Example 1: train test split pandas
from sklearn.model_selection import train_test_split
train, test = train_test_split(df, test_size=0.2)
Example 2: 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)
Example 3: sklearn split train test
import numpy as np
from sklearn.model_selection import train_test_split
X, y = np.arange(10).reshape((5, 2)), range(5)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.33, random_state=42)
X_train
# array([[4, 5],
# [0, 1],
# [6, 7]])
y_train
# [2, 0, 3]
X_test
# array([[2, 3],
# [8, 9]])
y_test
# [1, 4]
Example 4: pandas split dataframe to train and test
train=df.sample(frac=0.8,random_state=200) #random state is a seed value
test=df.drop(train.index)
Example 5: sklearn train_test_split
import numpy as np
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
)
Example 6: splitting data into training and testing sklearn
train_features, test_features, train_labels, test_labels =
train_test_split(features, labels)
#This is using sklearn