Source Kevin Markham

Import libraries

‘generic import’ of math module

import math
math.sqrt(25)

5.0

import a function

from math import sqrt
sqrt(25)    # no longer have to reference the module

5.0

import multiple functions at once

from math import sqrt, exp

import all functions in a module (strongly discouraged)

from os import *

define an alias

import nltk
import numpy as np
np.sqrt(9)

np.float64(3.0)

show all functions in math module

content = dir(math)

Basic operations

Numbers

10 + 4          # add (returns 14)
10 - 4          # subtract (returns 6)
10 * 4          # multiply (returns 40)
10 ** 4         # exponent (returns 10000)
10 / 4          # divide (returns 2 because both types are 'int')
10 / float(4)   # divide (returns 2.5)
5 % 4           # modulo (returns 1) - also known as the remainder
10 / 4          # true division (returns 2.5)
10 // 4         # floor division (returns 2)

2

Boolean operations

comparisons (these return True)

5 > 3
5 >= 3
5 != 3
5 == 5

True

Boolean operations (these return True)

5 > 3 and 6 > 3
5 > 3 or 5 < 3
not False
False or not False and True     # evaluation order: not, and, or

True

Data types

Determine the type of an object

type(2)         # returns 'int'
type(2.0)       # returns 'float'
type('two')     # returns 'str'
type(True)      # returns 'bool'
type(None)      # returns 'NoneType'

Check if an object is of a given type

isinstance(2.0, int)            # returns False
isinstance(2.0, (int, float))   # returns True

True

Convert an object to a given type

float(2)
int(2.9)
str(2.9)

'2.9'

zero, None, and empty containers are converted to False

bool(0)
bool(None)
bool('')    # empty string
bool([])    # empty list
bool({})    # empty dictionary

False

Non-empty containers and non-zeros are converted to True

bool(2)
bool('two')
bool([2])

True

Lists

Different objects categorized along a certain ordered sequence, lists are ordered, iterable, mutable (adding or removing objects changes the list size), can contain multiple data types.

Creation

Empty list (two ways)

empty_list = []
empty_list = list()

List with values

simpsons = ['homer', 'marge', 'bart']

Examine a list

simpsons[0]     # print element 0 ('homer')
len(simpsons)   # returns the length (3)

3

Modify a list (does not return the list)

Append

simpsons.append('lisa')                 # append element to end
simpsons.extend(['itchy', 'scratchy'])  # append multiple elements to end
# insert element at index 0 (shifts everything right)

Insert

simpsons.insert(0, 'maggie')
# searches for first instance and removes it

Remove

simpsons.remove('bart')
simpsons.pop(0)                         # removes element 0 and returns it
# removes element 0 (does not return it)
del simpsons[0]
simpsons[0] = 'krusty'                  # replace element 0

Concatenate lists (slower than ‘extend’ method)

neighbors = simpsons + ['ned', 'rod', 'todd']

Replicate

rep = ["a"] * 2 + ["b"] * 3

Find elements in a list

'lisa' in simpsons
simpsons.count('lisa')      # counts the number of instances
simpsons.index('itchy')     # returns index of first instance

2

List slicing (selection) [start:end:stride]

weekdays = ['mon', 'tues', 'wed', 'thurs', 'fri']
weekdays[0]         # element 0
weekdays[0:3]       # elements 0, 1, 2
weekdays[:3]        # elements 0, 1, 2
weekdays[3:]        # elements 3, 4
weekdays[-1]        # last element (element 4)
weekdays[::2]       # every 2nd element (0, 2, 4)

['mon', 'wed', 'fri']

Reverse list

weekdays[::-1]      # backwards (4, 3, 2, 1, 0)
# alternative method for returning the list backwards
list(reversed(weekdays))

['fri', 'thurs', 'wed', 'tues', 'mon']

Sort list

Sort a list in place (modifies but does not return the list)

simpsons.sort()
simpsons.sort(reverse=True)     # sort in reverse
simpsons.sort(key=len)          # sort by a key

Return a sorted list (but does not modify the original list)

sorted(simpsons)
sorted(simpsons, reverse=True)
sorted(simpsons, key=len)

['lisa', 'itchy', 'krusty', 'scratchy']

Tuples

Like lists, but their size cannot change: ordered, iterable, immutable, can contain multiple data types

# create a tuple
digits = (0, 1, 'two')          # create a tuple directly
digits = tuple([0, 1, 'two'])   # create a tuple from a list
# trailing comma is required to indicate it's a tuple
zero = (0,)

# examine a tuple
digits[2]           # returns 'two'
len(digits)         # returns 3
digits.count(0)     # counts the number of instances of that value (1)
digits.index(1)     # returns the index of the first instance of that value (1)

# elements of a tuple cannot be modified
# digits[2] = 2       # throws an error

# concatenate tuples
digits = digits + (3, 4)

# create a single tuple with elements repeated (also works with lists)
(3, 4) * 2          # returns (3, 4, 3, 4)

# tuple unpacking
bart = ('male', 10, 'simpson')  # create a tuple

Strings

A sequence of characters, they are iterable, immutable

# create a string
s = str(42)         # convert another data type into a string
s = 'I like you'

# examine a string
s[0]                # returns 'I'
len(s)              # returns 10

# string slicing like lists
s[:6]               # returns 'I like'
s[7:]               # returns 'you'
s[-1]               # returns 'u'

# basic string methods (does not modify the original string)
s.lower()           # returns 'i like you'
s.upper()           # returns 'I LIKE YOU'
s.startswith('I')   # returns True
s.endswith('you')   # returns True
s.isdigit()         # returns False (True if every character is a digit)
s.find('like')      # returns index of first occurrence
s.find('hate')      # returns -1 since not found
s.replace('like', 'love')    # replaces all instances of 'like' with 'love'

# split a string into a list of substrings separated by a delimiter
s.split(' ')        # returns ['I','like','you']
s.split()           # same thing
s2 = 'a, an, the'
s2.split(',')       # returns ['a',' an',' the']

# join a list of strings into one string using a delimiter
stooges = ['larry', 'curly', 'moe']
' '.join(stooges)   # returns 'larry curly moe'

# concatenate strings
s3 = 'The meaning of life is'
s4 = '42'
s3 + ' ' + s4       # returns 'The meaning of life is 42'
s3 + ' ' + str(42)  # same thing

# remove whitespace from start and end of a string
s5 = '  ham and cheese  '
s5.strip()          # returns 'ham and cheese'

'ham and cheese'

