Removing non-numerical value from a dataframe

Question

My intention is to remove the values that appear with '...' as shown above and replace with an empty field.

The code I’m using to try to remove is this:

df['Energy Supply'].str.replace('[.]*', '')

however returns an output where all values become Nan

How could I fix this problem?

Answer 1

One option is to use the regex option of replace: df.replace('\.+', np.nan, regex=True)

Following example replicable:

import pandas as pd
import numpy as np

df = pd.DataFrame({'A': [0, 1, 2, 3, 4],
                   'B': ['......', 6, '.......', 8, 9],
                   'C': ['........', 'b', 'c', '....', 'e']})

print(df.replace('\.+', np.nan, regex=True))

Upshot:

   A    B    C
0  0  NaN  NaN
1  1  6.0    b
2  2  NaN    c
3  3  8.0  NaN
4  4  9.0    e