drop | creating dropdowns and other floating elements | Menu library
kandi X-RAY | drop Summary
kandi X-RAY | drop Summary
Drop.js is a powerful Javascript and CSS library for creating dropdowns and other floating displays.
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Lines of Code : 6
License : No License
Copyconst dropRightWhile = (arr, func) => {
let rightIndex = arr.length;
while (rightIndex-- && !func(arr[rightIndex]));
return arr.slice(0, rightIndex + 1);
};
dropRightWhile([1, 2, 3, 4], n => n < 3); // [1, 2]
const dropWhile = (arr, func) => {
while (arr.length > 0 && !func(arr[0])) arr = arr.slice(1);
return arr;
};
dropWhile([1, 2, 3, 4], n => n >= 3); // [3, 4]
const dropRight = (arr, n = 1) => arr.slice(0, -n);
dropRight([1, 2, 3]); // [1, 2]
dropRight([1, 2, 3], 2); // [1]
dropRight([1, 2, 3], 42); // []
public void dropToPersistantStorage(Message message, Logger logger) {
try {
// Get message reference
MessageReference messageReference = (MessageReference) message.getMessageHeader().getData();
// Create a unique name for the def _get_batched_dataset_attributes(d):
"""Get `batch_size`, `drop_remainder` of dataset."""
# pylint: disable=protected-access
assert isinstance(d,
(dataset_ops.BatchDataset, batching._MapAndBatchDataset))
if isinstance(d Community Discussions
Trending Discussions on drop
QUESTION
Background
I have a complex nested JSON object, which I am trying to unpack into a pandas df in a very specific way.
JSON Object
this is an extract, containing randomized data of the JSON object, which shows examples of the hierarchy (inc. children) for 1x family (i.e. 'Falconer Family'), however there is 100s of them in total and this extract just has 1x family, however the full JSON object has multiple -
ANSWER
Answered 2022-Feb-16 at 06:41I think this gets you pretty close; might just need to adjust the various name columns and drop the extra data (I kept the grouping column).
The main idea is to recursively use pd.json_normalize with pd.concat for all availalable children levels.
EDIT: Put everything into a single function and added section to collapse the name columns like the expected output.
QUESTION
The following code:
...ANSWER
Answered 2022-Feb-13 at 19:56From the documentation, pandas.DataFrame.drop has the following parameters:
Parameters
labels: single label or list-like Index or column labels to drop.
axis: {0 or ‘index’, 1 or ‘columns’}, default 0 Whether to drop labels from the index (0 or ‘index’) or columns (1 or ‘columns’).
index: single label or list-like Alternative to specifying axis (labels, axis=0 is equivalent to index=labels).
columns: single label or list-like Alternative to specifying axis (labels, axis=1 is equivalent to columns=labels).
level: int or level name, optional For MultiIndex, level from which the labels will be removed.
inplace: bool, default False If False, return a copy. Otherwise, do operation inplace and return None.
errors: {‘ignore’, ‘raise’}, default ‘raise’ If ‘ignore’, suppress error and only existing labels are dropped.
Moving forward, only labels (the first parameter) can be positional.
So, for this example, the drop code should be as follows:
QUESTION
There are so many ways to define colour scales within ggplot2. After just loading ggplot2 I count 22 functions beginging with scale_color_* (or scale_colour_*) and same number beginging with scale_fill_*. Is it possible to briefly name the purpose of the functions below? Particularly I struggle with the differences of some of the functions and when to use them.
