text_plain_output_1.png
import matplotlib.pyplot as plt

import numpy as np

import pandas as pd

import seaborn as sns

Sample = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv', index_col='Id')
Test = pd.read_csv('/kaggle/input/home-data-for-ml-course/test.csv', index_col='Id')

Sample.shape

Tgt_Col = 'SalePrice'
Num_Col = Sample.select_dtypes(exclude='object').drop(Tgt_Col, axis=1).columns
Cat_Col = Sample.select_dtypes(include='object').columns

plt.ticklabel_format(style='plain', axis='y')

sns.distplot(np.log(Sample[Tgt_Col] + 1))
plt.ticklabel_format(style='plain', axis='y')
plt.title("SalePrice's Distribution")
plt.show()
print('Skewness : ', str(np.log(Sample[Tgt_Col] + 1).skew()))