airbnb_price_per_month.py

import pandas as pd
import numpy as np

# load the data
df_train = pd.read_csv('calendar_train.csv')
df_test = pd.read_csv('calendar_test.csv')

# convert dates
def convert_date(df):
  df = df[~ df.price.isnull()]
  df.date = pd.to_datetime(df.date, format='%m/%d/%y')
  df['month'] = df.date.dt.month
  df['month_name'] = df.date.dt.month_name()
  df['weekday'] = df.date.dt.weekday
  df['weekday_name'] = df.date.dt.day_name()
  return df

df_train_2 = convert_date(df_train)

# display few listings
df_train_2.head()

# plot average price per month
ax = df_train_2.groupby(['month','month_name']) \
                    .agg({'price': np.mean}).reset_index() \
                    .sort_values(by=['month']).plot.bar(x='month_name', 
                                                        y='price', 
                                                        color='skyblue', 
                                                        title='Average price per month')
ax.set_xlabel('month')
ax.set_ylabel('average price')
ax.legend().set_visible(False);

# plot average price per weekday
ax = df_train_2.groupby(['weekday','weekday_name']) \
                    .agg({'price': np.mean}).reset_index() \
                    .sort_values(by=['weekday']).plot.bar(x='weekday_name', 
                                                          y='price', 
                                                          color='lightgreen', 
                                                          title='Average price per weekday')
ax.set_xlabel('weekday')
ax.set_ylabel('average price')
ax.legend().set_visible(False);