import pandas as pd
import numpy as np


df = pd.read_csv("data/train.csv", index_col=0)
# df['Deck'] = df[~df['Cabin'].isnull()]['Cabin'].str[0]
df['Title'] = df['Name'].apply(lambda x: x.split(',')[1].split('.')[0].strip())
df['FamSize'] = df['Parch'] + df['SibSp']

decks_df = df['Cabin'].str.extract('(?P<Deck>\w)(?P<CabinNumber>\d+)')
deck_dummies = pd.get_dummies(decks_df['Deck'], prefix='Deck')
title_dummies = pd.get_dummies(df['Title'])
gender_dummies = pd.get_dummies(df['Sex'])
class_dummies = pd.get_dummies(df['Pclass'], prefix='Class')

# saving to variables and using .concat() once seems to be much faster
df = pd.concat([df, decks_df, deck_dummies, title_dummies, gender_dummies, class_dummies], axis=1)

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split


df = pd.read_csv("data/train.csv", index_col=0)
# df['Deck'] = df[~df['Cabin'].isnull()]['Cabin'].str[0]
df['Title'] = df['Name'].apply(lambda x: x.split(',')[1].split('.')[0].strip())
df['FamSize'] = df['Parch'] + df['SibSp']

decks_df = df['Cabin'].str.extract('(?P<Deck>\w)(?P<CabinNumber>\d+)')
deck_dummies = pd.get_dummies(decks_df['Deck'], prefix='Deck')
title_dummies = pd.get_dummies(df['Title'])
gender_dummies = pd.get_dummies(df['Sex'])
class_dummies = pd.get_dummies(df['Pclass'], prefix='Class')

# saving to variables and using .concat() once seems to be much faster
df = pd.concat([df, decks_df, deck_dummies, title_dummies, gender_dummies, class_dummies], axis=1)

add train_test_split to imports