PYTHON

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 train

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

train, test = train_test_split(df, test_size=0.2)

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 python

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)

split data train, test by id python

train_inds, test_inds = next(GroupShuffleSplit(test_size=.20, n_splits=2, random_state = 7).split(df, groups=df['Group_Id']))

train = df.iloc[train_inds]
test = df.iloc[test_inds]

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:]

how to split a dataframe into train and test

# Dataframe splitting helper function
def SplitDataframe(df, y_column, test_size=3):
    train_count = int(round(test_size*10/len(df)*100))
    
    train_ds = df[train_count:]
    test_ds = df[:train_count]
    
    train_ds_X = train_ds.drop([y_column], axis=1)
    train_ds_y = train_ds[y_column]
    
    test_ds_X = test_ds.drop([y_column], axis=1)
    test_ds_y = test_ds[y_column]
    
    return (train_ds_X, train_ds_y), (test_ds_X, test_ds_y)