熊猫根据列的每个唯一元素添加行

Question

I've got a dataframe, like so:

ID A 
0  z
2  z
2  y
5  x

To which I want to add rows for each unique value of an ID column:

ID A
0  z
2  z
2  y
5  x
0  b
2  b
5  b

I'm currently doing so in a very naïve way, which is quite inefficient/slow:

IDs = df["ID"].unique()
for ID in IDs: 
    df = df.append(pd.DataFrame([[ID, "b"]], columns=df.columns), ignore_index=True)

How would I go to accomplish the same without the explicit foreach, only pandas function calls?

Answer 1

Use drop_duplicates , rewrite column by assign and append or concat to original DataFrame :

df = df.append(df.drop_duplicates("ID").assign(A='B'), ignore_index=True)
#alternative
#df = pd.concat([df, df.drop_duplicates("ID").assign(A='B')], ignore_index=True)
print (df)
   ID  A
0   0  z
1   2  z
2   2  y
3   5  x
4   0  B
5   2  B
6   5  B