pandas concating two default-indexed dataframes

Question

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?

Incidentally, don't use `list` as a variable name to *store* a list. Doing so, overwrites its [`built-in functionality`](https://docs.python.org/3/library/stdtypes.html#list). E.g. use `my_list` or something. — ouroboros1, Oct 15 '22 at 12:54

score 1 · Answer 1 · answered Oct 15 '22 at 12:30

1

you have to use index=None option. try this:

frame.to_csv('file.csv',index=None)

answered Oct 15 '22 at 12:30

Bushmaster

4,196
3
8
28

score 1 · Answer 2 · answered Oct 15 '22 at 12:31

1

Solution

To do this just use the index=None keyword argument in the .to_csv function like such:

.to_csv('foo.csv',index=None)

answered Oct 15 '22 at 12:31

NodeX4

150
1
10

score 1 · Accepted Answer · edited Oct 15 '22 at 12:55

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

pandas concating two default-indexed dataframes

3 Answers3

Solution