I have an import that is only used in a doctest. The linter (Pyflakes) that is used by Spyder to generate warnings right next to the code doesn't like this. I tried a bunch of ways of turning off the "imported but unused" warning, but the orange triangle in the left margin doesn't go away.
Since these triangles do not stem from Pylint but from Pyflakes, options for pylint naturally don't work:
import pandas as pd # pylint: disable=unused-import
import pandas as pd # pylint: disable=W0611
How can I turn the warnings off? I installed Spyder via WinPython.
With the new Spyder version 5.3.3, the following methods are now available in order to suppress the orange warning triangles: Append either # noqa or # analysis:ignore to the end of the line in question.
Suppressing only specific error types does not seem to work at this point in time.
(This information is mostly based on the closing post of the GitHub issue which Ilya mentioned.)
Apparently, this is a known bug in Spyder.
https://github.com/spyder-ide/spyder/issues/7879
I am new to Python using Spyder. I have written the following code:
import os
import numpy as np
os.environ["CDF_LIB"] = "D:\Anaconda\Lib"
from spacepy import pycdf
cdf = pycdf.CDF('Sample.cdf')
print(cdf) # Print the titles of the CDF file
Diff_en=cdf['diff'][:]
For some reason I keep getting the following error and I'm not sure why (I don't know what bool is). Any help is appreciated:
Diff_en=cdf['diff'][:]
D:\Anaconda\lib\site-packages\spacepy\pycdf\__init__.py:3957: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
This is a harmless warning. If you can live with it, just leave it be.
It comes about because the NumPy package recently deprecated its numpy.bool in favour of the standard Python bool (or alternatively the awkward numpy.bool_), as stated on the documentation page linked in your warning message.
If you can't stand the warning, I suggest you try updating spacepy. You can also silence the warning explicitly from within your project.
As the other answer notes, it's a warning and generally won't affect anything.
The following code can be used to selectively silence this particular DeprecationWarning
from warnings import filterwarnings
filterwarnings(action='ignore', category=DeprecationWarning, message='`np.bool` is a deprecated alias')
I'm struggling to suppress a specific warning related to fasttext.
The warning is Warning : 'load_model' does not return WordVectorModel or SupervisedModel any more, but a 'FastText' object which is very similar.
And here is the offending block of code:
with warnings.catch_warnings():
warnings.filterwarnings('ignore')
return fasttext.load_model(str(model_path)) # this line
I've attempted several ways to suppress the warning, mostly from this thread without success.
I'm using Python 3.8, fasttext v0.9.2.
For fasttext v0.9.2 this can be solved by adding the monkey patch below to your code (as per this GitHub issue).
import fasttext
fasttext.FastText.eprint = lambda x: None
As mentioned in that same GitHub issue, the warning message was removed in commit 9ef22d9 in May 2020, which will likely be in the next official release (fasttext v0.9.3), whenever that is.
There are many questions related to the question title above and all basically tell you to do:
import warnings
warnings.filterwarnings('ignore')
and to make sure this is placed before the first import.
However, even after doing this I get many warnings from seaborn and sklearn. I get UserWarning, DataConversionWarning and RuntimeWarning which, according to documentation, all inherit from Warning and should be covered by the above code.
Is there another way to hide those warnings?
(I cannot really solve most of them anyway)
EDIT
Example 1:
C:\Anaconda3\lib\site-packages\sklearn\preprocessing\data.py:645: DataConversionWarning: Data with input dtype int32, int64 were all converted to float64 by StandardScaler.
return self.partial_fit(X, y)
Example 2
C:\Anaconda3\lib\site-packages\seaborn\distributions.py:340: UserWarning: Attempted to set non-positive bottom ylim on a log-scaled axis.
Invalid limit will be ignored.
ax.set_ylim(0, auto=None)
Example2
It's a bit hard to track down; seaborn imports statsmodels. And in statsmodels/tools/sm_exceptions.py you find this line
warnings.simplefilter('always', category=UserWarning)
in which reverses any previous setting for user warnings.
A solution for now would be to remove that line or to set the warning state after the import of seaborn (and hence statsmodels). In a future version of statsmodels this will be fixed by PR 4712, so using the development version of statsmodels would also be an option.
