Question

我有一个3列的数据框，看起来像

                   Addresses  Floor      Time
0   fd00::212:4b00:1957:ce5f  B2WL3  08:00:00
1   fd00::212:4b00:1957:ce4f  B2WL2  08:00:00
2   fd00::212:4b00:1957:d184  B2WL4  08:00:00
3   fd00::212:4b00:1957:d1ac  B2WL1  08:00:00
4   fd00::212:4b00:1957:ce21  B2EL4  08:00:00
5   fd00::212:4b00:1957:d186  B2WKK  08:00:00
6   fd00::212:4b00:1957:ce65  B2EL2  08:00:00
7   fd00::212:4b00:1957:e7d7   D3L2  07:30:00
8   fd00::212:4b00:1957:f02d   D3L0  07:30:00
9   fd00::212:4b00:1957:f01f   D2L1  07:30:00

我想过滤时间为8的行。当我做df.dtypes时，所有列都是对象类型。我尝试过

df[df['Time']=='08:00:00']

但是这将返回一个空的数据帧。我不确定为什么会这样吗？有人可以帮我吗？

Answer 1

时间列中有空或nan值。

使用df[df['Time']=='08:00:00']进行过滤应该可以。

我将示例数据集中的值之一替换为一个空字符串，现在dtypes的值是对象，并且过滤给出了一个空的数据框

>>> df
                  Addresses  Floor                 Time
0  fd00::212:4b00:1957:ce4f  B2WL2  2019-12-13 08:00:00
1  fd00::212:4b00:1957:d184  B2WL4  2019-12-13 08:00:00
2  fd00::212:4b00:1957:d1ac  B2WL1  2019-12-13 08:00:00
3  fd00::212:4b00:1957:ce21  B2EL4  2019-12-13 08:00:00
4  fd00::212:4b00:1957:d186  B2WKK
5  fd00::212:4b00:1957:ce65  B2EL2  2019-12-13 08:00:00
6  fd00::212:4b00:1957:e7d7   D3L2  2019-12-13 07:30:00
7  fd00::212:4b00:1957:f02d   D3L0  2019-12-13 07:30:00
8  fd00::212:4b00:1957:f01f   D2L1  2019-12-13 07:30:00

>>> df.dtypes
Addresses    object
Floor        object
Time         object
dtype: object
>>> df[df.Time == '08:00:00']
Empty DataFrame
Columns: [Addresses, Floor, Time]
Index: []

所以我建议删除空单元格，然后重试

>>> df.Time = pd.to_datetime(df.Time.replace('', np.nan))
>>> df.dtypes
Addresses            object
Floor                object
Time         datetime64[ns]
dtype: object
>>> df[df.Time == '08:00:00']
                  Addresses  Floor                Time
0  fd00::212:4b00:1957:ce4f  B2WL2 2019-12-13 08:00:00
1  fd00::212:4b00:1957:d184  B2WL4 2019-12-13 08:00:00
2  fd00::212:4b00:1957:d1ac  B2WL1 2019-12-13 08:00:00
3  fd00::212:4b00:1957:ce21  B2EL4 2019-12-13 08:00:00
5  fd00::212:4b00:1957:ce65  B2EL2 2019-12-13 08:00:00

无法检测熊猫数据框中的日期列

1 个答案: