Analysis of data on House Sales in King County, USA

import pandas as pd
import DSTOOLS as ds
import matplotlib.pyplot as plt
import numpy as np

path = r'your path'
data = pd.read_csv(path)

"""
id - Unique ID for each home sold
date - Date of the home sale
price - Price of each home sold
bedrooms - Number of bedrooms
bathrooms - Number of bathrooms, where .5 accounts for a room with a toilet but no shower
sqft_living - Square footage of the apartments interior living space
sqft_lot - Square footage of the land space
floors - Number of floors
waterfront - A dummy variable for whether the apartment was overlooking the waterfront or not
view - An index from 0 to 4 of how good the view of the property was
condition - An index from 1 to 5 on the condition of the apartment,
grade - An index from 1 to 13, where 1-3 falls short of building construction and design, 7 has an average level of construction and design, and 11-13 have a high quality level of construction and design.
sqft_above - The square footage of the interior housing space that is above ground level
sqft_basement - The square footage of the interior housing space that is below ground level
yr_built - The year the house was initially built
yr_renovated - The year of the house’s last renovation
zipcode - What zipcode area the house is in
lat - Lattitude
long - Longitude
sqft_living15 - The square footage of interior housing living space for the nearest 15 neighbors
sqft_lot15 - The square footage of the land lots of the nearest 15 neighbors"""

#Function to modify the number of rows and columns allowed to be displayed on the screen, by default there is no limit.
def display_options(max_columns = None, max_rows = None):
    pd.options.display.max_columns = max_columns
    pd.options.display.max_rows = max_rows
    return print('Changes applied')

#Set display options to None in max_columns and max_rows
display_options()

#Take a quick look at the data structure
#data.head() #This output has been omitted to shorten the document.

#Use custom tool to see data structure (shape, null values)
#ds.data_estructure(data) #This output has been omitted to shorten the document.

#Use tool to see data types
#ds.categ_data(data) #This output has been omitted to shorten the document.

#Summary of each numerical attribute 
#data.describe() #This output has been omitted to shorten the document.

#Visualize data distribution
data.hist(bins=50, figsize=(15,15))

#Load a real image to compare with data
from IPython.display import Image
img_path = r'C:\Users\anton\Documents\Datasets\KC_HOUSES\kc_map.png'
Image(img_path)

#Graphic to visuzalice density area of houses
fig ,ax = plt.subplots(figsize=(15,10))
plt.scatter(data['long'],data['lat'],alpha=0.2)
plt.title('Geographical data')
plt.xlabel('Longitude')
plt.ylabel('Latitude')

#Heat map to show the price level of different households
data.plot(kind="scatter", x="long", y="lat", alpha=0.3,label="houses prices",
          figsize=(10,7),c="price", cmap=plt.get_cmap("jet"), colorbar=True )
plt.title('Price of the houses')
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.legend() 

#We look up how many different zip code values there are
valores = data['zipcode'].value_counts()

#We observe them
#valores

#We assign only one zip code of each type
zips = valores.index

#We searched the internet to find the location by zip code.
locations = ['Seattle','Maple Valley','Seattle','Redmond','Seattle','Kent','Kirkland','Seattle','Federal way','Bellevue','Seattle','Renton','Renton',
                     'Seattle','Sammamish', 'Kirkland','Issaquah','Seattle','Renton','Redmond','Auburn','Sammamish','Seattle','Auburn','Seattle','Seattle',
                     'Seattle','Issaquah','Bellevue', 'Seattle','Snoqualmie','Seattle','Seattle','Bellevue','Kenmore','Mercer Island','Seattle','Federal way',
                    'Kent', 'Woodinville','Seattle','Seattle','Renton','Seattle','Seattle','Seattle','Kent','Seattle','Seattle','Enumclaw','Seattle','North Bend',
                    'Auburn','Woodinville','Bothell','Duvall','Seattle','Seattle','Bellevue','Bellevue','Seattle','Kent','Carnation','Vashon','Seattle','Seattle',
                    'Black Diamond','Fall City','Seattle','Medina']

#We create a dictionary to assign the district to each ZIP code.
zip_locations = {}
for x in range(len(zips)):
    zip_locations[zips[x]] = locations[x]

#We look at the dictionary
#zip_locations This output has been omitted to shorten the document.

#We create a list with each district assigned to the zip code where it belongs from the length of the data.
def_locations = []
for x in range(len(data)):
    def_locations.append(zip_locations[data['zipcode'][x]])

#We assign a new column with the locations
data = data.assign(Locations = def_locations)

#Observamos como se ven los datos ahora con la nueva columna
#data.head() #This output has been omitted to shorten the document.

