Python | Pandas Series.transform()
Last Updated :
05 Feb, 2019
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.
import pandas as pd
sr = pd.Series(['New York', 'Chicago', 'Toronto', 'Lisbon', 'Rio', 'Moscow'])
didx = pd.DatetimeIndex(start ='2014-08-01 10:00', freq ='W',
periods = 6, tz = 'Europe/Berlin')
sr.index = didx
print(sr)
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Output :
Now we will use Series.transform() function to append ‘_City’ at the end of each city name.
sr.transform(lambda x : x + '_City')
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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.
import pandas as pd
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(df)
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Output :
Now we will use Dataframe.transform() function to increase the ticket cost by 1000
df['Cost'] = df['Cost'].transform(lambda x : x + 1000)
print(df)
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Output :
As we can see in the output, the Dataframe.transform() function has successfully increased the ticket cost of each event by 1000.
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