK,24-083,90,0,0,0,0,0,0,0,0,33,0,0,0,0,0.0,0.0,0.0,0,0,24,32,75.0,0,21,0,0,0,466fb2c5
14 Players,,,,,990,1,1,0,0,10,2,2,0,599,158,25,17,21,1.1,1.1,0.5,18,2,378,465,81.3,35,361,36,11,15,-9999
'''
#you can just replace data with user input
def tryNum(x):#input a value and if its a number then it returns a number, if not it returns itself back
try:
x = float(x)
return x
except:
return x
rows = [i.split(',')[:-1] for i in data.split('\n')[2:-2]]#removing useless lines
col_names = [i for i in rows[0]]#fetching all column names
cols = [[tryNum(rows[j][i]) for j in range(1,len(rows))] for i in range(len(rows[0]))]#get all column info by transposing the "matrix" if you will
full = {}#setting up the dictionary
for i,y in zip(col_names,cols):#putting the data in the dict
full[i]=y
df = pd.DataFrame(data = full)#uploading it all to the df
print(df.head())

Pandas Add New Column using Lookup using Multiple Columns from another DataFrame

I have two dataframes.
df1 = pd.DataFrame({
'id':[1,1,1,1,1,1,2,2,2,2,2,2],
'pp':[3,'',2,'',1,0,4, 3, 2, 1, '', 0],
'pc':[6,5,4,3,2,1,6,5,4,3,2,1]
})
| | id | pp | pc |
|---:|-----:|:-----|-----:|
| 0 | 1 | 3 | 6 |
| 1 | 1 | | 5 |
| 2 | 1 | 2 | 4 |
| 3 | 1 | | 3 |
| 4 | 1 | 1 | 2 |
| 5 | 1 | 0 | 1 |
| 6 | 2 | 4 | 6 |
| 7 | 2 | 3 | 5 |
| 8 | 2 | 2 | 4 |
| 9 | 2 | 1 | 3 |
| 10 | 2 | | 2 |
| 11 | 2 | 0 | 1 |
df2 = pd.DataFrame({
'id':[1,1,1,2,2,2],
'pp':['', 3, 4, 1, 2, ''],
'yu':[1,2,3,4,5,6]
})
| | id | pp | yu |
|---:|-----:|:-----|-----:|
| 0 | 1 | | 1 |
| 1 | 1 | 3 | 2 |
| 2 | 1 | 4 | 3 |
| 3 | 2 | 1 | 4 |
| 4 | 2 | 2 | 5 |
| 5 | 2 | | 6 |
I'd like to merge the two so that final results look like this.
| | id | pp | pc | yu |
|---:|-----:|:-----|:-----|-----:|
| 0 | 1 | | | 1 |
| 1 | 1 | 0 | 1 | 2 |
| 2 | 1 | 3 | 6 | 3 |
| 3 | 2 | 1 | 3 | 4 |
| 4 | 2 | 2 | 4 | 5 |
| 5 | 2 | | | 6 |
Basically, the df1 has the value that I need to lookup from.
df2 is the has id and pp column that are used to lookup.
However when I do
pd.merge(df2, df1, on=['id', 'pp'], how='left') results in
| | id | pp | pc | yu |
|---:|-----:|:-----|-----:|-----:|
| 0 | 1 | | 5 | 1 |
| 1 | 1 | | 3 | 1 |
| 2 | 1 | 3 | 6 | 2 |
| 3 | 1 | 4 | nan | 3 |
| 4 | 2 | 1 | 3 | 4 |
| 5 | 2 | 2 | 4 | 5 |
| 6 | 2 | | 2 | 6 |
This is not correct because it looks at empty rows as well.
If the value in df2 is empty, there should be no mapping.
I do want to keep the empty rows in df2 as it showed so can't use inner join

We can dropna for empty row in df1
out = pd.merge(df2, df1.replace({'':np.nan}).dropna(), on=['id', 'pp'], how='left')
Out[121]:
id pp yu pc
0 1 1 NaN
1 1 3 2 6.0
2 1 4 3 NaN
3 2 1 4 3.0
4 2 2 5 4.0
5 2 6 NaN

Multiplying pandas columns based on multiple conditions

I have a df like this
| count | people | A | B | C |
|---------|--------|-----|-----|-----|
| yes | siya | 4 | 2 | 0 |
| no | aish | 4 | 3 | 0 |
| total | | 4 | | 0 |
| yes | dia | 6 | 4 | 0 |
| no | dia | 6 | 2 | 0 |
| total | | 6 | | 0 |
I want a output like below
| count | people | A | B | C |
|---------|--------|-----|-----|-----|
| yes | siya | 4 | 2 | 8 |
| no | aish | 4 | 3 | 0 |
| total | | 4 | | 0 |
| yes | dia | 6 | 4 | 0 |
| no | dia | 6 | 2 | 2 |
| total | | 6 | | 0 |
The goal is calculate column C by mulytiplying A and B only when the count value is "yes" but if the column People values are same that is yes for dia and no for also dia , then we have to calculate for the count value "no"
I tried this much so far
df.C= df.groupby("Host", as_index=False).apply(lambda dfx : df.A *
df.B if (df['count'] == 'no') else df.A *df.B)
But not able to achieve the goal, any idea how can I achieve the output

import numpy as np
#Set Condtions
c1=df.groupby('people')['count'].transform('nunique').eq(1)&df['count'].eq('yes')
c2=df.groupby('people')['count'].transform('nunique').gt(1)&df['count'].eq('no')
#Put conditions in list
c=[c1,c2]
#Mke choices corresponding to condition list
choice=[df['A']*df['B'],len(df[df['count'].eq('no')])]
#Apply np select
df['C']= np.select(c,choice,0)
print(df)
count people A B C
0 yes siya 4 2.0 8.0
1 no aish 4 3.0 0.0
2 total NaN 4 0.0 0.0
3 yes dia 6 4.0 0.0
4 no dia 6 2.0 2.0
5 total NaN 6 NaN 0.0

