概述

我如何绘制线性回归结果用于大熊猫的线性回归？

import pandas as pd
from pandas.stats.api import ols

df = pd.read_csv('Samples.csv',index_col=0)
control = ols(y=df['Control'],x=df['Day'])
one = ols(y=df['Sample1'],x=df['Day'])
two = ols(y=df['Sample2'],x=df['Day'])

我试过plot()但它没有用.我想在一个图上绘制所有三个样本是否有任何pandas代码或matplotlib代码以这些摘要的格式的hadle数据？

无论如何,结果看起来像这样：

控制

------------------------Summary of Regression Analysis-------------------------

Formula: Y ~ 
  
   degrees of Freedom:   2

R-squared:         0.5642
Adj R-squared:     0.4770

Rmse:              4.6893

F-stat (1,5):     6.4719,p-value:     0.0516


   degrees of Freedom: model 1,resid 5

-----------------------Summary of Estimated Coefficients------------------------
      Variable       Coef    Std Err     t-stat    p-value    CI 2.5%   CI 97.5%
--------------------------------------------------------------------------------
             x    -0.4777     0.1878      -2.54     0.0516    -0.8457    -0.1097
     intercept    41.4621     2.9518      14.05     0.0000    35.6766    47.2476
---------------------------------End of Summary---------------------------------

  

一

-------------------------Summary of Regression Analysis-------------------------

Formula: Y ~ 
  
   degrees of Freedom:   2

R-squared:         0.8331
Adj R-squared:     0.7914

Rmse:              2.0540

F-stat (1,4):    19.9712,p-value:     0.0111


   degrees of Freedom: model 1,resid 4

-----------------------Summary of Estimated Coefficients------------------------
      Variable       Coef    Std Err     t-stat    p-value    CI 2.5%   CI 97.5%
--------------------------------------------------------------------------------
             x    -0.4379     0.0980      -4.47     0.0111    -0.6300    -0.2459
     intercept    29.6731     1.6640      17.83     0.0001    26.4116    32.9345
---------------------------------End of Summary---------------------------------

  

二

-------------------------Summary of Regression Analysis-------------------------

Formula: Y ~ 
  
   degrees of Freedom:   2

R-squared:         0.8788
Adj R-squared:     0.8384

Rmse:              1.0774

F-stat (1,3):    21.7542,p-value:     0.0186


   degrees of Freedom: model 1,resid 3

-----------------------Summary of Estimated Coefficients------------------------
      Variable       Coef    Std Err     t-stat    p-value    CI 2.5%   CI 97.5%
--------------------------------------------------------------------------------
             x    -0.2399     0.0514      -4.66     0.0186    -0.3407    -0.1391
     intercept    24.0902     0.9009      26.74     0.0001    22.3246    25.8559
---------------------------------End of Summary---------------------------------

  

我试图找到一些我的代码用Pandas做一个ols情节,但是不能把它放在它上面.一般来说你可能会更好地使用Statsmodels,它知道Pandas数据结构..所以过渡不是太难.然后我的答案和参考的例子将更有意义..

另见：http://nbviewer.ipython.org/gist/dartdog/9008026

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