Calculator

>>> 1+2+3+4
10
# this is comment line intended for human
# python itself does not care what is written here
>>> 3 + 5.
8.0
# note .0 at the end, we are working with floating points now

Remember that simple math with integers can do arbitrary precision

>>> 10**600 + 10**30 + 2*10**50
1000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000200000000000000000001000000000000000000000000000000
# unfortunately output does not fit on a slide

Variables and assignment operator

Very handy to use variables (i.e. aliases to numbers or other values).

>>> a=345.23
>>> b=2.4
>>> a/b
143.84583333333336

Note that = reads as assign to be, it is not an equality

>>> a=34
>>> b=11
>>> a=b
>>> # what is a? what is b?
>>> a
11
>>> b
11

Editing the inputs

The command line interface really shines when you need to modify you input based on previous inputs

Line editing

It depends on you setting, but the default is usually EMACS readline library shortcuts

Ctrl+w delete a word before cursor
Crtl+k delete the rest of the line after the cursor
Crtl+y delete the rest of the line before the cursor
Ctrl+ArrowLeft or Ctrl+ArrowRight move cursor one word left or right

Tab completion

if python knows about function or variable name you do not need to type the full name
just start the beginning and hit Tab key, it will offer you reasonable choices

Reusing history

type ArrowUp or ArrowLeft, to scroll through previous commands
hit Ctrl+r and type search string

There other tricks, but above are my favorite and most used

Package import

As we discussed the advance math functions were afterthought for Python. So we need to get them into context of the python execution. We will use math library (collection of methods) which in Python called module. We will use words: library, module, or package interchangeably

>>> import math
# Above loads math library, or import it to Python context.
# Now we have many useful function, routines, and goodies.
# They will all start with `math.` So typing is a bit wordy
# e.g. `math.sin` instead of just `sin`
>>> math.pow(2,3)  # i.e. 2^3, we can get same result by 2**3
8.0  # note that math function works with floats even though 2 and 3 are integers

If you want to see what else is imported from math module. Type math.<TAB> and see the choices.

Help related commands

This is where ipython really shines over bare bone python. If you type name of the method and a question mark, you will get a help description (if it exists)

In [1]: import math

In [2]: math.pow?
Signature: math.pow(x, y, /)
Docstring: Return x**y (x to the power of y).
Type:      builtin_function_or_method


In [3]: math.degrees?
Signature: math.degrees(x, /)
Docstring: Convert angle x from radians to degrees.
Type:      builtin_function_or_method

Of course we can always search online for available functions and their descriptions. In this case see https://docs.python.org/3/library/math.html

Plotting

Another useful package which commonly used for plotting is matplotlib. There are others but this one quite common.

In [1]: %matplotlib
Using matplotlib backend: qtagg

In [2]: import matplotlib.pyplot as plt
   ...: # note that we made an alias `plt` for `matplotlib.pyplot`
   ...: # so we can type less

In [3]: plt.plot([1,2,3,2,1])
Out[3]: [<matplotlib.lines.Line2D at 0x7f040c0a4a50>]

Lists and Arrays

What is [1,2,3,2,1]?

It is the list of numbers something which could be assign to a variable and used letter

>>> x = [1, 2, 3, 2 , 1]
>>> plt.plot(x)

List can store anything, so it does not have to be just number it can keep other things.

>>> y = [1, 2, "I am string", 33, 3]
>>> y
[1, 2, 'I am string', 33, 3]

But while it is very powerful idea, in numerical word the array is more useful.

Array is the list of similar type objects, in our case numbers. Then we are able to do operations on arrays in one go. But we need another package: numpy.

Note: It is possible that we need to install numpy separately. One of the ways:

pip install numpy

>>> import numpy as np
>>> x=np.array([1,2,3,4])
>>> x
array([1, 2, 3, 4])
>>> x*3
array([ 3,  6,  9, 12])
>>> x+2*x
array([ 3,  6,  9, 12])

By the way, numpy also has math functions but it can do a more powerful things

>>> x
array([1, 2, 3, 4])
>>> x**3
array([ 1,  8, 27, 64])
>>>
>>> y=[2, 3, 4, 1]
>>> x-y
array([-1, -1, -1,  3])

Data import and export

Typing arrays is quite error prone and besides programs need to exchange data in a more efficient way.

Coma separated values (CSV) is quite good for it. It is not the most storage efficient but it is human readable and many external tools understand it.

Content of the file data_example.csv

col1, col2
1, 10
2, 12
3, 13
4, 14
5, 10
6, 12
7, 13

import numpy as np
data=np.loadtxt('data_example.csv', delimiter=',', skiprows=1)
import matplotlib.pyplot as plt
plt.plot(data, "o-")
plt.savefig("data_plot.png") # export our image in to the figure file in `png` format

resulting plot in file data_plot.png

Introduction to python

Basics of Python

Installing python