What is the best way to remove columns in pandas

What is the best way to remove columns in pandas

In pandas, you can remove columns from a DataFrame using various methods. Here are some of the most common ways to remove columns:

  1. Using drop() Method: The drop() method allows you to remove one or more columns by specifying their labels. By default, it returns a new DataFrame with the specified columns removed, while leaving the original DataFrame unchanged.

    import pandas as pd # Create a DataFrame data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]} df = pd.DataFrame(data) # Remove columns 'B' and 'C' new_df = df.drop(['B', 'C'], axis=1) print(new_df)

  2. Using Indexing: You can use indexing to select the columns you want to keep, effectively removing the rest of the columns.

    # Create a DataFrame data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]} df = pd.DataFrame(data) # Keep only columns 'A' and 'B' new_df = df[['A', 'B']] print(new_df)

  3. Using del Statement: You can use the del statement to delete a column in-place from the DataFrame.

    # Create a DataFrame data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]} df = pd.DataFrame(data) # Delete column 'C' in-place del df['C'] print(df)

  4. Using pop() Method: The pop() method removes a column from the DataFrame and returns its values. This method modifies the original DataFrame in-place.

    # Create a DataFrame data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]} df = pd.DataFrame(data) # Remove column 'B' and get its values removed_column = df.pop('B') print(df) print(removed_column)

Choose the method that best fits your workflow and whether you want to keep the original DataFrame unchanged or modify it in-place.

Examples

    import pandas as pd # Create a sample DataFrame data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]} df = pd.DataFrame(data) # Remove columns by name df.drop(columns=['B'], inplace=True) print(df) 
      import pandas as pd # Create a sample DataFrame data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]} df = pd.DataFrame(data) # Remove columns by index df.drop(df.columns[1], axis=1, inplace=True) print(df) 
        import pandas as pd # Create a sample DataFrame with missing values data = {'A': [1, 2, None], 'B': [4, None, 6], 'C': [None, 8, 9]} df = pd.DataFrame(data) # Remove columns with missing values df.dropna(axis=1, inplace=True) print(df) 
          import pandas as pd # Create a sample DataFrame data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]} df = pd.DataFrame(data) # Remove columns based on condition condition = df['B'] > 4 columns_to_remove = df.columns[condition] df.drop(columns=columns_to_remove, inplace=True) print(df) 
            import pandas as pd # Create a sample DataFrame data = {'A': [1, 2, 3], 'B': ['x', 'y', 'z'], 'C': [4.0, 5.0, 6.0]} df = pd.DataFrame(data) # Remove columns with certain data type df = df.select_dtypes(exclude=['object']) print(df) 
              import pandas as pd # Create a sample DataFrame data = {'A_col1': [1, 2, 3], 'A_col2': [4, 5, 6], 'B_col1': [7, 8, 9]} df = pd.DataFrame(data) # Remove columns by prefix prefix = 'A_' columns_to_remove = [col for col in df.columns if col.startswith(prefix)] df.drop(columns=columns_to_remove, inplace=True) print(df) 
                import pandas as pd # Create a sample DataFrame data = {'A': [1, 1, 1], 'B': [2, 2, 2], 'C': [3, 3, 3]} df = pd.DataFrame(data) # Remove constant columns constant_columns = df.columns[df.nunique() == 1] df.drop(columns=constant_columns, inplace=True) print(df) 
                  import pandas as pd # Create a sample DataFrame data = {'col_1A': [1, 2, 3], 'col_2A': [4, 5, 6], 'col_1B': [7, 8, 9]} df = pd.DataFrame(data) # Remove columns by suffix suffix = 'A' columns_to_remove = [col for col in df.columns if col.endswith(suffix)] df.drop(columns=columns_to_remove, inplace=True) print(df) 
                    import pandas as pd # Create a sample DataFrame data = {'A': [1, 2, 3], 'B': [4, 0, 6], 'C': [7, 8, 9]} df = pd.DataFrame(data) # Remove columns with certain values columns_to_remove = df.columns[(df == 0).any()] df.drop(columns=columns_to_remove, inplace=True) print(df)

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