NumPy interp estimates a value between known samples by joining neighboring points with straight line segments. Give it the x-coordinate you need, the sorted sample positions, and the matching sample values.<\/p>\n\n\n\n
The function returns the first or last sample value when your query falls outside the sample range unless you provide left or right replacements. The sample positions in xp must increase, and xp and fp must have the same length.<\/p>\n\n\n
\nimport numpy as np\n\nxp = [0, 10, 20]\nfp = [0, 100, 80]\nvalue = np.interp(15, xp, fp)\nprint(value)\n<\/pre><\/div>\n\n\nThe output is 90.0. The query 15 lies halfway between 10 and 20, so NumPy moves halfway from 100 to 80.<\/p>\n\n\n
\n90.0\n<\/pre><\/div>\n\n\nInterpolate several queries at once<\/h2>\n\n\n\n
Pass an array of query positions when you need a batch of estimates. NumPy applies the same neighboring-segment rule to each value and returns an array with the matching shape.<\/p>\n\n\n
\nqueries = [0, 5, 15, 25]\nprint(np.interp(queries, xp, fp))\n<\/pre><\/div>\n\n\n[ 0. 50. 90. 80.]\n<\/pre><\/div>\n\n\nChoose boundary values explicitly<\/h2>\n\n\n\n
Outside the sample range, the defaults are fp[0] on the left and fp[-1] on the right. Set left and right when those defaults would hide a missing-data condition.<\/p>\n\n\n
\nprint(np.interp([-5, 25], xp, fp, left=np.nan, right=np.nan))\n<\/pre><\/div>\n\n\n[nan nan]\n<\/pre><\/div>\n\n\nCommon input errors<\/h2>\n\n\n\n\n- xp and fp must contain the same number of values.<\/li>\n\n\n
- Use one-dimensional sequences for xp and fp.<\/li>\n\n\n
- A period of zero raises ValueError when periodic interpolation is requested.<\/li>\n\n\n
- Keep xp increasing. Duplicate sample positions can produce unexpected results.<\/li>\n<\/ul>\n\n\n\n
Reference<\/h2>\n\n\n\n