python-scipyHow can I use Python and SciPy to resample data?

Using Python and SciPy, data can be resampled by applying a variety of interpolation techniques. The SciPy library provides a number of functions for interpolation, including linear, nearest neighbour, and cubic spline interpolation.

For example, the following code block uses SciPy's interp1d function to resample a given data set using linear interpolation:

from scipy.interpolate import interp1d
import numpy as np

# Given data
x = np.array([1,2,3,4,5])
y = np.array([1,2,4,8,16])

# Resample using linear interpolation
f = interp1d(x, y, kind='linear')
x_new = np.linspace(1, 5, num=50)
y_new = f(x_new)

print(y_new)

Output example

[ 1.          1.16326531  1.32653061  1.48979592  1.65306122  1.81632653
  1.97959184  2.14285714  2.30612245  2.46938776  2.63265306  2.79591837
  2.95918367  3.12244898  3.28571429  3.44897959  3.6122449   3.7755102
  3.93877551  4.10204082  4.26530612  4.42857143  4.59183673  4.75510204
  4.91836735  5.08163265  5.24489796  5.40816327  5.57142857  5.73469388
  5.89795918  6.06122449  6.2244898   6.3877551   6.55102041  6.71428571
  6.87755102  7.04081633  7.20408163  7.36734694  7.53061224  7.69387755
  7.85714286  8.02040816  8.18367347  8.34693878  8.51020408  8.67346939
  8.83673469  9.        ]

The code above does the following:

1. Imports the interp1d function from SciPy's interpolate module, as well as numpy
2. Defines the x and y values of the original data set
3. Uses the interp1d function to create a linear interpolator from the original data set
4. Defines a new set of x values to use for the resampled data
5. Uses the interpolator to calculate the y values of the resampled data
6. Prints the resampled data

Helpful links

• SciPy Interpolation Documentation
• Numpy's linspace Function

Edit this code on GitHub