pandas 熊猫删除所有不是“日期时间”类型的行
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Pandas delete all rows that are not a 'datetime' type
提问by Lukasz
I've got a large file with login information for a list of users. The problem is that the file includes other information in the Datecolumn. I would like to remove all rows that are not of type datetimein the Datecolumn. My data resembles
我有一个包含用户列表登录信息的大文件。问题是该文件在Date列中包含其他信息。我想删除该类型不是所有行datetime的Date列。我的数据类似于
df=
Name Date
name_1 | 2012-07-12 22:20:00
name_1 | 2012-07-16 22:19:00
name_1 | 2013-12-16 17:50:00
name_1 | 4345 # type = 'int'
# type = 'float'
name_2 | 2010-01-11 19:54:00
name_2 | 2010-02-06 12:10:00
...
name_2 | 2012-07-18 22:12:00
name_2 | 4521
...
name_5423 | 2013-11-23 10:21:00
...
name_5423 | 7532
I've tried modifying the solution to
我已经尝试将解决方案修改为
finding non-numeric rows in dataframe in pandas?
Remove rows where column value type is string Pandas
and How-should-I-delete-rows-from-a-DataFrame-in-Python-Pandas
以及我应该如何从 Python-Pandas 中的 DataFrame 中删除行
to fit my needs.
以满足我的需要。
The problem is that whenever I attempt the change I either get an error or the entire dataframe gets deleted
问题是，每当我尝试更改时，我都会收到错误或整个数据框被删除
回答by piRSquared
Use pd.to_datetimewith parameter errors='coerce'to make non-dates into NaTnull values. Then you can drop those rows
使用pd.to_datetimewith 参数errors='coerce'使非日期变为NaT空值。然后你可以删除这些行
df['Date'] = pd.to_datetime(df['Date'], errors='coerce')
df = df.dropna(subset=['Date'])
df
