Deleting Specific List in txt file-CodePudding

I'm trying to delete specific list from a text file and overwrite the original text file without using os.replace. However,i tried using truncate but nothing happen to my txt file..

Input:

def delete_tenants():
    a = []
    tnf = False

    # while not found:
    with open("tenant_application.txt", "r") as file:
        lines = file.readlines()

    with open("tenant_application.txt", "r") as file:
        for line in file:
            a = line.split(",")  # split lines using comma
            print(a)  # display all the data in txt file

    with open("tenant_application.txt", "r") as file:
        delete = input("Enter the primary key to delete: ")
        for line in file:
            a = line.split(",")  # split lines using comma
            b = a[10].strip()
            c = delete.strip()
            d = c == b
            if d:
                print("Line that you want to delete:", line)
                tnf = True
                break

    if tnf == False:
        print("\nInvalid Input\n")
        return delete_tenants()

    if tnf == True:
        input1 = "tenant_application.txt"
        #
        with open(input1, "r ") as file1:
            lines = file1.readlines()
            file1.seek(0)
            for i in lines:
                if i not in a:
                    file1.write(i)
            file1.truncate()

My text file:

Celine ,43,Female,Selangor,Kuala Lumpur,Happy Garden,Persiaran Sungai Long 2 ,2003-04-03 00:00:00,43000,111,0001
Jackson ,43,Female,Selangor,Kuala Lumpur,Happy Garden,Persiaran Sungai Long 2 ,2003-04-03 00:00:00,43000,111,0002
Wong ,43,Female,Selangor,Kuala Lumpur,Happy Garden,Persiaran Sungai Long 2 ,2003-04-03 00:00:00,43000,111,0003
Liew ,43,Female,Selangor,Kuala Lumpur,Happy Garden,Persiaran Sungai Long 2 ,2003-04-03 00:00:00,43000,111,0004

CodePudding user response：

This is a CSV file. Python can read and parse CSV file through the [csv module] but an even better option is to use Pandas to read the file, remove the row you want and then save it back. This can be done in just a few lines.

You can read a CSV into a DataFrame (roughly an in-memory table) using the pandas.read_csv function:

import pandas as pd

df=pd.read_csv(path,header=None)

If the file has a header, the dataframe's columns will use the header names. If not, the column names will be numbers.

Given the question's example

import pandas as pd
from io import StringIO

csv="""Celine ,43,Female,Selangor,Kuala Lumpur,Happy Garden,Persiaran Sungai Long 2 ,2003-04-03 00:00:00,43000,111,0001
Jackson ,43,Female,Selangor,Kuala Lumpur,Happy Garden,Persiaran Sungai Long 2 ,2003-04-03 00:00:00,43000,111,0002
Wong ,43,Female,Selangor,Kuala Lumpur,Happy Garden,Persiaran Sungai Long 2 ,2003-04-03 00:00:00,43000,111,0003
Liew ,43,Female,Selangor,Kuala Lumpur,Happy Garden,Persiaran Sungai Long 2 ,2003-04-03 00:00:00,43000,111,0004"""

df=pd.read_csv(StringIO(csv),header=None)

The first column values can be retrieved with df[0]:

df[0]
----------------
0     Celine
1    Jackson
2       Wong
3       Liew

Instead of deleting a row based on some condition, we can filter them to exclude the rows that don't match the condition:

df=df[df[10]!=4]
df[0]
---------------
0     Celine
1    Jackson
2       Wong

Once we have the dataframe we want we can save it with to_csv :

df.to_csv(path,header=False,index=false)

It's possible to manipulate string columns to eg strip them of whitespace :

df[0]=df[0].str.strip()

The answers to this SO question shows various ways to strip all string columns, eg :

df=df.applymap(lambda x: x.strip() if isinstance(x, str) else x)

Putting all this together:

import pandas as pd

df=pd.read_csv(path,header=None)
id= input("Enter the primary key to delete: ")
df=df[df[10]!=id]
df=df.applymap(lambda x: x.strip() if isinstance(x, str) else x)
df.to_csv(path,header=False,index=false)

CodePudding user response：

i don't know but Most popular RDBMS (eg MySQL, Oracle, PostgresSQL, Teradata) allow tables to be joined during DELETE, allowing complex balancing in short statements.

Now suppose we want to aggregate (Aggregate) a table from the target table based on date and not ID. Let's also say that we want the data to be deleted from the source only after the date field of the aggregate table has been filled.