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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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Difference between Python List and Numpy array

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schedule Mar 9, 2022
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There are 2 major benefits of Numpy arrays over Python lists:

  • Broadcasting: when you need to perform calculations over all the items in an array / list

  • Indexing: when you have a 2D list /array, it is easier and more efficient to retrieve a single column using arrays

Broadcasting

For example, say we need to divide all student raw scores in a test by 2 to arrive at the final score. If we try to perform the following using a Python list:

raw_scores = [50, 60, 70]
final_scores = raw_scores / 2
final_scores
TypeError: unsupported operand type(s) for /: 'list' and 'int'

We get a TypeError as such operations are not supported by Python lists.

Instead using a Numpy Array:

raw_scores = np.array([50, 60, 70])
final_scores = raw_scores / 2
final_scores
array([25., 30., 35.])

As we can see, each element in the array has been divided by 2.

WARNING

All elements in an array have to be of the same data type. (Not the case for Python list, which can handle elements of different types).

Indexing

To retrieve first column of 2D Numpy Array:

numpy_2d_array = np.array([[1,2,3], [4,5,6]])
numpy_2d_array[:, 0]
array([1, 4])

To retrieve first column of 2D list we must use list comprehension:

list2d = [[1,2,3], [4,5,6]]
[row[0] for row in list2d ]
[1, 4]
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Published by Arthur Yanagisawa
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