Answered: The csv file "house price.csv contains…

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Question

The csv file "house price.csv contains the basic Information about houses. Rerad the data from the csy file "house price.csv", and create a dataframe object. Plot the histogram chart and the density chart of the distribution of bedrooms (i.e. the column "bedrooms").

Plot the scatter chart of the relationship between the column "sqft Jiving" and the column "price". Pict the bar chart of the average selling price (Le. the column "price") of houses in different cities (Le. the calumn "city") and also compute the standard deviation. Plat the box chart of the selling price (ie. the column "price" )of houses in different cities (ie. the column "city"). In [2]: Import pandas as pd import matplotlib.pyplot as pit import seaborn as sns in [9]: #1 Read the data iffile ruth pathhouse price csv in [14] df = pd. read rsv ( path) df Out [14]: \table[[]

date price,bedrooms, bathrooms.sqft_living,sqft lot, floors, waterfront, view,condition,sqft_above, sqft basement.yr_built yr_renovated], [0, \table][2014

2. The csv file "house_price.csv" contains the basic information about houses.
1. Read the data from the csv file "house_price.csv", and create a dataframe object.
2. Plot the histogram chart and the density chart of the distribution of bedrooms (i.e. the column "bedrooms").
3. Plot the scatter chart of the relationship between the column "sqft_living" and the column "price".
4. Plot the bar chart of the average selling price (i.e. the column "price") of houses in different cities (i.e. the column "city")
and also compute the standard deviation.
5. Plot the box chart of the selling price (i.e. the column "price") of houses in different cities (i.e. the column "city").
In [2] import pandas as pd
Out [14]:
import matplotlib. pyplot as plt
import seaborn as sns
In [9]: #1 Read the data
#file ruth
path='house_price.csv'
In [14]: df pd. read_csv (path)
df
0
date
price bedrooms bathrooms sqft_living sqft_lot floors waterfront view condition sqft_above sqft_basement yr_built yr_renovated
2014-
05-02 3.130000e+05
00:00:00
3.0
1.50
1340
7912
1.5
0
0
3
1340
0
1955
2005

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