Plotting Data with Pandas

Prerequisites: Basic Python, pandas basics and DataFrame concepts

Table of Contents

1. Course Overview
2. The Python Visualization Ecosystem
   • Available Visualization Tools
   • matplotlib and pyplot
   • Three-Step Framework for Creating a Plot
   • Demo: First Plot in Jupyter Notebook
3. Creating Charts with pandas
   • Line Plot
   • Loading and Selecting Data
   • Scatter Plot
   • Bar Chart
   • Histogram
   • Area Chart
4. Reference Diagrams
   • Hierarchy of pandas Chart Types
   • Flow: df.plot() → matplotlib Backend
5. Reference Tables
   • Main df.plot() Parameters
   • kind Parameter Values
6. Datasets Used in the Course

1. Course Overview

This course introduces matplotlib, the most widely used visualization framework for Python, through the built-in visualization capabilities of pandas. Data visualization is essential at two moments in analytical work:

• During analysis — to explore and understand data.
• For communication — to share results with other stakeholders.

Topics Covered

2. The Python Visualization Ecosystem

Available Visualization Tools

There are four main packages for visualizing data with Python/pandas:

Key rule: All plot objects created by pandas are matplotlib objects. Calling .plot() on a DataFrame is equivalent to making a matplotlib call.

matplotlib and pyplot

pyplot is a matplotlib submodule that exposes simple functions to manipulate chart elements (lines, legends, axes). Its syntax is deliberately similar to MATLAB.

import matplotlib.pyplot as plt

%matplotlib inline is a Jupyter Notebook magic command that requests charts to be displayed directly inline in the notebook, alongside the code.

%matplotlib inline

Three-Step Framework for Creating a Plot

┌─────────────────────────────────────────────────────┐
│  Step 1: Import the necessary packages              │
│  Step 2: Prepare / import data                      │
│  Step 3: Call the visualization function            │
└─────────────────────────────────────────────────────┘

Demo: First Plot in Jupyter Notebook

# Step 1 — Imports
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

# Step 2 — Data
x = [2, 3, 4]
y = [4, 3, 2]

# Step 3 — Visualization
plt.plot(x, y)

Result: a descending line plot connecting points (2,4), (3,3), (4,2).

3. Creating Charts with pandas

Line Plot

By default, the .plot() method creates a line plot that connects each data point with a line.

With a simple array

import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

# Daily temperatures (°F) in Seattle over 5 days
y = [54, 58, 60, 57, 55]
pd.Series(y).plot()

Matplotlib assigns a default integer index (0, 1, 2…) on the X axis.

With an explicit X axis

x = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri']
y = [54, 58, 60, 57, 55]

plt.plot(x, y)
plt.title('Seattle High Temp (5-day)')
plt.xlabel('Day')
plt.ylabel('Temperature (°F)')
plt.show()

Loading and Selecting Data

read_csv

The read_csv function loads a CSV file into a DataFrame:

df = pd.read_csv('homes.csv')
df.head()

Common read_csv options:

Example from homes.csv:

Sell,List,Living,Rooms,Beds,Baths,Age,Acres,Taxes
142,160,28,10,5,3,60,0.28,3167
175,180,18,8,4,1,12,0.43,4033
129,132,13,6,3,1,41,0.33,1471
...

Selecting columns for a plot

# Plot the relationship between two columns
df.plot(x='List', y='Rooms')

By default, pandas plots each column as a line on the Y axis with an automatic X index. To isolate the relationship between two specific variables, explicitly specify x= and y=.

Scatter Plot

A scatter plot is ideal for visualizing the relationship between two numerical variables. Use the kind='scatter' parameter.

df.plot(kind='scatter', x='Age', y='List')

Filtering data before plotting

# Filter homes 50 years old or less
house_plot_data = df[df['Age'] <= 50]
house_plot_data = house_plot_data[house_plot_data['List'] < 600000]

house_plot_data.plot(kind='scatter', x='Age', y='List',
                     title='List Price vs. Age of Home (≤50 yrs)')

Best practice: Filter outliers into a new DataFrame before creating the chart to keep the code clear.

Bar Chart

A bar chart is suitable for comparing categories or data segments. File: cities.csv.

cities_df = pd.read_csv('cities.csv')

# Filter year 2019 only
cities_2019 = cities_df[cities_df['Year'] == 2019]

# Vertical bars
cities_2019.plot(kind='bar', x='City', y='Population',
                 title='2019 Population', legend=None)

Horizontal bars

# Horizontal bars — better rendering for long city names
cities_2019.plot(kind='barh', x='City', y='Population',
                 title='2019 Population', legend=None)

Notable parameters

Histogram

A histogram represents the frequency distribution of a numerical variable. Values are grouped into bins (intervals).

