Python | Pandas Series.transform()

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. Python3

# 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. Python3 1==

# 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. Python3

# 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 Python3 1==

# 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.