Home  >  Article  >  Backend Development  >  How to Efficiently Replace Whitespace Values with NaN in Pandas DataFrames?

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

Mary-Kate Olsen
Mary-Kate OlsenOriginal
2024-10-27 05:03:30225browse

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.

The above is the detailed content of How to Efficiently Replace Whitespace Values with NaN in Pandas DataFrames?. For more information, please follow other related articles on the PHP Chinese website!

Statement:
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn