pandas dataframe of temps and numpy.linalg.lstsq

Question

The problem is this. Forecast model data indicates at 0700Z the 1000 milibar temps will be -4C°. The forecaster knows that is incorrect, so they choose to make a correction. This correction should have a diminishing affect across the axes. The forecaster can set the threshold of correction via 2 sliders. time and height axis_0_weight axis_1_weight.

data temps

data = [{'2022-02-19 06:00:00': -61, '2022-02-19 07:00:00': -61, '2022-02-19 08:00:00': -61, '2022-02-19 09:00:00': -62, '2022-02-19 10:00:00': -63, '2022-02-19 11:00:00': -63, '2022-02-19 12:00:00': -64, '2022-02-19 13:00:00': -63, '2022-02-19 14:00:00': -62, '2022-02-19 15:00:00': -63}, {'2022-02-19 06:00:00': -60, '2022-02-19 07:00:00': -60, '2022-02-19 08:00:00': -60, '2022-02-19 09:00:00': -61, '2022-02-19 10:00:00': -60, '2022-02-19 11:00:00': -61, '2022-02-19 12:00:00': -61, '2022-02-19 13:00:00': -61, '2022-02-19 14:00:00': -60, '2022-02-19 15:00:00': -60}, {'2022-02-19 06:00:00': -60, '2022-02-19 07:00:00': -60, '2022-02-19 08:00:00': -60, '2022-02-19 09:00:00': -59, '2022-02-19 10:00:00': -59, '2022-02-19 11:00:00': -60, '2022-02-19 12:00:00': -59, '2022-02-19 13:00:00': -59, '2022-02-19 14:00:00': -59, '2022-02-19 15:00:00': -58}, {'2022-02-19 06:00:00': -57, '2022-02-19 07:00:00': -57, '2022-02-19 08:00:00': -56, '2022-02-19 09:00:00': -55, '2022-02-19 10:00:00': -55, '2022-02-19 11:00:00': -55, '2022-02-19 12:00:00': -55, '2022-02-19 13:00:00': -55, '2022-02-19 14:00:00': -55, '2022-02-19 15:00:00': -55}, {'2022-02-19 06:00:00': -55, '2022-02-19 07:00:00': -54, '2022-02-19 08:00:00': -53, '2022-02-19 09:00:00': -52, '2022-02-19 10:00:00': -53, '2022-02-19 11:00:00': -52, '2022-02-19 12:00:00': -52, '2022-02-19 13:00:00': -52, '2022-02-19 14:00:00': -52, '2022-02-19 15:00:00': -52}, {'2022-02-19 06:00:00': -52, '2022-02-19 07:00:00': -52, '2022-02-19 08:00:00': -51, '2022-02-19 09:00:00': -51, '2022-02-19 10:00:00': -51, '2022-02-19 11:00:00': -50, '2022-02-19 12:00:00': -50, '2022-02-19 13:00:00': -50, '2022-02-19 14:00:00': -50, '2022-02-19 15:00:00': -49}, {'2022-02-19 06:00:00': -50, '2022-02-19 07:00:00': -50, '2022-02-19 08:00:00': -49, '2022-02-19 09:00:00': -49, '2022-02-19 10:00:00': -49, '2022-02-19 11:00:00': -49, '2022-02-19 12:00:00': -48, '2022-02-19 13:00:00': -48, '2022-02-19 14:00:00': -48, '2022-02-19 15:00:00': -47}, {'2022-02-19 06:00:00': -50, '2022-02-19 07:00:00': -50, '2022-02-19 08:00:00': -50, '2022-02-19 09:00:00': -50, '2022-02-19 10:00:00': -50, '2022-02-19 11:00:00': -50, '2022-02-19 12:00:00': -50, '2022-02-19 13:00:00': -49, '2022-02-19 14:00:00': -49, '2022-02-19 15:00:00': -48}, {'2022-02-19 06:00:00': -50, '2022-02-19 07:00:00': -50, '2022-02-19 08:00:00': -50, '2022-02-19 09:00:00': -51, '2022-02-19 10:00:00': -52, '2022-02-19 