Python | Pandas DataFrame.blocks

Pandas DataFrame is a two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). Arithmetic operations align on both row and column labels. It can be thought of as a dict-like container for Series objects. This is the primary data structure of the Pandas. Pandas DataFrame.blocks attribute is synonym for as_blocks() function. It basically convert the frame to a dict of dtype -> Constructor Types that each has a homogeneous dtype.

Syntax: DataFrame.blocks Parameter : None Returns : dict

Example #1: Use DataFrame.blocks attribute to return a dictionary containing the data in blocks of separate data types. Python3

# importing pandas as pd
import pandas as pd

# Creating the DataFrame
df = pd.DataFrame({'Weight':[45, 88, 56, 15, 71],
                   'Name':['Sam', 'Andrea', 'Alex', 'Robin', 'Kia'],
                   'Age':[14, 25, 55, 8, 21]})

# Create the index
index_ = ['Row_1', 'Row_2', 'Row_3', 'Row_4', 'Row_5']

# Set the index
df.index = index_

# Print the DataFrame
print(df)

Output : Now we will use DataFrame.blocks attribute to return the block representation of the given dataframe. Python3 1==

# return a dictionary
result = df.blocks

# Print the result
print(result)

Output : As we can see in the output, the DataFrame.blocks attribute has successfully returned a dictionary containing the data of the dataframe. Homogeneous columns are places in the same block.   Example #2: Use DataFrame.blocks attribute to return a dictionary containing the data in blocks of separate data types. Python3

# importing pandas as pd
import pandas as pd

# Creating the DataFrame
df = pd.DataFrame({"A":[12, 4, 5, None, 1], 
                   "B":[7, 2, 54, 3, None], 
                   "C":[20, 16, 11, 3, 8], 
                   "D":[14, 3, None, 2, 6]}) 

# Create the index
index_ = ['Row_1', 'Row_2', 'Row_3', 'Row_4', 'Row_5']

# Set the index
df.index = index_

# Print the DataFrame
print(df)

Output : Now we will use DataFrame.blocks attribute to return the block representation of the given dataframe. Python3 1==

# return a dictionary
result = df.blocks

# Print the result
print(result)

Output : As we can see in the output, the DataFrame.blocks attribute has successfully returned a dictionary containing the data of the dataframe. Homogeneous columns are places in the same block.