python/pandas中的脚本可以工作，但如果放在函数的一侧，则不工作

Question

我运行这个脚本是为了创建一个数据框来总结一些统计数据：

month = [may,june,july,august,sept]
month_str = [5,6,7,8,9]
avg_age = []
avg_use = []
avg_kwh = []
avg_coll = []
avg_cred = []
for i in month:
    avg_age.append(i[i['Age']!=0]['Age'].mean())
    avg_use.append(i[i['AverageBilledUsage']!=0]['AverageBilledUsage'].mean())
    avg_kwh.append(i[i['AverageKWH']!=0]['AverageKWH'].mean())
    avg_coll.append(i[i['Total Collected']!=0]['Total Collected'].mean())
    avg_cred.append(i[(i['credit_score']!=0) & (i['credit_score']!=99999)]['credit_score'].mean())
pd.DataFrame(data = [avg_age,avg_use,avg_kwh,avg_coll,avg_cred],columns = month_str,index = ['Age','Usage','kwh','collected','creditscore'])

它返回的正是我想看到的。但当我把它放在一个函数中时，我会得到以下错误：

AssertionError: 5 columns passed, passed data had 1 columns

以下是函数内部的代码：

def get_nums():
    months = [may,june,july,august,sept]
    month_str = [5,6,7,8,9]
    avg_age = []
    avg_use = []
    avg_kwh = []
    avg_coll = []
    avg_cred = []
    for i in months:
        avg_age.append(i[i['Age']!=0]['Age'].mean())
        avg_use.append(i[i['AverageBilledUsage']!=0]['AverageBilledUsage'].mean())
        avg_kwh.append(i[i['AverageKWH']!=0]['AverageKWH'].mean())
        avg_coll.append(i[i['Total Collected']!=0]['Total Collected'].mean())
        avg_cred.append(i[(i['credit_score']!=0) & (i['credit_score']!=99999)]['credit_score'].mean())
        this_df = pd.DataFrame(data = [avg_age,avg_use,avg_kwh,avg_coll,avg_cred],columns = month_str,index = ['Age','Usage','kwh','collected','creditscore'])
    return this_df

Answer 1

函数中for循环的最后一行有问题。在循环的每次迭代中都会定义这个函数

更正后的代码如下

def get_nums():
    months = [may,june,july,august,sept]
    month_str = [5,6,7,8,9]
    avg_age = []
    avg_use = []
    avg_kwh = []
    avg_coll = []
    avg_cred = []
    for i in months:
        avg_age.append(i[i['Age']!=0]['Age'].mean())
        avg_use.append(i[i['AverageBilledUsage']!=0]['AverageBilledUsage'].mean())
        avg_kwh.append(i[i['AverageKWH']!=0]['AverageKWH'].mean())
        avg_coll.append(i[i['Total Collected']!=0]['Total Collected'].mean())
        avg_cred.append(i[(i['credit_score']!=0) & (i['credit_score']!=99999)]['credit_score'].mean())
    this_df = pd.DataFrame(data = [avg_age,avg_use,avg_kwh,avg_coll,avg_cred],columns = month_str,index = ['Age','Usage','kwh','collected','creditscore'])
    return this_df

Answer 2

根据我的理解，这里不需要for循环

month = [may,june,july,august,sept]
month_str = [5,6,7,8,9]
df=pd.concat(month,keys=month_str)

df=df.mask(df==0|df==99999)

df.groupby(level=0).mean().T