How to Subtract Two Columns in Pandas DataFrame?
Last Updated :
19 Dec, 2021
In this article, we will discuss how to subtract two columns in pandas dataframe in Python.
Dataframe in use:
Method 1: Direct Method
This is the __getitem__ method syntax ([]), which lets you directly access the columns of the data frame using the column name.
Example: Subtract two columns in Pandas dataframe
Python3
import numpy as np
import pandas as pd
data = np.arange(0, 20).reshape(4, 5)
df1 = pd.DataFrame(data,
index=['Row 1', 'Row 2', 'Row 3', 'Row 4'],
columns=['Column 1', 'Column 2', 'Column 3',
'Column 4', 'Column 5'])
df1['Column 1'] - df1['Column 2']
|
Output:
Method 2: Defining a function
We can create a function specifically for subtracting the columns, by taking column data as arguments and then using the apply method to apply it to all the data points throughout the column.
Example: Subtract two columns in Pandas dataframe
Python3
import numpy as np
import pandas as pd
def diff(a, b):
return b - a
data = np.arange(0, 20).reshape(4, 5)
df = pd.DataFrame(data,
index=['Row 1', 'Row 2', 'Row 3', 'Row 4'],
columns=['Column 1', 'Column 2', 'Column 3',
'Column 4', 'Column 5'])
df['Difference_2_1'] = df.apply(
lambda x: diff(x['Column 2'], x['Column 2']), axis=1)
|
Output :
Method 3: Using apply()
Since the operation we want to perform is simple we can you can directly use the apply() method without explicitly defining a function. Provide the axis argument as 1 to access the columns.
Syntax:
s.apply(func, convert_dtype=True, args=())
Parameters:
- func: .apply takes a function and applies it to all values of pandas series.
- convert_dtype: Convert dtype as per the function’s operation.
- args=(): Additional arguments to pass to function instead of series.
Return Type: Pandas Series after applied function/operation.
Example: Subtract two columns in Pandas Dataframe
Python3
import pandas as pd
import numpy as np
data = np.arange(0, 20).reshape(4, 5)
df = pd.DataFrame(data,
index=['Row 1', 'Row 2', 'Row 3', 'Row 4'],
columns=['Column 1', 'Column 2', 'Column 3',
'Column 4', 'Column 5'])
df['diff_3_4'] = df.apply(lambda x: x['Column 3'] - x['Column 4'], axis=1)
df
|
Output:
assign() method assign new columns to a DataFrame, returning a new object (a copy) with the new columns added to the original ones.
Example: Subtract two columns in Pandas dataframe
Python3
import numpy as np
import pandas as pd
data = np.arange(0, 20).reshape(4, 5)
df = pd.DataFrame(data,
index=['Row 1', 'Row 2', 'Row 3', 'Row 4'],
columns=['Column 1', 'Column 2', 'Column 3',
'Column 4', 'Column 5'])
df = df.assign(diff_1_5=df['Column 1'] - df['Column 5'])
df
|
Output :
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