Basic Bar Chart¶

Bar charts display categorical data with rectangular bars, where bar length represents the value for each category.

Simple Bar Chart¶

Create a basic vertical bar chart with ax.bar().

1. Import and Setup¶

python import matplotlib.pyplot as plt import numpy as np

2. Define Categories and Values¶

python categories = ['A', 'B', 'C', 'D', 'E'] values = [23, 45, 56, 78, 32]

3. Create Bar Chart¶

python fig, ax = plt.subplots() ax.bar(categories, values) ax.set_xlabel('Category') ax.set_ylabel('Value') ax.set_title('Basic Bar Chart') plt.show()

Horizontal Bar Chart¶

Use ax.barh() for horizontal orientation.

1. Basic Horizontal Bars¶

python fig, ax = plt.subplots() ax.barh(categories, values) ax.set_xlabel('Value') ax.set_ylabel('Category') plt.show()

2. Long Category Names¶

```python categories = ['Category Alpha', 'Category Beta', 'Category Gamma', 'Category Delta', 'Category Epsilon'] values = [23, 45, 56, 78, 32]

fig, ax = plt.subplots() ax.barh(categories, values) plt.tight_layout() plt.show() ```

3. Reversed Order¶

python fig, ax = plt.subplots() ax.barh(categories[::-1], values[::-1]) plt.show()

Pandas Plot Method¶

Use DataFrame's built-in plotting for quick visualizations.

1. Single Column¶

```python import pandas as pd

data = {'Courses': ['Language', 'History', 'Math', 'Chemistry', 'Physics'], 'Number of Teachers': [7, 3, 9, 3, 4]} df = pd.DataFrame(data).set_index('Courses')

fig, ax = plt.subplots(figsize=(12, 3)) df.plot(kind='bar', ax=ax) ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) plt.show() ```

2. Multiple Columns¶

```python data = {'Student': ['Brandon', 'Vanessa', 'Daniel', 'Kevin', 'William'], 'Midterm': [85, 60, 60, 65, 100], 'Final': [90, 90, 65, 80, 95]} df = pd.DataFrame(data).set_index('Student')

fig, ax = plt.subplots(figsize=(12, 3)) df.plot(kind='bar', ax=ax) ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) plt.show() ```

3. Horizontal with Pandas¶

python fig, ax = plt.subplots(figsize=(12, 3)) df.plot(kind='barh', ax=ax) ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) plt.show()

Numeric X-Axis¶

Use numeric positions instead of categorical labels.

1. Integer Positions¶

```python x = np.arange(5) values = [23, 45, 56, 78, 32]

fig, ax = plt.subplots() ax.bar(x, values) ax.set_xticks(x) ax.set_xticklabels(['A', 'B', 'C', 'D', 'E']) plt.show() ```

2. Custom Spacing¶

```python x = [0, 1, 3, 4, 6] # Non-uniform spacing values = [23, 45, 56, 78, 32]

fig, ax = plt.subplots() ax.bar(x, values, width=0.8) plt.show() ```

3. Centered Labels¶

python x = np.arange(5) fig, ax = plt.subplots() ax.bar(x, values) ax.set_xticks(x) ax.set_xticklabels(['A', 'B', 'C', 'D', 'E'], ha='center') plt.show()

Data Sources¶

Various ways to provide data to bar charts.

1. Lists¶

python categories = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'] values = [10, 25, 15, 30, 20] ax.bar(categories, values)

2. NumPy Arrays¶

python x = np.arange(5) values = np.array([10, 25, 15, 30, 20]) ax.bar(x, values)

3. Pandas DataFrame¶

```python import pandas as pd

df = pd.DataFrame({ 'day': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'], 'sales': [10, 25, 15, 30, 20] })

fig, ax = plt.subplots() ax.bar(df['day'], df['sales']) plt.show() ```

Adding Value Labels¶

Display values on top of bars.

