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How to remove "[]" from within a list?


By : adamnalisnick
Date : July 31 2020, 06:00 PM
this will help Where I have empty elements within lists, [] appears? I'm not sure how to remove this? My list that I'm appending to is copying text from another file. It ends up looking like so: , Simple solution:
code :
x = [2,[], 3, [], 4]
x = [i for i in x if i != []]
print(x)
[2, 3, 4]


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Unused "IBOutlet" cannot remove from "File's Owner" "Connection Inspector", "OutLets&


By : Archdevel
Date : March 29 2020, 07:55 AM
wish help you to fix your issue Sometimes I have the same problem. Make sure you remove it from the Interface Builder, Product -> Clean and then restart Xcode.
That usually does the trick for me

How to remove "Quick edit" option in "register_post_meta" list page


By : Mohit Maheshwari
Date : March 29 2020, 07:55 AM
I wish did fix the issue. I am using register_post_type in list page automatically it is four actions Edit/Quick Edit/trash/View. I want to remove "Quick edit" option from list page. How I can do this.
code :
/*------------------------------------------------------------------------------------
    remove quick edit for custom post type videos just to check if less mem consumption
------------------------------------------------------------------------------------*/
add_filter( 'post_row_actions', 'remove_row_actions', 10, 2 );
function remove_row_actions( $actions, $post )
{
  global $current_screen;
    if( $current_screen->post_type != 'videos' ) return $actions;
    unset( $actions['edit'] );
    unset( $actions['view'] );
    unset( $actions['trash'] );
    unset( $actions['inline hide-if-no-js'] );
    //$actions['inline hide-if-no-js'] .= __( 'Quick Edit' );

    return $actions;
}

How to remove "My Wish List" and "Compare Products" blocks from Magento 2.0 Luma template?


By : Ashish
Date : March 29 2020, 07:55 AM
I hope this helps you . I have spent a lot of hours trying to remove "My Wish List" and "Compare Products" blocks from Luma template. Besides, I need to change and remove some of the bottom links. , I've tried following step it worked for me.
1. My Wish List
code :
<referenceBlock name="catalog.compare.sidebar" remove="true"/>

System "Pin to this list" and "Remove from this list" jump list context menu actions don't do anythi


By : David Paramonov
Date : March 29 2020, 07:55 AM
hop of those help? Unfortunately it seems that this functionality was not yet implemented by Microsoft into UWP apps and is currently available only for the system-managed default group of items.
You can create named groups of jumplist items, but that will cause the pin button to be displayed albeit it is not functional.
code :
JumpListItem jumplistItem = JumpListItem.CreateWithArguments(mruToken, file.Name);

//comment this out
//jumplistItem.GroupName = "Popular files"; 

_jumpList.Items.Add(jumplistItem);

await _jumpList.SaveAsync();

How to remove List special characters ("()", "'",",") from the output of a bi / tri-gram i


By : somazero
Date : March 29 2020, 07:55 AM
I wish this help you A better question would have been what are those ("()", "'",",") in the ngrams output?
code :
>>> from nltk import ngrams
>>> from nltk import word_tokenize

# Split a sentence into a list of "words"
>>> word_tokenize("This is a foo bar sentence")
['This', 'is', 'a', 'foo', 'bar', 'sentence']
>>> type(word_tokenize("This is a foo bar sentence"))
<class 'list'>

# Extract bigrams.
>>> list(ngrams(word_tokenize("This is a foo bar sentence"), 2))
[('This', 'is'), ('is', 'a'), ('a', 'foo'), ('foo', 'bar'), ('bar', 'sentence')]

# Okay, so the output is a list, no surprise.
>>> type(list(ngrams(word_tokenize("This is a foo bar sentence"), 2)))
<class 'list'>
>>> list(ngrams(word_tokenize("This is a foo bar sentence"), 2))[0]
('This', 'is')
>>> first_thing_in_output = list(ngrams(word_tokenize("This is a foo bar sentence"), 2))[0]
>>> type(first_thing_in_output)
<class 'tuple'>
>>> print(first_thing_in_output)
('This', 'is')
>>> print(str(first_thing_in_output))
('This', 'is')
>>> print(' '.join((first_thing_in_output)))
This is
from nltk import word_tokenize
from nltk.util import ngrams
from nltk.corpus import stopwords

