pandas 将预测值和残差附加到熊猫数据框
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原文地址: http://stackoverflow.com/questions/32101233/
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Appending predicted values and residuals to pandas dataframe
提问by Uncle Milton
It's a useful and common practice to append predicted values and residuals from running a regression onto a dataframe as distinct columns. I'm new to pandas, and I'm having trouble performing this very simple operation. I know I'm missing something obvious. There was a very similar questionasked about a year-and-a-half ago, but it wasn't really answered.
将运行回归的预测值和残差作为不同的列附加到数据帧上是一种有用且常见的做法。我是Pandas的新手，在执行这个非常简单的操作时遇到了麻烦。我知道我错过了一些明显的东西。有一个非常类似的问题询问了一年和半前，但它并没有真正回答。
The dataframe currently looks something like this:
数据框目前看起来像这样：
y x1 x2
880.37 3.17 23
716.20 4.76 26
974.79 4.17 73
322.80 8.70 72
1054.25 11.45 16
And all I'm wanting is to return a dataframe that has the predicted value and residual from y = x1 + x2 for each observation:
我想要的只是返回一个数据帧，该数据帧具有 y = x1 + x2 的每个观察的预测值和残差：
y x1 x2 y_hat res
880.37 3.17 23 840.27 40.10
716.20 4.76 26 752.60 -36.40
974.79 4.17 73 877.49 97.30
322.80 8.70 72 348.50 -25.70
1054.25 11.45 16 815.15 239.10
I've tried resolving this using statsmodels and pandas and haven't been able to solve it. Thanks in advance!
我已经尝试使用 statsmodels 和 pandas 来解决这个问题，但一直无法解决。提前致谢！
回答by Josef
Here is a variation on Alexander's answer using the OLS model from statsmodels instead of the pandas ols model. We can use either the formula or the array/DataFrame interface to the models.
这是亚历山大使用来自 statsmodels 的 OLS 模型而不是 pandas ols 模型的答案的变体。我们可以使用模型的公式或数组/DataFrame 接口。
fittedvaluesand residare pandas Series with the correct index.
predictdoes not return a pandas Series.
fittedvalues并且resid是Pandas系列与正确的索引。
predict不返回Pandas系列。
import numpy as np
import pandas as pd
import statsmodels.api as sm
import statsmodels.formula.api as smf
df = pd.DataFrame({'x1': [3.17, 4.76, 4.17, 8.70, 11.45],
'x2': [23, 26, 73, 72, 16],
'y': [880.37, 716.20, 974.79, 322.80, 1054.25]},
index=np.arange(10, 20, 2))
result = smf.ols('y ~ x1 + x2', df).fit()
df['yhat'] = result.fittedvalues
df['resid'] = result.resid
result2 = sm.OLS(df['y'], sm.add_constant(df[['x1', 'x2']])).fit()
df['yhat2'] = result2.fittedvalues
df['resid2'] = result2.resid
# predict doesn't return pandas series and no index is available
df['predicted'] = result.predict(df)
print(df)
x1 x2 y yhat resid yhat2 resid2 \
10 3.17 23 880.37 923.949309 -43.579309 923.949309 -43.579309
12 4.76 26 716.20 890.732201 -174.532201 890.732201 -174.532201
14 4.17 73 974.79 656.155079 318.634921 656.155079 318.634921
16 8.70 72 322.80 610.510952 -287.710952 610.510952 -287.710952
18 11.45 16 1054.25 867.062458 187.187542 867.062458 187.187542
predicted
10 923.949309
12 890.732201
14 656.155079
16 610.510952
18 867.062458
As preview, there is an extended prediction method in the model results in statsmodels master (0.7), but the API is not yet settled:
作为预览，statsmodels master(0.7)中的模型结果中有一个扩展的预测方法，但API尚未确定：
>>> print(result.get_prediction().summary_frame())
mean mean_se mean_ci_lower mean_ci_upper obs_ci_lower \
10 923.949309 268.931939 -233.171432 2081.070051 -991.466820
12 890.732201 211.945165 -21.194241 1802.658643 -887.328646
14 656.155079 269.136102 -501.844105 1814.154263 -1259.791854
16 610.510952 282.182030 -603.620329 1824.642233 -1339.874985
18 867.062458 329.017262 -548.584564 2282.709481 -1214.750941
obs_ci_upper
10 2839.365439
12 2668.793048
14 2572.102012
16 2560.896890
18 2948.875858
回答by Alexander
This should be self explanatory.
这应该是不言自明的。
import pandas as pd
df = pd.DataFrame({'x1': [3.17, 4.76, 4.17, 8.70, 11.45],
'x2': [23, 26, 73, 72, 16],
'y': [880.37, 716.20, 974.79, 322.80, 1054.25]})
model = pd.ols(y=df.y, x=df.loc[:, ['x1', 'x2']])
df['y_hat'] = model.y_fitted
df['res'] = model.resid
>>> df
x1 x2 y y_hat res
0 3.17 23 880.37 923.949309 -43.579309
1 4.76 26 716.20 890.732201 -174.532201
2 4.17 73 974.79 656.155079 318.634921
3 8.70 72 322.80 610.510952 -287.710952
4 11.45 16 1054.25 867.062458 187.187542
回答by Andy Kubiak
So, it's polite to form your questions such that it's easy for contributors to run your code.
因此，形成您的问题是礼貌的，以便贡献者可以轻松地运行您的代码。
import pandas as pd
y_col = [880.37, 716.20, 974.79, 322.80, 1054.25]
x1_col = [3.17, 4.76, 4.17, 8.70, 11.45]
x2_col = [23, 26, 73, 72, 16]
df = pd.DataFrame()
df['y'] = y_col
df['x1'] = x1_col
df['x2'] = x2_col
Then calling df.head()yields:
然后调用df.head()产量：
y x1 x2
0 880.37 3.17 23
1 716.20 4.76 26
2 974.79 4.17 73
3 322.80 8.70 72
4 1054.25 11.45 16
Now for your question, it's fairly straightforward to add columns with calculated values, though I'm not agreeing with your sample data:
现在对于您的问题，添加具有计算值的列是相当简单的，尽管我不同意您的示例数据：
df['y_hat'] = df['x1'] + df['x2']
df['res'] = df['y'] - df['y_hat']
For me, these yield:
对我来说，这些收益：
y x1 x2 y_hat res
0 880.37 3.17 23 26.17 854.20
1 716.20 4.76 26 30.76 685.44
2 974.79 4.17 73 77.17 897.62
3 322.80 8.70 72 80.70 242.10
4 1054.25 11.45 16 27.45 1026.80
Hope this helps!
希望这可以帮助！
