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.
Syntax: Dataframe/series.apply(func, convert_dtype=True, args=())
Parameters: This method will take following parameters :
func: It 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.
Method 1: Using Dataframe.apply() and lambda function.
Example 1: For Column
# 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 new_df |
Output :
Example 2: For Row.
# 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 row # whose row index is 'b' new_df = df.apply(lambda x: np.square(x) if x.name == 'b' else x, axis = 1) # Output new_df |
Output :
Method 2: Using Dataframe/series.apply() & [ ] Operator.
Example 1: For Column.
# 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 df |
Output :
Example 2: For Row.
# 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) # Output df |
Output :
Method 3: Using numpy.square() method and [ ] operator.
Example 1: For Column
# 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 :
Example 2: For Row.
# 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 df |
Output :
We can also apply a function to more than one column or row in the dataframe.
Example 1: For Column
# 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 new_df |
Output :
Example 2: For Row.
# 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 new_df |
Output :
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