对于数值型数据，pandas使用浮点值NaN来代表缺失。而缺失值为NA，表示数据不存在或者存在但无法观察。有一些函数可以用于处理：

string_data = pd.Series(['aardvark', 'artichoke', np.nan, 'avocado'])
print(string_data)	==>
0     aardvark
1    artichoke
2          NaN
3      avocado
dtype: object
print(string_data.isnull())	==>
0    False
1    False
2     True
3    False
dtype: bool

过滤缺失值

dropna在过滤数据时起到了很大的作用

data = pd.Series([1,NA,3.5,NA,7])
print(data)	==>
0    1.0
1    NaN
2    3.5
3    NaN
4    7.0
dtype: float64
print(data.dropna())	==>
0    1.0
2    3.5
4    7.0
dtype: float64

对于DataFrame对象，dropna默认会删除所有含缺失值的行，若仅仅是要删除所有值为NA的行则传入参数how= ‘all’，如果要对列进行操作则传入axis=1,如果要指定丢弃的行数则传入参数thresh=

data = pd.DataFrame([[1., 6.5, 3.], [1., NA, NA],[NA, NA, NA],[NA, 6.5,3.]])
print(data)	==>
     0    1    2
0  1.0  6.5  3.0
1  1.0  NaN  NaN
2  NaN  NaN  NaN
3  NaN  6.5  3.0
print(data.dropna())	==>
     0    1    2
0  1.0  6.5  3.0
print(data.dropna(how='all'))	==>
     0    1    2
0  1.0  6.5  3.0
1  1.0  NaN  NaN
3  NaN  6.5  3.0
print(data.dropna(how='all', axis=1))	==>
     0    1    2
0  1.0  6.5  3.0
1  1.0  NaN  NaN
2  NaN  NaN  NaN
3  NaN  6.5  3.0
print(data.dropna(thresh=1))	==>
     0    1    2
0  1.0  6.5  3.0
1  1.0  NaN  NaN
3  NaN  6.5  3.0

补全缺失值

可以使用fillna函数来进行缺失值的补充

可以使用常数补全，指定列字典补全。默认情况下fillna生成一个新的对象，但也可以使用inplace参数修改已经存在的对象

df = pd.DataFrame(np.random.randn(7,3))
df.iloc[:4, 1] = NA
df.iloc[:2, 2] = NA
print(df)	==>
          0         1         2
0 -1.192710       NaN       NaN
1  0.507454       NaN       NaN
2 -0.573454       NaN  0.235207
3 -0.308547       NaN  1.769382
4 -1.696863  1.103388 -0.433570
5  0.304338  0.176991 -0.282484
6 -0.134053 -0.213646  0.800938
print(df.fillna(0))	==>
          0         1         2
0 -1.192710  0.000000  0.000000
1  0.507454  0.000000  0.000000
2 -0.573454  0.000000  0.235207
3 -0.308547  0.000000  1.769382
4 -1.696863  1.103388 -0.433570
5  0.304338  0.176991 -0.282484
6 -0.134053 -0.213646  0.800938
print(df.fillna({1: 0.5,2:0}))	==>
          0         1         2
0 -1.192710  0.500000  0.000000
1  0.507454  0.500000  0.000000
2 -0.573454  0.500000  0.235207
3 -0.308547  0.500000  1.769382
4 -1.696863  1.103388 -0.433570
5  0.304338  0.176991 -0.282484
6 -0.134053 -0.213646  0.800938

也可以用之前用于重建索引的相同的插值方法method=’ffill’（同时也可以带上limit参数用于限制个数）

df = pd.DataFrame(np.random.randn(6,3))
df.iloc[2:, 1] = NA
df.iloc[4:, 2] = NA
print(df)	==>
          0         1         2
0 -0.571684 -1.130371 -0.565300
1  1.243802 -1.536666  0.361830
2  1.291224       NaN  0.514455
3 -1.232330       NaN  2.057907
4  0.364088       NaN       NaN
5 -0.598980       NaN       NaN
print(df.fillna(method='ffill'))	==>
          0         1         2
0 -0.571684 -1.130371 -0.565300
1  1.243802 -1.536666  0.361830
2  1.291224 -1.536666  0.514455
3 -1.232330 -1.536666  2.057907
4  0.364088 -1.536666  2.057907
5 -0.598980 -1.536666  2.057907
print(df.fillna(method='ffill', limit=2))	==>
          0         1         2
0 -0.571684 -1.130371 -0.565300
1  1.243802 -1.536666  0.361830
2  1.291224 -1.536666  0.514455
3 -1.232330 -1.536666  2.057907
4  0.364088       NaN  2.057907
5 -0.598980       NaN  2.057907

还有一些创造性的填充方法可结合具体问题使用：

data = pd.Series([1.,NA,3.5,NA,7])
print(data)	==>
0    1.0
1    NaN
2    3.5
3    NaN
4    7.0
dtype: float64
print(data.fillna(data.mean()))	==>
0    1.000000
1    3.833333
2    3.500000
3    3.833333
4    7.000000
dtype: float64

下面是一些参数的参考