Learn Matplotlib and Seaborn for histograms, scatter plots, box plots, line charts and categorical comparisons in Python. Every Matplotlib & Seaborn example includes a visible checkpoint so you can verify the reasoning instead of trusting the code blindly. This page targets the practical intent behind Python plotting, not repetition of the phrase itself.
Editorial note: Publication metadata for Matplotlib & Seaborn is intentionally unset until this URL is actually released; the lesson already includes its own examples, CSV and notebook.
Choose the question before the chart
To compare delivery times across regions, a box plot quickly shows medians, spread and potential outliers, while a line chart is better for a daily trend over time.
- Matplotlib provides low-level control over figures and axes, while Seaborn adds statistical defaults and DataFrame-friendly plotting.
- Use explicit figure and axes objects for reusable plotting code instead of relying on global state.
- Labels, units and readable scales matter more than decorative styling.
- For dense data, transparency, aggregation or sampling can reveal patterns that an overplotted scatter chart hides.
Build and inspect a reproducible visual
For Matplotlib & Seaborn, predict one visible result before each snippet runs—a row count, shape, statistic, ranking or error pattern—then compare it with the printed checkpoint.
# Matplotlib & Seaborn · example 1
import seaborn as sns
import matplotlib.pyplot as plt
sns.boxplot(data=df, x='region', y='delivery_minutes')
plt.xlabel('Region')
plt.ylabel('Delivery time (minutes)')
plt.tight_layout()
plt.show()Matplotlib & Seaborn checkpoint 1: Run the cell and inspect the printed values before changing anything.
# Matplotlib & Seaborn · example 2
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv('practice.csv')
print(df.dtypes)
print(df.head(3).to_string(index=False))Matplotlib & Seaborn checkpoint 2: Check that numeric axes are numeric and categories are not accidentally encoded as measures.
# Matplotlib & Seaborn · example 3
num = df.select_dtypes('number').columns[0]
ax = df[num].plot(kind='hist', bins=12, edgecolor='black')
ax.set(title=f'Distribution of {num}', xlabel=num)
plt.tight_layout()Matplotlib & Seaborn checkpoint 3: A histogram appears; look for skew, gaps and extreme values before summarizing the center.
# Matplotlib & Seaborn · example 4
cat = df.select_dtypes(exclude='number').columns[0]
num = df.select_dtypes('number').columns[0]
print(df.groupby(cat)[num].agg(['count','mean','median']).round(2))Matplotlib & Seaborn checkpoint 4: The table shows group sizes beside mean and median so small groups are visible.
# Matplotlib & Seaborn · example 5
fig, ax = plt.subplots(figsize=(8,4.5))
df[num].plot(kind='box', ax=ax, vert=False)
ax.set_title(f'{num}: spread and possible outliers')
fig.tight_layout()
fig.savefig('practice-chart.png', dpi=160)Matplotlib & Seaborn checkpoint 5: practice-chart.png is saved with fixed dimensions and resolution.
Check scales, groups and misleading encodings
Labels, units and readable scales matter more than decorative styling.
For dense data, transparency, aggregation or sampling can reveal patterns that an overplotted scatter chart hides.
Self-check with answers
For Matplotlib & Seaborn, what result should you be able to explain without the code?
Matplotlib provides low-level control over figures and axes, while Seaborn adds statistical defaults and DataFrame-friendly plotting.
Which assumption matters most in this Matplotlib & Seaborn exercise?
Labels, units and readable scales matter more than decorative styling.
What failure mode would make this Matplotlib & Seaborn result untrustworthy?
For dense data, transparency, aggregation or sampling can reveal patterns that an overplotted scatter chart hides.
Use the same data to compare alternative views
Try it yourself workspace
Edit this Matplotlib & Seaborn starter, copy it, and run it in the downloadable notebook or your local Jupyter environment.
Questions specific to this lesson
How do I choose the right chart for Matplotlib and Seaborn?
Use explicit figure and axes objects for reusable plotting code instead of relying on global state.
What makes a visualization misleading for Matplotlib & Seaborn?
Truncated axes, inconsistent scales, hidden denominators and overloaded color can all change the story a reader takes from the same data.
What should I inspect before plotting for Matplotlib & Seaborn?
Check variable type, units, missingness, duplicates and sample size first. Many chart problems are really data-quality problems.
How do I make the chart reproducible for Matplotlib & Seaborn?
Keep the transformation and plotting code together, set explicit labels and save the output with fixed dimensions so another person can rebuild it.
Primary references and further reading
For Matplotlib & Seaborn, use these primary references when exact syntax, defaults or formal definitions matter beyond the examples on this page.
