Pandas append row without specifying columns

I wanted to add or append a row (in the form of a list) to a dataframe. All the methods requires that I turn the list into another dataframe first, eg.

df = df.append(another dataframe)
df = df.merge(another dataframe)
df = pd.concat(df, another dataframe)

I've found a trick if the index is in running number at https://www.statology.org/pandas-add-row-to-dataframe/

import pandas as pd

#create DataFrame
df = pd.DataFrame({'points': [10, 12, 12, 14, 13, 18],
                   'rebounds': [7, 7, 8, 13, 7, 4],
                   'assists': [11, 8, 10, 6, 6, 5]})

#view DataFrame
df

    points  rebounds assists
0   10  7    11
1   12  7    8
2   12  8    10
3   14  13   6
4   13  7    6
5   18  4    5

#add new row to end of DataFrame
df.loc[len(df.index)] = [20, 7, 5]

#view updated DataFrame
df

        points  rebounds assists
0   10  7    11
1   12  7    8
2   12  8    10
3   14  13   6
4   13  7    6
5   18  4    5
6   20  7    5

However, the dataframe must have index in running number or else, the add/append will override the existing data.

So my question is: Is there are simple, foolproof way to just append/add a list to a dataframe ?

Thanks very much !!!

Solution 1:

>>> df
  points rebounds assists
3     10        7      11
1     12        7       8
2     12        8      10

If the indexes are "numbers" - you could add 1 to the max index.

>>> df.loc[max(df.index) + 1] = 'my', 'new', 'row'
>>> df
  points rebounds assists
3     10        7      11
1     12        7       8
2     12        8      10
4     my      new     row