算术和数据对齐

In [20]: s1 = pd.Series([7.3, -2.5, 3.4, 1.5], index = ['a', 'c', 'd', 'e'])
In [22]: s2 = pd.Series([-2.1, 3.6, -1.5, 4, 3.1],
    ...:                index = ['a', 'c', 'e', 'f', 'g'])

In [23]: s1
Out[23]:
a    7.3
c   -2.5
d    3.4
e    1.5
dtype: float64

In [24]: s2
Out[24]:
a   -2.1
c    3.6
e   -1.5
f    4.0
g    3.1
dtype: float64

In [25]: s1 + s2
Out[25]:
a    5.2
c    1.1
d    NaN
e    0.0
f    NaN
g    NaN
dtype: float64

没有交叉值时为 NaN

In [27]: df1 = pd.DataFrame(np.arange(9.).reshape((3, 3)), columns=list('bcd'),
    ...:                   index=['Ohio', 'Texas', 'Colorado'])

In [28]: df2 = pd.DataFrame(np.arange(12.).reshape((4, 3)), columns=list('bde'),
    ...:                   index=['Utah', 'Ohio', 'Texas', 'Oregon'])

In [29]:

In [29]: df1
Out[29]:
            b    c    d
Ohio      0.0  1.0  2.0
Texas     3.0  4.0  5.0
Colorado  6.0  7.0  8.0

In [30]: df2
Out[30]:
          b     d     e
Utah    0.0   1.0   2.0
Ohio    3.0   4.0   5.0
Texas   6.0   7.0   8.0
Oregon  9.0  10.0  11.0

In [31]: df1 + df2
Out[31]:
            b   c     d   e
Colorado  NaN NaN   NaN NaN
Ohio      3.0 NaN   6.0 NaN
Oregon    NaN NaN   NaN NaN
Texas     9.0 NaN  12.0 NaN
Utah      NaN NaN   NaN NaN

使用填充值的算术方法

In [33]: df1 = pd.DataFrame(np.arange(12.).reshape((3, 4)),
    ...:                    columns=list('adcd'))

In [34]: df2 = pd.DataFrame(np.arange(20.).reshape((4, 5)),
    ...:                    columns=list('abcde'))

In [35]: df1 + df2
Out[35]:
      a   b     c     d     d   e
0   0.0 NaN   4.0   4.0   6.0 NaN
1   9.0 NaN  13.0  13.0  15.0 NaN
2  18.0 NaN  22.0  22.0  24.0 NaN
3   NaN NaN   NaN   NaN   NaN NaN
In [37]: df1.add(df2, fill_value=0)
Out[37]:
      a     b     c     d     d     e
0   0.0   1.0   4.0   4.0   6.0   4.0
1   9.0   6.0  13.0  13.0  15.0   9.0
2  18.0  11.0  22.0  22.0  24.0  14.0
3  15.0  16.0  17.0  18.0  18.0  19.0

灵活算术方法

DataFrame 和 Series 间的操作

In [38]: arr = np.arange(12.).reshape((3, 4))

In [39]: arr
Out[39]:
array([[ 0.,  1.,  2.,  3.],
       [ 4.,  5.,  6.,  7.],
       [ 8.,  9., 10., 11.]])

In [40]: arr[0]
Out[40]: array([0., 1., 2., 3.])

In [41]: arr - arr[0]
Out[41]:
array([[0., 0., 0., 0.],
       [4., 4., 4., 4.],
       [8., 8., 8., 8.]])

广播机制

frame = pd.DataFrame(np.arange(12.).reshape((4, 3)),
    ...:                      columns=list('bde'),
    ...:                      index=['Utah', 'Ohio', 'Texas', 'Oregon'])
In [44]: series = frame.iloc[0]

In [45]: series
Out[45]:
b    0.0
d    1.0
e    2.0
Name: Utah, dtype: float64

In [46]: frame  - series
Out[46]:
          b    d    e
Utah    0.0  0.0  0.0
Ohio    3.0  3.0  3.0
Texas   6.0  6.0  6.0
Oregon  9.0  9.0  9.0

In [47]: series2 = pd.Series(range(3), index=['b', 'e', 'f'])

In [48]: series2
Out[48]:
b    0
e    1
f    2
dtype: int64

In [49]: frame + series2
Out[49]:
          b   d     e   f
Utah    0.0 NaN   3.0 NaN
Ohio    3.0 NaN   6.0 NaN
Texas   6.0 NaN   9.0 NaN
Oregon  9.0 NaN  12.0 NaN

在列上广播，行上匹配

In [50]: series3 = frame['d']

In [51]: frame
Out[51]:
          b     d     e
Utah    0.0   1.0   2.0
Ohio    3.0   4.0   5.0
Texas   6.0   7.0   8.0
Oregon  9.0  10.0  11.0

In [52]: series3
Out[52]:
Utah       1.0
Ohio       4.0
Texas      7.0
Oregon    10.0
Name: d, dtype: float64

In [53]: frame.sub(series3, axis='index')
Out[53]:
          b    d    e
Utah   -1.0  0.0  1.0
Ohio   -1.0  0.0  1.0
Texas  -1.0  0.0  1.0
Oregon -1.0  0.0  1.0

axis 用于匹配的轴 axis='index' 或 axis=0