Python_Cheat_Sheet_Keywords_1664634397.pdf

Python Cheat Sheet - Keywords
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Keyword Description Code example
False, True Data values from the data type Boolean False == (1 > 2), True == (2 > 1)
and, or, not Logical operators:
(x and y) → both x and y must be True
(x or y) → either x or y must be True
(not x) → x must be false
x, y = True, False
(x or y) == True # True
(x and y) == False # True
(not y) == True # True
break Ends loop prematurely while(True):
break # no infinite loop
print("hello world")
continue Finishes current loop iteration while(True):
continue
print("43") # dead code
class
def
Defines a new class → a real-world concept
(object oriented programming)
Defines a new function or class method. For latter,
first parameter (“self”) points to the class object.
When calling class method, first parameter is implicit.
class Beer:
def __init__(self):
self.content = 1.0
def drink(self):
self.content = 0.0
becks = Beer() # constructor - create class
becks.drink() # beer empty: b.content == 0
if, elif, else Conditional program execution: program starts with
“if” branch, tries the “elif” branches, and finishes with
“else” branch (until one branch evaluates to True).
x = int(input("your value: "))
if x > 3: print("Big")
elif x == 3: print("Medium")
else: print("Small")
for, while # For loop declaration
for i in [0,1,2]:
print(i)
# While loop - same semantics
j = 0
while j < 3:
print(j)
j = j + 1
in Checks whether element is in sequence 42 in [2, 39, 42] # True
is Checks whether both elements point to the same
object
y = x = 3
x is y # True
[3] is [3] # False
None Empty value constant def f():
x = 2
f() is None # True
lambda Function with no name (anonymous function) (lambda x: x + 3)(3) # returns 6
return Terminates execution of the function and passes the
flow of execution to the caller. An optional value after
the return keyword specifies the function result.
def incrementor(x):
return x + 1
incrementor(4) # returns 5

Python Cheat Sheet - Basic Data Types
Description Example
Boolean The Boolean data type is a truth value, either
True or False.
The Boolean operators ordered by priority:
not x → “if x is False, then x, else y”
x and y → “if x is False, then x, else y”
x or y → “if x is False, then y, else x”
These comparison operators evaluate to True:
1 < 2 and 0 <= 1 and 3 > 2 and 2 >=2 and
1 == 1 and 1 != 0 # True
## 1. Boolean Operations
x, y = True, False
print(x and not y) # True
print(not x and y or x) # True
## 2. If condition evaluates to False
if None or 0 or 0.0 or '' or [] or {} or set():
# None, 0, 0.0, empty strings, or empty
# container types are evaluated to False
print("Dead code") # Not reached
Integer,
Float
An integer is a positive or negative number
without floating point (e.g. 3). A float is a
positive or negative number with floating point
precision (e.g. 3.14159265359).
The ‘//’ operator performs integer division.
The result is an integer value that is rounded
towards the smaller integer number
(e.g. 3 // 2 == 1).
## 3. Arithmetic Operations
x, y = 3, 2
print(x + y) # = 5
print(x - y) # = 1
print(x * y) # = 6
print(x / y) # = 1.5
print(x // y) # = 1
print(x % y) # = 1s
print(-x) # = -3
print(abs(-x)) # = 3
print(int(3.9)) # = 3
print(float(3)) # = 3.0
print(x ** y) # = 9
String Python Strings are sequences of characters.
The four main ways to create strings are the
following.
1. Single quotes
'Yes'
2. Double quotes
"Yes"
3. Triple quotes (multi-line)
"""Yes
We Can"""
4. String method
str(5) == '5' # True
5. Concatenation
"Ma" + "hatma" # 'Mahatma'
These are whitespace characters in strings.
● Newline n
● Space s
● Tab t
## 4. Indexing and Slicing
s = "The youngest pope was 11 years old"
print(s[0]) # 'T'
print(s[1:3]) # 'he'
print(s[-3:-1]) # 'ol'
print(s[-3:]) # 'old'
x = s.split() # creates string array of words
print(x[-3] + " " + x[-1] + " " + x[2] + "s")
# '11 old popes'
## 5. Most Important String Methods
y = " This is lazytn "
print(y.strip()) # Remove Whitespace: 'This is lazy'
print("DrDre".lower()) # Lowercase: 'drdre'
print("attention".upper()) # Uppercase: 'ATTENTION'
print("smartphone".startswith("smart")) # True
print("smartphone".endswith("phone")) # True
print("another".find("other")) # Match index: 2
print("cheat".replace("ch", "m")) # 'meat'
print(','.join(["F", "B", "I"])) # 'F,B,I'
print(len("Rumpelstiltskin")) # String length: 15
print("ear" in "earth") # Contains: True

