- Seaborn Tutorial
- Seaborn - Home
- Seaborn - Introduction
- Seaborn - Environment Setup
- Importing Datasets and Libraries
- Seaborn - Figure Aesthetic
- Seaborn- Color Palette
- Seaborn - Histogram
- Seaborn - Kernel Density Estimates
- Visualizing Pairwise Relationship
- Seaborn - Plotting Categorical Data
- Distribution of Observations
- Seaborn - Statistical Estimation
- Seaborn - Plotting Wide Form Data
- Multi Panel Categorical Plots
- Seaborn - Linear Relationships
- Seaborn - Facet Grid
- Seaborn - Pair Grid

- Seaborn Useful Resources
- Seaborn - Quick Guide
- Seaborn - Useful Resources
- Seaborn - Discussion

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# Seaborn - Kernel Density Estimates

Kernel Density Estimation (KDE) is a way to estimate the probability density function of a continuous random variable. It is used for non-parametric analysis.

Setting the **hist** flag to False in **distplot** will yield the kernel density estimation plot.

## Example

import pandas as pd import seaborn as sb from matplotlib import pyplot as plt df = sb.load_dataset('iris') sb.distplot(df['petal_length'],hist=False) plt.show()

## Output

## Fitting Parametric Distribution

**distplot()** is used to visualize the parametric distribution of a dataset.

### Example

import pandas as pd import seaborn as sb from matplotlib import pyplot as plt df = sb.load_dataset('iris') sb.distplot(df['petal_length']) plt.show()

### Output

## Plotting Bivariate Distribution

Bivariate Distribution is used to determine the relation between two variables. This mainly deals with relationship between two variables and how one variable is behaving with respect to the other.

The best way to analyze Bivariate Distribution in seaborn is by using the **jointplot()** function.

Jointplot creates a multi-panel figure that projects the bivariate relationship between two variables and also the univariate distribution of each variable on separate axes.

## Scatter Plot

Scatter plot is the most convenient way to visualize the distribution where each observation is represented in two-dimensional plot via x and y axis.

### Example

import pandas as pd import seaborn as sb from matplotlib import pyplot as plt df = sb.load_dataset('iris') sb.jointplot(x = 'petal_length',y = 'petal_width',data = df) plt.show()

### Output

The above figure shows the relationship between the **petal_length** and **petal_width** in the Iris data. A trend in the plot says that positive correlation exists between the variables under study.

### Hexbin Plot

Hexagonal binning is used in bivariate data analysis when the data is sparse in density i.e., when the data is very scattered and difficult to analyze through scatterplots.

An addition parameter called ‘kind’ and value ‘hex’ plots the hexbin plot.

### Example

import pandas as pd import seaborn as sb from matplotlib import pyplot as plt df = sb.load_dataset('iris') sb.jointplot(x = 'petal_length',y = 'petal_width',data = df,kind = 'hex') plt.show()

## Kernel Density Estimation

Kernel density estimation is a non-parametric way to estimate the distribution of a variable. In seaborn, we can plot a kde using **jointplot().**

Pass value ‘kde’ to the parameter kind to plot kernel plot.

### Example

import pandas as pd import seaborn as sb from matplotlib import pyplot as plt df = sb.load_dataset('iris') sb.jointplot(x = 'petal_length',y = 'petal_width',data = df,kind = 'hex') plt.show()