Python | Pandas Series.as_blocks()
Last Updated :
27 Feb, 2019
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.
import pandas as pd
sr = pd.Series([ 'New York' , 'Chicago' , 'Toronto' , 'Lisbon' , 'Rio' ])
index_ = [ 'City 1' , 'City 2' , 'City 3' , 'City 4' , 'City 5' ]
sr.index = index_
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.
result = sr.as_blocks()
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.
Example #2 : Use Series.as_blocks()
function to return the given series object as a dictionary.
import pandas as pd
sr = pd.Series([ 11 , 21 , 8 , 18 , 65 , 18 , 32 , 10 , 5 , 32 , None ])
index_ = pd.date_range( '2010-10-09 08:45' , periods = 11 , freq = 'Y' )
sr.index = index_
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.
result = sr.as_blocks()
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.
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