UNIT-1
Syllabus
An Introduction to Data Visualization in Python, Types of Plots- statistical
plots, Images, Networks/ Graphs, Geographical, 3D and Interactive, Grids and
Meshes
Introduction to Data Visualization in Python
Data Visualization:
Data Visualization is the graphical representation of data to understand
patterns, trends, relationships, and insights easily. In Python, visualization
helps beginners and professionals to convert raw data into meaningful
visuals for decision-making.
Uses:
Simplifies complex data
Identifies trends and outliers
Supports data-driven decisions
Improves communication of results
Common Python Libraries
Matplotlib – basic plotting
Seaborn – statistical visualization
Pandas – data handling + plots
Plotly – interactive plots
NetworkX – networks/graphs
OpenCV / PIL – images
GeoPandas / Folium – geographical data
Mayavi / Plotly – 3D plots
Data visualization is an essential part of data analysis and decision-making.
Python provides a rich ecosystem of libraries that help in visualizing data in
different forms such as charts, graphs, images, maps, and 3D models. The
commonly used Python libraries for data visualization are e
1. Matplotlib – Basic Plotting
Matplotlib is the most fundamental plotting library in Python. It is mainly
used to create static, two-dimensional plots such as line graphs, bar charts,
histograms, and scatter plots. It provides full control over plot appearance
,including titles, labels, colors, and grids. Matplotlib is widely used for
academic purposes and basic data analysis.
Example use cases include plotting student marks, temperature variations,
or stock prices over time.
2. Seaborn – Statistical Visualization
Seaborn is a high-level visualization library built on top of Matplotlib. It is
specifically designed for statistical data visualization and provides visually
attractive plots with minimal code. Seaborn supports box plots, violin plots,
heatmaps, and regression plots, which are useful for understanding data
distributions and relationships.
It is commonly used in data science and machine learning for exploratory
data analysis.
3. Pandas – Data Handling with Plots
Pandas is primarily a data manipulation library, but it also provides built-in
plotting capabilities. Using Pandas, data can be easily read from CSV or Excel
files and directly visualized using simple commands. Internally, Pandas uses
Matplotlib for plotting.
This library is widely used for business reports, financial analysis, and
academic research.
4. Plotly – Interactive Visualization
Plotly is used to create interactive and web-based visualizations. The plots
support features such as zooming, hovering, and rotation. Plotly is very
useful for dashboards and presentations where user interaction is required.
It is extensively used in analytics dashboards and real-time data
visualization.
5. NetworkX – Networks and Graphs
NetworkX is used to create, analyze, and visualize network or graph data
structures. It represents data in terms of nodes and edges. NetworkX is
useful for visualizing social networks, computer networks, and transportation
systems.
It helps in understanding relationships and connections between entities.
6. OpenCV and PIL – Image Visualization
OpenCV and PIL (Python Imaging Library) are used for image processing and
visualization. OpenCV is mainly used in computer vision applications such as
face detection and motion tracking. PIL is simpler and used for basic image
loading, displaying, and editing.
These libraries are used in medical imaging, surveillance systems, and image
analysis.
7. GeoPandas and Folium – Geographical Visualization
GeoPandas is used to work with geographical and spatial data such as maps
, and shapefiles. Folium is used to create interactive maps using latitude and
longitude information. These libraries are helpful in visualizing population
data, weather maps, and location-based services.
They are widely used in GIS applications.
8. Mayavi and Plotly – 3D Visualization
Mayavi and Plotly support three-dimensional data visualization. Mayavi is
mainly used for scientific and engineering data visualization, while Plotly
provides interactive 3D plots suitable for web applications.
These tools are used in simulations, scientific research, and engineering
design.
Statistical Plots
Statistical plots are used to understand data distribution, comparison, and
relationships.
Common Types
Line plot
Bar chart
Histogram
Box plot
Scatter plot
Required Modules
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
Line Plot:
A line plot (or line graph) is a type of data visualization used to display data
points connected by straight line segments, usually to show changes or
trends over time or over a continuous variable.
Each point on the line represents a data value, and the line helps in
understanding the pattern, increase, decrease, or variation in the data.