# Distribution of acreage for homes sold
house_plot_data['Acres'].plot(kind='hist',
                              title='Acreage Distribution')

Interpretation: The majority of home sales have an acreage below 0.5, and nearly all are below 1.5 acres.

Customizing bins

house_plot_data['Acres'].plot(kind='hist', bins=20,
                              title='Acreage Distribution',
                              xlabel='Acres',
                              ylabel='Count')

Area Chart

An area chart is useful when the data represents a total with components over a time dimension. File: enrollment.csv.

Raw data

Year,Class,Enrollment
2018,Freshman,1500
2018,Sophomore,1400
2018,Junior,1300
2018,Senior,1200
...

Step 1 — Load and explore

enrolled_df = pd.read_csv('enrollment.csv')
enrolled_df.head()

Step 2 — Pivot the DataFrame (long → wide format)

# pivot: one row per year, one column per class level
pivot_enrolled = enrolled_df.pivot(index='Year',
                                   columns='Class',
                                   values='Enrollment')
pivot_enrolled.head()

Result of the pivot:

Step 3 — Create the stacked area chart

pivot_enrolled.plot(kind='area',
                    title='College Enrollment by Class Level')

Important: Without the pivot, the area chart will not be properly stacked. The pivot() method reorganizes the data so that each column corresponds to a distinct series.

4. Reference Diagrams

Hierarchy of pandas Chart Types

graph TD
    A["df.plot()"] --> B["kind='line'\nLine Plot"]
    A --> C["kind='scatter'\nScatter Plot"]
    A --> D["kind='bar'\nBar Chart vertical"]
    A --> E["kind='barh'\nBar Chart horizontal"]
    A --> F["kind='hist'\nHistogram"]
    A --> G["kind='area'\nArea Chart"]
    A --> H["kind='box'\nBox Plot"]
    A --> I["kind='pie'\nPie Chart"]
    A --> J["kind='kde'\nKDE / Density"]
    A --> K["kind='hexbin'\nHexbin Plot"]

    B --> L[("Time trends\nContinuous relationships")]
    C --> M[("Relationships between\n2 variables")]
    D --> N[("Categorical\ncomparison")]
    E --> N
    F --> O[("Frequency\ndistribution")]
    G --> P[("Total evolution\nover time")]
    H --> Q[("Advanced statistical\ndistribution")]
    I --> R[("Parts of a whole")]

Flow: df.plot() → matplotlib Backend

flowchart LR
    A["DataFrame\n(pandas)"] -->|".plot(kind=...)"\nor direct call| B["pandas Plot API"]
    B -->|"internal call"| C["matplotlib Figure\n& Axes"]
    C -->|"rendered via"| D{"Backend"}
    D -->|"Jupyter Notebook"| E["%matplotlib inline\nInline display"]
    D -->|"Python Script"| F["plt.show()\nNative window"]
    D -->|"Save"| G["plt.savefig()\n.png / .svg / .pdf"]

    style A fill:#4CAF50,color:#fff
    style B fill:#2196F3,color:#fff
    style C fill:#FF9800,color:#fff
    style D fill:#9C27B0,color:#fff

5. Reference Tables

Main df.plot() Parameters

kind Parameter Values

6. Datasets Used in the Course

homes.csv — Real Estate Sales

Usage: scatter plot (Age vs List), histogram (Acres).

cities.csv — US City Populations

Usage: bar chart and barh chart (2019 population by city).

enrollment.csv — University Enrollments

Usage: stacked area chart (enrollment evolution by class level).

Essential Commands Summary

import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

# --- Load data ---
df = pd.read_csv('file.csv')

# --- Line plot (default) ---
df['column'].plot(title='My title')

# --- Scatter plot ---
df.plot(kind='scatter', x='col_x', y='col_y')

# --- Vertical bar chart ---
df.plot(kind='bar', x='category', y='value', legend=False)

# --- Horizontal bar chart ---
df.plot(kind='barh', x='category', y='value', legend=False)

# --- Histogram ---
df['column'].plot(kind='hist', bins=20, title='Distribution')

# --- Area chart (with prior pivot) ---
pivot_df = df.pivot(index='year', columns='category', values='value')
pivot_df.plot(kind='area', title='Evolution by category')

# --- Save ---
plt.savefig('my_chart.png', dpi=150, bbox_inches='tight')
plt.show()

Search Terms

plotting · data · pandas · python · foundations · analysis · engineering · analytics · plot · chart · area · df.plot · matplotlib · parameters · reference · selecting · visualization