我有一个pandas数据框，我希望能够根据 B 列和 C 列中的值预测 A 列的值。这是一个玩具示例：
import pandas as pd
df = pd.DataFrame({"A": [10,20,30,40,50],
"B": [20, 30, 10, 40, 50],
"C": [32, 234, 23, 23, 42523]})
理想情况下，我会有类似的东西ols(A ~ B + C, data = df)但当我看到examples http://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html来自算法库，例如scikit-learn它似乎使用行列表而不是列列表将数据提供给模型。这将需要我将数据重新格式化为列表内的列表，这似乎首先违背了使用 pandas 的目的。对 pandas 数据框中的数据运行 OLS 回归（或更一般的任何机器学习算法）的最 Pythonic 方法是什么？
我认为你几乎可以完全按照你的想法去做，使用统计模型 http://statsmodels.sourceforge.net/包是其中之一pandas' 之前的可选依赖项pandas' 版本 0.20.0（它用于一些事情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 14.952480
B 0.401182
C 0.000352
dtype: float64
>>> print(result.summary())
OLS Regression Results
==============================================================================
Dep. Variable: A R-squared: 0.579
Model: OLS Adj. R-squared: 0.158
Method: Least Squares F-statistic: 1.375
Date: Thu, 14 Nov 2013 Prob (F-statistic): 0.421
Time: 20:04:30 Log-Likelihood: -18.178
No. Observations: 5 AIC: 42.36
Df Residuals: 2 BIC: 41.19
Df Model: 2
==============================================================================
coef std err t P>|t| [95.0% Conf. Int.]
------------------------------------------------------------------------------
Intercept 14.9525 17.764 0.842 0.489 -61.481 91.386
B 0.4012 0.650 0.617 0.600 -2.394 3.197
C 0.0004 0.001 0.650 0.583 -0.002 0.003
==============================================================================
Omnibus: nan Durbin-Watson: 1.061
Prob(Omnibus): nan Jarque-Bera (JB): 0.498
Skew: -0.123 Prob(JB): 0.780
Kurtosis: 1.474 Cond. No. 5.21e+04
==============================================================================
Warnings:
[1] The condition number is large, 5.21e+04. This might indicate that there are
strong multicollinearity or other numerical problems.
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