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.to_sparse
Pandas DataFrame.to_sparse() function convert to SparseDataFrame. The function implements the sparse version of the DataFrame meaning that any data matching a specific value it’s omitted in the representation. The sparse DataFrame allows for more efficient storage.
Syntax: DataFrame.to_sparse(fill_value=None, kind=’block’)
Parameter :
- fill_value : The specific value that should be omitted in the representation.
- kind : {‘block’, ‘integer’}, default ‘block’
Returns : SparseDataFrame
Pandas SparseDataFrame Example
Example 1: Use DataFrame.to_sparse() function to convert the given Dataframe to a SparseDataFrame for efficient storage.
Python3
# importing pandas as pdimport pandas as pd# Creating the DataFramedf = pd.DataFrame({'Weight': [45, 88, 56, 15, 71], 'Name': ['Sam', 'Andrea', 'Alex', 'Robin', 'Kia'], 'Age': [14, 25, 55, 8, 21]})# Create the indexindex_ = pd.date_range('2010-10-09 08:45', periods=5, freq='H')# Set the indexdf.index = index_# Print the DataFrameprint(df) |
Output :
Now we will use DataFrame.to_sparse() function to convert the given dataframe to a SparseDataFrame.
Python3
# convert to SparseDataFrameresult = df.to_sparse()# Print the resultprint(result)# Verify the result by checking the# type of the object.print(type(result)) |
Output :
As we can see in the output, the DataFrame.to_sparse() function has successfully converted the given dataframe to a SparseDataFrame type.
Example 2: Use DataFrame.to_sparse() function to convert the given Dataframe to a SparseDataFrame for efficient storage.
Python3
# importing pandas as pdimport pandas as pd# Creating the DataFramedf = 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 indexindex_ = ['Row_1', 'Row_2', 'Row_3', 'Row_4', 'Row_5']# Set the indexdf.index = index_# Print the DataFrameprint(df) |
Output :
Now we will use DataFrame.to_sparse() function to convert the given dataframe to a SparseDataFrame.
Python3
# convert to SparseDataFrameresult = df.to_sparse()# Print the resultprint(result)# Verify the result by checking the# type of the object.print(type(result)) |
Output :
As we can see in the output, the DataFrame.to_sparse() function has successfully converted the given Dataframe to a SparseDataFrame type.
