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Solution in java8 Approach 1. import java.io.*; import java.util.*; import java.text.*; import java.math.*; import java.util.regex.*; public class Solution{ public static void main(String[] args){ Pattern pattern = Pattern.compile("<([^>]+)>([^<]+)</\\1>"); Scanner in = new Scanner(System.in); int
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mean The mean tool computes the arithmetic mean along the specified axis. import numpy my_array = numpy.array([ [1, 2], [3, 4] ]) print numpy.mean(my_array, axis = 0) #Output : [ 2. 3.] print numpy.mean(my_array, axis = 1) #Output : [ 1.5 3.5] print numpy.mean(my_array, axis
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poly The poly tool returns the coefficients of a polynomial with the given sequence of roots. print numpy.poly([-1, 1, 1, 10]) #Output : [ 1 -11 9 11 -10] roots The roots tool returns the roots of a polynomial with the given coefficients. print numpy.roots([1, 0, -1]) #Output
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The NumPy module also comes with a number of built-in routines for linear algebra calculations. These can be found in the sub-module linalg. linalg.det The linalg.det tool computes the determinant of an array. print numpy.linalg.det([[1 , 2], [2, 1]]) #Output : -3.0 linalg.eig The linalg.
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Solution in python3 Approach 1. import numpy print(numpy.eye(*map(int,input().split()))) Approach 2. import numpy N, M = map(int, input().split()) print(numpy.eye(N, M)) Solution in python import numpy N,M = map(int,raw_input().split()) print numpy.eye(N,M)
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Solution in python3 Approach 1. import numpy N = list(map(int, input().split())) print(numpy.zeros(N, int)) print(numpy.ones(N, int)) Approach 2. import numpy size = tuple(map(int,input().strip().split())) print( numpy.zeros(size, int) ) print( numpy.ones(size, int) ) Approach 3. import numpy N = [int(
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Solution in python3 Approach 1. import numpy a = numpy.array(input().split(),float) print(numpy.floor(a),numpy.ceil(a),numpy.rint(a),sep="\n") Approach 2. from numpy import * A = array([float(x) for x in input().split()],float) print(floor(A),ceil(A),rint(A),sep
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Basic mathematical functions operate element-wise on arrays. They are available both as operator overloads and as functions in the NumPy module. import numpy a = numpy.array([1,2,3,4], float) b = numpy.array([5,6,7,8], float) print a + b #[ 6. 8. 10. 12.] print numpy.add(a,
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Solution in python3 Approach 1. import numpy print( numpy.array(input().split(" "),int).reshape(3,3) ) Approach 2. import numpy l=input().split() a=numpy.array(l,int) a.shape=(3,3) print(a) Approach 3. import numpy A = numpy.array(input().strip().split(),int) print(numpy.reshape(
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You are given an HTML code snippet of lines. Your task is to detect and print all the HTML tags, attributes and attribute values. Print the detected items in the following format:Tag1Tag2-> Attribute2[0] > Attribute_value2[0]-> Attribute2[1] > Attribute_value2[1]->
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*This section assumes that you understand the basics discussed in HTML Parser - Part 1 .handle_comment(data) This method is called when a comment is encountered (e.g. <!--comment-->). The data argument is the content inside the comment tag: from HTMLParser import HTMLParser class MyHTMLParser(HTMLParser): def
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HTML Hypertext Markup Language is a standard markup language used for creating World Wide Web pages. Parsing Parsing is the process of syntactic analysis of a string of symbols. It involves resolving a string into its component parts and describing their syntactic roles. HTMLParser An HTMLParser instance is fed HTML