Python Library: Pandas for Beginners

Python Library: Pandas for Beginners

Learn the basics of data analysis with Pandas and master one of the most popular open-source Python libraries.

Simon Sez IT
Updated Jul 22, 2024

What you'll learn

  • Understand the fundamentals of Pandas.
  • Install Pandas on your computer system.
  • Apply Series and DataFrame in Pandas.
  • View data imported from external sources.
  • Organize input data with indexing and filtering.
  • Utilize Pandas for effective data preprocessing.
  • Handle missing values and eliminate duplicate rows.
  • Format data efficiently with Pandas.
  • Process various data types effectively.
  • Perform data manipulation using string functions and format the date and time in your data.
Course Description

Pandas is one of the most popular Python libraries, used for data analysis and manipulation. It is commonly used in data science, machine learning and artificial intelligence. This comprehensive course introduces you to the basics of data analysis with the Pandas library. You'll begin by working with two primary data structures in Pandas, Series and DataFrame. Then you will see how to read data from a file and explore input data using indexing and filtering. At this point, you are ready to start data preprocessing. You will see how to handle missing values and duplicate rows, and to transform your data into a more efficient format. Next, you'll discover how to manipulate the data and do some processing.

Finally, you'll delve into creating simple plots to visualize your data. Upon completion of this course, you will have a firm grasp of one of the most popular, easy to use, open-source Python libraries, enabling you to work with large quantities of data. you'll be able to harness its fast and efficient data manipulation, aggregation, and pivoting, flexible time series functionality, and more. You don't need any previous Pandas experience, but since Pandas is a package built for Python, you need to have a fundamental understanding of basic Python syntax.