{"id":78572,"date":"2020-09-12T09:00:33","date_gmt":"2020-09-12T03:30:33","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=78572"},"modified":"2021-05-09T13:13:24","modified_gmt":"2021-05-09T07:43:24","slug":"scipy-interpolation","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/scipy-interpolation\/","title":{"rendered":"SciPy Interpolation"},"content":{"rendered":"

The SciPy library is generally for mathematical and statistical computations. There are sub-packages in the library that manage more specific functionalities. There is the scipy.interpolation that has specific usage in the field of statistics. Let us learn more about SciPy Interpolation.<\/p>\n

SciPy Interpolation<\/h2>\n

The process of interpolation means locating a value between two points. We can consider the two points to be on a line or over a curve. The meaning of the word interpolation is split as \u201cinter\u201d which means \u201center\u201d, that means estimating a set of data by looking into it.<\/p>\n

Interpolation is basically looking between two data points for the estimation of the value in between the two. It is generally used in any field requiring value estimation from a set of data.<\/p>\n

Interpolation is carried out using the scipy.interpoation package. Its working is dependent on other libraries like numpy and matplotlib.
\nWe create an array and then apply the interpolation functions.<\/p>\n

 <\/p>\n

import numpy as np\r\nx = np.linspace(0,3,15)\r\ny = np.sin(x**3\/9)\r\nprint (x)\r\nprint(y)\r\n<\/pre>\n
Output<\/strong><\/p>\n
[0. 0.21428571 0.42857143 0.64285714 0.85714286 1.07142857
\n1.28571429 1.5 1.71428571 1.92857143 2.14285714 2.35714286
\n2.57142857 2.78571429 3. ]
\n[0. 0.00109329 0.00874624 0.02951466 0.06991376 0.13623681
\n0.23396278 0.36627253 0.53098857 0.7152709 0.88814574 0.99332329
\n0.94973234 0.67401073 0.14112001]<\/div>\n
We have used the linspace() and sin() function to generate two sets of arrays. Now we will use the plot function to check the value plot. We have to import the matplotlib package.<\/p>\n
import matplotlib.pyplot as plt  \r\nplt.plot(x,y,'o')  \r\nplt.show() \r\n<\/pre>\n
Output<\/strong><\/p>\n
\"\"<\/a><\/p>\n
1D Interpolation in SciPy<\/h2>\n
The interpolation module provides the interp1d class that is meant to create functions on the basis of the input data set. We can create new functions with different interpolation techniques. There is a variety of interpolation techniques:<\/p>\n