11:00:00': -51, '2022-02-19 12:00:00': -51, '2022-02-19 13:00:00': -50, '2022-02-19 14:00:00': -49, '2022-02-19 15:00:00': -49}, {'2022-02-19 06:00:00': -51, '2022-02-19 07:00:00': -51, '2022-02-19 08:00:00': -52, '2022-02-19 09:00:00': -53, '2022-02-19 10:00:00': -53, '2022-02-19 11:00:00': -53, '2022-02-19 12:00:00': -53, '2022-02-19 13:00:00': -52, '2022-02-19 14:00:00': -50, '2022-02-19 15:00:00': -49}, {'2022-02-19 06:00:00': -51, '2022-02-19 07:00:00': -53, '2022-02-19 08:00:00': -54, '2022-02-19 09:00:00': -55, '2022-02-19 10:00:00': -54, '2022-02-19 11:00:00': -55, '2022-02-19 12:00:00': -55, '2022-02-19 13:00:00': -54, '2022-02-19 14:00:00': -51, '2022-02-19 15:00:00': -49}, {'2022-02-19 06:00:00': -51, '2022-02-19 07:00:00': -51, '2022-02-19 08:00:00': -51, '2022-02-19 09:00:00': -51, '2022-02-19 10:00:00': -51, '2022-02-19 11:00:00': -51, '2022-02-19 12:00:00': -51, '2022-02-19 13:00:00': -50, '2022-02-19 14:00:00': -49, '2022-02-19 15:00:00': -48}, {'2022-02-19 06:00:00': -50, '2022-02-19 07:00:00': -50, '2022-02-19 08:00:00': -48, '2022-02-19 09:00:00': -48, '2022-02-19 10:00:00': -47, '2022-02-19 11:00:00': -47, '2022-02-19 12:00:00': -47, '2022-02-19 13:00:00': -47, '2022-02-19 14:00:00': -48, '2022-02-19 15:00:00': -47}, {'2022-02-19 06:00:00': -47, '2022-02-19 07:00:00': -47, '2022-02-19 08:00:00': -45, '2022-02-19 09:00:00': -44, '2022-02-19 10:00:00': -44, '2022-02-19 11:00:00': -44, '2022-02-19 12:00:00': -44, '2022-02-19 13:00:00': -44, '2022-02-19 14:00:00': -44, '2022-02-19 15:00:00': -44}, {'2022-02-19 06:00:00': -44, '2022-02-19 07:00:00': -43, '2022-02-19 08:00:00': -43, '2022-02-19 09:00:00': -41, '2022-02-19 10:00:00': -41, '2022-02-19 11:00:00': -40, '2022-02-19 12:00:00': -40, '2022-02-19 13:00:00': -41, '2022-02-19 14:00:00': -41, '2022-02-19 15:00:00': -41}, {'2022-02-19 06:00:00': -40, '2022-02-19 07:00:00': -40, '2022-02-19 08:00:00': -40, '2022-02-19 09:00:00': -39, '2022-02-19 10:00:00': -38, '2022-02-19 11:00:00': -38, '2022-02-19 12:00:00': -37, '2022-02-19 13:00:00': -37, '2022-02-19 14:00:00': -38, '2022-02-19 15:00:00': -38}, {'2022-02-19 06:00:00': -37, '2022-02-19 07:00:00': -37, '2022-02-19 08:00:00': -37, '2022-02-19 09:00:00': -36, '2022-02-19 10:00:00': -36, '2022-02-19 11:00:00': -35, '2022-02-19 12:00:00': -34, '2022-02-19 13:00:00': -34, '2022-02-19 14:00:00': -35, '2022-02-19 15:00:00': -35}, {'2022-02-19 06:00:00': -34, '2022-02-19 07:00:00': -34, '2022-02-19 08:00:00': -34, '2022-02-19 09:00:00': -34, '2022-02-19 10:00:00': -34, '2022-02-19 11:00:00': -33, '2022-02-19 12:00:00': -32, '2022-02-19 13:00:00': -32, '2022-02-19 14:00:00': -32, '2022-02-19 15:00:00': -33}, {'2022-02-19 06:00:00': -31, '2022-02-19 07:00:00': -31, '2022-02-19 08:00:00': -31, '2022-02-19 09:00:00': -31, '2022-02-19 10:00:00': -31, '2022-02-19 11:00:00': -31, '2022-02-19 