# House buyers avoid completing on the 13th of the month

Using the database of the UK land registry's record of house sales, we can extract the total number of sales for each day as follows:

import psycopg2
conn = psycopg2.connect(database='house_prices',

cursor = conn.cursor()

query = ("SELECT date_of_transfer, SUM(1) FROM pp"
" GROUP BY 1 ORDER BY 1;")

cursor.execute(query)

with open('sales_by_day.txt', 'w') as fo:
for row in cursor.fetchall():
print(*row, file=fo)


The PostGreSQL statement here sums and orders the sales according to their dates of transfer.

The file sales_by_day.txt now looks like:

\$ head sales_by_day.txt
1995-01-01 56
1995-01-02 65
1995-01-03 981
1995-01-04 1098
1995-01-05 1292
1995-01-06 5054
1995-01-07 30
1995-01-08 74
1995-01-09 1279
1995-01-10 952


We can use Pandas to analyse these data in different ways. For example, to determine the total number of house sales by day of the month:

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

# Read in the number of house sales each day
names=('date_of_transfer', 'nsales'), parse_dates=[0])

# Aggregate and sum by day of month
df['day'] = df.set_index('date_of_transfer').index.day
nsales_by_day = df.groupby('day').sum()

fig = plt.figure()
bars = ax.bar(np.arange(31)+1, nsales_by_day['nsales']/1000,
align='center', color='#cc88cc')
# Highlight the 13th in yellow
bars[12].set_facecolor('#ffff00')
ax.set_xlabel('Day of month')
ax.set_ylabel('Total number of sales \'000s')
ax.set_xlim(0,32)
ax.tick_params(length=5, width=2, direction='out',color='#cccccc')
ax.yaxis.grid(color='#cccccc', linestyle='-', lw=2)

for pos in ax.spines:
ax.spines[pos].set_visible(False)

ax.set_title('UK house sales by day of month for completion')
ax.set_axisbelow(True)

plt.show()


By default, the Pandas read_table function identifies fields as separated by tabs so we need to pass sep=' ' explicitly. We also need to tell Python to treat the first column as a date (parse_dates=[0]).

We can extract the day number and add it as an extra column with

df['day'] = df.set_index('date_of_transfer').index.day


And then take the sum of the sales, grouped by day number with

nsales_by_day = df.groupby('day').sum()


The plotted data show a significant minimum on the 13th. Presumably house-buyers are superstitious.

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