Getting Started with Plotly
The Plotly Python library is an interactive open-source graphing library. It is a very helpful tool for data visualization and understanding the data simply and easily. Plotly graph objects are a high-level interface to plotly which are easy to use. It can plot various types of graphs and charts like scatter plots, line charts, bar charts, box plots, histograms, pie charts, etc.
In this article, we will explore the basics of plotly, how to get started with it, and it’s key features that make it peferred choice for creating interactive visualization.
Table of Content
What is Plotly?
Plotly is a Python library built on top of the Plotly JavaScript library (plotly.js). It supports over 40 unique chart types, including statistical, financial, geographic, scientific, and 3-dimensional visualizations. Plotly is known for its ability to create beautiful, interactive web-based visualizations that can be displayed in Jupyter notebooks, saved as standalone HTML files, or integrated into web applications using Dash.
History and Evolution of Plotly
Plotly was initially released in 2013 and has evolved significantly since then. It was designed to be an open-source library for creating interactive, publication-quality graphs. Over the years, Plotly has added numerous features and chart types, expanded its integrations with other libraries, and improved its performance and ease of use.
Understanding the evolution of Plotly helps appreciate its current capabilities and its role in the data visualization ecosystem.
Why Use Plotly?
Plotly offers several advantages that make it an attractive choice for data visualization:
- Interactivity: Plotly has hover tool capabilities that allow us to detect any outliers or anomalies in a large number of data points.
- Ease-of-Use: It is visually attractive that can be accepted by a wide range of audiences.
- Customization: It allows us for the endless customization of our graphs that makes our plot more meaningful and understandable for others.
- Export Options: Plotly supports exporting visualizations to various formats, including static images and HTML files.
Installation and Setup with Plotly
Installation:
To install this module type the below command in the terminal.
pip install plotly
This may take some time as it will install the dependencies as well. For upgrading the version dependency refer to : How To Upgrade Plotly To Latest Version?
Plotly’s Architecture in Python
Plotly is designed with a modular architecture that provides flexibility in creating visualizations. At the heart of Plotly’s architecture are two primary interfaces: Plotly Express and Plotly Graph Objects. Each serves a distinct purpose and caters to different user needs, offering varying levels of complexity and customization.
1. Plotly Express
Plotly Express is a high-level, user-friendly interface for creating plots. It is designed to simplify the process of generating common types of visualizations with minimal code. Plotly Express is built on top of Plotly Graph Objects and provides a more streamlined and intuitive API for users who need to quickly produce interactive plots without delving into the intricacies of Plotly’s lower-level functionalities.
Features:
- Simplicity and Ease of Use: Plotly Express allows users to create plots using a concise and straightforward syntax. This simplicity is achieved by abstracting many of the underlying complexities of Plotly’s core functionalities, making it accessible even for users who are new to data visualization.
- Quick Visualization: Plotly Express is ideal for exploratory data analysis and quick visualizations. Users can generate a wide range of common plot types—such as scatter plots, bar charts, and line plots—using just a few lines of code.
- Automatic Handling: Plotly Express automatically manages many aspects of the plot, such as axis labels, titles, and legends. This automation helps users focus on data analysis rather than the technical details of plot configuration.
Example:
Here’s a simple example of creating a scatter plot using Plotly Express:
import plotly.express as px
import pandas as pd
# Sample data
df = pd.DataFrame({
'x': [1, 2, 3, 4, 5],
'y': [10, 11, 12, 13, 14],
'category': ['A', 'B', 'A', 'B', 'A']
})
# Create a scatter plot
fig = px.scatter(df, x='x', y='y', color='category', title='Simple Scatter Plot')
fig.show()
Output:
Plotly Express
In this example, Plotly Express simplifies the process of creating a scatter plot, automatically managing the color differentiation based on the ‘category’ column.
2. Plotly Graph Objects
Plotly Graph Objects (often abbreviated as Plotly GO) is a lower-level interface that provides more granular control over plot creation and customization. Unlike Plotly Express, which abstracts many details, Plotly Graph Objects allows users to construct plots by manually specifying each component.
