Trouble Changing csv row based on column values in python3 and pandas

Question

I am having issues trying to modify a value if columnA contains lets say 0,2,12,44,75,81 (looking at 33 different numerical values in columnA. If columnA contains 1 of the 33 vaules defined, i need to then change the same row of colulmnB to "approved".

I have tried this: df[(df.columnA == '0|2|12|44|75|81')][df.columnB] = 'approved'

I get an error that there are none of index are in the columns but, i know that isn't correct looking at my csv file data. I must be searching wrong or using the wrong syntax.

the | separated string you've made implements or logic when used for regex searches with str.contains; the equality requires it to be exactly that string with all the letters and vertical bars in that exact order. Instead, I'm guessing you want to create a list of your values and use .isin: stackoverflow.com/questions/19960077/… — ALollz
– ALollz, Commented Mar 8, 2021 at 22:20

Boskosnitch · Accepted Answer · 2021-03-09 01:40:39Z

2

As others have said, if you need the value in column A to match exactly with a value in the list, then you can use .isin. If you need a partial match, then you can convert to string dtypes and use .contains.

setup:

nums_to_match = [0,2,9]
df
   A  B
0  6  e
1  1  f
2  3  b
3  6  g
4  6  i
5  0  f
6  9  a
7  6  i
8  6  a
9  2  h

Exact match:

df.loc[df.A.isin(nums_to_match),'B']='approved'
df:
   A         B
0  6         e
1  1         f
2  3         b
3  6         g
4  6         i
5  0  approved
6  9  approved
7  6         i
8  6         a
9  2  approved

partial match:

nums_to_match_str = list(map(str,nums_to_match))
df['A']=df['A'].astype(str)
df.loc[df.A.str.contains('|'.join(nums_to_match_str),case=False,na=False),'B']='approved'
df
   A         B
0  1         h
1  4         c
2  6         i
3  7         d
4  3         d
5  9  approved
6  5         i
7  1         c
8  0  approved
9  5         d

answered Mar 9, 2021 at 1:40

Boskosnitch

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1 Comment

Germ650 Over a year ago

This worked perfectly. Thank you for the additional context and options as well to help understand better.

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