Which way to efficiently treat a concatenation of series in Python?

I am currently working on a Python function that creates a DataFrame based on 3 different values columns.
I efficiently compute those values but my question is more on how to stack them the best way possible in order to return a csv file where I get 3 columns.

Here is an image of what I want to do:

My final dataset will be around 300k rows.

What is the most efficient way to get a csv file with those 3 columns?
For the moment I am doing:

s = {"Text A" : value1, "Text B" : value2, "Score" : value3}
dataframe_return = pd.concat([dataframe_return, pd.Series(s).to_frame().T], ignore_index=True)

But it takes too much time.

If values are generated in loop better is create list of dictionaries and pass to DataFrame constructor if need most efficient solution:

In [171]: %%timeit
     ...: L = []
     ...: for x in range(1000):
     ...:     d = {"Text A" : 'cc', "Text B" : 'ee', "Score" :  x}
     ...:     L.append(d)
     ...:     
     ...: df = pd.DataFrame(L)
     ...: 
1.64 ms ± 8.58 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

#Timeless solution 
In [172]: %%timeit
     ...: df = pd.DataFrame(columns=['Text A','Text B','Score'])
     ...: 
     ...: for x in range(1000):
     ...:     
     ...:     d = {"Text A" : 'cc', "Text B" : 'ee', "Score" :  x}
     ...:     df = pd.concat([df, pd.DataFrame(d, index=[len(df)])])
     ...:     
1.35 s ± 167 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

#original solution
In [175]: %%timeit
     ...: df = pd.DataFrame(columns=['Text A','Text B','Score'])
     ...: 
     ...: for x in range(1000):
     ...:     
     ...:     s = {"Text A" : 'cc', "Text B" : 'ee', "Score" :  x}
     ...:     df = pd.concat([df, pd.Series(s).to_frame().T], ignore_index=True)
     ...:     
1.46 s ± 394 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

Solutions with loc are slowier.

I would do it this way (with concat) :

s = {"Text A" : "« text hhhhhh 5 »", "Text B": "« text aaaaaa 5 »" , "Score": 0}
​
dataframe_return = pd.concat([df, pd.DataFrame(s, index=[len(df)])])

​
Output :

print(dataframe_return)

              Text A             Text B  Score
0    « text hhhhhh »    « text aaaaaa »   0.40
1  « text hhhhhh 2 »  « text aaaaaa 2 »   0.23
2  « text hhhhhh 3 »  « text aaaaaa 3 »   0.12
3  « text hhhhhh 4 »  « text aaaaaa 4 »   1.40
4  « text hhhhhh 5 »  « text aaaaaa 5 »   0.00