在Pandas DataFrame中计算缺少值的行数的最佳方法

对于第二个计数，我认为只需从以下方法返回的行数中减去行数dropna：

In [14]:

from numpy.random import randn
df = pd.DataFrame(randn(5, 3), index=['a', 'c', 'e', 'f', 'h'],
               columns=['one', 'two', 'three'])
df = df.reindex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])
df
Out[14]:
        one       two     three
a -0.209453 -0.881878  3.146375
b       NaN       NaN       NaN
c  0.049383 -0.698410 -0.482013
d       NaN       NaN       NaN
e -0.140198 -1.285411  0.547451
f -0.219877  0.022055 -2.116037
g       NaN       NaN       NaN
h -0.224695 -0.025628 -0.703680
In [18]:

df.shape[0] - df.dropna().shape[0]
Out[18]:
3

第一个可以使用内置方法来实现：

In [30]:

df.isnull().values.ravel().sum()
Out[30]:
9

时机

In [34]:

%timeit sum([True for idx,row in df.iterrows() if any(row.isnull())])
%timeit df.shape[0] - df.dropna().shape[0]
%timeit sum(map(any, df.apply(pd.isnull)))
1000 loops, best of 3: 1.55 ms per loop
1000 loops, best of 3: 1.11 ms per loop
1000 loops, best of 3: 1.82 ms per loop
In [33]:

%timeit sum(df.isnull().values.ravel())
%timeit df.isnull().values.ravel().sum()
%timeit df.isnull().sum().sum()
1000 loops, best of 3: 215 µs per loop
1000 loops, best of 3: 210 µs per loop
1000 loops, best of 3: 605 µs per loop

因此，对于这种尺寸的df，我的选择要快一些

更新资料

因此，对于具有80,000行的df，我得到以下信息：

In [39]:

%timeit sum([True for idx,row in df.iterrows() if any(row.isnull())])
%timeit df.shape[0] - df.dropna().shape[0]
%timeit sum(map(any, df.apply(pd.isnull)))
%timeit np.count_nonzero(df.isnull())
1 loops, best of 3: 9.33 s per loop
100 loops, best of 3: 6.61 ms per loop
100 loops, best of 3: 3.84 ms per loop
1000 loops, best of 3: 395 µs per loop
In [40]:

%timeit sum(df.isnull().values.ravel())
%timeit df.isnull().values.ravel().sum()
%timeit df.isnull().sum().sum()
%timeit np.count_nonzero(df.isnull().values.ravel())
1000 loops, best of 3: 675 µs per loop
1000 loops, best of 3: 679 µs per loop
100 loops, best of 3: 6.56 ms per loop
1000 loops, best of 3: 368 µs per loop

其实np.count_nonzero赢得了这一手。