Question

请检查以下代码，我想将dtype int64转换为其原生python类型int。

dfCredit = pd.DataFrame(credits_List)
dfCredit['date'] = pd.to_datetime(dfCredit['date'], format='%d-%m-%Y')
sum_Credit_Bal = dfCredit.groupby(pd.Grouper(key='date', freq='1M')).sum()
avg_Credit_Bal = dfCredit.groupby(pd.Grouper(key='date', freq='1M')).mean()
avg_Credit_Bal['No. of transactions'] = sum_Credit_Bal['No. of transactions'].astype(int)
print("--------------")
print("\nAverage amount Credited per month :\n\n ", avg_Credit_Bal)
print("--------------")
print(avg_Credit_Bal.dtypes)


js =  [{"Average amount Credited per month": avg_Credit_Bal.to_dict()}]
s3object = s3.Object("bank-statement-demo","BankOutput.json")
s3object.put(Body=(bytes(json.dumps(js).encode('UTF-8'))))

我试图在Amazon Lambda服务中运行代码，但出现以下错误

“ TypeError：类型为'int64'的对象不可JSON序列化”。那就是为什么我需要将其转换为它的本机python类型

输出

Average amount Credited per month :

                Credit  No. of transactions
Month                                    
Jun-18   4644.500000                    4
Jul-18  11142.000000                    2
Aug-18  12148.750000                    4
Sep-18   2830.477143                    7
Oct-18   4664.250000                    4
Nov-18   8381.500000                    2
--------------
Credit                 float64
No. of transactions      int64
dtype: object

期望的答案

No. of transactions      int

Answer 1

因此，看来Amazon s3对dtypes有点敏感，因此为了使其兼容，您可以先转换为int，然后再转换为object，以便兼容：

avg_Credit_Bal['No. of transactions'] = sum_Credit_Bal['No. of transactions'].astype(int).astype(object)

如果您查看元素的类型，它将输出object，表明它是一个通用的python对象：

type(avg_Credit_Bal['No. of transactions'][0])

将输出object

Answer 2

要将numpy dtypes转换为本地Python dtypes，您有两个选择：

选项1：

sum_Credit_Bal['No. of transactions'].item()

选项2：

np.asscalar(sum_Credit_Bal['No. of transactions'])

无法将numpy dtypes转换为其本机python类型（将int64转换为int）

2 个答案: