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# What is the purpose of a density plot or kde plot?

## Density Plot

A density plot, also known as a kernel density estimate (KDE) plot, is a graphical display of data that shows the probability density function (PDF) of the data. It is used to visualize the distribution of the data and identify patterns and trends in the data.

The purpose of a density plot is to give you a visual representation of the underlying distribution of the data. It can help you understand the shape and spread of the data and identify any unusual values or outliers. It can also be used to compare the distribution of multiple variables or groups.

Because they are unaffected by the number of bins, density plots have an advantage over histograms in that they are better at identifying the shape of the distribution. Density charts include, for example, normal distribution curves.

## Application & Interpretation

Application & Interpretation: Let's say we have a dataset with 1000 credit card users' ages. We are interested in how the age distribution is distributed.

We can see that the peak in the graph below is a little over 45. We would have discovered in a histogram that the values' concentration is in the 45–50 range (if the bucket was five years wide). However, this density figure provides us with a more exact position. A continuous distribution view is also provided.

## How to Interpret Density Curves

The distribution of values in a particular dataset may be quickly and visually understood with the help of density curves, which exist in various sizes and forms. They are particularly helpful in aiding our ability to visualize −

### Number of Peaks

We can rapidly determine the number of " peaks " in a particular distribution by using density curves. Because there was just one peak in each of the above-case distributions, we would categorize those distributions as unimodal.

However, certain distributions—referred to as bimodal distributions—can have two peaks. Additionally, multimodal distributions with two or more peaks are occasionally possible. We can rapidly determine the number of peaks in the distribution by drawing a density curve for the dataset.

### Skewness

Skewness is a term used to define a distribution's symmetry. We can immediately determine from density curves if a graph is left, right, or has no skew.

### The location of the mean & median

We can rapidly determine whether the mean or median is greater in a particular distribution based on the skewness of a density curve. more specifically

When a density curve is left-skewed, the mean is less than the median.

The mean is bigger than the median when a density curve is right-skewed.

The mean and median are identical when a density curve has no skew.

## Properties of Density Curves

The qualities of density curves are as follows −

Every time, the area under the curve adds up to 100%.

Never will the curve deviate from the x-axis.

When you generate or evaluate density curves for various distributions, keep these two truths in mind.

## Kde Plot

A density plot, also known as a kernel density estimate (KDE) plot, is a graphical display of data that shows the probability density function (PDF) of the data. It is used to visualize the distribution of the data and identify patterns and trends in the data.

The purpose of a density plot is to give you a visual representation of the underlying distribution of the data. It can help you understand the shape and spread of the data and identify any unusual values or outliers. It can also be used to compare the distribution of multiple variables or groups.

A histogram, a stack of rectangles, will always seem wavy regardless of the interval length chosen (think bricks again). We occasionally want to compute a smoother estimate since it could be more accurate. We can slightly alter our strategy to account for that.

The histogram technique converts each data point into a rectangle with a defined area, which is then placed "near" the corresponding data point. What if we could pour a "pile of sand" on each data point and see how the sand builds instead of using rectangles?

## Conclusion

In conclusion, a density plot or KDE plot is a graphical display of data that shows the probability density function of the data. It is used to visualize the distribution of the data and identify patterns and trends in the data. The purpose of a density plot is to give you a visual representation of the underlying distribution of the data and help you understand the shape and spread of the data. It can be used to compare the distribution of multiple variables or groups, and to identify any unusual values or outliers in the data.