Python Guide for Beginners with Cheatsheet

Python is an object-oriented programming language and is one of the most extensively used languages globally. The language has a very simple syntax and is very easy to learn. Due to the adaptive nature of this language and its flexibility, Python can be used for web development, application development, and more such projects.

Even though a lot of developers prefer languages such as Java or C++ for architectural programming and hard coding (hardware-based), Python is gradually replacing these two languages for these kinds of tasks. Python can be as simple to use as commanding the interpreter with common English keywords and mathematical operators, but the language can be powerful enough to form the substrata of platforms such as Uber, Spotify, and Netflix.

Python is especially useful for Data Science and Machine Learning, almost making it seem like the language was specifically created for these domains. With the availability of incredibly powerful libraries such as Pandas (Data Analysis), NumPy (Scientific Computing), or Matplotlib (Visualizations/2D plotting) for it, Python can be extended to become a very powerful holistic tool for implementations of Artificial Intelligence, Machine Learning, Analytics, etc.

If you wish to work with Data Science using Python, you must start from the basics and learn how to use basic data structures, operations, and functions. You must also learn about various keywords, syntax, and operators. During the initial stage of learning Python, it is highly recommended you refer to a cheat sheet as it will help you commit fewer mistakes and learn more effectively.

A Holistic Python Cheat Sheet

Python is a relatively easy programming language to use for beginners, however, remembering everything is not always easy. Predictive suggestions in IDEs (Integrated Development Environments do help, but there is still some scope for messing up operators or functions. This is why a cheat sheet is always helpful as a reference.

Messing up multiple lines of code can result in a very cumbersome troubleshooting process. The errors are generally returned in the output of IDEs, which allows you to identify and locate the problem in your code. However, confirming syntax, operators, or expressions is always a better alternative than revisiting your code and fixing errors.

Even minor differences in the code can end up creating vastly different results or might result in bad syntax (invalid syntax/SyntaxError). For instance, just a minor change in the operators can carry out a different function. Let us see how that can happen with the same values and syntax.

Here is an example of a simple addition function where the program combines the total value of x and y. Here is the input:

x = 1

y = 6

output_file=(x+y)

print(output_file)

Here, the output will be 7 as the value of x and y are added together.

Now, just by adding ‘[]’ operators around the values of x and y, we can change the program into a function that acts similarly to the union of two sets. Here is the input:

x = [1]

y = [6]

output_file=(x+y)

print(output_file)

Here, the output will be a new set, [1, 6], as the sets x and y are combined. In mathematics, this is also known as the union of sets. However, this function was not intended to be a set function, and the result simply has returned the output like a union of two sets.

If there were more elements in x and y, their union would combine them all together. For instance, if the value of x was [4, 7, 8] and the value of y was [2, 8, 9], the new set would be [4, 7, 8, 2, 8, 9]. In mathematics, it is represented as {x}∪{y}. Even in programming,  to use sets, you must use ‘{}’ (curly bracket operators) and not ‘[]’.

The two programs above help in providing us with an example of why having a cheat sheet can always help, especially when we are dealing with much larger programs with more code.

Here are the basic Math operators in Python with examples:

• ** : This will initiate an exponent operation. Example: 3 ** 2 = 6
• % : This will initiate a modulus or remainder operation. Example: 10 % 3 = 1
• // : This will initiate an integer division operation. Example: 25 // 7 = 3
• / : This will initiate a division operation. Example: 40 / 4 = 10
• * : This will initiate a multiplication operation. Example: 6 * 8 = 48
• – : This will initiate a subtraction operation. Example: 60 – 34 = 26
• + : This will initiate an additional operation. Example: 7 + 9 = 16

Here are the basic data types in Python with examples:

• Integers: Integers are positive or negative numbers without decimal values.

Example: x, y = 2, 5

print(x + y)

Output:  7

• Floating numbers: Floating numbers are positive or negative numbers with decimal values.

Example: x, y = 1.5, 5

print(x/y)

Output: 0.3

• Strings: Strings are fundamentally sequences of characters. You can use strings with single quotes, double quotes, and triple quotes (multi-line).

