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Python | Pandas Series.filter()
  • Last Updated : 13 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.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 pd
import pandas as pd
  
# Creating the Series
sr = pd.Series([80, 25, 3, 25, 24, 6])
  
# Create the Index
index_ = ['Coca Cola', 'Sprite', 'Coke', 'Fanta', 'Dew', 'ThumbsUp']
  
# set the index
sr.index = index_
  
# Print the series
print(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 values
result = sr.filter(regex = '. .')
  
# Print the result
print(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 pd
import pandas as pd
  
# Creating the Series
sr = pd.Series(['New York', 'Chicago', 'Toronto', 'Lisbon', 'Rio'])
  
# Create the Index
index_ = ['City 1', 'City 2', 'City 3', 'City 4', 'City 5'
  
# set the index
sr.index = index_
  
# Print the series
print(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 values
result = sr.filter(items = ['City 2', 'City 4'])
  
# Print the result
print(result)

Output :

As we can see in the output, the Series.filter() function has successfully returned the desired values from the given series object.

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