How to replace 'any strings' with nan in pandas DataFrame using a boolean mask?

Question

I have a 227x4 DataFrame with country names and numerical values to clean (wrangle ?).

Here\'s an abstraction of the DataFrame:

import pandas as pd
i         


        

Answer 1

Use numeric with errors coerce i.e

cols = ['Measure1','Measure2']
df[cols] = df[cols].apply(pd.to_numeric,errors='coerce')

 Country Name  Measure1  Measure2
0          PuB       7.0       6.0
1          JHq       2.0       NaN
2          opE       4.0       3.0
3          pxl       3.0       6.0
4          ouP       NaN       4.0
5          qZR       4.0       6.0

Answer 2

Assign only columns of interest:

cols = ['Measure1','Measure2']
mask = df[cols].applymap(lambda x: isinstance(x, (int, float)))

df[cols] = df[cols].where(mask)
print (df)
  Country Name Measure1 Measure2
0          uFv        7        8
1          vCr        5      NaN
2          qPp        2        6
3          QIC       10       10
4          Suy      NaN        8
5          eFS        6        4

A meta-question, Is it normal that it takes me more than 3 hours to formulate a question here (including research) ?

In my opinion yes, create good question is really hard.

Answer 3

cols = ['Measure1','Measure2']
df[cols] = df[cols].applymap(lambda x: x if not isinstance(x, str) else np.nan)

or

df[cols] = df[cols].applymap(lambda x: np.nan if isinstance(x, str) else x)

Result:

In [22]: df
Out[22]:
  Country Name  Measure1  Measure2
0          nBl      10.0       9.0
1          Ayp       8.0       NaN
2          diz       4.0       1.0
3          aad       7.0       3.0
4          JYI       NaN      10.0
5          BJO       9.0       8.0