筛选DataFrame缺失值

DataFrame下dropna()代码示例

1.默认情况下，dropna()会删除包含缺失值的行 #这种情况下，其实是drop(axis=0,how=‘any’)

data = pd.DataFrame([[1,6.5,3],[1,None,None],
                     [None,None,None],[None,6.5,3]])
cleaned = data.dropna()
print("data:\n" + data)
print("cleaned: \n" + cleaned)


==Output:==

data:
      0     1    2
0   1.0   6.5   3.0
1   1.0   NaN   NaN
2   NaN   NaN   NaN
3   Nan   NaN   3.0

cleaned:
      0    1    2 
0   1.0  6.5  3.0 

2.若要删除所有值均为NaN的行，需要传入how = ‘all’

cleaned = data.dropna(how='all')

==Output:==

cleaned:
      0     1     2 
0   1.0   6.5   3.0 
1   1.0   NaN   NaN
3   NaN   6.5   3.0

3.可以用dropna(how=‘any’,axis=1)的方法删除列

cleaned = data.dropna(how='any',axis=1)
==Output:==

cleaned:
#因为删除掉了所有包含NaN的列，所以输出为空