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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())

Match and Store Python Dataframe

I'm trying to figure out a way to match a given value in a dataframe column to another dataframe column, and then storing an AGE from df1 in df2.
e.g. Matching VAL in df1 to VAL in df2. If the two are equal, store AGE from df1 in AGE df2.
| df1 | VAL | AGE |
|:--- |:---:|----:|
| 0 | 20 | 25 |
| 1 | 10 | 29 |
| 2 | 50 | 21 |
| 4 | 20 | 32 |
| 5 | 00 | 19 |
| df2 | VAL | AGE |
|:--- |:---:|----:|
| 0 | 00 | [] |
| 1 | 10 | [] |
| 2 | 20 | [] |
| 4 | 30 | [] |
| 5 | 40 | [] |
| 6 | 50 | [] |
edit: AGE in df2 stores an array of values rather than a single value

Try:
x = df1.groupby("VAL").agg(list)
df2["AGE"] = df2["VAL"].map(x["AGE"]).fillna({i: [] for i in df2.index})
print(df2)
Prints:
VAL AGE
0 0 [19]
1 10 [29]
2 20 [25, 32]
4 30 []
5 40 []
6 50 [21]

python: calculate column values ​based on previous rows and specified conditions

I have table like below:
|input | output|
|---- | --- |
| 5 | 0 |
| 5 | 0 |
| 5 | 0 |
| 0 | 2 |
| 0 | 3 |
| 0 | 8 |
| 5 | 0 |
| 5 | 0 |
I need to add a "capacity" column, the values ​​of which should be based on the conditions from other columns and the values ​​of the previous "capacity" row.
Variable: START_CAPACITY = 20
Conditions for the first row:
if input > 0: START_CAPACITY + (input/4)
else: START_CAPACITY - (output/4)
Conditions for the rest rows:
if input > 0: capacity before + (input/4)
else: capacity before - (output/4)
Expected result:
| input | output | capacity |
| ----- | ---- | ---- |
| 5 | 0 | `START_CAPACITY` + (5/4) = 21.25|
| 5 | 0 | 21.25 + (5/4) = 22.50 |
| 5 | 0 | 22.50 + (5/4) = 23.75 |
| 0 | 2 | 23.75 - (2/4) = 23.25 |
| 0 | 3 | 23.25 - (3/4) = 22.50 |
| 0 | 8 | 22.50 - (8/4) = 20.50 |
| 5 | 0 | 20.50 + (5/4) = 21.75 |
| 5 | 0 | 21.75 + (5/4) = 23.00 |
Calculations in the capacity column are illustrative, for better understanding.
I'm a little fresh with python, tried the "cumsum" tricks, but in this case I don't know how to handle it.
Do you have any ideas for this?
Thanks

I am not sure what you mean by "table", but when using lists, you could do something like this:
inputlist = [5,5,5,0,0,0,5,5]
outputlist = [0,0,0,2,3,8,0,0]
start_capacity = 20
assert(len(inputlist) == len(outputlist))
capacity = []
for i in range(0, len(inputlist)):
if i == 0:
if inputlist[i] > 0:
capacity.append(start_capacity + inputlist[i] / 4)
else:
capacity.append(start_capacity - outputlist[i] / 4)
else:
if inputlist[i] > 0:
capacity.append(capacity[i-1] + inputlist[i] / 4)
else:
capacity.append(capacity[i-1] - outputlist[i] / 4)
print('Capacity: ' + str(capacity))

Created New DataFrame Column, Unsigned Mininmum Less Than Zero

I'm very new to using Pandas DataFrames and was seeking help on creating a new column which is the unsigned minimum less than zero of an exiting column, something like...
df["B"] = abs(min(0, df["A"]))
...in other words, if A is greater than 0 then B equals 0,
otherwise B equals -A

Use pandas.DataFrame.apply
df['B'] = df.apply(lambda x:x['B'] if x['A'] > 0 else -x['A'], axis=1)

I guess this is how your data looks like
+---+-----+
| | A |
+---+-----+
| 0 | -10 |
| 1 | 20 |
| 2 | 50 |
| 3 | -25 |
| 4 | 8 |
+---+-----+
import numpy as np
df['B'] = np.where(df['A'] > 0, 0,-df['A'])
This will give the following result
+---+-----+----+
| | A | B |
+---+-----+----+
| 0 | -10 | 10 |
| 1 | 20 | 0 |
| 2 | 50 | 0 |
| 3 | -25 | 25 |
| 4 | 8 | 0 |
+---+-----+----+

where condition is met, get last row pandas

+------------+----+----+----+-----+----+----+----+-----+
| WS | 1 | 2 | 3 | 4 | 1 | 2 | 3 | 4 |
+------------+----+----+----+-----+----+----+----+-----+
| w1 | 0 | 0 | 0 | 50 | 0 | 0 | 0 | 50 |
| w2 | 0 | 30 | 0 | 0 | 0 | 30 | 0 | 0 |
| d1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| d2 | 62 | 0 | 0 | 0 | 62 | 0 | 0 | 0 |
| Total | 62 | 30 | 0 | 50 | 62 | 30 | 0 | 50 |
| Cumulative | 62 | 92 | 92 | 142 | 62 | 92 | 92 | 142 |
+------------+----+----+----+-----+----+----+----+-----+
Based on the condition of the column having value more than 0, I would like to get the corresponding value of row "Cumulative".
As shown in the image, when 50 > 0, I would like to get the corresponding "Cumulative" value of 142.
+------------+----+----+---+-----+----+----+---+-----+
| WS | 1 | 2 | 3 | 4 | 1 | 2 | 3 | 4 |
+------------+----+----+---+-----+----+----+---+-----+
| Cumulative | 62 | 92 | 0 | 142 | 62 | 92 | 0 | 142 |
+------------+----+----+---+-----+----+----+---+-----+
I have tried pandas loc and iloc but they cannot perform what I wanted.
Thank you in advanced!

You definitely should have designed your question better, but anyway, here is a possible solution:
Assuming df is your DataFrame:
df[df>5].loc['Cumulative'].dropna()