Dask DataFrames

Dask DataFrames are abstractions of pandas DataFrames. They are processed in parallel and partitioned into multiple smaller pandas DataFrames, as shown in the following diagram:

These small DataFrames can be stored on local or distributed remote machines. Dask DataFrames can compute large-sized DataFrames by utilizing all the available cores in the system. They coordinate the DataFrames using indexing and support standard pandas operations such as groupby, join, and time series. Dask DataFrames perform operations such as element-wise, row-wise, isin(), and date faster compared to set_index() and join() on index operations. Now, let's experiment with the performance or execution speed of Dask:

# Read csv file using pandas
import pandas as pd
%time temp = pd.read_csv("HR_comma_sep.csv")

This results in the following output:

CPU times: user 17.1 ms, sys: 8.34 ms, total: 25.4 ms

Wall time: 36.3 ms

In the preceding code, we tested the read time of a file using...