NumPy 
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Accessing a value in a 2D arrayAccessing columns of a 2D arrayAccessing rows of a 2D arrayCalculating the determinant of a matrixChecking allowed values for a NumPy data typeChecking if a NumPy array is a view or copyChecking the version of NumPyChecking whether a NumPy array contains a given rowComputing Euclidean distance using NumpyConcatenating 1D arraysConverting array to lowercaseConverting type of NumPy array to stringCreating a copy of an arrayDifference between Python List and Numpy arrayDifference between the methods array_equal and array_equivDifference between the methods mod and fmodDifference between the methods power and float_powerFinding the closest value in an arrayFinding the Index of Largest Value in a Numpy ArrayFinding the Index of Smallest Value in a Numpy ArrayFinding the most frequent value in a NumPy arrayFlattening Numpy arraysGetting constant PiGetting elements from a two dimensional array using two dimensional array of indicesGetting indices of N maximum valuesGetting indices of N minimum valuesGetting the number of columns of a 2D arrayGetting the number of non-zero elements in a NumPy arrayGetting the number of rows of a 2D arrayInitializing an array of onesInitializing an array of zerosInitializing an identity matrixLimiting array values to a certain rangePerforming linear regressionPrinting full or truncated NumPy arrayPrinting large Numpy arrays without truncationRemoving rows containing NaN in a NumPy arrayReversing a NumPy arraySaving NumPy array to a fileShape of Numpy ArraysSorting value of one array according to anotherSuppressing scientific notation
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 Finding the Index of Largest Value in a NumPy Array
 schedule Aug 12, 2023 
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There are two simple ways to find the index of the largest value in a Numpy array.
The first way is to use the argmax(~) function of the Numpy array:
        
        
            
                
                
                    x = np.array([3,5,2,1])x.argmax()
                
            
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    Here, 1 is returned because the largest value (i.e. 5) is located at index 1.
The second way is to use the argmax(~) static function of the Numpy module:
        
        
            
                
                
                    x = np.array([3,5,2,1])np.argmax(x)
                
            
            1
        
      Published by Isshin Inada
 Edited by 0 others
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