{"id":6692,"date":"2021-04-05T12:57:39","date_gmt":"2021-04-05T12:57:39","guid":{"rendered":"https:\/\/9series-blog.staging9.com\/?p=6692"},"modified":"2026-03-03T10:49:45","modified_gmt":"2026-03-03T10:49:45","slug":"top-5-python-data-science-libraries-must-look-2021","status":"publish","type":"post","link":"https:\/\/www.9series.com\/blog\/top-5-python-data-science-libraries-must-look-2021\/","title":{"rendered":"Top 5 Python Data Science Libraries Must Look in 2021"},"content":{"rendered":"

\"Top<\/a>
\nIn recent years thanks to technology and the availability of the internet we have seen immense growth in different kinds of technologies. Particularly, when it comes to data we often amused by the fact that It has given so much information and we could do loads of stuff with data science only if we have the right amount of data and a platform that would enable us to read these data.<\/p>\n

9series helps you to build all these kinds of applications or platforms. Our Enthusiasts and coding lovers can build a bunch of things such as python applications, docker containerization, mobile app development website development, and DevOps consulting. So, If you wanted to hire Python developer from 9series<\/a>. Just reach out to us or shoot a message.<\/p>\n

However, coming back to the topic. We have been thinking about different kinds of Python libraries<\/a> that are being used for data science, its application and their differences. So after an avid discussion, we have written down the crux for all our wishful people and clients.<\/p>\n

Here we are suggesting the 5 best Python libraries to anyone who wanted to learn and develop in the data science field.<\/p>\n

1. Numpy:<\/h2>\n

Numpy is a far more important and fundamental library that is meant for computing applications. It contains a powerful N-dimensional array object. We as developers use this library for random functions. And the library work quite well compares to the standard library.<\/p>\n

Fun Fact:<\/h3>\n

NumPy polyfit function becomes increasingly popular when it comes to analytics tasks like linear or polynomial regression.<\/p>\n

Most Use:<\/strong> Machine Learning<\/strong><\/p>\n

Key Features:<\/strong><\/h3>\n
    \n
  • Easy to use<\/li>\n
  • Interactive<\/li>\n
  • Simplify the process<\/li>\n
  • Open source contribution<\/li>\n<\/ul>\n

    2. Scikit-Learn:<\/h2>\n

    The most fascinating stuff that you can do on python is to write a code of Machine Learning or predictive analysis. You can use it for a cross-validation feature that allows us to use more metrics. It has a number of other features like reducing dimensionality, clustering, regression, etc.<\/p>\n

    Fun Fact:<\/strong><\/h3>\n

    is that The name \u201cScikit learn\u201d is meant as it sounds. It originally means \u201cMachine Learning in Python\u201d It is a fundamental source for any early-stage data science learner enthusiast. It clears your basics in vast areas such as classification methods, clustering methods, multiplication methods, etc.<\/p>\n

    Most Use: <\/strong>Machine Learning, Data mining, Data analysis.<\/p>\n

    Key features:<\/strong><\/p>\n

      \n
    • Reusable context<\/li>\n
    • Methods of checking the accuracy<\/li>\n<\/ul>\n

      3. Pandas:<\/h2>\n

      One of the most used libraries in the Data Science field. With Pandas, we could analyze data’s structure, assign specific values, labels even you can run functions such as sum, subtraction, multiplication, median, min, max, etc. The most useful feature of the pandas is to translate the complex data into simpler ones.<\/p>\n

      Fun fact:<\/strong> You often heard the panda as \u201cthe SQL of Python\u201d and It indeed plays that kind of role. Panda will enable us to analyze two-dimensional data tables in Python itself.<\/p>\n

      Main Use:<\/strong> Data analysis in Python<\/p>\n

      Key Features:<\/strong><\/h3>\n
        \n
      • Analyze the data of Industry like finance, education, etc.<\/li>\n
      • Very specific Functionality.<\/li>\n<\/ul>\n

        4. SciPy:<\/strong><\/h2>\n

        This is another application that is used in Data science and If we say specific then It would be in the field of computing. It is open source Library. The good thing about SciPy is all functions are well-curated and documented so it would be obvious to say that Scipy is the extended version of Numpy and good than numPy. It is mainly used for signal and Image processing and statistical formulas.<\/p>\n

        Fun Fact<\/strong>:<\/h3>\n

        SciPy is the name. SciPy is called a \u201cScipy stack\u201d which means scientific computing applications, particularly in Python.<\/p>\n

        Main Use:<\/strong> Computing, Data analysis, Machine Learning<\/p>\n

        Key Features:<\/strong><\/h3>\n
          \n
        • Use of Algebra<\/li>\n
        • Solve Fourier transformation and differentiation<\/li>\n
        • Support signal processing<\/li>\n<\/ul>\n

          5. TensorFlow:<\/h2>\n

          This library is generally used by high-performance developers because it consists of far more big and complex computation. It is an open-source library and one can say that It is called a framework that ultimately produces value. Tensor flow has a flexible architecture and as we said it is a framework so you can easily transport your code into mobile or any other devices and you don\u2019t need to rewrite any codes.<\/p>\n

          Fun Fact:<\/strong><\/h3>\n

          Tensor is the usage. Google has successfully established products like Google Photos and Google voice search assistant. Now, both two are using Tensorflow when Google is building the application.<\/p>\n

          Most use:<\/strong> Designing deep learning models<\/p>\n

          Key Features:<\/strong><\/h3>\n
            \n
          • Video detect<\/li>\n
          • Image, voice recognized<\/li>\n
          • Library management<\/li>\n
          • Support of machine learning<\/li>\n
          • Allows us to transfer the application.<\/li>\n<\/ul>\n

            So, That\u2019s it these are the main and top 5 libraries for Python and we recommend anyone to use them accordingly. We hope you will get enough learning from these articles.<\/p>\n","protected":false},"excerpt":{"rendered":"

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