Numpy Arrays in Python
Python : Numpy Arrays
What is Numpy Arrays?
Numpy
stands for Numerical Python, and it is a library used for numerical computing with Python. One of the main features of numpy is itsndarray
, which is anN-dimensional
array object that provides a fast and efficient way of working with large datasets.
Creating Numpy Arrays:
To create a numpy array, we first need to import the numpy library.
import numpy as np
Now, we can create an array in various ways:
From a Python List:
arr = np.array([1, 2, 3, 4, 5]) print(arr) # Output: [1 2 3 4 5] arr = np.array([[1, 2, 3], [4, 5, 6]]) print(arr) # Output [[1 2 3] [4 5 6]]
Using Built-in Functions:
# Creating an array of zeros arr_zeros = np.zeros((2, 3)) print(arr_zeros) # Output # [[0. 0. 0.] # [0. 0. 0.]] # Creating an array of ones arr_ones = np.ones((2, 3)) print(arr_ones) # Output # [[1. 1. 1.] # [1. 1. 1.]]
Numpy Array Attributes:
Once we have created a numpy array, we can access its various attributes.
arr = np.array([1, 2, 3, 4, 5])
print(arr.shape)
print(arr.ndim)
print(arr.size)
print(arr.dtype)
# Output
# (5,)
# 1
# 5
# int64
Numpy Array Indexing and Slicing:
We can access the elements of a numpy array using indexing and slicing.
arr = np.array([1, 2, 3, 4, 5])
print(arr[0])
print(arr[-1])
print(arr[1:3])
# Output
# 1
# 5
# [2 3]
arr = np.array([[1, 2, 3], [4, 5, 6]])
print(arr[0, 1])
print(arr[:, 1])
print(arr[1, :2])
# Output
# 2
# [2 5]
# [4 5]
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