nan | Native Abstractions for Node.js | Runtime Evironment library
kandi X-RAY | nan Summary
kandi X-RAY | nan Summary
[Build status] Thanks to the crazy changes in V8 (and some in Node core), keeping native addons compiling happily across versions, particularly 0.10 to 0.12 to 4.0, is a minor nightmare. The goal of this project is to store all logic necessary to develop native Node.js addons without having to inspect NODE_MODULE_VERSION and get yourself into a macro-tangle. This project also contains some helper utilities that make addon development a bit more pleasant.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of nan
nan Key Features
nan Examples and Code Snippets
aggregate(func: Function): Any
aggregate(func: Function, seed: Any): Any
let val = iter([2, 3, 4, 5])
.aggregate((carry, current) => {
return carry + current;
}, 1);
//=> 15
let val = iter([1, 2, 3, 4, 5])
.aggregate((carr
parseInt("f*ck"); // -> NaN
parseInt("f*ck", 16); // -> 15
//
parseInt("Infinity", 10); // -> NaN
// ...
parseInt("Infinity", 18); // -> NaN...
parseInt("Infinity", 19); // -> 18
// ...
parseInt("Infinity", 23); // -> 18...
parseI
// 1)
typeof NaN === 'number' // true
// 🤔 ("not a number" is a "number"...)
// 2)
isNaN('1') // false
// 🤔 the string '1' is not-"not a number"... so it's a number??
// 3)
isNaN('one') // true
// 🤔 'one' is NaN but `NaN === 'one'` is false...
/*********************************************************************
* NAN - Native Abstractions for Node.js
*
* Copyright (c) 2018 NAN contributors
*
* MIT License
********************************************************************/
var a
def has_inf_or_nan(datum, tensor):
"""A predicate for whether a tensor consists of any bad numerical values.
This predicate is common enough to merit definition in this module.
Bad numerical values include `nan`s and `inf`s.
The signature of
def _make_nan_reduction(np_fun_name, reduction, init_val):
"""Helper to generate nan* functions."""
@np_utils.np_doc(np_fun_name)
def nan_reduction(a, axis=None, dtype=None, keepdims=False):
a = np_array_ops.array(a)
v = np_array_ops.a
Community Discussions
Trending Discussions on nan
QUESTION
Using the defined-or operator ( //
) in a function call produces the result I'd expect:
ANSWER
Answered 2022-Mar-04 at 10:15I'd say, it's a grammar bug, as
QUESTION
I'm using Mac M1 and I've just upgraded to Node 14.17.6LTS.
I tried to rebuild better_sqlite3 (7.4.3) using with electron builder (22.11.7) and I'm getting the following errors:
no member named 'GetContents' in 'v8::ArrayBuffer'
Any ideas how to solve this? Thanks in advance!
...
ANSWER
Answered 2021-Sep-23 at 01:15I'm using Mac M1 and I've just upgraded to Node 14.17.6LTS.
An interesting choice, given that Node 16 officially introduced M1 support.
no member named 'GetContents' in 'v8::ArrayBuffer'
See this doc. In short, GetContents
was replaced by GetBackingStore
in late 2019. Being a compatibility layer, nan adapted to this in early 2020.
So you'll probably have to ensure that the versions of all involved packages (Node, nan, electron, ...) match each other (in the sense of having been released around the same time and targeting each other).
QUESTION
The operation pandas.DataFrame.lookup
is "Deprecated since version 1.2.0", and has since invalidated a lot of previous answers.
This post attempts to function as a canonical resource for looking up corresponding row col pairs in pandas versions 1.2.0 and newer.
Some previous answers to this type of question (now deprecated):
- Vectorized lookup on a pandas dataframe
- Python Pandas Match Vlookup columns based on header values
- Using DataFrame.lookup to get rows where columns names are a subset of a string
- Python: pandas: match row value to column name/ key's value
Some Current Answers to this Question:
- Reference DataFrame value corresponding to column header
- Pandas/Python: How to create new column based on values from other columns and apply extra condition to this new column
Given the following DataFrame:
...ANSWER
Answered 2021-Nov-18 at 21:34The documentation on Looking up values by index/column labels recommends using NumPy indexing via factorize
and reindex
as the replacement for the deprecated DataFrame.lookup
.
QUESTION
When I use .Internal(inspect())
to NA_real_
and NaN
, it returns,
ANSWER
Answered 2021-Dec-24 at 10:45NA
is a statistical or data integrity concept: the idea of a "missing value". Eg if your data comes from people filling in forms, a bad entry or missing entry would be treated as NA
.
NaN
is a numerical or computational concept: something that is "not a number". Eg 0/0 is NAN
, because the result of this computation is undefined (but note that 1/0 is Inf
, or infinity, and similarly -1/0 is -Inf
).
The way that R handles these concepts internally isn't something that you should ever be concerned about.
QUESTION
Given the following dataframe:
...ANSWER
Answered 2021-Dec-20 at 01:49One way is to aggregate your amounts by date first, then compute the rolling sum, and join this sum to the original list of dates to apply the rolling sum to all dates
QUESTION
I have a DataFrame:
name age 0 Paul 25 1 John 27 2 Bill 23I know that if I enter:
...
ANSWER
Answered 2021-Nov-13 at 13:51That's because for the loc
assignment all index axes are aligned, including the columns: Since age
and name
do not match, there is no data to assign, hence the NaNs.
You can make it work by renaming the columns:
QUESTION
I'm trying to recover some records but I don't remember the exact time or date they were deleted:
I remember that these records that were deleted all had 'nan' values in link
and name
columns, and the asof_date
values were between July & October.
I believe they were deleted 1 day ago so I tried this (since 1440 min in a day) but nothing populates:
...ANSWER
Answered 2021-Oct-14 at 13:49Unfortunately, since your data retention period is only 1 day, you will not be able to make use of time travel or fail safe.
For future reference, provided that you have Enterprise edition or above, then you can set the retention period to be up to 90 days, and then you have 7 days fail-safe protection after that.
QUESTION
I have dataframe which should be filled by understanding rows understanding like we do in excel. If its continious integer it fill by next number itself.
Is there any function in python like this?
...ANSWER
Answered 2021-Oct-08 at 12:41I tested some stuff out and did some more research. It appears pandas does not currently offer the functionality you're looking for.
df['cat'].interpolate(method='linear')
will only work if the first/last values are filled in already. You would have to manually assign df.loc[5, 'cat1'] = 6
in this example, then a linear interpolation would work.
Some Options:
If the data is small enough, you can always export to Excel and use the fill there, then bring back into pandas.
Analyze the patterns yourself and design your own fill methods. For example, to get the year, you can use
df['year'] = df.index.to_series().apply(lambda x: 2019 if x % 2 == 0 else 2020)
.
There are other Stack Overflow questions very similar to this, and none that I saw have a generic answer.
QUESTION
I have a df with numbers:
...ANSWER
Answered 2021-Oct-06 at 08:46You can use a bit of numpy
vectorial operations to generate masks, and use them to select
your labels:
QUESTION
In my opinion, things like float('nan')
should be optimized, but apparently they aren't in Python.
ANSWER
Answered 2021-Sep-26 at 11:50Membership testing
Two different instances of float('nan')
are not equal to each other. They are "Not a Number" so it makes sense that they shouldn't also have to be equal. They are different instances of objects which are not numbers:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install nan
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page