Intro to Python for Data Science Learning 7 - 2D NumPy Arrays

2D NumPy Arrays

from:https://campus.datacamp.com/courses/intro-to-python-for-data-science/chapter-4-numpy?ex=9

• Your First 2D NumPy Array

# Create baseball, a list of lists
baseball = [[180, 78.4],
[215, 102.7],
[210, 98.5],
[188, 75.2]]

# Import numpy
import numpy as np

# Create a 2D numpy array from baseball: np_baseball
np_baseball = np.array(baseball)

# Print out the type of np_baseball
print(type(np_baseball))

# Print out the shape of np_baseball. Use np_baseball.shape.
print(np_baseball.shape)

• Baseball data in 2D form

Change list to 2D array, it will get a form with two columns.

# baseball is available as a regular list of lists

baseball = [[74, 180], [74, 215], [72, 210], [72, 210], [73, 188], [69, 176], [69, 209], [71, 200], [76, 231], [71, 180], [73, 188], [73, 180], [74, 185], [74, 160], [69, 180], [70, 185], [73, 189], [75, 185], [78, 219], [79, 230], [76, 205], [74, 230], [76, 195], [72, 180], [71, 192], [75, 225], [77, 203], [74, 195], [73, 182], [74, 188], [78, 200], [73, 180], [75, 200], [73, 200], [75, 245], [75, 240], [74, 215], [69, 185], [71, 175], [74, 199], [73, 200], [73, 215], [76, 200], [74, 205], [74, 206], [70, 186], [72, 188], [77, 220], [74, 210], [70, 195], [73, 200], [75, 200], [76, 212], [76, 224], [78, 210], [74, 205], [74, 220], [76, 195], [77, 200], [81, 260], [78, 228], [75, 270], [77, 200], [75, 210], [76, 190], [74, 220], [72, 180], [72, 205], [75, 210], [73, 220], [73, 211], [73, 200], [70, 180], [70, 190], [70, 170], [76, 230], [68, 155], [71, 185], [72, 185], [75, 200], [75, 225], [75, 225], [75, 220], [68, 160], [74, 205], [78, 235], [71, 250], [73, 210], [76, 190], [74, 160], [74, 200], [79, 205], [75, 222], [73, 195], [76, 205], [74, 220], [74, 220], [73, 170], [72, 185], [74, 195], [73, 220], [74, 230], [72, 180], [73, 220], [69, 180], [72, 180], [73, 170], [75, 210], [75, 215], [73, 200], [72, 213], [72, 180], [76, 192], [74, 235], [72, 185], [77, 235], [74, 210], [77, 222], [75, 210], [76, 230], [80, 220], [74, 180], [74, 190], [75, 200], [78, 210], [73, 194], [73, 180], [74, 190], [75, 240], [76, 200], [71, 198], [73, 200], [74, 195], [76, 210], [76, 220], [74, 190], [73, 210], [74, 225], [70, 180], [72, 185], [73, 170], [73, 185], [73, 185], [73, 180], [71, 178], [74, 175], [74, 200], [72, 204], [74, 211], [71, 190], [74, 210], [73, 190], [75, 190], [75, 185], [79, 290], [73, 175], [75, 185], [76, 200], [74, 220], [76, 170], [78, 220], [74, 190], [76, 220], [72, 205], [74, 200], [76, 250], [74, 225], [75, 215], [78, 210], [75, 215], [72, 195], [74, 200], [72, 194], [74, 220], [70, 180], [71, 180], [70, 170], [75, 195], [71, 180], [71, 170], [73, 206], [72, 205], [71, 200], [73, 225], [72, 201], [75, 225], [74, 233], [74, 180], [75, 225], [73, 180], [77, 220], [73, 180], [76, 237], [75, 215], [74, 190], [76, 235], [75, 190], [73, 180], [71, 165], [76, 195], [75, 200], [72, 190], [71, 190], [77, 185], [73, 185], [74, 205], [71, 190], [72, 205], [74, 206], [75, 220], [73, 208], [72, 170], [75, 195], [75, 210], [74, 190], [72, 211], [74, 230], [71, 170], [70, 185], [74, 185], [77, 241], [77, 225], [75, 210], [75, 175], [78, 230], [75, 200], [76, 215], [73, 198], [75, 226], [75, 278], [79, 215], [77, 230], [76, 240], [71, 184], [75, 219], [74, 170], [69, 