For a plotly figure factory distribution plot, the default distribution is kde
(kernel density estimation):
You can override the default by setting curve = 'normal'
to get:
But how can you show both kde and the normal curve in the same plot? Assigning a list like curve_type = ['kde', 'normal']
will not work.
import plotly.figure_factory as ff
import plotly.graph_objects as go
import plotly.express as px
import numpy as np
np.random.seed(2)
x = np.random.randn(1000)
hist_data = [x]
group_labels = ['distplot'] # name of the dataset
mean = np.mean(x)
stdev_pluss = np.std(x)
stdev_minus = np.std(x)*-1
fig = ff.create_distplot(hist_data, group_labels, curve_type='kde')
fig.update_layout(template = 'plotly_dark')
fig.show()
A histogram puts all samples between the boundaries of each bin will fall into the bin. It doesn't differentiate whether the value falls close the left, to the right or the center of the bin. A kde plot, on the other hand, takes each individual sample value and draws a small gaussian bell curve over it.
Description. As known as Kernel Density Plots, Density Trace Graph. A Density Plot visualises the distribution of data over a continuous interval or time period. This chart is a variation of a Histogram that uses kernel smoothing to plot values, allowing for smoother distributions by smoothing out the noise.
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.
KDE Plot is known as Kernel Density Estimate Plot which is generally used for estimating the e Probability Density function of a continuous variable. It is a method for visualizing the distribution of observations in a dataset, analogous to a histogram. It represents the data using a continuous probability density curve in one or more dimensions.
# Plotting the histogram. The normal distribution chart is characterized by two parameters: The average value, which represents the maximum value of the chart, and the chart is always symmetrical. And the standard deviation, which determines the amount of change beyond the mean.
A density plot is a smoothed, continuous version of a histogram estimated from the data. The most common form of estimation is known as kernel density estimation (KDE) . In this method, a continuous curve (the kernel) is drawn at every individual data point and all of these curves are then added together to make a single smooth density estimation.
The create_2d_density () function in module plotly.figure_factory._2d_density returns a figure object for a 2D density plot. Following code is used to produce 2D Density plot over histogram data.
The easiest thing to do is build another figure fig2
with curve_type = 'normal'
and pick up the values from there using:
fig2 = ff.create_distplot(hist_data, group_labels, curve_type = 'normal')
normal_x = fig2.data[1]['x']
normal_y = fig2.data[1]['y']
And then inlclude those values in the first fig
using fid.add_trace(go.Scatter())
like this:
fig2 = ff.create_distplot(hist_data, group_labels, curve_type = 'normal')
normal_x = fig2.data[1]['x']
normal_y = fig2.data[1]['y']
fig.add_traces(go.Scatter(x=normal_x, y=normal_y, mode = 'lines',
line = dict(color='rgba(0,255,0, 0.6)',
#dash = 'dash'
width = 1),
name = 'normal'
))
fig.show()
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