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.get_dtype_counts() function return the counts of unique dtypes in this object.
Syntax: Series.get_values()
Parameter : None
Returns : dtype : Series
Example #1: Use Series.get_dtype_counts() function to return the count of unique dtype in the given series object.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series(['New York', 'Chicago', 'Toronto', None, '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.get_dtype_counts() function to return the count of unique dytpe in the given series object.
# return the count of dtypes result = sr.get_dtype_counts() # Print the result print(result) |
Output :
As we can see in the output, the Series.get_dtype_counts() function has returned the count of dtype in the given series object. It has returned object.
Example #2 : Use Series.get_dtype_counts() function to return the count of unique dtype in 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]) # Create the Index index_ = pd.date_range('2010-10-09', periods = 11, freq ='M') # set the index sr.index = index_ # Print the series print(sr) |
Output :
Now we will use Series.get_dtype_counts() function to return the count of unique dytpe in the given series object.
# return the count of dtypes result = sr.get_dtype_counts() # Print the result print(result) |
Output :
As we can see in the output, the Series.get_dtype_counts() function has returned the count of dtype in the given series object. It has returned int64.
