{"id":21836001,"url":"https://github.com/prabhupavitra/numpy-guide-for-data-science","last_synced_at":"2026-05-06T23:40:19.643Z","repository":{"id":218120583,"uuid":"197093173","full_name":"prabhupavitra/NumPy-Guide-for-Data-Science","owner":"prabhupavitra","description":"A Hands-On NumPy Tutorial for Data Scientists","archived":false,"fork":false,"pushed_at":"2019-12-12T04:24:17.000Z","size":120,"stargazers_count":2,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-01-26T10:09:30.485Z","etag":null,"topics":["aggregations","data-science","fancyindexing","jupyter-notebook","numpy","numpy-tutorial","python","python3","sorting","vectorized-computation"],"latest_commit_sha":null,"homepage":null,"language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/prabhupavitra/NumPy-Guide-for-Data-Science/blob/master/)\n\n# NumPy-Guide-for-Data-Science\nA Hands-On NumPy Tutorial for Data Scientists\n\nNumPy, short for Numerical Python, is one of the indispensable foundational packages for scientific computing in Python.\nAt the core of NumPy is, ndarray, an efficient multidimensional array that is a container of homogeneous data. \nIt is predominantly used in a majority of tasks in the data science ecosystem and is the fundamental package for \nscientific computing with Python.\n\nIts key features include:\n\n- Provides efficient data storage compared to the built-in python sequences since NumPy internally stores data in a\n  contiguous block of memory.\n\n- Operations are efficient whether it’s for complex computations or as the arrays grow larger in size since it does \n  not need \"for\" loops.\n\n- It provides an abundance of functions for mathematical, logical, matrix manipulation, discrete Fourier transforms, \n  basic linear algebra, basic statistical operations, random simulation, sorting, I/O and various other computations.\n\nThis Hands-on NumPy tutorial covers all the core aspects of NumPy and the features one needs to know, as a beginner \nin Data science. \n\nFor usability reasons, this tutorial is divided into three sections.\nThe following list of contents is a walk-through of NumPy features discussed in this tutorial:\n\n1.Basics of NumPy \nThis notebook gives an overview of basic concepts of NumPy.\n-\tNumPy Installation\n-\tNumPy Array Creation\n-\tNumPy Array Attributes\n-\tNumPy Array Manipulation \n\t(includes simple array indexing, array slicing, creating copies of    \n\tarrays, reshaping arrays, raveling arrays, flattening arrays, concatenating arrays, splitting arrays, \n  repeating elements in arrays)\n\n2.NumPy Vectorized Computations\nThis notebook introduces concepts of vectorization, implemented through NumPy’s universal functions that enables \nNumPy to make repeated calculations on array elements much more efficient. Ufuncs perform Element-Wise operations \non data in ndarrays. It’s important to note that ufuncs can return multiple arrays (E.g. divmod) although less \nfrequently used.\n-\tUnary ufuncs\n-\tUnary ufuncs for nan\n-\tBinary ufuncs\n-\tBroadcasting\n\n3.Other NumPy Essentials\nThis notebook introduces concepts of fancy indexing, sorting, aggregations, masking, set operations and various \nothers that demonstrates the computational efficiency of NumPy in the data science world.\n-\tFancy Indexing\n-\tSorting Arrays\n-\tAggregations**\n-\tSet Operations**\n\n**explained using real world datasets from energy markets and finance\n\nReferences:\n\n1.\tNumPy Documentation: https://docs.scipy.org/doc/numpy/reference/#\n2.\tMcKinney, Wes (2017). Python for Data Analysis (2nd ed.). \n3.\tVanderPlas, Jake (2016). Python Data Science Handbook.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprabhupavitra%2Fnumpy-guide-for-data-science","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fprabhupavitra%2Fnumpy-guide-for-data-science","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprabhupavitra%2Fnumpy-guide-for-data-science/lists"}