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]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)pandas split train test
from sklearn.model_selection import train_test_split
y = df.pop('output')
X = df
X_train,X_test,y_train,y_test = train_test_split(X.index,y,test_size=0.2)
X.iloc[X_train] # return dataframe trainpandas split train test
from sklearn.model_selection import train_test_split
train, test = train_test_split(df, test_size=0.2)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:]Copyright © 2021 Codeinu
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