IST 718 LAB 6¶
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# !pip install fbprophet
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%matplotlib inline
import pandas as pd
from fbprophet import Prophet
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
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df = pd.read_csv('https://raw.githubusercontent.com/danielcaraway/data/master/Zip_Zhvi_SingleFamilyResidence.csv', encoding='latin')
PART ONE: Arkansas Metro Areas¶
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hs = df[df['Metro'].str.contains('Hot Springs', na=False)]
lr = df[df['Metro'].str.contains('Little Rock', na=False)]
f = df[df['Metro'].str.contains('Fayetteville', na=False)]
s = df[df['Metro'].str.contains('Searcy', na=False)]
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def graph_prices_for(df, location_name):
df_t = df.loc[:, '1996-04'::].T
df_t['avg'] = df_t.mean(numeric_only=True, axis=1)
df_t.reset_index(inplace=True)
columns = ['index', 'avg']
df = pd.DataFrame(df_t, columns = columns)
df = df.rename(index=str, columns={"avg": "y", "index": "ds"})
ax = df.set_index('ds').plot(figsize=(12, 8))
ax.set_ylabel('Home Prices in ' + location_name)
ax.set_xlabel('Date')
plt.show()
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graph_prices_for(hs, "Hot Springs")
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graph_prices_for(lr, "Little Rock")
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graph_prices_for(f, "Fayetteville")
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graph_prices_for(s, "Searcy")
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ca = df[df['State'] == 'CA']
graph_prices_for(ca, "California")
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def transform(df, location_name):
df_t = df.loc[:, '1996-04'::].T
df_t['avg'] = df_t.mean(numeric_only=True, axis=1)
df_t.reset_index(inplace=True)
columns = ['index', 'avg']
df = pd.DataFrame(df_t, columns = columns)
df['place'] = location_name
return df
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hs_t = transform(hs, 'Hot Springs')
lr_t = transform(lr, 'Little Rock')
f_t = transform(f, 'Fayetteville')
s_t = transform(s, 'Searcy')
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big_df = hs_t.append([lr_t,f_t,s_t])
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import seaborn as sns
sns.lineplot(x="index", y="avg", hue="place", data=big_df)
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big_df['year'] = big_df.apply(lambda x: x['index'].split('-')[0], axis=1)
big_df['month'] = big_df.apply(lambda x: x['index'].split('-')[1], axis=1)
big_df['day'] = '01'
big_df['Date'] = pd.to_datetime(big_df[['year','month','day']])
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# by_place = pd.DataFrame(big_df_year.groupby(['place','year'])['avg'].mean())
# by_place.reset_index(inplace=True)
# sns.tsplot(data=by_place, time="year", condition="place", unit="place", value="avg")
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# sns.tsplot(data=big_df, time="Date", condition="place", unit="place", value="avg")
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for state in set(df['State']):
state_df = df[df['State'] == state]
graph_prices_for(state_df, str(state))
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def prophet_prices_for(df, location_name):
df_t = df.loc[:, '1997-01':'2017-12'].T
df_t['avg'] = df_t.mean(numeric_only=True, axis=1)
df_t.reset_index(inplace=True)
columns = ['index', 'avg']
df = pd.DataFrame(df_t, columns = columns)
df = df.rename(index=str, columns={"avg": "y", "index": "ds"})
m = Prophet()
m.fit(df)
future = m.make_future_dataframe(periods=12, freq='M')
forecast = m.predict(future)
print("====",state,"====")
components_fig = m.plot_components(forecast)
axes = components_fig.get_axes()
axes[0].set_xlabel('DATE')
axes[0].set_ylabel(state)
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for state in set(df['State']):
state_df = df[df['State'] == state]
prophet_prices_for(state_df, str(state))
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ca
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len(set(ca.RegionName))
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