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, 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# 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_matOutput:
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)