python - Remove rows in a csv file based on the format of column value

Question

I have a csv file which contains three columns - computer_name, software_code, software_update_date. The file contains computers that I don't need in my final report. I only need the data for computers whose name starts with 40- , 46- or 98-. Here is the sample file:

computer_name   software_code   software_update_date
07-0708                   436   2019-02-07 0:00
30-0207                 35170   2021-01-18 0:00
40-0049                    41   2017-06-21 23:00
46-0001                    11   2013-11-23 0:00

So I would like to delete rows 07-0708 and 30-0207. I tried with pandas but the generated file is exactly the same with no error message. I am quite new to python and still grasping the concepts. I wrote the below code:

import csv
import pandas as pd

fname = 'RAWfile.csv'

df=pd.read_csv(fname,encoding='ISO-8859-1')

#Renaming columns from the report
df.rename(columns = {'computer_name':'PC_NO', 'software_code':'SOFT_CODE', 'software_update_date':'UPDATE_DATE'}, inplace=True)

computers = ['40-','46-','98-']
searchstr = '|'.join(computers)
df[df['PC_NO'].str.contains(searchstr)]

df.to_csv('updatedfile.csv',index=False,quoting=csv.QUOTE_ALL,line_terminator='
')

UPDATE: There are almost 70,000 rows in the csv file. Corrected the values in computers list to match the question.

question from:https://stackoverflow.com/questions/65850595/remove-rows-in-a-csv-file-based-on-the-format-of-column-value

Answer 1

You can try this,

# String to be searched in start of string


search = ("40-", "46-", "98-")
  
# boolean series returned with False at place of NaN 
series = df["computer_name"].str.startswith(search, na = False) 
  
# displaying filtered dataframe 
df[series]