pandas 对比 np.nan 和 isnull() 的区别
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The difference between comparison to np.nan and isnull()
提问by sergzach
I supposed that
我以为
data[data.agefm.isnull()]
and
和
data[data.agefm == numpy.nan]
are equivalent. But no, the first truly returns rows where agefm is NaN, but the second returns an empty DataFrame. I thank that omitted values are always equal to np.nan, but it seems wrong.
是等价的。但是不，第一个真正返回agefm 为NaN 的行，但第二个返回一个空的DataFrame。我感谢省略的值总是等于np.nan，但这似乎是错误的。
agefm column has float64 type:
agefm 列具有 float64 类型：
(Pdb) data.agefm.describe()
count 2079.000000
mean 20.686388
std 5.002383
min 10.000000
25% 17.000000
50% 20.000000
75% 23.000000
max 46.000000
Name: agefm, dtype: float64
Could you explain me please, what does data[data.agefm == np.nan]mean exactly?
你能解释一下data[data.agefm == np.nan]吗，具体是什么意思？
回答by piRSquared
np.nanis not comparable to np.nan... directly.
np.nan不能np.nan直接与……相提并论。
np.nan == np.nan
False
While
尽管
np.isnan(np.nan)
True
Could also do
也可以做
pd.isnull(np.nan)
True
examples
Filters nothing because nothing is equal to np.nan
示例 不
过滤任何内容，因为没有任何内容等于np.nan
s = pd.Series([1., np.nan, 2.])
s[s != np.nan]
0 1.0
1 NaN
2 2.0
dtype: float64
Filters out the null
过滤掉空值
s = pd.Series([1., np.nan, 2.])
s[s.notnull()]
0 1.0
2 2.0
dtype: float64
Use odd comparison behavior to get what we want anyway. If np.nan != np.nanis Truethen
无论如何，使用奇怪的比较行为来获得我们想要的东西。如果np.nan != np.nan是True那么
s = pd.Series([1., np.nan, 2.])
s[s == s]
0 1.0
2 2.0
dtype: float64
Just dropna
只是 dropna
s = pd.Series([1., np.nan, 2.])
s.dropna()
0 1.0
2 2.0
dtype: float64
