How do I replicate row in a group with size < 3 within a DataFrame?

I’m new to pandas. I have the following dataframe:

I want to replicate every row 3 times for a group with a size < 3. The dataframe will look like this:

Does anyone have ideas? Thanks in advance.

>Solution :

Use Series.value_counts for count column, and if less like 3 set values to 3 else 1 for no repeat, then use Series.map and repeat rows by Index.repeat in DataFrame.loc:

s = df['ID'].value_counts().lt(3).map({True:3, False:1})

df = df.loc[df.index.repeat(df['ID'].map(s))]
print (df)
   ID     A     B
0   0  Data  Data
0   0  Data  Data
0   0  Data  Data
1   1  Data  Data
1   1  Data  Data
1   1  Data  Data
2   2  Data  Data
2   2  Data  Data
2   2  Data  Data
3   3  Data  Data
4   3  Data  Data
5   3  Data  Data
6   3  Data  Data

If there are some group with 2 values after repeat get:

print (df)
   ID     A     B
0   0  Data  Data
1   1  Data  Data
2   2  Data  Data
3   2  Data  Data
4   3  Data  Data
5   3  Data  Data
6   3  Data  Data

s = df['ID'].value_counts().lt(3).map({True:3, False:1})
print (s)
3    1
2    3
0    3
1    3
Name: ID, dtype: int64

df = df.loc[df.index.repeat(df['ID'].map(s))]
print (df)
   ID     A     B
0   0  Data  Data
0   0  Data  Data
0   0  Data  Data
1   1  Data  Data
1   1  Data  Data
1   1  Data  Data
2   2  Data  Data
2   2  Data  Data
2   2  Data  Data
3   2  Data  Data
3   2  Data  Data
3   2  Data  Data
4   3  Data  Data
5   3  Data  Data
6   3  Data  Data

But if need repeat if 1 values 3 times, if 2 values 2 times else no repeat (repeat 1) solution is change:

s = df['ID'].value_counts().map({1:3, 2:2}).fillna(1)
print (s)
3    1.0
2    2.0
0    3.0
1    3.0
Name: ID, dtype: float64

df = df.loc[df.index.repeat(df['ID'].map(s))]
print (df)
   ID     A     B
0   0  Data  Data
0   0  Data  Data
0   0  Data  Data
1   1  Data  Data
1   1  Data  Data
1   1  Data  Data
2   2  Data  Data
2   2  Data  Data
3   2  Data  Data
3   2  Data  Data
4   3  Data  Data
5   3  Data  Data
6   3  Data  Data