What Will You Achieve by Completing This Course?

Learn what Python Pandas offers.

We'll cover the following

Pandas library

Pandas is a highly functional and efficient library that covers all data analysis and manipulation needs. By completing this course, we’ll learn how to perform the following tasks with Pandas:

  • Handle missing values.
  • Filter data based on the desired conditions.
  • Remove noise from data.
  • Manipulate strings or textual data.
  • Manipulate dates and times.
  • Analyze data to extract insights and more useful information.
  • Visualize data to understand it thoroughly.
  • Combine data from different resources.

These operations are required for more advanced tasks, such as customer segmentation, customer churn prediction, time-series analysis, and demand forecasting. To accomplish these tasks, we’ll need to clean and process the raw data.

Let’s say we’re working on a machine-learning problem. Algorithms used in machine learning expect the input data in a certain format. These algorithms aren’t able to clean or process the data. It’s our task to format the data for the algorithm. If we feed the raw data as is to a machine-learning algorithm, we’re likely to get unsatisfying results. Thus, data cleaning and processing is an important step in machine learning as well.

The best way to learn a software tool or library is through practice. To that end, we’ll cover several examples and challenges to discover how to use the functions and methods of Pandas.