Python pandas: dynamically change values in unspecified number of columns

Question

I have a simple data frame which might look like this:

| Label   | Average BR_1 | Average BR_2 | Average BR_3 | Average BR_4 |
| ------- | ------------ | ------------ | ------------ | ------------ |
| Label 1 | 50           | 30           | 50           | 50           |
| Label 2 | 60           | 20           | 50           | 50           |
| Label 3 | 65           | 50           | 50           | 50           |

What I would like to be able to do is to add a % symbol in every column.

I know that I can do something like this for every column:

df['Average BR_1'] = df['Average BR_1'].astype(str) + '%'

However, the problem is, that I read in the data from a CSV file which might contain more of these columns, so instead of Average BR_1 to Average BR_4, it might contain Average BR_1 to say Average BR_10.

So I would like this change to happen automatically for every column which contains Average BR_ in its column name.

I have been reading about .loc but I managed only to change column values to an entirely new value like so:

df.loc[:, ['Average BR_1', 'Average BR_2']] = "Hello"

Also, I haven't yet been able to implement regex here.

I tried with a list:

colsArr = [c for c in df.columns if 'Average BR_' in c]
print(colsArr)

But I did not manage to implement this with .loc.

I suppose I could do this using a loop, but I feel like there must be some better pandas solution, but I can not figure it out.

Could you help and point me in the right direction?

Thank you

akuiper · Accepted Answer · 2020-11-21 20:34:52Z

2

# extract the column names that need to be updated
cols = df.columns[df.columns.str.startswith('Average BR')]

# update the columns
df[cols] = df[cols].astype(str).add('%')

print(df)
     Label Average BR_1 Average BR_2 Average BR_3 Average BR_4
0  Label 1          50%          30%          50%          50%
1  Label 2          60%          20%          50%          50%
2  Label 3          65%          50%          50%          50%

working example

answered Nov 21, 2020 at 20:34

akuiper

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Comments

Michael Szczesny · Accepted Answer · 2020-11-21 20:50:17Z

2

You can use df.update and df.filter

df.update(df.filter(like='Average BR_').astype('str').add('%'))
df

Out:

   Label     Average BR_1   Average BR_2   Average BR_3   Average BR_4
0  Label 1            50%            30%            50%            50%
1  Label 2            60%            20%            50%            50%
2  Label 3            65%            50%            50%            50%

edited Nov 21, 2020 at 20:50

answered Nov 21, 2020 at 20:44

Michael Szczesny

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