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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)