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 twodimensional 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 Arraysarr = [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 Elementsnp.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

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

Shape (dimensions) of an arrayThe .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 Distributionnp.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.

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