Integer types (int8, int16, int32, int64) in NumPy - Step-by-Step Execution

Concept Flow - Integer types (int8, int16, int32, int64)

Choose integer type

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Create numpy array with type

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Store values in array

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Check memory size and range

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Use array in calculations

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Observe overflow or correct results

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END

This flow shows how you pick an integer type, create a numpy array with it, store values, and see how size and range affect calculations.

Execution Sample

NumPy

import numpy as np
arr = np.array([120, 130], dtype=np.int8)
print(arr)
print(arr.dtype)
print(arr.nbytes)

Create a numpy array with int8 type, print values, type, and memory size.

Execution Table

Step	Action	Value/Result	Explanation
1	Create array with values [120, 130] and dtype int8	[120, -126]	130 overflows int8 max (127), wraps to -126
2	Print array dtype	int8	Array stores values as 8-bit signed integers
3	Print array memory size	2	Each int8 uses 1 byte, total 2 bytes for 2 elements
4	END	-	Execution ends after printing values and info

💡 All steps completed; overflow shown in step 1 due to int8 range limits

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
arr	undefined	[120, -126] (int8 array)	[120, -126] (int8 array)	[120, -126] (int8 array)	[120, -126] (int8 array)
arr.dtype	undefined	int8	int8	int8	int8
arr.nbytes	undefined	2	2	2	2

Key Moments - 2 Insights

Why does the value 130 become -126 in the int8 array?

int8 can only store values from -128 to 127. When 130 is stored, it overflows and wraps around, resulting in -126 as shown in execution_table step 1.

How do we know the memory size used by the array?

The arr.nbytes attribute shows total bytes used. Since int8 uses 1 byte per element and there are 2 elements, total is 2 bytes (execution_table step 3).

Visual Quiz - 3 Questions

Look at the execution table step 1, what is the stored value for 130 in the int8 array?

A 130

B -126

C 127

D -130

💡 Hint

Check the 'Value/Result' column in step 1 of the execution_table.

At which step do we see the memory size of the array?

A Step 1

B Step 2

C Step 3

D Step 4

💡 Hint

Look for arr.nbytes output in the execution_table.

If we change dtype to int16, what will happen to the stored value of 130?

A It will store 130 correctly

B It will still overflow and wrap

C It will become -130

D It will cause an error

💡 Hint

int16 can store values from -32768 to 32767, so 130 fits without overflow.

Concept Snapshot

Integer types in numpy define how many bits store each number.
int8 uses 8 bits (-128 to 127), int16 uses 16 bits, etc.
Choosing smaller types saves memory but can cause overflow.
Overflow wraps values around within the type's range.
Use dtype parameter to set integer type when creating arrays.

Full Transcript

This lesson shows how numpy integer types like int8 store numbers with limited range. We create an array with int8 type and values 120 and 130. Since int8 can only hold -128 to 127, 130 overflows and wraps to -126. We print the array, its type, and memory size. The memory size is 2 bytes because each int8 uses 1 byte. Key points: integer types limit value range, overflow wraps values, and smaller types use less memory. Changing to int16 would store 130 correctly without overflow.