In this data analysis with Python and Pandas tutorial, we cover function mapping and rolling_apply with Pandas.

The idea of function mapping and rolling apply is to allow you to fully customize Pandas to do whatever you need. If there isn't a pre-built method or function for you to run against to your dataframe to do analysis or manipulation, you can use function mapping, creating your own function entirely.

Sample code and text-based version of this tutorial: pythonprogramming.net/rolling-apply-mapping-functions-data-analysis-python-pandas-tutorial/

If you need to do something similar to this, but in a rolling fashion with a moving window, then you can do this with rolling_apply. Both will be covered here.

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1. Introduction to Pandas
2. Pandas Basics
3. IO Basics
4. Building dataset
5. Concatenating and Appending dataframes
6. Joining and Merging Dataframes
7. Pickling
8. Percent Change and Correlation Tables
9. Resampling
10. Handling Missing Data
11. Rolling statistics
12. Applying Comparison Operators to DataFrame
13. Joining 30 year mortgage rate
14. Adding other economic indicators
15. Rolling Apply and Mapping Functions
16. Scikit Learn Incorporation

In this 16-video tutorial series from PythonProgramming.net, learn how to employ the Pandas library in Python to conduct data analysis operations. Pandas is a Python module, and Python is the programming language that we're going to use. The Pandas module is a high performance, highly efficient, and high level data analysis library.

At its core, it is very much like operating a headless version of a spreadsheet, like Excel. Most of the datasets you work with will be what are called dataframes. You may be familiar with this term already, it is used across other languages, but, if not, a dataframe is most often just like a spreadsheet. Columns and rows, that's all there is to it! From here, we can utilize Pandas to perform operations on our data sets at lightning speeds.