比较布尔值的两个数据框列 [英] comparing two dataframe columns of booleans

问题描述

我有两个数据帧，每个数据帧分别表示实际降雨和预测的降雨情况。实际降雨数据帧是恒定的，因为这是已知结果。预测的降雨数据帧如下所示。

I have two dataframes each denoting actual rain and predicted rain condition. Actual rain dataframe is constant as it is a known result. Predicted rain dataframe They are given below.

actul = 

index  rain
Day1   True
Day2   False
Day3   True
Day4   True

预测的降雨数据帧在下面给出。该数据框会根据使用的预测模型不断变化。

Predicted rain dataframe is given below. This dataframe keeps on changing based on predicted model used.

prdt = 

index  rain
Day1   False
Day2   True
Day3   True
Day4   False

我正在开发上述给定模型的预测精度

I am developing prediction accuracy of above prediction model as given below:

#Following computes the number days on which raining was predicted correctly        
a = sum(np.where(((actul['rain'] == True)&(prdt['rain']==True)),True,False))  
#Following computes the number days on which no-rain was predicted correctly    
b = sum(np.where(((actul['rain'] == False)&(prdt['rain']==False)),True,False))
#Following computes the number days on which raining was incorrectly predicted 
c = sum(np.where(((actul['rain'] == True)&(prdt['rain']==False)),True,False))
#Following computes the number days on which no-rain was incorrectly predicted     
d = sum(np.where(((actul['rain'] == False)&(prdt['rain']==True)),True,False))

predt_per =  (a+b)*100/(a+b+c+d)

我上面的代码花了太多时间来计算。有没有更好的方法可以达到上述效果？

My above code is taking too much time to compute. Is there a better way to achieve above result?

现在，低于接受的答案可以解决上述问题。看起来下面的代码有问题，因为我得到所有数据帧的 100％预测百分比。我的代码是：

Now, below accepted answer solved my above problem. Looks like something is wrong in my code given below because I am getting 100% prediction percentage for all dataframes. My code is:

alldates_df = 

index       met1_r2    useful     met1_r2>0.5
0          0.824113     True        True
1          0.903828     True        True
2          0.500765     True        True
3          0.889757     True        True
4          0.890102     True        True
5          0.893995     True        True
6          0.933482     True        True
7          0.872847     True        True
8          0.913142     True        True
9          0.901424     True        True
10         0.910941     True        True
11         0.927310     True        True
12         0.934538     True        True
13         0.946092     True        True
14         0.653831     True        True
15         0.390702     True        False
16         0.878493     True        True
17         0.899739     True        True
18         0.938481     True        True
19      -850.978703     False       False
20       -21.802518     False       False

met1_detacu = [] # Method1_detection accuracy at various settings
var_flset = np.arange(-5,1,0.01) # various filter settings
for i in var_flset:
    pdt_usefl =  alldates_df.assign(result=alldates_df['met1_r2']>i)
    x = pd.concat([alldates_df['useful'],pdt_usefl['result']],axis=1).sum(1).isin([0,2]).mean()*100
    met1_detacu.append(x)
plt.plot(var_flset,met1_detacu)

我上面的代码可以正常工作，但我得到但我得到了所有<$在所有变量过滤器设置中，c $ c> 100％的检测精度。这里不对劲。
获得的地块：

My above code is working fine but I am getting but I am getting all 100% detection accuracy at all the varible filter settings. Something is wrong here. Obtained plot:

期望的图是：

@WeNYoBen

@WeNYoBen

推荐答案

在您的情况下，假设索引是df的索引，因此我们可以在 concat <之后使用 sum / code>，因为True + True == 2和False + False == 0

In your case assuming the index is the index of df , so we can using sum after concat , since True + True ==2 and False + False ==0

pd.concat([df1,df2],axis=1).sum(1).isin([0,2]).mean()*100
25.0

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更新

---

Update

met1_detacu = [] # Method1_detection accuracy at various settings
var_flset = np.arange(-5,1,0.01) # various filter settings
for i in var_flset:
    pdt_usefl =  alldates_df.assign(result=alldates_df['met1_r2']>i)
    x = pd.concat([alldates_df['useful'],pdt_usefl['result']],axis=1).sum(1).isin([0,2]).mean()*100
    met1_detacu.append(x)
plt.plot(var_flset,met1_detacu)

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