Course details

Python for Data Science: Introduction to Pandas

Python for Data Science: Introduction to Pandas

Expected Duration
Lesson Objectives
Course Number
Expertise Level


This Skillsoft Aspire course explores how to use Pandas, a Python software library, to work with series and tabular data, including initialization, population, and manipulation of Pandas Series and DataFrames. For this 14-video course, learners do not need prior experience working with Pandas, but should be familiar with Python3, and Jupyter Notebooks as a development environment. These data structures simplify various tasks in data analysis. You will learn to define your own index for a Pandas.Series object. Learners will explore Pandas DataFrames, a two-dimensional data structure and containing rows and columns. You will also learn to create a Pandas DataFrames by loading data from a CSV (comma separated values) file. Next, explore how to add and remove data from an existing DataFrames, and how to analyze just a part of the DataFrames. This course examines how to reshape or reorient data, and to create a pivot table. Finally, you will learn to use the concept of multiIndexes, or hierarchical indexes, to represent multidimensional data in a two-dimensional DataFrames.

Expected Duration (hours)

Lesson Objectives

Python for Data Science: Introduction to Pandas

  • Course Overview
  • understand the various applications of Pandas and why it is a building block in the field of data science
  • install Pandas and create a Pandas Series
  • work with Pandas Series by accessing elements using the default and a custom index
  • define a Pandas DataFrame and describe how data can be stored and accessed in these data structures
  • initialize and populate a simple Pandas DataFrame
  • load data into a DataFrame from a CSV file
  • edit individual cells and entire rows and columns in a Pandas DataFrame
  • access specific rows and columns of a Pandas DataFrame using the index and labels
  • access parts of a Pandas DataFrame based on specific conditions
  • describe the concept of hierarchical index or multi-index and why can be useful
  • re-orient a DataFrame as a pivot table to better visualize data
  • apply a multi-index to a DataFrame and reshape it using the stack and melt operations
  • work with Pandas for basic tabular data manipulation
  • Course Number:

    Expertise Level