Example of strings: ‘Alpha’, “Bravo”, ‘“Charlie’”

Example of use: stringexample = ‘codingiscool’

print(stringexample)

Output: codingiscool

• Boolean: Boolean data types are simply truth values that can either be ‘True’ or ‘False’.

Example: if y is False, then z, else x

Here are some basic keywords in Python with examples:

• and: This is a logical operator to indicate multiple targets in the line are true.
• or: This is a logical operator to indicate that either one of the targets is true.
• not: This is a logical operator to flip the boolean.
• break: Break helps in ending loops prematurely.
• continue: Continue helps in finishing the iteration of the current loop.
• class: Class helps in defining a new class
• def: Def helps in defining new functions or class methods.
• if: This initiates an ‘if’ condition.
• elif: This initiates an ‘else if’ condition.
• else: This initiates a fallback condition or else branch.
• for, while: For and while are both used for initiating a loop.
• in: This confirms a membership inside a sequence.
• is: This is used for confirming the location of the same object memory.
• none: This is used for an empty value constant.
• lambda: Lambda is used for anonymous functions.
• return: Return is used for terminating a function.
• len: Len helps in returning the length of a list or finding out the total number of members in a list.

Here are some more important data structures in Python with examples:

• List: Lists are data structures that store sequences of elements. Lists can be modified, unlike strings.

By using ‘remove’, you can remove elements from a list.

Example: [4, 6].insert(8, 9)

Output: [4, 6, 8, 9]

Similarly, by using ‘insert’ or ‘append’, you can add elements to a list.

Example: [4, 6, 8, 9].remove(4)

Output: [6, 8, 9]

• Set: Sets are collections of unorganized elements that can each be individual values. Curly bracks or ‘{}’ are used to store elements inside the sets.

Example: Arms = {‘M416’, ‘AK47’, ‘TYPE56’}

• Dictionary: Dictionaries are data structures that are used for storing pairs of keys and values (key:value format).

Weight = {‘Aritra’ : 90, ‘Anubhab’ : 80}

How to use Class, Instance, and Self in Python:

• Class: Classes encapsulate data with associated functions. The data will become the attributes of the class, while the functions become the methods. For example, a class ‘animal’ can have attributes species and color while the methods can be a command and an activity type of the animal.
• Instance: Instances are perfect implementations of classes. For example, a ‘wolf’ and a ‘tiger’ are instances of the class ‘animal’. Regardless of the different species and their colors, they both share the same class. The species and colour attributes of these two animals must have fixed values and cannot be undefined.
• Self: The self-argument is the first argument that is used for defining a method. Self helps in specifying the instances that will call on the methods.

Tips and Tricks for Machine Learning and Data Science using Python

Now, if you wish to learn python for data science and AI implementations, you must know how to import some important libraries such as NumPy and Pandas. Similarly, for Machine Learning with Python, you must import libraries such as Scikit-learn.

When using Python for Machine Learning, you might also need to do plotting or create visualizations. For this, you can use Matplotlib, one of the best libraries for 2D plotting.

Here are the commands for importing NumPy, SciPy, and Pandas:

• NumPy: import numpy as np

numpy.__version__

• SciPy: import scipy as sp

scipy.__version__

• Pandas: important pandas as pd

After the successful import of NumPy, SciPy, and Pandas, you can pip install Matplotlib and Scikit-learn with these:

• Matplotlib: pip install matplotlib
• Scikit-learn: pip install -U scikit-learn

After installing these two, you must import them and confirm their versions. You can import them by just adding ‘import’ before the names of these libraries in lowercase.

Example: import matplotlib

matplotlib.__version__

Data Science or Machine Learning with Python is extremely fun. However, to use this amazing programming language to its full extent in these domains, it is highly recommended that you learn how to work with the libraries that we covered. It is extremely important to know how to use library-specific operations, functions, and elements in arrays.

More than anything, you must focus on learning the basic operations of Python first. You can use the cheat sheet for reference initially, but eventually, you will not even need it to effectively use this simple language.

There are many more advanced functions and operations that are there in Python, which can be learned with the help of a good Data Science or Machine Learning online course. Most courses catering to these domains have Python in their syllabus.

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