RDM 2020: Python, Numpy, and Pandas
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The Princeton Research Data Management workshop, breakout session on Python. https://github.com/henryiii/pandas-notebook
![Numpy
● Adds an array type
● Fast computations
array-at-a-time
● Python and Numpy now
define a standard protocol
for arrays
● A library that replaces
langagues like ADL
import numpy as np
v = np.array([1,2,3])
print(v**2)
# Prints 1, 4, 9](https://image.slidesharecdn.com/sessionebreakoutpython-200204174202/85/RDM-2020-Python-Numpy-and-Pandas-14-320.jpg)
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RDM 2020: Python, Numpy, and Pandas
- 1. Princeton Research Data Management Workshop 2020 Co-sponsored by the Center for Digital Humanities, the Center for Statistics and Machine Learning, the Office of the Dean for Research, and Data-Driven Social Science Initiative Organized by Princeton University Library’s Princeton Research Data Service, Princeton Institute for Computational Science and Engineering, and OIT Research Computing Day Two: Break-out Session: Python, Numpy, Pandas
- 2. Python, Numpy, and Pandas Henry Schreiner, PICSiE/PHY [email protected] 2020 Research Data Management Workshop
- 3. Python for data science ● Second most popular language on GitHub ● General purpose ● Only Data Science language in top 10 ● Over 200K PyPI packages, 1.6 billion releases
- 4. Python for data science ● Another metric (PYPL, Google-based) has it #1 ● Data Science languages shown below ● Python fastest growing ● R peaked around 2017 ● Others also in decline ● Note the log scale!
- 5. Timeline ● 1994: Python 1.0 released ● 1995: First array package: Numeric ● 2003: Matplotlib ● 2005: Numeric and numarray merged into Numpy ● 2008: Pandas introduced ● 2012: The Anaconda python distribution
- 6. Timeline ● 2012: Numba JIT compiler ● 2014: IPython becomes Jupyter project & notebook ● 2016: LIGO's discovery: Jupyter Notebook + Python ● 2017: Google releases TensorFlow (Python) ● Now: All Machine Learning libraries are primarily or exclusively used via Python
- 7. Why Python? What makes Python special? ● Great interactivity ● General purpose ● Weaknesses filled by libraries and services
- 8. Python: the language ● Simple ● Easy to learn ● Flexible and powerful ● Object Oriented def square(x): return x**2 print(square(4)) # Prints 4
- 9. IPython ● Adds interactive features to Python ○ Timing chunks of code ○ Shell-like features ○ Fancy display system %cd my_dir %%timeit run_long() ! ./program
- 10. Jupyter Notebooks ● Cell-based HTML document ● Supports many kernels (IPython was first and is the most popular) ● Interleave documentation, code, and output
- 11. Jupyter Lab ● Holds multiple views of ○ Notebooks ○ Output ○ Editors ○ Terminals
- 12. Jupyter Hub ● Multiuser notebook or lab instances ● Available at mybinder.org or through Princeton Research Computing Example: Runge-Kutta static notebook, runnable mybinder
- 13. Libraries PyPI ● The core service for Python libraries ● Uses pip to install ● Environment management separate Anaconda ● Can package Python and complex libraries ● Uses conda to install ● Environment manager too (reproducible) ● conda-forge is community effort
- 14. Numpy ● Adds an array type ● Fast computations array-at-a-time ● Python and Numpy now define a standard protocol for arrays ● A library that replaces langagues like ADL import numpy as np v = np.array([1,2,3]) print(v**2) # Prints 1, 4, 9
- 15. Pandas ● Tabular data ○ A library that replaces languages like R and Excel ○ Designed with interactivity in mind ● Other libraries mimic Pandas’ API
- 16. Numba ● Adds full JIT (just in time) compiler to Python ● Compiles normal python functions into LLVM ● Growing subset of Python and Numpy ● Can be as fast as any compiled language ● Supports parallel computation, GPUs, and more
- 17. Other libraries of note ● CuPY: CUDA with a numpy interface ● TensorFlow/PyTorch: Machine learning libraries ● Matplotlib: The plotting library for Python ● PyQt/PySide: Bindings to Qt Graphical User Interface ● PyBind11: Easy C++ bindings
- 18. Summary ● Python is wildly popular, simple to learn, and well supported ● Python has an impressive collection of tools ○ Interactivity: IPython, Jupyter ○ Package delivery: PyPI (pip), Conda ○ Libraries: Numpy, Pandas, and many more
- 19. Demo ● The second half is devoted to a Pandas demo session