Skip to main content
Data Visualization
CHAPTER 26 Beginner

Data Visualization with Pandas

Updated: May 18, 2026
5 min read

# CHAPTER 26

Data Visualization with Pandas

1. Chapter Introduction

Pandas has a built-in .plot() method that creates Matplotlib charts directly from DataFrames — perfect for rapid EDA without writing chart code from scratch. This chapter masters Pandas plotting for the complete data analysis workflow.

2. Pandas plot() API

python
123456789101112131415161718192021222324252627282930313233343536373839 
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

np.random.seed(42)
df = pd.DataFrame({
    'Month': pd.date_range('2024-01', periods=12, freq='MS'),
    'Revenue':   np.random.normal(80000, 15000, 12).clip(50000, 120000),
    'Cost':      np.random.normal(55000, 10000, 12).clip(35000, 80000),
    'Customers': np.random.randint(100, 300, 12)
}).set_index('Month')
df['Profit'] = df['Revenue'] - df['Cost']
df['Margin'] = df['Profit'] / df['Revenue'] * 100

plt.style.use('seaborn-v0_8-whitegrid')
plt.rcParams['figure.figsize'] = (10, 5)

# Line chart
ax = df[['Revenue', 'Cost', 'Profit']].plot(
    title='Monthly Financials 2024',
    ylabel='Amount ($)',
    color=['#1565C0', '#E53935', '#4CAF50'])
ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'${x/1000:.0f}K'))
plt.tight_layout()
plt.savefig('pandas_line.png', dpi=150)
plt.show()

# Bar chart
df['Revenue'].plot(kind='bar', title='Monthly Revenue', color='#1565C0', rot=30)
plt.tight_layout()
plt.savefig('pandas_bar.png', dpi=150)
plt.show()

# Stacked area
df[['Revenue', 'Cost']].plot(kind='area', alpha=0.5, title='Revenue vs Cost',
                               color=['#1565C0', '#E53935'])
plt.tight_layout()
plt.savefig('pandas_area.png', dpi=150)
plt.show()

3. GroupBy + Plot for EDA

python
12345678910111213141516171819202122232425262728293031323334353637 
# Grouped visual analysis
np.random.seed(42)
sales_df = pd.DataFrame({
    'Date':    pd.date_range('2024-01', periods=200, freq='D'),
    'Region':  np.random.choice(['North', 'South', 'East', 'West'], 200),
    'Product': np.random.choice(['Laptop', 'Phone', 'Monitor'], 200),
    'Revenue': np.random.normal(5000, 1500, 200).clip(1000, 10000),
    'Units':   np.random.randint(1, 20, 200)
})

fig, axes = plt.subplots(2, 2, figsize=(14, 10))

# GroupBy bar chart
sales_df.groupby('Region')['Revenue'].sum().sort_values().plot(
    kind='barh', ax=axes[0,0], color='#1565C0', title='Revenue by Region')

# Pivot + stacked bar
pivot = sales_df.groupby(['Region', 'Product'])['Revenue'].sum().unstack()
pivot.plot(kind='bar', ax=axes[0,1], stacked=True, title='Revenue by Region & Product', rot=30)

# Line chart — monthly trend
sales_df.set_index('Date')['Revenue'].resample('W').sum().plot(
    ax=axes[1,0], color='#4CAF50', title='Weekly Revenue Trend')

# Box plot comparison
sales_df.boxplot(column='Revenue', by='Region', ax=axes[1,1], grid=False)
axes[1,1].set_title('Revenue Distribution by Region')
axes[1,1].set_xlabel('Region')

for ax in axes.flatten():
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)

plt.suptitle('Sales EDA Dashboard', fontsize=14, fontweight='bold')
plt.tight_layout()
plt.savefig('pandas_eda.png', dpi=150, bbox_inches='tight')
plt.show()

4. Common Mistakes

  • df.plot() ignoring non-numeric columns: Pandas plotting automatically includes all numeric columns. Use column selection df[['A','B']].plot() to control what's plotted.
  • Not resetting index after groupby: df.groupby().sum() returns an index-based Series. Call .resetindex() for better Pandas plot axis labels.

5. MCQs

Question 1

df.plot(kind='bar') uses?

Question 2

df[['A','B']].plot() plots?

Question 3

df.resample('W').sum().plot() shows?

Question 4

df.boxplot(by='Region') groups by?

Question 5

kind='area' creates?

Question 6

stacked=True in plot(kind='bar') creates?

Question 7

df.groupby('Region')['Revenue'].sum().sortvalues() before plot?

Question 8

pivot.plot(kind='bar') on grouped pivot table?

Question 9

.unstack() before pivot plot?

Question 10

Pandas plot for EDA is best because?

6. Interview Questions

  • Q: How do you create a grouped bar chart using Pandas groupby?
  • Q: What is the difference between df.plot() and plt.plot()?

7. Summary

Pandas .plot() wraps Matplotlib for one-line charts from DataFrames. Key kind values: line, bar, barh, area, hist, box. Combine groupby().sum().plot() for instant category comparison. resample().plot() for time series EDA. Use for exploration — switch to Matplotlib/Seaborn for publication polish.

8. Next Chapter Recommendation

In Chapter 27: Advanced Visualization Techniques, we explore animations, 3D surfaces (correctly used), custom chart types, and large-scale visual analytics.

Finish this Chapter

Save your progress on your learning path and prepare for coding interview challenges.

Previous Chapter Common Visualization Mistakes
Next Chapter Advanced Visualization Techniques

Discussion

Join the discussion

Log in or create a free account to participate.

Sort: ·

Explore More

📖 Related Tutorials 5

🧪 Related Labs 6

🗺️ Related Roadmaps 1