how to append a dataframe without overwriting existing dataframe using for loop in python

use concat() from the pandas module.

import pandas as pd
df_new = pd.concat([df_empty, df_additional])

read more about it in the pandas Docs.

regarding the question in the comment...

df = pd.DataFrame(insert columns which your to-be-appended-df has too)

for i in range(10):
    function_to_get_df_new()
    df = pd.concat([df, df_new])

Let you have list of dataframes list_of_df = [df1, df2, df3].

You have empty dataframe df = pd.Dataframe()

If you want to append all dataframes in list into that empty dataframe df:

for i in list_of_df:
    df = df.append(i)

Above loop will not change df1, df2, df3. But df will change. Note that doing df.append(df1) will not change df, unless you assign it back to df so that df = df.append(df1)

You can't also use set:

df_new = pd.concat({df_empty, df_additional})

Because pandas.DataFrame objects can't be hashed, set needs hashed so that's why

Or tuple:

df_new = pd.concat((df_empty, df_additional))

They are little quicker...

Update for for loop:

df = pd.DataFrame(data)

for i in range(your number):
    df_new=function_to_get_df_new()
    df = pd.concat({df, df_new}) # or tuple: df = pd.concat((df, df_new))

The question is already well answered, my 5cts are the suggestion to use ignore_index=True option to get a continuous new index, not duplicate the older ones.

import pandas as pd

df_to_append = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB')) # sample
df = pd.DataFrame() # this is a placeholder for the destination

for i in range(3):
    df = df.append(df_to_append, ignore_index=True)

I don't think you need to use for loop here, try concat()

import pandas
result = pandas.concat([emptydf,additionaldf])

pandas.concat documentation