Show Menu
Cheatography

Import Statement

import numpy as np

Creating Arrays

# Create a numpy array
array_1 = np.array([92, 94, 88, 91, 87])

# Create a numpy array from a CSV
test_2 = np.genfromtxt('test_2.csv', delimiter=',')

# Create a two-dimensional array
test_1 = np.array([92, 94, 88, 91, 87])
test_2 = np.array([79, 100, 86, 93, 91])
test_3 = np.array([87, 85, 72, 90, 92])

np.array([[92, 94, 88, 91, 87],
          [79, 100, 86, 93, 91],
          [87, 85, 72, 90, 92]])

Operations with Arrays

arr = [1, 2, 3, 4, 5]

# Adding 3 to each entry
>>> a = np.array(arr)
>>> a_plus_3 = a + 3

# Adding arrays
>>> a = np.array([1, 2, 3, 4, 5])
>>> b = np.array([6, 7, 8, 9, 10])
>>> c = a + b

# Logical Operations
>>> a = np.array([10, 2, 2, 4, 5, 3, 9, 8, 9, 7])

>>> a > 5
array([True, False, False, False, False, False, True, True, True, True], dtype=bool)

>>> a[a > 5]
array([10, 9, 8, 9, 7])

>>> a[(a > 5) | (a < 2)]
array([10, 9, 8, 9, 7])

-> c: array([ 7, 9, 11, 13, 15])

Selecting from Arrays (1 Dimension)

a = np.array([5, 2, 7, 0, 11])

>>> a[0]
-> 5

>>> a[-1]
-> 11

>>> a[-2]
-> 0

>>> a[0:5:2]
-> *array([5, 7, 11])

>>> a[1:3]
-> array([2, 7])

>>> a[:3]
-> array([5, 2, 7])

>>> a[-3:]
-> array([7, 0, 11])

Selecting from Arrays (2 Dimens­ions)

-> Basic Procedure a[row,column]

a = np.array([[32, 15, 6, 9, 14],
              [12, 10, 5, 23, 1],
              [2, 16, 13, 40, 37]])

# selects the first column
>>> a[:,0]
-> array([32, 12, 2])

# selects the second row
>>> a[1,:]
-> array([12, 10, 5, 23, 1])

# selects the first three elements of the first row
>>> a[0,0:3]
-> array([32, 15, 6])

Selecting Elements

np.count_nonzero(poodle_colors == "brown")
-> returns the number of poodles with brown hair
 

Mean and Logical Operations (On arrays)

np.mean(array > 8)
-> returns the percentage of values in the array that meet the criteria
We can use np.m­ean to calculate the percent of array elements that have a certain property.

Mean over 2 Dimens­ional Arrays

>>> ring_toss = np.array([[1, 0, 0],
                          [0, 0, 1],
                          [1, 0, 1]])

>>> np.mean(ring_toss)
0.44 -> Overall Average

>>> np.mean(ring_toss, axis=1)
array([ 0.33, 0.33, 0.67]) -> Average per row

>>> np.mean(ring_toss, axis=0)
array([ 0.67, 0. , 0.67]) -> Average per column

Dealing with Outliers

# Sort the Dataset
np.sort(array)
-> Outliers are clearly visible now

Percen­tiles

d = np.array([1, 2, 3, 4, 4, 4, 6, 6, 7, 8, 8])
np.percentile(d, 40)
-> 4.00

Shape (dimen­sions) of an array

The .shape attribute for NumPy arrays returns the dimensions of the array. If array has n rows × m columns, then array.s­hape returns (n, m).
 

Generate Normal Distri­bution

# Generate own Normal Distribution Set
-> np.random.normal(loc, scale, size)
loc: the mean for the normal distribution
scale: the standard deviation of the distribution
size: the number of random numbers to generate
68% of our samples will fall between +/- 1 standard deviat­ion of the mean

95% of our samples will fall between +/- 2 standard deviat­ions of the mean

99.7% of our samples will fall between +/- 3 standard deviat­ions of the mean

Binomial Distri­bution

np.random.binomial(N, P, size)

N: The number of samples or trials
P: The probability of success
size: The number of experiments

#Basketball Example
 Let's generate 10,000 "experiments"
 N = 10 shots
 P = 0.30 (30% he'll get a free throw)
-> a = np.random.binomial(10, 0.3, 10000)

# Probability that he makes 4 Shots:
prob = np.mean(a == 4)
The binomial distri­but­ion can help us. It tells us how likely it is for a certain number of “succe­sses” to happen, given a probab­ility of success and a number of trials.

Help Us Go Positive!

We offset our carbon usage with Ecologi. Click the link below to help us!

We offset our carbon footprint via Ecologi
 

Comments

No comments yet. Add yours below!

Add a Comment

Your Comment

Please enter your name.

    Please enter your email address

      Please enter your Comment.

          More Cheat Sheets by Justin1209