Strings formatting

# string substitutions: all of these return 'raining cats and dogs'
'raining %s and %s' % ('cats', 'dogs')                       # old way
'raining {} and {}'.format('cats', 'dogs')                   # new way
'raining {arg1} and {arg2}'.format(arg1='cats', arg2='dogs')  # named arguments

# String formatting
# See: https://realpython.com/python-formatted-output/
# Old method
print('6 %s' % 'bananas')
print('%d %s cost $%.1f' % (6, 'bananas', 3.14159))

# Format method positional arguments
print('{0} {1} cost ${2:.1f}'.format(6, 'bananas', 3.14159))

6 bananas
6 bananas cost $3.1
6 bananas cost $3.1

Strings encoding

Normal strings allow for escaped characters. The default strings use unicode string (u string)

print('first line\nsecond line')  # or
print(u'first line\nsecond line')
print('first line\nsecond line' == u'first line\nsecond line')

first line
second line
first line
second line
True

Raw strings treat backslashes as literal characters

print(r'first line\nfirst line')
print('first line\nsecond line' == r'first line\nsecond line')

first line\nfirst line
False

Sequence of bytes are not strings, should be decoded before some operations

s = b'first line\nsecond line'
print(s)
print(s.decode('utf-8').split())

b'first line\nsecond line'
['first', 'line', 'second', 'line']

Dictionaries

Dictionary is the must-known data structure. Dictionaries are structures which can contain multiple data types, and is ordered with key-value pairs: for each (unique) key, the dictionary outputs one value. Keys can be strings, numbers, or tuples, while the corresponding values can be any Python object. Dictionaries are: unordered, iterable, mutable

Creation

# Empty dictionary (two ways)
empty_dict = {}
empty_dict = dict()

simpsons_roles_dict = {'Homer': 'father', 'Marge': 'mother',
                       'Bart': 'son', 'Lisa': 'daughter', 'Maggie': 'daughter'}

simpsons_roles_dict = dict(Homer='father', Marge='mother',
                           Bart='son', Lisa='daughter', Maggie='daughter')

simpsons_roles_dict = dict([('Homer', 'father'), ('Marge', 'mother'),
                            ('Bart', 'son'), ('Lisa', 'daughter'), ('Maggie', 'daughter')])

print(simpsons_roles_dict)

{'Homer': 'father', 'Marge': 'mother', 'Bart': 'son', 'Lisa': 'daughter', 'Maggie': 'daughter'}

Access

# examine a dictionary
simpsons_roles_dict['Homer']   # 'father'
len(simpsons_roles_dict)       # 5
simpsons_roles_dict.keys()     # list: ['Homer', 'Marge', ...]
simpsons_roles_dict.values()   # list:['father', 'mother', ...]
simpsons_roles_dict.items()    # list of tuples: [('Homer', 'father') ...]
'Homer' in simpsons_roles_dict  # returns True
'John' in simpsons_roles_dict  # returns False (only checks keys)

# accessing values more safely with 'get'
simpsons_roles_dict['Homer']                       # returns 'father'
simpsons_roles_dict.get('Homer')                   # same thing

try:
    simpsons_roles_dict['John']               # throws an error
except KeyError as e:
    print("Error", e)

simpsons_roles_dict.get('John')               # None
# returns 'not found' (the default)
simpsons_roles_dict.get('John', 'not found')

Error 'John'

'not found'

Modify a dictionary (does not return the dictionary)

simpsons_roles_dict['Snowball'] = 'dog'              # add a new entry
simpsons_roles_dict['Snowball'] = 'cat'              # add a new entry
simpsons_roles_dict['Snoop'] = 'dog'                 # edit an existing entry
del simpsons_roles_dict['Snowball']                  # delete an entry

simpsons_roles_dict.pop('Snoop')  # removes and returns ('dog')
simpsons_roles_dict.update(
    {'Mona': 'grandma', 'Abraham': 'grandpa'})  # add multiple entries
print(simpsons_roles_dict)

{'Homer': 'father', 'Marge': 'mother', 'Bart': 'son', 'Lisa': 'daughter', 'Maggie': 'daughter', 'Mona': 'grandma', 'Abraham': 'grandpa'}

Intersecting two dictionaries

simpsons_ages_dict = {'Homer': 45, 'Marge': 43,
                      'Bart': 11, 'Lisa': 10, 'Maggie': 1}

print(simpsons_roles_dict.keys() & simpsons_ages_dict.keys())

inter = simpsons_roles_dict.keys() & simpsons_ages_dict.keys()

l = list()

for n in inter:
    l.append([n, simpsons_ages_dict[n], simpsons_roles_dict[n]])

[[n, simpsons_ages_dict[n], simpsons_roles_dict[n]] for n in inter]

{'Maggie', 'Marge', 'Homer', 'Lisa', 'Bart'}

[['Maggie', 1, 'daughter'], ['Marge', 43, 'mother'], ['Homer', 45, 'father'], ['Lisa', 10, 'daughter'], ['Bart', 11, 'son']]

String substitution using a dictionary: syntax %(key)format, where format is the formatting character e.g. s for string.

print('Homer is the %(Homer)s of the family' % simpsons_roles_dict)

Homer is the father of the family

Sets

Like dictionaries, but with unique keys only (no corresponding values). They are: unordered, iterable, mutable, can contain multiple data types made up of unique elements (strings, numbers, or tuples)

Creation

# create an empty set
empty_set = set()

# create a set
languages = {'python', 'r', 'java'}         # create a set directly
snakes = set(['cobra', 'viper', 'python'])  # create a set from a list

Examine a set

len(languages)              # 3
'python' in languages       # True

True

Set operations

languages & snakes          # intersection: {'python'}
languages | snakes          # union: {'cobra', 'r', 'java', 'viper', 'python'}
languages - snakes          # set difference: {'r', 'java'}
snakes - languages          # set difference: {'cobra', 'viper'}

# modify a set (does not return the set)
languages.add('sql')        # add a new element
# try to add an existing element (ignored, no error)
languages.add('r')
languages.remove('java')    # remove an element

try:
    languages.remove('c')   # remove a non-existing element: throws an error
except KeyError as e:
    print("Error", e)

# removes an element if present, but ignored otherwise
languages.discard('c')
languages.pop()             # removes and returns an arbitrary element
languages.clear()           # removes all elements
languages.update('go', 'spark')  # add multiple elements (list or set)

# get a sorted list of unique elements from a list
sorted(set([9, 0, 2, 1, 0]))    # returns [0, 1, 2, 9]

Error 'c'

[0, 1, 2, 9]

