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.as_blocks() function is used to convert the frame to a dict of dtype -> Constructor Types that each has a homogeneous dtype.
Syntax: Series.as_blocks(copy=True)
Parameter :
copy : boolean, default TrueReturns : values : a dict of dtype -> Constructor Types
Example #1: Use Series.as_blocks() function to return the given series object as a dictionary.
# importing pandas as pd import pandas as pd   # Creating the Series sr = pd.Series(['New York', 'Chicago', 'Toronto', 'Lisbon', '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 :
City 1 New York City 2 Chicago City 3 Toronto City 4 Lisbon City 5 Rio dtype: object
Now we will use Series.as_blocks() function to return the given series object as a dictionary.
# return a dictionary result = sr.as_blocks() Â Â # Print the result print(result) |
Output :
{'object': City 1 New York
City 2 Chicago
City 3 Toronto
City 4 Lisbon
City 5 Rio
dtype: object}
As we can see in the output, the Series.as_blocks() function has successfully returned the given series object as a dictionary.
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Example #2 : Use Series.as_blocks() function to return the given series object as a dictionary.
# importing pandas as pd import pandas as pd   # Creating the Series sr = pd.Series([11, 21, 8, 18, 65, 18, 32, 10, 5, 32, None])   # Create the Index # apply yearly frequency index_ = pd.date_range('2010-10-09 08:45', periods = 11, freq ='Y')   # set the index sr.index = index_   # Print the series print(sr) |
Output :
2010-12-31 08:45:00 11.0 2011-12-31 08:45:00 21.0 2012-12-31 08:45:00 8.0 2013-12-31 08:45:00 18.0 2014-12-31 08:45:00 65.0 2015-12-31 08:45:00 18.0 2016-12-31 08:45:00 32.0 2017-12-31 08:45:00 10.0 2018-12-31 08:45:00 5.0 2019-12-31 08:45:00 32.0 2020-12-31 08:45:00 NaN Freq: A-DEC, dtype: float64
Now we will use Series.as_blocks() function to return the given series object as a dictionary.
# return a dictionary result = sr.as_blocks() Â Â # Print the result print(result) |
Output :
{'float64': 2010-12-31 08:45:00 11.0
2011-12-31 08:45:00 21.0
2012-12-31 08:45:00 8.0
2013-12-31 08:45:00 18.0
2014-12-31 08:45:00 65.0
2015-12-31 08:45:00 18.0
2016-12-31 08:45:00 32.0
2017-12-31 08:45:00 10.0
2018-12-31 08:45:00 5.0
2019-12-31 08:45:00 32.0
2020-12-31 08:45:00 NaN
Freq: A-DEC, dtype: float64}
As we can see in the output, the Series.as_blocks() function has successfully returned the given series object as a dictionary.
