Plotly express: Grouped and stacked of specific data columns

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05-10-2024

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Unveiling the Power of Grouped and Stacked Charts with Plotly Express

Plotly Express is a high-level interface for creating interactive and visually appealing plots in Python. Its ease of use and versatility make it ideal for data exploration and visualization. One powerful feature is the ability to create grouped and stacked charts, which can provide insightful comparisons between different categories within your data.

Let's explore how to create grouped and stacked charts using Plotly Express, focusing on specific data columns.

Scenario: Sales Data Analysis

Imagine you have a dataset containing sales data for various products across different regions. You want to visually analyze the sales trends by product type and region. To achieve this, you can leverage the grouped and stacked chart functionality in Plotly Express.

Here's a sample code snippet using the fictitious sales_data dataframe:

import plotly.express as px

sales_data = {
    'product': ['A', 'A', 'B', 'B', 'A', 'B'],
    'region': ['East', 'West', 'East', 'West', 'East', 'West'],
    'sales': [100, 150, 80, 120, 130, 90]
}

fig = px.bar(sales_data, x='region', y='sales', color='product', barmode='group')
fig.show()

In this code:

• px.bar is the function for creating a bar chart.
• x='region' specifies the 'region' column for the x-axis.
• y='sales' specifies the 'sales' column for the y-axis.
• color='product' uses the 'product' column to group the bars by different colors.
• barmode='group' instructs Plotly Express to create grouped bars.

This code produces a grouped bar chart where each region has bars representing different product sales.

Stacked Charts: A Deeper Look

For a more nuanced view of the data, consider a stacked bar chart. To achieve this, simply modify the barmode parameter:

fig = px.bar(sales_data, x='region', y='sales', color='product', barmode='stack')
fig.show()

This code creates a stacked bar chart where each region has bars representing the total sales, with each product's contribution stacked on top of each other.

Enhanced Visualization with Plotly Express

Plotly Express offers numerous customization options to refine your charts. You can:

• Change colors: Use the color_discrete_sequence parameter to specify custom colors for each product category.
• Add labels: Use the labels parameter to modify the axis titles and legend labels.
• Set titles: Use the title parameter to add a chart title.
• Adjust layout: Use the layout parameter to control chart elements such as gridlines, margins, and annotations.

Conclusion

Plotly Express empowers you to create visually compelling and informative charts with ease. By leveraging the grouped and stacked chart functionality, you can uncover hidden patterns and gain deeper insights from your data. Remember to experiment with customization options to tailor your charts to your specific analysis needs.

For more detailed documentation and advanced features of Plotly Express, refer to the official documentation: https://plotly.com/python/plotly-express/