Python Cheat Sheet - Complex Data Types
Description Example
List A container data type that stores a
sequence of elements. Unlike strings, lists
are mutable: modification possible.
l = [1, 2, 2]
print(len(l)) # 3
Adding
elements
Add elements to a list with (i) append, (ii)
insert, or (iii) list concatenation.
The append operation is very fast.
[1, 2, 2].append(4) # [1, 2, 2, 4]
[1, 2, 4].insert(2,2) # [1, 2, 2, 4]
[1, 2, 2] + [4] # [1, 2, 2, 4]
Removal Removing an element can be slower. [1, 2, 2, 4].remove(1) # [2, 2, 4]
Reversing This reverses the order of list elements. [1, 2, 3].reverse() # [3, 2, 1]
Sorting Sorts a list. The computational complexity
of sorting is O(n log n) for n list elements.
[2, 4, 2].sort() # [2, 2, 4]
Indexing Finds the first occurence of an element in
the list & returns its index. Can be slow as
the whole list is traversed.
[2, 2, 4].index(2) # index of element 4 is "0"
[2, 2, 4].index(2,1) # index of element 2 after pos 1 is "1"
Stack Python lists can be used intuitively as stack
via the two list operations append() and
pop().
stack = [3]
stack.append(42) # [3, 42]
stack.pop() # 42 (stack: [3])
stack.pop() # 3 (stack: [])
Set A set is an unordered collection of
elements. Each can exist only once.
basket = {'apple', 'eggs', 'banana', 'orange'}
same = set(['apple', 'eggs', 'banana', 'orange'])
Dictionary The dictionary is a useful data structure for
storing (key, value) pairs.
calories = {'apple' : 52, 'banana' : 89, 'choco' : 546}
Reading and
writing
elements
Read and write elements by specifying the
key within the brackets. Use the keys() and
values() functions to access all keys and
values of the dictionary.
print(calories['apple'] < calories['choco']) # True
calories['cappu'] = 74
print(calories['banana'] < calories['cappu']) # False
print('apple' in calories.keys()) # True
print(52 in calories.values()) # True
Dictionary
Looping
You can loop over the (key, value) pairs of
a dictionary with the items() method.
for k, v in calories.items():
print(k) if v > 500 else None # 'chocolate'
Membership
operator
Check with the ‘in’ keyword whether the
set, list, or dictionary contains an element.
Set containment is faster than list
containment.
basket = {'apple', 'eggs', 'banana', 'orange'}
print('eggs' in basket} # True
print('mushroom' in basket} # False
List and Set
Comprehens
ion
List comprehension is the concise Python
way to create lists. Use brackets plus an
expression, followed by a for clause. Close
with zero or more for or if clauses.
Set comprehension is similar to list
comprehension.
# List comprehension
l = [('Hi ' + x) for x in ['Alice', 'Bob', 'Pete']]
print(l) # ['Hi Alice', 'Hi Bob', 'Hi Pete']
l2 = [x * y for x in range(3) for y in range(3) if x>y]
print(l2) # [0, 0, 2]
# Set comprehension
squares = { x**2 for x in [0,2,4] if x < 4 } # {0, 4}

Python Cheat Sheet - Classes
Description Example
Classes A class encapsulates data and functionality - data as
attributes, and functionality as methods. It is a blueprint
to create concrete instances in the memory.
class Dog:
""" Blueprint of a dog """
# class variable shared by all instances
species = ["canis lupus"]
def __init__(self, name, color):
self.name = name
self.state = "sleeping"
self.color = color
def command(self, x):
if x == self.name:
self.bark(2)
elif x == "sit":
self.state = "sit"
else:
self.state = "wag tail"
def bark(self, freq):
for i in range(freq):
print("[" + self.name
+ "]: Woof!")
bello = Dog("bello", "black")
alice = Dog("alice", "white")
print(bello.color) # black
print(alice.color) # white
bello.bark(1) # [bello]: Woof!
alice.command("sit")
print("[alice]: " + alice.state)
# [alice]: sit
bello.command("no")
print("[bello]: " + bello.state)
# [bello]: wag tail
alice.command("alice")
# [alice]: Woof!
# [alice]: Woof!
bello.species += ["wulf"]
print(len(bello.species)
== len(alice.species)) # True (!)
Instance You are an instance of the class human. An instance is a
concrete implementation of a class: all attributes of an
instance have a fixed value. Your hair is blond, brown, or
black - but never unspecified.
Each instance has its own attributes independent of
other instances. Yet, class variables are different. These
are data values associated with the class, not the
instances. Hence, all instance share the same class
variable species in the example.
Self The first argument when defining any method is always
the self argument. This argument specifies the
instance on which you call the method.
self gives the Python interpreter the information about
the concrete instance. To define a method, you use self
to modify the instance attributes. But to call an instance
method, you do not need to specify self.
Creation You can create classes “on the fly” and use them as
logical units to store complex data types.
class Employee():
pass
employee = Employee()
employee.salary = 122000
employee.firstname = "alice"
employee.lastname = "wonderland"
print(employee.firstname + " "
+ employee.lastname + " "
+ str(employee.salary) + "$")
# alice wonderland 122000$

