📊 Python NumPy Basics

NumPy stands for Numerical Python. NumPy is a powerful Python library used for numerical computations.

NumPy was created in 2005 by Travis Oliphant for working with arrays.

  import numpy as np

  # Creating a NumPy array
  arr = np.array([1, 2, 3, 4, 5])
  print(arr)
            

📊 Which Language is NumPy written in?

NumPy is a Python library and is written partially in Python, but most of the parts that require fast computation are written in C or C++

📊 Installation of NumPy in Python

If you have Python and PIP already installed on a system, then installation of NumPy is very easy. Install it using this command:

  C:\Users\Your Name>pip install numpy
            

📊 Checking NumPy Version in Python

The version string is stored under __version__ attribute.

You can test NumPy installation by running the following command in Python:

  import numpy as np

  print(np.__version__)
            

❗ If You Face Any Problem Installing NumPy

👉 Watch this short video guide: 📺 How to Install NumPy in Python

📊 NumPy Creating Arrays in Python

NumPy is used to work with arrays. The array object in NumPy is called ndarray.

We can create a NumPy ndarray object by using the array() function.

To create an ndarray, we can pass a list, tuple or any array-like object into the array() method, and it will be converted into an ndarray:

Syntax: numpy.array(object, dtype=None, copy=True, order='K', ndmin=0)

  import numpy as np

  arr = np.array([1, 2, 3, 4, 5])

  print(arr)

  print(type(arr))
            

📊 Dimensions in NumPy Arrays

NumPy arrays can have multiple dimensions depending on the data. Let's look at each:

🔹 0-D Array (Scalar) A single value (no dimension)

  import numpy as np
  arr0d = np.array(5)
  print(arr0d)
            

🔹 1-D Array (Vector) A simple list of values

  arr1d = np.array([10, 20, 30])
  print(arr1d)
            

🔹 2-D Array (Matrix) Array of arrays – like rows and columns

  arr2d = np.array([[1, 2], [3, 4]])
  print(arr2d)
            

3-D Array (Tensor) Array of 2D matrices

  arr3d = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
  print(arr3d)
            

🔍 How to Check Number of Dimensions

  print(arr3d.ndim)
            

📊 Difference Between NumPy Array and Python List

📊 NumPy Attributes in Python

After creating a NumPy array, you can check its key properties using built-in attributes.

📊 NumPy Array Creation Methods in Python

NumPy Array Creation Methods are used to create arrays in various ways — such as from existing data, with fixed values, numerical ranges, or random values.

📊 Indexing of NumPy Arrays in Python

Indexing means accessing individual elements in a NumPy array using their position (index).

  import numpy as np

  # 1D Array
  a = np.array([10, 20, 30, 40, 50])
  print("1D Array:", a)
  print("a[2] =", a[2])        

  # 2D Array
  b = np.array([[1, 2, 3], [4, 5, 6]])
  print("\n2D Array:\n", b)
  print("b[1, 2] =", b[1, 2])  

  # 3D Array
  c = np.array([
      [[1, 2], [3, 4]],
      [[5, 6], [7, 8]]
  ])
  print("\n3D Array:\n", c)
  print("c[1, 0, 1] =", c[1, 0, 1])  
            

📊 Slicing of NumPy Arrays in Python

Slicing means accessing a group (range) of elements from the array using a specific pattern or range of indices.

Syntax: array[start : stop : step]

  import numpy as np

  # 1D Array
  a = np.array([10, 20, 30, 40, 50])
  print("1D Array:", a)
  print("a[1:4] =>", a[1:4])       # Output: [20 30 40]
  print("a[::2] =>", a[::2])       # Output: [10 30 50]

  # 2D Array
  b = np.array([
      [1, 2, 3],
      [4, 5, 6],
      [7, 8, 9]
  ])
  print("\n2D Array:\n", b)
  print("b[0:2, 1:] =>\n", b[0:2, 1:])  # Output: [[2 3] [5 6]]
  print("b[:, ::2] =>\n", b[:, ::2])   # Output: [[1 3] [4 6] [7 9]]

  # 3D Array
  c = np.array([
      [[11, 12], [13, 14]],
      [[15, 16], [17, 18]]
  ])
  print("\n3D Array:\n", c)
  print("c[0, :, :] =>\n", c[0, :, :])     # First block
  print("c[:, 1, :] =>\n", c[:, 1, :])     # Second row from each block
  print("c[:, :, 1] =>\n", c[:, :, 1])     # Second column from each block
            

📊 Joining NumPy Arrays in Python

In NumPy, joining means combining two or more arrays into one. This can be done using several functions.

  import numpy as np
              
  # Define two 1D arrays
  a = np.array([1, 2])
  b = np.array([3, 4])
              
  # 1. np.concatenate() — join arrays along existing axis
  c1 = np.concatenate((a, b))
  print("concatenate:", c1)  # Output: [1 2 3 4]
              
  # 2. np.hstack() — stack arrays horizontally (like concatenate for 1D)
  c2 = np.hstack((a, b))
  print("hstack:    ", c2)  # Output: [1 2 3 4]
              
  # 3. np.vstack() — stack arrays vertically (row-wise)
  c3 = np.vstack((a, b))
  print("vstack:    \n", c3)  
  # Output:
  # [[1 2]
  #  [3 4]]
            

📊 Splitting NumPy Arrays in Python

Splitting means dividing a NumPy array into multiple smaller arrays. You can split arrays either equally or at specific positions.

  import numpy as np

  arr = np.array([10, 20, 30, 40, 50, 60])

  # Equal split into 3 parts
  print("np.split:", np.split(arr, 3))

  # Unequal split into 4 parts
  print("np.array_split:", np.array_split(arr, 4))

  # 2D array for hsplit and vsplit
  arr2d = np.array([[1, 2, 3, 4],
                    [5, 6, 7, 8]])

  # Horizontal split (by columns)
  print("np.hsplit:", np.hsplit(arr2d, 2))

  # Vertical split (by rows)
  print("np.vsplit:", np.vsplit(arr2d, 2))
            

📊 Arithmetic Operations On NumPy Arrays in Python