CODEWITHSUNDEEP
pythonpandasrename
an10b3
an10b3

Reputation: 273

add suffix to column name based on condition

i want to add the prefix '_nan' to columns that are all nan. I have the following code that prints what I want but does not reassign the columns in the actual dataframe and I am not sure why. Does anyone have any ideas why this is happening? Thanks in advance

df = pd.DataFrame({ 'a':[1, 0, 0, 0],
                    'b':[np.nan, np.nan, np.nan, np.nan],
                    'c':[np.nan, np.nan, np.nan, np.nan]})

a = df.loc[:,df.isna().all()].columns

df[[*a]] = df[[*a]].add_suffix('_nan')

Upvotes: 0

Views: 893

Answers (2)

Daweo
Daweo

Reputation: 36450

why this is happening?

After some experiments I found that when you assign pandas.DataFrame slice to slice of pandas.DataFrame then pandas apparently does bother solely about order of columns (given in list), not their names, consider following example:

import pandas as pd
df_1 = pd.DataFrame({'a':[1,2,3],'b':[4,5,6],'c':[7,8,9]})
df_2 = pd.DataFrame({'x':[10,20,30],'y':[400,500,600]})
df_1[['a','b']] = df_2[['x','y']]
print(df_1)

output

    a    b  c
0  10  400  7
1  20  500  8
2  30  600  9

whilst

...
df_1[['a','b']] = df_2[['y','x']]
print(df_1)

produce

     a   b  c
0  400  10  7
1  500  20  8
2  600  30  9

Upvotes: 0

Arkadiusz
Arkadiusz

Reputation: 1875

You can use list comprehension:

df.columns = [x + '_nan' if df[x].isna().all() else x for x in df.columns]

Output:

    a   b_nan   c_nan
0   1     NaN     NaN
1   0     NaN     NaN
2   0     NaN     NaN
3   0     NaN     NaN

Upvotes: 1

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