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How to create a Python heatmap with Seaborn? [Comprehensive Explanation]

Companies in the big data era are overwhelmed by the large amount of data every day. However, what matters is not the amount of data involved, but how the data is processed.Therefore, you need to analyze big data to gain the final insights. Direct a better decision Affect strategic business movements.

Still, it’s not enough to analyze the data and leave it there.The next step is Data visualization It displays the data in a visual format to see and understand patterns, trends, and outliers in the data. Python heatmaps are one of many data visualization techniques.

Data visualization refers to the graphic representation of data and may include graphs, charts, maps, and other visual elements. It is very important for analyzing vast amounts of information and making informed decisions.

This article describes the concept of heatmaps in Python and how to use Seaborn to create heatmaps.

What is a heat map?

Python heatmaps are a color-based data visualization technique that shows how a value of interest changes with the values ​​of two other variables. This is a two-dimensional graphic representation of the data whose values ​​are color-encoded, thereby providing a visually compelling view full of simplified insights of information. The image below is a simplified representation of the heatmap.

Heatmaps are usually data tables with rows and columns that represent different sets of categories. Each cell in the table contains a Boolean value or number that determines the color of the cell based on a particular color palette. Therefore, heatmaps use color to emphasize relationships between data values ​​that are difficult to understand when placed in a regular table using raw numbers.

Heatmaps find applications in some real-world scenarios. For example, consider the following heatmap. This is a stock index heatmap that identifies general trends in the stock market. The heatmap uses a cold to hot color scheme to show bearish and bullish stocks. The former is represented in red and the latter in green.

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Heatmaps are used in several other areas. Some examples include website heatmaps, geographic heatmaps, and sports heatmaps. For example, you can use a heatmap to understand how rainfall changes from month to month across a series of cities. Heatmaps are also very useful for studying human behavior.

Correlation heat map

Correlation heatmaps are two-dimensional matrices that show the correlation between two different variables. The rows of the table show the values ​​of the first variable and the second variable as columns. Like regular heatmaps, correlated heatmaps come with color bars to read and understand the data.

The color scheme used is such that one end of the color scheme represents the low data points and the other end represents the high data points. Therefore, correlated heatmaps are ideal for data analysis because they display patterns in an easy-to-read format while highlighting data variability.

The following is a classic representation of a correlation heatmap.

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Create a Seaborn heatmap in Python

Seaborn is a Python library used for data visualization and is based on matplotlib. It provides a useful and visually appealing medium for displaying data in statistical graph format. In heatmaps created using seaborn, the color palette represents variations of related data. If you are a beginner and want to acquire data science expertise, Data science course.

Steps to create a heatmap in Python

The following steps give a high-level overview of how to create a simple heatmap in Python.

  • Import all required packages
  • Import the file that saved the data
  • Plot the heatmap
  • Display heatmap using matplotlib

Now let’s see how to use seaborn with matplotlib and pandas to generate a heatmap.

This example creates a sea heatmap in Python for the stocks of 30 pharmaceutical companies. The resulting heatmap shows the stock symbol and each daily percentage price change. First, we collect market data for the stocks of pharmaceutical companies and create a CSV (comma-separated values) file consisting of the stock symbol and the corresponding price volatility in the first two columns of the CSV file.

We are working with 30 pharmaceutical companies to create a 6-by-5 ​​heatmap matrix. In addition, the heatmap should show the rate of change in price in descending order. So we’ll sort the CSV file stocks in descending order and add two more columns to show the location of each stock on the X and Y axes of the maritime heatmap.

step 1: Import Python packages.

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Step 2: Loading the dataset.

The dataset is read using the pandas read_csv function. In addition, use the print statement to visualize the first 10 lines.

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Step 3: Creating a PythonNumpy array.

With a 6×5 matrix in mind, create an n-dimensional array of “Symbols” and “Modify” columns.

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Step 4: Create a pivot in Python.

From the specified data frame object “df”, the pivot function creates a new derived table. Pivot functions take three arguments: index, column, and value. The value of the cell in the new table is taken from the Change column.

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Step 5: Create an array to annotate the heatmap.

The next step is to create an array to annotate the ocean heatmap. To do this, call the flatten method on the arrays “percentage” and “symbol” to flatten the Python list of the list in one line. In addition, the zip function compresses the list in Python. Run a Python for loop and use the format function to format the stock symbol and stock volatility values ​​as needed.

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Step 6: Create a matplotlib diagram and define a plot.

In this step, you will create an empty matplotlib plot and define the size of your figure. In addition, add a title for the plot, set the font size for the title, and use the set_position method to fix the distance from the plot. Finally, we want to see only the stock symbol and its corresponding daily percentage price change, so hide the X-axis and Y-axis tick marks and remove the axis from the plot.

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Step 7: Creating a heat map

The final step is to create a heatmap using the heatmap functions from the seaborn Python package. The heatmap function for the Seaborn Python package looks like this: Set of arguments:

This is a 2D dataset that can be forced into an array. If you specify Pandas DataFrame, rows and columns are labeled with index / column information.

This is an array of the same shape as the data and annotates the heatmap.

This is a matplotlib object or colormap name that maps data values ​​to the color space.

The string format code used when adding annotations.

Sets the width of the line that divides each cell.

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The final output of the marine heatmap for the selected pharmaceutical company is as follows:

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Conclusion

Statisticians and data analysts use numerous tools and techniques to sort collated data and display it in an easy-to-understand and user-friendly way. In this regard, heatmaps as a data visualization technique have helped companies in all sectors better visualize and understand their data.

In summary, heatmaps are widely used and are still used as one of the statistical and analytical tools of choice. It provides a visually appealing and accessible data display mode, is easy to understand, versatile, adaptable, and displays all values ​​in one frame for traditional data analysis. And to eliminate the tedious steps of the interpretation process.

How do you plot the heatmap?

Heatmaps are the standard way to plot grouped data in a two-dimensional graph format. The basic idea behind a heatmap plot is that the graph is divided into squares or rectangles, each representing one cell, one dataset, and one row in the data table. Squares or rectangles are color-coded according to the value of that cell in the table.

Does the heatmap show the correlation?

Correlation heatmaps are a graphic representation of a correlation matrix that represents the correlation between various variables. Correlation heatmaps are very effective when used properly because they make it easy to identify highly correlated variables.

Why is seaborn used in Python?

Seaborn is an open source Python library based on matplotlib. Used for exploratory data analysis and visualization, it makes it easy to work with data frames and Pandas libraries. In addition, graphs created using seaborn can be easily customized.

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