How to train/test/split data based on labels?

Question

How can I split the data into train and test dataset based on its labels? the labels are 1 and 0 and I want to use all 1 as train dataset and 0 as test dataset. the csv file looks like this:

1   Pixar classic is one of the best kids' movies of all time.
1   Apesar de representar um imenso avanço tecnológico, a força do filme reside no carisma de seus personagens e no charme de sua história.
1   When Woody perks up in the opening scene, it's not only the toy cowboy who comes alive - we're watching the rebirth of an art form.
0   The humans are wooden, the computer-animals have that floating, jerky gait of animated fauna.
1   Introduced not one but two indelible characters to the pop culture pantheon: cowboy rag-doll Woody (Tom Hanks) and plastic space ranger Buzz Lightyear (Tim Allen). [Blu-ray]
1   it is easy to see how virtually everything that is good in animation right now has some small seed in Toy Story
0   All the effects in the world can't disguise the thin plot.
1   Though some of the animation seems dated compared to later Pixar efforts and not nearly as detailed, what's here is done impeccably well.

Answer 1

Normally you would not want to do that but, following solution can work. I tried on a very small dataframe but seems to do the job.

import pandas as pd  

Df = pd.DataFrame()
Df['label'] = ['S', 'S', 'S', 'P', 'P', 'S', 'P', 'S']
Df['value'] = [1, 2, 3, 4, 5, 6, 7, 8]
Df

X = Df[Df.label== 'S']
Y = Df[Df.label == 'P']

from sklearn.model_selection import train_test_split
xtrain, ytrain = train_test_split(X, test_size=0.3,random_state=25, shuffle=True)
xtest, ytest = train_test_split(Y, test_size=0.3,random_state=25, shuffle=True)

I got the followin results

xtrain

    label   value
5   S       6
2   S       3
7   S       8

xtest

    label   value
6   P       7
3   P       4

ytest

    label   value
4   P       5

ytrain

    label   value
0   S       1
1   S       2

Answer 2

try this,

mask = df['label']==1
df_train = df[mask]
df_test = df[~mask]

you just need filtering of dataframe.

Answer 3

d = {'col1': [1, 1, 1, 1, 0, 0, 0, 0], 'text': ["a", "b", "c", "d", "e", "f", "g", "h"]}
df = pd.DataFrame(data=d)
df.head()

    label   text
0   1       a
1   1       b
2   1       c
3   1       d
4   0       e

You can filter based on each row value using the code below, this captures data from col1 when it equals 1.

traindf = df[df["label"] == 1]
traindf

    label   text
0   1       a
1   1       b
2   1       c
3   1       d

testdf = df[df["label"] == 0]
testdf

    label   text
4   0       e
5   0       f
6   0       g
7   0       h