NumPy with Matplotlib

Introduction

We use NumPy to create and handle numbers easily. Matplotlib helps us draw pictures from those numbers. Together, they make it simple to see data as graphs.

When you want to draw a line chart of daily temperatures.

When you need to show how sales change over months.

When you want to plot points to see a pattern in data.

When you want to compare two sets of numbers visually.

Syntax

NumPy

import numpy as np
import matplotlib.pyplot as plt

x = np.array([numbers])
y = np.array([numbers])
plt.plot(x, y)
plt.show()

Use np.array to create number lists for plotting.

plt.plot() draws the graph, and plt.show() displays it.

Examples

Simple line graph with 4 points.

NumPy

import numpy as np
import matplotlib.pyplot as plt

x = np.array([1, 2, 3, 4])
y = np.array([10, 20, 25, 30])
plt.plot(x, y)
plt.show()

Plotting a smooth sine wave using many points.

NumPy

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 10, 100)  # 100 points from 0 to 10

y = np.sin(x)  # sine values for each x

plt.plot(x, y)
plt.title('Sine Wave')
plt.xlabel('x values')
plt.ylabel('sin(x)')
plt.show()

Two lines on the same graph with labels.

NumPy

import numpy as np
import matplotlib.pyplot as plt

x = np.array([1, 2, 3, 4])
y1 = np.array([10, 20, 25, 30])
y2 = np.array([15, 18, 22, 28])

plt.plot(x, y1, label='Line 1')
plt.plot(x, y2, label='Line 2')
plt.legend()
plt.show()

Sample Program

This program draws sine and cosine waves on the same graph to compare them.

NumPy

import numpy as np
import matplotlib.pyplot as plt

# Create 50 points from 0 to 4*pi
x = np.linspace(0, 4 * np.pi, 50)

# Calculate sine and cosine values
sin_y = np.sin(x)
cos_y = np.cos(x)

# Plot sine and cosine
plt.plot(x, sin_y, label='Sine')
plt.plot(x, cos_y, label='Cosine')

# Add title and labels
plt.title('Sine and Cosine Waves')
plt.xlabel('x values')
plt.ylabel('y values')

# Show legend
plt.legend()

# Display the plot
plt.show()

Important Notes

Always import NumPy as np and Matplotlib pyplot as plt by convention.

Use np.linspace(start, stop, num_points) to create smooth ranges of numbers.

Adding labels and titles helps understand the graph better.

Summary

NumPy helps create and manage numbers easily.

Matplotlib draws graphs from those numbers.

Together, they make data easy to see and understand.

Practice

1. What is the main purpose of using NumPy together with Matplotlib in data science?

easy

A. To create numerical data and then visualize it with graphs

B. To write text documents and print them

C. To build websites and style them

D. To store large files on the computer

Solution

1. Step 1: Understand NumPy's role

   NumPy helps create and manage numerical data easily.

2. Step 2: Understand Matplotlib's role

   Matplotlib is used to draw graphs to visualize data.

3. Final Answer:

   To create numerical data and then visualize it with graphs -> Option A

4. Quick Check:

   NumPy + Matplotlib = Data + Graphs [OK]

Hint: NumPy handles numbers, Matplotlib draws pictures [OK]

Common Mistakes:

• Confusing Matplotlib with text editing tools
• Thinking NumPy is for website design
• Mixing file storage with data visualization

2. Which of the following code snippets correctly imports NumPy and Matplotlib for use?

easy

A. import np
import matplotlib.pyplot

B. import numpy
import matplotlib as plt

C. from numpy import matplotlib
import pyplot as plt

D. import numpy as np
import matplotlib.pyplot as plt

Solution

1. Step 1: Check NumPy import syntax

   Correct import is 'import numpy as np' to use 'np' as shortcut.

2. Step 2: Check Matplotlib import syntax

   Correct import is 'import matplotlib.pyplot as plt' to use 'plt' for plotting.

