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I interpolated temperature data observed on an urban area formed by 12 locations. Now i would like to remove all interpolated values that are outside the shapefile layer. How can i do it? The shapefile links: https://www.dropbox.com/s/0u76k3yegvr09sx/LimiteAMG.shp?dl=0 https://www.dropbox.com/s/yxsmm3v2ey3ngsp/LimiteAMG.cpg?dl=0 https://www.dropbox.com/s/yx05n31dfkggbb6/LimiteAMG.dbf?dl=0 https://www.dropbox.com/s/a6nk0xczgjeen2d/LimiteAMG.prj?dl=0 https://www.dropbox.com/s/royw7s51n2f0a6x/LimiteAMG.qpj?dl=0 https://www.dropbox.com/s/7k44dcl1k5891qc/LimiteAMG.shx?dl=0 The Data is: Lat Lon T 0 20.8208 … 
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I have el next dataframe from pandas import * from numpy import * data=read_csv('enero.csv') data Fecha DirViento MagViento 0 2011/07/01 00:00 318 6.6 1 2011/07/01 00:15 342 5.5 2 2011/07/01 00:30 329 6.6 3 2011/07/01 00:45 279 7.5 4 2011/07/01 01:00 318 6.0 5 2011/07/01 01:15 329 7.1 6 2011/07/01 … 
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The following script try to calculate the resulting average of the direction and magnitude of the wind well as daily averages of Temperature, Moisture and Sum of Precipitation. My monthly dataframe has the following column: data Fecha Hora DirViento MagViento Temperatura Humedad PreciAcu 0 2011/07/01 00:00 318 6.6 21.22 100 … |
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I have a long list of data structured in the following way Date , Time, Temperature, Moisture, Accumulated precipitation 1/01/2011, 00:00, 23, 50, 2, 1/01/2011, 00:15, 22, 45, 1, 1/01/2011, 00:30, 20, 39, 0, 1/01/2011, 01:00, 25, 34, 0, 1/01/2011, 01:15, 23, 50, 0, . . . . 1/01/2011, 23:45, …
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I have a monthly database and the daily data are every 15 minutes. Develop a script to calculate the average and sum of some variables. The data have the following structure: MagViento, DirViento, u, v, Humedad, Precipitacion 5.67, 245 , 7.7898, 6.5660, 100, 4.1 When I run the script, I … |
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I have a database with the following structure: lat lon id 19.7 -95.2 1 19.7 -94.7 1 19.3 -93.9 1 19 -93.5 1 19 -92.8 1 19.2 -92.6 1 19.9 -93 1 20 -92.8 1 23.1 -100.2 2 23.2 -100 2 23.3 -100 2 23.3 -100.2 2 23.4 -99 2 …
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#! /usr/bin/python import numpy as np data = np.loadtxt('path-tracks.csv',dtype=np.str,delimiter=',',skiprows=1) print data [['19.70' '-95.20' '2/5/04 6:45 AM' '1' '-38' 'CCM'] ['19.70' '-94.70' '2/5/04 7:45 AM' '1' '-48' 'CCM'] ['19.30' '-93.90' '2/5/04 8:45 AM' '1' '-60' 'CCM'] ['19.00' '-93.50' '2/5/04 9:45 AM' '1' '-58' 'CCM'] ['19.00' '-92.80' '2/5/04 10:45 AM' '1' '-50' …
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