numpy

name

numpy = Numeric Python

import

import numpy as pn #typical import

.array() #creates an array

.column_stack() #stacks values

.logical_and() #and in numpy for arrays

.logical_not() #notin numpy for arrays

.logical_or() #or in numpy for arrays

.mean() #calculates the mean i.e. arithmetic average

.median() #calculates the median

.nditer #iterates over a numpy array

.random() #creates random values

  .random.rand() #creates a random number

  .random.randint() #creates a random integer in numpy

  .random.seed(n) #sets seed n for random generator

.shape #gives the dimension of an array

.sum() #sorts values

.std() #standard deviation

.sum() #sums values

.transpose() #transposes a numpy array

you cannot calculate with a list, but you can turn a list into an array, and you can caluclate with arrays

the data types have to be all the same

.array() #the methods can hold as list

  .arry([ 1 , 2 , 3 ]) #creates the array 1, 2, 3 from the list of 1, 2, 3

.corrcoef() #correlation

.nditer #iterates over a numpy array

.shape #gives the dimension of an array

.std() #standard deviation

[ row , col ] #accesses an array by row and column index

  [ : , col ] #accesses all rows for the column

  [ row , : ] #accesses all columns fotr the row

for loop

for value in np.nditer(my_np_array):

  print(value)

np_height_in = numpy.array(np_baseball[:,0]) #fetches the first column over all rows

np.logical_and(myarray > 10, myarray <15) #and between arrays

  myarray[np.logical_and(myarray > 10, myarray <15) #and between arrays] #makes a list of True values

print(np.mean(np_height_in))

syntax example

random_walk = [0]

for event in range(100):

    step = random_walk[-1]

    dice = np.random.randint(1,7)

    if dice <= 2:

        step = step - 1

    elif dice <= 5:

        step = step + 1

    else:

        step = step + np.random.randint(1,7)

    random_walk.append(step)

print(random_walk)

for repeat in range(5):

    random_walk = [0]

    for event in range(100):

        step = random_walk[-1]

        dice = np.random.randint(1,7)

        if dice <= 2:

            step = max(0, step - 1)

        elif dice <= 5:

            step = step + 1

        else:

            step = step + np.random.randint(1,7)

        random_walk.append(step)

    all_walks.append(random_walk)

print(all_walks)