1. Text Annotation¶

```python fig, ax = plt.subplots() bars = ax.bar(categories, values)

for bar, value in zip(bars, values): ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 1, str(value), ha='center', va='bottom')

plt.show() ```

2. Using bar_label¶

python fig, ax = plt.subplots() bars = ax.bar(categories, values) ax.bar_label(bars) plt.show()

3. Formatted Labels¶

python fig, ax = plt.subplots() bars = ax.bar(categories, values) ax.bar_label(bars, fmt='%.1f', padding=3) plt.show()

Sorted Bar Charts¶

Order bars by value for better visualization.

1. Ascending Order¶

```python categories = ['A', 'B', 'C', 'D', 'E'] values = [23, 45, 56, 78, 32]

sorted_idx = np.argsort(values) sorted_categories = [categories[i] for i in sorted_idx] sorted_values = [values[i] for i in sorted_idx]

fig, ax = plt.subplots() ax.barh(sorted_categories, sorted_values) plt.show() ```

2. Descending Order¶

```python sorted_idx = np.argsort(values)[::-1] sorted_categories = [categories[i] for i in sorted_idx] sorted_values = [values[i] for i in sorted_idx]

fig, ax = plt.subplots() ax.barh(sorted_categories, sorted_values) plt.show() ```

3. Pandas Sorting¶

```python df = pd.DataFrame({'category': categories, 'value': values}) df_sorted = df.sort_values('value', ascending=True)

fig, ax = plt.subplots() ax.barh(df_sorted['category'], df_sorted['value']) plt.show() ```

Practical Example¶

Create a complete bar chart with styling.

1. Prepare Data¶

python products = ['Product A', 'Product B', 'Product C', 'Product D', 'Product E'] sales = [150, 230, 180, 310, 275]

2. Create Styled Chart¶

```python fig, ax = plt.subplots(figsize=(10, 6))

bars = ax.bar(products, sales, color='steelblue', edgecolor='navy', linewidth=1.5)

ax.set_xlabel('Product', fontsize=12) ax.set_ylabel('Sales ($K)', fontsize=12) ax.set_title('Quarterly Sales by Product', fontsize=14) ax.set_ylim(0, max(sales) * 1.15) ```

3. Add Annotations¶

```python ax.bar_label(bars, fmt='$%.0fK', padding=3) ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) ax.grid(axis='y', alpha=0.3)

