PYTHON

train test split sklearn

from sklearn.model_selection import train_test_split

X = df.drop(['target'],axis=1).values   # independant features
y = df['target'].values					# dependant variable

# Choose your test size to split between training and testing sets:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)

train test split python

from sklearn.model_selection import train_test_split
				
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)

train test split

import numpy as np
from sklearn.model_selection import train_test_split

# Data example 
X, y = np.arange(10).reshape((5, 2)), range(5)

# Split data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

train test split sklearn

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=42)
print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)

train-test split code in pandas

df_permutated = df.sample(frac=1)

train_size = 0.8
train_end = int(len(df_permutated)*train_size)

df_train = df_permutated[:train_end]
df_test = df_permutated[train_end:]

python train test val split

#You could just use sklearn.model_selection.train_test_split twice. First to split to train,
#test and then split train again into validation and train.
#Something like this:
X_train, X_test, y_train, y_test 
    = train_test_split(X, y, test_size=0.2, random_state=1)

X_train, X_val, y_train, y_val 
	= train_test_split(X_train, y_train, test_size=0.25, random_state=1) # 0.25 x 0.8 = 0.2

train test split

from sklearn.model_selection import train_test_split
# Split into training and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=42, stratify=y)

sklearn train test split

##sklearn train test split

from sklearn.model_selection import train_test_split

X = df.drop(['target'],axis=1).values   # independant features
y = df['target'].values					# dependant variable

# Choose your test size to split between training and testing sets:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)

#OR Randomly split your whole dataset to your desired percentage, insted of using a  ttarget variable:

training_data = df.sample(frac=0.8, random_state=25) #here we choose 80% as our training sample and for reproduciblity, we use random_state of 42
testing_data = df.drop(training_data.index) # testing sample is 20% of our initial data

train test split sklearn

import pandas as pd
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split

cal_housing = fetch_california_housing()
X = pd.DataFrame(cal_housing.data, columns=cal_housing.feature_names)
y = cal_housing.target

y -= y.mean()

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)