pandas Python 将时间戳与输入时间进行比较
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Python compare timestamp to input time
提问by yusica
I have some dataframe with timestamps as a column, I want to filter rows between 8:00:00 to 17:00:00 with np.where. I keep getting error messages on data/object types. Any help would be appreciated
我有一些时间戳作为列的数据框，我想用 np.where 过滤 8:00:00 到 17:00:00 之间的行。我不断收到有关数据/对象类型的错误消息。任何帮助，将不胜感激
example:
例子：
timestamp volume
2013-03-01 07:59:00 5
2013-03-01 08:00:00 6
2013-03-01 08:01:00 7
2013-03-01 08:02:00 8
Basically I want to end with:
基本上我想以：
2013-03-01 08:00:00 6
2013-03-01 08:01:00 7
2013-03-01 08:02:00 8
By using methods along the line of
通过使用沿线的方法
np.where(df['timestamp'] > dt.time('8:00:00')
采纳答案by MaxU
Try this:
尝试这个：
In [226]: df
Out[226]:
timestamp volume
0 2013-03-01 07:59:00 5
1 2013-03-01 08:00:00 6
2 2013-03-01 08:01:00 7
3 2013-03-01 08:02:00 8
In [227]: df.dtypes
Out[227]:
timestamp object
volume int64
dtype: object
In [228]: df['timestamp'] = pd.to_datetime(df['timestamp'], errors='coerce')
In [229]: df.dtypes
Out[229]:
timestamp datetime64[ns] # <---- it's `datetime64[ns]` now
volume int64
dtype: object
In [230]: df.set_index('timestamp').between_time('08:00','17:00').reset_index()
Out[230]:
timestamp volume
0 2013-03-01 08:00:00 6
1 2013-03-01 08:01:00 7
2 2013-03-01 08:02:00 8
回答by saloua
You can use between
您可以使用 between
I Generated a sample dataframe with
我生成了一个示例数据帧
import datetime
d = {'timestamp': pd.Series([datetime.datetime.now() +
datetime.timedelta(hours=i) for i in range(20)]),
'volume': pd.Series([s for s in range(20)])}
df = pd.DataFrame(d)
df['timeframe']is
df['timeframe']是
0 2017-02-13 22:37:54.515840
1 2017-02-13 23:37:54.515859
2 2017-02-14 00:37:54.515865
3 2017-02-14 01:37:54.515870
4 2017-02-14 02:37:54.515878
5 2017-02-14 03:37:54.515884
6 2017-02-14 04:37:54.515888
...
17 2017-02-14 15:37:54.515939
18 2017-02-14 16:37:54.515943
19 2017-02-14 17:37:54.515948
df.dtypes
df.dtypes
timestamp datetime64[ns]
volume int64
dtype: object
As in your example dtypeof df['timestamp']is objectyou can do
作为你的例子dtype的df['timestamp']是object，你可以做
df['timestamp'] = pd.to_datetime(df['timestamp'], coerce=True)
By setting param coerce=Trueif the conversion fails for any particular string then those rows are set to NaT.
coerce=True如果任何特定字符串的转换失败，则通过设置 param将这些行设置为NaT.
Then filtering can be done using betweenas below
然后过滤可以使用between如下
df[df.timestamp.dt.strftime('%H:%M:%S').between('11:00:00','18:00:00')]will return
df[df.timestamp.dt.strftime('%H:%M:%S').between('11:00:00','18:00:00')]将返回
13 2017-02-14 11:37:54.515922 13
14 2017-02-14 12:37:54.515926 14
15 2017-02-14 13:37:54.515930 15
16 2017-02-14 14:37:54.515935 16
17 2017-02-14 15:37:54.515939 17
18 2017-02-14 16:37:54.515943 18
19 2017-02-14 17:37:54.515948 19
回答by sameer_nubia
if you have a file with data as below : timestamp volume 2013-03-01 07:59:00 5 2013-03-01 08:00:00 6 2013-03-01 08:01:00 7 2013-03-01 08:02:00 8
如果您有一个包含以下数据的文件：时间戳卷 2013-03-01 07:59:00 5 2013-03-01 08:00:00 6 2013-03-01 08:01:00 7 2013-03-01 08:02:00 8
Then while reading only you can skip the first line and you will get output as timestamp volume 2013-03-01 08:00:00 6 2013-03-01 08:01:00 7 2013-03-01 08:02:00 8
然后，在只读时，您可以跳过第一行，您将获得时间戳卷的输出 2013-03-01 08:00:00 6 2013-03-01 08:01:00 7 2013-03-01 08:02:00 8
import pandas as pd
df=pd.read_csv("filename",skiprows=1)
print(df)
