技術標籤：資料分析python資料分析pandas

資料檢測與缺失值處理~

1.DataFrame.isnull()和notnull()函式

介紹

會返回DataFrame相同形狀的布林物件，指示是否有⽆空值。是⼀個⽆引數函式。

舉例

1 df = pd.DataFrame(dict(age=[5, 6, np.NaN],
2 born=[pd.NaT, pd.Timestamp('1939-05-27'),
3	pd.Timestamp('1940-04-25')],
4 name=['Alfred', 'Batman', ''],
5 toy=[None, 'Batmobile', 'Joker'
 
]))
6 df.isull()
7 >>	age	born	name	toy
8 0	False	True	False	True
9 1	False	False	False	False
10 True	False	False	False

2.DataFrame.fillna()函式

介紹

該函式⼀般⽤來填充缺失值，基本使⽤⽅法如下

pandas.DataFrame.fillna(value=None, method=None, axis=None, inplace=False, limit=None)

引數介紹

舉例

1 df = pd.DataFrame([[np.nan, 2, np.nan, 0], 
2 [3, 4, np.nan, 1], 
3 [np.nan, np.nan, np.nan, 5], 
4 [np.nan, 3, np.nan, 4]], 
5 columns=list('ABCD'
 
)) 
6 df 
7 >> A B C D 
8 0 NaN 2.0 NaN 0 
9 1 3.0 4.0 NaN 1 
10 2 NaN NaN NaN 5 
11 3 NaN 3.0 NaN 4 
12 
13 #直接填充0 
14 df.fillna(0)
15 >>	A	B	C	D
16 0	0.0	2.0	0.0	0
17 1	3.0	4.0	0.0	1
18 2	0.0	0.0	0.0	5
19 3	0.0	3.0	0.0	4

20
21 #按照⾏填充
22  values = {'A': 0, 'B': 1, 'C': 2, 'D': 3}
23  df.fillna(value=values)
24 >>	A	B	C	D
25 0	0.0	2.0	2.0	0
26 1	3.0	4.0	2.0	1
27 2 0.0 1.0 2.0 5 
28 3 0.0 3.0 2.0 4

3.DataFrame.dropna()函式

介紹

移除缺失的值。

pandas.DataFrame.dropna(self,axis=0,how='any', thresh=None, subset=None, inplace=False)

引數說明

便捷的刪除缺失值函式，有多種刪除缺失值⽅式。

舉例

1  df = pd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'],
2		"toy": [np.nan, 'Batmobile', 'Bullwhip'],
3		"born": [pd.NaT, pd.Timestamp("1940-04-25"),
4		pd.NaT]})
5
6 df		
7 >>	name	toy	born
8 0 Alfred NaN NaT 
9 1 Batman Batmobile 1940-04-25 
10 2 Catwoman Bullwhip NaT 
11 
12 df.dropna() 
13 >> name toy born 
14 1 Batman Batmobile 1940-04-25 
15 
16 df.dropna(how='all') 
17 >> name toy born 
18 0 Alfred NaN NaT 
19 1 Batman Batmobile 1940-04-25 
20 2 Catwoman Bullwhip NaT 
21 
22 df.dropna(subset=['name', 'toy']) 
23 >> name toy born 
24 1 Batman Batmobile 1940-04-25 
25 2 Catwoman Bullwhip NaT