{"id":3786,"date":"2021-05-24T22:24:00","date_gmt":"2021-05-24T20:24:00","guid":{"rendered":"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/uncategorized\/what-is-python-used-for\/"},"modified":"2023-08-31T14:19:50","modified_gmt":"2023-08-31T12:19:50","slug":"what-is-python-used-for","status":"publish","type":"post","link":"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/what-is-python-used-for\/","title":{"rendered":"What Is Python Used For?"},"content":{"rendered":"

Python is currently one of the most popular computer programming languages in use. In fact, it it\u00a0the\u00a0<\/strong>most popular coding language according to the Tiobe Index<\/a>.\u00a0Python can be used for a range of purposes, but its most ubiquitous (and fastest-growing) use is in the field of data analytics<\/a>.<\/p>\n

In this blog post, we\u2019ll explore why Python is a popular tool for data analysts, demonstrating its applications with some examples. We\u2019ll break this down into the following sections:<\/p>\n

    \n
  1. In brief: What is Python?<\/a><\/li>\n
  2. Why is Python good for data analytics?<\/a><\/li>\n
  3. What is Python used for?\u00a0<\/a><\/li>\n
  4. Summary<\/a><\/li>\n<\/ol>\n

    So, what is Python used for? Let\u2019s find out.<\/p>\n

    1. What is Python?<\/h2>\n

    Python is a computer programming language. It was first developed in the early nineties to create simple scripts. However, since the dawn of the digital age, its applications have ballooned. This is thanks to the simplicity of its code and its multi-purpose nature.<\/p>\n

    Organizations using Python include Netflix, the New York Stock Exchange, and even NASA. What\u2019s more, there are over 70,000 libraries of pre-existing tools available for free on the\u00a0Python Package Index (PyPI)<\/a>. These can be used to streamline everything from web development<\/a> to special effects and, of course, data analytics.<\/p>\n

    If you\u2019d like to learn more about what Python is, you\u2019ll find an in-depth introduction in this guide<\/a>.<\/p>\n

    2. Why is Python good for data analytics?<\/h2>\n

    To appreciate Python\u2019s applications, it helps to understand data analytics.<\/p>\n

    At the highest level, data analytics means finding patterns in data that can inform business decisions. “Data” can include everything from phone numbers or weights, to names, and even the price of potatoes\u2026you name it! “Analytics” means accumulating and sorting these data, drawing insights, and reporting them in a clear (often visual) manner.<\/p>\n

    Python can help streamline many of these tasks. Let\u2019s see why.<\/p>\n

    Python is easy to learn<\/h3>\n

    Python is an “abstract” language. This means that many computational aspects get handled internally. As such, Python users don\u2019t have to \u2018think like a computer\u2019. Rather than becoming experts in coding, they can focus on their goal\u2014obtaining and providing useful insights.<\/p>\n

    Python is good for writing scripts<\/h3>\n

    \u00a0<\/strong>In data analytics, agility is crucial. Whether you\u2019re gathering or analyzing data, Python\u2019s simplicity is perfect for fast coding and tweaking scripts. It\u2019s especially simple when compared to more complex programming languages, like C++.<\/p>\n

    Python has a strong online community<\/h3>\n

    With a huge online presence on community coding forums like\u00a0GitHub<\/a> and\u00a0Stack Overflow<\/a>, Python is well-supported. Users can get queries answered, and fast.<\/p>\n

    Python is open-source<\/h3>\n

    \u00a0<\/strong>Python is open-source. This means its code is freely available for distribution and modification. As a result, a massive number of tried-and-tested third-party libraries have sprung up on PyPI. These tools support a wide range of disciplines, but data analytics is a key one.<\/p>\n

    Still wondering if Python is a good language to learn? Here are five reasons you\u2019ll want to start learning Python right now<\/a>.<\/p>\n

    3. What is Python used for?<\/h2>\n

    In this section, we\u2019ll look at a small handful of Python\u2019s (many) third-party libraries. We\u2019ll explain what they do, and how some organizations use them to streamline data tasks. In practice, data analysts<\/a> have many more responsibilities than those outlined below, but we\u2019ll aim to cover some of the key ones.<\/p>\n

    Python for general data manipulation<\/h3>\n

    When you encounter graphs or statistics in daily life (such as in the news) they\u2019re always presented in a clear manner. Unfortunately, raw data rarely starts out this way! An analyst\u2019s first task is to make sense of their data. This means ordering it and searching for patterns. With huge datasets, doing so manually would take lots of time.<\/p>\n

