Creating a heatmap: In this data, I have used the same car dataset which contains a large amount of data and the graph below shows a representation of manufacturer names by the colors of cars.

Color Scale: Although the graphs have the same range count from 0 to 800, the color range in the graph below is less distinct and should be adjusted accordingly. While the highest count is identifiable, the differences in color distribution for companies with lower data are not clear without labels.

Sorting Data: For better visualization in heat maps, ensure the data is sorted in either ascending or descending order for both the x and y axes. Unsorted data, as seen in the graph below, results in a poor representation, making it difficult to interpret.

Maximum and Minimum: To highlight the highest and lowest values in the graph, using three distinct colors for your color range can make a significant difference. This graph can be used if the user wants focus  graph on the highest and lowest differences in the data.

Axis: Ensure that the x-axis accommodates a large number of categories. If the categories are placed on the y-axis, it transforms into a vertical box, which is equivalent to a table sorted from highest to lowest.