Python - Pandas Advanced Features

Python 3    |    Intermediate
  • 12 Videos | 1h 16m 18s
  • Includes Assessment
  • Earns a Badge
Likes 88 Likes 88
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.

WHAT YOU WILL LEARN

  • 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

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 37s
    UP NEXT
  • Playable
    2. 
    Grouping and Aggregations
    5m 17s
  • Locked
    3. 
    MultiIndex DataFrames
    4m 53s
  • Locked
    4. 
    Grouping and Aggregations with MultiIndex DataFrames
    6m 50s
  • Locked
    5. 
    General Aggregation Functions
    4m 47s
  • Locked
    6. 
    Filtering
    9m 31s
  • Locked
    7. 
    Masking Column Values
    5m 10s
  • Locked
    8. 
    Working with Duplicates
    6m 37s
  • Locked
    9. 
    Working with Categorical Data
    7m 49s
  • Locked
    10. 
    Filtering, Adding, and Removing Categories
    7m 21s
  • Locked
    11. 
    Reindexing
    6m 35s
  • Locked
    12. 
    Exercise: Filtering, Duplicates and Categorical Data
    4m 53s

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