看以前的时间序列 [英] Looking at Previous Time series
本文介绍了看以前的时间序列的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我有一个数据集，如下所示.想法是每隔15分钟查看一次，而不是查看我们在分组功能中使用的频率.我想查看过去15分钟内发生的积极变化.
I Have a dataset as shown below. The idea is looking at every previous 15minutes not the frequency which we use in grouper function. I want to see the number of positive changes in the previous 15 minutes.
row Timestamp Direction Positive Neg Nut
1 1/20/19 12:15
2 1/20/19 12:17 Nut
3 1/20/19 12:17 Neg
4 1/20/19 12:18 Neg
5 1/20/19 12:19 Pos
6 1/20/19 12:20 Neg
7 1/20/19 12:21 Neg
8 1/20/19 12:22 Pos
9 1/20/19 12:23 Neg
10 1/20/19 12:24 Pos
11 1/20/19 12:25 Neg
12 1/20/19 12:26 Neg
13 1/20/19 12:27 Neg
14 1/20/19 12:29 Neg
15 1/20/19 12:29 Nut
16 1/20/19 12:30 Pos 4(o2:o16) 9 2
17 1/20/19 12:31 Nut 4(o3:o17) 9 3
18 1/20/19 12:32 Pos 5(o4:o18) 9 2
所以我在Excel中执行= COUNTIF(Direction2:Direction16，"Pos")以计算正"列.我不确定如何以Python方式进行操作.当我尝试应用相同的公式时，我最终分组15分钟，这不是我想要的.对于每分钟，我都会检查Excel中的前15分钟.有人可以让我知道我需要遵循的方法.因此，目标是获得正"，负"和中性"列.给定的是时间戳记和方向"列
So i am doing =COUNTIF(Direction2:Direction16,"Pos") in excel to calculate the Positive column. I am not sure how to do in Pythonic way. When i tried to apply same formula i end up grouping 15minutes which is not what i wanted. For every minute i check previous 15minutes in excel. Could someone please let me know the approach i need to follow. So the goal is to get Positive, Negative and Neutral columns. Given is the Timestamp and Direction Column
错误:
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
/usr/local/lib/python3.6/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
3062 try:
-> 3063 return self._engine.get_loc(key)
3064 except KeyError:
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: 'timestamp'
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
<ipython-input-87-d00f59bea382> in <module>()
2 #df['timestamp'] = pd.to_datetime(df.timestamp)
3 #df = df.set_index('timestamp')
----> 4 df['timestamp'] = pd.to_datetime(df['timestamp'])
5 df = df.set_index('timestamp')
6
/usr/local/lib/python3.6/site-packages/pandas/core/frame.py in __getitem__(self, key)
2683 return self._getitem_multilevel(key)
2684 else:
-> 2685 return self._getitem_column(key)
2686
2687 def _getitem_column(self, key):
/usr/local/lib/python3.6/site-packages/pandas/core/frame.py in _getitem_column(self, key)
2690 # get column
2691 if self.columns.is_unique:
-> 2692 return self._get_item_cache(key)
2693
2694 # duplicate columns & possible reduce dimensionality
/usr/local/lib/python3.6/site-packages/pandas/core/generic.py in _get_item_cache(self, item)
2484 res = cache.get(item)
2485 if res is None:
-> 2486 values = self._data.get(item)
2487 res = self._box_item_values(item, values)
2488 cache[item] = res
/usr/local/lib/python3.6/site-packages/pandas/core/internals.py in get(self, item, fastpath)
4113
4114 if not isna(item):
-> 4115 loc = self.items.get_loc(item)
4116 else:
4117 indexer = np.arange(len(self.items))[isna(self.items)]
/usr/local/lib/python3.6/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
3063 return self._engine.get_loc(key)
3064 except KeyError:
-> 3065 return self._engine.get_loc(self._maybe_cast_indexer(key))
3066
3067 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: 'timestamp'
df.info()
RangeIndex: 31106 entries, 0 to 31105
Data columns (total 12 columns):
ID 31106 non-null int64
High 31106 non-null float64
Last 31106 non-null float64
Timestampvalue 31106 non-null int64
Bid 31106 non-null float64
VWap 31106 non-null float64
Volume 31106 non-null float64
Low 31106 non-null float64
Ask 31106 non-null float64
Openamt 31106 non-null float64
Type 31106 non-null object
timestamp 31106 non-null datetime64[ns]
dtypes: datetime64[ns](1), float64(8), int64(2), object(1)
memory usage: 2.8+ MB
推荐答案
您可以使用:
#create DatetimeIndex if necessary
#df = df.set_index('timestamp')
#get unique values with counts by comparing and sum of True
cols = df['Direction'].dropna().unique()
for c in cols:
df[c] = df['Direction'].eq(c).rolling('15min').sum()
#if necessary set first 14 minutes to NaNs
df.loc[:df.index[0] + pd.Timedelta(14 * 60, unit='s'), cols] = np.nan
print (df)
row Direction Positive Neg Nut Pos
timestamp
2019-01-20 12:15:00 1 NaN NaN NaN NaN NaN
2019-01-20 12:17:00 2 Nut NaN NaN NaN NaN
2019-01-20 12:17:00 3 Neg NaN NaN NaN NaN
2019-01-20 12:18:00 4 Neg NaN NaN NaN NaN
2019-01-20 12:19:00 5 Pos NaN NaN NaN NaN
2019-01-20 12:20:00 6 Neg NaN NaN NaN NaN
2019-01-20 12:21:00 7 Neg NaN NaN NaN NaN
2019-01-20 12:22:00 8 Pos NaN NaN NaN NaN
2019-01-20 12:23:00 9 Neg NaN NaN NaN NaN
2019-01-20 12:24:00 10 Pos NaN NaN NaN NaN
2019-01-20 12:25:00 11 Neg NaN NaN NaN NaN
2019-01-20 12:26:00 12 Neg NaN NaN NaN NaN
2019-01-20 12:27:00 13 Neg NaN NaN NaN NaN
2019-01-20 12:29:00 14 Neg NaN NaN NaN NaN
2019-01-20 12:29:00 15 Nut NaN NaN NaN NaN
2019-01-20 12:30:00 16 Pos 4(o2:o16) 9.0 2.0 4.0
2019-01-20 12:31:00 17 Nut 4(o3:o17) 9.0 3.0 4.0
2019-01-20 12:32:00 18 Pos 5(o4:o18) 8.0 2.0 5.0
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