# pandas – How to split data into 3 sets (train, validation and test)?

## pandas – How to split data into 3 sets (train, validation and test)?

Numpy solution. We will shuffle the whole dataset first (`df.sample(frac=1, random_state=42)`) and then split our data set into the following parts:

• 60% – train set,
• 20% – validation set,
• 20% – test set

``````In [305]: train, validate, test =
np.split(df.sample(frac=1, random_state=42),
[int(.6*len(df)), int(.8*len(df))])

In [306]: train
Out[306]:
A         B         C         D         E
0  0.046919  0.792216  0.206294  0.440346  0.038960
2  0.301010  0.625697  0.604724  0.936968  0.870064
1  0.642237  0.690403  0.813658  0.525379  0.396053
9  0.488484  0.389640  0.599637  0.122919  0.106505
8  0.842717  0.793315  0.554084  0.100361  0.367465
7  0.185214  0.603661  0.217677  0.281780  0.938540

In [307]: validate
Out[307]:
A         B         C         D         E
5  0.806176  0.008896  0.362878  0.058903  0.026328
6  0.145777  0.485765  0.589272  0.806329  0.703479

In [308]: test
Out[308]:
A         B         C         D         E
4  0.521640  0.332210  0.370177  0.859169  0.401087
3  0.333348  0.964011  0.083498  0.670386  0.169619
``````

`[int(.6*len(df)), int(.8*len(df))]` – is an `indices_or_sections ` array for numpy.split().

Here is a small demo for `np.split()` usage – lets split 20-elements array into the following parts: 80%, 10%, 10%:

``````In [45]: a = np.arange(1, 21)

In [46]: a
Out[46]: array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20])

In [47]: np.split(a, [int(.8 * len(a)), int(.9 * len(a))])
Out[47]:
[array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16]),
array([17, 18]),
array([19, 20])]
``````

However, one approach to dividing the dataset into `train`, `test`, `cv` with `0.6`, `0.2`, `0.2` would be to use the `train_test_split` method twice.

``````from sklearn.model_selection import train_test_split

x, x_test, y, y_test = train_test_split(xtrain,labels,test_size=0.2,train_size=0.8)
x_train, x_cv, y_train, y_cv = train_test_split(x,y,test_size = 0.25,train_size =0.75)
``````

### Note:

Function was written to handle seeding of randomized set creation. You should not rely on set splitting that doesnt randomize the sets.

``````import numpy as np
import pandas as pd

def train_validate_test_split(df, train_percent=.6, validate_percent=.2, seed=None):
np.random.seed(seed)
perm = np.random.permutation(df.index)
m = len(df.index)
train_end = int(train_percent * m)
validate_end = int(validate_percent * m) + train_end
train = df.iloc[perm[:train_end]]
validate = df.iloc[perm[train_end:validate_end]]
test = df.iloc[perm[validate_end:]]
return train, validate, test
``````

### Demonstration

``````np.random.seed([3,1415])
df = pd.DataFrame(np.random.rand(10, 5), columns=list(ABCDE))
df
``````

``````train, validate, test = train_validate_test_split(df)

train
``````

``````validate
``````

``````test
``````