Use: Daily Temperature Distribution (LINE)
Code:
Syllabus
An Introduction to Data Visualization in Python, Types of Plots- statistical
plots, Images, Networks/ Graphs, Geographical, 3D and Interactive, Grids and
Meshes
Introduction to Data Visualization in Python
Data Visualization:
Data Visualization is the graphical representation of data to understand
patterns, trends, relationships, and insights easily. In Python, visualization
helps beginners and professionals to convert raw data into meaningful
visuals for decision-making.
Uses:
Simplifies complex data
Identifies trends and outliers
Supports data-driven decisions
Improves communication of results
Common Python Libraries
Matplotlib – basic plotting
Seaborn – statistical visualization
Pandas – data handling + plots
Plotly – interactive plots
NetworkX – networks/graphs
OpenCV / PIL – images
GeoPandas / Folium – geographical data
Mayavi / Plotly – 3D plots
Data visualization is an essential part of data analysis and decision-making.
Python provides a rich ecosystem of libraries that help in visualizing data in
different forms such as charts, graphs, images, maps, and 3D models. The
commonly used Python libraries for data visualization are e
1. Matplotlib – Basic Plotting
Matplotlib is the most fundamental plotting library in Python. It is mainly
used to create static, two-dimensional plots such as line graphs, bar charts,
histograms, and scatter plots. It provides full control over plot appearance
,including titles, labels, colors, and grids. Matplotlib is widely used for
academic purposes and basic data analysis.
Example use cases include plotting student marks, temperature variations,
or stock prices over time.
2. Seaborn – Statistical Visualization
Seaborn is a high-level visualization library built on top of Matplotlib. It is
specifically designed for statistical data visualization and provides visually
attractive plots with minimal code. Seaborn supports box plots, violin plots,
heatmaps, and regression plots, which are useful for understanding data
distributions and relationships.
It is commonly used in data science and machine learning for exploratory
data analysis.
3. Pandas – Data Handling with Plots
Pandas is primarily a data manipulation library, but it also provides built-in
plotting capabilities. Using Pandas, data can be easily read from CSV or Excel
files and directly visualized using simple commands. Internally, Pandas uses
Matplotlib for plotting.
This library is widely used for business reports, financial analysis, and
academic research.
4. Plotly – Interactive Visualization
Plotly is used to create interactive and web-based visualizations. The plots
support features such as zooming, hovering, and rotation. Plotly is very
useful for dashboards and presentations where user interaction is required.
It is extensively used in analytics dashboards and real-time data
visualization.
5. NetworkX – Networks and Graphs
NetworkX is used to create, analyze, and visualize network or graph data
structures. It represents data in terms of nodes and edges. NetworkX is
useful for visualizing social networks, computer networks, and transportation
systems.
It helps in understanding relationships and connections between entities.
6. OpenCV and PIL – Image Visualization
OpenCV and PIL (Python Imaging Library) are used for image processing and
visualization. OpenCV is mainly used in computer vision applications such as
face detection and motion tracking. PIL is simpler and used for basic image
loading, displaying, and editing.
These libraries are used in medical imaging, surveillance systems, and image
analysis.
7. GeoPandas and Folium – Geographical Visualization
GeoPandas is used to work with geographical and spatial data such as maps
, and shapefiles. Folium is used to create interactive maps using latitude and
longitude information. These libraries are helpful in visualizing population
data, weather maps, and location-based services.
They are widely used in GIS applications.
8. Mayavi and Plotly – 3D Visualization
Mayavi and Plotly support three-dimensional data visualization. Mayavi is
mainly used for scientific and engineering data visualization, while Plotly
provides interactive 3D plots suitable for web applications.
These tools are used in simulations, scientific research, and engineering
design.
Statistical Plots
Statistical plots are used to understand data distribution, comparison, and
relationships.
Common Types
Line plot
Bar chart
Histogram
Box plot
Scatter plot
Required Modules
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
Line Plot:
A line plot (or line graph) is a type of data visualization used to display data
points connected by straight line segments, usually to show changes or
trends over time or over a continuous variable.
Each point on the line represents a data value, and the line helps in
understanding the pattern, increase, decrease, or variation in the data.
Use: Daily Temperature Distribution (LINE)
Code:
