Table of Contents

• 1  Data Description
• 2  Imports
• 3  Load the data

Kernel Author:
Bhishan Poudel, Data Scientist, Ph.D Astrophysics .

Data Description

This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015.

Imports

import numpy as np
import pandas as pd
import seaborn as sns
sns.set(color_codes=True)

import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline

import os
import time

# random state
SEED = 0
RNG = np.random.RandomState(SEED)

# Jupyter notebook settings for pandas
pd.set_option('display.max_columns', 50)
pd.set_option('display.max_rows', 20) # None for all the rows
pd.set_option('display.max_colwidth', 50)

print([(x.__name__,x.__version__) for x in [np, pd,sns,matplotlib]])

[('numpy', '1.16.4'), ('pandas', '0.25.0'), ('seaborn', '0.9.0'), ('matplotlib', '3.1.1')]

import bokeh
import pixiedust
from pixiedust import display

%%javascript
IPython.OutputArea.auto_scroll_threshold = 9999;

Load the data

df = pd.read_csv('../data/raw/kc_house_data.csv')
print(df.shape)
df.head().T

(21613, 21)

	0	1	2	3	4
id	7129300520	6414100192	5631500400	2487200875	1954400510
date	20141013T000000	20141209T000000	20150225T000000	20141209T000000	20150218T000000
price	2.2e+05	5.4e+05	1.8e+05	6e+05	5.1e+05
bedrooms	3	3	2	4	3
bathrooms	1	2.2	1	3	2
sqft_living	1180	2570	770	1960	1680
sqft_lot	5650	7242	10000	5000	8080
floors	1	2	1	1	1
waterfront	0	0	0	0	0
view	0	0	0	0	0
...	...	...	...	...	...
grade	7	7	6	7	8
sqft_above	1180	2170	770	1050	1680
sqft_basement	0	400	0	910	0
yr_built	1955	1951	1933	1965	1987
yr_renovated	0	1991	0	0	0
zipcode	98178	98125	98028	98136	98074
lat	48	48	48	48	48
long	-1.2e+02	-1.2e+02	-1.2e+02	-1.2e+02	-1.2e+02
sqft_living15	1340	1690	2720	1360	1800
sqft_lot15	5650	7639	8062	5000	7503

21 rows × 5 columns

display(df)

Hey, there's something awesome here! To see it, open this notebook outside GitHub, in a viewer like Jupyter