Pandas.apply allow the users to pass a function and apply it on every single value of the Pandas series.
Syntax of pandas.DataFrame.apply
Syntax : DataFrame.apply(parameters)
Parameters :
- func : Function to apply to each column or row.
- axis : Axis along which the function is applied
- raw : Determines if row or column is passed as a Series or ndarray object.
- result_type : ‘expand’, ‘reduce’, ‘broadcast’, None; default None
- args : Positional arguments to pass to func in addition to the array/series.
- **kwds : Additional keyword arguments to pass as keywords arguments to func.
Returns : Series or DataFrame
Example 1: Pandas Apply Function to Single Column
In this example, we are passing only a single column and increment age with 2.
Python3
# import the moduleimport pandas as pd# creating a DataFramedf = pd.DataFrame({'String 1': ['Tom', 'Nick', 'Krish', 'Jack'], 'Age': [32, 24, 33, 21]})# function for prepending 'Geek'def prepend_geek(age): return age + 2# executing the functiondf[["Age"]] = df[["Age"]].apply(prepend_geek)# displaying the DataFramedisplay(df) |
Output:
Example 2: Pandas Apply Function to multiple Columns
Here, we apply a function to two columns of Pandas Dataframe using Python concatenation.
Python3
# import the moduleimport pandas as pd# creating a DataFramedf = pd.DataFrame({'String 1' :['Tom', 'Nick', 'Krish', 'Jack'], 'String 2' :['Jane', 'John', 'Doe', 'Mohan']})# function for prepending 'Geek'def prepend_geek(name): return 'Geek ' + name# executing the functiondf[["String 1", "String 2"]] = df[["String 1", "String 2"]].apply(prepend_geek)# displaying the DataFramedisplay(df) |
Output :
Example 3: Pandas Apply Function to All Columns
Here, we are multiplying all the columns by 2 by calling the multiply_by_2 function.
Python3
# import the moduleimport pandas as pd# creating a DataFramedf = pd.DataFrame({'Integers' :[1, 2, 3, 4, 5], 'Float' :[1.1, 2.2, 3.3, 4.4 ,5.5], "Even_no":[2, 4, 6, 8, 10]})# function for prepending 'Geek'def multiply_by_2(number): return 2 * number# executing the functiondf = df.apply(multiply_by_2)# displaying the DataFramedisplay(df) |
Output :
Example 4: Pandas Apply Function to single Columns using Numpy
In this example, we are applying panda’s apply function to only a single column i.e. Integers, and making it square using numpy.square.
Python3
# import the moduleimport pandas as pdimport numpy as np# creating a DataFramedf = pd.DataFrame({'Integers' :[1, 2, 3, 4, 5], 'Float' :[1.1, 2.2, 3.3, 4.4 ,5.5], "Even_no":[2, 4, 6, 8, 10]})# executing the functiondf["Integers"] = df["Integers"].apply(np.square)# displaying the DataFramedisplay(df) |
Example 5: Pandas Apply Function to All Columns using lambda
In this example, we are applying panda’s apply function to all the columns, and adding all the columns with 5 using Python lambda.
Python3
# import the moduleimport pandas as pd# creating a DataFramedf = pd.DataFrame({'Integers' :[1, 2, 3, 4, 5], 'Float' :[1.1, 2.2, 3.3, 4.4 ,5.5], "Even_no":[2, 4, 6, 8, 10]})# executing the functiondf = df.apply(lambda x: x+5)# displaying the DataFramedisplay(df) |
