Course details

Data Wrangling with Pandas: Advanced Features

Data Wrangling with Pandas: Advanced Features


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

This course uses Python, the preferred programming language for data science, to explore Pandas, a popular Python library, and is a part of the open-source PyData stack. In this 11-video Skillsoft Aspire course, learners will use Pandas DataFrame to perform advanced category grouping, aggregations, and filtering operations. You will see how to use Pandas to retrieve a subset of your data by performing filtering operations both on rows, as well as columns. You will perform analysis on multilevel data by using the GROUPBY operation on Dataframe. You will then learn to use data masking or data obfuscation to protect classified or commercially sensitive data. Learners will work with duplicate data, an important part of data cleaning. You will examine the two broad categories of data continuous data which comprise of a continuous range of value, and categorical data has discrete, finite values. Pandas automatically generates indexes for each of our DataFrame rows, and here you will learn to different types of reindexing operations on Dataframe.



Expected Duration (hours)
1.2

Lesson Objectives

Data Wrangling with Pandas: Advanced Features

  • Course Overview
  • perform grouping and aggregations on data
  • work with multiple, hierarchical indexes
  • specify grouping and aggregations with multiple indexes
  • perform general user-defined aggregations
  • extract subsets of data using filtering
  • identify kinds of masking operations
  • troubleshoot data with duplicates
  • identify how categorical data differs from continuous
  • perform filtering operations on categorical data
  • recognize default and custom indexes and reindex DataFrames
  • perform filtering operations, drop duplicate data, and work with categories
  • Course Number:
    it_dsdwppdj_03_enus

    Expertise Level
    Intermediate