NumPy Basics Examples

Short, runnable programs that show numpy basics in action. Read the explanation, press Run, then change the code and try your own ideas. New to the topic? Start with the NumPy Basics lesson.

1Quick ways to create arrays

You do not always need a list to start from. NumPy has helpers for common arrays, and reshape() changes the shape without changing the values.

Quick ways to create arrays

import numpy as np

print(np.zeros(3))
print(np.ones((2, 3)))
print(np.arange(1, 6))
print(np.linspace(0, 1, 5))
print(np.arange(6).reshape(2, 3))

Expected output[0. 0. 0.]
[[1. 1. 1.]
 [1. 1. 1.]]
[1 2 3 4 5]
[0.   0.25 0.5  0.75 1.  ]
[[0 1 2]
 [3 4 5]]

2Indexing and slicing a 2-D array

Use [row, column] to reach one value. A colon on its own means "every row" or "every column", so seats[:, 2] is the whole third column.

Indexing and slicing a 2-D array

import numpy as np

seats = np.arange(1, 13).reshape(3, 4)
print(seats)
print("Row 1:", seats[1])
print("Column 2:", seats[:, 2])
print("Row 2, column 3:", seats[2, 3])
print(seats[:2, 1:3])

Expected output[[ 1  2  3  4]
 [ 5  6  7  8]
 [ 9 10 11 12]]
Row 1: [5 6 7 8]
Column 2: [ 3  7 11]
Row 2, column 3: 12
[[2 3]
 [6 7]]

3Cleaning sensor data with a mask

Real data often marks missing values with a placeholder such as -999. A mask finds them, ~ flips the mask, and assigning through a mask replaces values in place.

Cleaning sensor data with a mask

import numpy as np

# -999 means "sensor error" in this data
readings = np.array([21.5, -999.0, 22.1, 23.4, -999.0, 22.8])
valid = readings != -999.0
print("Valid readings:", valid.sum(), "of", readings.size)

average = readings[valid].mean()
print("Average:", round(average, 2))

readings[~valid] = average
print(readings)

Expected outputValid readings: 4 of 6
Average: 22.45
[21.5  22.45 22.1  23.4  22.45 22.8 ]

4Pass or fail for a whole class with np.where

np.where(condition, a, b) picks a where the condition is true and b elsewhere. A mask made from one array can filter another array of the same length.

Pass or fail for a whole class with np.where

import numpy as np

names = np.array(["Asha", "Ben", "Chen", "Dina", "Eli"])
scores = np.array([78, 45, 91, 52, 38])

print(np.where(scores >= 50, "pass", "fail"))
print("Passed:", names[scores >= 50])
print("Top student:", names[scores.argmax()])
print("Curved:", np.minimum(scores + 10, 100))

Expected output['pass' 'fail' 'pass' 'pass' 'fail']
Passed: ['Asha' 'Chen' 'Dina']
Top student: Chen
Curved: [ 88  55 100  62  48]

Practise next

NumPy Basics exercises → Back to the lesson

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