plt.tight_layout() plt.show() ```

Runnable Example: seaborn_categorical_plots.py¶

```python """ Tutorial 04: Categorical Plots in Seaborn

This tutorial covers plots designed specifically for categorical data. These plots help compare distributions and values across different categories.

Learning Objectives: - Create box plots and violin plots - Use strip plots and swarm plots - Build point plots and count plots - Understand when to use each categorical plot - Combine multiple plot types

Author: Educational Python Package Level: Intermediate Prerequisites: Tutorial 01-03 """

import seaborn as sns import matplotlib.pyplot as plt import pandas as pd import numpy as np

if name == "main":

sns.set_style("whitegrid")
sns.set_context("notebook")

# =============================================================================
# SECTION 1: BOX PLOTS - SHOWING DISTRIBUTION SUMMARY
# =============================================================================

"""
BOX PLOTS display the five-number summary:
- Minimum (excluding outliers)
- First quartile (Q1, 25th percentile)
- Median (Q2, 50th percentile)
- Third quartile (Q3, 75th percentile)
- Maximum (excluding outliers)
- Outliers shown as individual points

Box anatomy:
- Box: Interquartile range (IQR = Q3 - Q1)
- Line in box: Median
- Whiskers: Extend to 1.5 * IQR
- Points beyond whiskers: Outliers

Function: sns.boxplot()
"""

print("="*80)
print("SECTION 1: BOX PLOTS")
print("="*80)

tips = sns.load_dataset('tips')

# Example 1.1: Basic box plot
plt.figure(figsize=(10, 6))
sns.boxplot(data=tips, x='day', y='total_bill')
plt.title('Box Plot: Total Bill by Day', fontsize=14, fontweight='bold')
plt.xlabel('Day of Week', fontsize=12)
plt.ylabel('Total Bill ($)', fontsize=12)
plt.tight_layout()
plt.show()

print("✓ Basic box plot created")

# Example 1.2: Box plot with grouping
plt.figure(figsize=(12, 6))
sns.boxplot(data=tips, x='day', y='total_bill', hue='time')
plt.title('Box Plot with Grouping: Bill by Day and Time', fontsize=14, fontweight='bold')
plt.xlabel('Day of Week', fontsize=12)
plt.ylabel('Total Bill ($)', fontsize=12)
plt.legend(title='Time of Day')
plt.tight_layout()
plt.show()

print("✓ Grouped box plot created")

# Example 1.3: Horizontal box plot
plt.figure(figsize=(10, 8))
sns.boxplot(data=tips, y='day', x='total_bill', orient='h')
plt.title('Horizontal Box Plot', fontsize=14, fontweight='bold')
plt.ylabel('Day of Week', fontsize=12)
plt.xlabel('Total Bill ($)', fontsize=12)
plt.tight_layout()
plt.show()

print("✓ Horizontal box plot created")

# Example 1.4: Customized box plot
plt.figure(figsize=(10, 6))
sns.boxplot(
    data=tips, 
    x='day', 
    y='total_bill',
    palette='Set2',
    linewidth=2.5,
    width=0.6,  # Width of boxes
    fliersize=5  # Size of outlier points
)
plt.title('Customized Box Plot', fontsize=14, fontweight='bold')
plt.xlabel('Day of Week', fontsize=12)
plt.ylabel('Total Bill ($)', fontsize=12)
plt.tight_layout()
plt.show()

print("✓ Customized box plot created\n")

# =============================================================================
# SECTION 2: VIOLIN PLOTS - DISTRIBUTION SHAPE
# =============================================================================

"""
VIOLIN PLOTS combine box plots with KDE plots. They show:
- Distribution shape (width of violin)
- Quartiles and median (inner box)
- Full data density

Advantages over box plots:
- Show bimodal distributions
- Display distribution shape
- More informative about data density

Function: sns.violinplot()
"""

print("="*80)
print("SECTION 2: VIOLIN PLOTS")
print("="*80)

# Example 2.1: Basic violin plot
plt.figure(figsize=(10, 6))
sns.violinplot(data=tips, x='day', y='total_bill')
plt.title('Violin Plot: Total Bill by Day', fontsize=14, fontweight='bold')
plt.xlabel('Day of Week', fontsize=12)
plt.ylabel('Total Bill ($)', fontsize=12)
plt.tight_layout()
plt.show()

print("✓ Basic violin plot created")

# Example 2.2: Violin plot with inner representation options
fig, axes = plt.subplots(2, 2, figsize=(14, 10))

# Box: Shows box plot inside
sns.violinplot(data=tips, x='day', y='total_bill', inner='box', ax=axes[0, 0])
axes[0, 0].set_title("inner='box' - Box plot inside")

# Quartile: Shows quartile lines
sns.violinplot(data=tips, x='day', y='total_bill', inner='quartile', ax=axes[0, 1])
axes[0, 1].set_title("inner='quartile' - Quartile lines")

# Point: Shows all data points
sns.violinplot(data=tips, x='day', y='total_bill', inner='point', ax=axes[1, 0])
axes[1, 0].set_title("inner='point' - All data points")

# Stick: Shows each observation
sns.violinplot(data=tips, x='day', y='total_bill', inner='stick', ax=axes[1, 1])
axes[1, 1].set_title("inner='stick' - Individual observations")

plt.tight_layout()
plt.show()

print("✓ Violin plots with different inner types created")

