How to get first N number of elements from an array
To get the first n elements of an array, use const slicedArray = array.slice(0, n);
To get the first n elements of an array, use const slicedArray = array.slice(0, n);
You can use a HSV color thresholder script with trackbars to isolate the desired lower/upper HSV color range import cv2 import sys import numpy as np def nothing(x): pass # Create a window cv2.namedWindow(‘image’) # create trackbars for color change cv2.createTrackbar(‘HMin’,’image’,0,179,nothing) # Hue is from 0-179 for Opencv cv2.createTrackbar(‘SMin’,’image’,0,255,nothing) cv2.createTrackbar(‘VMin’,’image’,0,255,nothing) cv2.createTrackbar(‘HMax’,’image’,0,179,nothing) cv2.createTrackbar(‘SMax’,’image’,0,255,nothing) cv2.createTrackbar(‘VMax’,’image’,0,255,nothing) # Set … Read more
For a clearer “minimal, complete, verifiable example”, I’ll use the following input array in my demos: $array = [ [‘user_id’ => 82, ‘ac_type’ => 1], [‘user_id’ => 80, ‘ac_type’ => 5], [‘user_id’ => 76, ‘ac_type’ => 1], [‘user_id’ => 82, ‘ac_type’ => 2], [‘user_id’ => 80, ‘ac_type’ => 5] ]; // elements [0] and [3] … Read more
You can use UIVisualEffectView to achieve this effect. This is a native API that has been fine-tuned for performance and great battery life, plus it’s easy to implement. Swift: //only apply the blur if the user hasn’t disabled transparency effects if !UIAccessibility.isReduceTransparencyEnabled { view.backgroundColor = .clear let blurEffect = UIBlurEffect(style: .dark) let blurEffectView = UIVisualEffectView(effect: … Read more
With array_intersect_key and array_flip: var_dump(array_intersect_key($my_array, array_flip($allowed))); array(1) { [“foo”]=> int(1) }
First of all, it’s considered bad practice to extend Object.prototype. Instead, provide your feature as stand-alone function, or if you really want to extend a global, provide it as utility function on Object, just like there already are Object.keys, Object.assign, Object.is, …etc. I provide here several solutions: Using reduce and Object.keys As (1), in combination … Read more
If date column is the index, then use .loc for label based indexing or .iloc for positional indexing. For example: df.loc[‘2014-01-01′:’2014-02-01′] See details here http://pandas.pydata.org/pandas-docs/stable/dsintro.html#indexing-selection If the column is not the index you have two choices: Make it the index (either temporarily or permanently if it’s time-series data) df[(df[‘date’] > ‘2013-01-01’) & (df[‘date’] < ‘2013-02-01’)] … Read more
You can define a custom comparison function using array_udiff(). function udiffCompare($a, $b) { return $a[‘ITEM’] – $b[‘ITEM’]; } $arrdiff = array_udiff($arr2, $arr1, ‘udiffCompare’); print_r($arrdiff); Output: Array ( [3] => Array ( [ITEM] => 4 ) ) This uses and preserves the arrays’ existing structure, which I assume you want.
If you have multiple columns in your dataframe and would like to remove all rows that have outliers in at least one column, the following expression would do that in one shot. df = pd.DataFrame(np.random.randn(100, 3)) from scipy import stats df[(np.abs(stats.zscore(df)) < 3).all(axis=1)] description: For each column, it first computes the Z-score of each value … Read more
There is valuable information in the existing answers, but I think a more focused summary is helpful. Note that the original form of this answer advocated strict avoidance of script blocks ({…}) and AD-provider variable evaluation, but this has been replaced with more nuanced recommendations. tl;dr Get-ADUser -Filter ‘sAMAccountName -eq $SamAc’ Do not quote the … Read more