Bar Plots¶

Bar plots visualize categorical data by displaying rectangular bars with heights proportional to the values they represent. Pandas provides multiple ways to create bar plots.

Basic Bar Plot¶

Using plot(kind='bar')¶

```python import pandas as pd import matplotlib.pyplot as plt

Count categories¶

url = 'https://raw.githubusercontent.com/justmarkham/DAT8/master/data/drinks.csv' df = pd.read_csv(url)

fig, ax = plt.subplots(figsize=(10, 4)) df['continent'].value_counts().plot(kind='bar', ax=ax) ax.set_title('Countries by Continent') ax.set_ylabel('Count') plt.tight_layout() plt.show() ```

Horizontal Bar (kind='barh')¶

python fig, ax = plt.subplots(figsize=(8, 5)) df['continent'].value_counts().plot(kind='barh', ax=ax) ax.set_title('Countries by Continent') ax.set_xlabel('Count') plt.tight_layout() plt.show()

Single Bar Plot with Matplotlib¶

For more control, use matplotlib's ax.bar():

```python import matplotlib.pyplot as plt import pandas as pd

def load_teachers_data(): data = { 'Courses': ('Language', 'History', 'Geometry', 'Chemistry', 'Physics'), 'Number of Teachers': (7, 3, 9, 1, 2) } return pd.DataFrame(data).set_index('Courses')

df = load_teachers_data()

fig, ax = plt.subplots(figsize=(10, 4)) teacher_counts = df['Number of Teachers']

ax.bar( x=range(len(teacher_counts)), height=teacher_counts, tick_label=df.index, width=0.5 )

ax.set_xlabel('Courses') ax.set_ylabel('Number of Teachers') ax.set_title('Favorite Courses of Teachers') ax.spines[['right', 'top']].set_visible(False)

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

Grouped Bar Plot¶

Compare multiple metrics across categories:

```python import matplotlib.pyplot as plt import numpy as np import pandas as pd

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

df = load_student_scores()

Set up positions¶

positions = np.arange(len(df)) width = 0.35

fig, ax = plt.subplots(figsize=(10, 5))

Plot bars side by side¶

ax.bar(positions - width/2, df['Midterm'], width, label='Midterm') ax.bar(positions + width/2, df['Final'], width, label='Final')

Customize¶

ax.set_xticks(positions) ax.set_xticklabels(df.index) ax.set_xlabel('Student') ax.set_ylabel('Score') ax.set_title('Midterm and Final Scores') ax.legend() ax.spines[['right', 'top']].set_visible(False)

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

Using pandas plot() for Grouped Bars¶

python fig, ax = plt.subplots(figsize=(10, 5)) df.plot(kind='bar', ax=ax) ax.set_title('Student Scores') ax.set_ylabel('Score') plt.xticks(rotation=0) plt.tight_layout() plt.show()

Stacked Bar Plot¶

Show composition within categories:

```python

Using pandas¶

fig, ax = plt.subplots(figsize=(10, 5)) df.plot(kind='bar', stacked=True, ax=ax) ax.set_title('Student Scores (Stacked)') plt.xticks(rotation=0) plt.tight_layout() plt.show() ```

Segmented Bar Plot (100% Stacked)¶

Show proportions rather than absolute values:

```python import matplotlib.pyplot as plt import numpy as np

Data: Has Antibodies?¶

labels = ('Yes', 'No') antibody_pcts = ( np.array([95, 90, 40]), # Yes percentages np.array([5, 10, 60]) # No percentages ) age_groups = ('Adults', 'Children', 'Infants')

fig, ax = plt.subplots(figsize=(8, 5))

Initialize bottom for stacking¶

bottom = np.zeros(3)

Stack bars¶

for label, pct in zip(labels, antibody_pcts): ax.bar( x=np.arange(3), height=pct, width=0.5, bottom=bottom, label=label ) bottom += pct

ax.set_xticks(np.arange(3)) ax.set_xticklabels(age_groups) ax.set_ylabel('Percentage') ax.set_title('Has Antibodies?') ax.spines[['top', 'right']].set_visible(False) ax.legend(title='Response', loc='center left', bbox_to_anchor=(1.0, 0.5))

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

Bar Plot from Value Counts¶

Common pattern for categorical data:

```python url = "https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv" df = pd.read_csv(url)

fig, axes = plt.subplots(1, 3, figsize=(12, 4))

Survival counts¶

df['Survived'].value_counts().plot(kind='bar', ax=axes[0]) axes[0].set_title('Survival') axes[0].set_xticklabels(['Died', 'Survived'], rotation=0)

Passenger class¶

df['Pclass'].value_counts().sort_index().plot(kind='bar', ax=axes[1]) axes[1].set_title('Passenger Class') axes[1].set_xticklabels(['1st', '2nd', '3rd'], rotation=0)

Embarkation port¶

df['Embarked'].value_counts().plot(kind='bar', ax=axes[2]) axes[2].set_title('Embarkation Port')

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

Customization Options¶

Colors¶

```python

Single color¶

df['continent'].value_counts().plot(kind='bar', color='steelblue')

Multiple colors¶

colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b'] df['continent'].value_counts().plot(kind='bar', color=colors) ```

Edge Color¶

python df['continent'].value_counts().plot(kind='bar', edgecolor='black')

Bar Width¶

python df['continent'].value_counts().plot(kind='bar', width=0.8) # 0-1 range

Rotation¶

```python

Rotate x-tick labels¶

df.plot(kind='bar', rot=45) ```

Grid¶

python df.plot(kind='bar', grid=True)

Sorting Bars¶

```python

Sort by value (descending - default for value_counts)¶

df['continent'].value_counts().plot(kind='bar')

Sort by value (ascending)¶

df['continent'].value_counts().sort_values().plot(kind='bar')

Sort alphabetically¶

df['continent'].value_counts().sort_index().plot(kind='bar') ```

Adding Value Labels¶

```python fig, ax = plt.subplots(figsize=(10, 5)) counts = df['continent'].value_counts() bars = ax.bar(range(len(counts)), counts.values)

Add labels on bars¶

for bar, count in zip(bars, counts.values): ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.5, str(count), ha='center', va='bottom')

ax.set_xticks(range(len(counts))) ax.set_xticklabels(counts.index) ax.set_title('Countries by Continent') plt.tight_layout() plt.show() ```

Summary of Bar Plot Types¶

Quick Reference¶

```python

Basic bar from value counts¶

series.value_counts().plot(kind='bar')

Horizontal¶

series.value_counts().plot(kind='barh')

Multiple columns grouped¶

df.plot(kind='bar')

Stacked¶

df.plot(kind='bar', stacked=True)

Customized¶

series.value_counts().plot( kind='bar', color='steelblue', edgecolor='black', width=0.7, rot=45 ) ```

Exercises¶

Exercise 1. Write code that creates a bar plot from a DataFrame using df.plot.bar(). Add a title and axis labels.

Exercise 2. Create a grouped bar plot from a DataFrame with multiple numeric columns using df.plot.bar().

Exercise 3. Write code that creates a horizontal bar plot using df.plot.barh() and sorts the bars by value.

Exercise 4. Create a stacked bar plot using df.plot.bar(stacked=True) to show the composition of categories.