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np.py file\n\u003cp\u003eNumPy is a Python library used for working with arrays.\u003c/p\u003e\n\u003col\u003e\n  \u003cli\u003eCreate an Array\u003c/li\u003e\n  \u003cli\u003eArray Indexing\u003c/li\u003e\n  \u003cli\u003eArray Slicing\u003c/li\u003e\n  \u003cli\u003eData Types\u003c/li\u003e\n  \u003cli\u003eCopy Vs View\u003c/li\u003e\n  \u003cli\u003eIterating \u003c/li\u003e\n  \u003cli\u003eShape and Reshape\u003c/li\u003e\n  \u003cli\u003eSearch elements\u003c/li\u003e\n  \u003cli\u003eJoin and Split\u003c/li\u003e\n\u003c/ol\u003e\n\n\nNumPy Random - npRandom.py file\n\u003cp\u003eThe numpy. random module implements pseudo-random number generators (PRNGs or RNGs, for short) with the ability to draw samples from a variety of probability distributions.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eNumpy Random Data Distribution - You can create random data distributed according to different statistical properties\u003c/li\u003e\n\u003cli\u003eNumpy Random Permutations - The numpy.random.permutation function shuffles the elements of an array or returns a randomly permuted range of numbers.\u003c/li\u003e\n\u003cli\u003e\u003cb\u003eNumpy Seaborn - Seaborn is a library often used to visualize random data distributions.\u003c/b\u003e\u003c/li\u003e\n\u003cli\u003eNumpy Normal Distribution - The normal (Gaussian) distribution is one of the most commonly used. It is defined by its mean (center) and standard deviation (spread).\u003c/li\u003e\n\u003cli\u003eNumpy Binomial Distribution - The binomial distribution models the number of successes in a fixed number of trials.\u003c/li\u003e\n\u003cli\u003eNumpy Poisson Distribution - The Poisson distribution models the number of events in a fixed interval of time or space.\u003c/li\u003e\n\u003cli\u003eNumpy Uniform Distribution - The uniform distribution generates values evenly distributed between two bounds.\u003c/li\u003e\n\u003c/ol\u003e\n\n\nNumPy Random - npUfunc.py file\n\u003cp\u003eUniversal functions (ufunc) basics. A universal function (or ufunc for short) is a function that operates on ndarrays in an element-by-element fashion, supporting array broadcasting, type casting, and several other standard features \u003c/p\u003e\n\n\u003col\u003e\n\u003cli\u003eNumPy ufunc Create\u003c/li\u003e\n\u003cli\u003eNumPy ufunc Simple Arithmetic\u003c/li\u003e\n\u003cli\u003eNumPy ufunc Rounding Decimals\u003c/li\u003e\n\u003cli\u003eNumPy ufunc Logs\u003c/li\u003e\n\u003cli\u003eNumPy ufunc Summations\u003c/li\u003e\n\u003cli\u003eNumPy ufunc Products\u003c/li\u003e\n\u003c/ol\u003e\n\n\u003chr\u003e\n\nPython Matplotlib  - matplotlib_file.py file\n\u003cp\u003eMatplotlib is a popular data visualization library in Python and Matplotlib allows you to generate plots, histograms, bar charts, scatter plots, etc., with just a few lines of code.\u003c/p\u003e\n\u003col\u003e\n  \u003cli\u003eBasic plotting\u003c/li\u003e\n  \u003cli\u003eColor, Marker, Line Style\u003c/li\u003e\n  \u003cli\u003eLimits of axis\u003c/li\u003e\n  \u003cli\u003eSubplots \u003c/li\u003e\n  \u003cli\u003eLine chart \u003c/li\u003e\n  \u003cli\u003eBar chart \u003c/li\u003e\n  \u003cli\u003eHistogram \u003c/li\u003e\n  \u003cli\u003ePie Chart\u003c/li\u003e\n\u003c/ol\u003e\n\n\n\n\u003chr\u003e\n\nPython Pandas  - pandas_file.ipynb file\n\u003cp\u003ePandas is a Python library used for working with data sets. It has functions for analyzing, cleaning, exploring, and manipulating data. The name \"Pandas\" has a reference to both \"Panel Data\".\u003c/p\u003e\n\u003cul\u003e\n  \u003cli\u003eSeries\u003c/li\u003e\n  \u003cli\u003eData Frame\u003c/li\u003e\n  \u003cli\u003eDictionary\u003c/li\u003e\n  \u003cli\u003eImport CSV \u003c/li\u003e\n  \u003cli\u003eRemoving Columns in dataset \u003c/li\u003e\n  \u003cli\u003eGet useful data from dataset\u003c/li\u003e\n  \u003cli\u003eFilling the missing values with mean \u003c/li\u003e\n  \u003cli\u003eAdding conditions\u003c/li\u003e\n  \u003cli\u003eFiltering\u003c/li\u003e\n  \u003cli\u003eDataset Visualize\u003c/li\u003e\n\n\u003c/ul\u003e\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsahansilvadev%2Fworking-with-python-libraries","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsahansilvadev%2Fworking-with-python-libraries","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsahansilvadev%2Fworking-with-python-libraries/lists"}