Example1
I did not find a way to reproduce the first example from sklearn; so that may or may not have a different reason.
from slimit import minify
if __name__ == "__main__":
print("start")
# Normally, I pass real JavaScript. For this issue, an empty string reproduces problem.
minify("", mangle=True)
print("exit")
This triggers the following console output.
start
WARNING: Couldn't write lextab module <module 'slimit.lextab' from '/Users/kurtostfeld/samba/wrapad/venv/lib/python2.7/site-packages/slimit/lextab.pyc'>. Won't overwrite existing lextab module
WARNING: yacc table file version is out of date
WARNING: Token 'IMPORT' defined, but not used
WARNING: Token 'BLOCK_COMMENT' defined, but not used
WARNING: Token 'ENUM' defined, but not used
WARNING: Token 'EXTENDS' defined, but not used
WARNING: Token 'LINE_COMMENT' defined, but not used
WARNING: Token 'LINE_TERMINATOR' defined, but not used
WARNING: Token 'CONST' defined, but not used
WARNING: Token 'EXPORT' defined, but not used
WARNING: Token 'CLASS' defined, but not used
WARNING: Token 'SUPER' defined, but not used
WARNING: There are 10 unused tokens
WARNING: Couldn't create <module 'slimit.yacctab' from '/Users/kurtostfeld/samba/wrapad/venv/lib/python2.7/site-packages/slimit/yacctab.pyc'>. Won't overwrite existing tabmodule
exit
These warnings are flooding my application console output. How can I use minify without generating warnings?
I'm using Python 2.7.12, and what is currently the latest library versions: slimit 0.8.1, ply 3.10.
According to this issue on Github, slimit depends of the ply package. After few tries, it seems that theses warnings appear since version 3.8 of ply. . You could update ply to 3.6 which is the last version that doesn't bring these messages :
pip uninstall ply -y && pip install ply==3.6
It solved my problem.
UPDATE
Install a sooner version of ply was really a bad work around since some of my tests were failing. Original slimit version seems not maintained well so I suggest to update to a newer version, metatoaster did a good job to improve it and fixed that problem of warning message. The solution for me was to uninstall slimit and then install it's version:
pip install git+https://github.com/metatoaster/slimit.git#egg=slimit
FINAL UPDATE In fact, slimit seems not maintened anymore and its successor is called calmjs, there is few differences but it is really more stable and don't shows these annoying warning message. See: https://github.com/calmjs/calmjs.parse
Switching versions did not change anything for me, I found another workaround: simply delete (or move, if you want to be cautious) the mentioned files (yourpython/site-packages/slimit/yacctab.py and yourpython/site-packages/slimit/lextab.py).
I believe the module will re-create these files and stop bothering you with warning messages.
Slimit uses ply under the hood, which uses logging from stdlib. AFAICS slimit does not allow you to pass the logging parameters that ply's lex and yacc expect.
While you therefor can't (directly) access ply's logging, you should be able to suppress those messages my raising the global logging level:
import logging
...
logging.disable(logging.CRITICAL)
minify("", mangle=True)
logging.disable(logging.NOTSET)
You can use this parser, too: https://github.com/PiotrDabkowski/pyjsparser
It works and is easy to use. It does not handle comments though.
(Neither does calmjs seems to handle comments fully: Its parse function has a parameter to indicate that you want the comments, but as of now, some comments seem to get lost.)
Here was the solution that I went with. I made a custom variant of two slimit functions that pass an extra errorlog=ply.yacc.NullLogger() call to the ply.yacc.yacc function.
class SlimitNoLoggingParser(Parser):
"""
This is a simple customized variant to slimit.parser.Parser.
The only difference is that this passes a errorlog=ply.yacc.NullLogger() to ply.yacc.yacc to suppress unwanted
stderr logging output.
"""
def __init__(self, lex_optimize=True, lextab=lextab,
yacc_optimize=True, yacctab=yacctab, yacc_debug=False):
self.lex_optimize = lex_optimize
self.lextab = lextab
self.yacc_optimize = yacc_optimize
self.yacctab = yacctab
self.yacc_debug = yacc_debug
self.lexer = Lexer()
self.lexer.build(optimize=lex_optimize, lextab=lextab)
self.tokens = self.lexer.tokens
self.parser = ply.yacc.yacc(
module=self, optimize=yacc_optimize,
errorlog=ply.yacc.NullLogger(),
debug=yacc_debug, tabmodule=yacctab, start='program')
# https://github.com/rspivak/slimit/issues/29
# lexer.auto_semi can cause a loop in a parser
# when a parser error happens on a token right after
# a newline.
# We keep record of the tokens that caused p_error
# and if the token has already been seen - we raise
# a SyntaxError exception to avoid looping over and
# over again.
self._error_tokens = {}
# This is a simply variant of slimit.minify that suppresses unwanted noisy stderr logging output.
def warning_free_minify(text, mangle=False, mangle_toplevel=False):
parser = SlimitNoLoggingParser(lex_optimize=False)
tree = parser.parse(text)
if mangle:
mangler.mangle(tree, toplevel=mangle_toplevel)
minified = ECMAMinifier().visit(tree)
return minified