NumPy with Matplotlib - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is NumPy used for in data science?

NumPy is used to work with arrays and perform fast mathematical operations on large sets of numbers. It helps handle data efficiently.

beginner

What does Matplotlib help you do?

Matplotlib helps you create charts and graphs to visualize data, making it easier to understand patterns and results.

beginner

How do you create a simple line plot using NumPy and Matplotlib?

First, create data with NumPy arrays. Then use Matplotlib's plot() function to draw the line. Finally, call show() to display it.

intermediate

Why is it useful to combine NumPy with Matplotlib?

NumPy creates and processes data quickly, and Matplotlib shows that data visually. Together, they help analyze and explain data clearly.

intermediate

What is the role of the 'linspace' function in NumPy when plotting?

linspace creates evenly spaced numbers over a range. This helps make smooth curves or lines when plotting with Matplotlib.

Which library is used to create arrays for numerical data?

A NumPy

B Matplotlib

C Pandas

D Seaborn

NumPy is the library designed for creating and working with arrays.

What function from Matplotlib shows the plot on screen?

A plot()

B show()

C draw()

D display()

The show() function displays the plot window.

What does np.linspace(0, 10, 5) do?

A Creates 10 numbers spaced by 5

B Creates 5 random numbers between 0 and 10

C Creates an array of zeros with length 5

D Creates 5 evenly spaced numbers from 0 to 10

linspace creates evenly spaced numbers; here 5 numbers from 0 to 10.

Which of these is NOT a typical use of Matplotlib?

A Data cleaning

B Bar charts

C Line plots

D Scatter plots

Data cleaning is done with other tools; Matplotlib is for visualization.

Why combine NumPy with Matplotlib?

A To build websites

B To write text documents

C To create and visualize data efficiently

D To send emails

NumPy handles data; Matplotlib visualizes it, making analysis easier.

Explain how you would create a simple line graph using NumPy and Matplotlib.

Think about making data first, then drawing, then showing.

Describe why visualization is important when working with numerical data from NumPy.

Imagine explaining numbers to a friend using pictures.

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