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.to_dict() function is used to convert the given Series object to {label -> value} dict or dict-like object.
Syntax: Series.to_dict(into=)
Parameter :
into : The collections.Mapping subclass to use as the return object.Returns : value_dict : collections.Mapping
Example #1: Use Series.to_dict() function to convert the given series object to a dictionary.
# importing pandas as pd import pandas as pd   # Creating the Series sr = pd.Series(['New York', 'Chicago', 'Toronto', 'Lisbon', 'Rio', 'Moscow'])   # Create the Datetime Index didx = pd.DatetimeIndex(start ='2014-08-01 10:00', freq ='W',                      periods = 6, tz = 'Europe/Berlin')   # set the index sr.index = didx   # Print the series print(sr) |
Output :
Now we will use Series.to_dict() function to convert the given series object to a dictionary.
# convert to dictionary sr.to_dict() |
Output :
As we can see in the output, the Series.to_dict() function has successfully converted the given series object to a dictionary.
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Example #2: Use Series.to_dict() function to convert the given series object to a dictionary.
# importing pandas as pd import pandas as pd   # Creating the Series sr = pd.Series([19.5, 16.8, 22.78, 20.124, 18.1002])   # Print the series print(sr) |
Output :
Now we will use Series.to_dict() function to convert the given series object to a dictionary.
# convert to dictionary sr.to_dict() |
Output :
As we can see in the output, the Series.to_dict() function has successfully converted the given series object to a dictionary.
