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How to delete punctuation and number from element in the list? [closed]

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I have a list with many nams of columns that i work with for my project.
my list is like this:
list_colunm = ['solar [W].1', 'Wind [W].02', 'Caz [W].33']
(and other elements it's a long list).
if you can help me with same methods to delete .1 .02 and .33

Standard Python:
list_column = ['solar [W].1', 'Wind [W].02', 'Caz [W].33']
list_shortnames = [x.rsplit('.')[0] for x in list_column]
Output:
['solar [W]', 'Wind [W]', 'Caz [W]']
Pandas:
The most simple way is using rename() with a dict as a map.
import pandas as pd
mapping = {"solar [W].1": "solar [W]", "Wind [W].02": "Wind [W]", "Caz [W].33": "Caz [W]"}
df.rename(mapping, inplace=True, axis='columns')
More flexible alternative (#mozway):
df.rename(columns=lambda x: x.rsplit('.', n=1)[0])
Output:
solar [W] Wind [W] Caz [W]
0 1 2 3

Convert a text file comprised of data separated by commas to an excel file using python [closed]

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I am pretty new to python and am working with some data from a weather station. The data is output to a text file where the data is separated by commas and new lines. I was wondering how I could write this data to a CSV file so I can process and graph the data in excel? I have never worked with text files before only CSV files so am unsure how to go about this.
Ideally, I would want to group the data by each day and calculate daily averages at the end of each set. I have included a sample of the dataset below. The data is in the following format: date, time, outside temp, outside humidity, dew point, wind speed, wind gust, avg wind bearing, rainfall rate, total rainfall, sea lvl pressure, rainfall counter, inside temp, inside humidity, latest gust, wind chill, heat index, uv index, solar rad, ET, annual et.
01/09/21,00:28,7.1,75,3.0,3.7,3.7,292,0.0,0.0,1025.8,81.9,17.1,44,3.7,4.6,7.1,0,0,0.00,0.00,3.0,0,0.0,292,0.0,0.0
01/09/21,00:58,7.0,75,2.9,5.1,5.1,248,0.0,0.0,1025.9,81.9,17.0,44,5.1,3.8,7.0,0,0,0.00,0.00,1.9,0,0.0,248,0.0,0.0
01/09/21,01:28,6.9,74,2.6,4.1,4.1,248,0.0,0.0,1025.8,81.9,17.0,44,4.1,4.1,6.9,0,0,0.00,0.00,2.5,0,0.0,248,0.0,0.0
01/09/21,01:58,6.9,74,2.6,4.8,4.8,292,0.0,0.0,1025.7,81.9,16.9,45,4.8,3.8,6.9,0,0,0.00,0.00,2.0,0,0.0,292,0.0,0.0
01/09/21,02:28,6.8,76,2.9,3.7,3.7,270,0.0,0.0,1025.5,81.9,16.9,45,3.7,4.2,6.8,0,0,0.00,0.00,2.7,0,0.0,270,0.0,0.0
01/09/21,02:58,6.7,76,2.8,3.1,3.1,270,0.0,0.0,1025.4,81.9,16.8,45,3.1,4.5,6.7,0,0,0.00,0.00,3.0,0,0.0,270,0.0,0.0
01/09/21,03:28,6.6,77,2.9,4.1,4.1,248,0.0,0.0,1025.5,81.9,16.8,45,4.1,3.8,6.6,0,0,0.00,0.00,2.2,0,0.0,248,0.0,0.0
01/09/21,03:58,6.3,77,2.6,2.7,2.7,292,0.0,0.0,1025.7,81.9,16.8,45,2.7,4.3,6.3,0,0,0.00,0.00,2.8,0,0.0,292,0.0,0.0
