Repeat Rows of a Data.Frame N Times

Repeat Rows in Data Frame n Times

Use a combination of pd.DataFrame.loc and pd.Index.repeat

test.loc[test.index.repeat(test.times)]

  id  times
0  a      2
0  a      2
1  b      3
1  b      3
1  b      3
2  c      1
3  d      5
3  d      5
3  d      5
3  d      5
3  d      5

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To mimic your exact output, use reset_index

test.loc[test.index.repeat(test.times)].reset_index(drop=True)

   id  times
0   a      2
1   a      2
2   b      3
3   b      3
4   b      3
5   c      1
6   d      5
7   d      5
8   d      5
9   d      5
10  d      5

Repeat rows of a data.frame N times

EDIT: updated to a better modern R answer.

You can use replicate(), then rbind the result back together. The rownames are automatically altered to run from 1:nrows.

d <- data.frame(a = c(1,2,3),b = c(1,2,3))
n <- 3
do.call("rbind", replicate(n, d, simplify = FALSE))

A more traditional way is to use indexing, but here the rowname altering is not quite so neat (but more informative):

 d[rep(seq_len(nrow(d)), n), ]

Here are improvements on the above, the first two using purrr functional programming, idiomatic purrr:

purrr::map_dfr(seq_len(3), ~d)

and less idiomatic purrr (identical result, though more awkward):

purrr::map_dfr(seq_len(3), function(x) d)

and finally via indexing rather than list apply using dplyr:

d %>% slice(rep(row_number(), 3))

replicate rows by n times in python

Another method could be:

df.assign(Times = df.Times.apply(lambda x: range(1, x + 1))).explode('Times')
Out[]: 
  String Times
0      a     1
0      a     2
1      b     1
1      b     2
1      b     3
2      c     1
2      c     2
2      c     3
2      c     4
2      c     5

How can I replicate rows in Pandas?

Use np.repeat:

Version 1:

Try using np.repeat:

newdf = pd.DataFrame(np.repeat(df.values, 3, axis=0))
newdf.columns = df.columns
print(newdf)

The above code will output:

  Person   ID ZipCode  Gender
0  12345  882   38182  Female
1  12345  882   38182  Female
2  12345  882   38182  Female
3  32917  271   88172    Male
4  32917  271   88172    Male
5  32917  271   88172    Male
6  18273  552   90291  Female
7  18273  552   90291  Female
8  18273  552   90291  Female

np.repeat repeats the values of df, 3 times.

Then we add the columns with assigning new_df.columns = df.columns.

Version 2:

You could also assign the column names in the first line, like below:

newdf = pd.DataFrame(np.repeat(df.values, 3, axis=0), columns=df.columns)
print(newdf)

The above code will also output:

  Person   ID ZipCode  Gender
0  12345  882   38182  Female
1  12345  882   38182  Female
2  12345  882   38182  Female
3  32917  271   88172    Male
4  32917  271   88172    Male
5  32917  271   88172    Male
6  18273  552   90291  Female
7  18273  552   90291  Female
8  18273  552   90291  Female

Repeat rows of a data.frame

df <- data.frame(a = 1:2, b = letters[1:2]) 
df[rep(seq_len(nrow(df)), each = 2), ]

Pandas: repeat dataframe n times

Use:

N = 3
df = pd.concat([df] * N, ignore_index=True)
print (df)
    col
0     0
1    60
2   300
3   320
4     0
5    60
6   300
7   320
8     0
9    60
10  300
11  320

Repeat row of data table N times and join to another table in R

Do you mean something like this?

dt1[, id:=dt2$id]

Output:

|V1 |V2 |id            |
|:--|:--|:-------------|
|a  |a  |c(4,98,56,32) |
|a  |b  |c(4,98,56,32) |
|a  |c  |c(4,98,56,32) |
|b  |c  |c(4,98,56,32) |
|c  |d  |c(4,98,56,32) |

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