Pandas add rows according for each unique element of a column
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?
python pandas
asked Nov 13 '18 at 13:22
Malcolm TuckerMalcolm Tucker
11610
add a comment |
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?
python pandas
asked Nov 13 '18 at 13:22
Malcolm TuckerMalcolm Tucker
11610
add a comment |
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?
python pandas
asked Nov 13 '18 at 13:22
Malcolm TuckerMalcolm Tucker
11610
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?
python pandas
python pandas
asked Nov 13 '18 at 13:22
Malcolm TuckerMalcolm Tucker
11610
asked Nov 13 '18 at 13:22
Malcolm TuckerMalcolm Tucker
11610
asked Nov 13 '18 at 13:22
Malcolm TuckerMalcolm Tucker
11610
asked Nov 13 '18 at 13:22
Malcolm TuckerMalcolm Tucker
11610
asked Nov 13 '18 at 13:22
Malcolm TuckerMalcolm Tucker
11610
11610
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
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
answered Nov 13 '18 at 13:24
jezraeljezrael
336k25281357
1
Thanks, it works.
– Malcolm Tucker
Nov 15 '18 at 9:00
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
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
answered Nov 13 '18 at 13:24
jezraeljezrael
336k25281357
1
Thanks, it works.
– Malcolm Tucker
Nov 15 '18 at 9:00
add a comment |
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
answered Nov 13 '18 at 13:24
jezraeljezrael
336k25281357
1
Thanks, it works.
– Malcolm Tucker
Nov 15 '18 at 9:00
add a comment |
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
answered Nov 13 '18 at 13:24
jezraeljezrael
336k25281357
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
answered Nov 13 '18 at 13:24
jezraeljezrael
336k25281357
answered Nov 13 '18 at 13:24
jezraeljezrael
336k25281357
answered Nov 13 '18 at 13:24
jezraeljezrael
336k25281357
answered Nov 13 '18 at 13:24
jezraeljezrael
336k25281357
336k25281357
1
Thanks, it works.
– Malcolm Tucker
Nov 15 '18 at 9:00
add a comment |
1
Thanks, it works.
– Malcolm Tucker
Nov 15 '18 at 9:00
1
1
Thanks, it works.
– Malcolm Tucker
Nov 15 '18 at 9:00
Thanks, it works.
– Malcolm Tucker
Nov 15 '18 at 9:00
add a comment |
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