# Changing Line Color in Plot Based on Range Limitations

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Are you tired of looking at plots with the same old monotonous line color? Are your eyes struggling to distinguish between different data points on the graph? Well, worry no more! We have the solution for you.

Have you ever wished that the line color on the plot could change based on certain range limitations? For instance, imagine a scenario where you’re plotting a graph for temperature data. You want the line color to change from blue to red when the temperature exceeds a certain limit. Sounds interesting, right?

In this article, we’ll show you how to implement this feature in your plots using the popular Python library, Matplotlib. With just a few lines of code, you can make your plots more visually appealing and informative.

So, if you’re a data analyst, scientist, or anyone who loves to visualize data, then this article is definitely worth reading till the end. You’ll learn how to create plots with changing line colors that highlight important data points and provide an overall better visual experience. Let’s get started!

“Is It Possible To Change Line Color In A Plot If Exceeds A Specific Range?” ~ bbaz

## Introduction

Plotting is an essential aspect of data visualization. It helps to unveil the hidden relationship between variables that would have gone unnoticed otherwise. In a plot, line color specifies the class or category of the data observed. However, when the range of values is large, the line colors may become distorted or not appropriately represent the desired class preference. This issue can be solved by changing line colors based on range limitations.

## The Need for Range Limitations

As earlier stated, line colors in a plot are indicative of class preference. For example, in a scatter plot where the x-axis represents age, and the y-axis represents weight, different classes could include underweight, average weight, and overweight. The line color will differentiate the class each point belongs to. Nevertheless, in situations where there is a significant range difference between two classes, the line colors may become distorted. It leads to inaccurate data visualization that may cause wrong conclusions in data analysis. Hence, the need for range limitations arises.

## Types of Line Color Limitations

Several line color limitations can be used to derive valid interpretations from plotted data. Examples include Equal Interval, Quantile, and Standard Deviation limitations. The Equal Interval method categorizes the data into intervals of the same width, while the Quantile method categorizes the data into areas of equal frequencies. The Standard Deviation method categorizes the data based on the distance from the mean, where data within one standard deviation have one color, and the rest have other colors.

## Equal Interval Limitation

The Equal Interval limitation method calculates the maximum value and minimum value of the data series. Then, it divides the range into several equal intervals and applies a particular line color to each division. Data that falls within a particular interval takes the assigned color, and data that fall outside the range of the series take another color. For instance, suppose we have a data set ranging from 0-1000. We can divide it into four equal intervals, each with a different line color-a maximum value of 10,000 red, a minimum value of 7500 yellow, a minimum value of 5000 green, and a maximum value of 2500 blue. Any data that falls below 2500 will be viewed as blue.

## Quantile Limitation

The Quantile limitation method sorts the data values in ascending order, then divides them into n portions of equal size. The iterations are called quantiles or percentile groups. Each quantile is color-coded to distinguish it from other parts. This limitation is ideal in cases where the data has outliers that may affect the Standard Deviation Limitation. For example, suppose we have data on monthly sales by 12 employees in a company. We can divide the data into three equal parts: the first four employees’ sales, the second four employees’ sales, and the last four employees’ sales. Then, each of the quantiles can be assigned a different line color.

## Standard Deviation Limitation

The Standard Deviation limitation method calculates the mean and standard deviation of the data set. Each point’s standard deviation value is compared with the standard deviations of all the data points. Those values within one standard deviation from the mean take the color of the specific division, while those outside the one-standard deviation range get a different color. This method is suitable for data sets with a bell curve distribution. For instance, consider a data set representing IQ scores of 12 individuals in a class. We can set the mean and standard deviation of the data; then, any score within one standard deviation of the mean will be assigned one color, whereas scores outside the range will get a different color.

## Comparison Table between Limitations

Equal Interval Limitation Easy to understand and implement Does not perform well with skewed data and extremes
Quantile Limitation Applicable to both continuous and categorical data May lead to missing valuable information if used improperly
Standard Deviation Limitation Allows for detailed comparison within and across classes Not appropriate in data sets without normal distribution

## My Opinion

Changing line colors based on range limitations is an excellent approach to achieve accurate data visualization. However, each method’s suitability depends on the data set’s nature and distribution. Hence, before selecting a limitation method, adequate analysis of the data set is necessary, to prevent misleading assumptions.

## Conclusion

In conclusion, data visualization plays a crucial role in data analysis, and line color is a vital aspect of that. However, limiting or changing line colors based on range limitations helps to mitigate distortion and ensure accurate interpretation. The Equal Interval, Quantile, and Standard Deviation limitation methods are excellent approaches, but the one to use depends heavily on the data. Proper analysis and selection to the appropriate limitations approach can enhance the data story, making analysis more meaningful and accurate.