Pandas: Data Visualization

Plotting Basic Charts#

Pandas makes data visualization ridiculously easy—so easy that you’ll wonder why you ever suffered through Excel charts. Here’s how to get started:

• Line Plots using .plot()

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({"Year": [2018, 2019, 2020, 2021], "Sales": [200, 250, 400, 600]})
df.plot(x="Year", y="Sales", kind="line")
plt.show()

A line plot is perfect for showing trends over time—like your caffeine intake while debugging code.

• Histograms using .hist()

df["Sales"].hist(bins=5)
plt.show()

Histograms help visualize distribution, so you can quickly spot if your data is normal… or totally skewed.

• Box Plots using .boxplot()

df.boxplot(column=["Sales"])
plt.show()

Box plots reveal outliers—because there’s always that one absurd data point ruining everything.

• Bar and Scatter Plots

df.plot(kind="bar", x="Year", y="Sales")
df.plot(kind="scatter", x="Year", y="Sales")
plt.show()

Bar plots are great for categorical data, while scatter plots are perfect for spotting relationships.

Using Pandas with Matplotlib and Seaborn#

Matplotlib is the backbone of data visualization, and Seaborn is the stylish best friend that makes everything look better.

• Introduction to Matplotlib and Seaborn

import seaborn as sns
sns.set_style("darkgrid")

This instantly makes your plots less ugly.

• Creating Pandas Visualizations with Matplotlib Backend

df.plot(kind="line")
plt.title("Sales Over Time")
plt.show()

Pandas uses Matplotlib under the hood, so you can mix and match.

• Enhancing Pandas Plots with Seaborn Styles

sns.lineplot(data=df, x="Year", y="Sales")
plt.show()

Seaborn brings cleaner aesthetics and default settings that don’t look like they were made in the ‘90s.

Customizing Plots and Adding Labels#

Your plot is useless if no one knows what it means. Add some context:

• Adding Titles, Labels, and Legends

plt.title("Annual Sales Trend")
plt.xlabel("Year")
plt.ylabel("Sales")
plt.legend(["Revenue"])
plt.show()

This prevents people from guessing what your data means.

• Adjusting Figure Size and Color Schemes

plt.figure(figsize=(10,5))
df.plot(kind="bar", color=["#FF5733", "#33FF57"])
plt.show()

Change figure size and color for better readability.

• Using Subplots for Multiple Visualizations

fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))
df.plot(ax=axes[0], kind="bar")
df.plot(ax=axes[1], kind="line")
plt.show()

Multiple plots? No problem. Subplots let you visualize different angles in one go.

Advanced Visualization Techniques#

Let’s take things up a notch.

• Creating Multi-Faceted Plots with sns.FacetGrid

g = sns.FacetGrid(df, col="Year")
g.map_dataframe(sns.histplot, x="Sales")
plt.show()

Great for comparing distributions across different categories.

• Visualizing Correlations with Heatmaps

import numpy as np
corr_matrix = df.corr()
sns.heatmap(corr_matrix, annot=True, cmap="coolwarm")
plt.show()

Heatmaps show correlations at a glance—useful for spotting relationships in large datasets.

• Combining Multiple Plots for Storytelling

fig, ax = plt.subplots()
sns.lineplot(data=df, x="Year", y="Sales", ax=ax, label="Sales Trend")
sns.scatterplot(data=df, x="Year", y="Sales", color="red", ax=ax)
plt.show()

Layer different plots together to build a compelling narrative.

Hands-On Exercise#

1. Create Basic Plots: Generate line, histogram, and box plots using Pandas.
2. Enhance Visualizations with Matplotlib & Seaborn: Customize plots by adding labels, legends, and styling.
3. Use Advanced Techniques: Create heatmaps and multi-faceted plots using Seaborn.
4. Combine Plots for Insights: Develop a visualization dashboard using multiple Pandas plots.

References#

• Pandas Visualization