#We group the data according to district
Localizations = data['Locations'].value_counts()

#We plot the number of houses per district
fig ,ax = plt.subplots(figsize=(15,10))
plt.barh(Localizations.index, width=Localizations)

# Plot showing the number of houses per district
fig, ax = plt.subplots(figsize=(15,10))
plt.scatter(data['long'], data['lat'], alpha=0.7)
plt.title('Number of houses per district')
plt.xlabel('Longitude')
plt.ylabel('Latitude')


# Latitude coordinates
y = [47.67304300,47.51227600,47.61998500,47.68130900,47.67510800,47.30850100,47.36878300,47.62362300,47.30934700,
        47.49025500,47.40497400,47.76095200,47.56488900,47.75441400,47.56601100,47.17335000,47.45136500,47.75724700,
        47.73832800,47.66043000,47.41229600,47.31855000,47.62878100,47.62762400]

# Longitude coordinates
x = [-122.34276600,-122.18853600,-122.20697900,-122.12078000,-122.19386300,-122.21567100,-122.19665000,-122.04220000,
        -122.36310500,-121.97503800,-122.00456500,-122.13075800,-121.77285200,-122.24676700,-122.23207500,-121.64145000,
        -121.60338700,-122.19814100,-121.79412900,-121.89709700,-122.47238700,-121.99561700,-121.77954500,-122.24233400]

# District names with the number of houses per district
annotations = ['Seattle = 8977', 'Renton = 1597', 'Bellevue = 1407', 'Redmond = 979', 'Kirkland = 977', 'Auburn = 912', 'Kent = 1203', 
               'Sammamish = 800', 'Federal Way = 779', 'Issaquah = 733', 'Maple Valley = 590', 'Woodinville = 471', 'Snoqualmie = 310', 
               'Kenmore = 283', 'Mercer Island = 282', 'Enumclaw = 234', 'North Bend = 221', 'Bothell', 'Duvall = 195', 'Carnation = 190', 
               'Vashon = 124', 'Black Diamond = 100', 'Fall City = 81', 'Medina = 50']

# Plot red points for districts
plt.scatter(x, y, color='red')

# Assign annotations to the corresponding points
for i, label in enumerate(annotations):
    plt.annotate(annotations[i], (x[i], y[i]))

#We calculate the average price per location
med_price = data.groupby(['Locations']).mean()

#We plot the average price by location
fig ,ax = plt.subplots(figsize=(15,10))
plt.barh(med_price.index,med_price['price'])
plt.title('Mean price per district',fontsize=20)
plt.xlabel('Mean price')
plt.ylabel('Location')

#Creamos una nueva columna asignandole a cada distrito el precio medio de los hogares correspondiente
mean_d = {}
for x in range(len(med_price)):
    mean_d[med_price.index[x]] = med_price['price'][x]

mean_prices = []
for x in range(len(data)):
    mean_prices.append(mean_d[data['Locations'][x]])

#Añadimos la nueva columna a los datos
data = data.assign(mean_price_of_district = mean_prices)

# Heatmap to show the average price level per district
data.plot(kind="scatter", x="long", y="lat", alpha=0.5,label="houses",
          figsize=(15,10),c="mean_price_of_district", cmap=plt.get_cmap("gist_rainbow"), colorbar=True )

# District names
anotations = ['Seattle','Renton','Bellevue','Redmond', 'Kirkland','Auburn', 'Kent','Sammamish',
                        'Federal Way','Issaquah','Maple Valley','Woodinville','Snoqualmie','Kenmore',
                        'Mercer Island','Enumclaw','North Bend','Bothell','Duvall','Carnation','Vashon',
                        'Black Diamond','Fall City','Medina']

# Latitude coordinates
y = [47.67304300,47.51227600,47.61998500,47.68130900,47.67510800,47.30850100,47.36878300,47.62362300,47.30934700,
        47.49025500,47.40497400,47.76095200,47.56488900,47.75441400,47.56601100,47.17335000,47.45136500,47.75724700,
        47.73832800,47.66043000,47.41229600,47.31855000,47.62878100,47.62762400]

# Longitude coordinates
x = [-122.34276600,-122.18853600,-122.20697900,-122.12078000,-122.19386300,-122.21567100,-122.19665000,-122.04220000,
        -122.36310500,-121.97503800,-122.00456500,-122.13075800,-121.77285200,-122.24676700,-122.23207500,-121.64145000,
        -121.60338700,-122.19814100,-121.79412900,-121.89709700,-122.47238700,-121.99561700,-121.77954500,-122.24233400]

plt.title('Median house price per district')
plt.xlabel('Longitude')
plt.ylabel('Latitude')

# Add annotations to corresponding points
for i, label in enumerate(anotations):
    plt.annotate(anotations[i], (x[i], y[i]))
    
plt.legend() 

# We calculate the correlation matrix to see how variables are related
corr_matrix = data.corr()

# We check which variables have the highest correlation with the house price
corr_matrix['price'].sort_values(ascending=False)

# We plot the relationship between square footage and price
data.plot(kind="scatter", x='sqft_living', y='price', alpha=0.3)

#We plot the relationship between grade and price
data.plot(kind="scatter",x='grade',y='price',alpha=0.4)