Python Cheat Sheet - Functions and Tricks
Description Example Result
A
D
V
A
N
C
E
D
F
U
N
C
T
I
O
N
S
map(func, iter) Executes the function on all elements of
the iterable
list(map(lambda x: x[0], ['red',
'green', 'blue']))
['r', 'g', 'b']
map(func, i1, ...,
ik)
Executes the function on all k elements of
the k iterables
list(map(lambda x, y: str(x) + ' ' +
y + 's' , [0, 2, 2], ['apple',
'orange', 'banana']))
['0 apples', '2
oranges', '2
bananas']
string.join(iter) Concatenates iterable elements
separated by string
' marries '.join(list(['Alice',
'Bob']))
'Alice marries Bob'
filter(func,
iterable)
Filters out elements in iterable for which
function returns False (or 0)
list(filter(lambda x: True if x>17
else False, [1, 15, 17, 18]))
[18]
string.strip() Removes leading and trailing
whitespaces of string
print(" n t 42 t ".strip()) 42
sorted(iter) Sorts iterable in ascending order sorted([8, 3, 2, 42, 5]) [2, 3, 5, 8, 42]
sorted(iter,
key=key)
Sorts according to the key function in
ascending order
sorted([8, 3, 2, 42, 5], key=lambda
x: 0 if x==42 else x)
[42, 2, 3, 5, 8]
help(func) Returns documentation of func help(str.upper()) '... to uppercase.'
zip(i1, i2, ...) Groups the i-th elements of iterators i1, i2,
… together
list(zip(['Alice', 'Anna'], ['Bob',
'Jon', 'Frank']))
[('Alice', 'Bob'),
('Anna', 'Jon')]
Unzip Equal to: 1) unpack the zipped list, 2) zip
the result
list(zip(*[('Alice', 'Bob'),
('Anna', 'Jon')]
[('Alice', 'Anna'),
('Bob', 'Jon')]
enumerate(iter) Assigns a counter value to each element
of the iterable
list(enumerate(['Alice', 'Bob',
'Jon']))
[(0, 'Alice'), (1,
'Bob'), (2, 'Jon')]
T
R
I
C
K
S
python -m http.server
<P>
Share files between PC and phone? Run command in PC’s shell. <P> is any port number 0–65535. Type < IP address of
PC>:<P> in the phone’s browser. You can now browse the files in the PC directory.
Read comic import antigravity Open the comic series xkcd in your web browser
Zen of Python import this '...Beautiful is better than ugly. Explicit is ...'
Swapping numbers Swapping variables is a breeze in Python.
No offense, Java!
a, b = 'Jane', 'Alice'
a, b = b, a
a = 'Alice'
b = 'Jane'
Unpacking arguments Use a sequence as function arguments
via asterisk operator *. Use a dictionary
(key, value) via double asterisk operator **
def f(x, y, z): return x + y * z
f(*[1, 3, 4])
f(**{'z' : 4, 'x' : 1, 'y' : 3})
13
13
Extended Unpacking Use unpacking for multiple assignment
feature in Python
a, *b = [1, 2, 3, 4, 5] a = 1
b = [2, 3, 4, 5]
Merge two dictionaries Use unpacking to merge two dictionaries
into a single one
x={'Alice' : 18}
y={'Bob' : 27, 'Ann' : 22}
z = {**x,**y}
z = {'Alice': 18,
'Bob': 27, 'Ann': 22}

Python Cheat Sheet: 14 Interview Questions
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Question Code Question Code
Check if list
contains
integer x
l = [3, 3, 4, 5, 2, 111, 5]
print(111 in l) # True
Get missing
number in
[1...100]
def get_missing_number(lst):
return set(range(lst[len(lst)-1])[1:]) - set(l)
l = list(range(1,100))
l.remove(50)
print(get_missing_number(l)) # 50
Find duplicate
number in
integer list
def find_duplicates(elements):
duplicates, seen = set(), set()
for element in elements:
if element in seen:
duplicates.add(element)
seen.add(element)
return list(duplicates)
Compute
the
intersection
of two lists
def intersect(lst1, lst2):
res, lst2_copy = [], lst2[:]
for el in lst1:
if el in lst2_copy:
res.append(el)
lst2_copy.remove(el)
return res
Check if two
strings are
anagrams
def is_anagram(s1, s2):
return set(s1) == set(s2)
print(is_anagram("elvis", "lives")) # True
Find max
and min in
unsorted list
l = [4, 3, 6, 3, 4, 888, 1, -11, 22, 3]
print(max(l)) # 888
print(min(l)) # -11
Remove all
duplicates from
list
lst = list(range(10)) + list(range(10))
lst = list(set(lst))
print(lst)
# [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
Reverse
string using
recursion
def reverse(string):
if len(string)<=1: return string
return reverse(string[1:])+string[0]
print(reverse("hello")) # olleh
Find pairs of
integers in list
so that their
sum is equal to
integer x
def find_pairs(l, x):
pairs = []
for (i, el_1) in enumerate(l):
for (j, el_2) in enumerate(l[i+1:]):
if el_1 + el_2 == x:
pairs.append((el_1, el_2))
return pairs
Compute
the first n
Fibonacci
numbers
a, b = 0, 1
n = 10
for i in range(n):
print(b)
a, b = b, a+b
# 1, 1, 2, 3, 5, 8, ...
Check if a
string is a
palindrome
def is_palindrome(phrase):
return phrase == phrase[::-1]
print(is_palindrome("anna")) # True
Sort list with
Quicksort
algorithm
def qsort(L):
if L == []: return []
return qsort([x for x in L[1:] if x< L[0]]) + L[0:1] +
qsort([x for x in L[1:] if x>=L[0]])
lst = [44, 33, 22, 5, 77, 55, 999]
print(qsort(lst))
# [5, 22, 33, 44, 55, 77, 999]
Use list as
stack, array,
and queue
# as a list ...
l = [3, 4]
l += [5, 6] # l = [3, 4, 5, 6]
# ... as a stack ...
l.append(10) # l = [4, 5, 6, 10]
l.pop() # l = [4, 5, 6]
# ... and as a queue
l.insert(0, 5) # l = [5, 4, 5, 6]
l.pop() # l = [5, 4, 5]
Find all
permutation
s of string
def get_permutations(w):
if len(w)<=1:
return set(w)
smaller = get_permutations(w[1:])
perms = set()
for x in smaller:
for pos in range(0,len(x)+1):
perm = x[:pos] + w[0] + x[pos:]
perms.add(perm)
return perms
print(get_permutations("nan"))
# {'nna', 'ann', 'nan'}

