Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
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.update() function modify Series in place using non-NA values from passed Series object. The function aligns on index.
Syntax: Series.update(other)
Parameter :
other: seriesReturns : None
Example #1: Use Series.update() function to update the values of some cities in the given Series object
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series(['New York', 'Chicago', None, 'Toronto', 'Lisbon', 'Rio', 'Chicago', 'Lisbon']) # Print the series print(sr) |
Output :
Now we will use Series.update() function to update the values identified the passed indexed in the given Series object.
# update the values at the passed index # from the values in the passed series object sr.update(pd.Series(['Melbourne', 'Moscow'], index = [2, 7])) |
Output :
As we can see in the output, the Series.update() function has successfully updated the values in the original series object from the passed series object.
Example #2: Use Series.update() function to update the values of some elements in the given Series object
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([100, 214, 325, 88, None, 325, None, 325, 100]) # Print the series print(sr) |
Output :
Now we will use Series.update() function to update the values identified the passed indexed in the given Series object.
# update the values at the passed index # from the values in the passed series object sr.update(pd.Series([5000, 6000], index = [4, 6])) |
Output :
As we can see in the output, the Series.update() function has successfully updated the values in the original series object from the passed series object.
