Arrays

Introduction to Python for Finance

Adina Howe

Instructor

Installing packages

pip3 install package_name_here

pip3 install numpy

import numpy

import numpy

my_array = numpy.array([0, 1, 2, 3, 4])

print(my_array)

[0, 1, 2, 3, 4]

print(type(my_array))

<class 'numpy.ndarray'>

import package_name
package_name.function_name(...)

import numpy as np
my_array = np.array([0, 1, 2, 3, 4])
print(my_array)

[0, 1, 2, 3, 4]

Arrays can handle very large datasets efficiently
- Computationally-memory efficient
- Faster calculations and analysis than lists
- Diverse functionality (many functions in Python packages)

my_array = np.array([3, 'is', True])

print(my_array)

['3' 'is' 'True']

my_list = [3, 'is', True]

print(my_list)

[3, 'is', True]

import numpy as np

array_A = np.array([1, 2, 3])
array_B = np.array([4, 5, 6])

print(array_A + array_B)

[5 7 9]

list_A = [1, 2, 3]
list_B = [4, 5, 6]

print(list_A + list_B)

[1, 2, 3, 4, 5, 6]

import numpy as np

months_array = np.array(['Jan', 'Feb', 'March', 'Apr', 'May'])

print(months_array[3])

Apr

print(months_array[2:5])

['March' 'Apr' 'May']

import numpy as np

months_array = np.array(['Jan', 'Feb', 'March', 'Apr', 'May'])

print(months_array[0:5:2])

['Jan' 'March' 'May']

Introduction to Python for Finance