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]]

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)

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

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]

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]]

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