Python 3 is a truly versatile programming language, loved  both by web developers, data scientists and software  engineers. And there are several good reasons for that! 

• Python is open-source and has a great support community,  • Plus, extensive support libraries.  
• Its data structures are user-friendly.

Python Basics: Getting Started 

Most Windows and Mac computers come with Python pre-installed. You can check  that via a Command Line search. The particular appeal of Python is that you can  write a program in any text editor, save it in .py format and then run via a Command  Line. But as you learn to write more complex code or venture into data science, you  might want to switch to an IDE or IDLE. 

What is IDLE (Integrated Development and Learning) 

IDLE (Integrated Development and Learning Environment) comes with every  Python installation. Its advantage over other text editors is that it highlights  important keywords (e.g. string functions), making it easier for you to interpret code.  Shell is the default mode of operation for Python IDLE. In essence, it’s a simple loop  that performs that following four steps: 

• Reads the Python statement 
• Evaluates the results of it  
• Prints the result on the screen 
• And then loops back to read the next statement.  

Python shell is a great place to test various small code snippets.  

Main Python Data Types 

Every value in Python is called an “object”. And every object has a specific data  type. The three most-used data types are as follows:  Integers (int) — an integer number to represent an object such as “number 3”. Integers -2, -1, 0, 1, 2, 3, 4, 5  Floating-point numbers (float) — use them to represent floating-point numbers. Floating-point numbers -1.25, -1.0, --0.5, 0.0, 0.5, 1.0, 1.25  Strings — codify a sequence of characters using a string. For example, the word  “hello”. In Python 3, strings are immutable. If you already defined one, you cannot  change it later on.   While you can modify a string with commands such as replace() or join(), they will  create a copy of a string and apply modification to it, rather than rewrite the original  one.  Strings ‘yo’, ‘hey’, ‘Hello!’, ‘what’s up!’  Plus, another three types worth mentioning are lists, dictionaries, and tuples. All of  them are discussed in the next sections.   For now, let’s focus on the strings. 

How to Create a String in Python 

You can create a string in three ways using single, double or triple quotes. Here’s an  example of every option:  Basic Python String  my_string = “Let’s Learn Python!”  another_string = ‘It may seem difficult first, but you  can do it!’  a_long_string = ‘’’Yes, you can even master multi-line  strings   that cover more than one line   with some practice’’’  IMP! Whichever option you choose, you should stick to it and use it consistently  within your program.   As the next step, you can use the print() function to output your string in the console  window. This lets you review your code and ensure that all functions well.   Here’s a snippet for that:   print(“Let’s print out a string!”)  String Concatenation  The next thing you can master is concatenation — a way to add two strings  together using the “+” operator. Here’s how it’s done:   string_one = “I’m reading “  string_two = “a new great book!”  string_three = string_one + string_two  Note: You can’t apply + operator to two different data types e.g. string + integer. If  you try to do that, you’ll get the following Python error:   TypeError: Can’t convert ‘int’ object to str implicitly 

String Replication 

As the name implies, this command lets you repeat the same string several times.  This is done using * operator. Mind that this operator acts as a replicator only with  string data types. When applied to numbers, it acts as a multiplier.   String replication example:  ‘Alice’ * 5 ‘AliceAliceAliceAliceAlice’  And with print ()  print(“Alice” * 5)  And your output will be Alice written five times in a row.  Math Operators  For reference, here’s a list of other math operations you can apply towards numbers:  Operators Operation Example ** Exponent 2 ** 3 = 8 % Modulus/Remainder 22 % 8 = 6 // Integer division 22 // 8 = 2 / Division 22 / 8 = 2.75 * Multiplication 3 * 3 = 9 - Subtraction 5 - 2 = 3 + Addition 2 + 2 = 4 

How to Store Strings in Variables 

Variables in Python 3 are special symbols that assign a specific storage location to  a value that’s tied to it. In essence, variables are like special labels that you place on  some value to know where it’s stored.   Strings incorporate data. So you can “pack” them inside a variable. Doing so makes  it easier to work with complex Python programs.   Here’s how you can store a string inside a variable.  my_str = “Hello World”  Let’s break it down a bit further: 

• my_str is the variable name.  
• = is the assignment operator.  
• “Just a random string” is a value you tie to the variable name.  

