Replicating rows in a pandas data frame by a column value

Question

I want to replicate rows in a Pandas Dataframe. Each row should be repeated n times, where n is a field of each row.

import pandas as pd  what_i_have = pd.DataFrame(data={   'id': ['A', 'B', 'C'],   'n' : [  1,   2,   3],   'v' : [ 10,  13,   8] })  what_i_want = pd.DataFrame(data={   'id': ['A', 'B', 'B', 'C', 'C', 'C'],   'v' : [ 10,  13,  13,   8,   8,   8] }) 

Is this possible?

Answer 1

You can use Index.repeat to get repeated index values based on the column then select from the DataFrame:

df2 = df.loc[df.index.repeat(df.n)]    id  n   v 0  A  1  10 1  B  2  13 1  B  2  13 2  C  3   8 2  C  3   8 2  C  3   8 

Or you could use np.repeat to get the repeated indices and then use that to index into the frame:

df2 = df.loc[np.repeat(df.index.values, df.n)]    id  n   v 0  A  1  10 1  B  2  13 1  B  2  13 2  C  3   8 2  C  3   8 2  C  3   8 

After which there's only a bit of cleaning up to do:

df2 = df2.drop("n", axis=1).reset_index(drop=True)    id   v 0  A  10 1  B  13 2  B  13 3  C   8 4  C   8 5  C   8 

Note that if you might have duplicate indices to worry about, you could use .iloc instead:

df.iloc[np.repeat(np.arange(len(df)), df["n"])].drop("n", axis=1).reset_index(drop=True)    id   v 0  A  10 1  B  13 2  B  13 3  C   8 4  C   8 5  C   8 

which uses the positions, and not the index labels.

Answer 2

You could use set_index and repeat

In [1057]: df.set_index(['id'])['v'].repeat(df['n']).reset_index() Out[1057]:   id   v 0  A  10 1  B  13 2  B  13 3  C   8 4  C   8 5  C   8 

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Details

In [1058]: df Out[1058]:   id  n   v 0  A  1  10 1  B  2  13 2  C  3   8