11 Things Every Python Developer Should Know

11 Things Every Python Developer Should Know

Python is one of the best coding languages that a developer should learn to boot his or her career. There are many big sites such as Instagram, Netflix, Uber, Pinterest, and Dropbox, which have been created using python programming language. In this case, skilled Python programmers are in high demand, not only because of the popularity of the language but mostly because it has grown to become a solution to many different problems in the software development world. Python has been used in various developments, such as web applications, machine learning, and data science. As a Python fan, I believe that there are certain essential concepts or facts that every Python developer should know. All these were important were necessary concepts within the period I learned using Python as my main programming language. One needs to be familiar with sites such as the official Python website, Python 2 and 3 documentations, and stack flow. In this article, I will discuss the 12 Things Every Python Developer Should Know.

The Different Versions of Python Programming Platforms

Although this is not a programming characteristic, it still remains to be crucial to understand the latest versions of Python so that everybody is familiar with the programming language. Python programming language versions are usually numbered as ABC, whereby the three letters epitomize the significant changes that the programming language has encountered. For example, changing from 2.7.4 to 2.7.5 shows that Python made some minor bug fixes to the platform, but going from Python 2 to Python 3 shows a major change took place between the two versions. For you to confirm your version of python program, you can use the statement:

import sys

print ("My version Number: {}".format(sys.version))

The Python Shell

The python shell comes auto-installed in the platform, and it can be executed by typing the command python in the command line (in the Windows OS). This will provide the default version, copyright notice, and r-angles >>> that ask for input. If your computer contains multiple versions of Python, then you will have to add in the python3.3 version number to get the correct version. Python shell allows a user to test some simple commands to detect whether there is a logical or syntax error, thus help avoid consuming much time or memory.

Understand the Python Frameworks and the Object Relational Mapper (ORM) Libraries

Having a clear understanding of the Python frameworks is very important, but, that does not mean that one has to know all of them. Based on the project that you are trying to execute, you will be required to know the most important ones for that project, but the most popular ones that are used often are CherryPy, Flask, and Django. Moreover, one needs to understand how to connect and use applications through the ORM, such as the Django ORM and SQLAlchemy. This makes it easier, efficient, and faster compared to writing an SQL.

Understand the Difference between the Front-End and Back-End Technologies

The front-end is what a user sees when visiting a page while the back-end is what happens behind the scenes. The back-end is where the programs are executed and queries data from the database to display it to the website. Python is one of the programming languages used in developing the back-end. However, a python developer is required to link with the front-end developers to link the client-side with the server-side. In this case, it is essential to understand how the front-end work and how the application will appear.

It Is Essential to Know How to Use the 'sys' and 'os.'

These modules are useful to a Python developer since they provide generality and consistency. The sys allows the developer to use the command line inputs to the program, to avoid going back to the text-editor or amend the program before re-executing. Modifying the inputs on the command line is faster and more efficient compared to retyping the variables in the text editor. This can be done using the sys.argv, which will take in the inputs from the command line. In addition, one can also ensure that the user inputs the accurate parameters. Other than speed, the command line arguments can be employed as part of a process that automates the script repeatedly.

List Comprehension Exemplifies the Simplicity and Beauty of Python

Python 2.7.5 documentation offers a vivid description of how to list comprehension is important to a developer. A list display produces a new list object since the contents are usually specified either as list comprehension or a list of expressions. Whenever a programmer uses a comma-separated list of expressions, the elements are usually evaluated from left to right and then arranged in that order within the list object. Whenever a list comprehension is used, it contains a single expression that is accompanied by at least one for clause and zero or more for or if clauses. In such a case, the new list elements will be those that will be produced by bearing in mind that each of the if or if clauses a block, nested from left to the right side, and assessing the expression to come up with a list element whenever the innermost block is reached.

list2 = [(x, x**2, y) for x in range(5) for y in range(3) if x != 2]

print(list2)

'''
list2 = [(0, 0, 0), (0, 0, 1), (0, 0, 2), (1, 1, 0), (1, 1, 1), (1, 1, 2), (3, 9, 0), (3, 9, 1), (3, 9, 2), (4, 16, 0), (4, 16, 1), (4, 16, 2)]

This expression can be easily understood as:

list2 = [(x, x**2, y) for x in range(5):
            for y in range(3)
                if x != 2]

'''

Just as the Python 2.7.5 documentation states, one can create a nested list via list comprehension, and it is mainly significant whenever a developer needs to initiate a matrix or a table.

