Normal distribution with normal() in NumPy - Step-by-Step Execution

Concept Flow - Normal distribution with normal()

Call np.random.normal(mean, std, size)

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Generate random values

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Return array of values

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Use values for analysis or plot

The function np.random.normal() generates random numbers following a bell-shaped curve defined by mean and standard deviation.

Execution Sample

NumPy

import numpy as np
values = np.random.normal(0, 1, 5)
print(values)

Generate 5 random numbers from a normal distribution with mean 0 and std 1, then print them.

Execution Table

Step	Action	Parameters	Generated Values	Output
1	Call np.random.normal	mean=0, std=1, size=5	N/A	N/A
2	Generate 5 random values	N/A	[0.5, -1.2, 0.3, 1.1, -0.7]	N/A
3	Return array	N/A	[0.5, -1.2, 0.3, 1.1, -0.7]	[0.5, -1.2, 0.3, 1.1, -0.7]
4	Print values	N/A	N/A	[0.5, -1.2, 0.3, 1.1, -0.7]

💡 All 5 values generated and printed, execution ends.

Variable Tracker

Variable	Start	After np.random.normal call	Final
values	undefined	[0.5, -1.2, 0.3, 1.1, -0.7]	[0.5, -1.2, 0.3, 1.1, -0.7]

Key Moments - 2 Insights

Why do the generated values look different each time I run the code?

Because np.random.normal generates random numbers each time, the values change on every run as shown in the execution_table rows 2 and 3.

What do the mean and std parameters control?

Mean shifts the center of the values, std controls how spread out they are. This is why in the execution_table step 1, mean=0 and std=1 define the shape of generated values.

Visual Quiz - 3 Questions

Look at the execution_table, what is the variable 'values' after step 2?

A undefined

B [0.5, -1.2, 0.3, 1.1, -0.7]

C N/A

D [0, 0, 0, 0, 0]

💡 Hint

Check the 'Generated Values' column in row 2 of execution_table.

At which step does the function return the generated array?

A Step 3

B Step 2

C Step 1

D Step 4

💡 Hint

Look for the 'Return array' action in execution_table.

If we change std from 1 to 2, how would the generated values change?

A Values would all be zero

B Values would be closer to the mean

C Values would be more spread out from the mean

D Values would not change

💡 Hint

Recall std controls spread as explained in key_moments and concept_flow.

Concept Snapshot

np.random.normal(mean, std, size) generates random numbers
from a normal distribution with given mean and std deviation.
Returns an array of 'size' values.
Mean shifts center; std controls spread.
Values differ each run due to randomness.

Full Transcript

This visual execution shows how np.random.normal works step-by-step. First, the function is called with mean 0, std 1, and size 5. Then, it generates 5 random values following the bell curve shape. These values are returned as an array and printed. The variable 'values' holds these numbers after generation. The randomness means values change each run. Mean and std control the center and spread of the numbers. This helps in simulations and data analysis where normal distribution is assumed.