NumPy Cheat Sheet

# *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]])

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])*

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])*

-> **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])*

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

np.**mean**(array > 8)
-> *returns the percentage of values in the array that meet the criteria*

>>> 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*

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

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

# **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

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)