Pandas DataFrame Density Plot.
Table Of Contents:
- Syntax ‘plot.density( )’ Method In Pandas.
- Examples ‘plot.density( )’ Method.
(1) Syntax:
DataFrame.plot.density(bw_method=None, ind=None, **kwargs)Description:
- Generate Kernel Density Estimate plot using Gaussian kernels.
- 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.
Parameters:
- bw_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_kdefor more information. ind : NumPy 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().
Returns:
- matplotlib.axes.Axes or numpy.ndarray of them
(2) Examples Of plot.density() Method:
# 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()Output:
#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)Output:
ax = s.plot.kde(bw_method=3)Output:
# 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])Output:
# 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],
})
df
ax = df.plot.kde()Output:
#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])