Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
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.dtype attribute returns the data type of the underlying data for the given Series object.
Syntax: Series.dtype
Parameter : None
Returns : data type
Example #1: Use Series.dtype attribute to find the data type of the underlying data for the given Series object.
# importing pandas as pdimport pandas as pd # Creating the Seriessr = pd.Series(['New York', 'Chicago', 'Toronto', 'Lisbon']) # Creating the row axis labelssr.index = ['City 1', 'City 2', 'City 3', 'City 4'] # Print the seriesprint(sr) |
Output :
Now we will use Series.dtype attribute to find the data type of the given Series object.
# return the data typesr.dtype |
Output :
As we can see in the output, the Series.dtype attribute has returned ‘O’ indicating the data type of the underlying data is object type.
Example #2 : Use Series.dtype attribute to find the data type of the underlying data for the given Series object.
# importing pandas as pdimport pandas as pd # Creating the Seriessr = pd.Series([1000, 5000, 1500, 8222]) # Print the seriesprint(sr) |
Output :
Now we will use Series.dtype attribute to find the data type of the given Series object.
# return the data typesr.dtype |
Output :
As we can see in the output, the Series.dtype attribute has returned ‘int64’ indicating the data type of the underlying data is of int64 type.
