In [1]:
%reload_ext autoreload
%autoreload 2
%matplotlib inline

Adjust Price Paid

Predict what properties would have sold for, if sold in 2019

In [2]:
import os
import pandas as pd
from ast import literal_eval
from datetime import datetime
import matplotlib.ticker as plticker

filepath = os.path.realpath('../data/shared/prepared.csv')
In [3]:
df = pd.read_csv(filepath)
df['transaction_at'] = pd.to_numeric(pd.to_datetime(df['transaction_at']))

Price paid distribution over time

In [4]:
ax = df.plot.scatter('transaction_at', 'price_paid', s=0.01, figsize=(25,15))
ax.set_ylim(0, 300000)
ax.set_xticklabels([datetime.fromtimestamp(ts / 1e9).strftime('%y/%m') for ts in ax.get_xticks()])
ax
Out[4]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f47b8e56e10>