Python Cheat Sheet: NumPy
Name Description Example
a.shape The shape attribute of NumPy array a keeps a tuple of
integers. Each integer describes the number of elements of
the axis.
a = np.array([[1,2],[1,1],[0,0]])
print(np.shape(a)) # (3, 2)
a.ndim The ndim attribute is equal to the length of the shape tuple. print(np.ndim(a)) # 2
* The asterisk (star) operator performs the Hadamard product,
i.e., multiplies two matrices with equal shape element-wise.
a = np.array([[2, 0], [0, 2]])
b = np.array([[1, 1], [1, 1]])
print(a*b) # [[2 0] [0 2]]
np.matmul(a,b), a@b The standard matrix multiplication operator. Equivalent to the
@ operator.
print(np.matmul(a,b))
# [[2 2] [2 2]]
np.arange([start, ]stop,
[step, ])
Creates a new 1D numpy array with evenly spaced values print(np.arange(0,10,2))
# [0 2 4 6 8]
np.linspace(start, stop,
num=50)
Creates a new 1D numpy array with evenly spread elements
within the given interval
print(np.linspace(0,10,3))
# [ 0. 5. 10.]
np.average(a) Averages over all the values in the numpy array a = np.array([[2, 0], [0, 2]])
print(np.average(a)) # 1.0
<slice> = <val> Replace the <slice> as selected by the slicing operator with
the value <val>.
a = np.array([0, 1, 0, 0, 0])
a[::2] = 2
print(a) # [2 1 2 0 2]
np.var(a) Calculates the variance of a numpy array. a = np.array([2, 6])
print(np.var(a)) # 4.0
np.std(a) Calculates the standard deviation of a numpy array print(np.std(a)) # 2.0
np.diff(a) Calculates the difference between subsequent values in
NumPy array a
fibs = np.array([0, 1, 1, 2, 3, 5])
print(np.diff(fibs, n=1))
# [1 0 1 1 2]
np.cumsum(a) Calculates the cumulative sum of the elements in NumPy
array a.
print(np.cumsum(np.arange(5)))
# [ 0 1 3 6 10]
np.sort(a) Creates a new NumPy array with the values from a
(ascending).
a = np.array([10,3,7,1,0])
print(np.sort(a))
# [ 0 1 3 7 10]
np.argsort(a) Returns the indices of a NumPy array so that the indexed
values would be sorted.
a = np.array([10,3,7,1,0])
print(np.argsort(a))
# [4 3 1 2 0]
np.max(a) Returns the maximal value of NumPy array a. a = np.array([10,3,7,1,0])
print(np.max(a)) # 10
np.argmax(a) Returns the index of the element with maximal value in the
NumPy array a.
a = np.array([10,3,7,1,0])
print(np.argmax(a)) # 0
np.nonzero(a) Returns the indices of the nonzero elements in NumPy array
a.
a = np.array([10,3,7,1,0])
print(np.nonzero(a)) # [0 1 2 3]

