Python - Using Pandas to Work with Series & DataFrames

Python 3    |    Beginner
  • 11 Videos | 1h 15m 26s
  • Includes Assessment
  • Earns a Badge
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Pandas, a popular Python library, is part of the open-source PyData stack. In this 10-video Skillsoft Aspire course, you will learn that Pandas represents data in a tabular format which makes it easy and intuitive to perform data manipulation, cleaning, and exploration. You will use Python's DataFrame a two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). To take this course, you should already be familiar with Python programming language; all code writing is in Jupyter notebooks. You will work with basic Pandas data structures, Pandas Series objects representing a single column of data which can store numerical values, strings, Booleans, and more complex data types. Learn how to use Pandas DataFrame, which represents data in table form. Finally, learn to append and sort series values, add missing data, add columns, and aggregate data in a DataFrame. The closing exercise involves instantiating a Pandas Series object by using both a list and a dictionary; changing the Series index to something other than default value; and practicing sorting Series values in place.

WHAT YOU WILL LEARN

  • install and work with Pandas
    create and configure Pandas Series objects
    perform data wrangling operations on Series objects
    use appending and sorting operations on Series objects
    create and configure Pandas DataFrame objects
  • perform indexing operations on DataFrames
    identify and troubleshoot missing data
    work with aggregations on columns
    perform statistical operations on DataFrames
    recall basic concepts and instantiate Series and DataFrame objects

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 29s
    UP NEXT
  • Playable
    2. 
    Installing Pandas
    4m 49s
  • Locked
    3. 
    Pandas Series Objects
    8m 17s
    FREE ACCESS
  • Locked
    4. 
    Operations on Series
    6m 34s
    FREE ACCESS
  • Locked
    5. 
    Appending and Sorting Series Values
    8m 50s
    FREE ACCESS
  • Locked
    6. 
    Pandas DataFrames
    8m 36s
    FREE ACCESS
  • Locked
    7. 
    Indexing Operations with DataFrames
    6m 10s
    FREE ACCESS
  • Locked
    8. 
    Missing Data
    5m 51s
    FREE ACCESS
  • Locked
    9. 
    Column Aggregations
    7m 42s
    FREE ACCESS
  • Locked
    10. 
    Statistical Operations
    6m 51s
    FREE ACCESS
  • Locked
    11. 
    Exercise: Operations on Series and DataFrames
    5m 49s
    FREE ACCESS

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