How and why to use Python for data analysis

by Alex
How and why to use Python for data analysis

Big Data and Business Analytic solutions bring in hundreds of billions each year, and revenues are growing steadily. This is no surprise as data analytics helps businesses predict consumer demands, personalize their policies, prevent potential setbacks and make better decisions. The industry’s popularity is steadily growing. In 2015, only 17% of companies used Big Data analytics capabilities, and in 2017 that number rose to 53%. To join this group, you need to know at least one programming language used for data science. In this piece, let’s break down Python and how it is used for data analytics.

Is Python suitable for data analysis?

Python has been around since 1990, but didn’t start gaining popularity until recently. In 2020, Python was the fourth most used programming language after JavaScript, HTML/CSS and SQL, with 44.1% of developers using it. Python is an interpreted, high-level, object-oriented general-purpose language used for API development, artificial intelligence, web development, Internet of Things, and so on. Part of the reason Python became so popular is because of data scientists. It is one of the easiest languages to learn. It offers many libraries that apply to all phases of data analysis. Therefore, the language is definitely suitable for these purposes.

How is Python used for data analysis?

Python works great at all stages. Various libraries help with this first and foremost. Searching, processing, modeling (along with visualization) are the 3 most popular scenarios for using the language for data analysis.

Data Retrieval

Engineers use Scrapy and BeautifulSoup to search for data using Python.You can use Scrapy to create programs that collect structured data on the web. It can also be used to collect data from APIs. BeautifulSoup is used where getting data from the API is not an option; it collects data and arranges it in a specific format. Как и зачем использовать Python для анализа данных

Data processing and modeling

At this point, among the most used libraries are NumPy and Pandas. NumPy (Numerical Python) is used to sort large data sets. It simplifies mathematical operations and their vectorization on arrays. Pandas offers two data structures: Series (a list of items) and Data Frames (a table with multiple columns). This library converts data into Data Frames, allowing you to delete and add new columns and perform different operations. Обработка и моделирование данных

Data Visualization

Matplotlib and Seaborn are widely used for data visualization. They help convert huge lists of numbers into handy graphs, histograms, charts, heat maps, and so on. Of course, there are many more libraries. Python offers countless tools for data analysis projects and can help with any task in the process. Как и зачем использовать Python для анализа данных

Advantages and disadvantages of Python for data analysis

It’s almost impossible to find the perfect language for data analysis, as each has its own advantages and disadvantages. One is better for visualization and the other works better with large amounts of data. The choice also depends on the personal preferences of the developer. Let’s look at the advantages and disadvantages of Python for data analysis. Как и зачем использовать Python для анализа данных

Advantages of Python

Great community

Programming has never been easy, and even developers with a lot of experience run into problems. Fortunately, every language has a community to help you find the right solutions. On GitHub, for example, there are over 90,000 repositories with Python projects. So you can almost always find an answer to your question.

Easy to learn

Python is one of the easiest languages to learn because of its simple syntax and readability. It also requires far fewer lines of code. A developer can think less about the code itself and more about what it does. Debugging in Python is also much easier.

Flexible and Scalable

Python is used in a wide variety of industries because of its flexibility and wide range of tools.

Variety of Libraries

There are a huge number of libraries for Python that can be used at different stages of data analysis. Plus, most of them are free. This all affects the ease of working with data using Python.

Disadvantages

Dynamic Typing

Python is a general purpose language and wasn’t just created for data analysis. It’s much easier to develop with dynamic typing, but it slows down the search for errors in data related to different types.

Where to learn data analysis

One of the best courses is the year-long Data Scientist Profession: Data Analytics program from Skillbox. Analysts from Ivi, QIWI, Rambler and Epam teach data analytics and commentary homework. Course syllabus:

  1. Python for Data Science
  2. Analytics. Elementary level
  3. Statistics and Probability Theory
  4. Fundamentals of Mathematics for Data Science
  5. Analytics. Middle level
  6. Programmer’s Universal Knowledge
  7. English for IT Professionals

After completing the course, you’ll do a thesis project and get help with employment. There is a discount and installment plan available now, see the Skillbox website for details.

Alternatives to Python for Data Analysis

Although Python is considered one of the main languages for data analysis, there are other options. Each of these languages is designed for a specific task (data mining, visualization, or working with large amounts of data), and some were designed specifically for data analysis and statistical calculations.

R

R is the second most popular language for data analysis, which is often compared to Python. It was developed for statistical calculations and graphics, which is great for data analysis. It has tools for data visualization. It is compatible with any statistical application, works offline, and various data management and graphing packages are available to developers.

SQL

A widely used language for data querying and editing. It is also a great tool for storing and retrieving data. SQL works well with large databases and can retrieve data from the network faster than other languages.

Julia

Julia was developed for data science and scientific computing. It is a relatively new language that is quickly gaining popularity among professionals in the field. Its main goal is to overcome the shortcomings of Python and become the #1 choice among engineers. Julia is a compilable language, which implies higher performance. However, the syntax is similar to Python, albeit with an emphasis on mathematics. Libraries from Python, C and Forton can be used in Julia. The language is also famous for parallel computing, which is faster and more complex than in Python.

Scala

Scala and the Spark framework are often used to work with large databases. You don’t even have to load all the data to do this – you can work in chunks. Scala runs on the JVM and can be built into enterprise code. It offers a lot of data processing tools that are faster than Python and R. These are the 4 most popular languages among data scientists. However, MATLAB for statistical analysis, TensorFlow for BigData, graphs and parallel computing, and JavaScript for visualization are also worth mentioning.

Conclusions

Data is an important part of any business. There are many languages available today for data analysis, including R, SQL, Julia, and Scala. Each performs a specific set of tasks and handles them better than the others. Overall, there is no one perfect language for a project. Nevertheless, Python remains the most popular programming language for data analysis. It offers an array of libraries, has a huge community, and is easy to learn.

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