5 Essential Matplotlib Tricks for Data Science 🚀

Published: 31 May 2025
on channel: CodeVisium

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1. Create a simple line plot

Drawing a line plot is the foundation for visualizing trends over a continuous variable. In Matplotlib, you import the pyplot module and call plot() on your x–y arrays.

Long form

import matplotlib.pyplot as plt
x = [0, 1, 2, 3, 4]
y = [0, 1, 4, 9, 16]
plt.figure(figsize=(6,4))
plt.plot(x, y, linestyle='-', marker='o')
plt.show()

One-liner

__import__('matplotlib.pyplot').pyplot.plot([0,1,2,3,4],[0,1,4,9,16],linestyle='-',marker='o'); __import__('matplotlib.pyplot').pyplot.show()

2. Customize plot labels and title

Adding axis labels and a title provides essential context for your viewers. Use xlabel(), ylabel(), and title() to annotate your figure.

Long form

import matplotlib.pyplot as plt
x = [0, 1, 2, 3, 4]
y = [0, 1, 4, 9, 16]
plt.figure(figsize=(6,4))
plt.plot(x, y, color='green')
plt.xlabel('X Axis Label')
plt.ylabel('Y Axis Label')
plt.title('Square Numbers Line Plot')
plt.grid(True)
plt.show()

One-liner

__import__('matplotlib.pyplot').pyplot.plot([0,1,2,3,4],[0,1,4,9,16],color='green'); __import__('matplotlib.pyplot').pyplot.xlabel('X Axis Label'); __import__('matplotlib.pyplot').pyplot.ylabel('Y Axis Label'); __import__('matplotlib.pyplot').pyplot.title('Square Numbers Line Plot'); __import__('matplotlib.pyplot').pyplot.grid(True); __import__('matplotlib.pyplot').pyplot.show()

3. Create multiple subplots in one figure

Subplots let you display multiple related plots side by side. Use plt.subplots() to create a grid of axes, then call plotting functions on each axis.

Long form

import matplotlib.pyplot as plt
x = [0, 1, 2, 3, 4]
y1 = [0, 1, 4, 9, 16]
y2 = [16, 9, 4, 1, 0]
fig, axes = plt.subplots(1, 2, figsize=(10,4))
axes[0].plot(x, y1, color='blue')
axes[0].set_title('Ascending Squares')
axes[0].set_xlabel('X')
axes[0].set_ylabel('X²')
axes[1].plot(x, y2, color='red')
axes[1].set_title('Descending Squares')
axes[1].set_xlabel('X')
axes[1].set_ylabel('16−X²')
plt.tight_layout()
plt.show()

4. Generate a scatter plot with color mapping

Scatter plots visualize the relationship between two numeric variables, and adding a colormap based on a third variable reveals additional insight. Use scatter(x, y, c=values, cmap='viridis').

Long form

import matplotlib.pyplot as plt
import numpy as np
x = np.random.rand(50)
y = np.random.rand(50)
c = x + y
plt.figure(figsize=(6,5))
plt.scatter(x, y, c=c, cmap='viridis', s=50, edgecolor='k')
plt.colorbar(label='x + y')
plt.xlabel('x values')
plt.ylabel('y values')
plt.title('Scatter Plot with Color Mapping')
plt.show()

5. Plot a histogram with a density curve

Histograms show the distribution of a single variable, and overlaying a density curve (via kernel density estimate or a fitted distribution) helps visualize shape more smoothly.

Long form

import matplotlib.pyplot as plt
import numpy as np
data = np.random.normal(loc=0, scale=1, size=1000)
plt.figure(figsize=(6,4))
counts, bins, patches = plt.hist(data, bins=30, density=True, alpha=0.6, color='skyblue')
density = (1 / (np.sqrt(2 * np.pi) * 1)) * np.exp(- (bins**2) / 2)
plt.plot(bins, density, color='darkblue', linewidth=2)
plt.xlabel('Value')
plt.ylabel('Probability Density')
plt.title('Histogram with Normal Density Curve')
plt.show()

5 Interview Questions (with Answers):

1. Q: How do you change the figure size in Matplotlib, and why might you want to?

A: Use plt.figure(figsize=(width, height)) or fig, ax = plt.subplots(figsize=(w, h)). Larger dimensions improve readability, accommodate more subplots, and produce higher-resolution images for presentations or publication.

2. Q: What is the difference between plt.plot() and plt.scatter()?

A: plt.plot() draws a line (and optionally markers) connecting data points in sequence. plt.scatter() places individual markers at specified (x, y) coordinates without connecting lines and can map a third variable to color or marker size.

3. Q: How do you create a grid of subplots having 2 rows and 3 columns?

A: Call fig, axes = plt.subplots(2, 3, figsize=(width, height)). Here, axes is a 2×3 NumPy array of Axes objects. You can index axes[row, col] to plot on each subplot.

4. Q: How can you save a Matplotlib figure to disk without displaying it on-screen?

A: Replace plt.show() with plt.savefig('filename.png', dpi=300, bbox_inches='tight') to save the current figure at 300 DPI. Omitting show() keeps the GUI window from appearing.

5. Q: Describe how to add annotation text pointing to a data point in a plot.

A: Use plt.annotate(text, xy=(x_coord, y_coord), xytext=(x_text, y_text), arrowprops=dict(arrowstyle='-v')). This places text at xytext and draws an arrow from the text to the data point at xy.

#matplotlib #Python #DataScience #Visualization #CodingTips #Analytics #DeveloperShortcuts

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