ndarray as the core data structure in NumPy - Step-by-Step Execution

Concept Flow - ndarray as the core data structure

Create ndarray

↓

Store data in contiguous memory

↓

Access elements by index

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Perform operations element-wise

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Return new ndarray or modify in-place

This flow shows how an ndarray is created, stores data, allows element access, and supports element-wise operations.

Execution Sample

NumPy

import numpy as np
arr = np.array([1, 2, 3])
print(arr)
print(arr[1])
print(arr * 2)

Create a 1D ndarray, print it, access an element, and multiply all elements by 2.

Execution Table

Step	Action	Code Line	Variable State	Output
1	Import numpy as np	import numpy as np	np module loaded
2	Create ndarray from list [1, 2, 3]	arr = np.array([1, 2, 3])	arr = ndarray([1, 2, 3])
3	Print entire array	print(arr)	arr unchanged	[1 2 3]
4	Access element at index 1	print(arr[1])	arr unchanged	2
5	Multiply array by 2 element-wise	print(arr * 2)	arr unchanged	[2 4 6]

💡 All steps executed; array operations completed.

Variable Tracker

Variable	Start	After Step 2	After Step 3	After Step 4	After Step 5
arr	undefined	[1 2 3]	[1 2 3]	[1 2 3]	[1 2 3]

Key Moments - 2 Insights

Why does multiplying arr by 2 not change arr itself?

Because arr * 2 creates a new ndarray with multiplied values but does not modify arr in-place, as shown in execution_table step 5.

How does arr[1] access the second element?

Indexing starts at 0, so arr[1] accesses the element at position 1, which is 2, as shown in execution_table step 4.

Visual Quiz - 3 Questions

Look at the execution table, what is the output of step 3?

A [2 4 6]

B [1 2 3]

C 2

D Error

💡 Hint

Check the Output column for step 3 in the execution_table.

At which step is the element 2 accessed from arr?

A Step 2

B Step 3

C Step 4

D Step 5

💡 Hint

Look at the Action and Output columns in execution_table for element access.

If we assign arr = arr * 2, what changes in variable_tracker after step 5?

A arr becomes [2 4 6]

B arr remains [1 2 3]

C arr becomes 2

D arr becomes undefined

💡 Hint

Multiplying creates a new array; assigning it updates arr, check variable_tracker logic.

Concept Snapshot

ndarray is numpy's main data structure
Stores data in contiguous memory
Supports indexing and slicing
Allows element-wise operations
Operations return new ndarrays by default

Full Transcript

We start by importing numpy as np. Then we create an ndarray named arr from a Python list [1, 2, 3]. This ndarray stores the numbers in a continuous block of memory. We print the entire array, which outputs [1 2 3]. Next, we access the element at index 1, which is 2, and print it. Finally, we multiply the array by 2, which creates a new array [2 4 6] without changing the original arr. This shows how ndarray supports element-wise operations and indexing.