You can group the data by target and then sample,

df = pd.DataFrame({'col':np.random.randn(12000), 'target':np.random.randint(low = 0, high = 2, size=12000)})
new_df = df.groupby('target').apply(lambda x: x.sample(n=5000)).reset_index(drop = True)

new_df.target.value_counts()

1    5000
0    5000

Edit: Use DataFrame.sample

You get similar results using DataFrame.sample

new_df = df.groupby('target').sample(n=5000)

If I understand what you are trying to get at, the following should be of help:

# Test dataframe
import numpy as np
import pandas as pd


data = pd.DataFrame({'file': np.repeat(['A', 'B', 'C'], 12),
                     'value_1': np.repeat([1,0,1],12),
                     'value_2': np.random.randint(20, 100, 36)})
# Select a file
data1 = data[data.file == np.random.choice(data['file'].unique())].reset_index(drop=True)

# Get a random index from data1
start_ix = np.random.choice(data1.index[:-3])

# Get a sequence starting at the random index from the previous step
print(data.loc[start_ix:start_ix+3])

IIUC, you could groupby on the condition whether "x" is 0 or not and sample the smallest-group-size number of entries from each group:

g = df.groupby(df['x']==0)['x']
out = g.sample(n=g.count().min()).sort_index()

(An example) output:

1    5
3    0
4    0
5    9
Name: x, dtype: int64

For the second case, we could use numpy.select and numpy.unique to get the groups (the rest are essentially the same as above):

import numpy as np
groups = np.select([df['x']==0, df['x']==5], [1,2], 3)
g = df.groupby(groups)['x']
out = g.sample(n=np.unique(groups, return_counts=True)[1].min()).sort_index()

An example output:

2    5
3    0
4    0
5    9
7    3
8    5
Name: x, dtype: int64

We can do np.random.choice by doing the index slice

n = len(ratings_top)
idx = np.random.choice(ratings_top.index.values, p=ratings_top['probability'], size=n*0.0007, replace=True)

Then

sample_df = df.loc[idx].copy()

You can use the == operator to make a list* of boolean values. And when said list is put into the getter ([]) it will filter the values. If you want to, you can use n=50 to create a sample size of 50 rows.

New code

df[df['a']==1].sample(n=50)

Full code

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randint(low=0, high=5, size=(10000, 2)),columns=['a', 'b'])

print(df[df['a']==1].sample(n=50))

*List isn't literally a list in this context, but it is a great word for explaining how it works. It's a technically a DataFrame that maps rows to a true/false value.

More obscure DataFrame sampling

If you want to sample all 50 where a is 1 or 0:

print(df[(df['a']==1) | (df['a']==0)].sample(n=50))

And if you want to sample 50 of each:

df1 = df[df['a']==1].sample(n=50)
df0 = df[df['a']==0].sample(n=50)
print(pd.concat([df1,df0]))

Assuming you have a unique-indexed dataframe (and if you don't, you can simply do .reset_index(), apply this, and then set_index after the fact), you could use DataFrame.sample. [Actually, you should be able to use sample even if the frame didn't have a unique index, but you couldn't use the below method to get df2.]

Note that I'm using A instead of T in this example because A is the only repeated value of Col2 in the example you gave, and I'll only select 1 randomly rather than 10.

>>> df1 = df[df.Col2 == "A"].sample(1)
>>> df2 = df[~df.index.isin(df1.index)]
>>> df1
   Col1 Col2
3     2    A
>>> df2
   Col1 Col2
0     1    T
1     1    B
2     3    S
4     1    C
5     2    A