Python 如何在数据帧上循环并创建列表

因此，我有下面的数据，我想循环数据帧并执行一些函数，最后将函数的结果保存在一个列表中。我无法创建列表。我只得到列表中的一个值，而不是我想要得到的两个值。任何人如果有更有效的方法来解决这个问题，请分享

     dict = {'PassengerId' : [0.0, 0.001, 0.002, 0.003, 0.004, 0.006, 0.007, 0.008, 0.009, 0.01], 
'Survived' : [0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0], 
'Pclass' : [1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.5],
'Age' : [0.271, 0.472, 0.321, 0.435, 0.435, np.nan, 0.673, 0.02, 0.334, 0.171], 
'SibSp' : [0.125, 0.125, 0.0, 0.125, 0.0, 0.0, 0.0, 0.375, 0.0, 0.125], 
'Parch' : [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.167, 0.333, 0.0], 
'Fare' : [0.014, 0.139, 0.015, 0.104, 0.016, 0.017, 0.101, 0.041, 0.022, 0.059]}


        
import pandas as pd
dicts = pd.DataFrame(dicts, columns = dicts.keys())
def Mean(self):
    list_mean = []
    list_all = []
    for i, row in dicts.iterrows():
        if (row['Age'] > 0.2) & (row['Fare'] < 0.1):
            list_all.append(row['PassengerId'])
        elif (row['Age'] > 0.2) & (row['Fare'] > 0.1):
            list_all.clear()
            list_all.append(row['PassengerId'])
    return list_mean.append(np.mean(list_all))
            
               
Mean()

dict={'PassengerId'：[0.0,0.001,0.002,0.003,0.004,0.006,0.007,0.008,0.009,0.01]，
“生存下来的”：[0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0]，
“Pclass”：[1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.5]，
‘年龄’：[0.271,0.472,0.321,0.435,0.435,np.nan,0.673,0.02,0.334,0.171]，
“SibSp”：[0.125,0.125,0.0,0.125,0.0,0.0,0.0,0.375,0.0,0.125]，
“Parch”：[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.167,0.333,0.0]，
‘票价’：[0.014,0.139,0.015,0.104,0.016,0.017,0.101,0.041,0.022,0.059]}
作为pd进口熊猫
dicts=pd.DataFrame（dicts，columns=dicts.keys（））
def平均值（自我）：
列表_平均值=[]
列出所有=[]
对于我，在dicts.iterrows（）中的行：
如果（第[‘年龄’]>0.2行）和（第[‘票价’]<0.1行）：
list_all.append（第['PassengerId'行]）
elif（第[‘年龄’]>0.2行）和（第[‘票价’]>0.1行）：
列出所有。清除（）
list_all.append（第['PassengerId'行]）
返回列表\平均值追加（np.平均值（列表\全部））
平均数（）

---

请帮帮我

要解决此问题，您必须在解决方案中进行一些更改。对于矢量化答案，请查看我的代码部分。

1. 返回语句

返回列表\u意思是

应该放在

功能块中

而不是

if块中

更改：

. . .         
if (row['Age'] > self.age) & (row['Fare'] < self.fare):
                list_mean.append(row['PassengerId'])
                return list_mean            
. . .

. . .
list_mean = []
for i, row in dicts.iterrows():
    if (row['Age'] > self.age) & (row['Fare'] < self.fare):
         list_mean.append(row['PassengerId'])
return list_mean
. . .

(0.00375, 0.0036666666666666666)

将熊猫作为pd导入
将numpy作为np导入
dict={'PassengerId'：[0.0,0.001,0.002,0.003,0.004,0.006,0.007,0.008,0.009,0.01]，
“生存下来的”：[0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0]，
“Pclass”：[1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.5]，
‘年龄’：[0.271,0.472,0.321,0.435,0.435,np.nan,0.673,0.02,0.334,0.171]，
“SibSp”：[0.125,0.125,0.0,0.125,0.0,0.0,0.0,0.375,0.0,0.125]，
“Parch”：[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.167,0.333,0.0]，
‘票价’：[0.014,0.139,0.015,0.104,0.016,0.017,0.101,0.041,0.022,0.059]}
df=pd.DataFrame（dict，columns=dict.keys（））
def calculate_mean（）：
l1，l2=[]，[]
对于i，df.iterrows（）中的行：
如果第['Age']>0.2行和第['Fare']<0.1行：
l1.追加（第['PassengerId'行]）
elif第['Age']>0.2行和第['Fare']>0.1行：
l2.追加（第['PassengerId'行]）
返回np.平均值（l1），np.平均值（l2）
打印（计算平均值（））#（0.00375，0.0036666）

如果我正确理解了这个问题，那么您只会得到列表中的一项，这是因为只要满足数据帧中第一个值的If条件，您就会返回。我相信您应该返回最终值，即在for循环完成时返回。@SomuSinhhaa谢谢您的回答，但是我能够解决问题，我现在有一个新的挑战，您能帮我检查一下吗？我已经修改了代码。抱歉，我仍然看到您的旧代码，您正在尝试在if块中返回。您应该按照其中一个答案中的说明返回，即仅在您将所有列表元素存储在list_mean中后，即在完成for循环后返回。此外，如果您有不同的问题，我建议您打开一个新的线程。@SomuSinhhaa我已经编辑了它，您现在可以检查它。请您详细说明这一行。不太清楚“我只在列表中得到一个值，而不是我想要得到的两个值”我猜，但不确定您是否希望附加到列表中，以防您的任何一个条件匹配，那么在这种情况下，您必须使用逻辑或组合这两个条件，而不是elifthank you非常@Somusinhai编辑问题，你能帮我找到最有效的解决方法吗？@deeplearningEngineer请检查解决方案并让我知道任何问题。如果回答是肯定的，看起来还可以。然而，我希望是否有一种方法可以使用循环来实现它？该代码是更大代码的一部分，循环将更有效，而不是在explorore X中使用大块代码

(0.00375, 0.0036666666666666666)

import pandas as pd
import numpy as np

dict = {'PassengerId' : [0.0, 0.001, 0.002, 0.003, 0.004, 0.006, 0.007, 0.008, 0.009, 0.01],
'Survived' : [0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0],
'Pclass' : [1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.5],
'Age' : [0.271, 0.472, 0.321, 0.435, 0.435, np.nan, 0.673, 0.02, 0.334, 0.171],
'SibSp' : [0.125, 0.125, 0.0, 0.125, 0.0, 0.0, 0.0, 0.375, 0.0, 0.125],
'Parch' : [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.167, 0.333, 0.0],
'Fare' : [0.014, 0.139, 0.015, 0.104, 0.016, 0.017, 0.101, 0.041, 0.022, 0.059]}

df = pd.DataFrame(dict, columns = dict.keys())

def calculate_mean():
    l1, l2 = [], []
    for i, row in df.iterrows():
        if row['Age'] > 0.2 and row['Fare'] < 0.1:
            l1.append(row['PassengerId'])
        elif row['Age'] > 0.2 and row['Fare'] > 0.1:
            l2.append(row['PassengerId'])
    return np.mean(l1), np.mean(l2)


print(calculate_mean()) # (0.00375, 0.0036666666666666666)