import cudf # Creating a dataframe df1 = cudf.DataFrame({'key': [0, 1, 2, 3, 4], 'value': ['apple', 'banana', 'orange', 'kiwi', 'grape']}) # Creating another dataframe df2 = cudf.DataFrame({'key': [0, 1, 2, 3, 4], 'value': ['red', 'yellow', 'orange', 'brown', 'green']}) # Merging the dataframes using the 'key' column merged_df = df1.merge(df2, on='key') # Displaying the merged dataframe print(merged_df)
key value_x value_y 0 0 apple red 1 1 banana yellow 2 2 orange orange 3 3 kiwi brown 4 4 grape green
import cudf # Creating a dataframe df1 = cudf.DataFrame({'key': [0, 1, 2, 3, 4], 'value': ['apple', 'banana', 'orange', 'kiwi', 'grape']}) # Creating another dataframe df2 = cudf.DataFrame({'key': [2, 3], 'value': ['orange', 'brown']}) # Merging the dataframes using the 'key' column merged_df = df1.merge(df2, on='key') # Displaying the merged dataframe print(merged_df)
key value_x value_y 0 2 orange orange 1 3 kiwi brownHere, we have created two dataframes with a common 'key' column, but the second dataframe has only a subset of the keys present in the first dataframe. The merge operation is performed only on the common keys, and the resulting merged dataframe contains only the rows with the common keys. Overall, python cudf.core DataFrame merge is a powerful function for combining multiple dataframes into a single dataframe based on one or more common columns. It is particularly useful for large datasets that can benefit from GPU acceleration.