How to Replace Empty Strings (Whitespace) with NaN in a Pandas DataFrame?

Replacing Blank Values (White Space) with NaN in Pandas

How can you efficiently replace blank values (whitespace) with NaN in a Pandas dataframe?

Initial Approach:

The following code is capable of replacing blank values with None, but it is inefficient and not the most Pythonic solution:

<code class="python">for i in df.columns:
    df[i][df[i].apply(lambda i: True if re.search('^\s*$', str(i)) else False)] = None</code>

Optimal Solution:

Pandas offers a more concise and efficient solution through the df.replace() method:

<code class="python">df = pd.DataFrame([
    [-0.532681, 'foo', 0],
    [1.490752, 'bar', 1],
    [-1.387326, 'foo', 2],
    [0.814772, 'baz', ' '],     
    [-0.222552, '   ', 4],
    [-1.176781,  'qux', '  '],         
], columns='A B C'.split(), index=pd.date_range('2000-01-01','2000-01-06'))

# replace field that's entirely space (or empty) with NaN
print(df.replace(r'^\s*$', np.nan, regex=True))</code>

This code replaces blank values (regular expressions: ^s*$) with NaN, producing the desired output:

                   A    B   C
2000-01-01 -0.532681  foo   0
2000-01-02  1.490752  bar   1
2000-01-03 -1.387326  foo   2
2000-01-04  0.814772  baz NaN
2000-01-05 -0.222552  NaN   4
2000-01-06 -1.176781  qux NaN

Note:

If you need to handle valid data that may contain white spaces, you can modify the regular expression to r'^s $', which only matches fields consisting entirely of white space.

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