Execution control statements

Conditional statements

if statement

x = 3
if x > 0:
    print('positive')

positive

if/else statement

if x > 0:
    print('positive')
else:
    print('zero or negative')

positive

Single-line if/else statement, known as a ‘ternary operator’

sign = 'positive' if x > 0 else 'zero or negative'
print(sign)

positive

if/elif/else statement

if x > 0:
    print('positive')
elif x == 0:
    print('zero')
else:
    print('negative')

positive

Loops

Loops are a set of instructions which repeat until termination conditions are met. This can include iterating through all values in an object, go through a range of values, etc

# range returns a list of integers
# returns [0, 1, 2]: includes first value but excludes second value
range(0, 3)
range(3)        # same thing: starting at zero is the default
range(0, 5, 2)  # returns [0, 2, 4]: third argument specifies the 'stride'

range(0, 5, 2)

Iterate on list values

fruits = ['Apple', 'Banana', 'cherry']
for fruit in fruits:
    print(fruit.upper())

APPLE
BANANA
CHERRY

Iterate with index

for i in range(len(fruits)):
    print(fruits[i].lower())

apple
banana
cherry

Iterate with index and values: enumerate

for i, val in enumerate(fruits):
    print(i, val.upper())

# Use range when iterating over a large sequence to avoid actually
# creating the integer list in memory
v = 0
for i in range(10 ** 6):
    v += 1

0 APPLE
1 BANANA
2 CHERRY

List comprehensions, iterators, etc.

List comprehensions

List comprehensions provides an elegant syntax for the most common processing pattern:

1. iterate over a list,

2. apply some operation

3. store the result in a new list

Classical iteration over a list

nums = [1, 2, 3, 4, 5]
cubes = []
for num in nums:
    cubes.append(num ** 3)

Equivalent list comprehension

cubes = [num**3 for num in nums]    # [1, 8, 27, 64, 125]

Classical iteration over a list with if condition: create a list of cubes of even numbers

cubes_of_even = []
for num in nums:
    if num % 2 == 0:
        cubes_of_even.append(num**3)

Equivalent list comprehension with if condition syntax: [expression for variable in iterable if condition]

cubes_of_even = [num**3 for num in nums if num % 2 == 0]    # [8, 64]

Classical iteration over a list with if else condition: for loop to cube even numbers and square odd numbers

cubes_and_squares = []
for num in nums:
    if num % 2 == 0:
        cubes_and_squares.append(num**3)
    else:
        cubes_and_squares.append(num**2)

Equivalent list comprehension (using a ternary expression) for loop to cube even numbers and square odd numbers syntax: [true_condition if condition else false_condition for variable in iterable]

cubes_and_squares = [num**3 if num % 2 == 0 else num**2 for num in nums]
print(cubes_and_squares)

[1, 8, 9, 64, 25]

Nested loops: flatten a 2d-matrix

matrix = [[1, 2], [3, 4]]
items = []
for row in matrix:
    for item in row:
        items.append(item)

Equivalent list comprehension with Nested loops

items = [item for row in matrix
         for item in row]

print(items)

[1, 2, 3, 4]

Set comprehension

fruits = ['apple', 'banana', 'cherry']
unique_lengths = {len(fruit) for fruit in fruits}
print(unique_lengths)

{5, 6}

Dictionary comprehension

Create a dictionary from a list

fruit_lengths = {fruit: len(fruit) for fruit in fruits}
print(fruit_lengths)

{'apple': 5, 'banana': 6, 'cherry': 6}

Iterate over keys and values. Increase age of each subject:

simpsons_ages_ = {key: val + 1 for key, val in simpsons_ages_dict.items()}
print(simpsons_ages_)

{'Homer': 46, 'Marge': 44, 'Bart': 12, 'Lisa': 11, 'Maggie': 2}

Combine two dictionaries sharing key. Example, a function that joins two dictionaries (intersecting keys) into a dictionary of lists

simpsons_info_dict = {name: [simpsons_roles_dict[name], simpsons_ages_dict[name]]
                      for name in simpsons_roles_dict.keys() &
                      simpsons_ages_dict.keys()}
print(simpsons_info_dict)

{'Maggie': ['daughter', 1], 'Marge': ['mother', 43], 'Homer': ['father', 45], 'Lisa': ['daughter', 10], 'Bart': ['son', 11]}

Iterators itertools package

import itertools

Example: Cartesian product

print([[x, y] for x, y in itertools.product(['a', 'b', 'c'], [1, 2])])

[['a', 1], ['a', 2], ['b', 1], ['b', 2], ['c', 1], ['c', 2]]

Example, use loop, dictionary and set to count words in a sentence

quote = """Tick-tow
our incomes are like our shoes; if too small they gall and pinch us
but if too large they cause us to stumble and to trip
"""

words = quote.split()
len(words)

count = {word: 0 for word in set(words)}

for word in words:
    count[word] += 1
    # count[word] = count[word] + 1

print(count)

import numpy as np
freq_veq = np.array(list(count.values())) / len(words)

{'they': 2, 'but': 1, 'too': 2, 'gall': 1, 'to': 2, 'small': 1, 'incomes': 1, 'shoes;': 1, 'if': 2, 'our': 2, 'pinch': 1, 'are': 1, 'like': 1, 'stumble': 1, 'Tick-tow': 1, 'us': 2, 'large': 1, 'and': 2, 'trip': 1, 'cause': 1}

Exceptions handling

dct = dict(a=[1, 2], b=[4, 5])

key = 'c'
try:
    dct[key]
except:
    print("Key %s is missing. Add it with empty value" % key)
    dct['c'] = []

print(dct)

Key c is missing. Add it with empty value
{'a': [1, 2], 'b': [4, 5], 'c': []}

Functions

Functions are sets of instructions launched when called upon, they can have multiple input values and a return value

Function with no arguments and no return values

def print_text():
    print('this is text')


# call the function
print_text()

this is text

Function with one argument and no return values

def print_this(x):
    print(x)


# call the function
print_this(3)       # prints 3
n = print_this(3)   # prints 3, but doesn't assign 3 to n
# because the function has no return statement
print(n)

3
3
None

Dynamic typing

Important remarque: Python is a dynamically typed language, meaning that the Python interpreter does type checking at runtime (as opposed to compiled language that are statically typed). As a consequence, the function behavior, decided, at execution time, will be different and specific to parameters type. Python function are polymorphic.

def add(a, b):
    return a + b


print(add(2, 3), add("deux", "trois"), add(["deux", "trois"], [2, 3]))

5 deuxtrois ['deux', 'trois', 2, 3]

Default arguments

def power_this(x, power=2):
    return x ** power


print(power_this(2), power_this(2, 3))

4 8

Docstring to describe the effect of a function IDE, ipython (type: ?power_this) to provide function documentation.

def power_this(x, power=2):
    """Return the power of a number.

