pandas is a library that defines three data structures and algorithms that are useful in the context of data analysis and data science. It represents Series, DataFrame, and Panel, or 1D, 2D, and 3D arrays. DataFrame is especially useful, and defines methods such as pivot_table, and query, and has many facilities to deal with missing data.

For analysis purposes, pandas has some nice plotting features that are easy to use.

1. agt_analysis.ipynb: a notebook illustrating the analysis and visualization of water levels as measured by variouus sensors.
2. agt_data: three CSV files using in the notebook.
3. data_generation.ipynb: notebook that generates some simulated gene expression data using numpy and 'pandas`.
4. pandas_intro.ipynb: illustrates various aspects of using pandas such as importing data, using Series, DataFrame, cleaning and formatting data, dealing with missing data, adding and removing columns, and various algorithms and visualizations.
5. data: some data sets used in the notebook above.
6. patients.ipynb: runninng example used in the Python slides.
7. patient_data.ipynb: extended version of therunninng example used in the Python slides.
8. pipes.ipynb: consolidating data processing using pipes.
9. screenshots: screenshots made for the slides.
10. generate_csv_files.py: script to generate CSV files in different formats.