12:00:00': -30, '2022-02-19 13:00:00': -29, '2022-02-19 14:00:00': -29, '2022-02-19 15:00:00': -31}, {'2022-02-19 06:00:00': -28, '2022-02-19 07:00:00': -28, '2022-02-19 08:00:00': -28, '2022-02-19 09:00:00': -28, '2022-02-19 10:00:00': -28, '2022-02-19 11:00:00': -28, '2022-02-19 12:00:00': -28, '2022-02-19 13:00:00': -27, '2022-02-19 14:00:00': -28, '2022-02-19 15:00:00': -30}, {'2022-02-19 06:00:00': -26, '2022-02-19 07:00:00': -25, '2022-02-19 08:00:00': -25, '2022-02-19 09:00:00': -26, '2022-02-19 10:00:00': -26, '2022-02-19 11:00:00': -26, '2022-02-19 12:00:00': -27, '2022-02-19 13:00:00': -26, '2022-02-19 14:00:00': -27, '2022-02-19 15:00:00': -29}, {'2022-02-19 06:00:00': -23, '2022-02-19 07:00:00': -23, '2022-02-19 08:00:00': -23, '2022-02-19 09:00:00': -23, '2022-02-19 10:00:00': -23, '2022-02-19 11:00:00': -23, '2022-02-19 12:00:00': -24, '2022-02-19 13:00:00': -24, '2022-02-19 14:00:00': -27, '2022-02-19 15:00:00': -28}, {'2022-02-19 06:00:00': -21, '2022-02-19 07:00:00': -21, '2022-02-19 08:00:00': -21, '2022-02-19 09:00:00': -21, '2022-02-19 10:00:00': -21, '2022-02-19 11:00:00': -21, '2022-02-19 12:00:00': -22, '2022-02-19 13:00:00': -23, '2022-02-19 14:00:00': -27, '2022-02-19 15:00:00': -28}, {'2022-02-19 06:00:00': -19, '2022-02-19 07:00:00': -19, '2022-02-19 08:00:00': -19, '2022-02-19 09:00:00': -19, '2022-02-19 10:00:00': -19, '2022-02-19 11:00:00': -19, '2022-02-19 12:00:00': -21, '2022-02-19 13:00:00': -23, '2022-02-19 14:00:00': -26, '2022-02-19 15:00:00': -27}, {'2022-02-19 06:00:00': -17, '2022-02-19 07:00:00': -17, '2022-02-19 08:00:00': -17, '2022-02-19 09:00:00': -17, '2022-02-19 10:00:00': -17, '2022-02-19 11:00:00': -17, '2022-02-19 12:00:00': -19, '2022-02-19 13:00:00': -23, '2022-02-19 14:00:00': -25, '2022-02-19 15:00:00': -27}, {'2022-02-19 06:00:00': -16, '2022-02-19 07:00:00': -15, '2022-02-19 08:00:00': -15, '2022-02-19 09:00:00': -15, '2022-02-19 10:00:00': -15, '2022-02-19 11:00:00': -16, '2022-02-19 12:00:00': -18, '2022-02-19 13:00:00': -21, '2022-02-19 14:00:00': -23, '2022-02-19 15:00:00': -24}, {'2022-02-19 06:00:00': -15, '2022-02-19 07:00:00': -14, '2022-02-19 08:00:00': -14, '2022-02-19 09:00:00': -13, '2022-02-19 10:00:00': -13, '2022-02-19 11:00:00': -15, '2022-02-19 12:00:00': -17, '2022-02-19 13:00:00': -20, '2022-02-19 14:00:00': -21, '2022-02-19 15:00:00': -22}, {'2022-02-19 06:00:00': -14, '2022-02-19 07:00:00': -13, '2022-02-19 08:00:00': -13, '2022-02-19 09:00:00': -12, '2022-02-19 10:00:00': -13, '2022-02-19 11:00:00': -15, '2022-02-19 12:00:00': -17, '2022-02-19 13:00:00': -18, '2022-02-19 14:00:00': -19, '2022-02-19 15:00:00': -20}, {'2022-02-19 06:00:00': -12, '2022-02-19 07:00:00': -13, '2022-02-19 08:00:00': -12, '2022-02-19 09:00:00': -12, '2022-02-19 10:00:00': -13, '2022-02-19 11:00:00': -15, '2022-02-19 12:00:00': -16, '2022-02-19 