01/09/21,04:28,6.5,77,2.8,1.7,1.7,292,0.0,0.0,1025.7,81.9,16.7,45,1.7,5.4,6.5,0,0,0.00,0.00,3.8,0,0.0,292,0.0,0.0
01/09/21,04:58,6.4,77,2.7,1.7,1.7,292,0.0,0.0,1026.1,81.9,16.7,45,1.7,5.3,6.4,0,0,0.00,0.00,3.7,0,0.0,292,0.0,0.0
01/09/21,05:28,6.3,79,2.9,3.7,3.7,270,0.0,0.0,1026.5,81.9,16.7,45,3.7,3.6,6.3,0,0,0.00,0.00,2.2,0,0.0,270,0.0,0.0
01/09/21,05:58,6.3,80,3.1,3.7,3.7,292,0.0,0.0,1026.9,81.9,16.7,45,3.7,3.6,6.3,0,0,0.00,0.00,2.2,0,0.0,292,0.0,0.0
01/09/21,06:28,6.2,81,3.2,0.7,0.7,292,0.0,0.0,1027.3,81.9,16.6,45,0.7,6.2,6.2,0,0,0.00,0.00,4.2,0,0.0,292,0.0,0.0
01/09/21,06:58,6.4,80,3.2,1.7,1.7,270,0.0,0.0,1027.3,81.9,16.7,45,1.7,5.3,6.4,0,6,0.00,0.00,3.7,1,0.5,270,0.0,0.0
01/09/21,07:28,6.9,79,3.5,2.0,2.0,270,0.0,0.0,1027.5,81.9,16.7,45,2.0,5.6,6.9,0,19,0.00,0.00,4.1,40,0.5,270,0.0,0.0
01/09/21,07:58,7.5,77,3.7,2.7,2.7,270,0.0,0.0,1027.7,81.9,16.9,45,2.7,5.7,7.5,0,29,0.00,0.00,4.2,119,0.5,270,0.0,0.0
01/09/21,08:28,8.3,75,4.1,1.0,1.0,248,0.0,0.0,1027.6,81.9,17.3,43,1.0,8.3,8.3,0,36,0.00,0.00,6.3,212,0.5,248,0.0,0.0
01/09/21,08:58,9.1,74,4.7,2.0,2.0,270,0.0,0.0,1027.7,81.9,17.7,43,2.0,8.1,9.1,0,40,0.00,0.00,6.5,304,0.5,270,0.0,0.0
01/09/21,09:28,9.6,71,4.6,1.7,1.7,292,0.0,0.0,1027.7,81.9,18.0,43,1.7,9.0,9.6,0,44,0.00,0.00,7.2,391,0.5,292,0.0,0.0
01/09/21,09:58,10.8,67,4.9,6.8,6.8,248,0.0,0.0,1027.2,81.9,18.5,43,6.8,8.0,10.8,3,365,0.00,0.00,4.9,467,1.0,248,0.0,0.0
01/09/21,10:28,11.6,64,5.0,6.1,6.1,292,0.0,0.0,1027.0,81.9,18.8,43,6.1,9.2,11.6,3,402,0.00,0.00,6.2,531,1.5,292,0.0,0.0
01/09/21,10:58,12.1,63,5.3,7.1,7.1,314,0.0,0.0,1027.3,81.9,19.1,42,7.1,9.6,12.1,4,433,0.00,0.00,6.1,581,1.5,315,0.0,0.0
01/09/21,11:28,12.5,62,5.4,8.8,8.8,292,0.0,0.0,1027.1,81.9,19.4,42,8.8,9.7,12.5,4,492,0.00,0.00,5.3,616,2.0,292,0.0,0.0
01/09/21,11:58,12.6,62,5.5,8.2,8.2,270,0.0,0.0,1027.1,81.9,19.7,41,8.2,10.0,12.6,4,429,0.00,0.00,5.8,635,2.0,270,0.0,0.0
01/09/21,12:28,12.5,62,5.4,7.8,7.8,270,0.0,0.0,1026.8,81.9,20.0,40,7.8,10.0,12.5,1,124,0.00,0.00,6.0,637,2.0,270,0.0,0.0
01/09/21,12:58,13.0,60,5.4,4.8,4.8,292,0.0,0.0,1026.3,81.9,20.1,40,4.8,11.4,13.0,4,414,0.00,0.00,8.6,623,2.0,292,0.0,0.0
01/09/21,13:28,12.9,60,5.3,3.1,3.1,270,0.0,0.0,1025.9,81.9,20.2,40,3.1,11.9,12.9,0,59,0.00,0.00,9.7,592,2.0,270,0.0,0.0
01/09/21,13:58,12.8,60,5.2,3.1,3.1,270,0.0,0.0,1025.7,81.9,20.3,40,3.1,11.8,12.8,1,101,0.00,0.00,9.6,546,2.0,270,0.0,0.0
01/09/21,14:28,12.9,59,5.1,2.7,2.7,270,0.0,0.0,1025.5,81.9,20.3,40,2.7,12.1,12.9,0,47,0.00,0.00,9.9,485,2.0,270,0.0,0.0
01/09/21,14:58,12.8,59,5.0,3.1,3.1,270,0.0,0.0,1025.1,81.9,20.3,39,3.1,11.8,12.8,0,44,0.00,0.00,9.5,412,2.0,270,0.0,0.0
01/09/21,15:28,13.0,60,5.4,2.7,2.7,270,0.0,0.0,1025.2,81.9,20.5,39,2.7,12.3,13.0,0,40,0.00,0.00,10.1,328,2.0,270,0.0,0.0
01/09/21,15:58,12.5,60,4.9,5.1,5.1,270,0.0,0.0,1025.0,81.9,20.3,39,5.1,10.7,12.5,0,35,0.00,0.00,7.8,237,2.0,270,0.0,0.0
