Querying for NaN and other names in Pandas

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Say I have a dataframe df with a column value holding some float values and some NaN. How can I get the part of the dataframe where we have NaN using the query syntax?

The following, for example, does not work:

df.query( '(value < 10) or (value == NaN)' )

I get name NaN is not defined (same for df.query('value ==NaN'))

Generally speaking, is there any way to use numpy names in query, such as inf, nan, pi, e, etc.?

In general, you could use @local_variable_name, so something like

>>> 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)")
1      4
2      9

would work, but nan isn't equal to itself, so value == NaN will always be false. One way to hack around this is to use that fact, and use value != value as an isnan check. We have

>>> df.query("(value < 10) or (value == @nan)")
0      3
1      4
2      9


>>> df.query("(value < 10) or (value != value)")
0      3
1      4
2      9
5    NaN

Querying for NaN and other names in Pandas, You can use: df.query('value < 10 | value.isnull()', engine='python'). If you wish to Learn more about Pandas visit this Pandas Tutorial. I get name NaN is not defined (same for df.query('value ==NaN')) Generally speaking, is there any way to use numpy names in the query, such as inf, nan, pi, e, etc.? pandas

According to this answer you can use:

df.query('value < 10 | value.isnull()', engine='python')

I verified that it works.

Working with Missing Data in Pandas, How can I check if a device is NaN in pandas? In Pandas,.count() will return the number of non-null/NaN values. To get the same result as the SQL COUNT, use.size(). Below, we group on more than one field. Pandas will sort things on the same

For rows where value is not null

df.query("value == value")

For rows where value is null

df.query("value != value")

Checking If Any Value is NaN in a Pandas DataFrame, Series in order to find null values in a series. This is the reason that I'm thinking of looking for other solutions like running an SQL server and querying the tables from there (looks a bit too complicated), or looking another library in spite of Pandas, or use my own (that I want to get rid of). Thx – Gyula Sámuel Karli Aug 26 '13 at 20:52

Pandas DataFrame: isin() function, () function is used to check each element in the DataFrame is contained in values or not. The result will only be true at a location if all the labels match. If values is a Series, that's the index. If values is a dict, the keys must be the column names, which must match. Values considered “missing”¶ As data comes in many shapes and forms, pandas aims to be flexible with regard to handling missing data. While NaN is the default missing value marker for reasons of computational speed and convenience, we need to be able to easily detect this value with data of different types: floating point, integer, boolean, and general object.

7 Ways To Filter A Pandas Dataframe, Within pandas, a missing value is denoted by NaN . In most cases, the terms missing and null are interchangeable, but to abide by the standards of pandas, we'll  In [4]: nms.dropna(thresh=2) Out[4]: movie name rating 0 thg John 3.0 1 thg NaN 4.0 3 mol Graham NaN It's possible that I was either mistaken 3 years ago or that the version of pandas I was running had a bug, both scenarios are entirely possible.

pandas.DataFrame.notna, It may come handy when your filter options are dynamic. Showing only the rows where the year is greater than 2012 OR name is “Frank”: df.query('year > 2012 |  The query() method uses a slightly modified Python syntax by default. For example, the & and | (bitwise) operators have the precedence of their boolean cousins, and and or. This is syntactically valid Python, however the semantics are different.

  • There should be a better way of doing this... but I like the hack.
  • The @nan "trick" is not working for numpy vars e.g. nan = numpy.nan. It does work to filter out other string's .
  • @javadba: er, the whole point of that section is to show that (value == @nan) doesn't work, because nan isn't equal to itself, hence my use of the value != value trick.
  • Ok I see that now. Setting value == value then works for excluding NaN's
  • Nice! I believe this is what the post author wanted.