text_plain_output_2.png text_plain_output_1.png
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import numpy as np
import pandas as pd
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
articles = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/customers.csv')
train_data = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')

customers_clean = customers
customers_clean['FN'].fillna(0, inplace=True)
customers_clean['FN'].fillna(0, inplace=True)
customers_clean[customers_clean['fashion_news_frequency'] == 'NONE'] = 'None'
customers_age_compare = customers_clean.dropna(subset=['age'])
customers_age_compare = customers_age_compare[customers_age_compare['age'] != 'None']
print(customers_age_compare['age'].unique())
print('')
print(customers_clean['FN'].unique())
print(customers_clean['Active'].unique())
print(customers_clean['club_member_status'].unique())
print(customers_clean['fashion_news_frequency'].unique())
print(customers_clean['age'].unique())
print('')
print(customers_clean.head())