01/09/21,16:28,12.3,60,4.8,2.7,2.7,270,0.0,0.0,1025.2,81.9,19.9,40,2.7,11.4,12.3,0,29,0.00,0.00,9.2,143,2.0,270,0.0,0.0
01/09/21,16:58,12.0,62,4.9,1.7,1.7,292,0.0,0.0,1025.2,81.9,21.4,34,1.7,11.7,12.0,0,22,0.00,0.00,9.7,58,2.0,292,0.0,0.0
01/09/21,17:28,11.4,63,4.6,1.7,1.7,248,0.0,0.0,1025.4,81.9,19.7,40,1.7,11.1,11.4,0,11,0.00,0.00,9.0,5,2.5,248,0.0,0.0
01/09/21,17:58,10.8,67,4.9,2.7,2.7,292,0.0,0.0,1025.5,81.9,19.0,41,2.7,9.6,10.8,0,0,0.00,0.00,7.8,0,2.5,292,0.0,0.0
01/09/21,18:28,10.4,70,5.2,3.1,3.1,292,0.0,0.0,1025.9,81.9,18.7,43,3.1,8.9,10.4,0,0,0.00,0.00,7.1,0,2.5,292,0.0,0.0
01/09/21,18:58,10.0,73,5.4,1.7,1.7,270,0.0,0.0,1026.3,81.9,18.5,43,1.7,9.4,10.0,0,0,0.00,0.00,7.8,0,2.5,270,0.0,0.0
01/09/21,19:28,9.8,76,5.8,1.7,1.7,270,0.0,0.0,1026.6,81.9,18.3,44,1.7,9.2,9.8,0,0,0.00,0.00,7.6,0,2.5,270,0.0,0.0
01/09/21,19:58,9.4,78,5.8,1.7,1.7,292,0.0,0.0,1026.6,81.9,18.2,44,1.7,8.7,9.4,0,0,0.00,0.00,7.2,0,2.5,292,0.0,0.0
01/09/21,20:28,9.0,79,5.6,1.7,1.7,270,0.0,0.0,1026.8,81.9,18.1,44,1.7,8.3,9.0,0,0,0.00,0.00,6.8,0,2.5,270,0.0,0.0
01/09/21,20:58,8.8,79,5.4,2.0,2.0,270,0.0,0.0,1027.1,81.9,18.0,45,2.0,7.8,8.8,0,0,0.00,0.00,6.3,0,2.5,270,0.0,0.0
01/09/21,21:28,8.6,79,5.2,2.7,2.7,292,0.0,0.0,1027.2,81.9,17.9,45,2.7,7.0,8.6,0,0,0.00,0.00,5.6,0,2.5,292,0.0,0.0
01/09/21,21:58,8.3,79,4.9,2.0,2.0,270,0.0,0.0,1027.2,81.9,17.8,45,2.0,7.2,8.3,0,0,0.00,0.00,5.8,0,2.5,270,0.0,0.0
01/09/21,22:28,8.2,79,4.8,1.0,1.0,270,0.0,0.0,1027.3,81.9,17.7,46,1.0,8.2,8.2,0,0,0.00,0.00,6.3,0,2.5,270,0.0,0.0
01/09/21,22:58,7.6,80,4.4,0.0,0.0,0,0.0,0.0,1027.2,81.9,17.7,46,0.0,7.6,7.6,0,0,0.00,0.00,6.4,0,2.5,0,0.0,0.0
01/09/21,23:28,7.2,81,4.2,1.0,1.0,270,0.0,0.0,1027.5,81.9,17.5,46,1.0,7.2,7.2,0,0,0.00,0.00,5.2,0,2.5,270,0.0,0.0
01/09/21,23:58,6.0,84,3.5,0.0,0.0,0,0.0,0.0,1027.5,81.9,17.4,46,0.0,6.0,6.0,0,0,0.00,0.00,4.6,0,2.5,0,0.0,0.0
02/09/21,00:28,5.9,83,3.2,0.0,0.0,0,0.0,0.0,1027.7,81.9,17.4,46,0.0,5.9,5.9,0,0,0.00,0.00,4.4,0,0.0,0,0.0,0.0
02/09/21,00:58,5.1,86,3.0,0.0,0.0,0,0.0,0.0,1028.0,81.9,17.3,46,0.0,5.1,5.1,0,0,0.00,0.00,3.6,0,0.0,0,0.0,0.0
02/09/21,01:28,5.1,87,3.1,0.7,0.7,270,0.0,0.0,1027.9,81.9,17.2,46,0.7,5.1,5.1,0,0,0.00,0.00,3.1,0,0.0,270,0.0,0.0
02/09/21,01:58,4.9,86,2.8,0.0,0.0,0,0.0,0.0,1027.8,81.9,17.2,46,0.0,4.9,4.9,0,0,0.00,0.00,3.4,0,0.0,0,0.0,0.0
02/09/21,02:28,4.5,87,2.5,0.0,0.0,0,0.0,0.0,1027.7,81.9,17.1,46,0.0,4.5,4.5,0,0,0.00,0.00,2.9,0,0.0,0,0.0,0.0
02/09/21,02:58,3.4,90,1.9,0.0,0.0,0,0.0,0.0,1027.6,81.9,17.0,45,0.0,3.4,3.4,0,0,0.00,0.00,1.7,0,0.0,0,0.0,0.0
02/09/21,03:28,3.1,90,1.6,0.0,0.0,0,0.0,0.0,1028.0,81.9,16.9,46,0.0,3.1,3.1,0,0,0.00,0.00,1.4,0,0.0,0,0.0,0.0
02/09/21,03:58,3.4,89,1.8,0.7,0.7,270,0.0,0.0,1027.8,81.9,16.9,46,0.7,3.4,3.4,0,0,0.00,0.00,1.2,0,0.0,270,0.0,0.0
02/09/21,04:28,4.0,87,2.0,0.0,0.0,0,0.0,0.0,1027.8,81.9,16.9,46,0.0,4.0,4.0,0,0,0.00,0.00,2.3,0,0.0,0,0.0,0.0
02/09/21,04:58,3.1,89,1.5,0.7,0.7,270,0.0,0.0,1027.9,81.9,16.8,45,0.7,3.1,3.1,0,0,0.00,0.00,0.9,0,0.0,270,0.0,0.0
