Python for Scientists

This presentation briefs on Python libraries / frameworks that helps Scientists.

Transcript

1. UNVEILING THE SCIENTIST IN YOU, PYTHONICALLY
2. TAKE AWAYS FROM THIS TALK ◉ Why python for solving scientific problems ◉ Frameworks that helps in solving ◉ Think forward like a data scientists /ˈsīəntəst/ is a person who is studying or has expert knowledge of one or more of the natural or physical sciences. 2
3. HELLO! I am Shreeram Vaidhyanathan I am here because I’m Pythonista and love to spread python You can find me at @iamshreeram 3
4. WHY DATA SCIENCE ? WHY PYTHON ?? Ask why before learning anything 1 4
5. WHAT EXCITES DATA SCIENTISTS? Godefroy Clair "Data science was a way to become a detective. For every new case, you have to go into a new field to try to understand how it works, to massage the data until you understand them, to try to acquire all the knowledge of the field without being a specialist” Kirk Borne "the excitement in data science is in the journey toward achieving three significant kinds of results: discovery, insights, and innovation” Lise Vaudor "Being able to obtain, process, and build models out of data, which will ultimately be used for their predictive capabilities is incredibly exciting in my view” 5
6. 6 “EVERY LIVING BEING IS PARTIALLY A DATA SCIENTIST”
7. STRONG CONTENDERS FOR DATA SCIENCE 7 PYTHON R SCALA JULIA Unique attribute is, being a general purpose programming language as being easy to use, when it comes to analytical and quantitative computing. It’s the best compromise between readability, scalability and sophistication (in terms of data processing).
8. GENERAL PURPOSE LANGUAGE python Flask, Django, Bottle Japranto, W eb2py Sklearn, tflow , keras, pytorch, theano Pandas, jupyter, num py, scipy, sym py Kviy, BeeW are NASA, Google, Microsoft, Dell, Walmart, DARPA, NVIDIA Machine learning Instagram, Instacart, Reddit, Spotify, Lyft Mobile Apps Microsoft, Yahoo, Walmart, Disney, Sony, Anaconda Data Analytics Pinterest, Instagram, Netflix, DreamWorks Web applications 8
9. Frameworks Tools that can help to solve scientific problems 9
10. Openpyxl WHY PYTHON FOR DATA SCIENCE Allows you to read, write, and create Excel spreadsheets. It does not require Excel to be installed Update or insert date, create formulae, add or change styles, even hide columns More info on how to use this module at https://openpyxl.readthedocs.io/en/stable/ 10
11. Jupyter INTERACTIVE PROGRAMMING A Jupyter notebook is a journal-like python interpreter that lives in a browser window. Code is grouped into cells, which can contain multiple statements, Cells can be edited, repeated, rearranged, and otherwise manipulated. Enhanced interpreter for exploratory computing which is efficient for data analysis (what-if? Scenarios) More info on how to use this module at https://realpython.com/jupyter-notebook-introduction/ 11
12. Pandas HIGH-PERFORMANCE DATA STRUCTURE Expressive data structures for working with “relational” or “labeled” data intuitively Data structures of pandas - Series (1- dimensional) and DataFrame (2-dimensional) Time series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging How to use this module at https://www.learndatasci.com/tutorials/python-pandas-tutorial- complete-introduction-for-beginners/ 12
13. Matplotlib 2D & 3D - DATA VISUALIZATION You can generate plots, histograms, power spectra, bar charts, error charts, scatterplots, etc., with just a few lines of code Update or insert date, create formulae, add or change styles, even hide columns More info on how to use this module at https://www.datacamp.com/community/tutorials/matplotlib- tutorial-python 13
14. Numpy EFFICIENT MULTI-DIEMENSIONAL CONTAINER Work with shapes, dimensions, and ranks. Broadcast changes across multiple array dimensions Broadcast changes across multiple array dimensions. Extract multidimensional slices Working with NumPy arrays can be 100 times faster than working with normal Python lists. More info on how to use this template at http://cs231n.github.io/python-numpy-tutorial/ 14
15. Scipy BASIC LIBRARY FOR SCIENTIFIC COMPUTING “Sigh Pie” - SciPy is built in top of the NumPy. SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing and other tasks common in science and engineering More info on how to use this module at https://scipy-lectures.org/intro/scipy.html Interesting documents - This template is free to use under Chttps://docs.scipy.org/doc/scipy/reference/tutorial/basic.html 15
16. Sympy SCIENTIFIC PYTHON Symbolic python - Creates symbolic representations for each mathematical functions and supports symbolic computation. It provides computer algebra capabilities either as a standalone application, as a library to other applications, or live on the web as SymPy Live or SymPy Gamma. More info on how to use this module at https://docs.sympy.org/latest/tutorial/index.html#tutorial 16
17. Dask ADVANCED PARALLELISM FOR ANALYTICS This is a flexible parallel computing library for analytics Dask's schedulers scale to thousand-node clusters and its algorithms have been tested on some of the largest supercomputers in the world. How to use this module at https://www.analyticsvidhya.com/blog/2018/08/dask-big-datasets- machine_learning-python/ 17
18. DEMO TALK IS CHEAP, SHOW US SOME CODE 18 3
19. THANKS! Any questions? You can find me at @iamshreeram 19