DataFrame.read_pickle() method in Pandas

Prerequisite : pd.to_pickle method()

The read_pickle() method is used to pickle (serialize) the given object into the file. This method uses the syntax as given below :

Syntax:

pd.read_pickle(path, compression='infer')

Parameters:

Arguments	Type	Description
path	str	File path where the pickled object will be loaded.
compression	{‘infer’, ‘gzip’, ‘bz2’, ‘zip’, ‘xz’, None}, default ‘infer’	For on-the-fly decompression of on-disk data. If ‘infer’, then use gzip, bz2, xz or zip if path ends in ‘.gz’, ‘.bz2’, ‘.xz’, or ‘.zip’ respectively, and no decompression otherwise. Set to None for no decompression.

Below is the implementation of the above method with some examples :

Example 1:

Python3

# importing packages
import pandas as pd

# dictionary of data
dct = {'ID': {0: 23, 1: 43, 2: 12,
            3: 13, 4: 67, 5: 89,
            6: 90, 7: 56, 8: 34},
    'Name': {0: 'Ram', 1: 'Deep',
                2: 'Yash', 3: 'Aman',
                4: 'Arjun', 5: 'Aditya',
                6: 'Divya', 7: 'Chalsea',
                8: 'Akash' },
    'Marks': {0: 89, 1: 97, 2: 45, 3: 78,
                4: 56, 5: 76, 6: 100, 7: 87,
                8: 81},
    'Grade': {0: 'B', 1: 'A', 2: 'F', 3: 'C',
                4: 'E', 5: 'C', 6: 'A', 7: 'B',
                8: 'B'}
    }

# forming dataframe
data = pd.DataFrame(dct)

# using to_pickle function to form file
# with name 'pickle_file'
pd.to_pickle(data,'./pickle_file.pkl')

# unpickled the data by using the
# pd.read_pickle method
unpickled_data = pd.read_pickle("./pickle_file.pkl")
print(unpickled_data)

Output :

Example 2:

Python3

# importing packages
import pandas as pd

# dictionary of data
dct = {"f1": range(6), "b1": range(6, 12)}

# forming dataframe
data = pd.DataFrame(dct)

# using to_pickle function to form file
# with name 'pickle_data'
pd.to_pickle(data,'./pickle_data.pkl')

# unpickled the data by using the
# pd.read_pickle method
unpickled_data = pd.read_pickle("./pickle_data.pkl")
print(unpickled_data)

Output :