Now when you print this out, you receive the string output.  print(my_str)  = Hello World  See? By using variables, you save yourself heaps of effort as you don’t need to  retype the complete string every time you want to use it. 

Built-in Functions in Python 

You already know the most popular function in Python — print(). Now let’s take a  look at its equally popular cousins that are in-built in the platform.  Input() Function  input() function is a simple way to prompt the user for some input (e.g. provide their  name). All user input is stored as a string.  Here’s a quick snippet to illustrate this:   name = input(“Hi! What’s your name? “)  print(“Nice to meet you “ + name + “!”)  age = input(“How old are you “)  print(“So, you are already “ + str(age) + “ years old, “ + name + “!”)  When you run this short program, the results will look like this:  Hi! What’s your name? “Jim”  Nice to meet you, Jim!  How old are you? 25  So, you are already 25 years old, Jim!  len() Function  len() function helps you find the length of any string, list, tuple, dictionary, or another  data type. It’s a handy command to determine excessive values and trim them to  optimize the performance of your program.  Here’s an input function example for a string:  # testing len()   str1 = “Hope you are enjoying our tutorial!”  print(“The length of the string is :”, len(str1))  Output:  The length of the string is: 35 

filter() 

Use the Filter() function to exclude items in an iterable object (lists, tuples,  dictionaries, etc) 

ages = [5, 12, 17, 18, 24, 32] 
def myFunc(x): 
 if x < 18: 
 return False 
 else: 
 return True 
adults = filter(myFunc, ages) 
for x in adults: 
 print(x)

How to Define a Function 

Apart from using in-built functions, Python 3 also allows you to define your own  functions for your program.   To recap, a function is a block of coded instructions that perform a certain action.  Once properly defined, a function can be reused throughout your program i.e. re-use  the same code.   Here’s a quick walkthrough explaining how to define a function in Python:  First, use def keyword followed by the function name():. The parentheses can  contain any parameters that your function should take (or stay empty).  def name():  Next, you’ll need to add a second code line with a 4-space indent to specify what  this function should do. 

def name(): 
 print(“What’s your name?”)

Now, you have to call this function to run the code.  name.py  def name():   print(“What’s your name?”)  hello()  Now, let’s take a look at a defined function with a parameter — an entity, specifying  an argument that a function can accept.  def add_numbers(x, y, z):   a = x + y   b = x + z   c = y + z   print(a, b, c)  add_numbers(1, 2, 3)  In this case, you pass the number 1 in for the x parameter, 2 in for the y parameter,  and 3 in for the z parameter. The program will that do the simple math of adding up  the numbers:  Output:   a = 1 + 2  b = 1 + 3  c = 2 + 3 

How to Pass Keyword Arguments to a Function 

A function can also accept keyword arguments. In this case, you can use  parameters in random order as the Python interpreter will use the provided  keywords to match the values to the parameters.  Here’s a simple example of how you pass a keyword argument to a function.  

# Define function with parameters 

def product_info(product name, price): 
 print(“productname: “ + product name) 
 print(“Price “ + str(dollars)) 

# Call function with parameters assigned as above product_info(“White T-shirt”, 15 dollars) 
# Call function with keyword arguments 
product_info(productname=”jeans”, price=45)