02/09/21,05:28,3.3,89,1.7,0.0,0.0,0,0.0,0.0,1028.0,81.9,16.8,45,0.0,3.3,3.3,0,0,0.00,0.00,1.6,0,0.0,0,0.0,0.0
02/09/21,05:58,3.3,90,1.8,0.7,0.7,270,0.0,0.0,1028.1,81.9,16.7,45,0.7,3.3,3.3,0,0,0.00,0.00,1.1,0,0.0,270,0.0,0.0
02/09/21,06:28,3.4,90,1.9,0.0,0.0,0,0.0,0.0,1028.4,81.9,16.7,45,0.0,3.4,3.4,0,0,0.00,0.00,1.7,0,0.0,0,0.0,0.0
02/09/21,06:58,3.3,90,1.8,0.0,0.0,0,0.0,0.0,1028.7,81.9,16.7,45,0.0,3.3,3.3,0,7,0.00,0.00,1.6,2,0.5,0,0.0,0.0
02/09/21,07:28,3.6,90,2.1,0.0,0.0,0,0.0,0.0,1028.7,81.9,16.8,45,0.0,3.6,3.6,0,19,0.00,0.00,1.9,44,0.5,0,0.0,0.0
02/09/21,07:58,5.0,87,3.0,0.0,0.0,0,0.0,0.0,1029.0,81.9,16.9,44,0.0,5.0,5.0,0,29,0.00,0.00,3.5,125,0.5,0,0.0,0.0
02/09/21,08:28,6.6,83,3.9,0.0,0.0,0,0.0,0.0,1029.4,81.9,17.2,43,0.0,6.6,6.6,0,38,0.00,0.00,5.3,218,0.5,0,0.0,0.0
02/09/21,08:58,8.4,76,4.4,0.0,0.0,0,0.0,0.0,1029.7,81.9,17.7,43,0.0,8.4,8.4,0,43,0.00,0.00,7.2,311,0.5,0,0.0,0.0
02/09/21,09:28,9.6,70,4.4,0.0,0.0,0,0.0,0.0,1029.8,81.9,18.1,43,0.0,9.6,9.6,0,48,0.00,0.00,8.4,397,0.5,0,0.0,0.0
02/09/21,09:58,12.3,65,5.9,0.7,0.7,270,0.0,0.0,1029.5,81.9,18.7,43,0.7,12.3,12.3,3,359,0.00,0.00,10.9,474,1.0,270,0.0,0.0
02/09/21,10:28,13.0,65,6.6,1.0,1.0,270,0.0,0.0,1029.9,81.9,19.0,42,1.0,13.0,13.0,4,411,0.00,0.00,11.5,538,1.5,270,0.0,0.0
02/09/21,10:58,14.6,63,7.6,0.0,0.0,0,0.0,0.0,1029.7,81.9,19.3,42,0.0,14.6,14.6,4,425,0.00,0.00,14.0,588,1.5,0,0.0,0.0
02/09/21,11:28,14.3,62,7.1,2.0,2.0,314,0.0,0.0,1029.9,81.9,19.7,41,2.0,14.2,14.3,4,480,0.00,0.00,12.2,623,2.0,315,0.0,0.0
02/09/21,11:58,14.5,62,7.3,1.7,1.7,314,0.0,0.0,1029.7,81.9,20.1,40,1.7,14.5,14.5,3,322,0.00,0.00,12.7,641,2.0,315,0.0,0.0
02/09/21,12:28,14.1,64,7.4,1.0,1.0,337,0.0,0.0,1029.9,81.9,20.3,40,1.0,14.1,14.1,1,164,0.00,0.00,12.8,643,2.0,338,0.0,0.0
02/09/21,12:58,14.6,60,6.9,1.0,1.0,337,0.0,0.0,1029.5,81.9,20.3,40,1.0,14.6,14.6,0,78,0.00,0.00,13.2,629,2.0,338,0.0,0.0
02/09/21,13:28,14.6,58,6.4,0.7,0.7,337,0.0,0.0,1029.3,81.9,20.5,40,0.7,14.6,14.6,1,101,0.00,0.00,13.3,598,2.0,338,0.0,0.0
02/09/21,13:58,15.1,57,6.6,1.7,1.7,337,0.0,0.0,1029.0,81.9,20.8,40,1.7,15.1,15.1,2,247,0.00,0.00,13.1,551,2.0,338,0.0,0.0
02/09/21,14:28,14.9,55,5.9,1.0,1.0,337,0.0,0.0,1029.1,81.9,20.8,40,1.0,14.9,14.9,1,143,0.00,0.00,13.3,491,2.0,338,0.0,0.0
02/09/21,14:58,14.5,60,6.8,1.0,1.0,337,0.0,0.0,1028.9,81.9,20.8,40,1.0,14.5,14.5,1,111,0.00,0.00,13.1,417,2.0,338,0.0,0.0
02/09/21,15:28,14.7,58,6.5,0.7,0.7,337,0.0,0.0,1029.0,81.9,21.0,39,0.7,14.7,14.7,0,55,0.00,0.00,13.4,333,2.0,338,0.0,0.0
02/09/21,15:58,13.9,67,7.9,0.7,0.7,337,0.0,0.0,1028.9,81.9,20.9,40,0.7,13.9,13.9,0,41,0.00,0.00,12.9,241,2.0,338,0.0,0.0
02/09/21,16:28,13.6,67,7.6,0.7,0.7,337,0.0,0.0,1029.3,81.9,20.5,40,0.7,13.6,13.6,0,32,0.00,0.00,12.5,147,2.0,338,0.0,0.0
02/09/21,16:58,13.2,68,7.4,1.0,1.0,337,0.0,0.0,1029.3,81.9,21.4,38,1.0,13.2,13.2,0,25,0.00,0.00,11.9,61,2.0,338,0.0,0.0