Python Cheat Sheet: Object Orientation Terms
Description Example
Class A blueprint to create objects. It defines the data (attributes) and functionality
(methods) of the objects. You can access both attributes and methods via
the dot notation.
class Dog:
# class attribute
is_hairy = True
# constructor
def __init__(self, name):
# instance attribute
self.name = name
# method
def bark(self):
print("Wuff")
bello = Dog("bello")
paris = Dog("paris")
print(bello.name)
"bello"
print(paris.name)
"paris"
class Cat:
# method overloading
def miau(self, times=1):
print("miau " * times)
fifi = Cat()
fifi.miau()
"miau "
fifi.miau(5)
"miau miau miau miau miau "
# Dynamic attribute
fifi.likes = "mice"
print(fifi.likes)
"mice"
# Inheritance
class Persian_Cat(Cat):
classification = "Persian"
mimi = Persian_Cat()
print(mimi.miau(3))
"miau miau miau "
print(mimi.classification)
Object
(=instance)
A piece of encapsulated data with functionality in your Python program that
is built according to a class definition. Often, an object corresponds to a
thing in the real world. An example is the object "Obama" that is created
according to the class definition "Person". An object consists of an arbitrary
number of attributes and methods, encapsulated within a single unit.
Instantiation The process of creating an object of a class. This is done with the
constructor method __init__(self, …).
Method A subset of the overall functionality of an object. The method is defined
similarly to a function (using the keyword "def") in the class definition. An
object can have an arbitrary number of methods.
Self The first argument when defining any method is always the self argument.
This argument specifies the instance on which you call the method.
self gives the Python interpreter the information about the concrete
instance. To define a method, you use self to modify the instance
attributes. But to call an instance method, you do not need to specify self.
Encapsulation Binding together data and functionality that manipulates the data.
Attribute A variable defined for a class (class attribute) or for an object (instance attribute). You
use attributes to package data into enclosed units (class or instance).
Class
attribute
(=class variable, static variable, static attribute) A variable that is created
statically in the class definition and that is shared by all class objects.
Instance
attribute
(=instance
variable)
A variable that holds data that belongs only to a single instance. Other instances do
not share this variable (in contrast to class attributes). In most cases, you create an
instance attribute x in the constructor when creating the instance itself using the self
keywords (e.g. self.x = <val>).
Dynamic
attribute
An instance attribute that is defined dynamically during the execution of the program
and that is not defined within any method. For example, you can simply add a new
attribute neew to any object o by calling o.neew = <val>.
Method
overloading
You may want to define a method in a way so that there are multiple options
to call it. For example for class X, you define a method f(...) that can be called
in three ways: f(a), f(a,b), or f(a,b,c). To this end, you can define the method
with default parameters (e.g. f(a, b=None, c=None).
Inheritance Class A can inherit certain characteristics (like attributes or methods) from class B.
For example, the class "Dog" may inherit the attribute "number_of_legs" from the
class "Animal". In this case, you would define the inherited class "Dog" as follows:
"class Dog(Animal): ..."

[Test Sheet] Help Alice Find Her Coding Dad!
„Continuous Improvement in Your Coffee Break Python!“
Solve puzzle 93!
Solve puzzle 332!
Solve
puzzle
441!
+ BONUS
Solve
puzzle
369!
Solve puzzle 366!
+ BONUS
Solve puzzle 377!
Solve
puzzle
137!
+
BONUS

[Cheat Sheet] 6 Pillar Machine Learning Algorithms
Logic
skills
Creativity skills
Decision
boundary
Support Vector Machine
Classification
https://blog.finxter.com/support-vector-machines-python/
Support vector
Computer Scientist
Artist
Complete Course: https://academy.finxter.com/
„Continuous Improvement in Your Coffee Break Python!“
Like maths?
Like language?
Like painting?
„Study
computer
science!“
„Study
linguistics!“
„Study art!“
„Study history!“
Y
Y
Y
N
N
N
Decision Tree Classification
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K-Means Clustering
https://blog.finxter.com/tutorial-how-to-run-k-means-
clustering-in-1-line-of-python/
House Size (square meter)
House
Price
($)
3NN
A: 50𝑚2
, $34,000
B: 55𝑚2
, $33,500
C: 45𝑚2
, $32,000
A
C
B
D: (52𝑚2
, ? )
D‘: 52𝑚2
,
$99,500
3
≈ (52𝑚2
, $33,167)
K Nearest Neighbors
https://blog.finxter.com/k-nearest-neighbors-as-a-
python-one-liner/
Linear Regression
https://blog.finxter.com/logistic-regression-in-one-line-python/
Multilayer Perceptron
https://blog.finxter.com/tutorial-how-to-create-your-first-neural-
network-in-1-line-of-python-code/

The Simple Git Cheat Sheet – A Helpful Illustrated Guide
„A Puzzle A Day
to Learn, Code,
and Play!“
The Centralized Git Workflow
Git Master Branch
Alice Bob
</>
• Every coder has own copy of project
• Independence of workflow
• No advanced branching and merging needed
Create new repository
git init
Clone repository
Remote Repository:
Master Branch
git clone alice@host:/path/repos
git add main.py
Add file „main.py“ to project
git commit –m “new file”
Commit change and add message
„new file“ to the master branch
„new file“
git push origin master
Send master branch to remote
repository </>
„new file“
Update local repository
with master branch
git pull
git clone bob@host:/path/repos
</>
„new file“
</> </>
git add *
Add all changes to master
git rm main.py
Remove file „main.py“ from
master branch
</>
git commit –m “add 2, rem 1”
Commit change and add
message to the master
„add 2,
rem 1“
git push origin master
Send master branch to
remote repository
</> </>
</>
„add 2, rem 1“
Clone
Push
Clone
Pull
Push

