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.filter() function returns subset rows or columns of dataframe according to labels in the specified index. Please note that this routine does not filter a dataframe on its contents. The filter is applied to the labels of the index.
Syntax: Series.filter(items=None, like=None, regex=None, axis=None)
Parameter :
items : List of axis to restrict to (must not all be present).
like : Keep axis where “arg in col == True”.
regex : Keep axis with re.search(regex, col) == True.
axis : The axis to filter on. By default this is the info axis, ‘index’ for Series, ‘columns’ for DataFrame.Returns : same type as input object
Example #1: Use Series.filter() function to filter out some values in the given series object using a regular expressions.
# importing pandas as pdimport pandas as pd # Creating the Seriessr = pd.Series([80, 25, 3, 25, 24, 6]) # Create the Indexindex_ = ['Coca Cola', 'Sprite', 'Coke', 'Fanta', 'Dew', 'ThumbsUp'] # set the indexsr.index = index_ # Print the seriesprint(sr) |
Output :
Now we will use Series.filter() function to filter those values from the given series object whose index label name has a space in its name.
# filter valuesresult = sr.filter(regex = '. .') # Print the resultprint(result) |
Output :
As we can see in the output, the Series.filter() function has successfully returned the desired values from the given series object.
Example #2 : Use Series.filter() function to filter out some values in the given series object using a list of index labels.
# importing pandas as pdimport pandas as pd # Creating the Seriessr = pd.Series(['New York', 'Chicago', 'Toronto', 'Lisbon', 'Rio']) # Create the Indexindex_ = ['City 1', 'City 2', 'City 3', 'City 4', 'City 5'] # set the indexsr.index = index_ # Print the seriesprint(sr) |
Output :
Now we will use Series.filter() function to filter the values corresponding to the passed index labels in the given series object.
# filter valuesresult = sr.filter(items = ['City 2', 'City 4']) # Print the resultprint(result) |
Output :
As we can see in the output, the Series.filter() function has successfully returned the desired values from the given series object.
