You can use the following syntax to replace empty strings with NaN values in pandas:
df = df.replace(r'^\s*$', np.nan, regex=True)
The following example shows how to use this syntax in practice.
Related: How to Replace NaN Values with String in Pandas
Example: Replace Empty Strings with NaN
Suppose we have the following pandas DataFrame that contains information about various basketball players:
import pandas as pd #create DataFrame df = pd.DataFrame({'team': ['A', 'B', ' ', 'D', 'E', ' ', 'G', 'H'], 'position': [' ', 'G', 'G', 'F', 'F', ' ', 'C', 'C'], 'points': [5, 7, 7, 9, 12, 9, 9, 4], 'rebounds': [11, 8, 10, 6, 6, 5, 9, 12]}) #view DataFrame df team position points rebounds 0 A 5 11 1 B G 7 8 2 G 7 10 3 D F 9 6 4 E F 12 6 5 9 5 6 G C 9 9 7 H C 4 12
Notice that there are several empty strings in both the team and position columns.
We can use the following syntax to replace these empty strings with NaN values:
import numpy as np
#replace empty values with NaN
df = df.replace(r'^\s*$', np.nan, regex=True)
#view updated DataFrame
df
team position points rebounds
0 A NaN 5 11
1 B G 7 8
2 NaN G 7 10
3 D F 9 6
4 E F 12 6
5 NaN NaN 9 5
6 G C 9 9
7 H C 4 127
Notice that each of the empty strings have been replaced with NaN.
Note: You can find the complete documentation for the replace function in pandas here.
Additional Resources
The following tutorials explain how to perform other common tasks in pandas:
How to Impute Missing Values in Pandas
How to Count Missing Values in Pandas
How to Fill NaN Values with Mean in Pandas