218], [71, 190], [76, 225], [72, 220], [72, 176], [70, 190], [72, 197], [73, 204], [71, 167], [72, 180], [71, 195], [73, 220], [72, 215], [73, 185], [74, 190], [74, 205], [72, 205], [75, 200], [74, 210], [74, 215], [77, 200], [75, 205], [73, 211], [72, 190], [71, 208], [74, 200], [77, 210], [75, 232], [75, 230], [75, 210], [78, 220], [78, 210], [74, 202], [76, 212], [78, 225], [76, 170], [70, 190], [72, 200], [80, 237], [74, 220], [74, 170], [71, 193], [70, 190], [72, 150], [71, 220], [74, 200], [71, 190], [72, 185], [71, 185], [74, 200], [69, 172], [76, 220], [75, 225], [75, 190], [76, 195], [73, 219], [76, 190], [73, 197], [77, 200], [73, 195], [72, 210], [72, 177], [77, 220], [77, 235], [71, 180], [74, 195], [74, 195], [73, 190], [78, 230], [75, 190], [73, 200], [70, 190], [74, 190], [72, 200], [73, 200], [73, 184], [75, 200], [75, 180], [74, 219], [76, 187], [73, 200], [74, 220], [75, 205], [75, 190], [72, 170], [73, 160], [73, 215], [72, 175], [74, 205], [78, 200], [76, 214], [73, 200], [74, 190], [75, 180], [70, 205], [75, 220], [71, 190], [72, 215], [78, 235], [75, 191], [73, 200], [73, 181], [71, 200], [75, 210], [77, 240], [72, 185], [69, 165], [73, 190], [74, 185], [72, 175], [70, 155], [75, 210], [70, 170], [72, 175], [72, 220], [74, 210], [73, 205], [74, 200], [76, 205], [75, 195], [80, 240], [72, 150], [75, 200], [73, 215], [74, 202], [74, 200], [73, 190], [75, 205], [75, 190], [71, 160], [73, 215], [75, 185], [74, 200], [74, 190], [72, 210], [74, 185], [74, 220], [74, 190], [73, 202], [76, 205], [75, 220], [72, 175], [73, 160], [73, 190], [73, 200], [72, 229], [72, 206], [72, 220], [72, 180], [71, 195], [75, 175], [75, 188], [74, 230], [73, 190], [75, 200], [79, 190], [74, 219], [76, 235], [73, 180], [74, 180], [74, 180], [72, 200], [74, 234], [74, 185], [75, 220], [78, 223], [74, 200], [74, 210], [74, 200], [77, 210], [70, 190], [73, 177], [74, 227], [73, 180], [71, 195], [75, 199], [71, 175], [72, 185], [77, 240], [74, 210], [70, 180], [77, 194], [73, 225], [72, 180], [76, 205], [71, 193], [76, 230], [78, 230], [75, 220], [73, 200], [78, 249], [74, 190], [79, 208], [75, 245], [76, 250], [72, 160], [75, 192], [75, 220], [70, 170], [72, 197], [70, 155], [74, 190], [71, 200], [76, 220], [73, 210], [76, 228], [71, 190], [69, 160], [72, 184], [72, 180], [69, 180], [73, 200], [69, 176], [73, 160], [74, 222], [74, 211], [72, 195], [71, 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225], [70, 187], [79, 245], [72, 185], [72, 185], [70, 175], [74, 200], [74, 180], [72, 188], [73, 225], [72, 200], [74, 210], [74, 245], [76, 213], [82, 231], [74, 165], [74, 228], [70, 210], [73, 250], [73, 191], [74, 190], [77, 200], [72, 215], [76, 254], [73, 232], [73, 180], [72, 215], [74, 220], [74, 180], [71, 200], [72, 170], [75, 195], [74, 210], [74, 200], [77, 220], [70, 165], [71, 180], [73, 200], [76, 200], [71, 170], [75, 224], [74, 220], [72, 180], [76, 198], [79, 240], [76, 239], [73, 185], [76, 210], [78, 220], [75, 200], [76, 195], [72, 220], [72, 230], [73, 170], [73, 220], [75, 230], [71, 165], [76, 205], [70, 192], [75, 210], [74, 205], [75, 200], [73, 210], [71, 185], [71, 195], [72, 202], [73, 205], [73, 195], [72, 180], [69, 200], [73, 185], [78, 240], [71, 185], [73, 220], [75, 205], [76, 205], [70, 180], [74, 201], [77, 190], [75, 208], [79, 240], [72, 