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:

1. Import the necessary libraries:

   import pandas as pd
   import numpy as np

2. 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)

3. 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, or pd.read_excel):

   data = data_loader.read_file()

4. The data variable now contains the loaded data from the file in a pandas DataFrame.

5. 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 an AssertionError.

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())