13:00:00': -16, '2022-02-19 14:00:00': -17, '2022-02-19 15:00:00': -18}, {'2022-02-19 06:00:00': -11, '2022-02-19 07:00:00': -12, '2022-02-19 08:00:00': -12, '2022-02-19 09:00:00': -13, '2022-02-19 10:00:00': -13, '2022-02-19 11:00:00': -15, '2022-02-19 12:00:00': -14, '2022-02-19 13:00:00': -15, '2022-02-19 14:00:00': -15, '2022-02-19 15:00:00': -16}, {'2022-02-19 06:00:00': -10, '2022-02-19 07:00:00': -10, '2022-02-19 08:00:00': -12, '2022-02-19 09:00:00': -13, '2022-02-19 10:00:00': -13, '2022-02-19 11:00:00': -14, '2022-02-19 12:00:00': -13, '2022-02-19 13:00:00': -13, '2022-02-19 14:00:00': -13, '2022-02-19 15:00:00': -15}, {'2022-02-19 06:00:00': -8, '2022-02-19 07:00:00': -9, '2022-02-19 08:00:00': -11, '2022-02-19 09:00:00': -12, '2022-02-19 10:00:00': -12, '2022-02-19 11:00:00': -12, '2022-02-19 12:00:00': -12, '2022-02-19 13:00:00': -11, '2022-02-19 14:00:00': -12, '2022-02-19 15:00:00': -15}, {'2022-02-19 06:00:00': -8, '2022-02-19 07:00:00': -8, '2022-02-19 08:00:00': -9, '2022-02-19 09:00:00': -11, '2022-02-19 10:00:00': -11, '2022-02-19 11:00:00': -11, '2022-02-19 12:00:00': -10, '2022-02-19 13:00:00': -10, '2022-02-19 14:00:00': -10, '2022-02-19 15:00:00': -14}, {'2022-02-19 06:00:00': -9, '2022-02-19 07:00:00': -8, '2022-02-19 08:00:00': -7, '2022-02-19 09:00:00': -9, '2022-02-19 10:00:00': -10, '2022-02-19 11:00:00': -9, '2022-02-19 12:00:00': -9, '2022-02-19 13:00:00': -8, '2022-02-19 14:00:00': -8, '2022-02-19 15:00:00': -12}, {'2022-02-19 06:00:00': -10, '2022-02-19 07:00:00': -8, '2022-02-19 08:00:00': -7, '2022-02-19 09:00:00': -7, '2022-02-19 10:00:00': -9, '2022-02-19 11:00:00': -8, '2022-02-19 12:00:00': -8, '2022-02-19 13:00:00': -7, '2022-02-19 14:00:00': -7, '2022-02-19 15:00:00': -10}, {'2022-02-19 06:00:00': -10, '2022-02-19 07:00:00': -8, '2022-02-19 08:00:00': -7, '2022-02-19 09:00:00': -6, '2022-02-19 10:00:00': -8, '2022-02-19 11:00:00': -7, '2022-02-19 12:00:00': -6, '2022-02-19 13:00:00': -6, '2022-02-19 14:00:00': -5, '2022-02-19 15:00:00': -8}, {'2022-02-19 06:00:00': -10, '2022-02-19 07:00:00': -9, '2022-02-19 08:00:00': -8, '2022-02-19 09:00:00': -6, '2022-02-19 10:00:00': -7, '2022-02-19 11:00:00': -6, '2022-02-19 12:00:00': -5, '2022-02-19 13:00:00': -4, '2022-02-19 14:00:00': -4, '2022-02-19 15:00:00': -6}, {'2022-02-19 06:00:00': -10, '2022-02-19 07:00:00': -9, '2022-02-19 08:00:00': -7, '2022-02-19 09:00:00': -6, '2022-02-19 10:00:00': -5, '2022-02-19 11:00:00': -4, '2022-02-19 12:00:00': -3, '2022-02-19 13:00:00': -3, '2022-02-19 14:00:00': -2, '2022-02-19 15:00:00': -4}, {'2022-02-19 06:00:00': -9, '2022-02-19 07:00:00': -8, '2022-02-19 08:00:00': -6, '2022-02-19 09:00:00': -5, '2022-02-19 10:00:00': -4, '2022-02-19 11:00:00': -3, '2022-02-19 12:00:00': -2, '2022-02-19 13:00:00': -1, '2022-02-19 14:00:00': -1, '2022-02-19 15:00:00': -3}]

