{"id":78340,"date":"2020-04-17T10:00:27","date_gmt":"2020-04-17T04:30:27","guid":{"rendered":"https:\/\/techvidvan.com\/tutorials\/?p=78340"},"modified":"2020-04-17T10:00:27","modified_gmt":"2020-04-17T04:30:27","slug":"python-libraries-for-data-scientist","status":"publish","type":"post","link":"https:\/\/techvidvan.com\/tutorials\/python-libraries-for-data-scientist\/","title":{"rendered":"Top 21 Python Libraries a Data Scientist must know"},"content":{"rendered":"

Python is an abundant source<\/strong> of libraries<\/strong>. A Python library is a gathering of functions<\/strong> that assist one to perform many actions<\/strong>. It has myriad inbuilt<\/strong> libraries<\/strong>. Python contains ample libraries for data science<\/strong>.<\/p>\n

This tutorial covers python libraries<\/strong> for data scientist.<\/p>\n

\"Python<\/a>
\nPython categorizes these libraries according to their title role<\/strong> in data science.<\/p>\n

Let’s see Python libraries for data scientist:<\/strong><\/p>\n

A. Data Cleaning and Data Manipulation<\/h3>\n
    \n
  • Pandas<\/li>\n
  • NumPy<\/li>\n
  • Spacy<\/li>\n
  • SciPy<\/li>\n<\/ul>\n

    B. Data Gathering<\/h3>\n
      \n
    • Beautiful Soap<\/li>\n
    • Scrapy<\/li>\n
    • Selenium<\/li>\n<\/ul>\n

      C. Data Visualisation<\/h3>\n
        \n
      • Matplotlib<\/li>\n
      • Seaborn<\/li>\n
      • Bokeh<\/li>\n
      • Plotly<\/li>\n<\/ul>\n

        D. Data Modelling<\/h3>\n
          \n
        • Scikit-Learn<\/li>\n
        • PyTorch<\/li>\n
        • TensorFlow<\/li>\n
        • Theano<\/li>\n<\/ul>\n

          E. Image Processing<\/h3>\n
            \n
          • Scikit-Image<\/li>\n
          • Pillow<\/li>\n
          • OpenCV<\/li>\n<\/ul>\n

            F. Audio Processing<\/h3>\n
              \n
            • pyAudioAnalysis<\/li>\n
            • Librosa<\/li>\n
            • Madmom<\/li>\n<\/ul>\n

              1) Pandas<\/h4>\n

              Pandas is one of the most popular data analysis<\/strong> and data manipulation<\/strong> libraries. It is an open-source library<\/strong>.<\/p>\n

              DataFrame<\/strong> is the chief data structure<\/strong> of the Pandas library. It stores<\/strong> and manages<\/strong> the data<\/strong> in the table. It can be done by manipulating rows<\/strong> and columns<\/strong>. It allows dataset joining<\/strong>, merging<\/strong> and reshaping<\/strong>.<\/p>\n

              Hence, when millions<\/strong> of petabytes<\/strong> of data<\/strong> are to be analyzed<\/strong>, Pandas is much helpful in this case. Using Pandas, Data can be easily<\/strong> and effectively analyzed<\/strong>.<\/p>\n

              2) NumPy<\/h4>\n

              Numerical Python<\/strong>, in short, NumPy, is an open-source library<\/strong>. It is an incredible Python library for scientific calculations<\/strong>. It also allows for accomplishing matrix operations<\/strong>.<\/p>\n

              NumPy is used to perform operations<\/strong> on the array<\/strong>. As it works on an array, it permits us to reorganize a large set of data<\/strong>.<\/p>\n

              3) Spacy<\/h4>\n

              Till now, Pandas and NumPy taught us to clean<\/strong> and manipulate data<\/strong>.<\/p>\n

              Spacy manipulates free data<\/strong> into structured data<\/strong>. It is used as an NLP (Natural Language Processing)<\/strong> library. Many human languages<\/strong> are also supported by this library.<\/p>\n

              4) SciPy<\/h4>\n

              It is an open-source library<\/strong> which is based on the concept of NumPy which provides<\/strong> many effective numerical routines<\/strong>. It can perform integration<\/strong> and linear algebra<\/strong> and has high-level features<\/strong> for data manipulating<\/strong> and visualizing<\/strong>.<\/p>\n

