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.ravel() function returns the flattened underlying data as an ndarray.
Syntax: Series.ravel(order=āCā)
Parameter : order
Returns : ndarray
Example #1: Use Series.ravel() function to return the elements of the given Series object as an ndarray.
# importing pandas as pd import pandas as pd Ā Ā # Creating the Series sr = pd.Series([10, 25, 3, 11, 24, 6]) Ā Ā # Create the Index index_ = ['Coca Cola', 'Sprite', 'Coke', 'Fanta', 'Dew', 'ThumbsUp'] Ā Ā # set the index sr.index = index_ Ā Ā # Print the series print(sr) |
Output :
Now we will use Series.ravel() function to return the underlying data of the given Series object as an ndarray.
# return an ndarray result = sr.ravel() Ā Ā # Print the result print(result) |
Output :
As we can see in the output, the Series.ravel() function has returned the an ndarray containing the data of the given series object.
Example #2: Use Series.ravel() function to return the elements of the given Series object as an ndarray.
# importing pandas as pd import pandas as pd Ā Ā # Creating the Series sr = pd.Series(['New York', 'Chicago', 'Toronto', 'Lisbon', 'Rio']) Ā Ā # Create the Index index_ = ['City 1', 'City 2', 'City 3', 'City 4', 'City 5']Ā Ā Ā # set the index sr.index = index_ Ā Ā # Print the series print(sr) |
Output :
Now we will use Series.ravel() function to return the underlying data of the given Series object as an ndarray.
# return an ndarray result = sr.ravel() Ā Ā # Print the result print(result) |
Output :
As we can see in the output, the Series.ravel() function has returned the an ndarray containing the data of the given series object.