[Machine Learning Cheat Sheet] Support Vector Machines
Logic
skills
Creativity skills
Decision boundary
Support Vector Machine Classification
Support vectors
Logic
skills
Creativity skills
Computer Scientist
Artist
Decision
boundaries
Machine Learning Classification
Main idea: Maximize width of separator zone → increases „margin of safety“ for classification
Based on Article: https://blog.finxter.com/support-vector-machines-python/
Support Vector Machines
Alternatives: SVM, support-vector networks
Learning: Classification, Regression
Advantages: Robust for high-dimensional space
Memory efficient (only uses support vectors)
Flexible and customizable
Disadvantages: Danger of overfitting in high-dimensional space
No classification probabilities like Decision trees
Boundary: Linear and Non-linear
## Dependencies
from sklearn import svm
import numpy as np
## Data: student scores in (math, language, creativity)
## --> study field
X = np.array([[9, 5, 6, "computer science"],
[10, 1, 2, "computer science"],
[1, 8, 1, "literature"],
[4, 9, 3, "literature"],
[0, 1, 10, "art"],
[5, 7, 9, "art"]])
## One-liner
svm = svm.SVC().fit(X[:,:-1], X[:,-1])
## Result & puzzle
student_0 = svm.predict([[3, 3, 6]])
print(student_0)
print(student_1)
What are basic SVM properties?
What‘s the most basic Python code example?
Explanation: A Study Recommendation System with SVM
• NumPy array holds labeled training data (one row per user and one
column per feature).
• Features: skill level in maths, language, and creativity.
• Labels: last column is recommended study field.
• 3D data → SVM separates data using 2D planes (the linear separator)
rather than 1D lines.
• One-liner:
1. Create model using constructor of scikit-learn’s svm.SVC class
(SVC = support vector classification).
2. Call fit function to perform training based on labeled training
data.
• Results: call predict function on new observations
• student_0 (skills maths=3, language=3, and creativity=6) →
SVM predicts “art”
• student_1 (maths=8, language=1, and creativity=1) → SVM
predicts “computer science”
• Final output of one-liner:
## Result & puzzle
print(student_0)
# ['art']
print(student_1)
## ['computer science']
What‘s the explanation of the code example?
„A Puzzle A Day to Learn, Code, and Play!“

Python Cheat Sheet: List Methods
Method Description Example
lst.append(x) Appends element x to the list lst. >>> l = []
>>> l.append(42)
>>> l.append(21)
[42, 21]
lst.clear() Removes all elements from the list
lst–which becomes empty.
>>> lst = [1, 2, 3, 4, 5]
>>> lst.clear()
[]
lst.copy() Returns a copy of the list lst. Copies only
the list, not the elements in the list (shallow
copy).
>>> lst = [1, 2, 3]
>>> lst.copy()
[1, 2, 3]
lst.count(x) Counts the number of occurrences of
element x in the list lst.
>>> lst = [1, 2, 42, 2, 1, 42, 42]
>>> lst.count(42)
3
>>> lst.count(2)
2
lst.extend(iter) Adds all elements of an iterable iter (e.g.
another list) to the list lst.
>>> lst = [1, 2, 3]
>>> lst.extend([4, 5, 6])
[1, 2, 3, 4, 5, 6]
lst.index(x) Returns the position (index) of the first
occurrence of value x in the list lst.
>>> lst = ["Alice", 42, "Bob", 99]
>>> lst.index("Alice")
0
>>> lst.index(99, 1, 3)
ValueError: 99 is not in list
lst.insert(i, x) Inserts element x at position (index) i in
the list lst.
>>> lst = [1, 2, 3, 4]
>>> lst.insert(3, 99)
[1, 2, 3, 99, 4]
lst.pop() Removes and returns the final element of
the list lst.
>>> lst = [1, 2, 3]
>>> lst.pop()
3
>>> lst
[1, 2]
lst.remove(x) Removes and returns the first occurrence
of element x in the list lst.
>>> lst = [1, 2, 99, 4, 99]
>>> lst.remove(99)
>>> lst
[1, 2, 4, 99]
lst.reverse() Reverses the order of elements in the list
lst.
>>> lst = [1, 2, 3, 4]
>>> lst.reverse()
>>> lst
[4, 3, 2, 1]
lst.sort() Sorts the elements in the list lst in
ascending order.
>>> lst = [88, 12, 42, 11, 2]
>>> lst.sort()
# [2, 11, 12, 42, 88]
>>> lst.sort(key=lambda x: str(x)[0])
# [11, 12, 2, 42, 88]