This approach is suited for users who need fine-grained control over their visualizations or who are working with more complex plotting requirements.
Features:
- Detailed Customization: Plotly Graph Objects offers extensive customization options, allowing users to control nearly every aspect of the plot. This includes setting individual trace properties, customizing layout options, and adding annotations or shapes.
- Complex Plotting: Users can create intricate and sophisticated visualizations by combining different types of traces and layers. This capability is essential for building complex plots that require multiple data series or detailed configuration.
- Manual Configuration: Plotly Graph Objects requires users to manually define the structure of the plot, including traces, layout, and configuration settings. This approach provides flexibility but requires a deeper understanding of Plotly’s syntax and parameters.
Example:
Here’s an example of creating a scatter plot using Plotly Graph Objects:
import plotly.graph_objects as go
import pandas as pd
# Sample data
df = pd.DataFrame({
'x': [1, 2, 3, 4, 5],
'y': [10, 11, 12, 13, 14],
'category': ['A', 'B', 'A', 'B', 'A']
})
# Create a scatter plot
fig = go.Figure()
# Add scatter traces
for category in df['category'].unique():
df_category = df[df['category'] == category]
fig.add_trace(go.Scatter(x=df_category['x'], y=df_category['y'], mode='markers', name=category))
# Update layout
fig.update_layout(title='Scatter Plot with Plotly Graph Objects', xaxis_title='x', yaxis_title='y')
fig.show()
Output:
Plotly Graph Objects
In this example, Plotly Graph Objects provides detailed control over each trace and the plot’s layout. Users manually add each trace and configure the plot’s appearance, which allows for a high level of customization.
Creating the Basic Plots with Plotly
Let’s start by creating a simple line plot using Plotly Express, the high-level interface for Plotly. Let’s create various plots using this module
- Scatter Plot: Scatter plot represent values for two different numeric variables. They are mainly used for the representation of the relationship between two variables.
# import all required libraries
import numpy as np
import plotly
import plotly.graph_objects as go
import plotly.offline as pyo
from plotly.offline import init_notebook_mode
init_notebook_mode(connected=True)
# generating 150 random integers
# from 1 to 50
x = np.random.randint(low=1, high=50, size=150)*0.1
# generating 150 random integers
# from 1 to 50
y = np.random.randint(low=1, high=50, size=150)*0.1
# plotting scatter plot
fig = go.Figure(data=go.Scatter(x=x, y=y, mode='markers'))
fig.show()
Output:
- Bar charts: Bar charts are used when we want to compare different groups of data and make inferences of which groups are highest and which groups are common and compare how one group is performing compared to others.
# import all required libraries
import numpy as np
import plotly
import plotly.graph_objects as go
import plotly.offline as pyo
from plotly.offline import init_notebook_mode
init_notebook_mode(connected = True)
# countries on x-axis
countries=['India', 'canada',
'Australia','Brazil',
'Mexico','Russia',
'Germany','Switzerland',
'Texas']
# plotting corresponding y for each
# country in x
fig = go.Figure([go.Bar(x=countries,
y=[80,70,60,50,
40,50,60,70,80])])
fig.show()
Output:
- Pie chart: A pie chart represents the distribution of different variables among total. In the pie chart each slice shows its contribution to the total amount.
