Cannot convert numpy dtypes to its native python types (int64 to int)

Question

Please check the below code, I want to convert the dtype int64 to its native python type 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'))))

I was trying to run my code in amazon lambda service and I got the following error

'TypeError: Object of type 'int64' is not JSON serializable'. that why I need to convert it into its native python type

Output

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

Expected answer

No. of transactions      int

Answer 1

So it seems that Amazon s3 is a bit sensitive to dtypes so in order for it to be compatible you can first cast to int and then to object so it's compatible:

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

If you look at the type of the elements it will output object indicating that it's a generic python object:

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

will output object

Answer 2

For converting numpy dtypes to native Python dtypes you have two options:

Option 1:

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

Option 2:

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