Core features of the Pandas library
In the Pandas_class.ipynb file, I explore the core features of the Pandas library, a vital tool for data manipulation in Python. The file covers the following topics:
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- Creating a Python Library and Accessing it in Google Colab: Mounting a Google Drive folder to integrate external files into the Colab environment.
- Importing Modules (Pandas): Setting up the environment for efficient data analysis.
- Pandas DataFrame: Reading data from a CSV file and exploring structured data tables.
- Indexing DataFrame: Utilizing
.locand.ilocmethods to perform operations such as retrieving values from specific rows and columns, selecting multiple rows, and specifying ranges of rows and columns. - Manipulating DataFrame: Executing operations such as adding new columns and rows, sorting the DataFrame, dropping rows and columns, removing duplicates, and checking for missing values.
- Reading Data from Different Sources: Loading datasets from formats like
.xlsx,.txt,.zip,.html, and.json.
This project highlights practical skills in data handling.