One kind of data visualization tool is the density plot. It is a histogram variant that plots the data using “kernel smoothing.” It is a histogram that is inferred from data that is continuous and smooth.
Kernel density estimation, or KDE, is a probability density function that is used in density charts, which is why they are often referred to as KDE plots. The area of the plot with the highest peak is where the majority of the data points fall between those values.
You may create density graphs using Pandas, Seaborn, etc. Let us focus on what a density plot really is.
What is a density plot?
A density plot, also known as a kernel density plot, is a way to visualize the probability density function of a continuous variable
This method involves drawing a kernel (continuous curve) for each unique data point, which is then combined with all the other curves to get a single smoothed density estimation. When we attempt to compare the data distribution of a single variable across numerous categories, the histogram is unsuccessful. In such a case, the density plot can be used to visualize the data.
The likelihood of a variable having a specific value is expressed by the probability density function of a vector, denoted by f(x)f(x). The histogram’s smoothed counterpart, the empirical probability density function, is the histogram. This is sometimes referred to as the kernel estimator or Parzen-Rosenblatt estimator

Importance of Histogram: It is vital to understand histograms before learning about density plots.
A density plot and a histogram are extremely similar. A histogram is used to show the distribution’s shape. By binning the data and keeping track of how many observations are in each bin, histograms can be produced. The y-axis of a histogram typically shows bin counts, but it can also show counts per unit, commonly known as densities.
Knowledge about The Density Plot
It should be quite clear by this point that density plots are simply plots of smoothed histograms. Density graphs frequently employ a kernel density estimate. The kernel density estimation offers smoother distributions by lowering noise.
Since density plots are unaffected by the number of bins, which is an important consideration when histograms are to be taken into account, we can more clearly observe how our data are distributed.
As a result, it resembles a histogram in that a smooth curve crosses the top of each bin.
The thickness The peaks on the plot show the places with the highest concentration of values, and the distribution of the data across the given time period is shown.
Density plots are better at determining the shape of the distribution because they are unaffected by the number of bins (the idea of the shape of a probability distribution comes up when deciding which distribution to use to model the statistical characteristics of a population given a sample from that population).
The most well-known kind of density plot is a kernel density estimate, in which a continuous curve is produced for each individual data point. Then, all of these curves are added together to provide a single smooth density estimation.
Let’s focus on both the benefits and drawbacks of creating a density map in Python. But before that lets implement it in python.
How to make a density plot in Python
The first step is to import all the required libraries such as matplolib, seaborn, and plotly in order to make a density plot.
| import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import plotly.graph_objs as go |
The second step is to load the titanic dataset using the pandas library and display the top 5 rows of the dataset using the head function.
| df= pd.read_csv(‘train.csv’) df.head() |
The next step is to create the density plot using the matplotlib library. Here is an example of a Python program that creates a density plot using the matplotlib library:
| df.Age.plot.density() plt.title(“Density plot of Age column”) plt.show() |
The next step is to create the density plot using the kernel density plot in seaborn
| sns.displot(df, x=”Age”, kind=”kde”) plt.title(“Density plot of Age column in seaborn”) plt.show() |
The next step is to make a density plot using the plotly library. In the below code, the density plot was created.
# Create the density plot trace = go.Histogram(x=column, histnorm=’probability density’, name=’density’) layout = go.Layout(title=’Density Plot of Numerical Column’, xaxis=dict(title=’Value’), yaxis=dict(title=’Density’)) fig = go.Figure(data=[trace], layout=layout) # Show the plot fig.show() |
The go.Histogram function creates the density plot, and the histnorm parameter is set to ‘probability density’ to show the probability density of the values within the numerical column. The resulting plot will show the probability density of the values within the numerical column.
Advantages of the Density plot
- As contrast to the histogram, the density plot may smooth the value distribution and remove noise. The peaks show where values are concentrated while the distribution of the data across a given time period is shown.
- A density plot is used to visualize the distribution of data across a continuous interval or time period. This histogram-like graph uses kernel smoothing to depict numbers, which creates smoother distributions by removing noise. The peaks of a density plot can be used to display the areas of an interval where values are concentrated.
- Density plots have an advantage over histograms in that they are better at detecting the distribution shape (each bar used in a conventional histogram) since the number of utilised bins has no effect on them. A histogram with twenty bins would produce a more recognisable distribution form than one with only four bins. Density charts do not have an issue with this, though.
- A density map may be used to visualise the distribution of a continuous numerical variable over a dataset. Sometimes, the phrase “Kernel Density Plots” is used.
- It’s a good idea to thoroughly understand your data before applying any machine learning algorithms to it.
Disadvantages of the Density plot
- The density plot is not without shortcomings though. The plot depends on selecting the proper bandwidth in order to present the data in the most optimal way; if the bandwidth is chosen wrong, the data may be distorted by being either excessively or under-smoothed.
- The density plot, as previously mentioned, shows a distribution for the majority of the smoothed races with the proper bandwidth selection, adequately removing noise while still highlighting important features, such as the locations of the distribution’s modes and spread. The density plot has additional advantages.
- Unlike a histogram, which would require numerous plots piled on top of each other and expose substantially more noise, the density plot’s lines may all be contained inside a single graph. Techniques like strip charts, which use various colours to denote the many races on the same scene, may also be effective. However, they would be significantly busier and very likely lot harder to read.
Conclusion
Python’s density plot offers a lot of advantages but also some disadvantages. The completeness of the density plot can help us distinguish overlapping distributions. We can compare airlines easily since the density map is less crowded than other graphs. Since we received the plot we desired, we draw the conclusion that the arrival delay distributions of all of these airlines are substantially the same. We may plot one of the other airlines in the dataset that is slightly different to demonstrate colouring the graph, another potential feature for density graphs.
Although we can also produce empirical cumulative density plots and quantile-quantile plots, for the time being we’ll stay with histograms and density plots (and rug plots, too!). Don’t worry if the options seem overwhelming; with practise, making sensible decisions will become easier, and you can always ask for help if you need it. There is usually no perfect alternative, therefore the “correct” answer will rely on personal preference and the visualization’s objectives.
The good news is that Python will be capable of handling any plot you decide to create! Visualizations may be used to convey results effectively, and by being aware of all our options, we can choose the best graph for our data.
