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agt_data
agt_data
 
 
bokeh
bokeh
 
 
data
data
 
 
screenshots
screenshots
 
 
.gitignore
.gitignore
 
 
README.md
README.md
 
 
agt_analysis.ipynb
agt_analysis.ipynb
 
 
apply.ipynb
apply.ipynb
 
 
copy_on_write.ipynb
copy_on_write.ipynb
 
 
data_generation.ipynb
data_generation.ipynb
 
 
from_long_to_wide_and_back_again.ipynb
from_long_to_wide_and_back_again.ipynb
 
 
generate_csv_files.py
generate_csv_files.py
 
 
indexing_and_querying.ipynb
indexing_and_querying.ipynb
 
 
missing_values.ipynb
missing_values.ipynb
 
 
numba_and_pandas.ipynb
numba_and_pandas.ipynb
 
 
pandas_datatypes.ipynb
pandas_datatypes.ipynb
 
 
pandas_intro.ipynb
pandas_intro.ipynb
 
 
patient_data.ipynb
patient_data.ipynb
 
 
patients.ipynb
patients.ipynb
 
 
pipes.ipynb
pipes.ipynb
 
 
pivot_versus_pivot_table.ipynb
pivot_versus_pivot_table.ipynb
 
 

README.md

Pandas

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.

What is it?

  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 formatg.
  11. copy_on_write.ipynb: Jupyter notebook that illustrates how data is shared between related notebooks and the role Copy-on-Write plays in order to prevent accidental data modifications in more than one dataframe.
  12. apply.ipynb: Jupyter notebook that illustrates the use of the apply method in pandas dataframes for applying functions along rows or columns. It includes a comparison of performance between using apply and vectorized operations.
  13. numba_and_pandas.ipynb: Jupyter notebook that demonstrates how to use Numba to optimize performance of operations on pandas dataframes.
  14. from_long_to_wide_and_back_again.ipynb: Jupyter notebook that illustrates how to reshape data using stack and pivot methods in pandas.