Data Wrangling in IronPython QuickStart Sample

Illustrates how to perform basic data wrangling or data munging operations on data frames using classes in the Numerics.NET.DataAnalysis namespace in IronPython.

This sample is also available in: C#, Visual Basic, F#.

Overview

This QuickStart sample demonstrates essential data wrangling operations using Numerics.NET’s DataFrame class. Data wrangling, also known as data munging, is the process of transforming and mapping data from one format into another to make it more suitable for analysis.

The sample covers several key data wrangling operations:

• Joining data frames using different join types (outer, inner, left, right)
• Working with different data sources and combining them into a single data frame
• Handling missing data during joins
• Performing one-to-one and one-to-many joins using indexes and keys
• Time series data alignment using nearest-neighbor joins
• Sorting data frames by index or column values

The example uses realistic scenarios like combining presidential data with state information and aligning time series data with different timestamps. It demonstrates both exact matching joins and approximate matching for time series data that may be offset by a few hours.

The code

Coming soon...