Numpy is a python library which is known for Numerical Python. It provides powerful datastructure called ndarray (N-dimensional Array).
For example:
We have a array
numbers = [1, 5, 3, 6, 8]To use Numpy, first import it.
import numpy as np
numbers = [1, 5, 3, 6, 8]
num = np.array[numbers]
print(num)
Why use Numpy?
Looping through arrays can be challenging for programmers. However, with NumPy, we can perform various types of calculations on entire arrays without using explicit loops. This is called vectorization.
For Example:
import numpy as np
number = [1, 2, 3, 4, 5]
addition = np.array(number)
print(addition+ 5)addition = np.array([1, 2, 3, 4, 5])
print(addition+ 5)
More examples:
print(addition * 2) # Multiply each element by 2
print(addition ** 2) # Square each element
Read: Python Exercises for Beginners
How to Install Numpy
If you have already installed Python, open Command Prompt and run the following command to install NumPy:
python -m pip install numpyCheck NumPy Installation and Version
After the installation is complete, open Python and run:
import numpy as np
print(np.__version__)If NumPy is installed successfully, you will see a version number, such as:
2.3.4This confirms that NumPy has been successfully installed and is ready to use in your Python programs.
Numpy Array
NumPy uses one-dimensional, two-dimensional, and multidimensional arrays to store, organize, and perform calculations on multiple elements.
One Dimensional Array
A single list is use as one dimensional array.
number = [5, 10, 15, 20]Two Dimensional Array
It contains elements arranged in rows and columns. It can be represented like a table or matrix.
number [[5, 10, 15], [20, 25, 30]]Multi-DimensionalArray
It is commonly used to store data in multiple layers like collection of 2D arrays.
import numpy as np
numbers = np.array([
[[1, 2, 3],
[4, 5, 6]],
[[7, 8, 9],
[10, 11, 12]
]])
print(numbers)
Note: Array indexing start from 0.
Max(), Min() and Mean()
These function widely use get fast result without multiple line code. max (): Maximum, min(): Minimum, and mean(): Mean().
import numpy as np
numbers = np.array([43, 52, 23, 44, 15])
print(numbers.mean())
print(numbers.max())
print(numbers.min())
Similarly we can calcuate: sum(), substract(), divide(), and multiply().
print("Total:", numbers.sum())
print("Substract:", np.substract(50, 14)) # value given manually,Not from Array
print("Multiply:", np.multiply(55, 5)) # value given manually, Not from Array
print("Divide:", numbers/5)Indexing and Slicing
Array indexing starts with zero (0), which means the first element has index 0, the second has index 1, and so on. While slicing allows us to extract a part of an Array.
import numpy as np
numbers = np.array([21, 23, 45, 63, 34, 67])
print(numbers[2])
print(numbers[1:4])
print(numbers[:2]) #first 2 elements
print(numbers[2:]) #from index 2 to last
print(numbers[-2]) #second last element
print(numbers[::2]) #every second elementShape, Size and ndim
This is widely use in data analysis and they are properties not function.shape - extract total element and row and columnsize - total elementsndim - dimensionimport numpy as np
numbers= np.array([2,4,6,3,7])
print(numbers.shape)
print(numbers.size)
print(numbers.ndim)Note: shape output changed according to the dimension and number of element in each dimension.More example:Two demension:import numpy as np
numbers= np.array([[2,4,3],[5, 6, 7]])
print(numbers.shape) # output: (2, 3) - 2 row, 3 column
print(numbers.size) #output: 6 - 2 x 3
print(numbers.ndim) #output: 2
Three Dimension
import numpy as np
numbers= np.array([
[[3,7],[4,2]],
[[8,1],[4,3]]
])
print(numbers.shape) #output(2, 2, 2) - 2 block, 2 row and 2 column
print(numbers.size) #output: 8 - 2 x 2 x 2
print(numbers.ndim) #output: 3Now practice 2D and 3D array so you can proficient in it.Reshape ()
Using reshape(), we can change the structure of an Array without changing in value.import numpy as np numbers = np.array([10, 20, 30, 40, 50, 60])
print(numbers.size) new_data = numbers.reshape(2, 3) print(new_data) print(new_data.size)Important point:Output:6[10, 20, 30,], [40, 50, 60]6You can see the shape value of the previous and after is 6. So whenever you reshape the array structure then the total number of value must be the same otherwise you will get an error.