Basic Data Structures
The Ultimate Python Cheat Sheet
Keywords
Keyword Description Code Examples
False,
True
Boolean data type False == (1 > 2)
True == (2 > 1)
and,
or,
not
Logical operators
→ Both are true
→ Either is true
→ Flips Boolean
True and True # True
True or False # True
not False # True
break Ends loop prematurely while True:
break # finite loop
continue Finishes current loop iteration while True:
continue
print("42") # dead code
class Defines new class class Coffee:
# Define your class
def Defines a new function or class
method.
def say_hi():
print('hi')
if,
elif,
else
Conditional execution:
- “if” condition == True?
- "elif" condition == True?
- Fallback: else branch
x = int(input("ur val:"))
if x > 3: print("Big")
elif x == 3: print("3")
else: print("Small")
for,
while
# For loop
for i in [0,1,2]:
print(i)
# While loop does same
j = 0
while j < 3:
print(j); j = j + 1
in Sequence membership 42 in [2, 39, 42] # True
is Same object memory location y = x = 3
x is y # True
[3] is [3] # False
None Empty value constant print() is None # True
lambda Anonymous function (lambda x: x+3)(3) # 6
return Terminates function. Optional
return value defines function
result.
def increment(x):
return x + 1
increment(4) # returns 5
Type Description Code Examples
Boolean The Boolean data type is
either True or False.
Boolean operators are
ordered by priority:
not → and → or
## Evaluates to True:
1<2 and 0<=1 and 3>2 and 2>=2 and 1==1
and 1!=0
## Evaluates to False:
bool(None or 0 or 0.0 or '' or [] or
{} or set())
Rule: None, 0, 0.0, empty strings, or empty container
types evaluate to False
Integer,
Float
An integer is a positive or
negative number without
decimal point such as 3.
A float is a positive or
negative number with
floating point precision
such as 3.1415926.
Integer division rounds
toward the smaller integer
(example: 3//2==1).
## Arithmetic Operations
x, y = 3, 2
print(x + y) # = 5
print(x - y) # = 1
print(x * y) # = 6
print(x / y) # = 1.5
print(x // y) # = 1
print(x % y) # = 1
print(-x) # = -3
print(abs(-x)) # = 3
print(int(3.9)) # = 3
print(float(3)) # = 3.0
print(x ** y) # = 9
String Python Strings are
sequences of characters.
String Creation Methods:
1. Single quotes
>>> 'Yes'
2. Double quotes
>>> "Yes"
3. Triple quotes (multi-line)
>>> """Yes
We Can"""
4. String method
>>> str(5) == '5'
True
5. Concatenation
>>> "Ma" + "hatma"
'Mahatma'
Whitespace chars:
Newline n,
Space s,
Tab t
## Indexing and Slicing
s = "The youngest pope was 11 years"
s[0] # 'T'
s[1:3] # 'he'
s[-3:-1] # 'ar'
s[-3:] # 'ars'
x = s.split()
x[-2] + " " + x[2] + "s" # '11 popes'
## String Methods
y = " Hello worldtn "
y.strip() # Remove Whitespace
"HI".lower() # Lowercase: 'hi'
"hi".upper() # Uppercase: 'HI'
"hello".startswith("he") # True
"hello".endswith("lo") # True
"hello".find("ll") # Match at 2
"cheat".replace("ch", "m") # 'meat'
''.join(["F", "B", "I"]) # 'FBI'
len("hello world") # Length: 15
"ear" in "earth" # True
Type Description Example
Dictionary Useful data structure for
storing (key, value) pairs
cal = {'apple' : 52, 'banana' : 89,
'choco' : 546} # calories
Reading
and
writing
elements
Read and write elements by
specifying the key within the
brackets. Use the keys()
and values() functions to
access all keys and values of
the dictionary
print(cal['apple'] < cal['choco'])
# True
cal['cappu'] = 74
print(cal['banana'] < cal['cappu'])
# False
print('apple' in cal.keys()) # True
print(52 in cal.values()) # True
Dictionary
Iteration
You can access the (key,
value) pairs of a dictionary
with the items() method.
for k, v in cal.items():
print(k) if v > 500 else ''
# 'choco'
Member-
ship
operator
Check with the in keyword if
set, list, or dictionary contains
an element. Set membership
is faster than list membership.
basket = {'apple', 'eggs',
'banana', 'orange'}
print('eggs' in basket) # True
print('mushroom' in basket) # False
List & set
comprehe
nsion
List comprehension is the
concise Python way to create
lists. Use brackets plus an
expression, followed by a for
clause. Close with zero or
more for or if clauses.
Set comprehension works
similar to list comprehension.
l = ['hi ' + x for x in ['Alice',
'Bob', 'Pete']]
# ['Hi Alice', 'Hi Bob', 'Hi Pete']
l2 = [x * y for x in range(3) for y
in range(3) if x>y] # [0, 0, 2]
squares = { x**2 for x in [0,2,4]
if x < 4 } # {0, 4}
Complex Data Structures
Type Description Example
List Stores a sequence of
elements. Unlike strings, you
can modify list objects (they're
mutable).
l = [1, 2, 2]
print(len(l)) # 3
Adding
elements
Add elements to a list with (i)
append, (ii) insert, or (iii) list
concatenation.
[1, 2].append(4) # [1, 2, 4]
[1, 4].insert(1,9) # [1, 9, 4]
[1, 2] + [4] # [1, 2, 4]
Removal Slow for lists [1, 2, 2, 4].remove(1) # [2, 2, 4]
Reversing Reverses list order [1, 2, 3].reverse() # [3, 2, 1]
Sorting Sorts list using fast Timsort [2, 4, 2].sort() # [2, 2, 4]
Indexing Finds the first occurrence of
an element & returns index.
Slow worst case for whole list
traversal.
[2, 2, 4].index(2)
# index of item 2 is 0
[2, 2, 4].index(2,1)
# index of item 2 after pos 1 is 1
Stack Use Python lists via the list
operations append() and pop()
stack = [3]
stack.append(42) # [3, 42]
stack.pop() # 42 (stack: [3])
stack.pop() # 3 (stack: [])
Set An unordered collection of
unique elements (at-most-
once) → fast membership O(1)
basket = {'apple', 'eggs',
'banana', 'orange'}
same = set(['apple', 'eggs',
'banana', 'orange'])
Subscribe to the 11x FREE Python Cheat Sheet Course:
https://blog.finxter.com/python-cheat-sheets/
1, 2, 3
1 2 3 4
0 1 2 3
Slice [::2]

