SciPy with Matplotlib for visualization - Step-by-Step Execution

Concept Flow - SciPy with Matplotlib for visualization

Import SciPy and Matplotlib

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Create or load data

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Use SciPy function (e.g., stats.norm.pdf)

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Generate values for plot

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Plot data with Matplotlib

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Show visualization

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End

This flow shows how to import SciPy and Matplotlib, create data, apply SciPy functions, and visualize results with Matplotlib.

Execution Sample

SciPy

import numpy as np
from scipy.stats import norm
import matplotlib.pyplot as plt

x = np.linspace(-3, 3, 100)
y = norm.pdf(x)
plt.plot(x, y)
plt.show()

This code plots the bell curve of the normal distribution using SciPy and Matplotlib.

Execution Table

Step	Action	Variable/Function	Value/Result	Output/Plot
1	Import numpy	np	<module 'numpy'>	No output
2	Import norm from scipy.stats	norm	<scipy.stats._distn_infrastructure.rv_frozen>	No output
3	Import matplotlib.pyplot	plt	<module 'matplotlib.pyplot'>	No output
4	Create x values	x	array from -3 to 3 (100 points)	No output
5	Calculate y = norm.pdf(x)	y	array of PDF values for x	No output
6	Plot x vs y	plt.plot(x, y)	Line plot prepared	Plot line ready
7	Show plot	plt.show()	Plot window opens	Bell curve displayed
8	End	-	-	Visualization complete

💡 Plot displayed and program ends

Variable Tracker

Variable	Start	After Step 4	After Step 5	Final
np	Not defined	Module imported	Module imported	Module imported
norm	Not defined	Imported	Imported	Imported
plt	Not defined	Imported	Imported	Imported
x	Not defined	Array [-3.0, ..., 3.0] (100 points)	Same	Same
y	Not defined	Not defined	Array of PDF values	Same

Key Moments - 3 Insights

Why do we use np.linspace(-3, 3, 100) before calling norm.pdf?

np.linspace creates 100 evenly spaced points between -3 and 3 to evaluate the PDF smoothly, as shown in execution_table step 4.

What does norm.pdf(x) compute exactly?

It computes the probability density function values for each x point, producing y values for the bell curve, as seen in execution_table step 5.

Why do we call plt.show() after plt.plot()?

plt.plot prepares the plot but does not display it; plt.show() opens the window to display the graph, as in execution_table step 7.

Visual Quiz - 3 Questions

Look at the execution table, what is the value of 'x' after step 4?

A An empty array

B An array of 100 points evenly spaced from -3 to 3

C A single number -3

D A list of random numbers

💡 Hint

Check the 'Value/Result' column for step 4 in the execution_table.

At which step does the plot window open to show the bell curve?

A Step 7

B Step 5

C Step 6

D Step 8

💡 Hint

Look for 'Plot window opens' in the Output/Plot column in execution_table.

If we change np.linspace(-3, 3, 100) to np.linspace(-1, 1, 50), how does the plot change?

A The plot will not change

B The plot will be wider with more points

C The plot will show a narrower range with fewer points

D The plot will show random noise

💡 Hint

Refer to variable_tracker for 'x' values and how they affect the plot range.

Concept Snapshot

SciPy with Matplotlib visualization:
- Import SciPy stats and Matplotlib
- Create data points with numpy.linspace
- Compute function values (e.g., norm.pdf)
- Plot with plt.plot(x, y)
- Display plot with plt.show()
Use this to visualize mathematical functions easily.

Full Transcript

This example shows how to use SciPy and Matplotlib together. First, we import numpy, scipy.stats.norm, and matplotlib.pyplot. Then, we create 100 points between -3 and 3 using numpy.linspace. Next, we calculate the normal distribution's probability density function values at those points with norm.pdf. We plot these points using plt.plot and finally display the plot with plt.show. This produces a bell curve visualization. Variables like x and y hold the data points and function values, changing step by step. Key moments include understanding why we create x points first, what norm.pdf computes, and why plt.show is needed to display the plot. The quizzes test understanding of these steps and how changing inputs affects the plot.