Welcome to Part 7 of our Data Analysis with Python and Pandas tutorial series. In the last couple tutorials, we learned how to combine data sets. In this tutorial, we're going to resume under the premise that we're aspiring real estate moguls. We're looking to protect our wealth by having diversified wealth, and, one component to this is real-estate.

Tutorial text and sample code: pythonprogramming.net/pickle-data-analysis-python-pandas-tutorial/

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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.