Data Viz with pandas tools

Quick examples of plotting with pandas and matplotlib (line, bar, scatter) and visualization best practices.

Data Visualization with pandas tools

This notebook shows simple, reusable examples of creating line, bar, and scatter charts directly from pandas using matplotlib as the backend. Each section includes a short explanation and code snippet.

# Import libraries and create sample DataFrame
import pandas as pd
import matplotlib.pyplot as plt

data = {
    'Year': [2015,2016,2017,2018,2019],
    'Revenue': [500,700,650,800,950]
}

df = pd.DataFrame(data)
df

	Year	Revenue
0	2015	500
1	2016	700
2	2017	650
3	2018	800
4	2019	950

df.plot(x='Year', y='Revenue', kind='line', marker='o', color='black', legend=False)
plt.title('Revenue over years')
plt.xlabel('Year')
plt.ylabel('Revenue')
plt.grid()
plt.show()

Line Chart: Revenue over Years

Line charts are useful for showing trends over time. Use pandas’ DataFrame.plot(kind='line') for quick exploration.

# Bar chart example data
data = {
    'City': ['New York', 'London', 'Tokyo', 'Sydney', 'Paris'],
    'Population': [850000, 890000, 900000, 520000, 1100000]
}

df_cities = pd.DataFrame(data)
df_cities

	City	Population
0	New York	850000
1	London	890000
2	Tokyo	900000
3	Sydney	520000
4	Paris	1100000

# Bar chart (sorted by population)
df_cities.sort_values(by='Population', ascending=False).plot(x='City', y='Population', kind='bar', color='tab:red', legend=False)
plt.title('Population in Major Cities')
plt.xlabel('City')
plt.ylabel('Population')
plt.tight_layout()
plt.show()

Scatter Plot: Relationship between X and Y

Scatter plots are useful to visualize relationships between two numeric variables. Use plt.scatter or DataFrame.plot(kind='scatter').

# Scatter data
df_scatter = pd.DataFrame({'X': [1, 2, 3, 4, 5], 'Y': [2, 3, 4, 8, 10]})
df_scatter

	X	Y
0	1	2
1	2	3
2	3	4
3	4	8
4	5	10

# Scatter plot
plt.scatter(df_scatter['X'], df_scatter['Y'], color='g')
plt.title('Scatter: X vs Y')
plt.xlabel('X')
plt.ylabel('Y')
plt.grid(alpha=0.3)
plt.show()

Best Practices

• Use descriptive titles and axis labels.
• Prefer plt.tight_layout() after plotting to avoid overlap in labels.
• For large datasets, consider sampling or using alpha blending (alpha) to avoid over-plotting.
• Use color palettes from matplotlib or seaborn for consistent visuals.

Summary

This notebook provided quick examples of line, bar, and scatter charts using pandas + matplotlib. Use these snippets as a starting point for exploratory data analysis and adapt styling as needed.