[英]Script in python/pandas works but doesn't work when placed in side a function
我正在运行以下脚本来尝试创建一个数据框以汇总一些统计信息:
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'])
它返回的正是我想要看到的。 但是,当我将其放置在函数中时,出现以下错误:
AssertionError: 5 columns passed, passed data had 1 columns
这是函数内部的代码:
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
函数中for循环的最后一行存在问题。 在循环的每次迭代中都定义了this_df。
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
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
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