Python Pandas read_feather() Method

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The read_feather() method in Python's Pandas library allows you to read/load the feather-format object from the file path into a Pandas object, enabling fast and efficient data retrieval.

The Feather file format is a portable file format for saving and retrieving the DataFrame. It is a fast and language-independent binary file format designed for efficient data interchange between data analysis languages. It is supported by both Python and R languages, ensuring easy data sharing, fast reading and writing capabilities with less memory usage.

Before using the read_feather() method, please ensure that the 'pyarrow' library is installed, as it is an optional dependency. We need to install it by using the following command −

pip install pyarrow

Syntax

Following is the syntax of the Python Pandas read_feather() method −

pandas.read_feather(path, columns=None, use_threads=True, storage_options=None, dtype_backend=<no_default>)

Parameters

The Python Pandas read_feather() method accepts the below parameters −

• path: This method accepts a string, path object, or file-like object that specifies the file path. It can be a local file path or a remote URL supporting schemes like http, ftp, s3, and file. For local files, the expected format is file://localhost/path/to/file.feather.

• columns: Specifies which columns to read. If not provided, all columns are read.

• use_threads: Specifies whether to parallelize reading using multiple threads.

• storage_options: Additional options for storage connections, such as host, port, username, password, etc.

• dtype_backend: Specifies the data type backend for the resulting DataFrame.

Return Value

The Pandas read_feather() method returns the same type of Pandas object that was stored in the feather format file.

Example: Loading a Feather File into Pandas Object

Here is a basic example demonstrating loading a Pandas DataFrame object from a feather file using the Pandas read_feather() method.

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({"Col_1": range(5), "Col_2": range(5, 10)})
print("Original DataFrame:")
print(df)

# Save the DataFrame as a feather file
df.to_feather("df_feather_file.feather")

# Load the DataFrame from the feather file
result = pd.read_feather("df_feather_file.feather")
print("DataFrame Loaded from Feather File:")
print(result)

When we run above program, it produces following result −

Original DataFrame:

	Col_1	Col_2
0	0	5
1	1	6
2	2	7
3	3	8
4	4	9

DataFrame Loaded from Feather File:

	Col_1	Col_2
0	0	5
1	1	6
2	2	7
3	3	8
4	4	9

Example: Reading Selected Columns from a Feather File

This example loads the Pandas DataFrame with the selected columns from a feather file using the Pandas read_feather() method.

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({"Col_1": range(5), "Col_2": range(5, 10)})
print("Original DataFrame:")
print(df)

# Save the DataFrame as a feather file
df.to_feather("df_feather_file.feather")

# Read only specific columns
df = pd.read_feather('df_feather_file.feather', columns=['Col_1'])
print("DataFrame with Selected Column:")
print(df)

While executing the above code we get the following output −

Original DataFrame:

	Col_1	Col_2
0	0	5
1	1	6
2	2	7
3	3	8
4	4	9

DataFrame with Selected Column:

	Col_1
0	0
1	1
2	2
3	3
4	4

Example: Loading a feather File with Compression

The following example initially saves a compressed feather file and then loads it using the read_feather() method to read the compressed feather file back into the Pandas object.

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({"Col_1": range(5), "Col_2": range(5, 10)})
print("Original DataFrame:")
print(df)

# Save the DataFrame to a feather file with compression
df.to_feather('compressed_data.feather', compression='zstd')
print("DataFrame saved with compression..")

# Load the compressed file
compressed_df = pd.read_feather("compressed_data.feather")
print("\nLoaded Compressed DataFrame:")
print(compressed_df)

Following is an output of the above code −

Original DataFrame:

	Col_1	Col_2
0	0	5
1	1	6
2	2	7
3	3	8
4	4	9

DataFrame saved with compression.. Loaded Compressed DataFrame:

	Col_1	Col_2
0	0	5
1	1	6
2	2	7
3	3	8
4	4	9

Example: Reading from an In-Memory Feather File

The read_feather() method can also read the In-Memory feather file. Here is an example demonstrates the same.

import pandas as pd
import io

# Create a Pandas DataFrame 
df = pd.DataFrame(data={'Col_1': [1, 2], 'Col_2': [3.0, 4.0]})

# Display the Input DataFrame
print("Original DataFrame:")
print(df)

# Save the DataFrame as In-Memory feather
buf = io.BytesIO()
df.to_feather(buf)

# Read the DataFrame from the in-memory buffer
loaded_df = pd.read_feather(buf)
print("DataFrame Loaded from In-Memory Feather:")
print(loaded_df)

Following is an output of the above code −

Original DataFrame:

	Col_1	Col_2
0	1	3.0
1	2	4.0

DataFrame Loaded from In-Memory Feather:

	Col_1	Col_2
0	1	3.0
1	2	4.0