Python Pandas – Remove leading and trailing whitespace from more than one column

To remove leading and trailing whitespace from multiple columns in a Pandas DataFrame, use the str.strip() method. This is useful for cleaning data that contains unwanted spaces.

Creating a DataFrame with Whitespace

First, let's create a DataFrame with whitespace in the string columns ?

import pandas as pd

# Create a DataFrame with whitespace in string columns
dataFrame = pd.DataFrame({
    'Product Category': [' Computer', ' Mobile Phone', 'Electronics ', 'Appliances', ' Furniture', 'Stationery'],
    'Product Name': ['Keyboard', 'Charger', ' SmartTV', 'Refrigerators', ' Chairs', 'Diaries'],
    'Quantity': [10, 50, 10, 20, 25, 50]
})

print("Original DataFrame:")
print(dataFrame)

Original DataFrame:
  Product Category    Product Name  Quantity
0        Computer        Keyboard        10
1     Mobile Phone         Charger        50
2    Electronics          SmartTV        10
3      Appliances  Refrigerators        20
4        Furniture          Chairs        25
5       Stationery         Diaries        50

Method 1: Using str.strip() on Individual Columns

Apply str.strip() to each column separately and assign back to the DataFrame ?

import pandas as pd

dataFrame = pd.DataFrame({
    'Product Category': [' Computer', ' Mobile Phone', 'Electronics ', 'Appliances', ' Furniture', 'Stationery'],
    'Product Name': ['Keyboard', 'Charger', ' SmartTV', 'Refrigerators', ' Chairs', 'Diaries'],
    'Quantity': [10, 50, 10, 20, 25, 50]
})

# Remove whitespace from specific columns
dataFrame['Product Category'] = dataFrame['Product Category'].str.strip()
dataFrame['Product Name'] = dataFrame['Product Name'].str.strip()

print("DataFrame after removing whitespaces:")
print(dataFrame)

DataFrame after removing whitespaces:
  Product Category    Product Name  Quantity
0         Computer        Keyboard        10
1     Mobile Phone         Charger        50
2      Electronics         SmartTV        10
3       Appliances   Refrigerators        20
4        Furniture          Chairs        25
5       Stationery         Diaries        50

Method 2: Using apply() for Multiple Columns

Use apply() to strip whitespace from multiple string columns at once ?

import pandas as pd

dataFrame = pd.DataFrame({
    'Product Category': [' Computer', ' Mobile Phone', 'Electronics ', 'Appliances', ' Furniture', 'Stationery'],
    'Product Name': ['Keyboard', 'Charger', ' SmartTV', 'Refrigerators', ' Chairs', 'Diaries'],
    'Quantity': [10, 50, 10, 20, 25, 50]
})

# Strip whitespace from multiple columns using apply
string_columns = ['Product Category', 'Product Name']
dataFrame[string_columns] = dataFrame[string_columns].apply(lambda x: x.str.strip())

print("DataFrame after removing whitespaces:")
print(dataFrame)

DataFrame after removing whitespaces:
  Product Category    Product Name  Quantity
0         Computer        Keyboard        10
1     Mobile Phone         Charger        50
2      Electronics         SmartTV        10
3       Appliances   Refrigerators        20
4        Furniture          Chairs        25
5       Stationery         Diaries        50

Method 3: Using applymap() for All String Columns

For DataFrames with only string columns, use applymap() to apply strip to all cells ?

import pandas as pd

# DataFrame with only string columns
string_df = pd.DataFrame({
    'Product Category': [' Computer', ' Mobile Phone', 'Electronics ', 'Appliances'],
    'Product Name': ['Keyboard ', 'Charger', ' SmartTV', 'Refrigerators ']
})

print("Original string DataFrame:")
print(string_df)

# Apply strip to all cells
cleaned_df = string_df.applymap(lambda x: x.strip() if isinstance(x, str) else x)

print("\nAfter removing whitespaces:")
print(cleaned_df)

Original string DataFrame:
  Product Category    Product Name
0        Computer       Keyboard 
1     Mobile Phone         Charger
2    Electronics          SmartTV
3      Appliances  Refrigerators 

After removing whitespaces:
  Product Category   Product Name
0         Computer       Keyboard
1     Mobile Phone        Charger
2      Electronics        SmartTV
3       Appliances  Refrigerators

Comparison of Methods

Conclusion

Use str.strip() on individual columns for specific cleaning. For multiple columns, apply() with lambda provides an efficient solution. Choose applymap() when all columns contain strings that need cleaning.