Python Pivot Table based on multiple criteria

I was asking the question in this link SUMIFS in python jupyter
However, I just realized that the solution didn't work because they can switch in and switch out on different dates. So basically they have to switch out first before they can switch in.
Here is the dataframe (sorted based on the date):
+---------------+--------+---------+-----------+--------+
| Switch In/Out | Client | Quality | Date | Amount |
+---------------+--------+---------+-----------+--------+
| Out | 1 | B | 15-Aug-19 | 360 |
| In | 1 | A | 16-Aug-19 | 180 |
| In | 1 | B | 17-Aug-19 | 180 |
| Out | 1 | A | 18-Aug-19 | 140 |
| In | 1 | B | 18-Aug-19 | 80 |
| In | 1 | A | 19-Aug-19 | 60 |
| Out | 2 | B | 14-Aug-19 | 45 |
| Out | 2 | C | 15-Aug-20 | 85 |
| In | 2 | C | 15-Aug-20 | 130 |
| Out | 2 | A | 20-Aug-19 | 100 |
| In | 2 | A | 22-Aug-19 | 30 |
| In | 2 | B | 23-Aug-19 | 30 |
| In | 2 | C | 23-Aug-19 | 40 |
+---------------+--------+---------+-----------+--------+
I would then create a new column and divide them into different transactions.
+---------------+--------+---------+-----------+--------+------+
| Switch In/Out | Client | Quality | Date | Amount | Rows |
+---------------+--------+---------+-----------+--------+------+
| Out | 1 | B | 15-Aug-19 | 360 | 1 |
| In | 1 | A | 16-Aug-19 | 180 | 1 |
| In | 1 | B | 17-Aug-19 | 180 | 1 |
| Out | 1 | A | 18-Aug-19 | 140 | 2 |
| In | 1 | B | 18-Aug-19 | 80 | 2 |
| In | 1 | A | 19-Aug-19 | 60 | 2 |
| Out | 2 | B | 14-Aug-19 | 45 | 3 |
| Out | 2 | C | 15-Aug-20 | 85 | 3 |
| In | 2 | C | 15-Aug-20 | 130 | 3 |
| Out | 2 | A | 20-Aug-19 | 100 | 4 |
| In | 2 | A | 22-Aug-19 | 30 | 4 |
| In | 2 | B | 23-Aug-19 | 30 | 4 |
| In | 2 | C | 23-Aug-19 | 40 | 4 |
+---------------+--------+---------+-----------+--------+------+
With this, I can apply the pivot formula and take it from there.
However, how do I do this in python? In excel, I can just use multiple SUMIFS and compare in and out. However, this is not possible in python.
Thank you!

One simple solution is to iterate and apply a check (function) over each element being the result a new column, so: map.
Using df.index.map we get the index for each item to pass as a argument, so we can play with the values, get and compare. In your case your aim is to identify the change to "Out" keeping a counter.
import pandas as pd
switchInOut = ["Out", "In", "In", "Out", "In", "In",
"Out", "Out", "In", "Out", "In", "In", "In"]
df = pd.DataFrame(switchInOut, columns=['Switch In/Out'])
counter = 1
def changeToOut(i):
global counter
if df["Switch In/Out"].get(i) == "Out" and df["Switch In/Out"].get(i-1) == "In":
counter += 1
return counter
rows = df.index.map(changeToOut)
df["Rows"] = rows
df
Result:
+----+-----------------+--------+
| | Switch In/Out | Rows |
|----+-----------------+--------|
| 0 | Out | 1 |
| 1 | In | 1 |
| 2 | In | 1 |
| 3 | Out | 2 |
| 4 | In | 2 |
| 5 | In | 2 |
| 6 | Out | 3 |
| 7 | Out | 3 |
| 8 | In | 3 |
| 9 | Out | 4 |
| 10 | In | 4 |
| 11 | In | 4 |
| 12 | In | 4 |
+----+-----------------+--------+

how to find max of a columns with same name

im having some trouble with this data frame where columns having the same name have to be reduced to values with at least one "1" as "1".
+---+---+---+---+---+---+---+---+---+
| a | a | a | b | c | c | c | d | d |
+---+---+---+---+---+---+---+---+---+
| 1 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 |
| 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 0 |
| 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 |
+---+---+---+---+---+---+---+---+---+
to something like this using "or" condition for every column for a huge dataset could be a time-consuming task so I am having trouble figuring it out. I used max(axis=1, level=0) still couldn't make it.
my desired output :
+---+---+---+---+
| a | b | c | d |
+---+---+---+---+
| 1 | 1 | 1 | 1 |
| 0 | 1 | 1 | 1 |
| 1 | 0 | 1 | 0 |
+---+---+---+---+

Check with max
df = df.max(level=0, axis=1)