    Args:
        x (float): the number
        power (int, optional): the power. Defaults to 2.
    """
    return x ** power

Return several values as tuple

def min_max(nums):
    return min(nums), max(nums)


# return values can be assigned to a single variable as a tuple
min_max_num = min_max([1, 2, 3])         # min_max_num = (1, 3)

# return values can be assigned into multiple variables using tuple unpacking
min_num, max_num = min_max([1, 2, 3])    # min_num = 1, max_num = 3

Reference and copy

References are used to access objects in memory, here lists. A single object may have multiple references. Modifying the content of the one reference will change the content of all other references.

Modify a a reference of a list

num = [1, 2, 3]
same_num = num   # create a second reference to the same list
same_num[0] = 0  # modifies both 'num' and 'same_num'
print(num, same_num)

[0, 2, 3] [0, 2, 3]

Copies are references to different objects. Modifying the content of the one reference, will not affect the others.

Modify a copy of a list

new_num = num.copy()
new_num = num[:]
new_num = list(num)
new_num[0] = -1  # modifies 'new_num' but not 'num'
print(num, new_num)

[0, 2, 3] [-1, 2, 3]

Examine objects

id(num) == id(same_num)  # returns True
id(num) == id(new_num)  # returns False
num is same_num         # returns True
num is new_num          # returns False
num == same_num         # returns True
num == new_num          # returns True (their contents are equivalent)

False

Functions’ arguments are references to objects. Thus functions can modify their arguments with possible side effect.

def change(x, index, newval):
    x[index] = newval

l = [0, 1, 2]
change(x=l, index=1, newval=33)
print(l)

[0, 33, 2]

Example: function, and dictionary comprehension

Example of a function join_dict_to_table(dict1, dict2) joining two dictionaries (intersecting keys) into a table, i.e., a list of tuples, where the first column is the key, the second and third columns are the values of the dictionaries.

simpsons_ages_dict = {'Homer': 45, 'Marge': 43, 'Bart': 11, 'Lisa': 10}
simpsons_roles_dict = {'Homer': 'father', 'Marge': 'mother', 'Bart': 'son',
                       'Maggie': 'daughter'}

def join_dict_to_table(dict1, dict2):
    table = [[key] + [dict1[key], dict2[key]]
             for key in dict1.keys() & dict2.keys()]
    return table

print("Roles:", simpsons_roles_dict)
print("Ages:", simpsons_ages_dict)
print("Join:", join_dict_to_table(simpsons_roles_dict,
                                  simpsons_ages_dict))

Roles: {'Homer': 'father', 'Marge': 'mother', 'Bart': 'son', 'Maggie': 'daughter'}
Ages: {'Homer': 45, 'Marge': 43, 'Bart': 11, 'Lisa': 10}
Join: [['Marge', 'mother', 43], ['Bart', 'son', 11], ['Homer', 'father', 45]]

Regular expression

Regular Expression (RE, or RegEx) allow to search and patterns in strings. See this page for the syntax of the RE patterns.

import re

Usual patterns

• . period symbol matches any single character (except newline ‘n’).

• pattern``+`` plus symbol matches one or more occurrences of the pattern.

• [] square brackets specifies a set of characters you wish to match

• [abc] matches a, b or c

• [a-c] matches a to z

• [0-9] matches 0 to 9

• [a-zA-Z0-9]+ matches words, at least one alphanumeric character (digits and alphabets)

• [\w]+ matches words, at least one alphanumeric character including underscore.

• \s Matches where a string contains any whitespace character, equivalent to [ tnrfv].

• [^\s] Caret ^ symbol (the start of a square-bracket) inverts the pattern selection .

# regex = re.compile("^.+(firstname:.+)_(lastname:.+)_(mod-.+)")
# regex = re.compile("(firstname:.+)_(lastname:.+)_(mod-.+)")

Compile (re.compile(string)) regular expression with a pattern that captures the pattern firstname:<subject_id>_lastname:<session_id>

pattern = re.compile("firstname:[\w]+_lastname:[\w]+")

/home/ed203246/git/pystatsml/python_lang/python_lang.py:936: SyntaxWarning: invalid escape sequence '\w'
  pattern = re.compile("firstname:[\w]+_lastname:[\w]+")

Match (re.match(string)) to be used in test, loop, etc. Determine if the RE matches at the beginning of the string.

yes_ = True if pattern.match("firstname:John_lastname:Doe") else False
no_ = True if pattern.match("blahbla_firstname:John_lastname:Doe") else False
no2_ = True if pattern.match("OUPS-John_lastname:Doe") else False
print(yes_, no_, no2_)

True False False

Match (re.search(string)) to be used in test, loop, etc. Determine if the RE matches at any location in the string.

yes_ = True if pattern.search("firstname:John_lastname:Doe") else False
yes2_ = True if pattern.search(
    "blahbla_firstname:John_lastname:Doe") else False
no_ = True if pattern.search("OUPS-John_lastname:Doe") else False
print(yes_, yes2_, no_)

True True False

Find (re.findall(string)) all substrings where the RE matches, and returns them as a list.

# Find the whole pattern within the string
pattern = re.compile("firstname:[\w]+_lastname:[\w]+")
print(pattern.findall("firstname:John_lastname:Doe blah blah"))

# Find words
print(re.compile("[a-zA-Z0-9]+").findall("firstname:John_lastname:Doe"))

# Find words with including underscore
print(re.compile("[\w]+").findall("firstname:John_lastname:Doe"))

/home/ed203246/git/pystatsml/python_lang/python_lang.py:963: SyntaxWarning: invalid escape sequence '\w'
  pattern = re.compile("firstname:[\w]+_lastname:[\w]+")
/home/ed203246/git/pystatsml/python_lang/python_lang.py:970: SyntaxWarning: invalid escape sequence '\w'
  print(re.compile("[\w]+").findall("firstname:John_lastname:Doe"))
['firstname:John_lastname:Doe']
['firstname', 'John', 'lastname', 'Doe']
['firstname', 'John_lastname', 'Doe']

Extract specific parts of the RE: use parenthesis (part of pattern to be matched) Extract John and Doe, such as John is suffixed with firstname: and Doe is suffixed with lastname:

pattern = re.compile("firstname:([\w]+)_lastname:([\w]+)")
print(pattern.findall("firstname:John_lastname:Doe \
    firstname:Bart_lastname:Simpson"))

