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Output:
[[[ 1 2 3]
[ 4 5 6]
[ 7 8 9]]
[[10 11 12]
[13 14 15]
[16 17 18]]
[[19 20 21]
[22 23 24]
[25 26 27]]]
In NumPy, arrays are homogeneous, which means all elements in an array must be of the same data type, for example,
integers, floats, etc.
You can install NumPy using pip. For installing NumPy, you need to open your terminal or command prompt and run
the following command:
pip install numpy
NumPy Library in Artificial Intelligence
Let us understand why and where we can use the NumPy library in Artificial Intelligence with the help of an example.
Suppose, you have a dataset containing daily temperature readings from weather stations across different cities. You can
utilise NumPy arrays to efficiently manage and analyse this data.
With NumPy's array operations, you can easily perform the following tasks:
• • Calculating the average temperature for each city over the recorded days.
• • Finding the total temperature recorded for each day across all cities.
• • Determining the overall average temperature across all cities and days.
• • Identifying the highest and lowest temperatures recorded.
NumPy's array operations streamline these computations that enables you to handle large datasets with ease. This
makes NumPy an indispensable tool for processing and analysing data in different fields.
Creating a NumPy Array
NumPy array are created in several ways:
1. Using np.array() to create arrays from lists or tuples
Program 32: To demonstrate the use of np.array() to create arrays from lists or tuples
import numpy as np
# From a list
arr1 = np.array([1, 2, 3, 4, 5])
print(arr1)
# From a tuple
arr2 = np.array((6, 7, 8, 9, 10))
print(arr2)
Python Programming 203