180], [77, 230], [73, 195], [75, 215], [75, 190], [75, 195], [73, 215], [73, 215], [76, 220], [77, 220], [75, 230], [70, 195], [71, 190], [71, 195], [75, 209], [74, 204], [69, 170], [70, 185], [75, 205], [72, 175], [75, 210], [73, 190], [72, 180], [72, 180], [72, 160], [76, 235], [75, 200], [74, 210], [69, 180], [73, 190], [72, 197], [72, 203], [75, 205], [77, 170], [76, 200], [80, 250], [77, 200], [76, 220], [79, 200], [71, 190], [75, 170], [73, 190], [76, 220], [77, 215], [73, 206], [76, 215], [70, 185], [75, 235], [73, 188], [75, 230], [70, 195], [69, 168], [71, 190], [72, 160], [72, 200], [73, 200], [70, 189], [70, 180], [73, 190], [76, 200], [75, 220], [72, 187], [73, 240], [79, 190], [71, 180], [72, 185], [74, 210], [74, 220], [74, 219], [72, 190], [76, 193], [76, 175], [72, 180], [72, 215], [71, 210], [72, 200], [72, 190], [70, 185], [77, 220], [74, 170], [72, 195], [76, 205], [71, 195], [76, 210], [71, 190], [73, 190], [70, 180], [73, 220], [73, 190], [72, 186], [71, 185], [71, 190], [71, 180], [72, 190], [72, 170], [74, 210], [74, 240], [74, 220], [71, 180], [72, 210], [75, 210], [72, 195], [71, 160], [72, 180], [72, 205], [72, 200], [72, 185], [74, 245], [74, 190], [77, 210], [75, 200], [73, 200], [75, 222], [73, 215], [76, 240], [72, 170], [77, 220], [75, 156], [72, 190], [71, 202], [71, 221], [75, 200], [72, 190], [73, 210], [73, 190], [71, 200], [70, 165], [75, 190], [71, 185], [76, 230], [73, 208], [68, 209], [71, 175], [72, 180], [74, 200], [77, 205], [72, 200], [76, 250], [78, 210], [81, 230], [72, 244], [73, 202], [76, 240], [72, 200], [72, 215], [74, 177], [76, 210], [73, 170], [76, 215], [75, 217], [70, 198], [71, 200], [74, 220], [72, 170], [73, 200], [76, 230], [76, 231], [73, 183], [71, 192], [68, 167], [71, 190], [71, 180], [74, 180], [77, 215], [69, 160], [72, 205], [76, 223], [75, 175], [76, 170], [75, 190], [76, 240], [72, 175], [74, 230], [76, 223], [74, 196], [72, 167], [75, 195], [78, 190], [77, 250], [70, 190], [72, 190], [79, 190], [74, 170], [71, 160], [68, 150], [77, 225], [75, 220], [71, 209], [72, 210], [70, 176], [72, 260], [72, 195], [73, 190], [72, 184], [74, 180], [72, 195], [72, 195], [75, 219], [72, 225], [73, 212], [74, 202], [72, 185], [78, 200], [75, 209], [72, 200], [74, 195], [75, 228], [75, 210], [76, 190], [74, 212], [74, 190], [73, 218], [74, 220], [71, 190], [74, 235], [75, 210], [76, 200], [74, 188], [76, 210], [76, 235], [73, 188], [75, 215], [75, 216], [74, 220], [68, 180], [72, 185], [75, 200], [71, 210], [70, 220], [72, 185], [73, 231], [72, 210], [75, 195], [74, 200], [70, 205], [76, 200], [71, 190], [82, 250], [72, 185], [73, 180], [74, 170], [71, 180], [75, 208], [77, 235], [72, 215], [74, 244], [72, 220], [73, 185], [78, 230], [77, 190], [73, 200], [73, 180], [73, 190], [73, 196], [73, 180], [76, 230], [75, 224], [70, 160], [73, 178], [72, 205], [73, 185], [75, 210], [74, 180], [73, 190], [73, 200], [76, 257], [73, 190], [75, 220], [70, 165], [77, 205], [72, 200], [77, 208], [74, 185], [75, 215], [75, 170], [75, 235], [75, 210], [72, 170], [74, 180], [71, 170], [76, 190], [71, 150], [75, 230], [76, 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208], [74, 215], [75, 180], [75, 200], [78, 230], [76, 211], [75, 230], [69, 190], [75, 220], [72, 180], [75, 205], [73, 190], [74, 180], [75, 205], [75, 190], [73, 195]]

