pandas DataFrame(4)-向量化运算

pandas DataFrame进行向量化运算时,是根据行和列的索引值进行计算的,而不是行和列的位置:

1. 行和列索引一致:

import pandas as pd
df1 = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6], 'c': [7, 8, 9]})
df2 = pd.DataFrame({'a': [10, 20, 30], 'b': [40, 50, 60], 'c': [70, 80, 90]})
print df1 + df2

    a   b   c
0  11  44  77
1  22  55  88
2  33  66  99

2. 行索引一致,列索引不一致:

df1 = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6], 'c': [7, 8, 9]})
df2 = pd.DataFrame({'d': [10, 20, 30], 'c': [40, 50, 60], 'b': [70, 80, 90]})
print df1 + df2

    a   b   c   d
0 NaN  74  47 NaN
1 NaN  85  58 NaN
2 NaN  96  69 NaN

没有对应索引的值,会用空来代替进行计算

3. 行索引不一致,列索引一致:

df1 = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6], 'c': [7, 8, 9]},
                       index=['row1', 'row2', 'row3'])
df2 = pd.DataFrame({'a': [10, 20, 30], 'b': [40, 50, 60], 'c': [70, 80, 90]},
                       index=['row4', 'row3', 'row2'])
print df1 + df2

         a     b     c
row1   NaN   NaN   NaN
row2  32.0  65.0  98.0
row3  23.0  56.0  89.0
row4   NaN   NaN   NaN

其实总结下来就是,行列索引相同的,进行计算,没有的全部用空进行计算