# 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 its `ndarray`, which is an `N-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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