What is the difference between save a pandas dataframe to pickle and to csv?

Question

I am learning python pandas. I see a tutorial which shows two ways to save a pandas dataframe.

1. pd.to_csv('sub.csv') and to open pd.read_csv('sub.csv')

2. pd.to_pickle('sub.pkl') and to open pd.read_pickle('sub.pkl')

The tutorial says to_pickle is to save the dataframe to disk. I am confused about this. Because when I use to_csv, I did see a csv file appears in the folder, which I assume is also save to disk right?

In general, why we want to save a dataframe using to_pickle rather than save it to csv or txt or other format?

Answer 1

csv

• ✅human readable
• ✅cross platform
• ⛔slower
• ⛔more disk space
• ⛔doesn't preserve types in some cases

pickle

• ✅fast saving/loading
• ✅less disk space
• ⛔non human readable
• ⛔python only

Also take a look at parquet format (to_parquet, read_parquet)

• ✅fast saving/loading
• ✅less disk space than pickle
• ✅supported by many platforms
• ⛔non human readable

Answer 2

Pickle is a serialized way of storing a Pandas dataframe. Basically, you are writing down the exact representation of the dataframe to disk. This means the types of the columns are and the indices are the same. If you simply save a file as csv, you are just storing it as a comma separated list. Depending on your data set, some information will be lost when you load it back up.

You can read more about pickle library in python, here.