Skip to content
Related Articles

Related Articles

Improve Article
Save Article
Like Article

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.

 Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.  

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning - Basic Level Course

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.




My Personal Notes arrow_drop_up
Recommended Articles
Page :

Start Your Coding Journey Now!