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‎Indexing & Slicing/Indexing_slicing.py‎

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import numpy as np
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# Creating a 1D NumPy array
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arr_1d = np.array([1.4, 2, 3.7, 4, 5, 6])
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# 🔍 NumPy Indexing, Slicing, and Filtering
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# Creating a 2D NumPy array (3x3 matrix)
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arr_2d = np.array([[1, 2, 30],
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[4, 50, 6],
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[70, 8, 9]])
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This repository demonstrates how to access and manipulate data in NumPy arrays using **indexing**, **slicing**, **fancy indexing**, and **boolean masking** techniques. These operations are essential for data manipulation and form the backbone of most data science and numerical computing workflows.
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# ----------------------------------------
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# INDEXING
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# ----------------------------------------
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# Indexing in NumPy:
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# For 1D arrays: arr[index]
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# For 2D arrays: arr[row, column]
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---
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# Example:
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# print(arr_1d[2]) # Access 3rd element (3.7)
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# print(arr_2d[1,2]) # Access value at 2nd row, 3rd column (6)
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## 📘 Table of Contents
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1. [Introduction](#introduction)
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2. [Indexing](#indexing)
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3. [Slicing](#slicing)
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4. [2D Array Slicing](#2d-array-slicing)
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5. [Fancy Indexing](#fancy-indexing)
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6. [Boolean Masking & Filtering](#boolean-masking--filtering)
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7. [Using `np.where`](#using-npwhere)
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8. [Conclusion](#conclusion)
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# ----------------------------------------
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# SLICING
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# ----------------------------------------
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# General form: arr[start:stop:step]
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# Note: 'stop' index is excluded
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---
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first = arr_1d[1:4] # Slicing from index 1 to 3 (i.e., 2, 3.7, 4)
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second = arr_1d[:4] # From start to index 3 (i.e., 1.4 to 4)
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third = arr_1d[::2] # Every second element (step size 2)
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fourth = arr_1d[::-1] # Reversing the array using negative step
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## 📌 Introduction
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print("First slice:", first)
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print("Second slice:", second)
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print("Third slice:", third)
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print("Reversed array:", fourth)
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NumPy is a foundational Python library used for efficient numerical operations. Understanding how to access and modify elements in arrays is key to working with NumPy. This guide walks through examples that explain:
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- Accessing single and multiple values
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- Selecting slices or subarrays
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- Applying conditional logic to filter data
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# ----------------------------------------
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# 2D ARRAY INDEXING AND SLICING
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# ----------------------------------------
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---
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# Accessing a specific row in a 2D array
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# Example: Second row (index 1)
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print('Second row:', arr_2d[1])
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## 🧹 Indexing
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# Accessing a specific column in a 2D array
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# Example: Third column (index 2)
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print('Third column:', arr_2d[:, 2])
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### 1D Array
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```python
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arr[index]
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```
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# Extracting a sub-array using slicing
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# Syntax: arr[row_start:row_end, col_start:col_end]
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# Example: Extracts rows 0–2 and columns 1–2 (excluding row 3 and column 3)
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sub_array = arr_2d[0:3, 1:3]
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print("2D Sliced Sub-array:\n", sub_array)
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### 2D Array
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```python
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arr[row, column]
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```
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🔹 Example:
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```python
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arr_1d = np.array([1.4, 2, 3.7, 4, 5, 6])
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arr_2d = np.array([[1, 2, 30], [4, 50, 6], [70, 8, 9]])
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# ----------------------------------------
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# FANCY INDEXING
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# ----------------------------------------
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# Select multiple specific elements by index
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print("Fancy indexing on 1D:", arr_1d[[0, 2, 4]]) # Fetch elements at index 0, 2, and 4
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print(arr_1d[2]) # Output: 3.7
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print(arr_2d[1, 2]) # Output: 6
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```
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---
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import numpy as np
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## ✂️ Slicing
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# ----------------------------------------
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# BOOLEAN MASKING ON ARRAYS
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# ----------------------------------------
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### Syntax
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```python
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arr[start:stop:step]
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```
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# Boolean masking to extract values based on a condition
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# Returns a 1D array of elements where the condition is True
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print("Boolean masking on 2D (elements > 25):", arr_2d[arr_2d > 25])
