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.
# 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.
# 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.
# 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.
# 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.
