reference: https://www.kaggle.com/pmarcelino/comprehensive-data-exploration-with-python

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

import seaborn as sns
from scipy.stats import skew, norm

import scipy.stats as stats
import warnings
warnings.filterwarnings('ignore')

from google.colab import files
upload_train = files.upload()

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Saving house-train.csv to house-train.csv

import pandas as pd
import io
train = pd.read_csv(io.BytesIO(upload_train['house-train.csv']), index_col=None)
train.shape

(1460, 81)

quantitative = [f for f in train.columns if train.dtypes[f] != 'object']
quantitative.remove('SalePrice')
quantitative.remove('Id')
qualitative = [f for f in train.columns if train.dtypes[f] == 'object']

# number of top correlated variables to price 
k = 10
corrmat = train.corr()
cols = corrmat.nlargest(k, 'SalePrice')['SalePrice'].index
cm = np.corrcoef(train[cols].values.T)
plt.subplots(figsize=(15, 15))
sns.set(font_scale=1.25)
sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
plt.show()