Python 在 Pandas 中查询 NaN 和其他名称
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/26535563/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
Querying for NaN and other names in Pandas
提问by Amelio Vazquez-Reina
Say I have a dataframe dfwith a column valueholding some float values and some NaN. How can I get the part of the dataframe where we have NaNusing the query syntax?
假设我有一个数据框，df其中一列包含value一些浮点值和一些NaN. 如何NaN使用查询语法获取数据帧的一部分？
The following, for example, does not work:
例如，以下内容不起作用：
df.query( '(value < 10) or (value == NaN)' )
I get name NaN is not defined(same for df.query('value ==NaN'))
我得到name NaN is not defined（相同的df.query('value ==NaN')）
Generally speaking, is there any way to use numpy names in query, such as inf, nan, pi, e, etc.?
一般来说，有没有办法使用查询numpy的名称，如inf，nan，pi，e，等？
采纳答案by DSM
In general, you could use @local_variable_name, so something like
一般来说，你可以使用@local_variable_name，所以像
>>> pi = np.pi; nan = np.nan
>>> df = pd.DataFrame({"value": [3,4,9,10,11,np.nan,12]})
>>> df.query("(value < 10) and (value > @pi)")
value
1 4
2 9
would work, but nanisn't equal to itself, so value == NaNwill always be false. One way to hack around this is to use that fact, and use value != valueas an isnancheck. We have
会工作，但nan不等于它自己，所以value == NaN总是假的。解决此问题的一种方法是使用该事实，并将其value != value用作isnan检查。我们有
>>> df.query("(value < 10) or (value == @nan)")
value
0 3
1 4
2 9
but
但
>>> df.query("(value < 10) or (value != value)")
value
0 3
1 4
2 9
5 NaN
回答by as - if
For rows where valueis not null
对于value不为空的行
df.query("value == value")
For rows where valueis null
对于value为空的行
df.query("value != value")
回答by Eric Ness
According to this answeryou can use:
根据此答案，您可以使用：
df.query('value < 10 | value.isnull()', engine='python')
I verified that it works.
我验证了它的工作原理。
回答by AreToo
Pandas fills empty cells in a DataFrame with NumPy's nan values. As it turns out, this has some funny properties. For one, nothingis equal to this kind of null, even itself. As a result, you can't search for it by checking for any particular equality.
Pandas 使用 NumPy 的 nan 值填充 DataFrame 中的空单元格。事实证明，这有一些有趣的特性。首先，没有任何东西等于这种空值，甚至是它本身。因此，您无法通过检查任何特定的相等性来搜索它。
In : 'nan' == np.nan
Out: False
In : None == np.nan
Out: False
In : np.nan == np.nan
Out: False
However, because a cell containing a np.nan value will not be equal to anything, including another np.nan value,we can check to see if it is unequal to itself.
但是，因为包含 np.nan 值的单元格将不等于任何值，包括另一个 np.nan 值，我们可以检查它是否不等于自身。
In : np.nan != np.nan
Out: True
You can take advantage of this using Pandas query method by simply searching for cells where the value in a particular column is unequal to itself.
您可以使用 Pandas 查询方法来利用这一点，只需搜索特定列中的值与其自身不相等的单元格即可。
df.query('a != a')
回答by James Page
df = pd.DataFrame({'value':[3,4,9,10,11,np.nan, 12]})
df.query("value < 10 or (~(value < 10) and ~(value >= 10))")
回答by Jarno
You can use the isnaand notnaSeriesmethods, which is concise and readable.
您可以使用isna和方法，它简洁易读。notnaSeries
import pandas as pd
import numpy as np
df = pd.DataFrame({'value': [3, 4, 9, 10, 11, np.nan, 12]})
available = df.query("value.notna()")
print(available)
# value
# 0 3.0
# 1 4.0
# 2 9.0
# 3 10.0
# 4 11.0
# 6 12.0
not_available = df.query("value.isna()")
print(not_available)
# value
# 5 NaN
Alternatively, you can use the toplevel pd.isnafunction, by referencing it as a local variable.
或者，您可以使用顶级pd.isna函数，将其作为局部变量引用。
import pandas as pd
import numpy as np
df = pd.DataFrame({'value': [3, 4, 9, 10, 11, np.nan, 12]})
df.query("@pd.isna(value)")
# value
# 5 NaN
