# How to create a stackplot with multiple stacked areas in matplotlib?

by sneha@openweaver.com Updated: Jul 13, 2023

Solution Kit

A stack plot is also a stacked area plot or stream graph. It is a type of data visualization. It is used to display the composition of different variables over time. It is particularly useful for illustrating the cumulative effect of many variables. It shows their relative contributions to a whole.

In a stack plot, the y-axis represents a quantitative value, such as time, population, or a numeric label. The x-axis represents the progression of time or another independent variable. The plot contains many layers, each representing a different variable or category. The areas are stacked on each other, and the combined area represents the total value at a given time.

The stack plot allows for easy comparison between different categories or variables. It is because the total height of each stack at any given point indicates the value or composition. It enables the visualization of changes in the composition over time. It is because the areas expand or contract based on the values of the variables being plotted. Stack plots are used in various fields. It includes finance, economics, environmental studies, and data analysis.

There are three different types of stack plots. It can be created including Stacked Bar Chart, Stacked Histogram, and Stacked 3D Chart.

• The stacked Bar graph Chart is a variation of the traditional bar chart. It is where bars are stacked on each other to show the composition of different categories. Each bar represents a specific category. The bar height represents the cumulative value of the variables being compared.
• A stacked histogram is a modification of a regular histogram. It is where many distributions or variables are stacked on top of each other. It shows their combined contribution to the distribution.
• A stacked 3D Chart is a three-dimensional representation of a stack plot. It is where extra depth is added to represent an extra dimension or variable. It helps in analyzing legend data and extracting insights.

Here are some different ways they can be used for data analysis. They help identify patterns, assess performance, Comparative Analysis, and Decision-Making. Creating effective subplots includes Choosing Appropriate Variables, Planning, and Arranging Stacks. It provides clear Axes Labels, Titles, and Using Effective Color Schemes. It can communicate with data in various ways, from simple to complex reports.

The approaches using stack plots are standalone visualizations, Comparative Analysis, and Interactive Dashboards. You can enhance your ability by using corresponding plots in data analysis practice. It helps identify respective matrices, patterns, and comparisons, uncover relationships, and gain deeper insights. You can improve your data analysis skills with practice and a curious mindset. It applies stack plots to various data analysis tasks.

Using stack plots is of great significance in improving your data analysis skills. It helps in understanding the underlying patterns within the world in data. You can unlock valuable insights by incorporating individual plots into your analytical toolkit. It enhances your ability to interpret and communicate complete data.

Here is an example of creating a stackplot with multiple stacked areas in matplotlib.

Fig1: Preview of the output when the code is run in IDE.

### Code

In this solution, we're creating stackplot with multiple stacked areas in matplotlib.

### Instructions

Follow the steps carefully to get the output easily.

1. Install Jupyter Notebook on your computer.
2. Open the terminal and install the required libraries with the following commands.
3. Install Numpy - pip install numpy
4. Install matplotlib - pip install matplotlib
5. Copy the snippet using the 'copy' button and paste it into that file.
6. Run the file using run button.

I hope you found this useful. I have added the link to dependent libraries, and version information in the following sections.

I found this code snippet by searching for "Stackplot with multiple stacked areas in matplotlib" in kandi. You can try any such use case!

### Dependent Libraries

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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matplotlibby matplotlib

Python 17559 Version:v3.7.1

matplotlib: plotting with Python

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matplotlibby matplotlib

matplotlib: plotting with Python
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You can also search for any dependent libraries on kandi like " numpy / matplotlib"

### Environment Tested

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

1. The solution is created in Python3.9.6.
2. The solution is tested on numpy 1.21.5 version.
3. The solution is tested on matplotlib 3.5.2 version.

Using this solution, we are able to create stackplot with multiple stacked areas in matplotlib.

This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us to create stackplot with multiple stacked areas in matplotlib.

### Support

1. For any support on kandi solution kits, please use the chat
2. For further learning resources, visit the Open Weaver Community learning page.

### FAQ:

1. What is a Stack plot, and how does it differ from an area plot?

A stack plot is also known as a stacked area plot or streamgraph. It is a type of data visualization. It displays the composition and changes of variables over time or across categories. It is constructed by plotting many datasets on top of each other. Each dataset is represented as a colored area. Then, the cumulative sum of the variables forms the vertical axis.

The key feature of a stack plot is that each variable's value is stacked on top of the previous variable. It helps in creating a layered representation. It shows the cumulative contribution of each variable to the whole. The stack's height at any given point represents the total value of all variables combined up to that point.

2. How do horizontal and vertical axes work in the context of stack plots?

In stack plots, the horizontal and vertical axes play important roles. It provides context and understanding of the data being visualized. Here's how the horizontal and vertical axes work in stack plots:

Horizontal Axis:

The horizontal axis represents the independent variable, time, or another categorical variable. It provides a chronological or categorical reference point for the data being plotted. Each point with the horizontal axis corresponds to a category label.

Vertical Axis:

The vertical axis represents the dependent or cumulative value of the stacked variables. It represents the cumulative contribution. It represents the total variable sum at any given point along the horizontal axis.

3. Can I create an individual plot with a Stack plot for data analysis?

You can create an individual plot using stack plots for data analysis. Stack plots can be a powerful tool for analyzing and visualizing data. It is when you want to understand the composition and changes of many variables over time. To create an individual plot with a stack plot for data analysis, you would follow these steps:

• Choose a Programming or Visualization Tool.
• Plotting the Stack Plot.
• Customize the Plot.
• Analyze the Stack Plot.
• Interpret and Draw Conclusions.

4. What kind of data can create a Stack plot using World in Data?

World in Data is a comprehensive online platform that provides various data. It can be on global trends, statistics, and indicators. While World in Data offers various data types, not all are suitable for creating a stack plot. Data suitability depends on the variable's nature and availability over time. Here are some types of data available on World in Data that can create a stack plot:

• Time Series Data
• Categorical Data
• Survey Data

5. How do column vectors and matrices help construct a stack plot?

Column vectors and matrices play a crucial role. It helps construct a stack plot by organizing and representing the data. They provide the necessary information for plotting the stacked areas. It helps determine the cumulative values at each point with the horizontal axis. Here's how column vectors and matrices help in constructing a stack plot:

Column Vectors:

Column vectors represent the individual variables or categories being plotted in stack plots. Each column vector and its elements correspond to a specific variable or category. It represents the values of that variable at different time points or within groups.

Matrices:

Matrices stores and organizes the column vectors representing the variables or categories. In a stack plot, the matrix would have dimensions. It matches the number of time points or categories and the number of plotted variables.

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