/home/ed203246/git/pystatsml/python_lang/python_lang.py:978: SyntaxWarning: invalid escape sequence '\w'
  pattern = re.compile("firstname:([\w]+)_lastname:([\w]+)")
[('John', 'Doe'), ('Bart', 'Simpson')]

Split (re.split(string)) splits the string where there is a match and returns a list of strings where the splits have occurred. Example, match any non alphanumeric character (digits and alphabets) [^a-zA-Z0-9] to split the string.

print(re.compile("[^a-zA-Z0-9]").split("firstname:John_lastname:Doe"))

['firstname', 'John', 'lastname', 'Doe']

Substitute (re.sub(pattern, replace, string)) returns a string where matched occurrences are replaced with the content of replace variable.

print(re.sub('\s', "_", "Sentence with white      space"))
print(re.sub('\s+', "_", "Sentence with white      space"))

/home/ed203246/git/pystatsml/python_lang/python_lang.py:995: SyntaxWarning: invalid escape sequence '\s'
  print(re.sub('\s', "_", "Sentence with white      space"))
/home/ed203246/git/pystatsml/python_lang/python_lang.py:996: SyntaxWarning: invalid escape sequence '\s'
  print(re.sub('\s+', "_", "Sentence with white      space"))
Sentence_with_white______space
Sentence_with_white_space

Remove all non-alphanumeric characters and space in a string

re.sub('[^0-9a-zA-Z\s]+', '', 'H^&ell`.,|o W]{+orld')

/home/ed203246/git/pystatsml/python_lang/python_lang.py:1001: SyntaxWarning: invalid escape sequence '\s'
  re.sub('[^0-9a-zA-Z\s]+', '', 'H^&ell`.,|o W]{+orld')

'Hello World'

System programming

Operating system interfaces (os)

import os

Get/set current working directory

# Get the current working directory
cwd = os.getcwd()
print(cwd)

# Set the current working directory
os.chdir(cwd)

/home/ed203246/git/pystatsml/python_lang

Temporary directory

import tempfile
tmpdir = tempfile.gettempdir()
print(tmpdir)

/tmp

Join paths

mytmpdir = os.path.join(tmpdir, "foobar")

Create a directory

os.makedirs(os.path.join(tmpdir, "foobar", "plop", "toto"), exist_ok=True)

# list containing the names of the entries in the directory given by path.
os.listdir(mytmpdir)

['myfile.txt', 'plop']

File input/output

filename = os.path.join(mytmpdir, "myfile.txt")
print(filename)
lines = ["Dans python tout est bon", "Enfin, presque"]

/tmp/foobar/myfile.txt

Write line by line

fd = open(filename, "w")
fd.write(lines[0] + "\n")
fd.write(lines[1] + "\n")
fd.close()

Context manager to automatically close your file

with open(filename, 'w') as f:
    for line in lines:
        f.write(line + '\n')

Read read one line at a time (entire file does not have to fit into memory)

f = open(filename, "r")
f.readline()    # one string per line (including newlines)
f.readline()    # next line
f.close()

# read the whole file at once, return a list of lines
f = open(filename, 'r')
f.readlines()   # one list, each line is one string
f.close()

# use list comprehension to duplicate readlines without reading entire file at once
f = open(filename, 'r')
[line for line in f]
f.close()

# use a context manager to automatically close your file
with open(filename, 'r') as f:
    lines = [line for line in f]

Explore, list directories

Walk through directories and subdirectories os.walk(dir)

WD = os.path.join(tmpdir, "foobar")

for dirpath, dirnames, filenames in os.walk(WD):
    print(dirpath, dirnames, filenames)

/tmp/foobar ['plop'] ['myfile.txt']
/tmp/foobar/plop ['toto'] []
/tmp/foobar/plop/toto [] []

Search for a file using a wildcard glob.glob(dir)

import glob
filenames = glob.glob(os.path.join(tmpdir, "*", "*.txt"))
print(filenames)

['/tmp/foobar/myfile.txt', '/tmp/plop2/myfile.txt']

Manipulating file names, basename and extension

def split_filename_inparts(filename):
    dirname_ = os.path.dirname(filename)
    filename_noext_, ext_ = os.path.splitext(filename)
    basename_ = os.path.basename(filename_noext_)
    return dirname_, basename_, ext_


print(filenames[0], "=>", split_filename_inparts(filenames[0]))

/tmp/foobar/myfile.txt => ('/tmp/foobar', 'myfile', '.txt')

File operations: (recursive) copy, move, test if exists: shutil package

import shutil

Copy

src = os.path.join(tmpdir, "foobar",  "myfile.txt")
dst = os.path.join(tmpdir, "foobar",  "plop", "myfile.txt")
shutil.copy(src, dst)
print("copy %s to %s" % (src, dst))

copy /tmp/foobar/myfile.txt to /tmp/foobar/plop/myfile.txt

Test if file exists ?

print("File %s exists ?" % dst, os.path.exists(dst))

File /tmp/foobar/plop/myfile.txt exists ? True

Recursive copy,deletion and move

src = os.path.join(tmpdir, "foobar",  "plop")
dst = os.path.join(tmpdir, "plop2")

try:
    print("Copy tree %s under %s" % (src, dst))
    # Note that by default (dirs_exist_ok=True), meaning that copy will fail
    # if destination exists.
    shutil.copytree(src, dst, dirs_exist_ok=True)

    print("Delete tree %s" % dst)
    shutil.rmtree(dst)

    print("Move tree %s under %s" % (src, dst))
    shutil.move(src, dst)
except (FileExistsError, FileNotFoundError) as e:
    pass

Copy tree /tmp/foobar/plop under /tmp/plop2
Delete tree /tmp/plop2
Move tree /tmp/foobar/plop under /tmp/plop2

Command execution with subprocess

For more advanced use cases, the underlying Popen interface can be used directly.

import subprocess

subprocess.run([command, args*])

• Run the command described by args.

• Wait for command to complete

• return a CompletedProcess instance.

• Does not capture stdout or stderr by default. To do so, pass PIPE for the stdout and/or stderr arguments.

p = subprocess.run(["ls", "-l"])
print(p.returncode)

0

Run through the shell

subprocess.run("ls -l", shell=True)

CompletedProcess(args='ls -l', returncode=0)

Capture output

out = subprocess.run(
    ["ls", "-a", "/"], stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
# out.stdout is a sequence of bytes that should be decoded into a utf-8 string
print(out.stdout.decode('utf-8').split("\n")[:5])

['.', '..', 'bin', 'bin.usr-is-merged', 'boot']

Multiprocessing and multithreading

Difference between multiprocessing and multithreading is essential to perform efficient parallel processing on multi-cores computers.

