How to Efficiently Replace Whitespace Values with NaN in Pandas DataFrames?

Replacing Blank Values (White Space) with NaN in Pandas

Problem:

Consider a Pandas dataframe with whitespace values present in certain columns. The goal is to replace these white spaces with NaN values.

Ugly 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>

This solution iterates through each column, generates a boolean mask using regex, and replaces white space values with None. However, it's inefficient and non-idiomatic.

Improved Solution:

<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'))

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

This solution takes advantage of Pandas' built-in replace() function, which can be used to replace specified values based on a regex pattern. By using r'^s*$', the regex matches and replaces any field that consists entirely of whitespace (or is empty) with NaN.

Optimizations:

• Check if the column data type is object, as whitespace values are typically found in object columns.
• Use r'^s $' instead of r'^s*$' if valid data contains whitespace characters.

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