NumPy with Matplotlib - Step-by-Step Execution

Concept Flow - NumPy with Matplotlib

Import NumPy and Matplotlib

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Create data array with NumPy

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Prepare plot with Matplotlib

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Plot data (line, scatter, etc.)

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Add labels and title

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Show plot on screen

This flow shows how we create data with NumPy and then use Matplotlib to draw and display a graph.

Execution Sample

NumPy

import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 10, 5)
y = x ** 2
plt.plot(x, y)
plt.show()

This code creates 5 points from 0 to 10, squares them, and plots the curve.

Execution Table

Step	Action	Variable/Function	Value/Result	Explanation
1	Import numpy	np	<module 'numpy'>	Load NumPy library as np
2	Import matplotlib.pyplot	plt	<module 'matplotlib.pyplot'>	Load plotting library as plt
3	Create x array	x	[ 0. 2.5 5. 7.5 10. ]	5 points evenly spaced from 0 to 10
4	Calculate y = x^2	y	[ 0. 6.25 25. 56.25 100. ]	Square each x value
5	Plot x vs y	plt.plot(x, y)	Line plot created	Draw line connecting points
6	Show plot	plt.show()	Graph window opens	Display the plot visually

💡 Plot displayed and program waits for user to close window

Variable Tracker

Variable	Start	After Step 3	After Step 4	Final
x	undefined	[ 0. 2.5 5. 7.5 10. ]	[ 0. 2.5 5. 7.5 10. ]	[ 0. 2.5 5. 7.5 10. ]
y	undefined	undefined	[ 0. 6.25 25. 56.25 100. ]	[ 0. 6.25 25. 56.25 100. ]

Key Moments - 3 Insights

Why do we use np.linspace instead of a simple list for x?

np.linspace creates evenly spaced numbers automatically, which is easier and less error-prone than typing a list manually. See execution_table step 3 where x is created.

What does plt.show() do and why is it important?

plt.show() opens the window to display the plot. Without it, the plot might not appear. See execution_table step 6.

Why do we square x using y = x ** 2 instead of a loop?

NumPy allows element-wise operations on arrays directly, making code shorter and faster. This is shown in execution_table step 4.

Visual Quiz - 3 Questions

Look at the execution table, what is the value of x after step 3?

A [ 0 1 2 3 4 ]

B [ 0. 2.5 5. 7.5 10. ]

C [ 1 2 3 4 5 ]

D [ 0 2 4 6 8 ]

💡 Hint

Check the 'Value/Result' column at step 3 in the execution_table.

At which step does the program create the y values by squaring x?

A Step 4

B Step 3

C Step 2

D Step 5

💡 Hint

Look for the action 'Calculate y = x^2' in the execution_table.

If we change np.linspace(0, 10, 5) to np.linspace(0, 10, 10), what happens to x?

A x will have 5 points

B x will be empty

C x will have 10 points

D x will have 0 to 5 points

💡 Hint

np.linspace(start, stop, num) creates 'num' points; see variable_tracker for x values.

Concept Snapshot

NumPy with Matplotlib quick steps:
1. Import numpy as np and matplotlib.pyplot as plt
2. Create data arrays with np.linspace or other functions
3. Use plt.plot(x, y) to draw graphs
4. Add labels or titles if needed
5. Call plt.show() to display the plot

Full Transcript

This lesson shows how to use NumPy and Matplotlib together. First, we import both libraries. Then, we create an array x with 5 points from 0 to 10 using np.linspace. Next, we calculate y by squaring each x value. We plot x and y using plt.plot, which draws a line graph. Finally, plt.show() displays the graph window. Variables x and y change as we create and calculate data. Key points include using np.linspace for easy data creation, vectorized operations for calculations, and plt.show() to see the plot.