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Alignment operation Pandas
Data cleaning is an important process to be aligned index calculation, if the position is not aligned complement NaN, NaN eventually be filled
Series operation alignment
1. Series aligned row index
Sample code:
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)
operation result:
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 operation alignment
Sample code:
# Series 对齐运算
s1 + s2
operation result:
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 alignment operation
1. DataFrame rows, aligned column index
Sample code:
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)
operation result:
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 alignment operation
Sample code:
# DataFrame对齐操作
df1 + df2
operation result:
a b c
0 2.0 2.0 NaN
1 2.0 2.0 NaN
2 NaN NaN NaN
Filling misaligned data calculates
1. fill_value
Use
add,sub,div,mulat the same time,By
fill_valuespecify a fill value, unaligned data and padding value calculation done
Sample code:
print(s1)
print(s2)
s1.add(s2, fill_value = -1)
print(df1)
print(df2)
df1.sub(df2, fill_value = 2.)
operation result:
# 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
