5 Best Ways to Export a DataFrame to Pickle File Format in Python and Read It Back

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💡 Problem Formulation: In data analysis, it is often necessary to save intermediate results in a compact format and load them back for further use. This article demonstrates how to export a pandas DataFrame to a binary pickle file and read it back into Python. For example, we may have a DataFrame ‘df’ that we want to save to ‘data.pkl’, and later, we want to read ‘data.pkl’ to retrieve the original DataFrame.

Method 1: Using pandas to_pickle() and read_pickle() methods

Pandas provides straightforward methods to serialize DataFrame objects to pickle format and deserialize from pickle format back to DataFrame objects. The to_pickle() method is used to save a DataFrame to a pickle file, while the read_pickle() method reads it back.

Here’s an example:

import pandas as pd

# Create a simple DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Save the DataFrame to a pickle file
df.to_pickle('./data.pkl')

# Read the DataFrame back from the pickle file
df_from_pickle = pd.read_pickle('./data.pkl')

The snippet above creates a simple DataFrame, exports it to ‘data.pkl’, and then reads it back into a new DataFrame.

Method 2: Using Python’s pickle module

Python’s built-in pickle module can serialize almost any Python object, including pandas DataFrames. Employing this module, you gain more control over the serialization process, such as setting the protocol level.

Here’s an example:

import pandas as pd
import pickle

# Create a DataFrame
df = pd.DataFrame({'X': [1, 2, 3], 'Y': ['A', 'B', 'C']})

# Save the DataFrame using pickle
with open('data.pickle', 'wb') as file:
    pickle.dump(df, file, protocol=pickle.HIGHEST_PROTOCOL)

# Read the DataFrame back
with open('data.pickle', 'rb') as file:
    df_from_pickle = pickle.load(file)

This snippet demonstrates serialization with the pickle module by using a context manager to ensure proper handling of file resources.

Method 3: Compressed Pickle Files

Saving DataFrame as compressed pickle files can be useful when dealing with large datasets. Various compression types like ‘gzip’, ‘bz2’, ‘xz’ can be used to reduce file size significantly.

Here’s an example:

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({'Column1': range(100), 'Column2': range(100, 200)})

# Save the DataFrame to a compressed pickle file
df.to_pickle('data_compressed.pkl.gz', compression='gzip')

# Read the compressed pickle file back into a DataFrame
df_from_compressed_pickle = pd.read_pickle('data_compressed.pkl.gz', compression='gzip')

The code sample saves a DataFrame to a gzip-compressed pickle file and then reads it back, effectively reducing file size and storage space.

Method 4: Using joblib for Efficient Serialization

joblib is a library that provides efficient serialization of Python objects, including NumPy arrays, which are often stored within DataFrames. It can sometimes be more efficient than pickle for large arrays of data.

Here’s an example:

from joblib import dump, load
import pandas as pd

# Create a DataFrame
df = pd.DataFrame({'ColA': list('abcdef'), 'ColB': list(range(6))})

# Save the DataFrame using joblib
dump(df, 'data.joblib')

# Load the DataFrame from a joblib file
df_from_joblib = load('data.joblib')

This code snippet uses the joblib library to serialize and then deserialize a pandas DataFrame. It is a good alternative for large numerical datasets.

Bonus One-Liner Method 5: Pickle DataFrames in a Single Line

For quick operations, the entire process of saving and loading into pickle format can be done in one line using a lambda function or chaining methods if only temporary usage of the file is needed.

Here’s an example:

(pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
 .to_pickle('data_one_liner.pkl')) and print(pd.read_pickle('data_one_liner.pkl'))

The one-liner above creates a DataFrame, pickles it to a file, and prints the unpickled DataFrame, all in a single line of code.

Summary/Discussion

  • Method 1: Pandas to_pickle/read_pickle. Easy to use. Limited to pandas objects.
  • Method 2: Python pickle module. More control over the serialization process. Requires file handling code.
  • Method 3: Compressed Pickle Files. Saves disk space. Slightly more complex with compression options.
  • Method 4: Using joblib. Efficient for large arrays. An extra library to install and use.
  • Bonus Method 5: Single Line Pickle. Quick and easy for temporary usage. Not suitable for complex scenarios.