Python | Pandas DataFrame.to_sparse - GeeksforGeeks

21 Feb, 2019
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() function convert to SparseDataFrame. The function implement 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 a 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
Example #1: Use DataFrame.to_sparse() function to convert the given Dataframe to a SparseDataFrame for efficient storage.
# 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.
# 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.
# 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.
# 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.
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