问题描述
我已将 SQL 查询读入 Pandas,并且这些值以 dtype 'object' 的形式传入,尽管它们是字符串、日期和整数.我能够将日期对象"转换为 Pandas 日期时间数据类型,但在尝试转换字符串和整数时出现错误.
这是一个例子:
>>>将熊猫导入为 pd>>>df = pd.read_sql_query('select * from my_table', conn)>>>df身份证日期购买1 abc1 2016-05-22 12 abc2 2016-05-29 03 abc3 2016-05-22 24 abc4 2016-05-22 0>>>df.dtypes标识对象日期对象购买对象数据类型:对象将 df['date']
转换为日期时间有效:
但是在尝试将 df['purchase']
转换为整数时出现错误:
注意:当我尝试 .astype('float')
当尝试转换为字符串时,似乎什么也没有发生.
>>>df['id'].apply(str)1 abc12 abc23 abc34 abc4名称:id,数据类型:对象根据 @piRSquared 的评论记录对我有用的答案.
我需要先转换为字符串,然后是整数.
>>>df['购买'].astype(str).astype(int)I've read an SQL query into Pandas and the values are coming in as dtype 'object', although they are strings, dates and integers. I am able to convert the date 'object' to a Pandas datetime dtype, but I'm getting an error when trying to convert the string and integers.
Here is an example:
>>> import pandas as pd
>>> df = pd.read_sql_query('select * from my_table', conn)
>>> df
id date purchase
1 abc1 2016-05-22 1
2 abc2 2016-05-29 0
3 abc3 2016-05-22 2
4 abc4 2016-05-22 0
>>> df.dtypes
id object
date object
purchase object
dtype: object
Converting the df['date']
to a datetime works:
>>> pd.to_datetime(df['date'])
1 2016-05-22
2 2016-05-29
3 2016-05-22
4 2016-05-22
Name: date, dtype: datetime64[ns]
But I get an error when trying to convert the df['purchase']
to an integer:
>>> df['purchase'].astype(int)
....
pandas/lib.pyx in pandas.lib.astype_intsafe (pandas/lib.c:16667)()
pandas/src/util.pxd in util.set_value_at (pandas/lib.c:67540)()
TypeError: long() argument must be a string or a number, not 'java.lang.Long'
NOTE: I get a similar error when I tried .astype('float')
And when trying to convert to a string, nothing seems to happen.
>>> df['id'].apply(str)
1 abc1
2 abc2
3 abc3
4 abc4
Name: id, dtype: object
Documenting the answer that worked for me based on the comment by @piRSquared.
I needed to convert to a string first, then an integer.
>>> df['purchase'].astype(str).astype(int)
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