index

index = [
 '50mb', '75mb', '100mb', '125mb', '150mb', '175mb', '200mb', '225mb',
 '250mb', '275mb', '300mb', '325mb', '350mb', '375mb', '400mb', '425mb',
 '450mb', '475mb', '500mb', '525mb', '550mb', '575mb', '600mb', '625mb',
 '650mb', '675mb', '700mb', '725mb', '750mb', '775mb', '800mb', '825mb',
 '850mb', '875mb', '900mb', '925mb', '950mb', '975mb', '1000mb'
 ]

main code

import pandas as pd
import numpy as np
CORRECTION=+20
data=...
index=...
def make_matrix(df:pd.DataFrame)->np.ndarray:
 x,y=df.shape
 arr_x,arr_y= np.vstack(np.linspace(0,1,x)),np.linspace(1,0,y)
 matrix = (arr_x+arr_y)-1
 matrix[matrix<0]=0
 return matrix
if __name__ == '__main__':
 celcius_temps = pd.DataFrame.from_records(data,index=index)
 celcius_temps.columns=pd.to_datetime(celcius_temps.columns)
 mat = make_matrix(celcius_temps)
 print(celcius_temps+np.around(mat*CORRECTION,2))

Results

original dataframe

 2022年02月19日 06:00:00 ...
50mb -61 -61 -61 -62 -63 -63 -64 -63 -62 -63
75mb -60 -60 -60 -61 -60 -61 -61 -61 -60 -60
100mb -60 -60 -60 -59 -59 -60 -59 -59 -59 -58
125mb -57 -57 -56 -55 -55 -55 -55 -55 -55 -55
150mb -55 -54 -53 -52 -53 -52 -52 -52 -52 -52
175mb -52 -52 -51 -51 -51 -50 -50 -50 -50 -49
200mb -50 -50 -49 -49 -49 -49 -48 -48 -48 -47
225mb -50 -50 -50 -50 -50 -50 -50 -49 -49 -48
250mb -50 -50 -50 -51 -52 -51 -51 -50 -49 -49
275mb -51 -51 -52 -53 -53 -53 -53 -52 -50 -49
300mb -51 -53 -54 -55 -54 -55 -55 -54 -51 -49
325mb -51 -51 -51 -51 -51 -51 -51 -50 -49 -48
350mb -50 -50 -48 -48 -47 -47 -47 -47 -48 -47
375mb -47 -47 -45 -44 -44 -44 -44 -44 -44 -44
400mb -44 -43 -43 -41 -41 -40 -40 -41 -41 -41
425mb -40 -40 -40 -39 -38 -38 -37 -37 -38 -38
450mb -37 -37 -37 -36 -36 -35 -34 -34 -35 -35
475mb -34 -34 -34 -34 -34 -33 -32 -32 -32 -33
500mb -31 -31 -31 -31 -31 -31 -30 -29 -29 -31
525mb -28 -28 -28 -28 -28 -28 -28 -27 -28 -30
550mb -26 -25 -25 -26 -26 -26 -27 -26 -27 -29
575mb -23 -23 -23 -23 -23 -23 -24 -24 -27 -28
600mb -21 -21 -21 -21 -21 -21 -22 -23 -27 -28
625mb -19 -19 -19 -19 -19 -19 -21 -23 -26 -27
650mb -17 -17 -17 -17 -17 -17 -19 -23 -25 -27
675mb -16 -15 -15 -15 -15 -16 -18 -21 -23 -24
700mb -15 -14 -14 -13 -13 -15 -17 -20 -21 -22
725mb -14 -13 -13 -12 -13 -15 -17 -18 -19 -20
750mb -12 -13 -12 -12 -13 -15 -16 -16 -17 -18
775mb -11 -12 -12 -13 -13 -15 -14 -15 -15 -16
800mb -10 -10 -12 -13 -13 -14 -13 -13 -13 -15
825mb -8 -9 -11 -12 -12 -12 -12 -11 -12 -15
850mb -8 -8 -9 -11 -11 -11 -10 -10 -10 -14
875mb -9 -8 -7 -9 -10 -9 -9 -8 -8 -12
900mb -10 -8 -7 -7 -9 -8 -8 -7 -7 -10
925mb -10 -8 -7 -6 -8 -7 -6 -6 -5 -8
950mb -10 -9 -8 -6 -7 -6 -5 -4 -4 -6
975mb -10 -9 -7 -6 -5 -4 -3 -3 -2 -4
1000mb -9 -8 -6 -5 -4 -3 -2 -1 -1 -3

