Time Var1 EvntType Var2 0 15 1 2 17 1 19 1 1 45 2 21 6 2 43 3 23 3 2 65 4 25 0 2 76 #this one should be skipped 5 26 2 2 35 6 28 3 2 25 7 31 5 1 16 8 33 1 2 25 9 36 5 1 36 10 39 1 2 21

Question

[我要忽略Var1等于0的行，然后计算类型1的事件之间的类型1的事件与类型2的事件之间的时间（Var1 == 0除外）的时间，因此在上述情况下：

Start_time: 19, Time_inbetween: 12, Event_count: 4
Start_time: 31, Time_inbetween: 5, Event_count: 1

我通过以下方式进行此操作：

i=0
eventCounter = 0
lastStartTime = 0
length = data[data['EvntType']==1].shape[0]
results = np.zeros((length,3),dtype=int)
for row in data[data['Var1'] > 0].iterrows():
    myRow = row[1]
    if myRow['EvntType'] == 1:
        results[i,0] = lastStartTime
        results[i,1] = myRow['Time'] - lastStartTime
        results[i,2] = eventCounter
        lastStartTime = myRow['Time']
        eventCounter = 0
        i += 1
    else:
        eventCounter += 1
这给了我想要的结果：

>>> results[1:]
array([[19, 12,  4],
       [31,  5,  1]])
但是这似乎确实可以绕开，并且在大型数据帧上花费很长时间。我该如何改善？

我在python 2.7中有一个pandas数据框，我想遍历行并获取两种类型的事件之间的时间以及之间的其他类型事件的计数（给定的条件...

Answer 1

mask = df['EvntType']==1
# 0     False
# 1      True
# ...
# 9      True
# 10    False
# Name: EvntType, dtype: bool

找到与Time为True的行关联的mask：

times = df.loc[mask, 'Time']
# 1    19
# 7    31
# 9    36
# Name: Time, dtype: int64

并且还找到mask为True的顺序索引：

idx = np.flatnonzero(mask)
# array([1, 6, 8])

start_time是times[:-1]中的所有值。

In [56]: times[:-1]
Out[56]: 
1    19
7    31
Name: Time, dtype: int64

time_inbetween是时间差，np.diff(times)

In [55]: np.diff(times)
Out[55]: array([12,  5])

event_count是idx的差值减去1。

In [57]: np.diff(idx)-1
Out[57]: array([4, 1])

import numpy as np
import pandas as pd

df = pd.DataFrame({'EvntType': [2, 1, 2, 2, 2, 2, 2, 1, 2, 1, 2],
                   'Time': [15, 19, 21, 23, 25, 26, 28, 31, 33, 36, 39],
                   'Var1': [1, 1, 6, 3, 0, 2, 3, 5, 1, 5, 1],
                   'Var2': [17, 45, 43, 65, 76, 35, 25, 16, 25, 36, 21]})

# Remove rows where Var1 equals 0
df = df.loc[df['Var1'] != 0]

mask = df['EvntType']==1
times = df.loc[mask, 'Time']
idx = np.flatnonzero(mask)

result = pd.DataFrame(
    {'start_time': times[:-1],
     'time_inbetween': np.diff(times),
     'event_count': np.diff(idx)-1})

print(result)
产量

   event_count  start_time  time_inbetween
1            4          19              12
7            1          31               5