Subtract two columns in dataframe in python

Subtract two columns in dataframe in python

To subtract two columns in a DataFrame in Python using a library like Pandas, you can directly perform the subtraction operation between the columns. Here's how you can do it:

import pandas as pd # Create a sample DataFrame data = {'Column1': [10, 20, 30, 40], 'Column2': [5, 8, 12, 18]} df = pd.DataFrame(data) # Subtract values in Column2 from Column1 df['SubtractionResult'] = df['Column1'] - df['Column2'] print(df)

Output:

 Column1 Column2 SubtractionResult 0 10 5 5 1 20 8 12 2 30 12 18 3 40 18 22

In this example, we created a DataFrame df with two columns 'Column1' and 'Column2'. To perform the subtraction operation between these columns, we simply subtract df['Column2'] from df['Column1'] and assign the result to a new column 'SubtractionResult'.

Examples

  1. How to subtract two columns in a pandas DataFrame in Python?

    • Description: This query seeks a method to subtract the values of two columns in a pandas DataFrame element-wise.
    import pandas as pd # Create a sample DataFrame df = pd.DataFrame({'A': [10, 20, 30], 'B': [5, 10, 15]}) # Subtract column B from column A df['Result'] = df['A'] - df['B'] print(df)

  2. Python code to subtract two columns and create a new column with the result

    • Description: This query aims to subtract two columns in a pandas DataFrame and store the result in a new column.
    import pandas as pd # Create a sample DataFrame df = pd.DataFrame({'A': [100, 200, 300], 'B': [50, 80, 120]}) # Subtract column B from column A and store the result in column 'Result' df['Result'] = df['A'] - df['B'] print(df)

  3. Subtracting columns in pandas DataFrame and handling missing values

    • Description: This query involves subtracting two columns in a pandas DataFrame while handling missing or NaN values.
    import pandas as pd import numpy as np # Create a sample DataFrame with missing values df = pd.DataFrame({'A': [10, 20, np.nan], 'B': [5, np.nan, 15]}) # Subtract column B from column A, handling missing values with fill_value=0 df['Result'] = df['A'].sub(df['B'], fill_value=0) print(df)

  4. How to subtract columns in pandas DataFrame and handle data types

    • Description: This query focuses on subtracting two columns in a pandas DataFrame and handling data type conversions if necessary.
    import pandas as pd # Create a sample DataFrame with different data types df = pd.DataFrame({'A': [10, 20, 30], 'B': [5.5, 8.2, 12.1]}) # Subtract column B from column A, ensuring consistent data types df['Result'] = df['A'].astype(float) - df['B'] print(df)

  5. Subtracting columns in pandas DataFrame and rounding the result

    • Description: This query involves subtracting two columns in a pandas DataFrame and rounding the resulting values to a specified number of decimal places.
    import pandas as pd # Create a sample DataFrame df = pd.DataFrame({'A': [10.75, 20.35, 30.90], 'B': [5.25, 8.50, 12.15]}) # Subtract column B from column A and round the result to 2 decimal places df['Result'] = (df['A'] - df['B']).round(2) print(df)

  6. Python code to subtract columns in DataFrame and handle column names

    • Description: This query looks for Python code to subtract two columns in a pandas DataFrame and handle custom column names for the result.
    import pandas as pd # Create a sample DataFrame df = pd.DataFrame({'First': [100, 200, 300], 'Second': [50, 80, 120]}) # Subtract column 'Second' from column 'First' and rename the result column df['Subtraction_Result'] = df['First'] - df['Second'] print(df)

  7. Subtracting columns in pandas DataFrame with different lengths

    • Description: This query involves subtracting two columns in a pandas DataFrame even when they have different lengths.
    import pandas as pd # Create sample DataFrames with different lengths df1 = pd.DataFrame({'A': [10, 20, 30]}) df2 = pd.DataFrame({'B': [5, 10]}) # Subtract column B from column A, filling missing values with NaN result = df1['A'].sub(df2['B'], fill_value=0) print(result)

  8. How to subtract columns in pandas DataFrame and handle datetime data

    • Description: This query focuses on subtracting two columns in a pandas DataFrame containing datetime data and handling the result appropriately.
    import pandas as pd # Create a sample DataFrame with datetime data df = pd.DataFrame({'Start_Time': ['2023-01-01 12:00:00', '2023-01-02 13:00:00'], 'End_Time': ['2023-01-01 14:30:00', '2023-01-02 16:45:00']}) # Convert string columns to datetime df['Start_Time'] = pd.to_datetime(df['Start_Time']) df['End_Time'] = pd.to_datetime(df['End_Time']) # Subtract 'End_Time' from 'Start_Time' to get duration df['Duration'] = df['End_Time'] - df['Start_Time'] print(df)

  9. Subtracting columns in pandas DataFrame and handling negative values

    • Description: This query involves subtracting two columns in a pandas DataFrame and handling scenarios where the result may be negative.
    import pandas as pd # Create a sample DataFrame df = pd.DataFrame({'A': [5, 10, 15], 'B': [10, 8, 6]}) # Subtract column B from column A and handle negative values by setting them to zero df['Result'] = df['A'] - df['B'] df['Result'] = df['Result'].apply(lambda x: max(x, 0)) print(df)

  10. Python code to subtract two columns in DataFrame and handle outliers

    • Description: This query looks for Python code to subtract two columns in a pandas DataFrame and handle outliers or extreme values in the result.
    import pandas as pd # Create a sample DataFrame df = pd.DataFrame({'A': [100, 200, 300], 'B': [50, 250, 100]}) # Subtract column B from column A and handle outliers by replacing them with NaN df['Result'] = df['A'] - df['B'] df['Result'] = df['Result'].apply(lambda x: x if abs(x) < 200 else pd.NA) print(df)

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