How to replace countries other than 'India' and 'U.S.A' by 'Other' in pandas dataframe?

I have the following df:

df = pd.DataFrame({
    'Q0_0': ["India", "Algeria", "India", "U.S.A", "Morocco", "Tunisia", "U.S.A", "France", "Russia", "Algeria"],
    'Q1_1': [np.random.randint(1,100) for i in range(10)],
    'Q1_2': np.random.random(10),
    'Q1_3': np.random.randint(2, size=10),
    'Q2_1': [np.random.randint(1,100) for i in range(10)],
    'Q2_2': np.random.random(10),
    'Q2_3': np.random.randint(2, size=10)
})

It has following display:

I need to change countries other than India and U.S.A to Òther in column Q0_0.

Desired output

Q0_0    Q1_1    Q1_2    Q1_3    Q2_1    Q2_2    Q2_3
0   India     21    0.326856    0   51  0.520506    0
1   Other      7    0.504580    1   43  0.953744    1
2   India     67    0.327273    1   34  0.840453    1
3   U.S.A     49    0.056478    0   67  0.309559    1
4   Other     71    0.743913    1   76  0.240706    1
5   Other     31    0.060707    1   78  0.576598    0
6   U.S.A     25    0.588239    1   61  0.133856    1
7   Other     99    0.991723    0   85  0.274825    1
8   Other     9 0.846950    1   61  0.279948    1
9   Other     79    0.176326    1   78  0.881051    1

I tried to use pandas.series.str.replace() but it didn’t work.

Any help from your side will be highly appreciated, thanks.

>Solution :

You can use pandas.Series.mask with pandas.Series.fillna :

df["Q0_0"]= df["Q0_0"].mask(~df["Q0_0"].isin(["India", "U.S.A"])).fillna("Other")

# Output :

print(df)

    Q0_0  Q1_1      Q1_2  Q1_3  Q2_1      Q2_2  Q2_3
0  India    43  0.681795     0    36  0.772289     0
1  Other    85  0.695352     1    14  0.989219     1
2  India    69  0.684015     1    85  0.687373     0
3  U.S.A    10  0.175235     1    52  0.825989     1
4  Other    90  0.998192     0    59  0.482667     0
5  Other    27  0.723308     0    90  0.054042     1
6  U.S.A    38  0.973819     0    69  0.536380     1
7  Other    10  0.815710     1     2  0.134707     1
8  Other    38  0.238863     1     1  0.872125     1
9  Other    96  0.078010     0    84  0.650347     0