to_pickle() to pickle pandas DataFrame and save

Pandas

`path ( file )`	Path with file name to store the byte string of pickled data
`compression`	(Optional )Type of compression, {'infer', 'gzip', 'bz2', 'zip', 'xz', None}
`protocol`	(Optional) , Int, we can use HIGHEST_PROTOCOL to get the latest

We can create a file with DataFrame data by using to_pickle(). We will create one DataFrame by using a dictionary.

import pandas as pd
import pickle
my_dict={
	'NAME':['Ravi','Raju','Alex'],
	'ID':[1,2,3],'MATH':[30,40,50],
	'ENGLISH':[20,30,40]
	}
my_data = pd.DataFrame(data=my_dict)
my_data.to_pickle("my_data.pkl") # pickled

This is the data written to the current directory. Check for the file my_data.pkl . You can use path to store the file in different location.

my_data.to_pickle("D:\my_data\my_data.pkl")

Compression

We can add compression option. ( You must take care while reading the same file )

my_data.to_pickle("my_data.pkl",compression='zip')

protocol

We can assign the integer or we can get the latest by using HIGHEST_PROTOCOL

my_data.to_pickle("my_data.pkl",protocol=pickle.HIGHEST_PROTOCOL)

While creating the pickle we have to note the compression used. When we de-serialize or un-pickle the same file, we must use the same type of compression.

read_pickle()
Read how to pickle data stored in a MySQL table.
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Data input and output from Pandas DataFrame

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