# import all required libraries
import numpy as np
import plotly
import plotly.graph_objects as go
import plotly.offline as pyo
from plotly.offline import init_notebook_mode
init_notebook_mode(connected = True)
# different individual parts in
# total chart
countries=['India', 'canada',
'Australia','Brazil',
'Mexico','Russia',
'Germany','Switzerland',
'Texas']
# values corresponding to each
# individual country present in
# countries
values = [4500, 2500, 1053, 500,
3200, 1500, 1253, 600, 3500]
# plotting pie chart
fig = go.Figure(data=[go.Pie(labels=countries,
values=values)])
fig.show()
Output:
- Histogram: A histogram plots the continuous distribution of variable as series of bars and each bar indicates the frequency of the occurring value in a variable. In order to use a histogram, we simply require a variable that takes continuous numeric values
# import all required libraries
import numpy as np
import plotly
import plotly.graph_objects as go
import plotly.offline as pyo
from plotly.offline import init_notebook_mode
init_notebook_mode(connected = True)
# save the state of random
np.random.seed(42)
# generating 250 random numbers
x = np.random.randn(250)
# plotting histogram for x
fig = go.Figure(data=[go.Histogram(x=x)])
fig.show()
Output:
- Box plot: A box plot is the representation of a statistical summary. Minimum, First Quartile, Median, Third Quartile, Maximum.
# import all required libraries
import numpy as np
import plotly
import plotly.graph_objects as go
import plotly.offline as pyo
from plotly.offline import init_notebook_mode
init_notebook_mode(connected = True)
np.random.seed(42)
# generating 50 random numbers
y = np.random.randn(50)
# generating 50 random numbers
y1 = np.random.randn(50)
fig = go.Figure()
# updating the figure with y
fig.add_trace(go.Box(y=y))
# updating the figure with y1
fig.add_trace(go.Box(y=y1))
fig.show()
Output:
Data Structures and Plotly
Plotly is designed to be flexible and versatile, supporting a range of data structures for creating visualizations. This flexibility is essential for accommodating different types of data and ensuring that users can effectively represent their information in visual formats.
- The primary data structures used with Plotly include Pandas DataFrames, dictionaries, lists, and NumPy arrays.
- Understanding how to work with these structures is crucial for effective data visualization.
Each data structure has its own strengths and use cases:
- Pandas DataFrames: Ideal for structured, tabular data and easy integration with Plotly Express.
- Dictionaries: Useful for custom data representation and detailed plot configuration.
- Lists: Effective for representing data series and managing multiple traces.
- NumPy Arrays: Suitable for efficient numerical handling and large datasets.
Best Practices for Using Plotly
Plotly is a powerful tool for creating interactive and informative visualizations, but to make the most of it, it’s important to follow best practices that ensure clarity, usability, and performance. Here are some key best practices to consider:
1. Choose the Right Plot Type
When to Use Plotly Express:
- Quick and Simple Visualizations: Use Plotly Express for rapid creation of common plot types (e.g., scatter plots, line charts, bar charts) with minimal code.
- Exploratory Data Analysis: Ideal for initial data exploration where ease of use and quick feedback are critical.
When to Use Plotly Graph Objects:
- Complex Visualizations: Opt for Plotly Graph Objects when you need to create intricate plots that require detailed customization.
- Fine-Grained Control: Use Graph Objects for advanced features like multiple traces, custom shapes, annotations, or intricate layout adjustments.
2. Keep Your Data Organized
Use Pandas DataFrames:
- Structured Data: Organize data in DataFrames for easy integration with Plotly Express, leveraging built-in support for DataFrames.
- Data Cleaning: Ensure data is clean and well-structured to avoid issues with plotting. This includes handling missing values and ensuring consistent data types.
Alternative Structures:
- Dictionaries and Lists: Use dictionaries and lists for more customized data setups or when working with non-tabular data. Be mindful of the data format and ensure it aligns with the plot requirements.
3. Optimize for Performance
- Large Datasets: For large datasets, consider sampling or aggregating data to avoid performance issues. Plotly can handle large datasets, but performance may degrade with very large amounts of data.
- Balance Interactivity and Performance: While interactive features are powerful, too many interactive elements can slow down performance. Use interactivity judiciously to balance usability and performance.
Conclusion
Plotly is a powerful tool for creating interactive and engaging visualizations in Python. Its flexibility, ease of use, and integration with other data science tools make it a valuable asset for data analysts, scientists, and engineers. By understanding Plotly’s features, architecture, and best practices, users can effectively leverage its capabilities to enhance their data visualization and analysis efforts.
