Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index.
Pandas Series.transform() function Call func (the passed function) on self producing a Series with transformed values and that has the same axis length as self.
Syntax: Series.transform(func, axis=0, *args, **kwargs)
Parameter :
func : If a function, must either work when passed a Series or when passed to Series.apply
axis : Parameter needed for compatibility with DataFrame.
*args : Positional arguments to pass to func.
**kwargs : Keyword arguments to pass to func.Returns : Returns series that must have the same length as self.
Example #1: Use Series.transform() function to transform the elements of the given Series object. Append ‘_City’ at the end of each city name.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series(['New York', 'Chicago', 'Toronto', 'Lisbon', 'Rio', 'Moscow']) # Create the Datetime Index didx = pd.DatetimeIndex(start ='2014-08-01 10:00', freq ='W', periods = 6, tz = 'Europe/Berlin') # set the index sr.index = didx # Print the series print(sr) |
Output :
Now we will use Series.transform() function to append ‘_City’ at the end of each city name.
# append '_City' sr.transform(lambda x : x + '_City') |
Output :
As we can see in the output, the Series.transform() function has successfully appended the desired keyword at the end of which city name.
Example #2: Use Dataframe.transform() function to transform the data of the given Dataframe. Increase the ticket cost of each even by 1000.
# importing pandas as pd import pandas as pd # Creating the Dataframe df = pd.DataFrame({'Date':['10/2/2011', '11/2/2011', '12/2/2011', '13/2/2011'], 'Event':['Music', 'Poetry', 'Theatre', 'Comedy'], 'Cost':[10000, 5000, 15000, 2000]}) # Print the dataframe print(df) |
Output :
Now we will use Dataframe.transform() function to increase the ticket cost by 1000
# transform the 'Cost' column df['Cost'] = df['Cost'].transform(lambda x : x + 1000) # Print the dataframe after modification print(df) |
Output :
As we can see in the output, the Dataframe.transform() function has successfully increased the ticket cost of each event by 1000.
