Mathew K AnalyticsLesson 13 · Data analytics zero to hero
Data Visualisation with Matplotlib | Data Analytics #13
Video thirteen of the 30-part series, and the start of the visualization block: line plots, bar charts, histograms, and scatter plots. We're back to the…
- CourseData analytics zero to hero
- Lesson13 of 30
- Video10 min
- FormatJupyter notebook · 8 code cells
- Data1 dataset
What you'll learn
Datasets used in this lesson
Save these next to the notebook. In Google Colab, upload them with the 📁 icon on the left first.
- mtcars.csv1.7 KB
📓 Full notebook
Download .ipynbData Analytics Zero to Hero, Video 13: Data Visualization with Matplotlib#
- Video thirteen of the 30-part series, and the start of the visualization block: line plots, bar charts, histograms, and scatter plots.
- We're back to the real mtcars dataset, 1974 Motor Trend car road tests, this time to actually see the data.
- Let's jump straight in.
Before You Start#
- Open a new Jupyter Notebook in VS Code and select your Python interpreter as the kernel.
- Install Matplotlib if you haven't already:
pip install matplotlib. - Place mtcars.csv in the same folder as this notebook.
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv('mtcars.csv')
print(df.shape)
Part 1: Line Plots and Bar Charts#
sorted_df = df.sort_values('mpg').reset_index(drop=True)
plt.plot(sorted_df.index, sorted_df['mpg'])
plt.title('Real MPG Values, Sorted')
plt.xlabel('Car Rank')
plt.ylabel('MPG')
plt.show()
top5 = df.nlargest(5, 'mpg')
plt.bar(top5['model'], top5['mpg'], color='steelblue')
plt.title('Top 5 Most Fuel-Efficient Real Cars')
plt.ylabel('MPG')
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()
Part 2: Scatter Plots#
plt.scatter(df['wt'], df['mpg'])
plt.title('Real Weight vs. MPG')
plt.xlabel('Weight (1000 lbs)')
plt.ylabel('MPG')
plt.show()
colors = df['cyl'].map({4: 'green', 6: 'orange', 8: 'red'})
plt.scatter(df['wt'], df['mpg'], c=colors)
plt.title('Real Weight vs. MPG, Colored by Cylinder Count')
plt.xlabel('Weight (1000 lbs)')
plt.ylabel('MPG')
plt.show()
Part 3: Histograms#
plt.hist(df['mpg'], bins=8, color='mediumseagreen', edgecolor='black')
plt.title('Distribution of Real MPG Values')
plt.xlabel('MPG')
plt.ylabel('Number of Cars')
plt.show()
Part 4: Subplots#
fig, axes = plt.subplots(1, 2, figsize=(10, 4))
axes[0].scatter(df['wt'], df['mpg'])
axes[0].set_title('Weight vs. MPG')
axes[1].hist(df['hp'], bins=8, color='coral')
axes[1].set_title('Horsepower Distribution')
plt.tight_layout()
plt.show()
Part 5: Saving a Figure#
plt.scatter(df['wt'], df['mpg'], c=colors)
plt.title('Real Weight vs. MPG, Colored by Cylinder Count')
plt.xlabel('Weight (1000 lbs)')
plt.ylabel('MPG')
plt.savefig('weight_vs_mpg.png', dpi=150, bbox_inches='tight')
plt.show()
Wrap-Up: What You Learned#
- Line plots and bar charts, with plot and bar.
- Scatter plots, including coloring points by a third categorical column.
- Histograms for visualizing a single column's distribution.
- Subplots, for comparing multiple charts in one figure.
- Saving a chart to a real image file with savefig.
- All of it built on the real mtcars dataset. Video fourteen covers Seaborn, a higher-level charting library built on top of Matplotlib, for faster, more polished statistical charts. Subscribe so it lands automatically see you there.
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