to do linear regression of tempi -> hourly entries

linear_model_OLS.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm

df = pd.read_csv('../improved_data_set/turnstile_weather_v2.csv', index_col=0)
dk = pd.DataFrame(df.groupby('tempi')['ENTRIESn_hourly'].mean())
dk['tempi'] = dk.index

y = dk.ENTRIESn_hourly  # response

X = dk['tempi']  # predictor
X = sm.add_constant(X)  # Adds a constant term to the predictor

est = sm.OLS(y, X)
est = est.fit()
print est.summary()
print est.params

# DRAW 
# We pick 100 hundred points equally spaced from the min to the max
X_prime = np.linspace(X.tempi.min(), X.tempi.max(), 100)[:, np.newaxis]
X_prime = sm.add_constant(X_prime)  # add constant as we did before

# Now we calculate the predicted values
y_hat = est.predict(X_prime)

plt.scatter(X.tempi, y, alpha=0.3)  # Plot the raw data
plt.xlabel("temp")
plt.ylabel("Hourly Entries")
plt.plot(X_prime[:, 1], y_hat, 'r', alpha=0.9)  # Add the regression line, colored in red
plt.show()