Repeat Dataframe Rows N Times According to the Unique Column Values and to Each Row Repeat Create a New Column With Different Values

Repeat dataframe rows n times according to the unique column values and to each row repeat create a new column with different values

One solution is to convert 'Cs' to a Categorical. Then use GroupBy + first:

df['Cs'] = df['Cs'].astype('category')

res = df.groupby(['Samp', 'Cs']).first().reset_index()
res['Age'] = res.groupby('Samp')['Age'].transform('first').astype(int)

Result

   Samp   Cs  Age
0     A  cin   51
1     A  ebv   51
2     A   gs   51
3     A  msi   51
4     B  cin   62
5     B  ebv   62
6     B   gs   62
7     B  msi   62
8     C  cin   55
9     C  ebv   55
10    C   gs   55
11    C  msi   55
12    D  cin   70
13    D  ebv   70
14    D   gs   70
15    D  msi   70
16    E  cin   56
17    E  ebv   56
18    E   gs   56
19    E  msi   56

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 in DataFrame N times based on len(list) in column with different list values

Use DataFrame.explode working in pandas 0.25+ and create new columns with DataFrame constructor:

print (date_df)
   a                                               date
0  4       [[2017-02-01 00:00:00, 2017-03-01 00:00:00]]
1  7  [[2017-02-01 00:00:00, 2017-04-01 00:00:00], [...

df = date_df.explode('date')
print (df)
   a                                        date
0  4  [2017-02-01 00:00:00, 2017-03-01 00:00:00]
1  7  [2017-02-01 00:00:00, 2017-04-01 00:00:00]
1  7  [2017-02-01 00:00:00, 2017-04-01 00:00:00]


df[['date_start','date_end']] = pd.DataFrame(df.pop('date').values.tolist(), index=df.index)
print (df)
   a date_start   date_end
0  4 2017-02-01 2017-03-01
1  7 2017-02-01 2017-04-01
1  7 2017-02-01 2017-04-01

EDIT:

Solution for oldier pandas versions:

s = date_df.pop('date')
df = date_df.loc[date_df.index.repeat(s.str.len())]
df[['date_start','date_end']] = pd.DataFrame(np.concatenate(s), index=df.index)
df = df.reset_index(drop=True)
print (df)
   a date_start   date_end
0  4 2017-02-01 2017-03-01
1  7 2017-02-01 2017-04-01
2  7 2017-02-01 2017-04-01

pandas - Copy each row 'n' times depending on column value

Use Index.repeat, DataFrame.loc, DataFrame.assign and DataFrame.reset_index

 new_df = df.loc[df.index.repeat(df['orig_qty'])].assign(fifo_qty=1).reset_index(drop=True)

[output]

         date  orig_qty  price  fifo_qty
0  2019-04-08         4  115.0         1
1  2019-04-08         4  115.0         1
2  2019-04-08         4  115.0         1
3  2019-04-08         4  115.0         1
4  2019-04-09         2  103.0         1
5  2019-04-09         2  103.0         1

How to keep duplicated rows that repeat exactly n times in pandas DataFame

use .transform and count with a boolean filter.

s = df.groupby('peak_start')['peak_start'].transform('count')

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df[s == 2]
   peak_start  peak_end  motif_start  motif_end strand
0         948       177      3210085    3210103      -
1         948       177      3210047    3210065      +


print(df[s == 3])

   peak_start  peak_end  motif_start  motif_end strand
2          62       419      3223269    3223287      -
3          62       419      3223229    3223247      +
4          62       419      3223232    3223250      +

Repeat rows in a pandas DataFrame based on column value

reindex+ repeat

df.reindex(df.index.repeat(df.persons))
Out[951]: 
   code  .     role ..1  persons
0   123  .  Janitor   .        3
0   123  .  Janitor   .        3
0   123  .  Janitor   .        3
1   123  .  Analyst   .        2
1   123  .  Analyst   .        2
2   321  .   Vallet   .        2
2   321  .   Vallet   .        2
3   321  .  Auditor   .        5
3   321  .  Auditor   .        5
3   321  .  Auditor   .        5
3   321  .  Auditor   .        5
3   321  .  Auditor   .        5

PS: you can add.reset_index(drop=True) to get the new index

Duplicating rows n times, where n is a value of a string

You could extend your example with the followign code:

set.seed(5)
df <- data.frame(state = c('A','B'), city = c('Other (3)','Other (2)'), count = c('250','50'))
times <- as.numeric(gsub(".*\\((.*)\\).*", "\\1", df$city))
df$count <- as.numeric(df$count)/times
output <- df[rep(seq_along(times),times),]

The key addition is the line creating output, which uses row indexing on the input dataframe to repeat each row as required.

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