3. Final Answer:

   import numpy as np\nimport matplotlib.pyplot as plt -> Option D

4. Quick Check:

   Standard imports = import numpy as np import matplotlib.pyplot as plt [OK]

Hint: Use 'as np' and 'as plt' for easy code [OK]

Common Mistakes:

• Using wrong aliases like 'import matplotlib as plt'
• Forgetting 'pyplot' in Matplotlib import
• Trying to import non-existing modules

3. What will be the output plot type when running this code?

import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 10, 100)
y = np.sin(x)
plt.plot(x, y)
plt.show()

medium

A. A line graph showing a sine wave

B. A bar chart with values from 0 to 10

C. A scatter plot with random points

D. No plot will show due to error

Solution

1. Step 1: Understand data creation

   np.linspace creates 100 points from 0 to 10; y is sine of x values.

2. Step 2: Understand plotting command

   plt.plot draws a line graph connecting (x, y) points; plt.show displays it.

3. Final Answer:

   A line graph showing a sine wave -> Option A

4. Quick Check:

   plot(x, sin(x)) = sine wave line [OK]

Hint: plt.plot with np.sin creates smooth wave line [OK]

Common Mistakes:

• Confusing plt.plot with bar or scatter plots
• Expecting random points instead of line
• Missing plt.show() to display plot

4. Identify the error in this code snippet that tries to plot a cosine wave:

import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 2*np.pi, 50)
y = np.cos(x)
plt.plot(x, y)
plt.show

medium

A. np.cos is not a valid NumPy function

B. np.linspace should use 100 points instead of 50

C. Missing parentheses after plt.show to display the plot

D. x and y arrays have different lengths

Solution

1. Step 1: Check plt.show usage

   plt.show is a function and must be called with parentheses: plt.show()

2. Step 2: Verify other parts

   np.linspace and np.cos are correct; x and y lengths match.

3. Final Answer:

   Missing parentheses after plt.show to display the plot -> Option C

4. Quick Check:

   plt.show() needed to display plot [OK]

Hint: Always call plt.show() with parentheses [OK]

Common Mistakes:

• Forgetting parentheses on plt.show
• Thinking np.cos is invalid
• Assuming array length mismatch without checking

5. You want to plot two functions, sine and cosine, on the same graph using NumPy and Matplotlib. Which code correctly does this?

hard

A. import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 2*np.pi, 100)
plt.plot(np.sin(x))
plt.plot(np.cos(x))
plt.show()

B. import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 2*np.pi, 100)
plt.plot(x, np.sin(x))
plt.plot(x, np.cos(x))
plt.legend(['sin', 'cos'])
plt.show()

C. import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 2*np.pi, 100)
plt.plot(x, np.sin(x), np.cos(x))
plt.show()

D. import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 2*np.pi, 100)
plt.scatter(x, np.sin(x))
plt.scatter(x, np.cos(x))
plt.show()

Solution

1. Step 1: Check plotting both functions

   import numpy as np import matplotlib.pyplot as plt x = np.linspace(0, 2*np.pi, 100) plt.plot(x, np.sin(x)) plt.plot(x, np.cos(x)) plt.legend(['sin', 'cos']) plt.show() plots sine and cosine separately with x values, correct usage.

2. Step 2: Check legend and show

   import numpy as np import matplotlib.pyplot as plt x = np.linspace(0, 2*np.pi, 100) plt.plot(x, np.sin(x)) plt.plot(x, np.cos(x)) plt.legend(['sin', 'cos']) plt.show() adds legend for clarity and calls plt.show() properly.

3. Final Answer:

   Code that plots sine and cosine with legend and shows plot -> Option B

4. Quick Check:

   Separate plt.plot calls + legend + plt.show() = import numpy as np import matplotlib.pyplot as plt x = np.linspace(0, 2*np.pi, 100) plt.plot(x, np.sin(x)) plt.plot(x, np.cos(x)) plt.legend(['sin', 'cos']) plt.show() [OK]

Hint: Plot each function with x, add legend, call plt.show() [OK]

Common Mistakes:

• Plotting without x values causing wrong x-axis
• Missing legend to identify lines
• Using scatter instead of line plot for smooth curves