Multiprocessing

A process is a program instance that has been loaded into memory and managed by the operating system. Process = address space + execution context (thread of control)

• Process address space is made of (memory) segments for (i) code, (ii) data (static/global), (iii) heap (dynamic memory allocation), and the execution stack (functions’ execution context).

• Execution context consists of (i) data registers, (ii) Stack Pointer (SP), (iii) Program Counter (PC), and (iv) working Registers.

OS Scheduling of processes: context switching (ie. save/load Execution context)

Pros/cons

• Context switching expensive.

• (potentially) complex data sharing (not necessary true).

• Cooperating processes - no need for memory protection (separate address spaces).

• Relevant for parallel computation with memory allocation.

Multithreading

• Threads share the same address space (Data registers): access to code, heap and (global) data.

• Separate execution stack, PC and Working Registers.

Pros/cons

• Faster context switching only SP, PC and Working Registers.

• Can exploit fine-grain concurrency

• Simple data sharing through the shared address space.

• But most of concurrent memory operations are serialized (blocked) by the global interpreter lock (GIL). The GIL prevents two threads writing to the same memory at the same time.

• Relevant for GUI, I/O (Network, disk) concurrent operation

In Python

• As long the GIL exists favor multiprocessing over multithreading

• Multithreading rely on threading module.

• Multiprocessing rely on multiprocessing module.

Example: Random forest

Random forest are the obtained by Majority vote of decision tree on estimated on bootstrapped samples.

Toy dataset

import time
import numpy as np
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import balanced_accuracy_score

# Toy dataset
X, y = make_classification(n_features=1000, n_samples=5000, n_informative=20,
                           random_state=1, n_clusters_per_class=3)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.8,
                                                    random_state=42)

Random forest algorithm: (i) In parallel, fit decision trees on bootstrapped data samples. Make predictions. (ii) Majority vote on predictions

1. In parallel, fit decision trees on bootstrapped data sample. Make predictions.

def boot_decision_tree(X_train, X_test, y_train, predictions_list=None):
    N = X_train.shape[0]
    boot_idx = np.random.choice(np.arange(N), size=N, replace=True)
    clf = DecisionTreeClassifier(random_state=0)
    clf.fit(X_train[boot_idx], y_train[boot_idx])
    y_pred = clf.predict(X_test)
    if predictions_list is not None:
        predictions_list.append(y_pred)
    return y_pred

Independent runs of decision tree, see variability of predictions

for i in range(5):
    y_test_boot = boot_decision_tree(X_train, X_test, y_train)
    print("%.2f" % balanced_accuracy_score(y_test, y_test_boot))

0.63
0.68
0.63
0.63
0.67

2. Majority vote on predictions

def vote(predictions):
    maj = np.apply_along_axis(
        lambda x: np.argmax(np.bincount(x)),
        axis=1,
        arr=predictions
    )
    return maj

Sequential execution

Sequentially fit decision tree on bootstrapped samples, then apply majority vote

nboot = 2
start = time.time()
y_test_boot = np.dstack([boot_decision_tree(X_train, X_test, y_train)
                         for i in range(nboot)]).squeeze()
y_test_vote = vote(y_test_boot)
print("Balanced Accuracy: %.2f" % balanced_accuracy_score(y_test, y_test_vote))
print("Sequential execution, elapsed time:", time.time() - start)

Balanced Accuracy: 0.63
Sequential execution, elapsed time: 1.3796963691711426

Multithreading

Concurrent (parallel) execution of the function with two threads.

from threading import Thread

predictions_list = list()
thread1 = Thread(target=boot_decision_tree,
                 args=(X_train, X_test, y_train, predictions_list))
thread2 = Thread(target=boot_decision_tree,
                 args=(X_train, X_test, y_train, predictions_list))

# Will execute both in parallel
start = time.time()
thread1.start()
thread2.start()

# Joins threads back to the parent process
thread1.join()
thread2.join()

# Vote on concatenated predictions
y_test_boot = np.dstack(predictions_list).squeeze()
y_test_vote = vote(y_test_boot)
print("Balanced Accuracy: %.2f" % balanced_accuracy_score(y_test, y_test_vote))
print("Concurrent execution with threads, elapsed time:", time.time() - start)

Balanced Accuracy: 0.62
Concurrent execution with threads, elapsed time: 0.7034385204315186

Multiprocessing

Concurrent (parallel) execution of the function with processes (jobs) executed in different address (memory) space. Process-based parallelism

Process() for parallel execution and Manager() for data sharing

Sharing data between process with Managers Therefore, sharing data requires specific mechanism using . Managers provide a way to create data which can be shared between different processes, including sharing over a network between processes running on different machines. A manager object controls a server process which manages shared objects.

from multiprocessing import Process, Manager

predictions_list = Manager().list()
p1 = Process(target=boot_decision_tree,
             args=(X_train, X_test, y_train, predictions_list))
p2 = Process(target=boot_decision_tree,
             args=(X_train, X_test, y_train, predictions_list))

# Will execute both in parallel
start = time.time()
p1.start()
p2.start()

# Joins processes back to the parent process
p1.join()
p2.join()

# Vote on concatenated predictions
y_test_boot = np.dstack(predictions_list).squeeze()
y_test_vote = vote(y_test_boot)
print("Balanced Accuracy: %.2f" % balanced_accuracy_score(y_test, y_test_vote))
print("Concurrent execution with processes, elapsed time:", time.time() - start)

Balanced Accuracy: 0.64
Concurrent execution with processes, elapsed time: 0.6105175018310547

Pool() of workers (processes or Jobs) for concurrent (parallel) execution of multiples tasks. Pool can be used when N independent tasks need to be executed in parallel, when there are more tasks than cores on the computer.

1. Initialize a Pool(), map(), apply_async(), of P workers (Process, or Jobs), where P < number of cores in the computer. Use cpu_count to get the number of logical cores in the current system, See: Number of CPUs and Cores in Python.