- scale_*_binned()
- scale_*_brewer()
- scale_*_continuous()
- scale_*_date()
- scale_*_datetime()
- scale_*_discrete()
- scale_*_distiller()
- scale_*_fermenter()
- scale_*_gradient()
- scale_*_gradient2()
- scale_*_gradientn()
- scale_*_grey()
- scale_*_hue()
- scale_*_identity()
- scale_*_manual()
- scale_*_ordinal()
- scale_*_steps()
- scale_*_steps2()
- scale_*_stepsn()
- scale_*_viridis_b()
- scale_*_viridis_c()
- scale_*_viridis_d()
What I tried
I've tried to make some research on the web but the more I read the more I get onfused. To drop some random example: "The default scale for continuous fill scales is scale_fill_continuous() which in turn defaults to scale_fill_gradient()". I do not get what the difference of both functions is. Again, this is just an example. Same is true for scale_color_binned() and scale_color_discrete() where I can not name the difference. And in case of scale_color_date() and scale_color_datetime() the destription says "scale_*_gradient creates a two colour gradient (low-high), scale_*_gradient2 creates a diverging colour gradient (low-mid-high), scale_*_gradientn creates a n-colour gradient." which is nice to know but how is this related to scale_color_date() and scale_color_datetime()? Looking for those functions on the web does not give me very informative sources either. Reading on this topic gets also chaotic because there are tons of color palettes in different packages which are sequential/ diverging/ qualitative plus one can set same color in different ways, i.e. by color name, rgb, number, hex code or palette name. In part this is not directly related to the question about the 2*22 functions but in some cases it is because providing a "wrong" palette results in an error (e.g. the error"Continuous value supplied to discrete scale).
Why I ask this
I need to do many plots for my work and I am supposed to provide some function that returns all kind of plots. The plots are supposed to have similiar layout so that they fit well together. One aspect I need to consider here is that the colour scales of the plots go well together. See here for example, where so many different kind of plots have same colour scale. I was hoping I could use some general function which provides a colour palette to any data, regardless of whether the data is continuous or categorical, whether it is a fill or col easthetic. But since this is not how colour scales are defined in ggplot2 I need to understand what all those functions are good for.
ANSWER
Answered 2022-Feb-01 at 18:14This is a good question... and I would have hoped there would be a practical guide somewhere. One could question if SO would be a good place to ask this question, but regardless, here's my attempt to summarize the various scale_color_*() and scale_fill_*() functions built into ggplot2. Here, we'll describe the range of functions using scale_color_*(); however, the same general rules will apply for scale_fill_*() functions.
There are 22 functions in all, but happily we can group them intelligently based on practical usage scenarios. There are three key criteria that can be used to define practically how to use each of the scale_color_*() functions:
Nature of the mapping data. Is the data mapped to the color aesthetic discrete or continuous? CONTINUOUS data is something that can be explained via real numbers: time, temperature, lengths - these are all continuous because even if your observations are
1and2, there can exist something that would have a theoretical value of1.5. DISCRETE data is just the opposite: you cannot express this data via real numbers. Take, for example, if your observations were:"Model A"and"Model B". There is no obvious way to express something in-between those two. As such, you can only represent these as single colors or numbers.The Colorspace. The color palette used to draw onto the plot. By default,
ggplot2uses (I believe) a color palette based on evenly-spaced hue values. There are other functions built into the library that use either Brewer palettes or Viridis colorspaces.The level of Specification. Generally, once you have defined if the scale function is continuous and in what colorspace, you have variation on the level of control or specification the user will need or can specify. A good example of this is the functions:
*_continuous(),*_gradient(),*_gradient2(), and*_gradientn().
We can start off with continuous scales. These functions are all used when applied to observations that are continuous variables (see above). The functions here can further be defined if they are either binned or not binned. "Binning" is just a way of grouping ranges of a continuous variable to all be assigned to a particular color. You'll notice the effect of "binning" is to change the legend keys from a "colorbar" to a "steps" legend.
The continuous example (colorbar legend):
QUESTION
With regard to the Log4j JNDI remote code execution vulnerability that has been identified CVE-2021-44228 - (also see references) - I wondered if Log4j-v1.2 is also impacted, but the closest I got from source code review is the JMS-Appender.
The question is, while the posts on the Internet indicate that Log4j 1.2 is also vulnerable, I am not able to find the relevant source code for it.
Am I missing something that others have identified?
Log4j 1.2 appears to have a vulnerability in the socket-server class, but my understanding is that it needs to be enabled in the first place for it to be applicable and hence is not a passive threat unlike the JNDI-lookup vulnerability which the one identified appears to be.
