Sample N Random Rows Per Group in a Dataframe

Sample n random rows per group in a dataframe with dplyr when some observations have less than n rows

You can sample minimum of number of rows or x for each group :

library(dplyr)

x <- 2
df %>% group_by(samples, groups) %>% sample_n(min(n(), x))

#  samples groups
#  <chr>    <dbl>
#1 A            1
#2 A            1
#3 A            2
#4 B            1
#5 B            1

However, note that sample_n() has been super-seeded in favor of slice_sample but n() doesn't work with slice_sample. There is an open issue here for it.

However, as @tmfmnk mentioned we don't need to call n() here. Try :

df %>% group_by(samples, groups) %>% slice_sample(n = x)

Sample n random rows per group in a dataframe

You can assign a random ID to each element that has a particular factor level using ave. Then you can select all random IDs in a certain range.

rndid <- with(df, ave(X1, color, FUN=function(x) {sample.int(length(x))}))
df[rndid<=3,]

This has the advantage of preserving the original row order and row names if that's something you are interested in. Plus you can re-use the rndid vector to create subset of different lengths fairly easily.

Python: Random selection per group

size = 2        # sample size
replace = True  # with replacement
fn = lambda obj: obj.loc[np.random.choice(obj.index, size, replace),:]
df.groupby('Group_Id', as_index=False).apply(fn)

Pandas: select rows by random groups while keeping all of the group's variables

You need create random ids first and then compare original column id by Series.isin in boolean indexing:

#number of groups
N = 2
df2 = df1[df1['id'].isin(df1['id'].drop_duplicates().sample(N))]
print (df2)
  id      std  number
0  A      1.0       1
1  A      0.0      12
5  C    134.0      90
6  C   1234.0     100
7  C  12345.0     111

Or:

N = 2
df2 = df1[df1['id'].isin(np.random.choice(df1['id'].unique(), N))]

Random sampling of groups after pandas groupby

Using iterrows from Pandas you can iterate over DataFrame rows as (index, Series) pairs, and get what you want:

new_df = df.groupby(['Nationality', 'Sex'], as_index=False).size()

for _, row in new_df.iterrows():
    print(df[(df.Nationality==row.Nationality)&(df.Sex==row.Sex)].sample(20))

How to randomly select fixed number of rows (if greater) per group else select all rows in pandas?

You can choose to sample only if you have more row:

n = 2
(df.groupby('Group_Id')
   .apply(lambda x: x.sample(n) if len(x)>n else x  )
   .reset_index(drop=True)
)

You can also try shuffling the whole data and groupby().head():

df.sample(frac=1).groupby('Group_Id').head(2)

Output:

  Name  Group_Id
5  DEF         3
0  AAA         1
2  BDF         1
3  CCC         2
4  XYZ         2

Python - Pandas random sampling per group

IIUC, the issue is that you do not want to groupby the column image name, but if that column is not included in the groupby, your will lose this column

You can first create the grouby object

gb = df.groupby(['type', 'Class'])

Now you can interate over the grouby blocks using list comprehesion

blocks = [data.sample(n=1) for _,data in gb]

Now you can concatenate the blocks, to reconstruct your randomly sampled dataframe

pd.concat(blocks)

Output

   Class    Value2 image name   type
7      A  0.817744    image02   long
17     B  0.199844    image01   long
4      A  0.462691    image01  short
11     B  0.831104    image02  short

You can modify your code and add the column image name to the groupby like this

df.groupby(['type', 'Class'])[['Value2','image name']].apply(lambda s: s.sample(min(len(s),2)))

                  Value2 image name
type  Class
long  A     8   0.777962    image01
            9   0.757983    image01
      B     19  0.100702    image02
            15  0.117642    image02
short A     3   0.465239    image02
            2   0.460148    image02
      B     10  0.934829    image02
            11  0.831104    image02

EDIT: Keeping image same per group

Im not sure if you can avoid using an iterative process for this problem. You could just loop over the groupby blocks, filter the groups taking a random image and keeping the same name per group, then randomly sample from the remaining images like this

import random

gb = df.groupby(['Class','type'])
ls = []

for index,frame in gb:
    ls.append(frame[frame['image name'] == random.choice(frame['image name'].unique())].sample(n=2))

pd.concat(ls)

Output

   Class    Value2 image name   type
6      A  0.850445    image02   long
7      A  0.817744    image02   long
4      A  0.462691    image01  short
0      A  0.444939    image01  short
19     B  0.100702    image02   long
15     B  0.117642    image02   long
10     B  0.934829    image02  short
14     B  0.721535    image02  short

How can I randomly sample a subgroup with multiple rows from within a larger group?

Like so perhaps:


set.seed( 100 )
df %>% group_by( ID, Group ) %>%
    sample_n(1) %>%
    select( -Score ) %>%
    left_join( df, by=c("ID","Group","Color") )

Think I misunderstood you at first, but this sounds like it could be it.