# Example 2.3: Split violin plot (comparing two groups)
plt.figure(figsize=(10, 6))
sns.violinplot(
    data=tips, 
    x='day', 
    y='total_bill', 
    hue='sex',
    split=True,  # Split violin in half for comparison
    palette='Set2'
)
plt.title('Split Violin Plot: Comparing by Gender', fontsize=14, fontweight='bold')
plt.xlabel('Day of Week', fontsize=12)
plt.ylabel('Total Bill ($)', fontsize=12)
plt.legend(title='Gender')
plt.tight_layout()
plt.show()

print("✓ Split violin plot created\n")

# =============================================================================
# SECTION 3: STRIP AND SWARM PLOTS - INDIVIDUAL POINTS
# =============================================================================

"""
STRIP PLOTS show all individual data points, optionally with jitter.
SWARM PLOTS arrange points to avoid overlap, showing density.

When to use:
- Strip: Simple, fast, good for small-medium datasets
- Swarm: More informative about density, slower for large datasets

Functions: sns.stripplot(), sns.swarmplot()
"""

print("="*80)
print("SECTION 3: STRIP AND SWARM PLOTS")
print("="*80)

# Example 3.1: Strip plot
plt.figure(figsize=(10, 6))
sns.stripplot(data=tips, x='day', y='total_bill', alpha=0.5)
plt.title('Strip Plot: Individual Data Points', fontsize=14, fontweight='bold')
plt.xlabel('Day of Week', fontsize=12)
plt.ylabel('Total Bill ($)', fontsize=12)
plt.tight_layout()
plt.show()

print("✓ Strip plot created")

# Example 3.2: Strip plot with jitter
plt.figure(figsize=(10, 6))
sns.stripplot(
    data=tips, 
    x='day', 
    y='total_bill',
    jitter=True,  # Add random noise to x-position
    alpha=0.5,
    size=4
)
plt.title('Strip Plot with Jitter', fontsize=14, fontweight='bold')
plt.xlabel('Day of Week', fontsize=12)
plt.ylabel('Total Bill ($)', fontsize=12)
plt.tight_layout()
plt.show()

print("✓ Strip plot with jitter created")

# Example 3.3: Swarm plot
plt.figure(figsize=(10, 6))
sns.swarmplot(data=tips, x='day', y='total_bill', size=4)
plt.title('Swarm Plot: Non-Overlapping Points', fontsize=14, fontweight='bold')
plt.xlabel('Day of Week', fontsize=12)
plt.ylabel('Total Bill ($)', fontsize=12)
plt.tight_layout()
plt.show()

print("✓ Swarm plot created")

# Example 3.4: Swarm plot with grouping
plt.figure(figsize=(12, 6))
sns.swarmplot(data=tips, x='day', y='total_bill', hue='time', size=4)
plt.title('Swarm Plot with Grouping', fontsize=14, fontweight='bold')
plt.xlabel('Day of Week', fontsize=12)
plt.ylabel('Total Bill ($)', fontsize=12)
plt.legend(title='Time of Day')
plt.tight_layout()
plt.show()

print("✓ Grouped swarm plot created\n")

# =============================================================================
# SECTION 4: COMBINING PLOT TYPES
# =============================================================================

"""
Combining different plot types gives the most complete picture.
Common combinations:
- Box + Strip: Shows summary and individual points
- Violin + Swarm: Shows distribution and all data
"""

print("="*80)
print("SECTION 4: COMBINING PLOT TYPES")
print("="*80)

# Example 4.1: Box plot with strip plot overlay
plt.figure(figsize=(10, 6))
sns.boxplot(data=tips, x='day', y='total_bill', color='lightgray', width=0.5)
sns.stripplot(data=tips, x='day', y='total_bill', color='black', alpha=0.3, size=3)
plt.title('Box Plot with Strip Plot Overlay', fontsize=14, fontweight='bold')
plt.xlabel('Day of Week', fontsize=12)
plt.ylabel('Total Bill ($)', fontsize=12)
plt.tight_layout()
plt.show()

print("✓ Combined box and strip plot created")

# Example 4.2: Violin plot with swarm plot overlay
plt.figure(figsize=(10, 6))
sns.violinplot(data=tips, x='day', y='total_bill', inner=None, color='lightblue', alpha=0.6)
sns.swarmplot(data=tips, x='day', y='total_bill', color='black', alpha=0.5, size=3)
plt.title('Violin Plot with Swarm Plot Overlay', fontsize=14, fontweight='bold')
plt.xlabel('Day of Week', fontsize=12)
plt.ylabel('Total Bill ($)', fontsize=12)
plt.tight_layout()
plt.show()

print("✓ Combined violin and swarm plot created\n")

# =============================================================================
# KEY TAKEAWAYS
# =============================================================================

"""
🎯 KEY TAKEAWAYS:

1. BOX PLOTS: Best for showing 5-number summary and outliers
2. VIOLIN PLOTS: Show distribution shape, good for multimodal data
3. STRIP PLOTS: Show all individual points, use jitter to reduce overlap
4. SWARM PLOTS: Like strip plots but automatically avoid overlap
5. COMBINE plots for comprehensive visualization
6. Use 'hue' for grouping, 'split' for split violins
7. Choose plot based on: data size, question, audience

NEXT: Tutorial 05 - Regression Plots
"""

print("="*80)
print("TUTORIAL 04 COMPLETE!")
print("="*80)

