Apply a function to single or selected columns or rows in Pandas Dataframe
Last Updated :
04 Sep, 2023
In this article, we will learn different ways to apply a function to single or selected columns or rows in Dataframe. We will use Dataframe/series.apply() method to apply a function.
Apply a function to single row in Pandas Dataframe
Here, we will use different methods to apply a function to single rows by using Pandas dataframe. First lets create a data frame.
Python3
# import pandas and numpy library
import pandas as pd
import numpy as np
# List of Tuples
matrix = [(1, 2, 3),
(4, 5, 6),
(7, 8, 9)]
# Create a DataFrame object
df = pd.DataFrame(matrix, columns = list('xyz'),
index = list('abc'))
print(df)
Output:
x y z
a 1 2 3
b 4 5 6
c 7 8 9
Pandas.apply() allow the users to pass a function and apply it on every single value row of the Pandas dataframe. Here, we squared the 'bth' row.
Python3
# Apply function numpy.square() to lambda
# whose row index is 'b'
df = df.apply(lambda x: np.square(x) if x.name == 'b' else x,
axis = 1)
# Output
print(df)
Output :
x y z
a 1 2 3
b 16 25 36
c 7 8 9
Pandas DataFrame.loc attribute accesses a group of rows in the given DataFrame to squared the 'bth' row.
Python3
# import pandas and numpy library
import pandas as pd
import numpy as np
# List of Tuples
matrix = [(1, 2, 3),
(4, 5, 6),
(7, 8, 9)]
# Create a DataFrame object
df = pd.DataFrame(matrix, columns=list('xyz'),
index=list('abc'))
# Apply a function to one row 'b'
# and assign it back to the same row
df.loc['b'] = df.loc['b'].apply(np.square)
print(df)
Output :
x y z
a 1 2 3
b 16 25 36
c 7 8 9
This mathematical function helps the user to calculate the square value of each element in the array. Here, we are passing b row to make a square of it.
Python3
# import pandas and numpy library
import pandas as pd
import numpy as np
# List of Tuples
matrix = [(1, 2, 3),
(4, 5, 6),
(7, 8, 9)]
# Create a DataFrame object
df = pd.DataFrame(matrix, columns=list('xyz'),
index=list('abc'))
# Apply a function to one row 'b' and
# assign it back to the same row
df.loc['b'] = np.square(df.loc['b'])
# Output
print(df)
Output :
x y z
a 1 2 3
b 16 25 36
c 7 8 9
Apply a function to single column in Pandas Dataframe
Here, we will use different methods to apply a function to single columns by using Pandas Dataframe.
Pandas.apply() allow the users to pass a function and apply it on every single value column of the Pandas Dataframe. Here, we squared the 'zth' column.
Python3
# import pandas and numpy library
import pandas as pd
import numpy as np
# List of Tuples
matrix = [(1, 2, 3),
(4, 5, 6),
(7, 8, 9)]
# Create a DataFrame object
df = pd.DataFrame(matrix, columns=list('xyz'),
index=list('abc'))
# Apply function numpy.square() to lambda
# to find the squares of the values of
# column whose column name is 'z'
new_df = df.apply(lambda x: np.square(x) if x.name == 'z' else x)
# Output
print(new_df)
Output :
x y z
a 1 2 9
b 4 5 36
c 7 8 81
Pandas DataFrame.loc attribute access a group of columns in the given DataFrame to square the 'zth' column.
Python3
# import pandas and numpy library
import pandas as pd
import numpy as np
# List of Tuples
matrix = [(1, 2, 3),
(4, 5, 6),
(7, 8, 9)]
# Create a DataFrame object
df = pd.DataFrame(matrix, columns=list('xyz'),
index=list('abc'))
# Apply a function to one column 'z'
# and assign it back to the same column
df['z'] = df['z'].apply(np.square)
# Output
print(df)
Output :
x y z
a 1 2 9
b 4 5 36
c 7 8 81
This mathematical function helps the user to calculate the square value of each element in the array. Here, we are passing z column to make a square of it.
Python3
# import pandas and numpy library
import pandas as pd
import numpy as np
# List of Tuples
matrix = [(1, 2, 3),
(4, 5, 6),
(7, 8, 9)]
# Create a DataFrame object
df = pd.DataFrame(matrix, columns=list('xyz'),
index=list('abc'))
# Apply a function to one column 'z' and
# assign it back to the same column
df['z'] = np.square(df['z'])
# Output
print(df)
Output:
x y z
a 1 2 9
b 4 5 36
c 7 8 81
Apply function to column and row in the Dataframe
Here. we will see how to apply a function to more than one row and column using df.apply() method.
For ColumnÂ
Here, we applied the function to the x, and y columns.
Python3
# import pandas and numpy library
import pandas as pd
import numpy as np
# List of Tuples
matrix = [(1, 2, 3),
(4, 5, 6),
(7, 8, 9)]
# Create a DataFrame object
df = pd.DataFrame(matrix, columns=list('xyz'),
index=list('abc'))
# Apply function numpy.square()
# for square the values of
# two columns 'x' and 'y'
new_df = df.apply(lambda x: np.square(x) if x.name in ['x', 'y'] else x)
# Output
print(new_df)
Output:
x y z
a 1 4 3
b 16 25 6
c 49 64 9
For Row
Here, we applied the function to the b, and c rows.
Python3
# import pandas and numpy library
import pandas as pd
import numpy as np
# List of Tuples
matrix = [(1, 2, 3),
(4, 5, 6),
(7, 8, 9)]
# Create a DataFrame object
df = pd.DataFrame(matrix, columns=list('xyz'),
index=list('abc'))
# Apply function numpy.square() to
# square the values of two rows
# 'b' and 'c'
new_df = df.apply(lambda x: np.square(x) if x.name in ['b', 'c'] else x,
axis=1)
# Output
print(new_df)
Output:
x y z
a 1 2 3
b 16 25 36
c 49 64 81
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