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Often you may want to plot a smooth curve in Matplotlib for a line chart. Fortunately this is easy to do with the help of the following SciPy functions:

This tutorial explains how to use these functions in practice.

**Example: Plotting a Smooth Curve in Matplotlib**

The following code shows how to create a simple line chart for a dataset:

import numpy as np import matplotlib.pyplot as plt #create data x = np.array([1, 2, 3, 4, 5, 6, 7, 8]) y = np.array([4, 9, 12, 30, 45, 88, 140, 230]) #create line chart plt.plot(x,y) plt.show()

Notice that the line chart isn’t completely smooth since the underlying data doesn’t follow a smooth line. We can use the following code to create a smooth curve for this dataset:

from scipy.interpolate import make_interp_spline, BSpline #create data x = np.array([1, 2, 3, 4, 5, 6, 7, 8]) y = np.array([4, 9, 12, 30, 45, 88, 140, 230]) #define x as 200 equally spaced values between the min and max of original x xnew = np.linspace(x.min(), x.max(), 200) #define spline spl = make_interp_spline(x, y, k=3) y_smooth = spl(xnew) #create smooth line chart plt.plot(xnew, y_smooth) plt.show()

Note that the higher the degree you use for the **k **argument, the more “wiggly” the curve will be. For example, consider the following chart with **k=7**:

from scipy.interpolate import make_interp_spline, BSpline #create data x = np.array([1, 2, 3, 4, 5, 6, 7, 8]) y = np.array([4, 9, 12, 30, 45, 88, 140, 230]) #define x as 200 equally spaced values between the min and max of original x xnew = np.linspace(x.min(), x.max(), 200) #define spline with degree k=7 spl = make_interp_spline(x, y, k=7) y_smooth = spl(xnew) #create smooth line chart plt.plot(xnew, y_smooth) plt.show()

Depending on how curved you want the line to be, you can modify the value for k.

**Additional Resources**

How to Show Gridlines on Matplotlib Plots

How to Remove Ticks from Matplotlib Plots

How to Create Matplotlib Plots with Log Scales