将日期时间字段格式化为python pandas中的MON

将日期时间字段格式化为python pandas中的MON-YYYY并忽略空值(Format datetime field as MON-YYYY in python pandas & ignore nulls)

我有一个python pandas数据帧“df”如下 -

NAME SETID VENDOR_ID vendor_created_date 0 Vendor1 SD 93 2002-11-22 11:04:33 1 Vendor2 SD 94 2003-08-09 11:40:59 2 Service1 SD 95 2003-10-31 10:29:21 3 Vendor3 SD 01 NaT 4 Vendor4 SD 02 NaT

vendor_created_date的格式为datetime64[ns] 。

现在我想创建一个名为fomatted_date的新字段，其中vendor_created_date字段值应采用MON-YYYY格式，我想删除日期字段中带有NaT的行。

你能告诉我指示吗？

I have a python pandas dataframe "df" as below -

NAME SETID VENDOR_ID vendor_created_date 0 Vendor1 SD 93 2002-11-22 11:04:33 1 Vendor2 SD 94 2003-08-09 11:40:59 2 Service1 SD 95 2003-10-31 10:29:21 3 Vendor3 SD 01 NaT 4 Vendor4 SD 02 NaT

The vendor_created_date is of the format datetime64[ns].

Now I want to create a new field called fomatted_date where the vendor_created_date field values should be in the format MON-YYYY & I want to delete the rows with NaT in the date field.

Can you please give me directions?

最满意答案

这是你想要的格式吗？

基本上我们可以先删除NaN行，然后调用apply并使用datetime.strftime来应用新格式：

In [24]: df = df.dropna() df['fomatted_date'] = df['vendor_created_date'].apply(lambda x: dt.datetime.strftime(x,'%b-%Y')) df Out[24]: NAME SETID VENDOR_ID vendor_created_date fomatted_date Index 0 Vendor1 SD 93 2002-11-22 11:04:33 Nov-2002 1 Vendor2 SD 94 2003-08-09 11:40:59 Aug-2003 2 Service1 SD 95 2003-10-31 10:29:21 Oct-2003

is this the format you wanted?

Basically we can drop the NaN rows first and then call apply and use datetime.strftime to apply a new format:

In [24]: df = df.dropna() df['fomatted_date'] = df['vendor_created_date'].apply(lambda x: dt.datetime.strftime(x,'%b-%Y')) df Out[24]: NAME SETID VENDOR_ID vendor_created_date fomatted_date Index 0 Vendor1 SD 93 2002-11-22 11:04:33 Nov-2002 1 Vendor2 SD 94 2003-08-09 11:40:59 Aug-2003 2 Service1 SD 95 2003-10-31 10:29:21 Oct-2003