Interview Questions and Answers

NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with mathematical functions to operate on these arrays.

You can install NumPy using the command 'pip install numpy'.

import numpy as npnmy_list = [1, 2, 3]narr = np.array(my_list)

You can use the attribute 'ndim'. For example, if 'arr' is your array, use 'arr.ndim'.

'np.zeros()' creates an array filled with zeros, while 'np.ones()' creates an array filled with ones.

You can use indices. For example, 'arr[1, 2]' accesses the element in the second row and third column of 'arr'.

You can use the attribute 'shape'. For example, 'arr.shape' returns the shape of 'arr'.

'np.eye()' creates an identity matrix with ones on the main diagonal and zeros elsewhere.

import numpy as npnarr1 = np.array([1, 2, 3])narr2 = np.array([4, 5, 6])nresult = arr1 + arr2

'np.arange()' creates an array with regularly spaced values within a specified range.

You can use 'np.mean()'. For example, 'mean_value = np.mean(arr)'.

Broadcasting is a powerful mechanism that allows NumPy to work with arrays of different shapes when performing arithmetic operations.

import numpy as npnarr1 = np.array([1, 2, 3])narr2 = np.array([4, 5, 6])nresult = arr1 * arr2

np.random.seed() is used to initialize the random number generator in NumPy, ensuring reproducibility of random results.

A ufunc in NumPy is a flexible function that operates element-wise on NumPy arrays, supporting broadcasting.

You can use 'np.vstack()' or 'np.concatenate()' with 'axis=0'.

'np.ravel()' returns a flattened 1D array from a multi-dimensional array.

'np.concatenate()' joins a sequence of arrays along an existing axis.

You can use 'np.matmul()' or the '@' operator. For example, 'result = np.matmul(arr1, arr2)' or 'result = arr1 @ arr2'.

np.newaxis is used to increase the dimension of the existing array by one more dimension when used once.

np.dot() performs matrix multiplication for 2-D arrays, while np.matmul() is equivalent to the '@' operator and is more general.

import numpy as npnarr = np.diag([1, 2, 3])

np.meshgrid() is used to create coordinate matrices from coordinate vectors, commonly used for 3D plotting.

'np.histogram()' computes the histogram of a set of data, returning the bin counts and bin edges.

You can use 'np.argmax()'. For example, 'np.argmax(arr)' returns the index of the maximum value in 'arr'.

'np.where()' returns the indices of elements that satisfy a given condition.

'np.linalg.inv()' computes the (multiplicative) inverse of a matrix.

'np.percentile()' calculates the nth percentile of a dataset.

'np.unique()' returns the unique elements of an array.

You can use 'arr.T'. For example, 'transposed_arr = arr.T'.