# Import numpy package
import numpy as np

# Create a 2D numpy array from baseball: np_baseball
np_baseball = np.array(baseball)

# Print out the shape of np_baseball
print(np_baseball.shape)

• Subsetting 2D NumPy Arrays

If your 2D numpy array has a regular structure, i.e. each row and column has a fixed number of values, complicated ways of subsetting become very easy. Have a look at the code below where the elements "a" and "c" are extracted from a list of lists.

# regular list of lists
x = [["a", "b"], ["c", "d"]]
[x[0][0], x[1][0]]
# numpy
import numpy as np
np_x = np.array(x)
np_x[:,0]

Remember that in Python, the first element is at index 0!

# baseball is available as a regular list of lists

# Import numpy package
import numpy as np

# Create np_baseball (2 cols)
np_baseball = np.array(baseball)

# Print out the 50th row of np_baseball
print(np_baseball[49,:])

# Select the entire second column of np_baseball: np_weight
np_weight = np_baseball[:,1]

# Print out height of 124th player
print(np_baseball[123,0])

• 2D Arithmetic

You can combine matrices with single numbers, with vectors, and with other matrices.Execute the code below in the IPython shell and see if you understand:

import numpy as np
np_mat = np.array([[1, 2],
                   [3, 4],
                   [5, 6]])
np_mat * 2
np_mat + np.array([10, 10])
np_mat + np_mat

Output:

print(np_mat * 2)
[[ 2 4]
[ 6 8]
[10 12]]

print(np_mat * 2)
[[ 2 4]
[ 6 8]
[10 12]]

print(np_mat * 2)
[[ 2 4]
[ 6 8]
[10 12]]

# baseball is available as a regular list of lists
# updated is available as 2D numpy array

# Import numpy package
import numpy as np

# Create np_baseball (3 cols)
np_baseball = np.array(baseball)

# Print out addition of np_baseball and updated
print(np_baseball + updated)

# Create numpy array: conversion. You want to convert the units of height and weight. As a first step, create a numpy array with three values: 0.0254, 0.453592 and 1. Name this array conversion.
conversion = [0.0254,0.453592,1]

# Print out product of np_baseball and conversion
print(np_baseball * conversion)