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🔹 Examples:
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```python
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first = arr_1d[1:4] # [2, 3.7, 4]
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second = arr_1d[:4] # [1.4, 2, 3.7, 4]
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third = arr_1d[::2] # [1.4, 3.7, 5]
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fourth = arr_1d[::-1] # [6, 5, 4, 3.7, 2, 1.4]
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```
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# ----------------------------------------
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# FILTERING ELEMENTS FROM 1D ARRAY
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# ----------------------------------------
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---
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# Create a 1D array
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numbers = np.array([1, 2, 0, 3, 5, 6, 7, 8, 9, 10, 12, 45, 68, 90])
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## 🧩 2D Array Slicing
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# Extract all even numbers using boolean masking
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even_numbers = numbers[numbers % 2 == 0]
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print("Even numbers:", even_numbers)
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You can slice rows and columns simultaneously:
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```python
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print(arr_2d[1]) # Second row
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print(arr_2d[:, 2]) # Third column
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print(arr_2d[0:3, 1:3]) # Sub-array from rows 0-2 and columns 1-2
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```
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---
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## 🎯 Fancy Indexing
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Fancy indexing lets you access multiple arbitrary indices at once.
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```python
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print(arr_1d[[0, 2, 4]]) # Output: [1.4, 3.7, 5]
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```
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---
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## 🧪 Boolean Masking & Filtering
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### Filtering 2D Array
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```python
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print(arr_2d[arr_2d > 25]) # Output: [30 50 70]
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```
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# ----------------------------------------
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# FILTERING WITH MASK
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# ----------------------------------------
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### Filtering Even Numbers from 1D
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```python
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numbers = np.array([1, 2, 0, 3, 5, 6, 7, 8, 9, 10, 12, 45, 68, 90])
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even_numbers = numbers[numbers % 2 == 0]
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print(even_numbers) # Output: [ 2 0 6 8 10 12 68 90]
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```
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# Create a boolean mask for elements greater than 5
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### Filtering with Boolean Mask
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```python
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mask = numbers > 5
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# Use the mask to filter values
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print("Numbers greater than 5:", numbers[mask])
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print(numbers[mask]) # Output: [ 6 7 8 9 10 12 45 68 90]
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```
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---
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# ----------------------------------------
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# FANCY INDEXING vs. np.where()
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# ----------------------------------------
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## 🧠 Using `np.where`
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# np.where returns the indices where condition is True
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### Getting Indices of Elements Matching a Condition
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```python
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where_result = np.where(numbers > 5)
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print("Indices where numbers > 5:", where_result)
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print("Values at those indices:", numbers[where_result])
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print(where_result) # Output: (array([...]),)
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print(numbers[where_result]) # Output: [ 6 7 8 9 10 12 45 68 90]
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```
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# np.where with 3 parameters: condition, value_if_true, value_if_false
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# This replaces numbers > 5 with number * 4, else keeps the number as-is
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### Conditional Replacement
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```python
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condition_array = np.where(numbers > 5, numbers * 4, numbers)
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print("Apply logic: numbers > 5 → number * 4 else keep same:\n", condition_array)
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print(condition_array)
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```
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# np.where returning custom labels for each condition
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### Conditional Labeling
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```python
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condition_array2 = np.where(numbers > 5, "True", "False")
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print("Condition labels (True/False):", condition_array2)
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# Equivalent pseudocode for the above logic:
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'''
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if numbers[i] > 5:
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result = numbers[i] * 4
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else:
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result = numbers[i]
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'''
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print(condition_array2)
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```
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---
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## ✅ Conclusion
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This guide covered the core techniques for working with subsets of NumPy arrays:
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- Indexing individual elements
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- Slicing ranges
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- Fancy indexing for custom selection
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- Boolean masks and conditionals for filtering
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These skills are essential for data preprocessing and manipulation in any scientific or machine learning pipeline.
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---
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## 💡 Pro Tip
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Practice modifying arrays and chaining these techniques to prepare for real-world data workflows.
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