python - 寻找最佳模型并将其信息纳入新专栏

Question

我有一个 df 如下。

p1_conf,p2_conf,p3_conf分别表示模型p1,的置信区间p2 和 p3。

我想知道如何选择每行具有最高置信区间的预测并将其存储在一些新列中。所以结果将是:

您可以使用下面的 df 作为原始 df:

df = pd.DataFrame({"id": [1,2,3,4,5],
                "Name": ["Dave","Max","Joe","Rose","Mark"],
                "model1":["Irish","German","USA","Japan","China"],
                "confidence1": [0.9,.99,.83,.45,.51],
                "prediction1": [True,False,True,False,False],
                "model2":["Oman","Nigeria","India","Russia","Brazil"],
                "confidence2": [0.1,.25,.26,.41,.01],
                "prediction2": [False,True,False,False,False],
                "model3":["Egypt","Cameron","Netherland","Canada","Mexcio"],
                "confidence3": [0.01,.23,.12,.34,.61],
                "prediction3": [True,False,True,True,False]})

结果应该是这样的:

df1 = pd.DataFrame({"id": [1,2,3,4,5],
                 "Name":["Dave","Max","Joe","Rose","Mark"],
                 "model_name":["1","2","1","3",None],
                 "predicted_gener":["Irish","Nigeria","USA","Canada",None],
                 "confidence":[0.9,0.25,.83,0.34,None],
                 "prediction":[True,True,True,True,None]})

感谢任何帮助。

Answer 1

我更新了我的答案以匹配您提供的新信息。希望这会有所帮助。

import pandas as pd

df=pd.DataFrame({"id": [1,2,3,4,5],
               "Name": ["Dave","Max","Joe","Rose","Mark"],
               "model1":["Irish","German","USA","Japan","China"],
                "confidence1": [0.9,.99,.83,.45,.51],
                "prediction1": [True,False,True,False,False],
                 "model2":["Oman","Nigeria","India","Russia","Brazil"],
                 "confidence2": [0.1,.25,.26,.41,.01],
                 "prediction2": [False,True,False,False,False],
                 "model3":["Egypt","Cameron","Netherland","Canada","Mexcio"],
                 "confidence3": [0.01,.23,.12,.34,.61],
                 "prediction3": [True,False,True,True,False]})

tweet_id = []
name = []
Model = []
Breed = []
Confidence = []

for i in range(len(df['id'])):
    confidences = [df['confidence{0}'.format(model)][i] for model in range(1,4)]
    models = ['p{0}'.format(model) for model in range(1,4)]
    breeds = [df['model{0}'.format(model)][i] for model in range(1,4)]
    isDog = [df['prediction{0}'.format(model)][i] for model in range(1,4)]

    best_one = max(zip(confidences, models, breeds, isDog), key=lambda M: M[0])

    model = best_one[1]
    breed = best_one[2]
    confidence = best_one[0]

    if not (True in isDog):
        model = breed = confidence = 'NaN'

    tweet_id.append(df['id'][i])
    name.append(df['Name'][i])
    Model.append(model)
    Breed.append(breed)
    Confidence.append(confidence)

print(pd.DataFrame({
                'tweet_id': tweet_id,
                'name': name,
                'Model': Model,
                'Breed': Breed,
                'Confidence': Confidence
                }))

输出

   tweet_id  name Model   Breed Confidence
0         1  Dave    p1   Irish        0.9
1         2   Max    p1  German       0.99
2         3   Joe    p1     USA       0.83
3         4  Rose    p1   Japan       0.45
4         5  Mark   NaN     NaN        NaN