我试图扩展我当前的模式,以适应额外的条件+-最后一个值的百分比,而不是严格的它是否匹配以前的值。你知道吗
data = np.array([[2,30],[2,900],[2,30],[2,30],[2,30],[2,1560],[2,30],
[2,300],[2,30],[2,450]])
df = pd.DataFrame(data)
df.columns = ['id','interval']
更新2(id修复):使用更多数据更新数据2:
data2 = np.array([[2,30],[2,900],[2,30],[2,29],[2,31],[2,30],[2,29],[2,31],[2,1560],[2,30],[2,300],[2,30],[2,450], [3,40],[3,900],[3,40],[3,39],[3,41], [3,40],[3,39],[3,41] ,[3,1560],[3,40],[3,300],[3,40],[3,450]])
df2 = pd.DataFrame(data2)
df2.columns = ['id','interval']
for i, g in df.groupby([(df.interval != df.interval.shift()).cumsum()]):
if len(g.interval.tolist())>=3:
print(g.interval.tolist())
导致[30,30,30]
然而,我真的想抓住近数条件,比如当一个数是前一个数的+-10%时。你知道吗
所以看一下df2,我想介绍一下这个系列[30,29,31]
for i, g in df2.groupby([(df2.interval != <???+- 10% magic ???>).cumsum()]):
if len(g.interval.tolist())>=3:
print(g.interval.tolist())
更新:这里是行尾处理代码,我将收集到的列表存储到一个以ID为键的字典中
leak_intervals = {}
final_leak_intervals = {}
serials = []
for i, g in df.groupby([(df.interval != df.interval.shift()).cumsum()]):
if len(g.interval.tolist()) >= 3:
print(g.interval.tolist())
serial = g.id.values[0]
if serial not in serials:
serials.append(serial)
if serial not in leak_intervals:
leak_intervals[serial] = g.interval.tolist()
else:
leak_intervals[serial] = leak_intervals[serial] + (g.interval.tolist())
更新:
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