Output:  Productname: White T-shirt  Price: 15  Productname: Jeans   Price: 45 

Lists 

Lists are another cornerstone data type in Python used to specify an ordered  sequence of elements. In short, they help you keep related data together and  perform the same operations on several values at once. Unlike strings, lists are  mutable (=changeable).  Each value inside a list is called an item and these are placed between square  brackets.  Example lists  my_list = [1, 2, 3]  my_list2 = [“a”, “b”, “c”]  my_list3 = [“4”, d, “book”, 5]  Alternatively, you can use list() function to do the same:  alpha_list = list((“1”, “2”, “3”))   print(alpha_list)  How to Add Items to a List  You have two ways to add new items to existing lists.   The first one is using append() function:   beta_list = [“apple”, “banana”, “orange”]  beta_list.append(“grape”)  print(beta_list)  The second option is to insert() function to add an item at the specified index:  beta_list = [“apple”, “banana”, “orange”]  beta_list.insert(“2 grape”)  print(beta_list) 

How to Remove an Item from a List 

Again, you have several ways to do so. First, you can use remove() function:   beta_list = [“apple”, “banana”, “orange”]  beta_list.remove(“apple”)  print(beta_list)  Secondly, you can use the pop() function. If no index is specified, it will remove the  last item.   beta_list = [“apple”, “banana”, “orange”]  beta_list.pop()  print(beta_list)  The last option is to use del keyword to remove a specific item:   beta_list = [“apple”, “banana”, “orange”]  del beta_list [1]  print(beta_list)  P.S. You can also apply del towards the entire list to scrap it.   Combine Two Lists   To mash up two lists use the + operator.  my_list = [1, 2, 3]  my_list2 = [“a”, “b”, “c”]  combo_list = my_list + my_list2  combo_list  [1, 2, 3, ‘a’, ‘b’, ‘c’]  Create a Nested List  You can also create a list of your lists when you have plenty of them 🙂  my_nested_list = [my_list, my_list2]  my_nested_list  [[1, 2, 3], [‘a’, ‘b’, ‘c’]] 

Sort a List 

Use the sort() function to organize all items in your list.   alpha_list = [34, 23, 67, 100, 88, 2]  alpha_list.sort()  alpha_list  [2, 23, 34, 67, 88, 100]  Slice a List  Now, if you want to call just a few elements from your list (e.g. the first 4 items),  you need to specify a range of index numbers separated by a colon [x:y]. Here’s an  example:  alpha_list[0:4]  [2, 23, 34, 67]  Change Item Value on Your List  You can easily overwrite a value of one list items:  beta_list = [“apple”, “banana”, “orange”]  beta_list[1] = “pear”  print(beta_list)  Output:  [‘apple’, ‘pear’, ‘cherry’]  Loop Through the List  Using for loop you can multiply the usage of certain items, similarly to what *  operator does. Here’s an example:  for x in range(1,4):   beta_list += [‘fruit’]   print(beta_list) 

Copy a List 

Use the built-in copy() function to replicate your data:  beta_list = [“apple”, “banana”, “orange”]  beta_list = beta_list.copy()  print(beta_list)  Alternatively, you can copy a list with the list() method:  beta_list = [“apple”, “banana”, “orange”]  beta_list = list (beta_list)   print(beta_list) 

List Comprehensions 

List comprehensions are a handy option for creating lists based on existing lists.  When using them you can build by using strings and tuples as well.   List comprehensions examples  list_variable = [x for x in iterable]  Here’s a more complex example that features math operators, integers, and the  range() function:   number_list = [x ** 2 for x in range(10) if x % 2 == 0] print(number_list) 

Tuples 

Tuples are similar to lists — they allow you to display an ordered sequence of  elements. However, they are immutable and you can’t change the values stored in a  tuple.   The advantage of using tuples over lists is that the former are slightly faster. So it’s  a nice way to optimize your code.   How to Create a Tuple  my_tuple = (1, 2, 3, 4, 5)  my_tuple[0:3]  (1, 2, 3)  Note: Once you create a tuple, you can’t add new items to it or change it in any other way! 