02/09/21,17:28,12.3,74,7.8,0.7,0.7,337,0.0,0.0,1029.7,81.9,20.3,40,0.7,12.3,12.3,0,12,0.00,0.00,11.3,6,2.5,338,0.0,0.0
02/09/21,17:58,12.0,73,7.3,0.7,0.7,337,0.0,0.0,1029.9,81.9,19.6,43,0.7,12.0,12.0,0,0,0.00,0.00,10.9,0,2.5,338,0.0,0.0
02/09/21,18:28,11.6,66,5.5,2.7,2.7,337,0.0,0.0,1030.2,81.9,19.2,44,2.7,10.6,11.6,0,0,0.00,0.00,8.7,0,2.5,338,0.0,0.0
02/09/21,18:58,11.0,68,5.3,1.7,1.7,292,0.0,0.0,1030.4,81.9,18.9,45,1.7,10.6,11.0,0,0,0.00,0.00,8.8,0,2.5,292,0.0,0.0
02/09/21,19:28,10.3,70,5.1,0.7,0.7,270,0.0,0.0,1031.1,81.9,18.8,46,0.7,10.3,10.3,0,0,0.00,0.00,8.7,0,2.5,270,0.0,0.0
02/09/21,19:58,10.5,73,5.9,0.7,0.7,225,0.0,0.0,1030.4,81.9,18.7,46,0.7,10.5,10.5,0,0,0.00,0.00,9.1,0,2.5,225,0.0,0.0
02/09/21,20:28,10.1,74,5.7,2.0,2.0,248,0.0,0.0,1031.0,81.9,18.5,46,2.0,9.3,10.1,0,0,0.00,0.00,7.7,0,2.5,248,0.0,0.0
02/09/21,20:58,10.0,75,5.8,2.0,2.0,292,0.0,0.0,1031.4,81.9,18.5,46,2.0,9.2,10.0,0,0,0.00,0.00,7.6,0,2.5,292,0.0,0.0
02/09/21,21:28,9.7,77,5.9,1.7,1.7,292,0.0,0.0,1031.7,81.9,18.3,46,1.7,9.1,9.7,0,0,0.00,0.00,7.6,0,2.5,292,0.0,0.0
02/09/21,21:58,9.4,79,5.9,1.0,1.0,292,0.0,0.0,1031.9,81.9,18.1,47,1.0,9.4,9.4,0,0,0.00,0.00,7.8,0,2.5,292,0.0,0.0
02/09/21,22:28,9.5,80,6.2,1.0,1.0,292,0.0,0.0,1031.8,81.9,18.1,47,1.0,9.5,9.5,0,0,0.00,0.00,7.9,0,2.5,292,0.0,0.0
02/09/21,22:58,9.4,80,6.1,2.0,2.0,292,0.0,0.0,1032.2,81.9,18.0,47,2.0,8.5,9.4,0,0,0.00,0.00,7.1,0,2.5,292,0.0,0.0
02/09/21,23:28,9.3,79,5.8,1.7,1.7,292,0.0,0.0,1032.2,81.9,18.0,47,1.7,8.6,9.3,0,0,0.00,0.00,7.2,0,2.5,292,0.0,0.0
02/09/21,23:58,8.9,80,5.6,0.0,0.0,0,0.0,0.0,1032.2,81.9,18.0,48,0.0,8.9,8.9,0,0,0.00,0.00,7.9,0,2.5,0,0.0,0.0
03/09/21,00:28,8.5,81,5.4,0.0,0.0,0,0.0,0.0,1032.5,81.9,17.9,48,0.0,8.5,8.5,0,0,0.00,0.00,7.5,0,0.0,0,0.0,0.0
03/09/21,00:58,7.6,85,5.2,0.0,0.0,0,0.0,0.0,1032.6,81.9,17.8,48,0.0,7.6,7.6,0,0,0.00,0.00,6.5,0,0.0,0,0.0,0.0
03/09/21,01:28,7.6,85,5.2,0.0,0.0,0,0.0,0.0,1032.6,81.9,17.7,48,0.0,7.6,7.6,0,0,0.00,0.00,6.5,0,0.0,0,0.0,0.0
03/09/21,01:58,7.6,85,5.2,0.0,0.0,0,0.0,0.0,1032.5,81.9,17.7,48,0.0,7.6,7.6,0,0,0.00,0.00,6.5,0,0.0,0,0.0,0.0
03/09/21,02:28,6.4,88,4.6,0.0,0.0,0,0.0,0.0,1032.4,81.9,17.6,48,0.0,6.4,6.4,0,0,0.00,0.00,5.2,0,0.0,0,0.0,0.0
03/09/21,02:58,5.8,89,4.1,0.0,0.0,0,0.0,0.0,1032.2,81.9,17.6,48,0.0,5.8,5.8,0,0,0.00,0.00,4.5,0,0.0,0,0.0,0.0
03/09/21,03:28,5.5,91,4.1,0.0,0.0,0,0.0,0.0,1032.4,81.9,17.4,48,0.0,5.5,5.5,0,0,0.00,0.00,4.2,0,0.0,0,0.0,0.0
03/09/21,03:58,5.4,90,3.9,0.0,0.0,0,0.0,0.0,1032.3,81.9,17.3,48,0.0,5.4,5.4,0,0,0.00,0.00,4.1,0,0.0,0,0.0,0.0
03/09/21,04:28,5.5,90,4.0,0.7,0.7,292,0.0,0.0,1032.5,81.9,17.3,48,0.7,5.5,5.5,0,0,0.00,0.00,3.7,0,0.0,292,0.0,0.0
03/09/21,04:58,5.1,90,3.6,0.0,0.0,0,0.0,0.0,1032.7,81.9,17.2,48,0.0,5.1,5.1,0,0,0.00,0.00,3.7,0,0.0,0,0.0,0.0
03/09/21,05:28,4.1,92,2.9,0.0,0.0,0,0.0,0.0,1032.9,81.9,17.2,48,0.0,4.1,4.1,0,0,0.00,0.00,2.6,0,0.0,0,0.0,0.0
03/09/21,05:58,4.0,93,3.0,0.0,0.0,0,0.0,0.0,1033.2,81.9,17.1,48,0.0,4.0,4.0,0,0,0.00,0.00,2.5,0,0.0,0,0.0,0.0
