1. Introduction to Python

1. Introduction to Python#

Python is a programming language developed in 1990s by Guido van Rossum.
Its major features are:

  • consice – (relatively) easy to read

  • extensible – (so-called) object oriented

  • free! – unlike Matlab

It was originally used for “scripting” sequences of processing.
Now it is widely used for scientific computing as well.

Installing Python#

Most Linux and Mac machines usually have Python pre-installed.
To install and setup a variety of packages, it is the best to install a curated distribution, such as:

Starting Python#

From a terminal, type

$ python

to start a python interpreter.

Python as a calculator#

At the python prompt >>>, try typing numbers and operators, like

>>> 1+1

1+1

2

2**8

256

# Uncomment (remove #) the line below
# exp(2)

The plain Python does not include math functions. You need to import numpy.

Jupyter Notebook#

For building a program step-by-step with notes and results attached, it is highly recommended to use a notebook interface, such as Jupyter Notebook (https://jupyter.org), which is included in Anaconda and other popular distributions.

To start Jupyter Notebook type in the terminal

$ jupyter notebook

which should open a web page showing your working directory.

You can create a new notebook from the New menu on the upper right corner, or open an existing .ipynb file like this.

Working with the notebook#

A notebook is made of “cells.”
You can make a new cell by “+” button on the Toolbar, or by typing ESC A (above) or ESC B (below).
You can make a cell as Markdown (documentation) by ESC M, as Code by ESC Y, or simply by the Toolbar menu.
You can delete a cell by ESC DD, or the Cut button on the Toolbar.

Markdown cell#

Markdown is a simple text formatting tool, with

#, ##, ###,... for headings

*, +, -,... for bullets

  • item 1

  • item 2

$  $ for Latex equations like \(\sum_{i=1}^n \alpha_i\) in line

$$  $$ for equations centered in a separate line

\[\sum_{i=1}^n \alpha_i\]

and two spaces at the end of the line
for a line break.

See adam-p/markdown-here for details.

You can format a Markdown cell by Shift+Return, and go back to Edit mode by Return

Code cell#

You can type Control+Return to run the cell or Shift+Return to run and move to the next cell.
You can also use the triangle button or “Cell” menu to run cells.

2*3

6

Integer and floating-point numbers#

A number can be an integer or floating-point, which sometimes needs distinction.

type(1)

int

type(1.5)

float

In Python 3, division of integers can produce a float.
In Python 2, it was truncated to an integer.

3 / 2  # 1.5 by Python 3; 1 by Python 2

1.5

You can perform integer division by // and get the remainder by %.

5 // 2

2

5 % 2

1

To make an integer as a floating point number, you can add .

type(1.)

float

Variables#

You can assing a number or result of computation to a variable.

a = 1

a

1

b = a + a
b

2

Multiple variables can be assigned at once.

a, b = 3, 4
a

3

b

4

Lists#

You can create a list by surrounding items by [ ].

b = [1, 2, 3, 4]
b

[1, 2, 3, 4]

An item can be referenced by [ ], with index starting from 0.

b[1]  # 2nd item

2

b[-1]  # last item

4

A colon can be used for indexing a part of list.

b[1:3]  # 2nd to 3rd

[2, 3]

b[:3]  # first to third

[1, 2, 3]

b[1:]  # 2nd to last

[2, 3, 4]

b[1::2]  # from 1st, step by 2

[2, 4]

b[::-1]  # all in reverse order

[4, 3, 2, 1]

For lists, + means concatenation

b + b

[1, 2, 3, 4, 1, 2, 3, 4]

You can create a nested list, like a matrix

A = [[1,2,3],[4,5,6]]
A

[[1, 2, 3], [4, 5, 6]]

An item in a nested list can be picked by [ ][ ], but not [ , ]

A[1]

[4, 5, 6]

A[1][2]

6

A[1,2]  # this causes an error for a list

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[32], line 1
----> 1 A[1,2]

TypeError: list indices must be integers or slices, not tuple

A list can contain different types of items with different lengths.

a = [1, 2, 3.14, 'apple', "orange", [1, 2]]
a

When you assign a list to another list, only the pointer is copied.

a = [1, 2, 3]
b = a
b[1] = 4
a

When you want to copy the content, use [:]

a = [1, 2, 3]
b = a[:]
b[1] = 4
a

Dictionary#

When you store data as a list, you have to remember what you stored 1st, 2nd, …

A dictionary allows you to access the value by name with key:value pairs.