How to Slide a Tuple 

The process is similar to slicing lists.  numbers = (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12) print(numbers[1:11:2])  Output:  (1, 3, 5, 7, 9) 

Convert Tuple to a List 

Since Tuples are immutable, you can’t change them. What you can do though is  convert a tuple into a list, make an edit and then convert it back to a tuple.   Here’s how to accomplish this:  x = (“apple”, “orange”, “pear”)  y = list(x)  y[1] = “grape”  x = tuple(y)  print(x) 

Dictionaries 

A dictionary holds indexes with keys that are mapped to certain values. These  key-value pairs offer a great way of organizing and storing data in Python. They are  mutable, meaning you can change the stored information.   A key value can be either a string, Boolean, or integer. Here’s an example dictionary  illustrating this:  Customer 1= {‘username’: ‘john-sea’, ‘online’: false,  ‘friends’:100}  How to Create a Python Dictionary  Here’s a quick example showcasing how to make an empty dictionary.  Option 1: new_dict = {}  Option 2: other_dict= dict()  And you can use the same two approaches to add values to your dictionary:  new_dict = {   “brand”: “Honda”,   “model”: “Civic”,   “year”: 1995  }  print(new_dict)   

How to Access a Value in a Dictionary 

You can access any of the values in your dictionary the following way: x = new_dict[“brand”]  You can also use the following methods to accomplish the same. • dict.keys() isolates keys 

• dict.values() isolates values 
• dict.items() returns items in a list format of (key, value) tuple pairs 

Change Item Value 

To change one of the items, you need to refer to it by its key name: #Change the “year” to 2020:  new_dict= {   “brand”: “Honda”,   “model”: “Civic”,   “year”: 1995  }  new_dict[“year”] = 2020  Loop Through the Dictionary  Again to implement looping, use for loop command.  Note: In this case, the return values are the keys of the dictionary. But, you can also return  values using another method.  #print all key names in the dictionary   for x in new_dict:   print(x)  #print all values in the dictionary   for x in new_dict:   print(new_dict[x])  #loop through both keys and values  for x, y in my_dict.items():   print(x, y) 

If Statements (Conditional  Statements) in Python 

Just like other programming languages, Python supports the basic logical  conditions from math:  

• Equals: a == b 
• Not Equals: a != b 
• Less than: a < b 
• Less than or equal to a <= b 
• Greater than: a > b 
• Greater than or equal to: a >= b 

You can leverage these conditions in various ways. But most likely, you’ll use them in  “if statements” and loops. 

If Statement Example 

The goal of a conditional statement is to check if it’s True or False.  if 5 > 1:   print(“That’s True!”)  Output:  That’s True! 

Nested If Statements 

For more complex operations, you can create nested if statements. Here’s how it  looks:  x = 35  if x > 20:   print(“Above twenty,”)   if x > 30:   print(“and also above 30!”) 

Elif Statements 

elif keyword prompts your program to try another condition if the previous one(s)  was not true. Here’s an example:  a = 45  b = 45  if b > a:   print(“b is greater than a”)  elif a == b:   print(“a and b are equal”) 

If Else Statements 

else keyword helps you add some additional filters to your condition clause. Here’s  how an if-elif-else combo looks:  if age < 4:   ticket_price = 0  elif age < 18:   ticket_price = 10  else: ticket_price = 15 

If-Not-Statements 

Not keyword let’s you check for the opposite meaning to verify whether the value is  NOT True:   new_list = [1, 2, 3, 4]  x = 10  if x not in new_list:   print(“’x’ isn’t on the list, so this is True!”) 

Pass Statements 

If statements can’t be empty. But if that’s your case, add the pass statement to avoid  having an error:   a = 33  b = 200  if b > a:   pass 

Python Loops 

Python has two simple loop commands that are good to know: 

• for loops  
• while loops  

Let’s take a look at each of these. 

For Loop 

As already illustrated in the other sections of this Python checklist, for loop is a  handy way for iterating over a sequence such as a list, tuple, dictionary, string, etc.   Here’s an example showing how to loop through a string:  for x in “apple”:   print(x)  Plus, you’ve already seen other examples for lists and dictionaries. 

