https://docs.scipy.org/doc/numpy-1.12.0/reference/

Numpy

install Numpy using pip

import numpy as np

ndarray - an N-dimensional array, which describes a collection of “items” of the same type

array(list) # constructor

asarray(a[, dtype, order]) # Convert the input to an array

Constants:

ndarray.shape tuple of array dimensions

ndarray.size number of elements in array

ndarray.itemsize size of one element

ndarray.dtype data type of elements

ndarray.flat 1D iterator over elements of array

Common Functions

np.tolist()

np.reshape(a, (3,2))

np.swapaxes(axis1, axis2)

np.copy()

arange()

Statistics Functions:

np.sum(a, axis)

np.prod

np.min

np.max

np.mean

np.std standard deviation

np.var

np.sort(axis)

Other Functions:

String operations

logical operations - AND, OR, XOR, NOT, >, <, =, ...

trig functions

complex numbers (real + imaginary)

polynomials

2D matrix operations

Fourier transforms

====================================================================================

import numpy as np

x = [0,1,2,3,4,5]

a = np.array(x)

index: a[2]

slice: a[start:stop:step]

a[1:4:2]

a[3:]

a[:3]

a.shape

a.size

a.itemsize

a.dtype

b = np.array([[1,2,3], [4,5,6]])

b.swapaxes(0,1)

a = np.arange(0,6)

a = np.arange(0,6).reshape(2,3)

========================================================================================

import numpy as np

pip install numpy

pip install numpy --upgrade

import numpy as np

a = np.array([2,3,4])

a = np.arange(1, 12, 2) # (from, to, step)

a = np.linspace(1, 12, 6) # (first, last, num_elements) float data type

a.reshape(3,2)

a = a.reshape(3,2)

a.size

a.shape

a.dtype

a.itemsize

# this works:

b = np.array([(1.5,2,3), (4,5,6)])

# but this does not work:

b = np.array(1,2,3) # square brackets are required

a < 4 # prints True/False

a * 3 # multiplies each element by 3

a *= 3 # saves result to a

a = np.zeros((3,4))

a = np.ones((2,3))

a = np.array([2,3,4], dtype=np.int16)

a = np.random.random((2,3))

np.set_printoptions(precision=2, suppress=True) # show 2 decimal places, suppress scientific notation

a = np.random.randint(0,10,5)

a.sum()

a.min()

a.max()

a.mean()

a.var() # variance

a.std() # standard deviation

a.sum(axis=1)

a.min(axis=0)

a.argmin() # index of min element

a.argmax() # index of max element

a.argsort() # returns array of indices that would put the array in sorted order

a.sort() # in place sort

# indexing, slicing, iterating

a = np.arange(10)**2

a[2]

a[2:5]

for i in a:

print (i ** 2)

a[::-1] # reverses array

for i in a.flat:

print (i)

a.transpose()

a.ravel() # flattens to 1D

# read in csv data file

data = np.loadtxt("data.txt", dtype=np.uint8, delimiter=",", skiprows=1 )

# loadtxt does not handle missing values. to handle such exceptions use genfromtxt instead.

data = np.loadtxt("data.txt", dtype=np.uint8, delimiter=",", skiprows=1, usecols=[0,1,2,3])

np.random.shuffle(a)

a = np.random.random(5)

np.random.choice(a)

np.random.random_integers(5,10,2) # (low, high inclusive, size)