03/09/21,06:28,3.8,92,2.6,0.0,0.0,0,0.0,0.0,1033.1,81.9,16.9,49,0.0,3.8,3.8,0,0,0.00,0.00,2.2,0,0.0,0,0.0,0.0
03/09/21,06:58,4.3,91,3.0,1.0,1.0,292,0.0,0.0,1033.7,81.9,16.9,49,1.0,4.3,4.3,0,6,0.00,0.00,2.1,2,0.5,292,0.0,0.0
03/09/21,07:28,4.5,91,3.2,0.0,0.0,0,0.0,0.0,1033.5,81.9,17.0,48,0.0,4.5,4.5,0,17,0.00,0.00,3.0,47,0.5,0,0.0,0.0
03/09/21,07:58,5.6,87,3.6,1.0,1.0,292,0.0,0.0,1034.1,81.9,17.2,47,1.0,5.6,5.6,0,25,0.00,0.00,3.5,130,0.5,292,0.0,0.0
03/09/21,08:28,6.7,83,4.0,1.7,1.7,270,0.0,0.0,1034.2,81.9,17.6,46,1.7,5.6,6.7,0,31,0.00,0.00,4.2,224,0.5,270,0.0,0.0
03/09/21,08:58,7.9,80,4.7,0.7,0.7,225,0.0,0.0,1034.4,81.9,17.9,45,0.7,7.9,7.9,0,36,0.00,0.00,6.2,317,0.5,225,0.0,0.0
03/09/21,09:28,9.4,77,5.6,1.0,1.0,225,0.0,0.0,1034.2,81.9,18.5,45,1.0,9.4,9.4,0,45,0.00,0.00,7.7,404,0.5,225,0.0,0.0
03/09/21,09:58,12.5,72,7.6,0.0,0.0,0,0.0,0.0,1034.3,81.9,18.9,44,0.0,12.5,12.5,3,368,0.00,0.00,11.9,481,1.0,0,0.0,0.0
03/09/21,10:28,14.7,63,7.7,0.0,0.0,0,0.0,0.0,1034.2,81.9,19.3,43,0.0,14.7,14.7,4,432,0.00,0.00,14.2,545,1.5,0,0.0,0.0
03/09/21,10:58,15.2,53,5.7,3.1,3.1,248,0.0,0.0,1033.9,81.9,19.6,43,3.1,14.7,15.2,4,432,0.00,0.00,12.0,595,1.5,248,0.0,0.0
03/09/21,11:28,15.7,46,4.1,2.7,2.7,203,0.0,0.0,1033.8,81.9,20.0,41,2.7,15.5,15.7,5,515,0.00,0.00,12.5,629,2.0,202,0.0,0.0
03/09/21,11:58,15.9,45,4.0,3.7,3.7,203,0.0,0.0,1033.4,81.9,20.3,41,3.7,15.3,15.9,3,391,0.00,0.00,12.0,648,2.0,202,0.0,0.0
03/09/21,12:28,15.9,44,3.7,0.7,0.7,203,0.0,0.0,1032.8,81.9,20.7,40,0.7,15.9,15.9,1,114,0.00,0.00,14.0,650,2.0,202,0.0,0.0
03/09/21,12:58,16.1,47,4.8,1.0,1.0,270,0.0,0.0,1032.6,81.9,20.9,40,1.0,16.1,16.1,2,227,0.00,0.00,14.2,635,2.0,270,0.0,0.0
03/09/21,13:28,16.7,49,5.9,1.0,1.0,292,0.0,0.0,1032.4,81.9,21.0,39,1.0,16.7,16.7,1,159,0.00,0.00,15.1,604,2.0,292,0.0,0.0
03/09/21,13:58,15.9,48,4.9,2.0,2.0,292,0.0,0.0,1032.3,81.9,21.1,38,2.0,15.9,15.9,0,77,0.00,0.00,13.4,557,2.0,292,0.0,0.0
03/09/21,14:29,15.8,48,4.8,0.7,0.7,314,0.0,0.0,1032.0,81.9,21.1,38,0.7,15.8,15.8,0,55,0.00,0.00,14.1,494,2.0,315,0.0,0.0
03/09/21,14:59,15.9,49,5.2,1.7,1.7,314,0.0,0.0,1031.8,81.9,21.1,37,1.7,15.9,15.9,0,47,0.00,0.00,13.6,419,2.0,315,0.0,0.0
03/09/21,15:29,16.1,48,5.1,3.1,3.1,314,0.0,0.0,1031.8,81.9,21.4,37,3.1,15.8,16.1,1,143,0.00,0.00,12.8,334,2.0,315,0.0,0.0
03/09/21,15:59,15.4,52,5.6,0.0,0.0,0,0.0,0.0,1031.6,81.9,21.2,37,0.0,15.4,15.4,0,38,0.00,0.00,14.4,242,2.0,0,0.0,0.0
03/09/21,16:29,15.1,52,5.3,4.8,4.8,292,0.0,0.0,1031.9,81.9,20.9,39,4.8,14.0,15.1,0,31,0.00,0.00,10.7,148,2.0,292,0.0,0.0
03/09/21,16:59,14.2,57,5.8,3.7,3.7,314,0.0,0.0,1031.5,81.9,20.8,39,3.7,13.3,14.2,0,24,0.00,0.00,10.6,61,2.0,315,0.0,0.0
03/09/21,17:29,13.2,61,5.8,2.7,2.7,314,0.0,0.0,1032.0,81.9,20.3,40,2.7,12.5,13.2,0,11,0.00,0.00,10.4,6,2.5,315,0.0,0.0
03/09/21,17:59,12.0,66,5.8,2.7,2.7,314,0.0,0.0,1032.2,81.9,19.9,41,2.7,11.1,12.0,0,0,0.00,0.00,9.2,0,2.5,315,0.0,0.0