2. Map N tasks to the P workers, here we use the function Pool.apply_async() that runs the jobs asynchronously. Asynchronous means that calling pool.apply_async does not block the execution of the caller that carry on, i.e., it returns immediately with a AsyncResult object for the task.

that the caller (than runs the sub-processes) is not blocked by the to the process pool does not block, allowing the caller that issued the task to carry on.# 3. Wait for all jobs to complete pool.join() 4. Collect the results

from multiprocessing import Pool, cpu_count
# Numbers of logical cores in the current system.
# Rule of thumb: Divide by 2 to get nb of physical cores
njobs = int(cpu_count() / 2)
start = time.time()
ntasks = 12

pool = Pool(njobs)
# Run multiple tasks each with multiple arguments
async_results = [pool.apply_async(boot_decision_tree,
                                  args=(X_train, X_test, y_train))
                 for i in range(ntasks)]

# Close the process pool & wait for all jobs to complete
pool.close()
pool.join()

# Collect the results
y_test_boot = np.dstack([ar.get() for ar in async_results]).squeeze()

# Vote on concatenated predictions

y_test_vote = vote(y_test_boot)
print("Balanced Accuracy: %.2f" % balanced_accuracy_score(y_test, y_test_vote))
print("Concurrent execution with processes, elapsed time:", time.time() - start)

Balanced Accuracy: 0.64
Concurrent execution with processes, elapsed time: 1.761357069015503

Scripts and argument parsing

Example, the word count script

import os
import os.path
import argparse
import re
import pandas as pd

if __name__ == "__main__":
    # parse command line options
    output = "word_count.csv"
    parser = argparse.ArgumentParser()
    parser.add_argument('-i', '--input',
                        help='list of input files.',
                        nargs='+', type=str)
    parser.add_argument('-o', '--output',
                        help='output csv file (default %s)' % output,
                        type=str, default=output)
    options = parser.parse_args()

    if options.input is None :
        parser.print_help()
        raise SystemExit("Error: input files are missing")
    else:
        filenames = [f for f in options.input if os.path.isfile(f)]

    # Match words
    regex = re.compile("[a-zA-Z]+")

    count = dict()
    for filename in filenames:
        fd = open(filename, "r")
        for line in fd:
            for word in regex.findall(line.lower()):
                if not word in count:
                    count[word] = 1
                else:
                    count[word] += 1

    fd = open(options.output, "w")

    # Pandas
    df = pd.DataFrame([[k, count[k]] for k in count], columns=["word", "count"])
    df.to_csv(options.output, index=False)

Networking

# TODO

FTP

FTP with ftplib

import ftplib

ftp = ftplib.FTP("ftp.cea.fr")
ftp.login()
ftp.cwd('/pub/unati/people/educhesnay/pystatml')
ftp.retrlines('LIST')

fd = open(os.path.join(tmpdir, "README.md"), "wb")
ftp.retrbinary('RETR README.md', fd.write)
fd.close()
ftp.quit()

-rwxrwxr-x    1 ftp      ftp          3019 Oct 16  2019 README.md
-rwxrwxr-x    1 ftp      ftp      10672252 Dec 18  2020 StatisticsMachineLearningPython.pdf
-rwxrwxr-x    1 ftp      ftp       9676120 Nov 12  2020 StatisticsMachineLearningPythonDraft.pdf
-rwxrwxr-x    1 ftp      ftp       9798485 Jul 08  2020 StatisticsMachineLearningPythonDraft_202007.pdf

'221 Goodbye.'

FTP file download with urllib

import urllib
ftp_url = 'ftp://ftp.cea.fr/pub/unati/people/educhesnay/pystatml/README.md'
urllib.request.urlretrieve(ftp_url, os.path.join(tmpdir, "README2.md"))

('/tmp/README2.md', <email.message.Message object at 0x733805637390>)

HTTP

# TODO

Sockets

# TODO

xmlrpc

# TODO

Object Oriented Programming (OOP)

Sources

• http://python-textbok.readthedocs.org/en/latest/Object_Oriented_Programming.html

Principles

• Encapsulate data (attributes) and code (methods) into objects.

• Class = template or blueprint that can be used to create objects.

• An object is a specific instance of a class.

• Inheritance: OOP allows classes to inherit commonly used state and behavior from other classes. Reduce code duplication

• Polymorphism: (usually obtained through polymorphism) calling code is agnostic as to whether an object belongs to a parent class or one of its descendants (abstraction, modularity). The same method called on 2 objects of 2 different classes will behave differently.

class Shape2D:
    def area(self):
        raise NotImplementedError()

# __init__ is a special method called the constructor


# Inheritance + Encapsulation
class Square(Shape2D):
    def __init__(self, width):
        self.width = width

    def area(self):
        return self.width ** 2


class Disk(Shape2D):
    def __init__(self, radius):
        self.radius = radius

    def area(self):
        return math.pi * self.radius ** 2

Object creation

square = Square(2)

Call a method of the object

square.area()

4

More sophisticated use

shapes = [Square(2), Disk(3)]

# Polymorphism
print([s.area() for s in shapes])

s = Shape2D()
try:
    s.area()
except NotImplementedError as e:
    print("NotImplementedError", e)

[4, 28.274333882308138]
NotImplementedError

Style guide for Python programming

See PEP 8

• Spaces (four) are the preferred indentation method.

• Two blank lines for top level function or classes definition.

• One blank line to indicate logical sections.

• Never use: from lib import *

• Bad: Capitalized_Words_With_Underscores

• Function and Variable Names: lower_case_with_underscores

• Class Names: CapitalizedWords (aka: CamelCase)

Documenting

See Documenting Python Documenting = comments + docstrings (Python documentation string)

• Docstrings are use as documentation for the class, module, and packages. See it as “living documentation”.

• Comments are used to explain non-obvious portions of the code. “Dead documentation”.

Docstrings for functions (same for classes and methods):

def my_function(a, b=2):
    """
    This function ...

    Parameters
    ----------
    a : float
        First operand.
    b : float, optional
        Second operand. The default is 2.

    Returns
    -------
    Sum of operands.

    Example
    -------
    >>> my_function(3)
    5
    """
    # Add a with b (this is a comment)
    return a + b


print(help(my_function))

Help on function my_function in module __main__:

my_function(a, b=2)
    This function ...

    Parameters
    ----------
    a : float
        First operand.
    b : float, optional
        Second operand. The default is 2.

    Returns
    -------
    Sum of operands.

    Example
    -------
    >>> my_function(3)
    5

None

Docstrings for scripts:

At the begining of a script add a pream:

"""
Created on Thu Nov 14 12:08:41 CET 2019

@author: firstname.lastname@email.com

Some description
"""

Modules and packages

Python packages and modules structure python code into modular “libraries” to be shared.

Package

Packages are a way of structuring Python’s module namespace by using “dotted module names”. A package is a directory (here, stat_pkg) containing a __init__.py file.

Example, package

stat_pkg/
├── __init__.py
└── datasets_mod.py

The __init__.py can be empty. Or it can be used to define the package API, i.e., the modules (*.py files) that are exported and those that remain internal.

