How to randomly select rows from Pandas dataframe based on a specific condition?

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Suppose I have a Pandas dataframe, df, which has the following structure:-

         Column 1      Column 2 ....     Column 100
Row 1    0.233           0.555              0
Row 2    0.231           0.514              2
..
Row 15000    0.232           0.455          3

Column 100 represents a particular class each row belongs to (which can be from 0-14). Each category/class has 1000 rows associated to it. For each category (denoted by integers in Column 100), I only want to select 200 samples randomly, and create a new dataframe df_new which will have a new dimension of 15x200 = 3000 rows. Any good way to reach this?

pandas dataframe random python
1 Answers

Use DataFrame.sample per groups - then last column is sorted:

np.random.seed(2019)
df = (pd.DataFrame(np.random.randint(15, size=(100000, 100)))
         .rename(columns=lambda x: f'Column {x+1}'))
#print (df.head())


N = 200
df1 = df.groupby('Column 100').apply(lambda x:x.sample(N)).reset_index(drop=True)
#print (df1.head())

print (len(df1))
3000
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