The 1000mb 2022年02月19日 06:00:00 receives 100% of the correction.

the 50mb row and 2022年02月19日 15:00:00 column remain unchanged

with broadcast diminished correction

 2022年02月19日 06:00:00 ...
50mb -61.00 -61.00 -61.00 -62.00 -63.00 -63.00 -64.00 -63.00 -62.00 -63.0
75mb -59.47 -60.00 -60.00 -61.00 -60.00 -61.00 -61.00 -61.00 -60.00 -60.0
100mb -58.95 -60.00 -60.00 -59.00 -59.00 -60.00 -59.00 -59.00 -59.00 -58.0
125mb -55.42 -57.00 -56.00 -55.00 -55.00 -55.00 -55.00 -55.00 -55.00 -55.0
150mb -52.89 -54.00 -53.00 -52.00 -53.00 -52.00 -52.00 -52.00 -52.00 -52.0
175mb -49.37 -51.59 -51.00 -51.00 -51.00 -50.00 -50.00 -50.00 -50.00 -49.0
200mb -46.84 -49.06 -49.00 -49.00 -49.00 -49.00 -48.00 -48.00 -48.00 -47.0
225mb -46.32 -48.54 -50.00 -50.00 -50.00 -50.00 -50.00 -49.00 -49.00 -48.0
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1000mb 11.00 9.78 9.56 8.33 7.11 5.89 4.67 3.44 1.22 -3.0

I've looked into the numpy.linalg.lstsq method but I'm not sure how to implement it.

• Jason Leaver: Data and index are as show in the original post data temps (record array) and index is a list of millibars strings

Answer 1

Passing df to make_matrix is not adequately loosely coupled. Instead you can just pass its size as two integers.

I do not trust your use of vstack. I would sooner add a new dimension via slice.

Rather than conditional zeroing of the matrix in place, you should probably use np.maximum.

celcius is spelled celsius.

Don't around where you've done it. If you want to round for print, there are better ways.

Suggested

import pandas as pd
import numpy as np
def make_matrix(x: int, y: int, correction: float = 20) -> np.ndarray:
 arr_x = np.linspace(0, correction, x)[:, np.newaxis]
 arr_y = np.linspace(0, -correction, y)
 matrix = np.maximum(arr_x + arr_y, 0)
 return matrix
def main() -> None:
 celsius_temps = pd.DataFrame.from_records(data, index=index)
 celsius_temps.columns = pd.to_datetime(celsius_temps.columns)
 mat = make_matrix(*celsius_temps.shape)
 result = celsius_temps + mat
 print(result)
if __name__ == '__main__':
 main()

• tdy: Or maybe just build the correction matrix in one shot? def make_matrix(x, y, correction)