Course details

Python for Data Science: Manipulating and Analyzing Data in Pandas DataFrames

Python for Data Science: Manipulating and Analyzing Data in Pandas DataFrames


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

This 10-video course explores some of the advanced features of Pandas DataFrames, a Python software library used for data manipulation and analysis. To take this Skillsoft Aspire course, learners should have some previous experience using Pandas, be able to load data into data frames, and be able to perform basic data manipulations in a Jupyter Notebook. You will learn to iterate data in DataFrames. This course covers various ways to export data from a DataFrames to Excel files, JSON (Javascript Object Notation) files, and CSV (comma separated values) files. You will learn to sort the contents in DataFrames, and how to utilize different techniques to manage missing data. The course conducts an in-depth examination of using a multi-index to group data. You will learn to merge data residing in different data frames into a single frame by using join and concatenate operations. Finally, since there are some similarities between relational databases and Pandas, you will learn when and where to integrate data by using structured query language (SQL)-like operations.



Expected Duration (hours)
0.8

Lesson Objectives

Python for Data Science: Manipulating and Analyzing Data in Pandas DataFrames

  • Course Overview
  • learn how to iterate over a DataFrame's rows and columns
  • export the contents of a DataFrame into files of various formats
  • describe and apply the different techniques involved in handling datasets where some information is missing
  • describe and apply the different techniques involved in handling datasets where some information is missing
  • implement a hierarchical index and access the DataFrame's contents based on that index
  • combine two similar DataFrames using the concat operation
  • apply a join operation on two related but dissimilar DataFrames using the merge function
  • load data into a Pandas DataFrame from a table in a relational database
  • use Pandas for advanced tabular data manipulation
  • Course Number:
    it_dspydsdj_04_enus

    Expertise Level
    Intermediate