```

Exercises¶

Exercise 1. Create a vertical bar chart showing the populations (in millions) of five countries: China (1412), India (1408), USA (332), Indonesia (276), and Brazil (215). Add value labels on top of each bar using ax.bar_label().

import matplotlib.pyplot as plt

countries = ['China', 'India', 'USA', 'Indonesia', 'Brazil']
populations = [1412, 1408, 332, 276, 215]

fig, ax = plt.subplots(figsize=(8, 5))
bars = ax.bar(countries, populations, color='steelblue', edgecolor='navy')
ax.bar_label(bars, padding=3)
ax.set_ylabel('Population (millions)')
ax.set_title('Population by Country')
plt.tight_layout()
plt.show()

Exercise 2. Create a horizontal bar chart of the same data from Exercise 1 using ax.barh(). Sort the bars by population in ascending order (smallest at top) and use a different color for each bar.

import matplotlib.pyplot as plt

countries = ['China', 'India', 'USA', 'Indonesia', 'Brazil']
populations = [1412, 1408, 332, 276, 215]

sorted_pairs = sorted(zip(populations, countries))
sorted_pops, sorted_countries = zip(*sorted_pairs)

colors = ['#e74c3c', '#3498db', '#2ecc71', '#f39c12', '#9b59b6']

fig, ax = plt.subplots(figsize=(8, 5))
ax.barh(sorted_countries, sorted_pops, color=colors)
ax.set_xlabel('Population (millions)')
ax.set_title('Population by Country (Sorted)')
plt.tight_layout()
plt.show()

Exercise 3. Create side-by-side vertical and horizontal bar charts in a 1x2 subplot layout. Use fruit sales data: categories ['Apples', 'Bananas', 'Cherries', 'Dates'] with values [45, 62, 28, 51]. Style the vertical chart with color='coral' and the horizontal chart with color='steelblue'. Add grid lines on the value axis for both.

import matplotlib.pyplot as plt

categories = ['Apples', 'Bananas', 'Cherries', 'Dates']
values = [45, 62, 28, 51]

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))

ax1.bar(categories, values, color='coral', edgecolor='black')
ax1.set_ylabel('Sales')
ax1.set_title('Vertical Bar Chart')
ax1.grid(axis='y', alpha=0.3)

ax2.barh(categories, values, color='steelblue', edgecolor='black')
ax2.set_xlabel('Sales')
ax2.set_title('Horizontal Bar Chart')
ax2.grid(axis='x', alpha=0.3)

plt.tight_layout()
plt.show()