data processing with python

I would recommend looking into the pandas library. You can do all sorts of neat manipulations of tabular data. First read it in:

>>> import pandas as pd
>>> df = pd.read_csv("cars.ssv", sep=";")
>>> df
     City Car model Color  Registration number
0  Moscow  Mercedes   Red                 1234
1  Moscow  Mercedes   Red                 2345
2    Kiev    Toyota  Blue                 3423
3  London      Fiat   Red                 4545

Index it in different ways:

>>> moscmerc = df[(df["City"] == "Moscow") & (df["Car model"] == "Mercedes")]
>>> moscmerc
     City Car model Color  Registration number
0  Moscow  Mercedes   Red                 1234
1  Moscow  Mercedes   Red                 2345
>>> len(moscmerc)
2

Write it out:

>>> moscmerc.to_csv("moscmerc.ssv", sep=";", header=None, index=None)
>>> !cat moscmerc.ssv
Moscow;Mercedes;Red;1234
Moscow;Mercedes;Red;2345

You can also work on multiple groups at once:

>>> df.groupby(["City", "Car model"]).size()
City    Car model
Kiev    Toyota       1
London  Fiat         1
Moscow  Mercedes     2
Dtype: int64

Update: @Anthon pointed out that the above only handles the case of a semicolon separator. If a file has a comma throughout, then you can just use , instead of ;, so that's trivial. The more interesting case is if the delimiter is inconsistent within the file, but that's easily handled too:

>>> !cat cars_with_both.txt
City;Car model,Color;Registration number
Moscow,Mercedes;Red;1234
Moscow;Mercedes;Red;2345
Kiev,Toyota;Blue,3423
London;Fiat,Red;4545
>>> df = pd.read_csv("cars_with_both.txt", sep="[;,]")
>>> df
     City Car model Color  Registration number
0  Moscow  Mercedes   Red                 1234
1  Moscow  Mercedes   Red                 2345
2    Kiev    Toyota  Blue                 3423
3  London      Fiat   Red                 4545

Update #2: and now the text is in Russian -- of course it is. :^) Still, if everything is correctly encoded, and your terminal is properly configured, that should work too:

>>> df = pd.read_csv("russian_cars.csv", sep="[;,]")
>>> df
     City Car model    Color  Registration number
0  Москва  Mercedes  красный                 1234
1  Москва  Mercedes  красный                 2345
2    Киев    Toyota    синий                 3423
3  Лондон      Fiat  красный                 4545