xmacis2py 2.2

A Python library that downloads data from the Applied Climate Information System (ACIS) Database, performs various types of analyses on the data and makes various types of graphical summaries of the data.

Anaconda Downloads:

PIP Downloads:

xmACIS2Py

(C) Eric J. Drewitz 2025-2026

ANNOUNCEMENT: xmACIS2Py < 2.0 is now depreciated and replaced with xmACIS2Py >= 2.0

How To Install

Copy and paste either command into your terminal or anaconda prompt:

Install via Anaconda

conda install xmacis2py

Install via pip

pip install xmacis2py

How To Update To The Latest Version

Copy and paste either command into your terminal or anaconda prompt:

Update via Anaconda

This is for users who initially installed WxData through Anaconda

conda update xmacis2py

Update via pip

This is for users who initially installed WxData through pip

pip install --upgrade xmacis2py

Documentation and Jupyter Lab Examples

xmACIS2Py 2.0 Series Documentation and Jupyter Lab Tutorials

Jupyter Lab Tutorials

1. xmACIS2Py Data Access & Analysis
2. xmACIS2Py Graphical Summaries

Documentation

Data Access

1. Get Data

Analysis Tools

1. Period Mean
2. Period Median
3. Period Mode
4. Period Percentile
5. Period Standard Deviation
6. Period Variance
7. Period Skewness
8. Period Kurtosis
9. Period Maximum
10. Period Minimum
11. Period Sum
12. Period Rankings
13. Running Sum
14. Running Mean
15. Detrend Data
16. Number of Missing Days
17. Number of Days At Or Below Value
18. Number of Days At Or Above Value
19. Number of Days Below Value
20. Number of Days Above Value
21. Number of Days At Value

Graphical Summaries

1. Compreheisive Temperature Summary
2. Maximum Temperature Summary
3. Minimum Temperature Summary
4. Average Temperature Summary
5. Average Temperature Departure Summary
6. Heating Degree Day Summary
7. Cooling Degree Day Summary
8. Growing Degree Day Summary
9. Precipitation Summary

Documentation For Legacy Users

xmACIS2Py 1.0 Series (Depreciated/Legacy) Documentation and Jupyter Lab Tutorials

References

1. xmACIS2: https://www.rcc-acis.org/docs_webservices.html

2. MetPy: May, R. M., Goebbert, K. H., Thielen, J. E., Leeman, J. R., Camron, M. D., Bruick, Z., Bruning, E. C., Manser, R. P., Arms, S. C., and Marsh, P. T., 2022: MetPy: A Meteorological Python Library for Data Analysis and Visualization. Bull. Amer. Meteor. Soc., 103, E2273-E2284, https://doi.org/10.1175/BAMS-D-21-0125.1.

3. NumPy: Harris, C.R., Millman, K.J., van der Walt, S.J. et al. Array programming with NumPy. Nature 585, 357–362 (2020). DOI: 10.1038/s41586-020-2649-2. (Publisher link).

4. Pandas: Pandas: McKinney, W., & others. (2010). Data structures for statistical computing in python. In Proceedings of the 9th Python in Science Conference (Vol. 445, pp. 51–56).

5. WxData: Eric J. Drewitz. (2025). edrewitz/WxData: WxData 1.1.4 Released (WxData1.1.4). Zenodo. https://doi.org/10.5281/zenodo.17862030

6. scipy: Virtanen, P., Gommers, R., Oliphant, T.E. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods 17, 261–272 (2020). https://doi.org/10.1038/s41592-019-0686-2