How do you plot kde in python?

DataFrame.plot.kde(bw_method=None, ind=None, **kwargs)[source]¶

Generate Kernel Density Estimate plot using Gaussian kernels.

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How do you plot a KDE plot in Python?
What is KDE in plotting?
What does KDE do in Python?
How do you plot a density curve in Python?

In statistics, kernel density estimation (KDE) is a non-parametric way to estimate the probability density function (PDF) of a random variable. This function uses Gaussian kernels and includes automatic bandwidth determination.

Parametersbw_methodstr, scalar or callable, optional

The method used to calculate the estimator bandwidth. This can be ‘scott’, ‘silverman’, a scalar constant or a callable. If None (default), ‘scott’ is used. See scipy.stats.gaussian_kde for more information.

indNumPy array or int, optional

Evaluation points for the estimated PDF. If None (default), 1000 equally spaced points are used. If ind is a NumPy array, the KDE is evaluated at the points passed. If ind is an integer, ind number of equally spaced points are used.

**kwargs

Additional keyword arguments are documented in DataFrame.plot().

Returnsmatplotlib.axes.Axes or numpy.ndarray of them

See also

scipy.stats.gaussian_kde

Representation of a kernel-density estimate using Gaussian kernels. This is the function used internally to estimate the PDF.

Examples

Given a Series of points randomly sampled from an unknown distribution, estimate its PDF using KDE with automatic bandwidth determination and plot the results, evaluating them at 1000 equally spaced points (default):

>>> s = pd.Series([1, 2, 2.5, 3, 3.5, 4, 5])
>>> ax = s.plot.kde()

A scalar bandwidth can be specified. Using a small bandwidth value can lead to over-fitting, while using a large bandwidth value may result in under-fitting:

>>> ax = s.plot.kde(bw_method=0.3)

>>> ax = s.plot.kde(bw_method=3)

Finally, the ind parameter determines the evaluation points for the plot of the estimated PDF:

>>> ax = s.plot.kde(ind=[1, 2, 3, 4, 5])

For DataFrame, it works in the same way:

>>> df = pd.DataFrame({
...     'x': [1, 2, 2.5, 3, 3.5, 4, 5],
...     'y': [4, 4, 4.5, 5, 5.5, 6, 6],
... })
>>> ax = df.plot.kde()

A scalar bandwidth can be specified. Using a small bandwidth value can lead to over-fitting, while using a large bandwidth value may result in under-fitting:

>>> ax = df.plot.kde(bw_method=0.3)

>>> ax = df.plot.kde(bw_method=3)

Finally, the ind parameter determines the evaluation points for the plot of the estimated PDF:

>>> ax = df.plot.kde(ind=[1, 2, 3, 4, 5, 6])

How do you plot a KDE plot in Python?

Kdeplot is a Kernel Distribution Estimation Plot which depicts the probability density function of the continuous or non-parametric data variables i.e. we can plot for the univariate or multiple variables altogether. Using the Python Seaborn module, we can build the Kdeplot with various functionality added to it.

What is KDE in plotting?

A kernel density estimate (KDE) plot is a method for visualizing the distribution of observations in a dataset, analogous to a histogram. KDE represents the data using a continuous probability density curve in one or more dimensions.

What does KDE do in Python?

Kernel density estimation (KDE) is a non-parametric method for estimating the probability density function of a given random variable. It is also referred to by its traditional name, the Parzen-Rosenblatt Window method, after its discoverers.

How do you plot a density curve in Python?

Approach:.

Import the necessary libraries..

Create or import a dataset from seaborn library..

Select the column for which we have to make a plot..

For making the plot we are using distplot() function provided by seaborn library for plotting Histogram and Density Plot together in which we have to pass the dataset column..