In [1]:

    

import pandas as pd


    

In [2]:

    

s = pd.Series(range(10))


    

In [3]:

    

print(s)


    

    




0    0
1    1
2    2
3    3
4    4
5    5
6    6
7    7
8    8
9    9
dtype: int64

In [4]:

    

print(s.rolling(3))


    

    




Rolling [window=3,center=False,axis=0]

In [5]:

    

print(type(s.rolling(3)))


    

    




<class 'pandas.core.window.rolling.Rolling'>

In [6]:

    

print(s.rolling(3).sum())


    

    




0     NaN
1     NaN
2     3.0
3     6.0
4     9.0
5    12.0
6    15.0
7    18.0
8    21.0
9    24.0
dtype: float64

In [7]:

    

print(s.rolling(2).sum())


    

    




0     NaN
1     1.0
2     3.0
3     5.0
4     7.0
5     9.0
6    11.0
7    13.0
8    15.0
9    17.0
dtype: float64

In [8]:

    

print(s.rolling(4).sum())


    

    




0     NaN
1     NaN
2     NaN
3     6.0
4    10.0
5    14.0
6    18.0
7    22.0
8    26.0
9    30.0
dtype: float64

In [9]:

    

print(s.rolling(3, center=True).sum())


    

    




0     NaN
1     3.0
2     6.0
3     9.0
4    12.0
5    15.0
6    18.0
7    21.0
8    24.0
9     NaN
dtype: float64

In [10]:

    

print(s.rolling(4, center=True).sum())


    

    




0     NaN
1     NaN
2     6.0
3    10.0
4    14.0
5    18.0
6    22.0
7    26.0
8    30.0
9     NaN
dtype: float64

In [11]:

    

print(s.rolling(3, min_periods=2).sum())


    

    




0     NaN
1     1.0
2     3.0
3     6.0
4     9.0
5    12.0
6    15.0
7    18.0
8    21.0
9    24.0
dtype: float64

In [12]:

    

print(s.rolling(3, min_periods=1).sum())


    

    




0     0.0
1     1.0
2     3.0
3     6.0
4     9.0
5    12.0
6    15.0
7    18.0
8    21.0
9    24.0
dtype: float64

In [13]:

    

df = pd.DataFrame({'a': range(10), 'b': range(10, 0, -1),
                   'c': range(10, 20), 'd': range(20, 10, -1)})


    

In [14]:

    

print(df.rolling(2).sum())


    

    




      a     b     c     d
0   NaN   NaN   NaN   NaN
1   1.0  19.0  21.0  39.0
2   3.0  17.0  23.0  37.0
3   5.0  15.0  25.0  35.0
4   7.0  13.0  27.0  33.0
5   9.0  11.0  29.0  31.0
6  11.0   9.0  31.0  29.0
7  13.0   7.0  33.0  27.0
8  15.0   5.0  35.0  25.0
9  17.0   3.0  37.0  23.0

In [15]:

    

print(df.rolling(2, axis=1).sum())


    

    




    a     b     c     d
0 NaN  10.0  20.0  30.0
1 NaN  10.0  20.0  30.0
2 NaN  10.0  20.0  30.0
3 NaN  10.0  20.0  30.0
4 NaN  10.0  20.0  30.0
5 NaN  10.0  20.0  30.0
6 NaN  10.0  20.0  30.0
7 NaN  10.0  20.0  30.0
8 NaN  10.0  20.0  30.0
9 NaN  10.0  20.0  30.0

In [16]:

    

print(s.rolling(3).mean())


    

    




0    NaN
1    NaN
2    1.0
3    2.0
4    3.0
5    4.0
6    5.0
7    6.0
8    7.0
9    8.0
dtype: float64

In [17]:

    

print(s.rolling(3).agg(['sum', 'mean', 'skew', 'cov',
                        max, min,
                        lambda x: max(x) - min(x)]))


    

    




    sum  mean          skew  cov  max  min  <lambda>
0   NaN   NaN           NaN  NaN  NaN  NaN       NaN
1   NaN   NaN           NaN  NaN  NaN  NaN       NaN
2   3.0   1.0  0.000000e+00  1.0  2.0  0.0       2.0
3   6.0   2.0 -7.993606e-15  1.0  3.0  1.0       2.0
4   9.0   3.0  2.398082e-14  1.0  4.0  2.0       2.0
5  12.0   4.0 -6.394885e-14  1.0  5.0  3.0       2.0
6  15.0   5.0 -7.993606e-14  1.0  6.0  4.0       2.0
7  18.0   6.0  1.918465e-13  1.0  7.0  5.0       2.0
8  21.0   7.0  2.238210e-13  1.0  8.0  6.0       2.0
9  24.0   8.0 -5.115908e-13  1.0  9.0  7.0       2.0