Example, file stat_pkg/__init__.py

# 1) import function for modules in the packages
from .module import make_regression

# 2) Make them visible in the package
__all__ = ["make_regression"]

Module

A module is a python file. Example, stat_pkg/datasets_mod.py

import numpy as np
def make_regression(n_samples=10, n_features=2, add_intercept=False):
    ...
    return X, y, coef

Usage

import stat_pkg as pkg

X, y, coef = pkg.make_regression()
print(X.shape)

(10, 2)

The search path

With a directive like import stat_pkg, Python will searches for

• a module, file named stat_pkg.py or,

• a package, directory named stat_pkg containing a stat_pkg/__init__.py file.

Python will search in a list of directories given by the variable sys.path. This variable is initialized from these locations:

• The directory containing the input script (or the current directory when no file is specified).

• ``PYTHONPATH`` (a list of directory names, with the same syntax as the shell variable PATH).

In our case, to be able to import stat_pkg, the parent directory of stat_pkg must be in sys.path. You can modify PYTHONPATH by any method, or access it via sys package, example:

import sys
sys.path.append("/home/ed203246/git/pystatsml/python_lang")

Unit testing

When developing a library (e.g., a python package) that is bound to evolve and being corrected, we want to ensure that: (i) The code correctly implements some expected functionalities; (ii) the modifications and additions don’t break those functionalities;

Unit testing is a framework to asses to those two points. See sources:

• Unit testing reference doc

• Getting Started With Testing in Python

unittest: test your code

1. Write unit tests (test cases)

In a directory usually called tests create a test case, i.e., a python file test_datasets_mod.py (general syntax is test_<mymodule>.py) that will execute some functionalities of the module and test if the output are as expected. test_datasets_mod.py file contains specific directives:

• import unittest,

• class TestDatasets(unittest.TestCase), the test case class. The general syntax is class Test<MyModule>(unittest.TestCase)

• def test_make_regression(self), test a function of an element of the module. The general syntax is test_<my function>(self)

• self.assertTrue(np.allclose(X.shape, (10, 4))), test a specific functionality. The general syntax is self.assert<True|Equal|...>(<some boolean expression>)

• unittest.main(), where tests should be executed.

Example:

import unittest
import numpy as np
from stat_pkg import make_regression

class TestDatasets(unittest.TestCase):

    def test_make_regression(self):
        X, y, coefs = make_regression(n_samples=10, n_features=3,
                                      add_intercept=True)
        self.assertTrue(np.allclose(X.shape, (10, 4)))
        self.assertTrue(np.allclose(y.shape, (10, )))
        self.assertTrue(np.allclose(coefs.shape, (4, )))

if __name__ == '__main__':
    unittest.main()

2. Run the tests (test runner)

The test runner orchestrates the execution of tests and provides the outcome to the user. Many test runners are available.

unittest is the first unit test framework, it comes with Python standard library. It employs an object-oriented approach, grouping tests into classes known as test cases, each containing distinct methods representing individual tests.

Unitest generally requires that tests are organized as importable modules, see details. Here, we do not introduce this complexity: we directly execute a test file that isn’t importable as a module.

python tests/test_datasets_mod.py

Unittest test discovery: (-m unittest discover) within (-s) tests directory, with verbose (-v) outputs.

python -m unittest discover -v -s tests

Doctest: add unit tests in docstring

Doctest is an inbuilt test framework that comes bundled with Python by default. The doctest module searches for code fragments that resemble interactive Python sessions and runs those sessions to confirm they operate as shown. It promotes Test-driven (TDD) methodology.

1) Add doc test in the docstrings, see python stat_pkg/supervised_models.py:

class LinearRegression:
    """Ordinary least squares Linear Regression.
    ...
    Examples
    --------
    >>> import numpy as np
    >>> from stat_pkg import LinearRegression
    >>> X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
    >>> # y = 1 * x_0 + 2 * x_1 + 3
    >>> y = np.dot(X, np.array([1, 2])) + 3
    >>> reg = LinearRegression().fit(X, y)
    >>> reg.coef_
    array([3., 1., 2.0])
    >>> reg.predict(np.array([[3, 5]]))
    array([16.])
    """
def __init__(self, fit_intercept=True):
    self.fit_intercept = fit_intercept
...

2. Add the call to doctest module ad the end of the python file:

if __name__ == "__main__":
    import doctest
    doctest.testmod()

3. Run doc tests:

python stat_pkg/supervised_models.py

Test failed with the output:

**********************************************************************
File ".../supervised_models.py", line 36, in __main__.LinearRegression
Failed example:
    reg.coef_
Expected:
    array([3., 1., 2.0])
Got:
    array([3., 1., 2.])
**********************************************************************
1 items had failures:
   1 of   7 in __main__.LinearRegression
***Test Failed*** 1 failures.

---

Exercises

Exercise 1: functions

Create a function that acts as a simple calculator taking three parameters: the two operand and the operation in “+”, “-”, and “*”. As default use “+”. If the operation is misspecified, return a error message Ex: calc(4,5,"*") returns 20 Ex: calc(3,5) returns 8 Ex: calc(1, 2, "something") returns error message

Exercise 2: functions + list + loop

Given a list of numbers, return a list where all adjacent duplicate elements have been reduced to a single element. Ex: [1, 2, 2, 3, 2] returns [1, 2, 3, 2]. You may create a new list or modify the passed in list.

Remove all duplicate values (adjacent or not) Ex: [1, 2, 2, 3, 2] returns [1, 2, 3]

Exercise 3: File I/O

1. Copy/paste the BSD 4 clause license (https://en.wikipedia.org/wiki/BSD_licenses) into a text file. Read, the file and count the occurrences of each word within the file. Store the words’ occurrence number in a dictionary.

2. Write an executable python command count_words.py that parse a list of input files provided after --input parameter. The dictionary of occurrence is save in a csv file provides by --output. with default value word_count.csv. Use: - open - regular expression - argparse (https://docs.python.org/3/howto/argparse.html)

Exercise 4: OOP

1. Create a class Employee with 2 attributes provided in the constructor: name, years_of_service. With one method salary with is obtained by 1500 + 100 * years_of_service.

2. Create a subclass Manager which redefine salary method 2500 + 120 * years_of_service.

3. Create a small dictionary-nosed database where the key is the employee’s name. Populate the database with: samples = Employee(‘lucy’, 3), Employee(‘john’, 1), Manager(‘julie’, 10), Manager(‘paul’, 3)

4. Return a table of made name, salary rows, i.e. a list of list [[name, salary]]

5. Compute the average salary