80/20 Principle
Book:
Complexity
Focus
Subscribe to the 11x FREE Python Cheat Sheet Course:
https://blog.finxter.com/python-cheat-sheets/
Minimum Viable
Product (MVP)
Clean Code Principles
Premature Optimization
Flow
Less Is More in Design
“A whole, made up
of parts—difficult to
analyze, understand,
or explain".
Complexity appears in
• Project Lifecycle
• Code Development
• Algorithmic Theory
• Processes
• Social Networks
• Learning & Your Daily Life
Project Lifecycle Cyclomatic Complexity Runtime Complexity
→ Complexity reduces productivity and focus. It’ll consume your precious time. Keep it simple!
Majority of effects come
from the minority of causes.
Pareto Tips
1. Figure out your success metrics.
2. Figure out your big goals in life.
3. Look for ways to achieve the same
things with fewer resources.
4. Reflect on your own successes
5. Reflect on your own failures
6. Read more books in your industry.
7. Spend much of your time
improving and tweaking existing
products
8. Smile.
9. Don't do things that reduce value
Maximize Success Metric:
#lines of code written
A minimum viable
product in the
software sense is code
that is stripped from
all features to focus on
the core functionality.
How to MVP?
• Formulate
hypothesis
• Omit needless
features
• Split test to validate
each new feature
• Focus on product-
market fit
• Seek high-value and
low-cost features
1. You Ain't Going to Need It
2. The Principle of Least Surprise
3. Don't Repeat Yourself
4. Code For People Not Machines
5. Stand on the Shoulders of Giants
6. Use the Right Names
7. Single-Responsibility Principle
8. Use Comments
9. Avoid Unnecessary Comments
10. Be Consistent
11. Test
12. Think in Big Pictures
13. Only Talk to Your Friends
14. Refactor
15. Don’t Overengineer
16. Don’t Overuse Indentation
17. Small is Beautiful
18. Use Metrics
19. Boy Scout Rule: Leave Camp
Cleaner Than You Found It
1. Simple’s Better Than
Complex
2. Small is Beautiful (Again)
3. Make Each Program Do One
Thing Well
4. Build a Prototype First
5. Portability Over Efficiency
6. Store Data in Flat Text Files
7. Use Software Leverage
8. Avoid Captive User
Interfaces
9. Program = Filter
10. Worse is Better
11. Clean > Clever Code
12. Design Connected Programs
13. Make Your Code Robust
14. Repair What You Can — But
Fail Early and Noisily
15. Write Programs to Write
Programs
"Programmers waste enormous
amounts of time thinking about […]
the speed of noncritical parts of their
programs. We should forget about
small efficiencies, say about 97 % of
the time: premature optimization is
the root of all evil." – Donald Knuth
Performance Tuning 101
1. Measure, then improve
2. Focus on the slow 20%
3. Algorithmic optimization
wins
4. All hail to the cache
5. Solve an easier problem
version
6. Know when to stop
“… the source code of ultimate human performance" – Kotler
How to Achieve Flow? (1) clear
goals, (2) immediate feedback, and
(3) balance opportunity & capacity.
Flow Tips for Coders
1. Always work on an explicit
practical code project
2. Work on fun projects that
fulfill your purpose
3. Perform from your
strengths
4. Big chunks of coding time
5. Reduce distractions:
smartphone + social
6. Sleep a lot, eat healthily,
read quality books, and
exercise → garbage in,
garbage out!
How to Simplify Design?
1. Use whitespace
2. Remove design elements
3. Remove features
4. Reduce variation of fonts,
font types, colors
5. Be consistent across UIs
You can take raw resources and
move them from a state of high
entropy into a state of low entropy—
using focused effort towards the
attainment of a greater plan.
Figure: Same effort, different result.
3-Step Approach of
Efficient Software Creation
1. Plan your code
2. Apply focused effort
to make it real.
3. Seek feedback
Unix Philosophy

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