Is my understanding - that Log4j v1.2 - is not vulnerable to the jndi-remote-code execution bug correct?
ReferencesThis blog post from Cloudflare also indicates the same point as from AKX....that it was introduced from Log4j 2!
Update #1 - A fork of the (now-retired) apache-log4j-1.2.x with patch fixes for few vulnerabilities identified in the older library is now available (from the original log4j author). The site is https://reload4j.qos.ch/. As of 21-Jan-2022 version 1.2.18.2 has been released. Vulnerabilities addressed to date include those pertaining to JMSAppender, SocketServer and Chainsaw vulnerabilities. Note that I am simply relaying this information. Have not verified the fixes from my end. Please refer the link for additional details.
...ANSWER
Answered 2022-Jan-01 at 18:43The JNDI feature was added into Log4j 2.0-beta9.
Log4j 1.x thus does not have the vulnerable code.
QUESTION
In vuejs2 app having select input with rather big options list it breaks design of my page on extra small devices. Searching in net I found “size” property, but that not what I I need : I want to have dropdown selection, which is the default. Are there some other decision, maybe with CSS to set max-height of dropdown selection area.
Modeified PART # 1: I made testing demo page at http://photographers.my-demo-apps.tk/sel_test it has 2 select inputs with custom design and events as in this example link How to Set Height for the Drop Down of Select box and following workaround at js fiddle:
...ANSWER
Answered 2022-Jan-15 at 16:00Unfortunately, you cannot chant the height of a dropdown list (while using ).
It is confirmed here.
you can build it yourself using divs & v-for (assuming you get the list from an outsource) and then you can style it as you wish.
apologies for barring bad news.
QUESTION
I have a matrix with many rows and columns, of the nature
...ANSWER
Answered 2022-Jan-02 at 17:02How about this?
QUESTION
I am trying to try a sample project in Flutter integration email and google based login, and planning to use firebase initialisation for doing it while I have followed all the steps as mentioned in tutorials I am getting this error as soon as firebase is attempted to be initialised.
...ANSWER
Answered 2021-Dec-25 at 09:13UPDATE:
For your firebase_core version is seems to be sufficient to pass the FirebaseOptions once you initialize firebase in your flutter code (and you don't need any script tags in your index.html):
QUESTION
I couldn't find a question similar to the one that I have here. I have a very large named list of named vectors that match column names in a dataframe. I would like to use the list of named vectors to replace values in the dataframe columns that match each list element's name. That is, the name of the vector in the list matches the name of the dataframe column and the key-value pair in each vector element will be used to recode the column.
Reprex below:
...ANSWER
Answered 2021-Dec-13 at 04:44One work around would be to use your map2_dfr code, but then bind the columns that are needed to the map2_dfr output. Though you still have to drop the names column.
QUESTION
I notice when I run the same code as my example over here but with a union or unionByName or unionAll instead of the join, my query planning takes significantly longer and can result in a driver OOM.
Code included here for reference, with a slight difference to what occurs inside the for() loop.
ANSWER
Answered 2021-Aug-16 at 17:48This is a known limitation of iterative algorithms in Spark. At the moment, every iteration of the loop causes the inner nodes to be re-evaluated and stacked upon the outer df variable.
This means your query planning process is taking O(exp(n)) where n is the number of iterations of your loop.
There's a tool in Palantir Foundry called Transforms Verbs that can help with this.
Simply import transforms.verbs.dataframes.union_many and call it upon the total set of dataframes you wish to materialize (assuming your logic will allow for it, i.e. one iteration of the loop doesn't depend upon the result of a prior iteration of the loop.
The code above should instead be modified to:
QUESTION
Here's two groupby operations on a pandas.DataFrame:
ANSWER
Answered 2021-Sep-12 at 10:34I think the easiest is to avoid .apply() which indeed do weird things when recombining. This is probably because the semantics of this function are so vague. You can return anything and pandas will do its best to guess what you meant
If you want consistent results with functions that apply to the whole sub-dataframe you’re better off running the function yourself:
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