是数据清洗的重要过程,可以按索引对齐进行运算,如果没对齐的位置则补NaN,最后也可以填充NaN
Series的对齐运算
1. Series 按行、索引对齐
示例代码:
s1 = pd.Series(range(10, 20), index = range(10)) s2 = pd.Series(range(20, 25), index = range(5)) print('s1: ' ) print(s1) print('') print('s2: ') print(s2)
运行结果:
s1: 0 10 1 11 2 12 3 13 4 14 5 15 6 16 7 17 8 18 9 19 dtype: int64 s2: 0 20 1 21 2 22 3 23 4 24 dtype: int64
2. Series的对齐运算
示例代码:
# Series 对齐运算 s1 + s2
运行结果:
0 30.0 1 32.0 2 34.0 3 36.0 4 38.0 5 NaN 6 NaN 7 NaN 8 NaN 9 NaN dtype: float64
DataFrame的对齐运算
1. DataFrame按行、列索引对齐
示例代码:
df1 = pd.DataFrame(np.ones((2,2)), columns = ['a', 'b']) df2 = pd.DataFrame(np.ones((3,3)), columns = ['a', 'b', 'c']) print('df1: ') print(df1) print('') print('df2: ') print(df2)
运行结果:
df1: a b 0 1.0 1.0 1 1.0 1.0 df2: a b c 0 1.0 1.0 1.0 1 1.0 1.0 1.0 2 1.0 1.0 1.0
2. DataFrame的对齐运算
示例代码:
# DataFrame对齐操作 df1 + df2
运行结果:
a b c 0 2.0 2.0 NaN 1 2.0 2.0 NaN 2 NaN NaN NaN
填充未对齐的数据进行运算
1. fill_value
使用add,sub,div,mul的同时,通过fill_value指定填充值,未对齐的数据将和填充值做运算
示例代码:
print(s1) print(s2) s1.add(s2, fill_value = -1) print(df1) print(df2) df1.sub(df2, fill_value = 2.)
运行结果:
# print(s1) 0 10 1 11 2 12 3 13 4 14 5 15 6 16 7 17 8 18 9 19 dtype: int64 # print(s2) 0 20 1 21 2 22 3 23 4 24 dtype: int64 # s1.add(s2, fill_value = -1) 0 30.0 1 32.0 2 34.0 3 36.0 4 38.0 5 14.0 6 15.0 7 16.0 8 17.0 9 18.0 dtype: float64 # print(df1) a b 0 1.0 1.0 1 1.0 1.0 # print(df2) a b c 0 1.0 1.0 1.0 1 1.0 1.0 1.0 2 1.0 1.0 1.0 # df1.sub(df2, fill_value = 2.) a b c 0 0.0 0.0 1.0 1 0.0 0.0 1.0 2 1.0 1.0 1.0
算术方法表: