Input-output operations with Pandas to a CSV are serialized, making them incredibly inefficient and time-consuming. It's frustrating when I see ample scope for parallelization here, but unfortunately, Pandas does not provide this functionality (yet). Although I am never in favor of creating CSVs in the first place with Pandas (read my post below to know why), I understand that there might be situations where one has no other choice but to work with CSVs.
Why I Stopped Dumping DataFrames to a CSV and Why You Should Too
It’s time to say goodbye to pd.to_csv() and pd.read_csv()
Therefore, in this post, we will explore Dask and DataTable, two of the most trending Pandas-like libraries for Data Scientists. We’ll rank Pandas, Dask and Datatable based on their performance on the following parameters:
- Time taken to read the CSV and obtain a PANDAS DATAFRAME
If we read a CSV through Dask and DataTable, they will generate a Dask DataFrame and DataTable DataFrame respectively, not the Pandas DataFrame. Assuming that we want to stick to the traditional Pandas syntax and functions (due to familiarity), we would have to convert these to a Pandas DataFrame first, as shown below.