Run an OLS regression with Pandas Data Frame
I think you can almost do exactly what you thought would be ideal, using the statsmodels package which was one of pandas' optional dependencies before pandas' version 0.20.0 (it was used for a few things in pandas.stats.)
>>> import pandas as pd>>> import statsmodels.formula.api as sm>>> df = pd.DataFrame({"A": [10,20,30,40,50], "B": [20, 30, 10, 40, 50], "C": [32, 234, 23, 23, 42523]})>>> result = sm.ols(formula="A ~ B + C", data=df).fit()>>> print(result.params)Intercept 14.952480B 0.401182C 0.000352dtype: float64>>> print(result.summary()) OLS Regression Results ==============================================================================Dep. Variable: A R-squared: 0.579Model: OLS Adj. R-squared: 0.158Method: Least Squares F-statistic: 1.375Date: Thu, 14 Nov 2013 Prob (F-statistic): 0.421Time: 20:04:30 Log-Likelihood: -18.178No. Observations: 5 AIC: 42.36Df Residuals: 2 BIC: 41.19Df Model: 2 ============================================================================== coef std err t P>|t| [95.0% Conf. Int.]------------------------------------------------------------------------------Intercept 14.9525 17.764 0.842 0.489 -61.481 91.386B 0.4012 0.650 0.617 0.600 -2.394 3.197C 0.0004 0.001 0.650 0.583 -0.002 0.003==============================================================================Omnibus: nan Durbin-Watson: 1.061Prob(Omnibus): nan Jarque-Bera (JB): 0.498Skew: -0.123 Prob(JB): 0.780Kurtosis: 1.474 Cond. No. 5.21e+04==============================================================================Warnings:[1] The condition number is large, 5.21e+04. This might indicate that there arestrong multicollinearity or other numerical problems.
Note: pandas.stats has been removed with 0.20.0
It's possible to do this with pandas.stats.ols:
>>> from pandas.stats.api import ols>>> df = pd.DataFrame({"A": [10,20,30,40,50], "B": [20, 30, 10, 40, 50], "C": [32, 234, 23, 23, 42523]})>>> res = ols(y=df['A'], x=df[['B','C']])>>> res-------------------------Summary of Regression Analysis-------------------------Formula: Y ~ <B> + <C> + <intercept>Number of Observations: 5Number of Degrees of Freedom: 3R-squared: 0.5789Adj R-squared: 0.1577Rmse: 14.5108F-stat (2, 2): 1.3746, p-value: 0.4211Degrees of Freedom: model 2, resid 2-----------------------Summary of Estimated Coefficients------------------------ Variable Coef Std Err t-stat p-value CI 2.5% CI 97.5%-------------------------------------------------------------------------------- B 0.4012 0.6497 0.62 0.5999 -0.8723 1.6746 C 0.0004 0.0005 0.65 0.5826 -0.0007 0.0014 intercept 14.9525 17.7643 0.84 0.4886 -19.8655 49.7705---------------------------------End of Summary---------------------------------Note that you need to have statsmodels package installed, it is used internally by the pandas.stats.ols function.
I don't know if this is new in sklearn or pandas, but I'm able to pass the data frame directly to sklearn without converting the data frame to a numpy array or any other data types.
from sklearn import linear_modelreg = linear_model.LinearRegression()reg.fit(df[['B', 'C']], df['A'])>>> reg.coef_array([ 4.01182386e-01, 3.51587361e-04])