The Pandas library is a powerful tool for data analysis and manipulation in Python. It provides various functionalities for reading, writing, and manipulating structured data.

When it comes to file reading, Pandas supports multiple file formats, including CSV (Comma Separated Values), XLSX (Excel), and TXT (text) files. Here's a brief description of how you can use Pandas to read these file formats:

1. CSV File:
Pandas provides the `read_csv()` function to read CSV files. You can pass the file path or URL as an argument to this function, and it will return a DataFrame containing the data from the CSV file.

Example:
```python
import pandas as pd

df = pd.read_csv('data.csv')
```

2. XLSX File:
To read an Excel file in XLSX format, Pandas provides the `read_excel()` function. You need to pass the file path or URL to this function, along with the sheet name or index if the Excel file contains multiple sheets. The function returns a DataFrame with the data from the specified sheet.

Example:
```python
import pandas as pd

df = pd.read_excel('data.xlsx')
```

3. TXT File:
For reading text files in Pandas, you can use the `read_csv()` function as well. The only difference is that you need to specify the appropriate delimiter or separator used in the text file, which is typically a space or a tab. You can use the `sep` parameter to specify the delimiter.

Example:
```python
import pandas as pd

df = pd.read_csv('data.txt')
```

These are just basic examples, and Pandas provides many more options and parameters for reading files, such as handling missing values, specifying column data types, and more.


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