Python | Pandas Series.as_blocks()

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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.

# 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() 
  
# 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.

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() 
  
# 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.