linear-interpolation

I have the following table which records a value per day. The problem is that sometimes days are missing. I want to write a SQL query that will: Return the missing days Calculate the missing value using linear interpolation So from the following source table: Date Value -------------------- 2010/01/10 10 2010/01/11 15 2010/01/13 25 2010/01/16 40 I want to return: Date Value -------------------- 2010/01/10 10 2010/01/11 15 2010/01/12 20 2010/01/13 25 2010/01/14 30 2010/01/15 35 2010/01/16 40 Any help would be greatly appreciated. declare @MaxDate date declare @MinDate date select @MaxDate = MAX

I would like to add all missing dates between min and max date in a data.frame and linear interpolate all missing values, like df <- data.frame(date = as.Date(c("2015-10-05","2015-10-08","2015-10-09", "2015-10-12","2015-10-14")), value = c(8,3,9,NA,5)) date value 2015-10-05 8 2015-10-08 3 2015-10-09 9 2015-10-12 NA 2015-10-14 5 date value approx 2015-10-05 8 8 2015-10-06 NA 6.33 2015-10-07 NA 4.67 2015-10-08 3 3 2015-10-09 9 9 2015-10-10 NA 8.20 2015-10-11 NA 7.40 2015-10-12 NA 6.60 2015-10-13 NA 5.80 2015-10-14 5 5 Is there a clear solution with dplyr and approx ? (I do not like my 10 line

I have the following problem. I am trying to find the fastest way to use the interpolation method of numpy on a 2-D array of x-coordinates. import numpy as np xp = [0.0, 0.25, 0.5, 0.75, 1.0] np.random.seed(100) x = np.random.rand(10) fp = np.random.rand(10, 5) So basically, xp would be the x-coordinates of the data points, x would be an array containing the x-coordinates of the values I want to interpolate, and fp would be a 2-D array containing y-coordinates of the datapoints. xp [0.0, 0.25, 0.5, 0.75, 1.0] x array([ 0.54340494, 0.27836939, 0.42451759, 0.84477613, 0.00471886, 0.12156912, 0

I'm running about 45,000 local linear regressions (essentially) on about 1.2 million observations, so I'd appreciate some help trying to speed things up because I'm impatient. I'm basically trying to construct year-by-position wage contracts--the function wage(experience given firm,year,position)--for a bunch of firms. Here's the data (basic structure of) set I'm working with: > wages firm year position exp salary 1: 0007 1996 4 1 20029 2: 0007 1996 4 1 23502 3: 0007 1996 4 1 22105 4: 0007 1996 4 2 23124 5: 0007 1996 4 2 22700 --- 1175141: 994 2012 5 2 47098 1175142: 994 2012 5 2 45488 1175143

I would like to perform a linear interpolation in a variable of a data frame which takes into account the: 1) time difference between the two points, 2) the moment when the data was taken and 3) the individual taken for measure the variable. For example in the next dataframe: df <- data.frame(time=c(1,2,3,4,5,6,7,1,2,3), Individuals=c(1,1,1,1,1,1,1,2,2,2), Value=c(1, 2, 3, NA, 5, NA, 7, 5, NA, 7)) df I would like to obtain: result <- data.frame(time=c(1,2,3,4,5,6,7,1,2,3), Individuals=c(1,1,1,1,1,1,1,2,2,2), Value=c(1, 2, 3, 4, 5, 6, 7, 5, 5.5, 6)) result I cannot use exclusively the function

I have a table with rate at certain date : Rates Id | Date | Rate ----+---------------+------- 1 | 01/01/2011 | 4.5 2 | 01/04/2011 | 3.2 3 | 04/06/2011 | 2.4 4 | 30/06/2011 | 5 I want to get the output rate base on a simple linear interpolation. So if I enter 17/06/2011: Date Rate ---------- ----- 01/01/2011 4.5 01/04/2011 3.2 04/06/2011 2.4 17/06/2011 30/06/2011 5.0 the linear interpolation is (5 + 2,4) / 2 = 3,7 Is there a way to do a simple query (SQL Server 2005), or this kind of stuff need to be done in a programmatic way (C#...) ? Something like this (corrected): SELECT CASE WHEN next