Two Dimensional Arrays

Introduction to Python for Finance

Adina Howe

Instructor

Two-dimensional arrays

import numpy as np

months = [1, 2, 3]
prices = [238.11, 237.81, 238.91]
cpi_array = np.array([months, prices])

print(cpi_array)
[[   1.      2.      3.  ]
 [ 238.11  237.81  238.91]]
Introduction to Python for Finance

Array Methods

print(cpi_array)
[[   1.      2.      3.  ]
 [ 238.11  237.81  238.91]]

.shape gives you dimensions of the array

print(cpi_array.shape)
(2, 3)

.size gives you total number of elements in the array

print(cpi_array.size)
6
Introduction to Python for Finance

Array Functions

import numpy as np

prices = [238.11, 237.81, 238.91]
prices_array = np.array(prices)

np.mean() calculates the mean of an input

print(np.mean(prices_array))
238.27666666666667

np.std() calculates the standard deviation of an input

print(np.std(prices_array))
0.46427960923946671
Introduction to Python for Finance

The `arange()` function

numpy.arange() creates an array with start, end, step

import numpy as np

months = np.arange(1, 13)

print(months)
[ 1  2  3  4  5  6  7  8  9 10 11 12]
months_odd = np.arange(1, 13, 2)

print(months_odd)
[ 1  3  5  7  9 11]
Introduction to Python for Finance

The `transpose()` function

numpy.transpose() switches rows and columns of a numpy array

print(cpi_array)
[[   1.      2.      3.  ]
 [ 238.11  237.81  238.91]]
cpi_transposed = np.transpose(cpi_array)
print(cpi_transposed)
[[   1.    238.11]
 [   2.    237.81]
 [   3.    238.91]]
Introduction to Python for Finance

Array Indexing for 2D arrays

print(cpi_array)
[[   1.      2.      3.  ]
 [ 238.11  237.81  238.91]]
# row index 1, column index 2 
cpi_array[1, 2]
238.91
# all row slice, third column
print(cpi_array[:, 2])
[   3.    238.91]
Introduction to Python for Finance

Let's practice!

Introduction to Python for Finance

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