Sorted Split - To create train valid test dataset using custom code

custom_code_train_valid_test_split_sorted.py

import pandas as pd

df = pd.read_csv('/kaggle/input/bluebook-for-bulldozers/TrainAndValid.csv', parse_dates=['saledate'], low_memory=False)

# Let's say we want to split the data in 80:10:10 for train:valid:test dataset
train_size = 0.8
valid_size=0.1

train_index = int(len(df)*train_size)

# First we need to sort the dataset by the desired column 
df.sort_values(by = 'saledate', ascending=True, inplace=True)

df_train = df[0:train_index]
df_rem = df[train_index:]

valid_index = int(len(df)*valid_size)

df_valid = df[train_index:train_index+valid_index]
df_test = df[train_index+valid_index:]

X_train, y_train = df_train.drop(columns='SalePrice').copy(), df_train['SalePrice'].copy()
X_valid, y_valid = df_valid.drop(columns='SalePrice').copy(), df_valid['SalePrice'].copy()
X_test, y_test = df_test.drop(columns='SalePrice').copy(), df_test['SalePrice'].copy()
        
print(X_train.shape), print(y_train.shape)
print(X_valid.shape), print(y_valid.shape)
print(X_test.shape), print(y_test.shape)