text_plain_output_1.png
from subprocess import check_output

import matplotlib.pyplot as plt

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import numpy as np
import pandas as pd
from subprocess import check_output
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')

cities_crosswalk = pd.read_csv('../input/cities_crosswalk.csv')
city_time_series = pd.read_csv('../input/City_time_series.csv')
county_time_series = pd.read_csv('../input/County_time_series.csv')
metro_time_series = pd.read_csv('../input/Metro_time_series.csv')
neighborhood_time_series = pd.read_csv('../input/Neighborhood_time_series.csv')
state_time_series = pd.read_csv('../input/State_time_series.csv')
zip_time_series = pd.read_csv('../input/Zip_time_series.csv')

state_time_series.Date = pd.to_datetime(state_time_series.Date)
state_without_null = state_time_series.dropna(subset=['ZHVI_AllHomes'], how='any')
state_without_null.Date = state_without_null.Date.dt.year
state_without_null.groupby(state_without_null.RegionName)['ZHVI_AllHomes'].mean().sort_values().tail(10).plot(kind='bar')

state_time_series.Date = pd.to_datetime(state_time_series.Date)
state_without_null = state_time_series.dropna(subset=['ZRI_AllHomes'], how='any')
state_without_null.Date = state_without_null.Date.dt.year
plt.subplots(figsize=(14, 6))
state_without_null.groupby(state_without_null.RegionName)['ZRI_AllHomes'].mean().sort_values().tail(10).plot(kind='bar')
plt.title('Top States with Most Rent Values According to Zillow', fontsize=25)
plt.xlabel('States')
plt.ylabel('dollar amount')