Read files of any format into pandas with just read_file(**kwargs) method
Project description
Code README
Overview
This Python code provides a Loader
class that simplifies the process of loading data from various file formats such as CSV, Parquet, and Excel (XLSX) using the pandas library. The code is designed to make it easy to read data from different file formats with a unified interface.
Prerequisites
Before using this code, make sure you have the following installed:
- Python 3.x
- pandas library
How to Use
To use the Loader
class to read data from a file, follow these steps:
-
Import the necessary libraries:
import pandas as pd import numpy as np
-
Create an instance of the
Loader
class by providing the file path as an argument to the constructor:file_path = "your_data.csv" # Replace with the path to your data file data_loader = Loader(file_path)
-
Use the
read_file
method to read the data from the specified file. You can pass any keyword arguments that are valid for the corresponding pandas read function (e.g.,pd.read_csv
,pd.read_parquet
, orpd.read_excel
):data = data_loader.read_file()
-
The
data
variable now contains the loaded data from the file in a pandas DataFrame. -
You can also specify additional keyword arguments when calling
read_file
. For example:data = data_loader.read_file(header=0, sep=',')
Supported File Formats
The Loader
class supports the following file formats:
- CSV (Comma-Separated Values)
- Parquet
- Excel (XLSX)
Error Handling
- If you create a
Loader
instance without providing a file path or with an unsupported file format, it will raise anAssertionError
.
Example
Here's an example of how to use the Loader
class to read a CSV file:
# Import libraries and create a Loader instance
import pandas as pd
import numpy as np
from loader import Loader # Import the Loader class from your module
file_path = "data.csv"
data_loader = Loader(file_path)
# Read the CSV file
data = data_loader.read_file()
# Now 'data' contains the loaded data in a pandas DataFrame
print(data.head())
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