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• install python first
• “Programming with Python” from the software carpentry workshop:
  • setup to download the data
  • sections:
    1. python fundamentals
    2. analyzing patient data
    3. visualizing tabular data
  • software carpentry’s python reference

first goal: basic programming concepts, not much about python itself.

• basic types: integers int vs. numbers with decimals float, and their binary representations
• dot notation: module.function or object.method
  No dots in variable names!
• python is 0-indexed (unlike R, but like C, Perl, Java). index: offset from first value, or # steps
  slice 0:4 has 0,1,2,3: 4-0=4 elements. think of it as [0-4[.
  last index: -1, second to last: -2

• [row, column] for numpy arrays

• how to get help:
  • module.<tab>, variable.<tab>, dir(variable)to list available functions / methods
  • help(function_name) or ?function_name or ?variable.method_name

first: basic python interpreter, not the notebook

cd swc-python/data
$ python # or `winpty python` for git bash users on Windows
Python 3.8.3 (default, Jul  2 2020, 11:26:31)
[Clang 10.0.0 ] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> 1+2
3
>>> quit() # or just type ^D: control and D at the same time
$ head -n 2 inflammation-01.csv
0,0,1,3,1,2,4,7,8,3,3,3,10,5,7,4,7,7,12,18,6,13,11,11,7,7,4,6,8,8,4,4,5,7,3,4,2,3,0,0
0,1,2,1,2,1,3,2,2,6,10,11,5,9,4,4,7,16,8,6,18,4,12,5,12,7,11,5,11,3,3,5,4,4,5,5,1,1,0,1

The integrated terminal is great in VS Code, to send commands from a file to the terminal smoothly.

touch myscript.py
code myscript.py

then within VS Code:

• install the python extension (you may need to click on ‘reload’ VS Code),
• select your Python interpreter: see it on the status bar near the bottom left, click to see option & pick the conda installation
• write your python code in file myscript.py,
• open the command palette (Ctrl+Shift+P on Linux, Command+Shit+P on Mac),
• write and click ‘Python: Start REPL’ to start python
• then send your commands from your script file to be executed with Shift-Enter.

python code:

import numpy
numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')
weight_kg = 55
weight_kg
print(weight_kg)
print('weight in pounds:', 2.2 * weight_kg)
weight_kg = 57.5
print('weight in kilograms is now:', weight_kg)
weight_lb = 2.2 * weight_kg
print('weight in kilograms:', weight_kg, 'and in pounds:', weight_lb)
weight_kg = 100.0
print('weight in kilograms is now:', weight_kg, 'and weight in pounds is still:', weight_lb)
%whos
quit()

%whos: does not work with a basic python, requires an “interactive” python like ipython

$ ipython
Python 3.8.3 (default, Jul  2 2020, 11:26:31)
Type 'copyright', 'credits' or 'license' for more information
IPython 7.16.1 -- An enhanced Interactive Python. Type '?' for help.

In [1]: 1+2
Out[1]: 3

In [2]: 3*5
Out[2]: 15

In [3]: weight_kg = 100.0

In [4]: %whos
Variable    Type     Data/Info
------------------------------
weight_kg   float    100.0

In [5]: quit()

jupyter notebooks

• fantastic for data analysis projects, to do an analysis then to share with collaborators & show to stakeholders: a notebook contains
  • code
  • output, including graphs
  • any comments and text, formatted with markdown syntax
• not good for python functions or for python code that will need to be re-used, such as for a script. For example, if you write code to parse some input file & create an output file with a different format, this code might need to be called as a script to be applied many times to many files. Then, it’s best to use a basic python intepreter (not a notebook) to develop and test this code.

ways to use notebooks:

1. straight with jupyter notebook, which will open a new window in your browser. when done, type ^C in your terminal to shut down the notebook.
2. integrated in a larger enviroment, for many more features than the “notebook”:
   • install jupyterlab like this: conda install -c conda-forge jupyterlab, then
   • run jupyter with jupyter lab
   • when done, type ^C in your terminal to shut down the lab.
3. within VS Code with the “Python extension”. It’s pretty seamless, no browser, no ^C at the end, integrated in the same environment with other files in the project: I recommend it.

about jupyter:

• blog showing lots of features of IPython notebooks, like key bindings.
• for Julia Python and R
• can run many more “kernels” than just Python –like Julia
• integrate a shell, editor for notebooks, kernels, etc.
• learn key shortcuts to talk to jupyter

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