How to Subtract Two Columns in Pandas DataFrame?

When working with Pandas DataFrames, you often need to perform arithmetic operations between columns. One common operation is subtracting two columns. This guide explores four different methods to subtract columns in a Pandas DataFrame, from simple arithmetic operators to specialized Pandas functions.

Method 1: Using Simple Arithmetic Operator (-)

The most straightforward approach is using the standard subtraction operator. This method is intuitive and commonly used for basic column operations.

Syntax

result = dataframe['column1'] - dataframe['column2']

Example

Let's create a DataFrame with sales and costs data and calculate the profit by subtracting costs from sales ?

import pandas as pd

data = {'Sales': [100, 200, 300, 400], 'Costs': [30, 80, 150, 200]}
df = pd.DataFrame(data)

df['Profit'] = df['Sales'] - df['Costs']
print(df)

   Sales  Costs  Profit
0    100     30      70
1    200     80     120
2    300    150     150
3    400    200     200

Method 2: Using the sub() Method

The sub() method is a Pandas function that provides more flexibility for subtraction operations, especially when handling missing values.

Syntax

result = dataframe['column1'].sub(dataframe['column2'])

Example

import pandas as pd

data = {'Sales': [100, 200, 300, 400], 'Costs': [30, 80, 150, 200]}
df = pd.DataFrame(data)

df['Profit'] = df['Sales'].sub(df['Costs'])
print(df)

   Sales  Costs  Profit
0    100     30      70
1    200     80     120
2    300    150     150
3    400    200     200

Method 3: Using apply() with Lambda Function

The apply() method with a lambda function allows for more complex row-wise operations and provides flexibility for custom calculations.

Syntax

result = dataframe.apply(lambda row: row['column1'] - row['column2'], axis=1)

Example

import pandas as pd

data = {'Sales': [100, 200, 300, 400], 'Costs': [30, 80, 150, 200]}
df = pd.DataFrame(data)

df['Profit'] = df.apply(lambda row: row['Sales'] - row['Costs'], axis=1)
print(df)

   Sales  Costs  Profit
0    100     30      70
1    200     80     120
2    300    150     150
3    400    200     200

Method 4: Using subtract() Function

The subtract() function is similar to sub() and provides additional parameters for handling alignment and missing values.

Syntax

result = dataframe['column1'].subtract(dataframe['column2'])

Example

import pandas as pd

data = {'Sales': [100, 200, 300, 400], 'Costs': [30, 80, 150, 200]}
df = pd.DataFrame(data)

df['Profit'] = df['Sales'].subtract(df['Costs'])
print(df)

   Sales  Costs  Profit
0    100     30      70
1    200     80     120
2    300    150     150
3    400    200     200

Comparison

Conclusion

For simple column subtraction, use the arithmetic operator (-) as it's the most readable and efficient. Use sub() or subtract() when you need better control over missing values, and apply() with lambda for complex calculations involving multiple columns.