Python | Pandas Series.as_blocks()

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 True Returns : values : a dict of dtype -> Constructor Types

Example #1: Use Series.as_blocks() function to return the given series object as a dictionary. 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. 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.   Example #2 : Use Series.as_blocks() function to return the given series object as a dictionary. 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. 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.