Why Should You Learn Python For Data Processing

In this python article, Codingcompiler will explain why you should learn Python and how to work with data? Recommendations for beginners who are going to start learning Python programming and what Python packages they need to know to get started working with data. Let’s get started with Python programming.

Table of Contents

Python Learning Table of contents:

Recommendations for beginners to learn Python
Python packages and their purpose

Python The Popular Programming Language

Python is known as a popular programming language that allows you to write very few lines of code to run an application or process. In recent years we are seeing more and more demand for working with data through language.

Benefits of Python Programming Language

Due to Python’s ability to provide an end-to-end solution when it comes to data manipulation. This is an easy-to-learn language, a language that has no limitations in data processing, use on any platform, and on any operating system. Read more at Advantages of Python Programming

Disadvantages of Python Programming Language

The disadvantage of an industry-recognized Python language is it’s slowness and low performance, compared to languages such as Java, Scala, and others, but in combination with the Anaconda platform, language has overcome the disadvantage and has become faster and more performance-intensive.

Today, with Python’s extensive use of data, it can be said that a combination of SQL and Python brings you to a solution for nearly 99% of analytical problems that arise.

Related Python Tutorials

Python for Data Analysis

So, you’ve made a decision to work with Python for data analysis, but you don’t have any prior knowledge of it. The question is,

Where to start learning Python?
What level of knowledge to get to work with data using Python?
How long will it take to learn Python?
Where to learn and how to practice learning Python?

There are a lot of questions and quite a bit of confusion for working with Python programming language. In this article, we will try to answer these questions.

Recommendations for beginners to learn Python

Don’t Make a Mistake

Before you start learning Python, many of you think you have to know the language at a very high level to use it for any purpose. People do frontal courses, online courses, read blogs, articles, and books, try to draw lots of technical information, try to do small projects.

Learning Python can take months. But what needs to be kept in mind is the ultimate goal: data processing rather than development. Ultimately, you are not supposed to become a Python developer, but to process data through language. Do not make a mistake of someone who has already gone through the long process of learning – do not waste precious time learning to develop in Python.

Finally, after exposure to Python code, your next goal is to learn 5 main packages for working with data: Numpy, Scipy, Pandas, Matplotlib, Scikit-learn. We will discuss them further below.

Ignore sources of information or learnings that appear to the general public:

There are a lot of sources of programming information in Python, lots of books, websites, courses. Note that most of them appeal to the general public and are not focused on the ultimate goal of Python and data. In addition, there are quite a few scientific books on Python, which are very oriented to more mathematical topics, and do not connect to the data world.

So long before reading and learning, choose the source of knowledge well, install the environment in which they are encoded and learn how to work with it. IPython – Jupyter Notebook, the most popular editor today to work with Python.

Study track:

Start with a few short courses, complete all the exercises in these courses, spend a few hours each day at least – practice is the most important part because you will apply the knowledge you will receive.

Next, learn about using an editor or the environment, eg Jupyter.

And now in the final stage, you are ready to begin learning the relevant packages, which we mentioned earlier, to work with data.

Python packages and their purpose

NumPy
Scipy
Pandas
Matplotlib
Scikit-learn

NumPy – our recommendation to start learning this package as a first package. This is a basic package to run Python code combined with heavy scientific and mathematical calculations. In addition, a good understanding of Numpy will help you use the following tool – Pandas – with high efficiency.

Scipy – is a complementary package to the NumPy package. The package is designed to work with functions that NumPy contains, and it provides additional numeric templates that are both user-friendly and performance-efficient. The package is also used for data optimization and integration.

Pandas – an excellent data- handling suite that offers broad functionality in working with different data structures and enables data manipulation for exploration in a fast and easy way. The package is built on NumPy and also helps to use NumPy.

Matplotlib – This is a data visualization tool that allows you to build graphs, charts, histograms, and other graphic elements. The package supports the GUI of all operating systems, and also allows exporting the final product to various file types, including PDF, SVG, JPG, PNG, BMP, GIF and more.

Scikit-learn – This is a package designed to build machine learning models above Scipy. The package provides a set of useful algorithms and helps to run them with ease and comfort. If you look at the list of algorithms in the package, you’ll see that they cover almost all the standard needs of data scientists: clustering, classification, regression.

In the context of learning packages, our additional recommendation is not to study the packages in parallel, each package has a different concept, and it is absolutely not simple to move from one to another and understand in depth the essence of each.

So, in conclusion, start basic programming in Python, then learn the packages we mentioned earlier, preferably in the above-mentioned order, and then start running small projects to gain practical knowledge.