沧海拾珠

Pandas 处理缺失数据

发表于 | 分类于 |

1. Series 中设置，查找缺失数据

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
import numpy as np
import pandas as pd 
from pandas import Series, DataFrame
missing = np.nan
series_obj = Series(['row 1', 'row 2', missing, 'row 4','row 5', 'row 6', missing, 'row 8'])
series_obj
0    row 1
1    row 2
2      NaN
3    row 4
4    row 5
5    row 6
6      NaN
7    row 8

查找缺失数据

1
2
3
4
5
6
7
8
9
series_obj.isnull()
0    False
1    False
2     True
3    False
4    False
5    False
6     True
7    False

2. DataFrame中设置，查找缺失数据

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
np.random.seed(25)
DF_obj = DataFrame(np.random.randn(36).reshape(6,6))
DF_obj
0	1	2	3	4	5
0	0.228273	1.026890	-0.839585	-0.591182	-0.956888	-0.222326
1	-0.619915	1.837905	-2.053231	0.868583	-0.920734	-0.232312
2	2.152957	-1.334661	0.076380	-1.246089	1.202272	-1.049942
3	1.056610	-0.419678	2.294842	-2.594487	2.822756	0.680889
4	-1.577693	-1.976254	0.533340	-0.290870	-0.513520	1.982626
5	0.226001	-1.839905	1.607671	0.388292	0.399732	0.405477
#设置缺失数据
DF_obj.iloc[3:5, 0] = missing
DF_obj.iloc[1:4, 5] = missing
DF_obj
0	1	2	3	4	5
0	0.228273	1.026890	-0.839585	-0.591182	-0.956888	-0.222326
1	-0.619915	1.837905	-2.053231	0.868583	-0.920734	NaN
2	2.152957	-1.334661	0.076380	-1.246089	1.202272	NaN
3	NaN	-0.419678	2.294842	-2.594487	2.822756	NaN
4	NaN	-1.976254	0.533340	-0.290870	-0.513520	NaN
5	NaN	-1.839905	1.607671	0.388292	0.399732	0.405477
1
2
3
4
5
6
7
8
9
10
#将缺失数据赋值
filled_DF = DF_obj.fillna(0)
filled_DF
0	1	2	3	4	5
0	0.228273	1.026890	-0.839585	-0.591182	-0.956888	-0.222326
1	-0.619915	1.837905	-2.053231	0.868583	-0.920734	0.000000
2	2.152957	-1.334661	0.076380	-1.246089	1.202272	0.000000
3	0.000000	-0.419678	2.294842	-2.594487	2.822756	0.000000
4	0.000000	-1.976254	0.533340	-0.290870	-0.513520	0.000000
5	0.000000	-1.839905	1.607671	0.388292	0.399732	0.405477
1
2
3
4
5
6
7
8
#计算缺失数据数
DF_obj.isnull().sum()
0    3
1    0
2    0
3    0
4    0
5    4
1
2
3
4
5
6
7
8
9
10
11
12
13
#删除缺失数据
DF_no_NaN = DF_obj.dropna(axis=1) # 去除含有缺失数据的列。如果去除含有缺失数据的行，则dropna()就行。
DF_no_NaN
1	2	3	4
0	1.026890	-0.839585	-0.591182	-0.956888
1	1.837905	-2.053231	0.868583	-0.920734
2	-1.334661	0.076380	-1.246089	1.202272
3	-0.419678	2.294842	-2.594487	2.822756
4	-1.976254	0.533340	-0.290870	-0.513520
5	-1.839905	1.607671	0.388292	0.399732
#如果只去除所有值都为缺失数据的行或者列。
DF_obj.dropna(how='all')