pandas 使用pandas.io.sql.read_frame，我可以像read_csv那样解析日期吗？
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Using pandas.io.sql.read_frame, can I parse_dates, as in read_csv?
提问by random.me
I am reading a data_framedirectly from a database using pandas.io.sql.read_frame:
我正在data_frame使用pandas.io.sql.read_frame以下命令直接从数据库中读取：
cnx = pandas.io.sql.connect(host='srv',user='me',password='pw',database='db')
df = pandas.io.sql.read_frame('sql_query',cnx)
It works nicely in retrieving the data. But I would like to parse one of the columns as a datetime64, akin to what can be done when reading from a CSV file, e.g.:
它在检索数据方面效果很好。但我想将其中一列解析为datetime64，类似于从 CSV 文件读取时可以执行的操作，例如：
df2 = pandas.io.read_csv(csv_file, parse_dates=[0])
But there is no parse_datesflag for read_frame. What alternative approach is recommended?
但是没有parse_dates标志read_frame。推荐什么替代方法？
The same question applies to the index_colin read_csv, which indicates which col. should be the index. Is there a recommended way to do this with read_frame?
同样的问题适用于index_colin read_csv，它指示哪个列。应该是索引。是否有推荐的方法来使用 read_frame 执行此操作？
回答by firelynx
This question is very old by now. pandas 0.10 is very old as well. In the newest version of pandas 0.16, the read_frame method has been depricated in favour of the read_sql. Even so, the documentation says that just like the read_csv function, it takes a parse_dates argument Pandas 0.16 read_frame
这个问题现在已经很老了。pandas 0.10 也很老了。在最新版本的 Pandas 0.16 中，read_frame 方法已被弃用，取而代之的是 read_sql。即便如此，文档说就像 read_csv 函数一样，它需要一个 parse_dates 参数Pandas 0.16 read_frame
It seems the parse_dates argument appeared in 0.14, at the same time as read_frame was depricated. The read_sql function seems to be a rename of the read_frame, so just updating your pandas version to 0.14 or higher and renaming your function will give you access to this argument. Here is the doc for the read_sql function: Pandas 0.16 read_sql
似乎 parse_dates 参数出现在 0.14 中，同时 read_frame 被弃用。read_sql 函数似乎是 read_frame 的重命名，因此只需将您的 Pandas 版本更新到 0.14 或更高版本并重命名您的函数即可访问此参数。这是 read_sql 函数的文档：Pandas 0.16 read_sql
回答by Kracit
回答by oDDsKooL
df = pandas.io.sql.read_frame('sql_query', index=['date_column_name'], con=cnx)
where date_column_nameis the name of the column in the database that contains date elements. sql_queryshould then be of the form select date_column_name, data_column_name from ...
其中date_column_name是数据库中包含日期元素的列的名称。sql_query那么应该是这样的形式select date_column_name, data_column_name from ...
Pandas (as of 0.13+) will then automatically parse it to a date format if it resembles a date string.
如果它类似于日期字符串，Pandas（从 0.13+ 开始）将自动将其解析为日期格式。
In [34]: df.index
Out[34]:
<class 'pandas.tseries.index.DatetimeIndex'>
