Python Crash Course: A Comprehensive Introduction to Programming in Python, Slides of Web Programming and Technologies

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General

• Conceived in the late 1980s by Guido van Rossum at CWI

in the Netherlands

• Successor to the ABC language
  • Improvement: small core language with a large standard library

and easily extensible

• Multi-paradigm programming language
  • Object-oriented
  • Structured
  • Functional
  • …

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Built-in Data Types: Numbers and

Booleans

• Integer:
  • Normal Integer, e.g. i = 345
  • Octal Literals, e.g. i = 0o
  • Hexadecimal Literals, e.g. i = 0x1F
  • Binary Literals, e.g. i = 0b
• Floating-point numbers
  • E.g. i = 1.234e-
• Complex numbers
  • Composed of + j, e.g. i = 3+4j
• Boolean Values
• True and False

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Built-in Data Types: String

• Strings are sequences of Unicode characters
• Marked by quotes:
  • Single-quote, e.g. ‘Hello, World!’
  • Double-quote, e.g. “Hello, World!”
  • Triple-quote, e.g. ‘‘‘Hello, “World”!’’’
• Access:

  s[2] 'N' M U N I C H 0 1 2 3 4 5 -6 -5 -4 -3 -2 - s = ' ' s[-1] 'H' s[2:] 'NICH' s[:-2] 'MUNI' s[2:-2] 'NI'

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Data Structures: Python Lists

[0b100, ['times', True ], 'is', 4.]

• Related to Java or C arrays, BUT more powerful
• List items do not need to have the same type
• Lists can grow dynamically
• Lists are ordered
• Lists are mutable and elements can be accessed by their index

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Data Structures: Python Lists (cont.)

• Lists (resp. Iterables) are supported by many built-in functions
  • sum()
  • len()
  • max(), min()
  • …
• List comprehension as an elegant way to create lists

a = [x2 for x in range(7)] a [0, 1, 4, 9, 16, 25, 36] sum(a) 91 a + [x2 for x in range(7,9)] [0, 1, 4, 9, 16, 25, 36, 49, 64] del a[:3] [9, 16, 25, 36, 49, 64]

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Excursus: Copying in Python (cont.)

a = ['one',['one'‚'two']] b = a[:] print(id(a),id(b)) 85992520 85995336 b[0] = 'three‚ >>> b[1][1] = 'three' print(id(a),id(b)) >>> print(id(a),id(b)) 85992520 85995336 85992520 85995336 one two

a

No Side Effect

Shallow Copy

one sublist

b^ one

sublist one two

a

one sublist

b^ three

sublist

Side Effect

one three

a

one sublist

b^ one

sublist Solution: the method deepcopy from the module copy

from copy import deepcopy b = deepcopy(a)

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Data Structures: Tuples

('A tuple with', 3, 'entries')

• A tuple is a sequence of comma separated values
• Values can have different types
• Tuples are immutable (but can contain mutable values)

t = 1, [2], 'tuple' #tuple packing t[2] 'tuple' t[0] = 3 TypeError t[1][0] = 3 t (1, [3], 'tuple') x, y, z = t #sequence unpacking

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Data Structures: Dictionaries (cont.)

• Each key must be unique, since values are obtainable via the key
• Dictionaries also support comprehension

d = { i2: i for i in range(7) } d {0: 0, 1: 1, 4: 2, 9: 3, 16: 4, 25: 5, 36: 6} d[4] 2 for entry in d.items(): if entry[0] == 4: print(entry) (4,2) [key for key in d.keys()] #iterating over values is supported, too [0, 1, 4, 9, 16, 25, 36] d[49] = 7 #delete values by using del key word, e.g. del d[36] d print(id(a),id(b)) 85992520 85995336 85992520 85995336 one two ##### a #### No Side Effect #### Shallow Copy one sublist ##### b^ one sublist one two ##### a one sublist ##### b^ three sublist #### Side Effect one three ##### a one sublist ##### b^ one sublist Solution: the method deepcopy from the module copy >>> from copy import deepcopy >>> b = deepcopy(a) DATABASE SYSTEMS GROUP ## Data Structures: Tuples ## ('A tuple with', 3, 'entries') - A tuple is a sequence of comma separated values - Values can have different types - Tuples are immutable (but can contain mutable values) >>> t = 1, [2], 'tuple' #tuple packing >>> t[2] 'tuple' >>> t[0] = 3 TypeError >>> t[1][0] = 3 >>> t (1, [3], 'tuple') >>> x, y, z = t #sequence unpacking DATABASE SYSTEMS GROUP ## Data Structures: Dictionaries (cont.) - Each key must be unique, since values are obtainable via the key - Dictionaries also support comprehension >>> d = { i2: i for i in range(7) } >>> d {0: 0, 1: 1, 4: 2, 9: 3, 16: 4, 25: 5, 36: 6} >>> d[4] 2 >>> for entry in d.items(): if entry[0] == 4: print(entry) (4,2) >>> [key for key in d.keys()] #iterating over values is supported, too [0, 1, 4, 9, 16, 25, 36] >>> d[49] = 7 #delete values by using del key word, e.g. del d[36] >>> d {0: 0, 1: 1, 4: 2, 49: 7, 9: 3, 16: 4, 25: 5, 36: 6}