              SciPy is a key library<\/strong> for data processing<\/strong>.<\/p>\n

              5) Beautiful Soap<\/h4>\n

              It is one of the most popular libraries used for data scraping<\/strong>. Further, this data is given the required format<\/strong>.<\/p>\n

              With the support of Beautiful Soap<\/strong>, specific content<\/strong> from the webpage<\/strong> can be extracted<\/strong>. Using the same, HTML markup<\/strong> can be detached<\/strong> and the information<\/strong> can be protected<\/strong>.<\/p>\n

              6) Scrapy<\/h4>\n

              Scrapy is an alternative library<\/strong> of Python<\/strong> used for large scale web scraping<\/strong>. It is an open-source<\/strong> Python library which is very dissolute<\/strong> and modest<\/strong> to operate. It is beneficial for mining<\/strong> the data<\/strong> from the website<\/strong>.<\/p>\n

              Scrapy is a collection<\/strong> of all the efficient tools<\/strong> required to abstract<\/strong> from websites<\/strong>, process<\/strong> them and structure<\/strong> them the way you want.<\/p>\n

              7) Selenium<\/h4>\n

              Selenium is a library which automatically<\/strong> tests the web browser<\/strong>. It is also used for testing purposes<\/strong> in industries<\/strong>. It offers essential features to draw-out data<\/strong> and captures<\/strong> it in a future usable format<\/strong>.<\/p>\n

              Selenium is slower<\/strong> from other Python libraries.<\/p>\n

              8) Matplotlib<\/h4>\n

              Matplotlib is one of the most famous 2D graphical<\/strong> Python libraries used for data visualization<\/strong>. Not only 2D graphs, but it can also be useful to generate 3D graphs<\/strong>. It is helpful to generate graphs<\/strong>, bar charts<\/strong>, histograms<\/strong>, scatterplots<\/strong>, etc.<\/p>\n

              9) Seaborn<\/h4>\n

              Seaborn is based on Matplotlib<\/strong>. It enhances the visualizing features<\/strong> of Matplotlib. This popular Python library provides a gallery full of visualizations including time series<\/strong>, joint plots<\/strong>, etc.<\/p>\n

              Seaborn offers efficient tools<\/strong> for revealing the pattern of data<\/strong> in a more colorful manner<\/strong>.<\/p>\n

              10) Bokeh<\/h4>\n

              Boke is a Python library used to provide interactive visualization<\/strong>. It is dependent<\/strong> on Matplotlib<\/strong>. It targets interactivity<\/strong> and offers interactive designs<\/strong> in a web browser<\/strong>.<\/p>\n

              11) Plotly<\/h4>\n

              Plotly supports interactive web apps<\/strong>. It provides the advantage<\/strong> to create<\/strong> an upmarket graph<\/strong> in very fewer lines of code<\/strong>.<\/p>\n

              Plotly can fulfill any kind of visual requirement<\/strong> in a short period.<\/p>\n

              12) Scikit-Learn<\/h4>\n

              Scikit-learn is used for modeling data<\/strong>. It is a savior for Machine Learning projects. It has numerous supervised<\/strong> and unsupervised<\/strong> ML algorithms<\/strong>. Its main target is on quality of code<\/strong>, performance<\/strong> and decent documentation<\/strong>.<\/p>\n

              13) PyTorch<\/h4>\n

              PyTorch is an open-source library<\/strong>. It is a useful tool for deep learning programs<\/strong> that provide high speed<\/strong>. It fulfills many data-centric<\/strong> demands.<\/p>\n

              The cloud-based<\/strong> environment is provided by PyTorch<\/strong> which enables easy scaling<\/strong> of resources<\/strong>.<\/p>\n

              14) TensorFlow<\/h4>\n

              TensorFlow is one of the most popular frameworks for Data Science<\/strong>, Deep Learning<\/strong> and Machine Learning<\/strong>. It is an open-source framework<\/strong> that enables you to build models<\/strong>, test them<\/strong> and train them<\/strong> accordingly. It is the best tool for voice recognition<\/strong> and object identification<\/strong>.<\/p>\n

