# How to create Scatter Plot using plotly

by gayathrimohan Updated: Oct 31, 2023

Solution Kit

A scatter plot is a graphical data representation. It displays individual data points as dots on a two-dimensional coordinate system.

Each dot on the plot represents the values of two variables. We can plot one variable on the horizontal and the other on the vertical. The pattern or distribution of these dots can reveal relationships or trends in the data.

## Scatter plots help with several purposes:

• Data Exploration
• Correlation Analysis
• Outlier Detection
• Pattern Recognition
• Comparison

A scatter plot is a powerful data visualization tool. That can display relationships between variables.

## Here are the different types of data that we can plot on a scatter plot:

• Numeric-Numeric Scatter Plot
• Categorical-Numeric Scatter Plot
• Categorical-Categorical Scatter Plot
• Time Series Scatter Plot
• Bubble Scatter Plot

Scatter plots, bar plots, and line plots are distinct types of data visualizations.

## Each has its purpose and characteristics:

• Scatter Plots: Used to display individual data points on a two-dimensional plane. We can represent each data point as a dot. The distribution and patterns of the dots can reveal correlations or trends.
• Bar Plots: Bar plots, also known as bar charts. It helps represent categorical data. Bar plots are great for comparing data across categories. They are like sales figures for different products.
• Line Plots: Line plots show data trends over a continuous range. Lines connect the data points, making it easy to track changes and patterns.

Scatter plots are powerful tools for visualizing data relationships and patterns.

## Here are some keyways to interpret them:

• Display Data Trends: Scatter plots are great for displaying trends in data.
• Measure Relationships: It helps you to assess the strength and direction. This assesses the relationship between two variables. A horizontal line indicates no correlation.
• Analyze Data Variability: A wide scatter suggests high variability. We can do it while a narrow scatter indicates low variability.
• Identify Outliers: Outliers data points that deviate from the general pattern. They are often easy to spot on a scatter plot. They can provide valuable insights into data anomalies or errors.
• Assess Linearity: Determine if the relationship between variables is linear or nonlinear. A linear relationship shows a clear trend in a straight line. A nonlinear relationship may exhibit curves or clusters of data points.
• Visualize Clusters: This may reveal clusters or groups of data points. This can state the presence of subpopulations within your data.
• Check for Heteroscedasticity: Helps you to identify heteroscedasticity. It is a situation where the spread of data points changes as you move along the x-axis. This is essential for understanding the reliability of regression models.
• Compare Many Relationships: Used to compare relationships between several pairs of variables. It is useful in multivariate analysis and identifying complex data interactions.

In conclusion, scatter plots are essential tools in data analysis and visualization. They allow us to identify patterns, relationships, and outliers in data. We can do it by making them crucial for understanding correlations. It makes informed decisions in various fields, such as statistics, research, and business.

Fig: Preview of the output that you will get on running this code from your IDE.

### Code

In this solution we are using plotly library in Python.

### Instructions

Follow the steps carefully to get the output easily.

2. Open the terminal and install the required libraries with the following commands.
3. Create a new Python file on your IDE.
4. Copy the snippet using the 'copy' button and paste it into your python file.
5. Run the current file to generate the output.﻿

I hope you found this useful.

I found this code snippet by searching for 'How to create Scatter Plot using plotly' in Kandi. You can try any such use case!

### Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

1. PyCharm Community Edition 2022.3.1
2. The solution is created in Python 3.11.1 Version
3. plotly.py v5.15.0 Version
4. pandas v2.0.2 Version
5. numpy v1.25.0rcl Version

Using this solution, we can able to use lemmatize method in textblob in python with simple steps. This process also facilities an easy way to use, hassle-free method to create a hands-on working version of code which would help us to use lemmatize method in textblob in python.

### Dependent Library

plotly.pyby plotly

Python 13630 Version:v5.15.0

The interactive graphing library for Python :sparkles: This project now includes Plotly Express!

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plotly.pyby plotly

Python 13630 Version:v5.15.0 License: Permissive (MIT)

The interactive graphing library for Python :sparkles: This project now includes Plotly Express!
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pandasby pandas-dev

Python 38689 Version:v2.0.2

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

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pandasby pandas-dev

Python 38689 Version:v2.0.2 License: Permissive (BSD-3-Clause)

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
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numpyby numpy

Python 23755 Version:v1.25.0rc1

The fundamental package for scientific computing with Python.

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numpyby numpy

Python 23755 Version:v1.25.0rc1 License: Permissive (BSD-3-Clause)

The fundamental package for scientific computing with Python.
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You can search for any dependent library on Kandi like 'plotly.py', 'pandas', and 'numpy'.

### Support

1. For any support on Kandi solution kits, please use the chat
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### FAQ:

1. What is Plotly-Scatter Plot, and how does it work?

It is a Python data visualization library. It helps create interactive and appealing plots and charts. A scatter plot displays individual data points on a two-dimensional graph. In this, creating a scatter plot is straightforward.

Here's how it works:

• Data Preparation: You start by preparing your data. It consists of two sets of numerical values, one for the x-axis and one for the y-axis. These data points represent the coordinates of individual points on the scatter plot.
• Import Plotly: Import the library into your Python script or Jupyter Notebook.
• Create a Scatter Plot: Use Plotly's functions to create a scatter plot. You can customize various aspects of the plot. Those plots include the data points' appearance, labels, and titles.
• Interactivity: It provides interactivity features. We can do it by allowing you to hover over data points. It helps see more information, zoom in, pan, or save the plot as an image.
• Display or Export: You can display the scatter plot in your Jupyter Notebook.

2. Are there different distribution plots that we can create with Python Plotly?

Yes, this is a popular Python data visualization library. It provides various types of distribution plots you can create.

Some common distribution plots include:

• Histograms
• Box Plots
• Violin Plots
• Density Plots
• Histograms with Marginal Box Plots
• ECDF (Empirical Cumulative Distribution Function) Plots
• QQ (Quantile-Quantile) Plots

3. How is Interactive Data Visualization used in creating a scatter plot?

Interactive data visualization helps enhance the creation. It enhances the exploration of scatter plots in various ways:

• Data Exploration
• Data Filtering
• Tooltips
• Data Highlighting
• Axis Customization
• Trend Lines
• Zoom and Pan
• Color and Size Mapping
• Interactivity for Many Variables
• Saving and Exporting

4. Are line plots available in Python, or only scatter plots?

Yes, Python supports both scatter plots and line plots. You can create line plots by specifying the mode in your trace to be 'lines' instead of 'markers' for scatter plots.

Here's a simple example of creating a line plot:

import plotly.graph_objects as go

# Sample data

x = [1, 2, 3, 4, 5]

y = [10, 11, 12, 11, 10]

# Create a line plot

fig = go.Figure(data=go.Scatter(x=x, y=y, mode='lines'))

# Show the plot fig.show()

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