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Conditional Statements and Loops

• Conditional Statements:

if : <block 1> elif :

<block 2> else : <block 3>

a = 1 if (b > 2) else 0

• Loops:

while : <block 1> else : #else case can be avoided by using break or simply be omitted <block 2> for in : <block 1> else : <block 2>

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map() , filter() and reduce()

• map(func,seq)

def feet_to_meter(x): return x0. feet = [5.92, 49000, 1066.3] list(map(feet_to_meter, feet)) [1.804416, 14935.2, 325.00824] list(map( lambda x: x0.3048, feet)) [1.804416, 14935.2, 325.00824]

• filter(func,seq)

list(filter( lambda x: x%2 == 1, [1,2,3,4,5])) [1, 3, 5]

• reduce(func,seq)

from functools import reduce reduce( lambda x,y: x+y, [1,2,3,4,5]) 15

5.920.3048 490000.3048 1066.3*0.

map

True False^ True False True

filter

plus(plus(plus(plus(1,2),3),4),5)

reduce

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NumPy

• The fundamental package for scientific computing and core part of the

SciPy stack

• Homogeneous multidimensional arrays as main objects
• Provides many arithmetic operations on arrays

import numpy as np A = np.array([[1,2],[1,1]]) A array([[1, 2], [1, 1]]) B = np.array([[0,1],[2,1]]) AB : <block 1> elif : <block 2> else : <block 3> >>> a = 1 if (b > 2) else 0 - Loops: >>> while : <block 1> else : #else case can be avoided by using break or simply be omitted <block 2> >>> for in : <block 1> else : <block 2> DATABASE SYSTEMS GROUP ## map() , filter() and reduce() - map(func,seq) >>> def feet_to_meter(x): return x0. >>> feet = [5.92, 49000, 1066.3] >>> list(map(feet_to_meter, feet)) [1.804416, 14935.2, 325.00824] >>> list(map( lambda x: x0.3048, feet)) [1.804416, 14935.2, 325.00824] - filter(func,seq) >>> list(filter( lambda x: x%2 == 1, [1,2,3,4,5])) [1, 3, 5] - reduce(func,seq) >>> from functools import reduce >>> reduce( lambda x,y: x+y, [1,2,3,4,5]) 15 5.920.3048 490000.3048 1066.30. #### map True False^ True False True #### filter plus(plus(plus(plus(1,2),3),4),5) #### reduce DATABASE SYSTEMS GROUP ## NumPy - The fundamental package for scientific computing and core part of the #### SciPy stack - Homogeneous multidimensional arrays as main objects - Provides many arithmetic operations on arrays >>> import numpy as np >>> A = np.array([[1,2],[1,1]]) >>> A array([[1, 2], [1, 1]]) >>> B = np.array([[0,1],[2,1]]) >>> A*B array([[0, 2],

elif : <block 2> else : <block 3> >>> a = 1 if (b > 2) else 0 - Loops: >>> while : <block 1> else : #else case can be avoided by using break or simply be omitted <block 2> >>> for in : <block 1> else : <block 2> DATABASE SYSTEMS GROUP ## map() , filter() and reduce() - map(func,seq) >>> def feet_to_meter(x): return x0. >>> feet = [5.92, 49000, 1066.3] >>> list(map(feet_to_meter, feet)) [1.804416, 14935.2, 325.00824] >>> list(map( lambda x: x0.3048, feet)) [1.804416, 14935.2, 325.00824] - filter(func,seq) >>> list(filter( lambda x: x%2 == 1, [1,2,3,4,5])) [1, 3, 5] - reduce(func,seq) >>> from functools import reduce >>> reduce( lambda x,y: x+y, [1,2,3,4,5]) 15 5.920.3048 490000.3048 1066.30. #### map True False^ True False True #### filter plus(plus(plus(plus(1,2),3),4),5) #### reduce DATABASE SYSTEMS GROUP ## NumPy - The fundamental package for scientific computing and core part of the #### SciPy stack - Homogeneous multidimensional arrays as main objects - Provides many arithmetic operations on arrays >>> import numpy as np >>> A = np.array([[1,2],[1,1]]) >>> A array([[1, 2], [1, 1]]) >>> B = np.array([[0,1],[2,1]]) >>> AB array([[0, 2], [2, 1]])

np.dot(A,B) array([[4, 3], [2, 2]])