              15) Theano<\/h4>\n

              Theano is the Python library used to perform large multi-dimensional<\/strong> array operations<\/strong>. It allows performing array-based mathematical operations<\/strong>.<\/p>\n

              Theano has GPU<\/strong> based infrastructure<\/strong>. Hence, it can perform operations in a faster<\/strong> manner as compared to CPU<\/strong>.<\/p>\n

              16) Scikit-Image<\/h4>\n

              Scikit-Image is a Python library which performs image processing<\/strong>. It is a combination<\/strong> of various functions<\/strong> that are helpful for multiple image processing<\/strong>.<\/p>\n

              Scikit-Learn is a tool that has ample functionality including Image segmentation<\/strong>, color modification<\/strong>, etc.<\/p>\n

              17) Pillow<\/h4>\n

              Pillow is an advanced version of the Python Imaging Library<\/strong>. This library offers several image processing standards<\/strong>.<\/p>\n

              Pillow is helpful in image-enhancing like blurring<\/strong>, smoothing<\/strong>, etc. Using Pillow, you can add text<\/strong> to an image<\/strong>.<\/p>\n

              18) OpenCV<\/h4>\n

              OpenCV resolves computer vision issues<\/strong>. It makes use of NumPy<\/strong> to convert OpenCV array<\/strong> to and from NumPy array<\/strong>. It performs many tasks including motion tracking<\/strong>, gesture recognition<\/strong>, etc.<\/p>\n

              19) pyAudioAnalysis<\/h4>\n

              pyAudioAnalysis is the Python library used for audio processing<\/strong>. It performs various audio features<\/strong> like classification<\/strong>, extraction<\/strong>, segmentation<\/strong>, etc.<\/p>\n

              pyAudioAnalysis is also efficient<\/strong> in classifying unknown sounds<\/strong> and extracting audio<\/strong>. This special tool of Python also helps to detect audio chunks<\/strong> and remove unnecessary slots<\/strong> from heavy recordings<\/strong>.<\/p>\n

              20) Librosa<\/h4>\n

              This Python library is rich in features that can analyze audio<\/strong> and music features<\/strong>. It can extract remarkable features of the audio segment such as beats<\/strong>, tempo<\/strong>, rhythm<\/strong>, etc.<\/p>\n

              Librosa can deliver building blocks<\/strong> that are useful parts to create<\/strong> a music retrieval system<\/strong>.<\/p>\n

              21) Madmom<\/h4>\n

              Madmom is an audio processing library<\/strong> capable of performing Music Information Retrieval (MIR)<\/strong> tasks. It is proficient in performing music data analysis tasks<\/strong>.<\/p>\n

              Some libraries like NumPy<\/strong>, SciPy<\/strong>, etc. are pre-requisite<\/strong> for the execution<\/strong> of Madmom<\/strong>.<\/p>\n

              Conclusion<\/h3>\n

              Python has ample libraries<\/strong> that fulfil the requirements of every field. It has various libraries<\/strong> that deal with a particular field<\/strong>. These python libraries for data scientist are extremely useful as it helps in decision making<\/strong>.<\/p>\n

              The bundle of libraries are capable enough to work on large sets of data<\/strong>.<\/p>\n","protected":false},"excerpt":{"rendered":"

              Python is an abundant source of libraries. A Python library is a gathering of functions that assist one to perform many actions. It has myriad inbuilt libraries. Python contains ample libraries for data science....<\/p>\n","protected":false},"author":1,"featured_media":78344,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1053],"tags":[2450,2451,2452,2453],"class_list":["post-78340","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-python","tag-python-frameworks-for-data-science","tag-python-libraries","tag-python-libraries-for-data-science","tag-python-libraries-for-data-scientist"],"yoast_head":"\nTop 21 Python Libraries a Data Scientist must know - TechVidvan<\/title>\n<meta name=\"description\" content=\"Learn top 21 Python Libraries that a Data scientist must know - Numpy, Scipy,pandas,Spacy, Tensorflow,Scikit-Learn,PyTorch,Theano,Scikit-Image,Pillow,OpenCV\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/techvidvan.com\/tutorials\/python-libraries-for-data-scientist\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Top 21 Python Libraries a Data Scientist must know - 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