pandas read_pickle method Implementation in Python

If you want to manipulate small or large datasets then Pandas library is the best. There are many inbuilt functions in pandas that allow you to manipulate, filter, read, save datasets in an efficient way. The pandas read_pikle() is one of them. This method is used to pickle (serialize) the given object into the file. In this entire tutorial, you will know how to implement the pandas read_pickle() method in python. You will know it through easy steps.

But before going to the coding demonstration part let’s understand the syntax of the pandas read_pickle() method.

pandas.read_pickle(filepath, compression='infer', storage_options=None)

filepath: It is the path the pickle object you want to read.

compression: For on-the-fly decompression of on-disk data. If ‘infer’ and ‘filepath_or_buffer’ is path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, ‘.xz’, or ‘.zst’ (otherwise no compression). If using ‘zip’, the ZIP file must contain only one data file to be read in. Set to None for no decompression.

storage_options: Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc.

Steps to implement Pandas read_pickle() method

In this section, you will learn all the steps for implementing read_pickle() method. You will learn how to save a dataframe as a pickle object and then read it using the pandas.

You should also note that you follow all the given steps for deep understanding.

Step 1: Import required package

In the first step, I will import all the required libraries. In this tutorial, I am using only the pandas package, so I will import it only using the import statement.

import pandas as pd

Step 2: Create a Sample Dataframe

In this step, I will first create a demo dataframe for implementing the pandas read_pickle() method. However, you can use your own dataset. Execute the below lines of code to create a dummy dataframe.

import pandas as pd
data = {"Name":["Sahil","Ron","Michel","Peter"],"Cars":["Audi","BMW","Audi","Ford"],"Age":[30,27,24,50]}
df = pd.DataFrame(data)
print(df)

Output

Sample dataframe for pandas read_pickle() method

Step 3: Save the Dataframe as a Pickle object.

Before reading the pickle object first you have to save the dataframe to pickle object. You can save it using the pandad.to_pickle() method. However, if you have already saved the pickle object then go to step 4. Save the dataframe by running the below code.

import pandas as pd
data = {"Name":["Sahil","Ron","Michel","Peter"],"Cars":["Audi","BMW","Audi","Ford"],"Age":[30,27,24,50]}
df = pd.DataFrame(data)
pd.to_pickle(df,"sample.pkl")

A pickle file named sample.pkl will be created on your present directory.

Step 4: Read the pickle object using the pandas read_pickle() method.

Now the last step is to read and display the dataframe by reading the dataframe from the pickle file. Execute the below lines of code to extract data from the pickle file.

import pandas as pd
data = {"Name":["Sahil","Ron","Michel","Peter"],"Cars":["Audi","BMW","Audi","Ford"],"Age":[30,27,24,50]}
pickled_df = pd.read_pickle("sample.pkl")
print(pickled_df)

You will get the same dataframe as you have created in the first step.

Output

Sample dataframe extracted from the pickle file

Conclusion

These are steps to implement the pandas read_pickle() method. I hope you have understood how to implement this method. If you have any queries then you can contact us for more information.

Source:

Pandas Documentation