Python has emerged as a powerful programming language that revolutionizes data engineering and artificial intelligence. Whether you are a beginner exploring the vast world of programming or an experienced developer seeking to enhance your skills, this blog is the perfect starting point.

Python’s simplicity, versatility, and vast ecosystem of libraries make it an ideal choice for data scientists and AI developers. In this blog, we will delve into the fundamental concepts of Python, providing you with a solid foundation to leverage its capabilities in your data science and AI projects.

In programming, data type is an important concept. Variables can store data of different types, and different types can do different things. Python Data Types are used to define the type of a variable. In Python, we do not need to declare a datatype while declaring a variable like in other languages. But if we want to see what type of numerical value it is  holding right now, we can use type(), like this:

#create a variable with integer value.

a=100

print(“The type of variable having value”, a, ” is “, type(a))

By default, python has the following built-in data types:

• Numeric data types: int, float, complex
• String data types: str
• Sequence types: list, tuple, range
• Binary types: bytes, bytearray, memoryview
• Mapping data type: dict
• Boolean type: bool
• Set data types: set, frozenset

Python numeric data type is used to hold numeric values like:

1. int – holds signed integers of non-limited length.
2. long – holds long integers(exists in Python 2.x, deprecated in Python 3.x).
3. float – holds floating precision numbers and it’s accurate up to 15 decimal places.
4. complex– holds complex numbers.

In Python, the string data type is used to represent and manipulate sequences of characters. Strings are immutable, meaning their values cannot be changed once they are created. You can create a string by enclosing characters in either single quotes (‘ ‘) or double quotes (” “).

Here are a few examples of creating strings:

my_string = ‘Data Engineering’
another_string = “Artificial Intelligence’

In the examples above, my_string and another_string are two different strings containing the specified text.

Python provides a wide range of string methods that allow you to perform various operations on strings. Some common methods include lower(), upper(), strip(), split(), replace(), and join(). Here’s an example of using a few string methods:

my_string = “Data engineering”

print(my_string.lower()) # Output: ‘ data engineering ‘

print(my_string.strip()) # Output: ‘Data Engineering

print(my_string.split(‘,’)) # Output: [‘Data’, ‘engineering’]

print(my_string.replace(‘engineering’, ‘science’)) # Output: ‘ Data science’

In Python, the list data type is a built-in data structure that allows you to store and manipulate a collection of items. Lists are ordered, mutable, and can contain elements of different data types. You can think of a list as a sequence of values enclosed in square brackets ([]), where a comma separates each value.

Here’s an example of creating a list in Python:

my_list = [1, 2, 3, 4, 5]

In this example, `my_list` contains the integers 1, 2, 3, 4, and 5.

Lists can contain elements of any data type, including numbers, strings, booleans, and even other lists. Here’s an example of a list with different data types:

mixed_list = [1, “apple”, True, 3.14]

Lists are mutable, which means you can modify their elements. You can access elements in a list using indexing. Indexing starts at 0, so the first element is at index 0, the second element is at index 1, and so on. Here’s an example:

my_list = [1, 2, 3, 4, 5]
print(my_list[0]) # Output: 1
print(my_list[2]) # Output: 3

You can also modify elements in a list by assigning a new value to a specific index:

my_list[1] = 10
print(my_list) # Output: [1, 10, 3, 4, 5]

Lists have various built-in methods and operations for common operations like adding or removing elements, finding the list length, sorting, and more. You can refer to the Python documentation for a comprehensive list of list methods and operations.

a tuple is an immutable sequence of elements enclosed in parentheses (). It is similar to a list but cannot be modified once created. Tuples are commonly used to store related pieces of information together. Here’s an example of creating a tuple:

my_tuple = (1, 2, 3, 4, 5)
print(my_tuple) # Output: (1, 2, 3, 4, 5)

Tuples can contain elements of different data types, including numbers, strings, booleans, or even other tuples. Here’s an example of a tuple with different data types:

mixed_tuple = (‘apple’, 42, True)
print(mixed_tuple) # Output: (‘apple’, 42, True)

You can access individual elements of a tuple using indexing, just like with lists. Indexing starts from 0 for the first element. Here’s an example:

my_tuple = (‘a’, ‘b’, ‘c’, ‘d’, ‘e’)
print(my_tuple[0]) # Output: ‘a’
print(my_tuple[2]) # Output: ‘c’

Tuples support negative indexing as well, where -1 refers to the last element, -2 refers to the second-to-last element, and so on.

