import numpy as np

a = np.array([1, 2, 3])
print(a)
print(a.shape)
print(a.dtype)

b = np.array([[0, 2, 4], [1, 3, 5]])
print(b)
print(b.shape)
print(b.dtype)

np.zeros(5)

np.ones(shape=(3, 4), dtype=np.int32)

c = b * 0.5
print(c)
print(c.shape)
print(c.dtype)

d = a + c
print(d)

d[0]

d[0, 0]

d[:, 0]

d.sum()

d.mean()

d.sum(axis=0)

d.mean(axis=1)

e = np.arange(12)
print(e)

# f is a view of contents of e
f = e.reshape(3, 4)
print(f)

# Set values of e from index 5 onwards to 0
e[5:] = 0
print(e)

# f is also updated
f

# OWNDATA shows f does not own its data
f.flags

a

b

d

np.concatenate([a, a, a])

# Use broadcasting when needed to do this automatically
np.vstack([a, b, d])

# In machine learning, useful to enrich or 
# add new/concatenate features with hstack
np.hstack([b, d])

%matplotlib inline

import pylab as plt
import seaborn

seaborn.set()

# Create evenly spaced numbers over the specified interval
x = np.linspace(0, 2, 10)
plt.plot(x, 'o-');
plt.show()

# Create sample data, add some noise
x = np.random.uniform(1, 100, 1000)
y = np.log(x) + np.random.normal(0, .3, 1000)

plt.scatter(x, y)
plt.show()