# Postal codes in our neighborhood
postal = {'onna':9040411, 'tancha':9040412, 'fuchaku':9040413, 'oist':9040495}

You can check the value for a key by [ ].

postal['oist']

if branch#

Branching by if statement looks like this. In Python, indentation specifies where a block of code starts and ends.

x = 1

if x>0:
    y = x
else:
    y = 0
y

There is a shorthand notation with the condition in the middle:

x if x>0 else 0

for loop#

A common way of for loop is by range() function.
Don’t forget a colon and indentation.

j = 0
for i in range(5):
    j = j + i
    print(i, j)

You can specify start, end and interval.

for i in range(3,9,2):
    print(i)

for loop can also be over a list.

a = [1, 2, 3]
for x in a:
    print(x**2)

s = "hello"
for c in s:  # characters in a string
    print(c)

s = ["hello", "goodby"]
for c in s:  # strings in a list
    print(c)

enumerate() function gives pairs of index and content of a list.

for i, c in enumerate(s):
    print(i, c)

You can also apply a for loop for a dictionary.

for k in postal: # get the key
    print('%8s:'%k, postal[k])

# get key-value pair
for (k, v) in postal.items():
    print('{0:8s}: {1}'.format(k, v))

List ‘comprehension’#

There is a quick way of constructing a list from a for loop.

y = [x**2 for x in range(5)]
y

Numpy arrays#

For most computation, you need to import numpy package by the following convention:

import numpy as np

Numpy ndarray is specialized for storing numbers of the same type.

b = np.array([1, 2, 3])
b

type(b)

type(b[0])

Like a list, the index starts from zero

b[1]

Operators work component-wise.

b + b

b * b

b + 1  # broadcast

arange() gives an evenly spaced array.

np.arange(10)

np.arange(0, 10, 0.5)

linspace() gives an array including the last point.

np.linspace(0, 10)

np.linspace(0, 10, num=11)

Nested array#

You can make a matrix as a nested array.

A = np.array([[1,2],[3,4]])
A

Components can be accessed by [ , ]

A[1][1]

A[1,0]  # this if fine for a numpy ndarray, not for a regular list

Take the first row

A[0]

A[0,:]

Take the second column

A[:,1]

Component-wise arithmetics

A + A

A * A

A matrix product is inner products of rows and columns, such as

\[\newcommand{\mat}[1]{\begin{pmatrix} #1 \end{pmatrix}}\]
\[\begin{split} \mat{ a & b\\ c & d}\mat{ v & x\\ w & y} = \mat{ av+bw & ax+by\\ cv+dw & cx+dy}. \end{split}\]

From Python 3.5, @ symbol does the matrix product.

# matrix product
A @ A  # it should give [[1*1+2*3, 1*2+2*4], [3*1+4*3, 3*2+4*4]]

Common matrices#

np.zeros([2,3])

np.eye(4)

np.empty([3,2])  # the contents are not specified

np.empty([3,2], dtype=int)   # to specify the data type

Magic functions#

In Jupyter notebook (or ipython), many magic functions preceded by % are available for working with the file system, etc.

# present working directory
%pwd

You can use %quickref to see the list of magic functions

%quickref

Or %magic for the full documentation.

%magic

You can also use ! to run an OS command or a program.

!pwd

!hostname

Saving and loading data#

You can work with files by open(), write() and read() functions.

with open('haisai.txt', 'w') as f:
    f.write('Haisai!\n')
    f.write('Mensore!\n')
# f is closed when the `with` block is finished

%cat haisai.txt

with open('haisai.txt', 'r') as f:
    s = f.read()
print(s)

A common way of writing/reading a data file is to use savetxt() and loadtxt() functions of numpy.

X = [ [i, i**2] for i in range(5)]
X

np.savetxt("square.txt", X)  # by default, delimited by a space

%cat square.txt

Another common format is CSV, comma-separated value.

np.savetxt("square.csv", X, delimiter=",", fmt="%1d, %.5f")
%ls s*

%cat square.csv

Y = np.loadtxt("square.txt")
Y

Y = np.loadtxt("square.csv", delimiter=",")
Y

Getting help#

Python offers several ways of getting help.

help()

help(np.savetxt)

In Jupyter notebook, you can use ? for quick help.

np.*txt?

np.loadtxt?

np.loadtxt??

You can use ‘Help’ menu to jump to a variety of documentations.

Installing a new package#

A python environment is usually set up with multiple packages, such as numpy.
You can check the currently installed packages by pip list, or conda list command if you installed Anaconda.

!pip list

If you wand to install a new package, you can use pip install or conda install command, or by Anaconda Navigator.

For example, if you have not installed scikit-learn, which we will use later, you can install that from your console by

$ pip install scikit-learn
or
$ conda install scikit-learn
from your console.

!pip or !conda may not work if y/n input is required.

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