numpy.apply_along_axis(func1d, axis, arr, *args): Applies the function func1d along an axis on 1D slices of arr.

numpy.arange([start,] stop[, step,], dtype=None): Creates a NumPy array with evenly spaced values within a specified range.

numpy.argsort(a, axis=-1, kind='quicksort', order=None): Returns the indices that would sort the input array.

numpy.argmax(a, axis=None): Returns the indices of the maximum values along an axis.

numpy.argmin(a, axis=None): Returns the indices of the minimum values along an axis.

numpy.argwhere(a): Finds the indices of non-zero elements.

numpy.array(object, dtype=None, copy=True, order=None, subok=False, ndmin=0): Creates a NumPy array from an array-like sequence, such as a Python list.

numpy.testing.assert_allclose((actual, desired, rtol=1e-07, atol=0, err_msg='', verbose=True): Raises an error if two objects are unequal up to a specified precision.

numpy.testing.assert_almost_equal(): Raises an exception if two numbers are not equal up to a specified precision.

numpy.testing.assert_approx_equal(): Raises an exception if two numbers are not equal up to a certain significance.

numpy.testing.assert_array_almost_equal(): Raises an exception if two arrays are not equal up to a specified precision.

numpy.testing.assert_array_almost_equal_nulp(x, y, nulp=1): Compares arrays to their unit of least precision (ULP).

numpy.testing.assert_array_equal(): Raises an exception if two arrays are not equal.

numpy.testing.assert_array_less(): Raises an exception if two arrays do not have the same shape, and the elements of the first array are strictly less than the elements of the second array.

numpy.testing.assert_array_max_ulp(a, b, maxulp=1, dtype=None): Determines whether the array elements differ by, at most, a specified number of ULP.

numpy.testing.assert_equal(): Tests whether two NumPy arrays are equal.

numpy.testing.assert_raises(): Fails if a specified exception is not raised by a callable invoked with defined arguments.

numpy.testing.assert_string_equal(): Asserts that two strings are equal.

numpy.testing.assert_warns(): Fails if a specified warning is not thrown.

numpy.bartlett(M): Returns the Bartlett window with M points. This window is similar to a triangular window.

numpy.random.binomial(n, p, size=None): Draws random samples from the binomial distribution.

numpy.bitwise_and(x1, x2[, out]): Calculates the bit-wise AND of arrays.

numpy.bitwise_xor(x1, x2[, out]): Calculates the bit-wise XOR of arrays.

numpy.blackman(M): Returns a Blackman window with M points, which is close to optimal and a little bit worse than a Kaiser window.

numpy.column_stack(tup): Stacks 1D arrays provided as a tuple column wise.

numpy.concatenate ((a1, a2, ...), axis=0): Concatenates a sequence of arrays.

numpy.convolve(a, v, mode='full'): Computes the linear convolution of 1D arrays.

numpy.dot(a, b, out=None): Calculates the dot product of two arrays.

numpy.diff(a, n=1, axis=-1): Computes the nth difference for a given axis.

numpy.dsplit(ary, indices_or_sections): Splits an array into subarrays along the third axis.

numpy.dstack(tup): Stacks arrays given as a tuple along the third axis.

numpy.eye(N, M=None, k=0, dtype=<type 'float'>): Returns the identity matrix.

numpy.extract(condition, arr): Selects elements of an array using a condition.

numpy.fft.fftshift(x, axes=None): Shifts the zero-frequency component of a signal to the center of the spectrum.

numpy.hamming(M): Returns the Hamming window with M points.

numpy.hanning(M): Returns the Hanning window with M points.

numpy.hstack(tup): Stacks arrays given as a tuple horizontally.

numpy.isreal(x): Returns a Boolean array, where True corresponds to an element of the input array, which is a real number (as opposed to a complex number).

numpy.kaiser(M, beta): Returns a Kaiser window with M points for a given beta parameter.

numpy.load(file, mmap_mode=None): Loads NumPy arrays or pickled objects from .npy, .npz or pickles. A memory-mapped array is stored in the filesystem and doesn't have to be completely loaded in memory. This is especially useful for large arrays.

numpy.loadtxt(fname, dtype=<type 'float'>, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0): Loads data from a text file into a NumPy array.

numpy.lexsort (keys, axis=-1): Sorts using multiple keys.

numpy.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None): Returns evenly spaced numbers over an interval.

numpy.max(a, axis=None, out=None, keepdims=False): Returns the maximum of an array along an axis.

numpy.mean(a, axis=None, dtype=None, out=None, keepdims=False): Calculates the arithmetic mean along the given axis.

numpy.median(a, axis=None, out=None, overwrite_input=False): Calculates the median along the given axis.

numpy.meshgrid(*xi, **kwargs): Returns coordinate matrices for coordinate vectors. For instance:

In: numpy.meshgrid([1, 2], [3, 4])
Out:
[array([[1, 2],
        [1, 2]]), array([[3, 3],
        [4, 4]])]

numpy.min(a, axis=None, out=None, keepdims=False): Returns the minimum of an array along an axis.

numpy.msort(a): Returns a copy of an array sorted along the first axis.

numpy.nanargmax(a, axis=None): Returns the indices of the maximums given an axis ignoring NaNs.

numpy.nanargmin(a, axis=None): Returns the indices of the minimums given an axis ignoring NaNs.

numpy.nonzero(a): Returns indices of non-zero array elements.

numpy.ones(shape, dtype=None, order='C'): Creates a NumPy array of specified shape and data type, containing 1s.

numpy.piecewise(x, condlist, funclist, *args, **kw): Evaluates a function piecewise.

numpy.polyder(p, m=1): Differentiates a polynomial to a given order.

numpy.polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False): Performs a least squares polynomial fit.

numpy.polysub(a1, a2): Subtracts polynomials.

numpy.polyval(p, x): Evaluates a polynomial at specified values.

numpy.prod(a, axis=None, dtype=None, out=None, keepdims=False): Returns the product of array elements over a specified axis.

numpy.ravel(a, order='C'): Flattens an array or returns a copy if necessary.

numpy.reshape(a, newshape, order='C'): Changes the shape of a NumPy array.

numpy.row_stack(tup): Stacks arrays row wise.

numpy.save(file, arr): Saves a NumPy array to a file in the NumPy .npy format.

numpy.savetxt(fname, X, fmt='%.18e', delimiter=' ', newline='\n', header='', footer='', comments='# '): Saves a NumPy array to a text file.

numpy.sinc(a): Computes the sinc function.

numpy.sort_complex(a): Sorts array elements with the real part first, then followed by the imaginary part.

numpy.split(a, indices_or_sections, axis=0): Splits an array into subarrays.

numpy.std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False): Returns the standard deviation along the given axis.

numpy.take(a, indices, axis=None, out=None, mode='raise'): Selects elements from an array using specified indices.

numpy.vsplit(a, indices_or_sections): Splits an array into subarrays vertically.

numpy.vstack(tup): Stacks arrays vertically.

numpy.where(condition, [x, y]): Selects array elements from input arrays based on a Boolean condition.

numpy.zeros(shape, dtype=float, order='C'): Creates a NumPy array of specified shape and data type, containing zeros.