Python - Manipulating & Analyzing Data in Pandas DataFrames

Python 3    |    Intermediate
  • 10 videos | 44m 10s
  • Includes Assessment
  • Earns a Badge
Likes 217 Likes 217
Explore advanced data manipulation and analysis with Pandas DataFrames, a Python library that shares similarities with relational databases. To take this course, prior basic experience is needed with Pandas DataFrames, data loading, and Jupyter Notebook data manipulation. You will learn to iterate data in your DataFrame. See how to export data to Excel files, JSON (JavaScript Object Notation) files, and CSV (comma separated values) files. Sort the contents of a DataFrame and manage missing data. Group data with a multi-index. Merge disparate data into a single DataFrame through join and concatenate operations. Finally, you will determine when and where to integrate data with structured queries, similar to SQL.

WHAT YOU WILL LEARN

  • 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 different techniques involved in sorting the contents of a pandas DataFrame
    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

IN THIS COURSE

  • 2m 8s
  • 3m 24s
    In this video, you will learn how to iterate over a DataFrame's rows and columns. FREE ACCESS
  • Locked
    3.  Exporting a DataFrame
    3m 27s
    Learn how to export the contents of a DataFrame into various file formats. FREE ACCESS
  • Locked
    4.  Sorting
    4m 46s
    Upon completion of this video, you will be able to describe and apply the different techniques involved in handling datasets where some information is missing. FREE ACCESS
  • Locked
    5.  Handling Missing Data
    5m 36s
    Upon completion of this video, you will be able to describe and apply the different techniques involved in handling datasets where some information is missing. FREE ACCESS
  • Locked
    6.  Grouping with a Multi-Index
    5m 17s
    In this video, you will implement a hierarchical index and access the DataFrame's contents based on that index. FREE ACCESS
  • Locked
    7.  Merging DataFrames
    3m 24s
    Find out how to combine two similar DataFrames using the concatenate operation. FREE ACCESS
  • Locked
    8.  Applying Join Operations on DataFrames
    5m 13s
    Find out how to apply a join operation on two related but dissimilar DataFrames using the merge function. FREE ACCESS
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    9.  Pandas and Relational Databases
    6m 34s
    In this video, you will learn how to load data into a Pandas DataFrame from a table in a relational database. FREE ACCESS
  • Locked
    10.  Exercise: Pandas for Advanced Data Manipulation
    4m 20s
    In this video, you will learn how to use Pandas for advanced manipulation of tabular data. FREE ACCESS

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