Pandas - Strip white space

Pandas - Strip white space

In Pandas, you can remove leading and trailing whitespace from strings in DataFrame columns using the .str.strip() method. This is useful to clean up the data and make sure your analysis isn't affected by unnecessary whitespace. Here's how you can use it:

Assuming you have a DataFrame named df, and you want to strip whitespace from the strings in a specific column (let's say 'Column1'):

import pandas as pd # Create or load your DataFrame data = {'Column1': [' apple', 'banana ', ' orange ']} df = pd.DataFrame(data) # Strip whitespace from the 'Column1' values df['Column1'] = df['Column1'].str.strip() print(df)

Output:

 Column1 0 apple 1 banana 2 orange

As you can see, the .str.strip() method removes the leading and trailing whitespace from the strings in the 'Column1' column.

If you want to remove only leading or trailing whitespace, you can use the .str.lstrip() (for leading) or .str.rstrip() (for trailing) methods, respectively. For example:

# Remove leading whitespace df['Column1'] = df['Column1'].str.lstrip() # Remove trailing whitespace df['Column1'] = df['Column1'].str.rstrip()

Remember that these operations create new Series with the stripped values. If you want to modify the original DataFrame, you need to assign the modified Series back to the DataFrame, as shown in the examples above.

Examples

  1. "Pandas strip whitespace from all columns"

    • Description: This query involves removing leading and trailing whitespace from all strings in all columns of a Pandas DataFrame.
    import pandas as pd # Sample DataFrame with whitespace df = pd.DataFrame({'A': [' apple ', ' banana ', ' orange '], 'B': [' red', ' green ', ' yellow ']}) # Strip whitespace from all columns df = df.applymap(lambda x: x.strip() if isinstance(x, str) else x)

  2. "Pandas strip whitespace from specific column"

    • Description: This query aims to remove leading and trailing whitespace from strings in a specific column of a Pandas DataFrame.
    import pandas as pd # Sample DataFrame with whitespace df = pd.DataFrame({'A': [' apple ', ' banana ', ' orange '], 'B': [' red', ' green ', ' yellow ']}) # Strip whitespace from column 'A' df['A'] = df['A'].str.strip()

  3. "Pandas strip whitespace from column names"

    • Description: This query involves removing leading and trailing whitespace from column names in a Pandas DataFrame.
    import pandas as pd # Sample DataFrame with whitespace in column names df = pd.DataFrame({' A ': [1, 2, 3], ' B ': [4, 5, 6]}) # Strip whitespace from column names df.columns = df.columns.str.strip()

  4. "Pandas strip whitespace from DataFrame index"

    • Description: This query focuses on removing leading and trailing whitespace from the index labels of a Pandas DataFrame.
    import pandas as pd # Sample DataFrame with whitespace in index labels df = pd.DataFrame({'A': [1, 2, 3]}, index=[' a ', ' b ', ' c ']) # Strip whitespace from DataFrame index df.index = df.index.str.strip()

  5. "Pandas strip whitespace from DataFrame values"

    • Description: This query aims to remove leading and trailing whitespace from all string values in a Pandas DataFrame.
    import pandas as pd # Sample DataFrame with whitespace in values df = pd.DataFrame({'A': [' apple ', ' banana ', ' orange '], 'B': [' red', ' green ', ' yellow ']}) # Strip whitespace from all values df = df.applymap(lambda x: x.strip() if isinstance(x, str) else x)

  6. "Pandas strip whitespace from specific row"

    • Description: This query involves removing leading and trailing whitespace from strings in a specific row of a Pandas DataFrame.
    import pandas as pd # Sample DataFrame with whitespace df = pd.DataFrame({'A': [' apple ', ' banana ', ' orange '], 'B': [' red', ' green ', ' yellow ']}) # Strip whitespace from row with index 1 df.loc[1] = df.loc[1].str.strip()

  7. "Pandas strip whitespace from DataFrame values conditionally"

    • Description: This query aims to remove leading and trailing whitespace from string values in a Pandas DataFrame based on a condition.
    import pandas as pd # Sample DataFrame with whitespace df = pd.DataFrame({'A': [' apple ', ' banana ', ' orange '], 'B': [' red', ' green ', ' yellow ']}) # Strip whitespace from values in column 'A' if string length is greater than 5 df['A'] = df['A'].where(df['A'].str.len() <= 5, df['A'].str.strip())

  8. "Pandas strip whitespace from DataFrame columns excluding numeric columns"

    • Description: This query involves removing leading and trailing whitespace from all columns in a Pandas DataFrame except numeric columns.
    import pandas as pd # Sample DataFrame with whitespace df = pd.DataFrame({'A': [' apple ', ' banana ', ' orange '], 'B': [1, 2, 3]}) # Strip whitespace from non-numeric columns non_numeric_cols = df.select_dtypes(exclude=['number']).columns df[non_numeric_cols] = df[non_numeric_cols].applymap(lambda x: x.strip() if isinstance(x, str) else x)

  9. "Pandas strip whitespace from DataFrame rows based on condition"

    • Description: This query involves removing leading and trailing whitespace from string values in DataFrame rows based on a condition.
    import pandas as pd # Sample DataFrame with whitespace df = pd.DataFrame({'A': [' apple ', ' banana ', ' orange '], 'B': [' red', ' green ', ' yellow ']}) # Strip whitespace from values in rows where column 'A' contains 'orange' df.loc[df['A'].str.contains('orange'), :] = df.loc[df['A'].str.contains('orange'), :].applymap(lambda x: x.strip() if isinstance(x, str) else x)

  10. "Pandas strip whitespace from DataFrame column with NaN values"

    • Description: This query involves removing leading and trailing whitespace from strings in a DataFrame column, handling NaN values appropriately.
    import pandas as pd import numpy as np # Sample DataFrame with whitespace and NaN values df = pd.DataFrame({'A': [' apple ', np.nan, ' orange '], 'B': [' red', ' green ', ' yellow ']}) # Strip whitespace from column 'A', handling NaN values df['A'] = df['A'].apply(lambda x: x.strip() if isinstance(x, str) else x)

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