Create Scatter Plot with smooth Line using Python

A curve can be smoothened to reach a well approximated idea of the visualization. In this article, we will be plotting a scatter plot with the smooth line with the help of the SciPy library. To plot a smooth line scatter plot we use the following function:

• scipy.interpolate.make_interp_spline() from the SciPy library computes the coefficients of interpolating B-spline. By importing, this function from the Scipy library and added the parameter, It is quite easier to get the smooth line to scatter plot.

Syntax:

 scipy.interpolate.make_interp_spline(x, y, k=3, t=None, bc_type=None, axis=0, check_finite=True)

Parameters:

• x:-Abscissas
• y:-Ordinates
• k:-B-spline degree
• t:-Knots
• bc_type:-Boundary conditions
• axis:-Interpolation axis
• check_finite:-Whether to check that the input arrays contain only finite numbers

Return: a BSpline object of the degree k and with knots t.

• np.linspace() function is imported from NumPy library used to get evenly spaced numbers over a specified interval used to draw a smooth line scatter plot.

Syntax:

 numpy.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0)

Parameters:

• start:-The starting value of the sequence.
• stop:-The end value of the sequence.
• num:-Number of samples to generate.
• endpoint:-If True, stop is the last sample.
• retstep:-If True, return (samples, step), where the step is the spacing between samples.
• dtype:-The type of the output array.
• axis:- The axis in the result to store the samples.

Return: A array of num equally spaced samples in the closed interval

Approach

• Import module
• Create or load data
• Create a scatter plot
• Create a smoothened curve from the points of the scatter plot
• Display plot

Let us start with a sample scatter plot.

Example: 

import numpy as np
import matplotlib.pyplot as plt

x = np.array([1, 2, 3, 4, 5])

y = np.array([4, 9, 1, 3, 5])

plt.scatter(x, y)

plt.show()

Output:

Now let's visualize the scatter plot by joining points of the plot so that an uneven curve can appear i.e. without smoothening so that difference can be apparent.

Example:

import numpy as np
import matplotlib.pyplot as plt

x = np.array([1, 2, 3, 4, 5])

y = np.array([4, 9, 1, 3, 5])

plt.plot(x, y)

plt.show()

Output:

Now, We will be looking at the same example as above with the use of np.linspace() and scipy.interpolate.make_interp_spline() function.

Example:

import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import make_interp_spline

x = np.array([1, 2, 3, 4, 5])
y = np.array([4, 9, 1, 3, 5])

xnew = np.linspace(x.min(), x.max(), 300)

gfg = make_interp_spline(x, y, k=3)

y_new = gfg(xnew)

plt.plot(xnew, y_new)

plt.show()

Output: