text_plain_output_1.png
import pandas as pd

import pandas as pd
raw_data = pd.read_csv('../input/train.csv')

column_full_names = {'MSSubClass': 'The Building Class', 'MSZoning': 'The General Zoning Classification', 'SalePrice': 'Sale Price', 'LotFrontage': 'Linear feet of street connected to property', 'LotArea': 'Lot size in square feet', 'Street': 'Type of Road Access', 'Alley': 'Type of Alley Access', 'LotShape': 'General shape of property', 'LandContour': 'Flatness of The Property', 'Utilities': 'Type of utilitists available', 'LotConfig': 'Lot configuration', 'LandSlope': 'Slope of property', 'Neighborhood': 'Physical locations within Ames city limits', 'Condition1': 'Proximity to main road or railroad', 'Condition2': 'Proximity to main road or railroad (if a second is present)', 'BldgType': 'Type of dwelling', 'HouseStyle': 'Style of dwelling', 'OverallQual': 'Overall material and finish quality (1 - 10 Likert scale)', 'OverallCond': 'Overall condition rating (1 - 9 Likert scale)', 'RoofStyle': 'Type of roof', 'RoofMatl': 'Roof material', 'Exterior1st': 'Exterior covering on house', 'Exterior2nd': 'Exterior covering on house (if more than one material)', 'MasVnrType': 'Masonry veneer type', 'MasVnrArea': 'Masonry veneer area in square feet', 'ExterQual': 'Exterior material quality', 'ExterCond': 'Present condition of the material on the exterior', 'Foundation': 'Type of foundation', 'BsmtQual': 'Height of the basement', 'BsmtCond': 'General condition of the basement', 'BsmtExposure': 'Walkout or garden level basement walls', 'BsmtFinType1': 'Quality of basement finished area', 'BsmtFinSF1': 'Type 1 finished square feet', 'BsmtFinType2': 'Quality of second finished area (if present)', 'BsmtFinSF2': 'Type 2 finished square feet', 'BsmtUnfSF': 'Unfinished square feet of basement area', 'TotalBsmtSF': 'Total square feet of basement area', 'Heating': 'Type of heating', 'HeatingQC': 'Heating quality and condition', 'CentralAir': 'Central air conditioning', 'Electrical': 'Electrical system', '1stFlrSF': 'First Floor square feet', '2ndFlrSF': 'Second floor square feet', 'LowQualFinSF': 'Low quality finished square feet (all floors)', 'GrLivArea': 'Above grade (ground) living area square feet', 'BsmtFullBath': 'Basement full bathrooms', 'BsmtHalfBath': 'Basement half bathrooms', 'FullBath': 'Full bathrooms above grade', 'HalfBath': 'Half baths above grade', 'Bedroom': 'Number of bedrooms above basement level', 'Kitchen': 'Number of kitchens', 'KitchenQual': 'Kitchen quality', 'TotRmsAbvGrd': 'Total rooms above grade (does not include bathrooms)', 'Functional': 'Home functionality rating', 'Fireplaces': 'Number of fireplaces', 'FireplaceQu': 'Fireplace quality', 'GarageType': 'Garage location', 'GarageYrBlt': 'Year garage was built', 'GarageFinish': 'Interior finish of the garage', 'GarageCars': 'Size of garage in car capacity', 'GarageArea': 'Size of garage in square feet', 'GarageQual': 'Garage quality', 'GarageCond': 'Garage condition', 'PavedDrive': 'Paved driveway', 'WoodDeckSF': 'Wood deck area in square feet', 'OpenPorchSF': 'Open porch area in square feet', 'EnclosedPorch': 'Enclosed porch area in square feet', '3SsnPorch': 'Three season porch area in square feet', 'ScreenPorch': 'Screen porch area in square feet', 'PoolArea': 'Pool area in square feet', 'PoolQC': 'Pool quality', 'Fence': 'Fence quality', 'MiscFeature': 'Miscellaneous feature not covered in other categories', 'MiscVal': 'Value of miscellaneous feature ($)', 'MoSold': 'Month Sold', 'YrSold': 'Year Sold', 'YearBuilt': 'Original construction date', 'YearRemodAdd': 'Remodel date', 'SaleType': 'Type of sale', 'SaleCondition': 'Condition of sale'}

columns = raw_data.columns.tolist()
if 'Id' in columns:
    columns.remove('Id')

columns_with_null_values = []
for c in columns:
    f = c
    feature_name = str(f)
    has_null = str(any(raw_data[f].isnull().values))
    if has_null == 'True':
        columns_with_null_values.append(f)
        column_type = raw_data[f].dtype

column_names = raw_data.columns.drop('Id')
int_columns, float_columns, object_columns = ([], [], [])
for c in column_names:
    column_dtype = str(raw_data.dtypes[c])
    if column_dtype == 'int64':
        int_columns.append(c)
    elif column_dtype == 'object':
        object_columns.append(c)
    elif column_dtype == 'float64':
        float_columns.append(c)

for f in float_columns:
    print(f + ' ' + column_full_names[f])