Comparing the Speed and Filesize of to_csv(), np.save(), to_hdf(), and to_pickle() Functions

Reading and writing files using Pandas and NumPy is an everyday task for Data Scientists and Engineers.

Let’s compare the most common functions that these libraries provide to write/read tabular data.

We can make our code much faster in these I/O operations, save time, and make our boss and ourselves happy.

We can also save serious amounts of disk space by choosing the appropriate save function.

Read more on Medium (Towards Data Science)