Here’s an example:

my_tuple = (‘a’, ‘b’, ‘c’, ‘d’, ‘e’)
print(my_tuple[-1]) # Output: ‘e’
print(my_tuple[-3]) # Output: ‘c’

A Python dictionary is a built-in data structure that allows you to store and retrieve data using key-value pairs. It is also known as an associative array or hash table in other programming languages. Dictionaries are mutable, unordered, and can contain elements of different data types. In Python, dictionaries are defined using curly braces {} and separating key-value pairs with a colon (:). Here’s an example of a dictionary:

# Creating a dictionary

my_dict = {

“name”: “John”,

“age”: 25,

“city”: “New York”

}

 

# Accessing values using keys

print(my_dict[“name”]) # Output: John

print(my_dict[“age”]) # Output: 25

 

# Modifying values

my_dict[“age”] = 26

print(my_dict[“age”]) # Output: 26

 

# Adding a new key-value pair

my_dict[“occupation”] = “Engineer”

print(my_dict) # Output: {‘name’: ‘John’, ‘age’: 26, ‘city’: ‘New York’, ‘occupation’: ‘Engineer’}

 

# Removing a key-value pair

del my_dict[“city”]

print(my_dict) # Output: {‘name’: ‘John’, ‘age’: 26, ‘occupation’: ‘Engineer’}

 

# Checking if a key exists

if “age” in my_dict:

print(“Age is present in the dictionary”)

 

# Getting the number of key-value pairs

print(len(my_dict)) # Output: 3

 

# Iterating over the keys

for key in my_dict:

print(key, my_dict[key])

Dictionaries provide a flexible way to organize and manipulate data, making them a powerful tool in Python programming.

In Python, expressions are combinations of values, variables, and operators that produce a new value. Variables, on the other hand, are names that represent values stored in computer memory. They are used to store and manipulate data in a program.

Let’s take a closer look at expressions and variables in Python.

Expressions

1. Numeric expressions:
   These involve numeric values and mathematical operators. For example:

   x = 5 + 3 * 2

   In this case, the expression 5 + 3 * 2 evaluates to 11, and the result is assigned to the variable x.

2. String expressions: These involve string values and string operations. For example:
   

   message = “Data” + “Engineering”

   Here, the expression “Data” + “Engineering” concatenates the two strings to form “Data Engineering”, which is then assigned to the variable message.

3. Boolean expressions: These involve boolean values and logical operators. For example:
   

   is_greater = 10 > 5

   In this case, the expression 10 > 5 evaluates to True, and the result is assigned to the variable is_greater.

Variables

Variables are used to store and refer to data in Python. They can hold different types of values, such as numbers, strings, or boolean values. Here’s an example of creating and using variables:

x = 10
y = “Hello”

z = x + 5
print(y + “, the result is:”, z)

In this code snippet, the variable x is assigned the value 10, and the variable y is assigned the string “Hello”. The variable z is assigned the value of x + 5, which is 15. Finally, the result is printed as “Hello, the result is: 15”.

Variables can also be updated with new values during the execution of a program. For example:

x = 5
x = x + 1
print(x)

In this case, the variable x is initially assigned the value 5. Then, the expression x + 1 evaluates to 6, and the updated value is stored in the variable x. The final output will be 6.

Remember that variable names in Python are case-sensitive and should follow certain naming rules, such as starting with a letter or an underscore and consisting of letters, numbers, and underscores.

Understanding the basics of Python is essential for anyone embarking on a journey into the realms of data science and AI development. Python’s simplicity, versatility, and extensive range of libraries make it a popular choice among professionals in these fields

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