03/09/21,18:29,10.8,71,5.7,0.0,0.0,0,0.0,0.0,1032.4,81.9,19.4,42,0.0,10.8,10.8,0,0,0.00,0.00,9.8,0,2.5,0,0.0,0.0
03/09/21,18:59,9.4,76,5.4,0.0,0.0,0,0.0,0.0,1032.5,81.9,19.2,42,0.0,9.4,9.4,0,0,0.00,0.00,8.4,0,2.5,0,0.0,0.0
03/09/21,19:29,8.1,81,5.0,0.0,0.0,0,0.0,0.0,1032.8,81.9,18.9,44,0.0,8.1,8.1,0,0,0.00,0.00,7.0,0,2.5,0,0.0,0.0
03/09/21,19:59,7.5,83,4.8,0.0,0.0,0,0.0,0.0,1033.0,81.9,18.8,44,0.0,7.5,7.5,0,0,0.00,0.00,6.3,0,2.5,0,0.0,0.0
03/09/21,20:29,6.6,85,4.3,0.0,0.0,0,0.0,0.0,1033.0,81.9,18.6,45,0.0,6.6,6.6,0,0,0.00,0.00,5.3,0,2.5,0,0.0,0.0
03/09/21,20:59,6.0,86,3.8,0.0,0.0,0,0.0,0.0,1033.1,81.9,18.5,45,0.0,6.0,6.0,0,0,0.00,0.00,4.7,0,2.5,0,0.0,0.0
03/09/21,21:29,5.5,87,3.5,0.0,0.0,0,0.0,0.0,1033.2,81.9,18.3,45,0.0,5.5,5.5,0,0,0.00,0.00,4.1,0,2.5,0,0.0,0.0
03/09/21,21:59,5.4,87,3.4,0.0,0.0,0,0.0,0.0,1033.6,81.9,18.1,45,0.0,5.4,5.4,0,0,0.00,0.00,4.0,0,2.5,0,0.0,0.0
03/09/21,22:29,5.2,87,3.2,1.0,1.0,314,0.0,0.0,1033.4,81.9,18.0,45,1.0,5.2,5.2,0,0,0.00,0.00,3.0,0,2.5,315,0.0,0.0
03/09/21,22:59,5.9,86,3.7,0.7,0.7,314,0.0,0.0,1033.7,81.9,18.0,45,0.7,5.9,5.9,0,0,0.00,0.00,4.0,0,2.5,315,0.0,0.0
03/09/21,23:29,6.6,85,4.3,0.7,0.7,314,0.0,0.0,1033.6,81.9,18.0,45,0.7,6.6,6.6,0,0,0.00,0.00,4.8,0,2.5,315,0.0,0.0
03/09/21,23:59,6.8,85,4.5,0.0,0.0,0,0.0,0.0,1033.4,81.9,18.0,45,0.0,6.8,6.8,0,0,0.00,0.00,5.6,0,2.5,0,0.0,0.0
04/09/21,00:29,6.5,87,4.5,0.0,0.0,0,0.0,0.0,1033.6,81.9,18.0,45,0.0,6.5,6.5,0,0,0.00,0.00,5.3,0,0.0,0,0.0,0.0
04/09/21,00:59,6.6,87,4.6,0.0,0.0,0,0.0,0.0,1033.7,81.9,18.0,45,0.0,6.6,6.6,0,0,0.00,0.00,5.4,0,0.0,0,0.0,0.0
04/09/21,01:29,7.0,86,4.8,0.0,0.0,0,0.0,0.0,1033.6,81.9,18.0,45,0.0,7.0,7.0,0,0,0.00,0.00,5.8,0,0.0,0,0.0,0.0
04/09/21,01:59,7.6,84,5.1,0.0,0.0,0,0.0,0.0,1033.4,81.9,17.9,45,0.0,7.6,7.6,0,0,0.00,0.00,6.5,0,0.0,0,0.0,0.0

You can use a library called pandas for this:
import pandas as pd # library you need to download and install, e.g. anaconda or pip
df = pd.read_csv('path\to\your\file.csv')

You can use pandas:
import pandas as pd
info = [
["01/09/21","00:28",7.1,75,3.0,3.7,3.7,292,0.0,0.0,1025.8,81.9,17.1,44,3.7,4.6,7.1,0,0,0.00,0.00,3.0,0,0.0,292,0.0,0.0],
["01/09/21","00:58",7.0,75,2.9,5.1,5.1,248,0.0,0.0,1025.9,81.9,17.0,44,5.1,3.8,7.0,0,0,0.00,0.00,1.9,0,0.0,248,0.0,0.0],
["01/09/21","01:28",6.9,74,2.6,4.1,4.1,248,0.0,0.0,1025.8,81.9,17.0,44,4.1,4.1,6.9,0,0,0.00,0.00,2.5,0,0.0,248,0.0,0.0],
["01/09/21","01:58",6.9,74,2.6,4.8,4.8,292,0.0,0.0,1025.7,81.9,16.9,45,4.8,3.8,6.9,0,0,0.00,0.00,2.0,0,0.0,292,0.0,0.0],
["01/09/21","02:28",6.8,76,2.9,3.7,3.7,270,0.0,0.0,1025.5,81.9,16.9,45,3.7,4.2,6.8,0,0,0.00,0.00,2.7,0,0.0,270,0.0,0.0],
["01/09/21","02:58",6.7,76,2.8,3.1,3.1,270,0.0,0.0,1025.4,81.9,16.8,45,3.1,4.5,6.7,0,0,0.00,0.00,3.0,0,0.0,270,0.0,0.0],
]
df = pd.DataFrame(info)
print(df.head())
df.to_csv("example.csv")
This should give you the results you are looking for.