    Luckily, Python\u2019s general-use data manipulation libraries come in handy. One of these,\u00a0Pandas<\/a>, allows users to amend numerical tables, change format types, merge datasets, and much more.<\/p>\n

    Related reading:<\/strong> Python Pandas Tutorial<\/a><\/p>\n

    Another, NumPy<\/a>, features multi-dimensional arrays. These are structures that allow users to store values of the same data type and manipulate them. This makes NumPy excellent for scientific use.<\/p>\n

    Python\u2019s general-use data manipulation libraries have a variety of applications. For instance, take travel-related search site,\u00a0trivago<\/a>. They reportedly use NumPy to get a clear overview of the huge amounts of search data they collect each day. This allows them to improve their algorithms and help users to book the right hotels more easily. And you thought you were just clicking a button!<\/p>\n

    Python for data scraping<\/h3>\n

    Another challenge analysts sometimes face is not having enough data, which means they must source it. Fortunately, Python has libraries that can automate this task, too. Popular examples are\u00a0Scrapy<\/a> and\u00a0Beautiful Soup<\/a>, which both scrape data from the web.<\/p>\n

    Beautiful Soup works by \u2018parsing\u2019 HTML and XML documents\u2014which are used for processing and presenting text on websites\u2014for data. In simple terms, this means that it reads the code, breaks it into simpler parts and analyzes it. It then pulls out whichever data the analyst has flagged as relevant. Scrapy, meanwhile, is used to write applications known as \u2018web spiders\u2019 (a term you may have heard in relation to Google\u2019s search algorithms). These trawl websites and extract data from them.<\/p>\n

    Data scraping is so important for modern businesses that many companies dedicate themselves to this task alone. It\u2019s not only marketing agencies that use them, though. The UK government utilizes Scrapy to aggregate data for businesses and individuals (you can see what this looks like here<\/a>.)<\/p>\n

    Python for data visualization and reporting<\/h3>\n

    Once data are collected and analyzed, it is common practice to represent insights visually. Why? Well, it must be accessible to analysts, yes\u2014but also to non-technical folk, like business leaders and other stakeholders. Again, PyPI delivers, with a wide array of data visualization libraries. Some are designed for niche disciplines like\u00a0eye movement research<\/a>, but there are many general-purpose libraries, too.<\/p>\n

    The most popular example is\u00a0Matplotlib<\/a>. Matplotlib can be used to report data in graphs, pie charts, and many other formats. What\u2019s more, these visuals can be static, animated, or interactive, depending on your need. For this reason, Matplotlib is sometimes known as the Swiss army knife of data plotting!<\/p>\n

    According to marketing intelligence provider, HG Insights, companies like\u00a0Facebook, Apple and Tesla<\/a> all use Matplotlib. This makes it a great Python library to play with if you want to expand your expertise.<\/p>\n

    Python for machine learning<\/h3>\n

    Another key tenet of data analytics is machine learning. This is a broad term. However, it essentially involves running complex algorithms with large numbers of mathematical equations, in order to make sense of patterns in data. This would take impractical amounts of time without automation. Once again, PyPI steps in with machine learning libraries such as\u00a0TensorFlow<\/a> (which has in-built image, text and speech recognition) and\u00a0PyTorch<\/a> (which is used for natural language processing).<\/p>\n

    Other organizations use Python to create their own machine learning tools. Spotify, for instance, has used Python to develop a proprietary machine learning module called Luigi. Luigi allows teams to quickly automate complex lists of commands, allowing them to power things like Spotify\u2019s Discover and Radio features. It\u2019s also used to suggest people you might want to follow. Python is just one of many languages used for machine learning<\/a>.<\/p>\n

    Python for image-based analysis<\/h3>\n

    What happens if your data are in the form of pictures, not text? No worries. PyPI has libraries dedicated to image-based processing. While some of the libraries we\u2019ve already discussed can support this function, an especially popular one is\u00a0OpenCV<\/a>.<\/p>\n

    Startups and global corporations such as Google, Yahoo, and Toyota all use OpenCV. Its applications are broad, too. It supports facial recognition and can even classify human movement in video footage. Less Orwellian applications include stitching images (e.g. Google Street View), removing red-eye from photos, checking airplane runways for debris, or inspecting product labels in factories. This area of data analytics is growing and improving fast, and Python is right at the forefront.<\/p>\n

    \"Python<\/p>\n

    4. Summary<\/h2>\n

    As we\u2019ve covered, Python is a very agile programming language. It\u2019s easy to learn, straightforward to use, and has strong online support. Most importantly, however, Python is open-source. This means there are thousands of third-party software libraries available, which can be used to streamline a range of analytics tasks:<\/p>\n