When to Use HeatMap plot for Visualization of Data

HeatMap (Matrix) Plot Visualization for the Data: When to Use?

Visual representation always helps in simplification either any real world entities or the data. Visualization  provides an pictorial representation so anyone can easily understand about the data and their insights(what they are representing and in which range the value is lying.

                                            

                                              

                                                                Source: HeatMap

Now when the data science becomes one of the popular domain in Computer science. It makes a big impact both in technology domain and in industries. Every industries now a days wants to find insights about their business data that are generated daily and improve and grow their business accordingly. So the data science jobs now become very trending.

To make a complete analysis of data one's should many times go through visualization phase. Because everyone is not a good statistician or a data scientist. So visualization is done using various prebuilt libraries and tools. My favourite one is seaborn library that is extended form of matplotlib library (Python based) to visualize data in more colorful, concise and precise manner.  Also there are various online plotting libraray and tools available includes plotly, bokeh etc.

In this article we are talking about HeatMap (Matrix) plot for visualization of Data. When to use and what is the benefits of Using it when we also have various plotting graphs and types are present.

HeatMap is based on coloring. It visualize the data through variations in coloring. It is better for examine the multi-variate data with representing through rows and columns. It helps through visualizing the variance in data by visualizing the color palette. It also display the any similarity between variables.

                               

                                       Fig:- Sample Heatmap Using random value(10,10)

Here interpolation = 'nearest'. It displays an image without trying to interpolate between pixels if the display resolution is not the same as the image resolution. (mostly cases)

When to Use HeatMap(Matrix) plot for Visualization of Data?

1.  Heatmap is a two dimensional plot, which maps x and y pairs to a value. So it means input to heatmap must be a 2 dimensional array. Hence when we have value in given format of 2D array used Heatmap.

2. When want to find the similarity between any variable whether any variable is similar to each other.

3. To check about variance in data use the heatmap. Heatmap matrix better represent the varaiance and correlations by plotting the data on matrix shaped (row-column based color palette).

4. It represents data in color form. For numerical data it generates color scale to specify range of the data dependency and how the values changes (specifies range) on color scale. Better and easy to visualize.

Here the Heat-Map for Days and Hours of Day (Used more-frequently in plotting with Date and Time)

            

Fig:- Colorful Heatmap Using Seaborn

By using the Heatmap we generate a colorful matrix which shows correlation, similarity and range of values on colorful palette. Heatmap is a necessary for visualizing correlated data and also represents both categorical and numerical data accordingly. For categorical data is color-coded and for numerical data it based on color scale for representation of particular range of values on scale of color gradient.

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