import numpy as np
digits = np.array(range(10))
digits.dtype

# Operations
digits.shape

np.sin(digits)

# Creation
np.arange(3)

np.ones(3)

np.zeros(3)

np.eye(3, 5)

np.diag(range(3))

np.linspace(0, 10, num=15)

# Random Creation
np.random.random(3)  # between [0,1)

rng = np.random.default_rng()
rng.integers(low=11, high=15, size=5)  # 5 between [11,15)

np.random.bytes(5)  # 5 bytes

np.random.randn(3)  # normal distribution

X = np.linspace(0, 110000, 1000)

# to make this notebook's output identical at every run
np.random.seed(42)

# Divide by 1.5 to limit the number of income categories
housing["income_cat"] = np.ceil(housing["median_income"] / 1.5)
# Label those above 5 as 5
housing["income_cat"].where(housing["income_cat"] < 5, 5.0, inplace=True)

housing["income_cat"].value_counts()

# Translates slice objects to concatenation along the first axis.
example_images = np.r_[X[:12000:600], X[13000:30600:600], X[30600:60000:590]]

# Vectorized operations
+, -, *, /, ** (exponentiation) work on 2 arrays of the same length, or an array and a scalar
If a is an array and b is a scalar, both a ** b and b ** a will work.
& (and), | (or), ~ (not) logical operators work on binary arrays
& (and), | (or), ~ (not) bitwise operators work on integer arrays (less commonly used)
>, >=, <, <=, ==, != comparison operators result in an array of boolean

# Index arrays
a = np.array([1, 2, 3, 4])
b = np.array([True, True, False, False])
a[b] == [1, 2]

# += operates in-place while + does not

# Modifying an array slice modifies the original array (different from native python arrays!)
# A NumPy array slice is a view on the original array

# Use random.randint() to create a fair coin
# Use random.choice() to create a biased coin

# get the 95% confidence interval
np.percentile(hnd, 2.5), np.percentile(hnd, 97.5)

# create a bell curve
np.random.normal(loc=mean, scale=stdev, size=1000)

# compute the p-value
obs_diff = experiment_ctr - control_ctr # from full population
(null_vals > obs_diff).mean()

# ⚠ element-wise matrix multiplication (Why are you doing this?)
a * b
np.multiply(a, b)

# ⚠ dot product (This API is slippery, read docs first.)
np.dot(a, b)
a.dot(b)

# matrix product - you probably want this one
a @ b
np.matmul(a, b)

# matrix transpose
m.T
m.transpose()

# create row matrix
features = [ 0.49671415, -0.1382643 ,  0.64768854]
np.array(features, ndmin=2)

# create column matrix
np.array(features, ndmin=2).T