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    NumPy Histogram

    The famous programming language python has a core library which is specifically designed for scientific computation that provides for tools to integrate languages like C and C ++ which is known as NumPy (meaning numerical python). It is specifically useful for coders who deal with data science and big data analysis with parallel mathematical operations being conducted which are provided by calling the predefined numpy functions or tools. Among one of the features present in this library is the histogram function known as NumPyHistogram().

    The numpy histogram function provides for the data scientist to perform graphical analysis on the basis of the data and their respective frequency distribution. The Numpy histogram function has two parameters called bins and input arrays. The bins are rectangular-shaped blocks that are distanced at equal horizontal width that correspond to the respective class interval. The difference in the height of these beans is representative of the difference in the frequency of these class intervals.


    Following is the representation in which code has to be drafted in the Python language for the application of the numpy histogram function:

    	import numpy as np
    	//The core library of numpy is being imported so that the histogram function can be applied which is a part of the numpy library
    	numpy.histogram (a, bins=10, range = None, normed = None, weights = None, density = None)

    The various criteria is set to define the histogram data are represented by bins, range, density, and weights.

    Another function called the plt() from the matplot library is used in converting the numeric data into histogram graphs. The function uses the data from the array as parameters converting it to a histogram.


    Here are the following Parameters of NumPy Histogram mention below

    1. a: array_like (Represent the set of values that has been input by the user)

      These values would be arranged in the set of arrays which would be flattened and computed to who returned the histogram.

    2. bins: can be either int or sequence (of values which are either string or scalar)

      If the bins are int (which define the total number of bins with equal width that have been mentioned in the range which is taken to be 10 as a default value)

      If the pins are sequence then that represents the monotonic increase in the array which effects on the bin’s width edges (this is inclusive of the rightmost age which gives rise to two non-uniform bin width)

      The histogram’s bin edges define the method of calculation which has to be used depending upon the optimal width of the bin. This is specifically true if the bin has string values.

    3. range : (float [upper value] , float [lower value]) (optional)

      Operations and the lower range of the bin are represented through the float values. If the limit has not been provided and automatic range is taken by the system which is represented by the syntax a.min(), a.max(). The range helps in ignoring or by-passing any values which lie outside of the range are not considered during the computation, which has a great impact on the automated bin computation. It must be noted that the value of the first element which has been mentioned in the range must be less than or at least equal to the second element.

    4. normed: bool (optional for code syntax) [It has been deprecated since the release of version 1.6.0 of Python]

      The density argument used in Python can be taken to be its equivalent in terms of functionality, but its application produces discrepancies in the result when there is and an equal distribution with regard to the width of the bins.

    5. weights : array_like (optional for code syntax)

      An array of weights, of the same shape as a. Each value in an only contributes its associated weight towards the bin count (instead of 1). If the density is True, the weights are normalized, so that the integral of the density over the range remains 1.

    6. density : bool (optional for code syntax)

      The bin count accounts for the contribution of each value in accordance with its associated weight. The density value true normalization of the weights occurs making the integral of the density remain over 1. If the value is false, there are a number of samples that are contained in each of the resultant bins.


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