image_output_1.png
import matplotlib.pyplot as plt

import matplotlib.style as style

import pandas as pd

import seaborn as sns

train = pd.read_csv('train.csv')

train.shape

train.describe().T

train.dtypes.value_counts()

total = train.isnull().sum().sort_values(ascending=False)[train.isnull().sum().sort_values(ascending=False) != 0]
percent = (train.isnull().sum() / train.isnull().count()).sort_values(ascending=False)[(train.isnull().sum() / train.isnull().count()).sort_values(ascending=False) != 0]
missing = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])

missing = train.isnull().sum()
missing = missing[missing > 0]
style.use('seaborn-darkgrid')
missing.sort_values(inplace=True)

duplicated = train.duplicated()

correlation = train.corr()

def customized_scatterplot(y, x):
    style.use('seaborn-darkgrid')

customized_scatterplot(train.SalePrice, train.GarageArea)