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

df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')

(df_train.shape, df_test.shape)

numerical_features = [cols for cols in df_train if df_train[cols].dtype in ['int', 'float']]
categorical_features = [cols for cols in df_train if df_train[cols].dtype == 'object']

numerical_missing_columns = list(df_train[numerical_features].columns[df_train[numerical_features].isna().any()])

(df_train[numerical_missing_columns].isnull().sum() / df_train.shape[0] * 100).sort_values(ascending=False)

df_train[numerical_missing_columns].describe()