Create a loop to process multiple files I have written the code below but currently I need to retype the same conditions for each file and, as there are over 100 files, this is not ideal I couldn't come up with a way to implement this using a loop that will read all of these files and filter the values in MP out Meanwhile, adding two new columns to each filter file as the written code below would be the only method I know so far I try to obtain a new combined data frame with all filter files with their conditions Please suggest ways of implementing this using a loop import pandas as pd import matplotlib pyplot as plt import numpy as np from scipy import signal dfi pd read csv('E Unmanned Cars Unmanned Cars 2017040810 052 csv') df2 pd read csv('E Unmanned Cars Unmanned Cars 2017040901 052 csv') df3 pd read csv('E Unmanned Cars Unmanned Cars 2017040902 052 csv') df1 df1 MP unique) df1 pd DataFrame(dfi, columns 'MP' ) df1 Dates 2017 04 08 df1 Inspection 10 df2 df2 MP unique) df2 pd DataFrame(df2, columns 'MP' ) df2 Dates 2017 04 09 df2 Inspection 01 df3 df3 MP unique) df3 pd DataFrame(df3, columns 'MP' ) df3 Dates 2017 04 09 df3 Inspection 02 Final pd concat( dfi, df2, df3, df4 , axis 0, sort False)

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Create a loop to process multiple files I have written the code below but currently I need to retype the same conditions for each file

Create a loop to process multiple files

I have written the code below but currently I need to retype the same conditions for each file and, as there are over 100 files, this is not ideal.

I couldn't come up with a way to implement this using a loop that will read all of these files and filter the values in MP out. Meanwhile, adding two new columns to each filter file as the written code below would be the only method I know so far. I try to obtain a new combined data frame with all filter files with their conditions

Please suggest ways of implementing this using a loop:

image text in transcribed

import pandas as pd import matplotlib.pyplot as plt import numpy as np from scipy import signal dfi = pd.read_csv('E:\Unmanned Cars\Unmanned Cars\2017040810_052.csv') df2 = pd.read_csv('E:\Unmanned Cars\Unmanned Cars\2017040901_052.csv') df3 = pd.read_csv('E:\Unmanned Cars\Unmanned Cars\2017040902_052.csv') df1 =df1["MP"].unique) df1=pd.DataFrame(dfi, columns=['MP']) df1["Dates"] = "2017-04-08" df1["Inspection"] = "10" ## df2 =df2["MP"]. unique) df2=pd.DataFrame(df2, columns=['MP']) df2["Dates"] = "2017-04-09" df2["Inspection"] = "01" ## df3 =df3["MP"]. unique) df3=pd. DataFrame(df3, columns=['MP']) df3["Dates"] = "2017-04-09" df3["Inspection"] = "02" Final = pd.concat([dfi, df2, df3, df4], axis = 0, sort = False)