pandas库之DataFrame滑动窗口的实现

DataFrame.rolling(window, min_periods=None, center=False, win_type=None, on=None, axis=0, closed=None, method='single')

>>> import pandas as pd
>>> import numpy as np
>>> df = pd.DataFrame({'B':[0,1,2,np.nan,4]})
>>> df
     B
0  0.0
1  1.0
2  2.0
3  NaN
4  4.0
>>> df.rolling(2).sum()
     B
0  NaN
1  1.0
2  3.0
3  NaN
4  NaN

>>> df_time = pd.DataFrame({'B':[0,1,2,np.nan,4]},
		       index = [
		       pd.Timestamp('20130101 09:00:00'),
			   pd.Timestamp('20130101 09:00:02'),
			   pd.Timestamp('20130101 09:00:03'), 
			   pd.Timestamp('20130101 09:00:05'),
			   pd.Timestamp('20130101 09:00:06')])
			                                                   
>>> df_time
                       B
2013-01-01 09:00:00  0.0
2013-01-01 09:00:02  1.0
2013-01-01 09:00:03  2.0
2013-01-01 09:00:05  NaN
2013-01-01 09:00:06  4.0

>>> df_time.rolling('2s').sum()
                       B
2013-01-01 09:00:00  0.0
2013-01-01 09:00:02  1.0
2013-01-01 09:00:03  3.0
2013-01-01 09:00:05  NaN
2013-01-01 09:00:06  4.0

# 设置前向窗口
>>> indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=2)
>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
>>> df.rolling(window=indexer,min_periods=1).sum()
     B
0  1.0
1  3.0
2  2.0
3  4.0
4  4.0

>>> df.rolling(2,min_periods=1).sum()
     B
0  0.0
1  1.0
2  3.0
3  2.0
4  4.0

>>> df.rolling(3, min_periods=1, center=True).sum()
     B
0  1.0
1  3.0
2  3.0
3  6.0
4  4.0
>>> df.rolling(3, min_periods=1, center=False).sum()
     B
0  0.0
1  1.0
2  3.0
3  3.0
4  6.0

>>> df.rolling(2,win_type='gaussian').sum(std=3)
          B
0       NaN
1  0.986207
2  2.958621
3       NaN
4       NaN

>>> import pandas as pd
>>> s = pd.Series(range(5))
>>> s
0    0
1    1
2    2
3    3
4    4
dtype: int64

# 5个分区
>>> for window in s.rolling(window=2):
	print(window)

	
0    0
dtype: int64
0    0
1    1
dtype: int64
1    1
2    2
dtype: int64
2    2
3    3
dtype: int64
3    3
4    4
dtype: int64

>>> s = pd.Series(range(5),index = pd.date_range('2020-01-01',periods=5,freq='1D'))
>>> s
2020-01-01    0
2020-01-02    1
2020-01-03    2
2020-01-04    3
2020-01-05    4
Freq: D, dtype: int64
>>> s.rolling(window='2D').sum()
2020-01-01    0.0
2020-01-02    1.0
2020-01-03    3.0
2020-01-04    5.0
2020-01-05    7.0
Freq: D, dtype: float64

>>> df = pd.DataFrame({'A':['a', 'b', 'a', 'b', 'a'],'B':range(5)})
>>> df
   A  B
0  a  0
1  b  1
2  a  2
3  b  3
4  a  4
>>> df.groupby('A').expanding().sum()
       B
A       
a 0  0.0
  2  2.0
  4  6.0
b 1  1.0
  3  4.0

>>> times = ['2020-01-01', '2020-01-03', '2020-01-04', '2020-01-05', '2020-01-29']
>>> s = pd.Series(range(5),index = pd.DatetimeIndex(times))
>>> s
2020-01-01    0
2020-01-03    1
2020-01-04    2
2020-01-05    3
2020-01-29    4
dtype: int64

# 两个观测值的窗口
>>> s.rolling(2).sum()
2020-01-01    NaN
2020-01-03    1.0
2020-01-04    3.0
2020-01-05    5.0
2020-01-29    7.0
dtype: float64

# 两天的窗口
>>> s.rolling('2D').sum()
2020-01-01    0.0
2020-01-03    1.0
2020-01-04    3.0
2020-01-05    5.0
2020-01-29    4.0
dtype: float64

>>> s = pd.Series(range(10))
>>> s.rolling(window=5).mean()
0    NaN
1    NaN
2    NaN
3    NaN
4    2.0
5    3.0
6    4.0
7    5.0
8    6.0
9    7.0
dtype: float64
>>> s.rolling(window=5, center=True).mean()
0    NaN
1    NaN
2    2.0
3    3.0
4    4.0
5    5.0
6    6.0
7    7.0
8    NaN
9    NaN
dtype: float64

>>> import numpy as np
>>> def mad(x):
	return np.fabs(x - x.mean()).mean()

>>> s = pd.Series(range(10))
>>> s.rolling(window=4).apply(mad, raw=True)
0    NaN
1    NaN
2    NaN
3    1.0
4    1.0
5    1.0
6    1.0
7    1.0
8    1.0
9    1.0
dtype: float64

>>> s = pd.Series(range(10))
>>> s.rolling(window=5, win_type="gaussian").mean(std=0.1)
0    NaN
1    NaN
2    NaN
3    NaN
4    2.0
5    3.0
6    4.0
7    5.0
8    6.0
9    7.0
dtype: float64