https://www.udemy.com/python-for-data-science-and-machine-learning-bootcamp/learn/v4/overview
Answers by Jenifer Yoon
Date 3/27/2019
import numpy as np
np.zeros(10)
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
np.ones(10)
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
np.ones(10)*5
array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])
np.arange(10, 51)
array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50])
np.arange(10, 51, 2)
array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50])
mat = np.arange(0, 9)
mat.reshape(3, 3)
array([[0, 1, 2], [3, 4, 5], [6, 7, 8]])
np.eye(3)
array([[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]])
np.eye(3, 3)
array([[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]])
np.random.rand(1)
# np.random.rand(d0, d1, d2, ...) selects a random number from [0, 1) range. Paramters are dimensions.]
array([0.27345171])
help(np.random.rand)
Help on built-in function rand: rand(...) method of mtrand.RandomState instance rand(d0, d1, ..., dn) Random values in a given shape. Create an array of the given shape and populate it with random samples from a uniform distribution over ``[0, 1)``. Parameters ---------- d0, d1, ..., dn : int, optional The dimensions of the returned array, should all be positive. If no argument is given a single Python float is returned. Returns ------- out : ndarray, shape ``(d0, d1, ..., dn)`` Random values. See Also -------- random Notes ----- This is a convenience function. If you want an interface that takes a shape-tuple as the first argument, refer to np.random.random_sample . Examples -------- >>> np.random.rand(3,2) array([[ 0.14022471, 0.96360618], #random [ 0.37601032, 0.25528411], #random [ 0.49313049, 0.94909878]]) #random
np.random.rand(25)
array([0.52566673, 0.88139993, 0.24887143, 0.2956426 , 0.17621077, 0.98663012, 0.43747115, 0.64025843, 0.02924842, 0.75702585, 0.51010062, 0.09094537, 0.1174884 , 0.53385174, 0.64869243, 0.37622658, 0.65330289, 0.41197009, 0.30165867, 0.63191869, 0.39511783, 0.81510441, 0.75499403, 0.21194784, 0.94456316])
np.random.rand(25)
array([0.7559873 , 0.08741562, 0.43933887, 0.13731684, 0.05901282, 0.86639925, 0.48606921, 0.33415014, 0.23369125, 0.79805061, 0.41485206, 0.6283579 , 0.84090133, 0.31386089, 0.13334902, 0.38771005, 0.18281798, 0.79785461, 0.82618869, 0.90973119, 0.28070039, 0.53574626, 0.56157786, 0.07180881, 0.8936017 ])
array([[ 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ], [ 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 ], [ 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 ], [ 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 ], [ 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 ], [ 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 ], [ 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 ], [ 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 ], [ 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 ], [ 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1. ]])
np.arange(0.01, 1.01, .01).reshape(10, 10)
array([[0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ], [0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 ], [0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 ], [0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 ], [0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 ], [0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 ], [0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 ], [0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 ], [0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 ], [0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1. ]])
np.linspace(0, 1, 20)
array([0. , 0.05263158, 0.10526316, 0.15789474, 0.21052632, 0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421, 0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211, 0.78947368, 0.84210526, 0.89473684, 0.94736842, 1. ])
np.linspace(0, 1, 20, endpoint=True)
# linspace returns an array of evenly spaced numbers. (start, spop, numbers, default include endpoint=True)
array([0. , 0.05263158, 0.10526316, 0.15789474, 0.21052632, 0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421, 0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211, 0.78947368, 0.84210526, 0.89473684, 0.94736842, 1. ])
Now you will be given a few matrices, and be asked to replicate the resulting matrix outputs:
mat = np.arange(1,26).reshape(5,5)
mat
array([[ 1, 2, 3, 4, 5], [ 6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25]])
# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW
# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T
# BE ABLE TO SEE THE OUTPUT ANY MORE
array([[12, 13, 14, 15], [17, 18, 19, 20], [22, 23, 24, 25]])
mat[2:6, 1:6]
# Indexing is same as list indexing.
array([[12, 13, 14, 15], [17, 18, 19, 20], [22, 23, 24, 25]])
# Try negative indexing.
mat[-3:, -4:]
array([[12, 13, 14, 15], [17, 18, 19, 20], [22, 23, 24, 25]])
# Negative indexing. Always counts from top-left. [Row start:end, Col start:end]
mat[:-3, :-4]
array([[1], [6]])
# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW
# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T
# BE ABLE TO SEE THE OUTPUT ANY MORE
mat[3, -1]
20
20
# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW
# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T
# BE ABLE TO SEE THE OUTPUT ANY MORE
mat
array([[ 1, 2, 3, 4, 5], [ 6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25]])
# mat 2nd column, 0 to 2 rows.
mat[:3, 1].reshape(3, 1)
array([[ 2], [ 7], [12]])
array([[ 2], [ 7], [12]])
# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW
# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T
# BE ABLE TO SEE THE OUTPUT ANY MORE
mat[-1, :]
array([21, 22, 23, 24, 25])
array([21, 22, 23, 24, 25])
# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW
# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T
# BE ABLE TO SEE THE OUTPUT ANY MORE
mat[-2:, :] # Last two rows, all columns.
array([[16, 17, 18, 19, 20], [21, 22, 23, 24, 25]])
array([[16, 17, 18, 19, 20], [21, 22, 23, 24, 25]])
mat.sum() # Method applied to mat object?
# mat is an ndarray object, a built-in numpy class.
# This class object has a method .sum() and .std().
325
325
mat.std()
7.211102550927978
7.2111025509279782
sum(mat)
# sum() function applied to mat ndarray object produce column sums by default.
array([55, 60, 65, 70, 75])
sum(mat[:3, -2:])
# [row 0 to 2, col 4 to 5]
array([27, 30])
## mat.sum(axis=0)
# Columns axis=0, not 1.
x = mat.sum(axis=0)
y = mat.sum(axis=1)
print(x, y)
[55 60 65 70 75] [ 15 40 65 90 115]
array([55, 60, 65, 70, 75])