我在
python中有数据列表,如下表所示.
基本上,它是通过观察我们的机器人在我们的迷宫/竞技场中所做的事情而产生的.我们有事件的时间戳,目前时间戳是事件驱动的而不是周期性的.
我需要以有效的方式找到在每个舞台上度过的时间.
TimeStamp Arena
101 Arena A
109 Arena A
112 Arena B
113 Arena A
118 Arena A
120 Arena D
125 Arena D
129 Arena D
138 Arena B
139 Arena B
148 Arena C
149 Arena C
150 Arena B
151 Arena B
159 Arena D
169 Arena D
171 Arena D
172 Arena D
175 Arena B
177 Arena B
180 Arena B
181 Arena A
182 Arena A
189 Arena E
200 Arena E
204 Arena E
208 Arena A
209 Arena A
基本上,我需要在下面得到这个.在每个舞台上花费的总时间.
Arena TimeStamp
Arena D 32
Arena B 23
Arena E 22
Arena A 16
Arena C 10
我写了一个简单的脚本,现在就这样做了.
import pandas as pd
data = pd.read_csv('arenas_visited.csv')
l = len(data[[1]])
first_arena = data.loc[0, 'Arena']
start_time = data.loc[0, 'TimeStamp']
summary = []
for i in range(0,l):
try:
next_arena = data.loc[i+1, 'Arena']
except:
break
first_arena = data.loc[i, 'Arena']
if first_arena != next_arena:
change_time = data.loc[i, 'TimeStamp']
time_spent = change_time - start_time
arena = str(data.loc[i, 'Arena'])
summary.append([arena, time_spent])
start_time = change_time
first_arena = data.loc[i+1, 'Arena']
if i == l-2:
if data.loc[i, 'Arena'] != data.loc[i+1, 'Arena']:
time_spent = 1
arena = str(data.loc[i+1, 'Arena'])
print (str(1) + " Spent in " + arena)
summary.append([arena, time_spent])
else:
pass
aggregated = pd.DataFrame(summary, columns = ['Arena', 'TimeStamp'])
time_per_arena = aggregated.groupby(['Arena']).sum().sort_values('TimeStamp', ascending=False).reset_index()
print time_per_arena
基本上,这虽然工作得很好.但是,我最终将拥有数百万行这些数据,我需要找到一种更快的方法来实现这一目标.
但是,除了遍历每一行之外,我没有看到任何其他方式做到这一点?
是我不考虑的事情?
最佳答案 创建时间增量的向量,然后对其进行分组和求和:
df['delta'] = df.TimeStamp - df.TimeStamp.shift()
df.groupby('Arena').delta.sum()
Out[62]:
Arena
Arena_A 21.0
Arena_B 23.0
Arena_C 10.0
Arena_D 32.0
Arena_E 22.0
Name: delta, dtype: float64