While Loops 

While loop enables you to execute a set of statements as long as the condition for  them is true.  #print as long as x is less than 8   i = 1  while i< 8:   print(x)   i += 1  How to Break a Loop  You can also stop the loop from running even if the condition is met. For that, use  the break statement both in while and for loops:  i = 1  while i < 8:   print(i)   if i == 4:   break   i += 1 

Class 

Since Python is an object-oriented programming language almost every element of  it is an object — with its methods and properties.   Class acts as a blueprint for creating different objects. Objects are an instance of a  class, where the class is manifested in some program. 

How to Create a Class 

Let’s create a class named TestClass, with one property named z:  class TestClass:   z = 5 

How To Create an Object 

As a next step, you can create an object using your class. Here’s how it’s done:  p1 = TestClass()  print(p1.x)  Further, you can assign different attributes and methods to your object. The  example is below:  class car(object):   “””docstring”””   def __init__(self, color, doors, tires):   “””Constructor”””   self.color = color   self.doors = doors   self.tires = tires   def brake(self):   “””   Stop the car   “””   return “Braking”   def drive(self):   “””   Drive the car   “””   return “I’m driving!” 

How to Create a Subclass 

Every object can be further sub-classified. Here’s an example  class Car(Vehicle):   “””   The Car class   “””   def brake(self):   “””   Override brake method   “””   return “The car class is breaking slowly!”  if __name__ == “main”:   car = Car(“yellow”, 2, 4, “car”)   car.brake()   ‘The car class is breaking slowly!’   car.drive()   “I’m driving a yellow car!” 

Dealing with Python Exceptions (Errors) 

Python has a list of in-built exceptions (errors) that will pop up whenever you make  a mistake in your code. As a newbie, it’s good to know how to fix these. 

The Most Common Python Exceptions 

• AttributeError — pops up when an attribute reference or assignment fails. 
• IOError — emerges when some I/O operation (e.g. an open() function) fails  for an I/O-related reason, e.g., “file not found” or “disk full”. 
• ImportError — comes up when an import statement cannot locate the  module definition. Also, when a from… import can’t find a name that must be  imported. 
• IndexError — emerges when a sequence subscript is out of range. • KeyError — raised when a dictionary key isn’t found in the set of existing keys. 
• KeyboardInterrupt — lights up when the user hits the interrupt key (such  as Control-C or Delete). 
• NameError — shows up when a local or global name can’t be found.
• OSError — indicated a system-related error.
• SyntaxError — pops up when a parser encounters a syntax error. 
• TypeError — comes up when an operation or function is applied to an object  of inappropriate type.  
• ValueError — raised when a built-in operation/function gets an argument  that has the right type but not an appropriate value, and the situation is not  described by a more precise exception such as IndexError. 
• ZeroDivisionError — emerges when the second argument of a division or  modulo operation is zero. 

How to Troubleshoot the Errors 

Python has a useful statement, design just for the purpose of handling exceptions —  try/except statement. Here’s a code snippet showing how you can catch KeyErrors  in a dictionary using this statement:   my_dict = {“a”:1, “b”:2, “c”:3}  try:   value = my_dict[“d”]   except KeyError:   print(“That key does not exist!”)  You can also detect several exceptions at once with a single statement. Here’s an  example for that:   my_dict = {“a”:1, “b”:2, “c”:3}  try:   value = my_dict[“d”]  except IndexError:   print(“This index does not exist!”)  except KeyError:   print(“This key is not in the dictionary!”) except:   print(“Some other problem happened!”) 

try/except with else clause 

Adding an else clause will help you confirm that no errors  were found: 

my_dict = {“a”:1, “b”:2, “c”:3} 
try: 
 value = my_dict[“a”] 
except KeyError: 
 print(“A KeyError occurred!”) 
else: 
 print(“No error occurred!”)

Conclusions