Python for Data Analysis EP 17 | Loading CSV, Excel Files with Pandas | Dataset Basics for Beginners
Welcome to Episode 17 of Python for Data Analysis. In this episode, you will learn one of the most important fundamentals in data science: how to load and understand datasets using Python.
Working with data files is a core skill for data analysts, data scientists, and machine learning engineers. Most real-world datasets are stored in formats such as CSV files and Excel spreadsheets, so learning how to read them properly is essential.
In this tutorial, we explore how Python libraries like pandas and csv allow you to import and work with datasets efficiently. The video explains the differences between CSV and Excel files and demonstrates how to load them into Python for analysis.
You will also learn how to explore your dataset using pandas functions such as head(), info(), and describe() to quickly understand the structure of your data. The episode also introduces important data preparation concepts including handling missing values, converting data types, and removing duplicates.
Another key topic covered is file paths in Python, including the difference between absolute paths and relative paths, and how they help Python locate datasets in your project folders.
By the end of this episode, you will understand how to:
• Load CSV files using Python
• Load Excel files using pandas
• Work with DataFrames for data analysis
• Explore datasets using pandas functions
• Clean and prepare data for analysis
• Understand absolute and relative file paths
Subscribe to the channel and follow the full course playlist to continue learning step by step.
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