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likhitha manikonda
likhitha manikonda

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NumPy in Python

#python #beginners #tutorial

If you're new to Python and want to learn how to work with numbers and data efficiently, NumPy is your best friend. This guide will walk you through the basics of NumPy in the simplest way possible—with clear explanations and code examples.

📌 What is NumPy?

NumPy stands for Numerical Python. It’s a Python library that makes it easy to work with arrays (like lists of numbers) and perform mathematical operations on them quickly and efficiently.

🛠️ How to Install NumPy

Before using NumPy, you need to install it. Open your terminal or command prompt and type:

pip install numpy
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If you're using Anaconda (a Python distribution), you can use:

conda install numpy
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🧱 Step-by-Step Concepts with Examples

1. Importing NumPy 

import numpy as np
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✅ This line tells Python to load the NumPy library and give it a nickname np so we can use it easily later.

2. Creating Arrays 

import numpy as np

# Creating a 1D array (like a list)
arr1 = np.array([1, 2, 3])
print(arr1)
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📝 Explanation: This creates a NumPy array with the numbers 1, 2, and 3. Arrays are like lists but more powerful for math operations.

# Creating a 2D array (like a table)
arr2 = np.array([[1, 2, 3], [4, 5, 6]])
print(arr2)
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📝 Explanation: This is a 2D array with 2 rows and 3 columns. Think of it like a spreadsheet.

# Arrays filled with zeros
zeros = np.zeros((2, 3))
print(zeros)
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📝 Explanation: This creates a 2-row, 3-column array filled with zeros.

# Arrays filled with ones
ones = np.ones((3, 2))
print(ones)
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📝 Explanation: This creates a 3-row, 2-column array filled with ones.

# Array with a range of numbers
range_arr = np.arange(0, 10, 2)
print(range_arr)
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📝 Explanation: This creates an array starting from 0 to 10 (not including 10), with steps of 2. So the output will be [0 2 4 6 8].

3. Array Properties 

print(arr2.shape)  # (2, 3)
print(arr2.ndim)   # 2
print(arr2.dtype)  # int64
print(arr2.size)   # 6
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📝 Explanation:

  • shape: tells you the size (rows, columns).
  • ndim: number of dimensions (1D, 2D, etc.).
  • dtype: data type of elements (e.g., integers).
  • size: total number of elements in the array.

4. Indexing and Slicing 

print(arr2[0, 1])  # Output: 2
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📝 Explanation: This gets the value in the 1st row and 2nd column (remember, Python starts counting from 0).

print(arr2[:, 1])  # Output: [2 5]
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📝 Explanation: This gets the 2nd column from all rows.

print(arr2[1])  # Output: [4 5 6]
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📝 Explanation: This gets the entire 2nd row.

5. Math with Arrays 

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])

print(a + b)  # Output: [5 7 9]
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📝 Explanation: Adds each element from a and b together: 1+4, 2+5, 3+6.

print(a * 2)  # Output: [2 4 6]
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📝 Explanation: Multiplies each element in a by 2.

print(np.dot(a, b))  # Output: 32
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📝 Explanation: This is the dot product: 1*4 + 2*5 + 3*6 = 32.

6. Useful Functions 

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

print(np.sum(arr))       # Output: 10
print(np.mean(arr))      # Output: 2.5
print(np.max(arr))       # Output: 4
print(np.min(arr))       # Output: 1
print(np.std(arr))       # Output: 1.118...
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📝 Explanation:

  • sum: adds all numbers.
  • mean: average.
  • max: largest number.
  • min: smallest number.
  • std: standard deviation (how spread out the numbers are).

7. Reshaping Arrays 

arr = np.arange(12)
reshaped = arr.reshape(3, 4)
print(reshaped)
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📝 Explanation: arange(12) creates an array from 0 to 11 as [0,1,2,3,4,5,6,7,8,9,10]. reshape(3, 4) turns it into a 3-row, 4-column array.

Output:

[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]
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8. Random Numbers 

rand_arr = np.random.rand(2, 3)
print(rand_arr)
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📝 Explanation: Creates a 2x3 array with random numbers
between 0 and 1.

🧠 Final Tips

  • NumPy is great for data analysis, machine learning, and scientific computing.
  • Arrays are faster and more efficient than regular Python lists.
  • Practice is key! Try changing the numbers and shapes to see what happens.

If you have any questions, suggestions, or corrections, please feel free to leave a comment. Your feedback helps me improve and create more accurate content.

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