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Hello all just learning dictionary in python. I have few data please let me know how to create a nested dictionary. Data are available below with duplicate values in excel file. Please do explain using for loop
Name Account Dept
John AC Lab1
Dev AC Lab1
Dilip AC Lab1,Lab2
Sat AC Lab1,Lab2
Dina AC Lab3
Surez AC Lab4
I need the result in below format:
{
'AC': {
'Lab1': ['John', 'Dev', 'Dilip', 'Sat'],
'Lab2': ['Dilip','Sat'],
'Lab3': ['Dina'],
'Lab4': ['Surez']
}
}

Something like this should get you closer to an answer but I'd need your input file to optimize it:
import xlrd
from collections import defaultdict
wb = xlrd.open_workbook("<your filename>")
sheet_names = wb.sheet_names()
sheet = wb.sheet_by_name(sheet_names[0])
d = defaultdict(defaultdict(list))
for row_idx in range(0, sheet.nrows):
cell_obj_0 = sheet.cell(row_idx, 0)
cell_obj_1 = sheet.cell(row_idx, 1)
cell_obj_2 = sheet.cell(row_idx, 2)
for lab in cell_obj_2.split(","):
d[cell_obj_1][lab].append(cell_obj_0)

Average data by each hour in python [closed]

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I have data like below:
Data Columns:
DateTime,Data1,Data2,Data3,Month,Date,Year,Hour,Minutes
1/1/2017 0:00,1.1,2.2,3.3,1,1,2017,0,00
1/1/2017 0:00,1.1,2.2,3.3,1,1,2017,0,15
1/1/2017 0:00,1.1,2.2,3.3,1,1,2017,0,30
1/1/2017 0:00,1.1,2.2,3.3,1,1,2017,1,45
I need to average columns 'WS', 'VWS' .... 'SR' data by each hour. The DateTime column is reported every 15 minutes.

I have an answer to my own question. Posting it here so that others can benefit:
import pandas as pd
df = pd.read_csv("MetData.csv")
df['NewDateTime'] = pd.to_datetime(df['DateTime'])
df.index = df['NewDateTime']
df_p = df.resample('H').mean()
df_p['Month'] = df['NewDateTime'].dt.month
df_p['Year'] = df['NewDateTime'].dt.year
df_p['Date'] = df['NewDateTime'].dt.day
df_p['Hour'] = df['NewDateTime'].dt.hour
writer = pd.ExcelWriter('MetData_Orig1.xlsx', engine='xlsxwriter')
df_p.to_excel(writer, sheet_name='Sheet1')
writer.save()

Storing data in Redis then fetch that data [closed]

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I have an Excel file that contains four columns. I want to fetch this data and store it in MySQL. Later on I want fetch the data from here and store in Redis, then run a validation on it. I have already done the importing of data from Excel to Python.

You have to reshape your 4 column excel data to a 1 column data.
The redis client for Matlab/GNU Octave is doing this e.g.: https://github.com/markuman/go-redis/wiki/Data-Structure#arrays
Take care that in this example, Matlab/Octave are using Column-Major-Order.
Python is using Row-Major-Order: https://en.wikipedia.org/wiki/Row-major_order
So you have to save your 4 column X rows data as a row-major-order list in redis as a list (RPUSH).
example
given this excel sheet
using this python3 code
#!/usr/bin/python3
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 20 23:02:53 2015
#author: markus
"""
import pandas as pd
import redis
# redis connection
r = redis.StrictRedis(host='localhost', port=6379, db=0)
# open the first worksheed
df = pd.read_excel('/home/markus/excel.xlsx',0)
# read in as a list
# [[1, 'two', 'python'], ['excel', 'redis', 'action']]
a = list(df.T.itertuples())
print("this is a, your excel list")
print(a)
for list in a:
for value in list:
r.rpush('myexceldata', str(value))
# read all back to python
b = r.lrange('myexceldata', '0', '-1')
print("A1 becomes 0, B1 becomes 3 ...")
print(b[3].decode('UTF-8'))
to save it serialized as a list in redis
127.0.0.1:6379> lrange myexceldata 0 -1
1) "1"
2) "two"
3) "python"
4) "excel"
5) "redis"
6) "action"
This is just one way to save a spreadsheet in redis. It always belong on your datastructure and what you're going to do with it.