Identifying the certain values and counting the event of maximum precipitation with it's date and pointID with python

HadiK — Mon, 20 Jun 2022 22:51:39 GMT

I have a csv file of my study area and I want to choose best area in this large study area to run my modelling for hydrogeology which is based on maximum event precipitation.

I have the code and I can indexing and get the value by thresholding.

For example: I choose index 1450 and show the value more than 100.

Problem is how can I identify the values that I want and get rid of the useless data, delete them and save it to new csv file?

Or how can I just display the valuable data with dates and pointid of that value?

I want to show me the value based on the date and count of how many times that event happened.

%pylab inline import pandas as pd import numpy as np from scipy import stats import time data_all= pd.read_csv('D:/Project_by_tiff_SA/CSV file/GPM2points_SA.csv') rain_all=data_all[data_all.columns[pd.Series(data_all.columns).str.startswith('D_')]] #Drop some fields rain_drop=data_all.drop(["OID_","pointid","grid_code","Mean","Max"],axis=1) #Transpose Data trans_all=rain_drop.T trans_all #If I index like this I'd get the NAN table #I need the values to be shown based on date for whole 17000 points thresh=trans_all[64500][trans_all[64500]>100] thresh #I was wondering if it's correct way to show value counts or not. trans_all[64500][trans_all[64500]>500].value_counts() #To save the result df.to_csv("H123.csv",index=True)

I have the csv file for 17000 points but I will be attached the light version

Attachment: It can also be done with the test data with only 24pixels. I'll attach the light version.

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Identifying the certain values and counting the event of maximum precipitation with it's date and pointID with python