text = '''The pure amnesia of her face,
newborn. I looked so far into her that, for a while, looked so far into her that, for a while  looked so far into her that, for a while looked so far into her that, for a while the visual 
held no memory. Little by little, I returned to myself, waking to nurse the visual held no  memory. Little by little, I returned to myself, waking to nurse
'''

stoplist = set(stopwords.words('english'))

tokens = [token for token in nltk.word_tokenize(text) if token not in stoplist]

list(ngrams(tokens, 2))
[('The', 'pure'),
 ('pure', 'amnesia'),
 ('amnesia', 'face'),
 ('face', ','),
 (',', 'newborn'),
 ('newborn', '.'),
 ('.', 'I'),
 ('I', 'looked'),
 ('looked', 'far'),
 ('far', ','),
 (',', ','), ...]
from string import punctuation
from nltk import word_tokenize
from nltk.util import ngrams
from nltk.corpus import stopwords

text = '''The pure amnesia of her face,
newborn. I looked so far into her that, for a while, looked so far into her that, for a while  looked so far into her that, for a while looked so far into her that, for a while the visual 
held no memory. Little by little, I returned to myself, waking to nurse the visual held no  memory. Little by little, I returned to myself, waking to nurse
'''

stoplist = set(stopwords.words('english') + list(punctuation))

tokens = [token for token in nltk.word_tokenize(text) if token not in stoplist]

list(ngrams(tokens, 2))
[('The', 'pure'),
 ('pure', 'amnesia'),
 ('amnesia', 'face'),
 ('face', 'newborn'),
 ('newborn', 'I'),
 ('I', 'looked'),
 ('looked', 'far'),
 ('far', 'looked'),
 ('looked', 'far'), ...]
>>> stopwords.words('english')
['i',
 'me',
 'my',
 'myself',
 'we',
 'our',
 'ours',
 'ourselves',
 'you',
 "you're", ...]
from string import punctuation
from nltk import word_tokenize
from nltk.util import ngrams
from nltk.corpus import stopwords

text = '''The pure amnesia of her face,
newborn. I looked so far into her that, for a while, looked so far into her that, for a while  looked so far into her that, for a while looked so far into her that, for a while the visual 
held no memory. Little by little, I returned to myself, waking to nurse the visual held no  memory. Little by little, I returned to myself, waking to nurse
'''

stoplist = set(stopwords.words('english') + list(punctuation))

tokens = [token for token in nltk.word_tokenize(text) if token.lower() not in stoplist]

list(ngrams(tokens, 2))
[('pure', 'amnesia'),
 ('amnesia', 'face'),
 ('face', 'newborn'),
 ('newborn', 'looked'),
 ('looked', 'far'),
 ('far', 'looked'),
 ('looked', 'far'),
 ('far', 'looked'),
 ('looked', 'far'),
 ('far', 'looked'), ...]
from string import punctuation
from nltk import word_tokenize
from nltk.util import ngrams
from nltk.corpus import stopwords
from nltk import FreqDist

text = '''The pure amnesia of her face,
newborn. I looked so far into her that, for a while, looked so far into her that, for a while  looked so far into her that, for a while looked so far into her that, for a while the visual 
held no memory. Little by little, I returned to myself, waking to nurse the visual held no  memory. Little by little, I returned to myself, waking to nurse
'''

stoplist = set(stopwords.words('english') + list(punctuation))

tokens = [token for token in nltk.word_tokenize(text) if token.lower() not in stoplist]

FreqDist(ngrams(tokens, 2)).most_common()
[(('looked', 'far'), 4),
 (('far', 'looked'), 3),
 (('visual', 'held'), 2),
 (('held', 'memory'), 2),
 (('memory', 'Little'), 2),
 (('Little', 'little'), 2),
 (('little', 'returned'), 2),
 (('returned', 'waking'), 2),
 (('waking', 'nurse'), 2),
 (('pure', 'amnesia'), 1),
 (('amnesia', 'face'), 1),
 (('face', 'newborn'), 1),
 (('newborn', 'looked'), 1),
 (('far', 'visual'), 1),
 (('nurse', 'visual'), 1)]
with open('bigrams-list.tsv', 'w') as fout:
    for bg, count in FreqDist(ngrams(tokens, 2)).most_common():
        print('\t'.join([' '.join(bg), str(count)]), end='\n', file=fout)
looked far  4
far looked  3
visual held 2
held memory 2
memory Little   2
Little little   2
little returned 2
returned waking 2
waking nurse    2
pure amnesia    1
amnesia face    1
face newborn    1
newborn looked  1
far visual  1
nurse visual    1
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