Pandas data frame repeat each row a certain number of times

Question

I have a pandas dataframe consisting of 7 values:

   Minutiae         LR
0         1   1.975476
1         2   1.082983
2         3   0.269608
3         4   0.878350
4         5   2.820141
5         6   8.686183
6         7  24.340116
7         8  46.475523
8         9  66.139377

What I'm attempting to do is multiply each row a set amount of times, keeping both values the same. For example minutiae 3 by 1129 and 4 by 1085 etc.

So far I've only been able to find methods that increase the amount for each row but not individually.

I appreciate any insights to this, Thank you.

are you looking for df.loc[df.index.repeat(df['Minutiae'])] ? — anky
– anky, Commented Sep 27, 2019 at 8:27

jezrael · Accepted Answer · 2019-09-27 08:38:22Z

Create dictionary for number of repeats for each Minute, Series.map and then repeat index with Index.repeat, last use DataFrame.loc for repeat rows:

print (df)
   Minutiae        LR
0         1  1.975476
1         2  1.082983
2         3  0.269608
3         4  0.878350

d = {1:2, 2:1, 3:5, 4:3}

df1 = df.loc[df.index.repeat(df['Minutiae'].map(d))]
print (df1)
   Minutiae        LR
0         1  1.975476
0         1  1.975476
1         2  1.082983
2         3  0.269608
2         3  0.269608
2         3  0.269608
2         3  0.269608
2         3  0.269608
3         4  0.878350
3         4  0.878350
3         4  0.878350

Detail:

print (df['Minutiae'].map(d))
0    2
1    1
2    5
3    3
Name: Minutiae, dtype: int64

print (df.index.repeat(df['Minutiae'].map(d)))
Int64Index([0, 0, 1, 2, 2, 2, 2, 2, 3, 3, 3], dtype='int64')

Or create new column for repeating:

df['repeat'] = [2,1,5,3]
print (df)
   Minutiae        LR  repeat
0         1  1.975476       2
1         2  1.082983       1
2         3  0.269608       5
3         4  0.878350       3

df2 = df.loc[df.index.repeat(df['repeat'])]
print (df2)
   Minutiae        LR  repeat
0         1  1.975476       2
0         1  1.975476       2
1         2  1.082983       1
2         3  0.269608       5
2         3  0.269608       5
2         3  0.269608       5
2         3  0.269608       5
2         3  0.269608       5
3         4  0.878350       3
3         4  0.878350       3
3         4  0.878350       3

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