Classes and Functions Definition

The def makes function definition in Python to be easier. For example:

def count_zeros(string):
    total = 0
    for c in string:
        if c == "0":
            total += 1
    
    return total

# 3
print (count_zeros ('00102'))

Moreover, the recursive functions are also not complicated, and they have a similar character as some object-oriented programming (OOP) languages. Unlike other programming languages such as Java, Python uses few classes, making a developer's expertise in the expertise to be quite limited. The Python 2.7 documentation describes classes as:

Python classes provide all the standard featutes of Object Otiented Programming: the class inheritance mechanism allows multiple base classes, a derived class can override any methods of its base class or classes, and a method can call the method of a base class with the same name. Objects can contain arbitrary amounts and kinds of data. As is true for modules, classes partake of the dynamic nature of Python: they are created at runtime, and can be modfied further after creation.

File Management

Most python scripts use files as their inputs, thus making it important to understand the best way of incorporating the files in your code. In this case, the open keyword serves a great purpose since it is straightforward, and the programmer can loop through the file to analyze it in each line. The alternative is that one can employ readlines () method which helps create a list that comprises of each line in the file, but it is only efficient for smaller files.

f = open ('test.txt', 'r')  
for line in f:
    f.close()

The f.close() helps in freeing up memory that has been occupied by the open file.

Basic Memory Management and Copying Structures

While making a list in Python since easier, copying is not as straightforward as making a list. In the beginning, I often made separate copies of lists using the simple assignment operators. For example:

>>> list1 = [1,2,3,4,5]  
>>> list2 = list1  
>>> list2.append(6)  
>>> list2  
[1, 2, 3, 4, 5, 6]  
>>> list1  
[1, 2, 3, 4, 5, 6]

This is a case where making lists that are equal to other lists creates two variable names that point to a similar list in the memory. This applies to any “container'' item such as the dictionary. Since the simple assignment operators do not generate distinct copies, Python contains generic copy operations and a built-in list statement which help is generating more distinct copies. Moreover, slicing can also be used in generating distinct copies.

>>> list3 = list(list1)  
>>> list1  
[a, b, c, d, e, f]  
>>> list3  
[a, b, c, d, e, f]  
>>> list3.remove (3)  
>>> list3  
[a, b, c, d, e, f]  
>>> list1  
[a, b, c, d, e, f]  
>>> import copy  
>>> list4 = copy.copy (list1)

However, a developer may encounter containers that are within other containers, such as dictionaries containing dictionaries. In this case, using the direct operation will make any changes to one dictionary to be reflected in the larger dictionaries. But, one can solve this using the deep copy operation, which will help in copying every detail. This operation is memory-intensive compared to other copying solutions.

Understand the Basic Concepts of Dictionaries and Sets

Although lists are the most common types of data structures that are used in Python, one can still use sets and dictionaries. A set is a container that contains items in a similar way as a list, but it only contains distinct elements. If an element x is added to a set that already contains another element x, the set will not change. This makes it advantageous over lists since there will be no need for duplication as required in lists. Moreover, creating a set that is based on the pre-existing list is more manageable since one only inputs the set (list_name). However, one disadvantage of the sets is that they do not support elements indexing, making it lack order. On the other hand, dictionaries are also important data structures that pair up elements together. One can search for values efficiently using a key consistently.

Slicing

This is a process that involves taking a subset of some data, and it is mostly applied to lists and strings. Slicing is not only limited to just eliminating one element from data. In this case, for programmers to have a better intuition about slicing, they have to understand how the process of indexing works for negative numbers. In the Python documentation, there is an ASCII-style diagram in the Strings section which advocates that the developer should think about Python indices as pointing between data elements. One can employ the Python shell to play around with semi-complicated slicing before using your code.

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