pandas concating two default-indexed dataframes

I have data like (simplified for the sake of example):

list = [
    {
     age: 1234,
     val: 0.5,
     val2: 0.2
    },
    {
     age: 1234,
     val: 0.2,
     val2: 0.8
    },
]

I create pandas dataframe by frame = pandas.DataFrame(list) and it creates unnamed index from 0 to len(list) – 1.
Frame looks like:

     age  val  val2
0    1234 0.5  0.2
1    1234 0.2  0.8

Then I save it to csv by frame.to_csv('file.csv') – it goes ok.
But then, I want to create another frame exactly like this, load previous frame from csv file and add them together, so new data comes after old. I don’t care about the index too, preferably it could go from 0 to new length with added data.
I tried doing it by pd.concat([old_frame, new_frame], ignore_index=True) but final data has now 2 columns with index values like this:

           age  val  val2
0     0    1234 0.5  0.2
1     1    1234 0.2  0.8

How to properly concat the frames without creating additional index column each time?

>Solution :

You need to change this part of code frame.to_csv('file.csv', index=False)

This is how it will look

import pandas as pd

my_list = [
    {
     "age": 1234,
     "val": 0.5,
     "val2": 0.2
    },
    {
     "age": 1234,
     "val": 0.2,
     "val2": 0.8
    },
]

frame = pd.DataFrame(list)
frame.to_csv('file.csv', index=False) # <- change here


old_frame = pd.read_csv('file.csv')
new_frame = pd.DataFrame(list)



combined_frame = pd.concat([old_frame, new_frame], ignore_index=True)

The result will be:

age  val  val2
0  1234  0.5   0.2
1  1234  0.2   0.8
2  1234  0.5   0.2
3  1234  0.2   0.8