text_plain_output_1.png image_output_1.png
from scipy import stats

from scipy.stats import norm

import matplotlib.pyplot as plt

import numpy as np

import pandas as pd

import seaborn as sns

train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')

duplicate_row = train[train.duplicated()]

train.dtypes.value_counts()

sns.distplot(train['Price'], fit=norm)
fig = plt.figure()
res = stats.probplot(train['Price'], plot=plt)
print('Skew is:', train.Price.skew())