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[英]Python DataFrame - Select dataframe rows based on values in a column of same dataframe
[英]Select rows with the same order of values in python dataframe?
我正在使用包含3个值'event1','event2'和'event3'的列事件的数据框。 并且我正在寻找一种方法来仅选择具有特定顺序的事件的行['event1','event2','event3']。
我试过了:
df[df['Event'].isin(['event1', 'event2', 'event3'])]
但是结果是整个数据帧。
import pandas as pd
df = pd.DataFrame([['event1','01:22:52.134'],['event2','03:21:31.123'], ['event1','21:12:52.544'],['event3','23:12:31.216'],['event1','10:22:02.134'],['event2','06:52:48.184'], ['event3','12:52:46.188'], ['event3','06:52:46.184'], ['event1','13:33:46.235'], ['event2','14:35:12.235'], ['event3','14:59:12.177']], columns=["Events",'Time'])
df
您需要3个条件:
m = df.Events.eq('event1')
& df.Events.shift(-1).eq('event2')
& df.Events.shift(-2).eq('event3')
现在向前移动遮罩:
df[(m | m.shift() | m.shift(2))]
Events Time
4 event1 10:22:02.134
5 event2 06:52:48.184
6 event3 12:52:46.188
8 event1 13:33:46.235
9 event2 14:35:12.235
10 event3 14:59:12.177
对于任何数量的事件,您都可以使用np.logical_and.reduce
进行概括:
events = ['event1', 'event2', 'event3']
m = pd.Series(
np.logical_and.reduce([
df.Events.shift(-i).eq(e) for i, e in enumerate(events)
])
)
第二步是np.logical_or.reduce
。
df[np.logical_or.reduce([
m.shift(i).fillna(False) for i in range(len(events))
])
]
Events Time
4 event1 10:22:02.134
5 event2 06:52:48.184
6 event3 12:52:46.188
8 event1 13:33:46.235
9 event2 14:35:12.235
10 event3 14:59:12.177
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