Matplotlib is just one of many Python libraries supporting data visualisation Python offers numerous tools for data analysis projects and can assist during any task within the process. Of course, there are way more libraries than we have mentioned. It means that they help to convert long lists of numbers into easy-to-understand graphics, histograms, pie charts, heatmaps, etc. Matplotlib and Seaborn are widely used for Python data visualization. This library converts data to the data frame allowing you to delete or add new columns to it and perform various operations.
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Pandas offers two data structures: series (a list of items) and data frames (a table with multiple columns). NumPy (Numerical Python) is used for arranging big data sets and makes math operations and their vectorization on arrays easier. Two main libraries are used at this stage: NumPy and Pandas. It is also widely used for collecting data from APIs.īeautifulSoup is used when one can not retrieve data from APIs: it scrapes data and arranges in the preferable format.īeautifulSoup in action, scraping data from the Web
With the help of Scrapy, one can build special programs that can collect structured data from the web. Data MiningĪ data engineer uses libraries such as Scrapy and BeautifulSoup for data mining Python-based approach. Data mining, data processing, and modeling along with data visualization are the 3 most popular ways of how Python is being used for data analysis. It is the Python libraries that were designed for data science that are so helpful. How is Python Used For Data Analysis?Īs we have mentioned, Python works well on every stage of data analysis. We will discuss its pros and cons later in the article so stick around to find a more detailed explanation to the question. So the short answer to the question of whether Python is good for data analysis is yes. It is one of the easiest languages to learn and has impressive libraries and works perfectly for every stage of data science. The part of why Python has become so popular is because it is widely used among data scientists. The language is used for API development, Artificial Intelligence, web development, Internet of Things, etc. Python is an interpreted, general-purpose, high-level language with an object-oriented approach. In 2020, Python became the fourth most used language after JavaScript, HTML/CSS, and SQL, with 44,1% of developers using it. Python was introduced back in 1990 but it began to gain popularity only a couple of years ago.
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Find out whether Python is good for data analysis, how to use Python for data analysis, its pros, and cons, and what alternatives there are for data analytics. In this article, we will take a look at one of these most widely-used data science programming languages – Python. In order to join the top companies that use data and benefit greatly from it, you have to know at least one programming language used for data science. If back in 2015 only 17% of companies have been utilizing big data analytics, in 2017 the percentage has grown to 53% and is getting higher each year.
This is no wonder, as data analytics helps businesses predict customer needs, personalize their approach to customers, prevent failures and make better business decisions.Ĭonsequently, the popularity of data analytics is constantly growing. According to a forecast from International Data Corporation, the worldwide revenues of Big Data and Business Analytics solutions would reach $260 billion by the end of 2020.
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