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.nlargest() function return the n largest element from the underlying data in the given series object.
Syntax: Series.nlargest(n=5, keep=’first’)
Parameter :
n : Return this many descending sorted values.
keep : {‘first’, ‘last’, ‘all’}, default ‘first’Returns : Series
Example #1: Use Series.nlargest() function to return the first n largest element from the given series object.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([10, 25, 3, 11, 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.nlargest() function to find the first 2 largest value in the given series object.
# return the first 2 of the largest # element result = sr.nlargest(n = 2) # Print the result print(result) |
Output :
As we can see in the output, the Series.nlargest() function has successfully returned the first 2 largest value in the given series object.
Example #2: Use Series.nlargest() function to return the first n largest element from the given series object.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([11, 21, 8, 18, 65, 84, 32, 10, 5, 24, 32]) # Print the series print(sr) |
Output :
Now we will use Series.nlargest() function to find the first 5 largest value in the given series object.
# return the first 5 of the largest # element result = sr.nlargest(n = 5) # Print the result print(result) |
Output :
As we can see in the output, the Series.nlargest() function has successfully returned the first 5 largest value in the given series object.
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