Academy-Course-PYT37067

Processing Agency Mortgage Data with Awk, Pandas and Django - Part 2: Performing Book

This crash course illustrates how to process loan-level US Agency mortgage data using awk, pandas and django. The second part of the course focuses on performing book attributes. It requires familiarity with command line tools (Ideally a linux environment) and Python. The repository includes various scripts used in the course.

Please Note: Replicating the data processing steps on an actual Fannie Mae dataset requires the Academy student to download such data themselves from the Data Dynamics website and abide with their terms and conditions.

Course Level and Type:

	Introductory Level	Core Level	Advanced Level
Non-Technical
Technical		CrashProgram PYT37067

Name		Name	Last commit message	Last commit date
Latest commit History 13 Commits
.idea		.idea
LICENSE		LICENSE
README.md		README.md
analyze_book_segments.py		analyze_book_segments.py
analyze_performing_book.py		analyze_performing_book.py
chunks.awk		chunks.awk
config.py		config.py
config_old.py		config_old.py
data_dictionaries.py		data_dictionaries.py
extract_dynamic_counterparty.py		extract_dynamic_counterparty.py
extract_dynamic_loan.py		extract_dynamic_loan.py
extract_dynamic_property_collateral.py		extract_dynamic_property_collateral.py
extract_portfolio_and_snapshots.py		extract_portfolio_and_snapshots.py
extract_static_counterparty.py		extract_static_counterparty.py
extract_static_loan.py		extract_static_loan.py
extract_static_property_collateral.py		extract_static_property_collateral.py
pyt37067_course_image.png		pyt37067_course_image.png
requirements.txt		requirements.txt
split_npl_book.py		split_npl_book.py
split_performing_book.py		split_performing_book.py
split_prepaid_book.py		split_prepaid_book.py
timeseries_example_1.py		timeseries_example_1.py
timeseries_example_2.py		timeseries_example_2.py
utils.py		utils.py

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