returns | Make your functions return something | Functional Programming library

 by   dry-python Python Version: 0.22.0 License: BSD-2-Clause

kandi X-RAY | returns Summary

kandi X-RAY | returns Summary

returns is a Python library typically used in Programming Style, Functional Programming applications. returns has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However returns build file is not available. You can install using 'pip install returns' or download it from GitHub, PyPI.

Make your functions return something meaningful, typed, and safe!.

            kandi-support Support

              returns has a medium active ecosystem.
              It has 2791 star(s) with 100 fork(s). There are 39 watchers for this library.
              There were 3 major release(s) in the last 12 months.
              There are 63 open issues and 342 have been closed. On average issues are closed in 96 days. There are 7 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of returns is 0.22.0

            kandi-Quality Quality

              returns has 0 bugs and 0 code smells.

            kandi-Security Security

              returns has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              returns code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              returns is licensed under the BSD-2-Clause License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              returns releases are available to install and integrate.
              Deployable package is available in PyPI.
              returns has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions, examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed returns and discovered the below as its top functions. This is intended to give you an instant insight into returns implemented functionality, and help decide if they suit your requirements.
            • Creates a decorator factory
            • Create a release function to release the given value
            • Creates a function that uses the given acquire function
            • Return a wrapped function
            • Interpret args and kwargs
            • Wrapper for eager_curry_curry
            • Check all laws of a given container type
            • Build a search strategy from a given container type
            • Collect elements from iterable
            • Collect the elements from the iterable
            • Collects all the elements from the iterable
            • Collects all elements from iterable
            • Applies the result to the given container
            • Return the result of the given expression
            • Return the type of the function
            • Create a function that applies the given function to each instance
            • Create a search strategy from a legal container type
            • Implements the expression expr
            • Wrap a function into a Future
            • Return a new instance type
            • Maps the value of the given container to a new value
            • Analyze a function call
            • Evaluate the expression
            • Marks a function as kinded
            • Binds a function to an async context
            • Analyze member access
            • Creates a ContextualFutureResult
            Get all kandi verified functions for this library.

            returns Key Features

            No Key Features are available at this moment for returns.

            returns Examples and Code Snippets

            Returns an RaggedTensor .
            pythondot img1Lines of Code : 82dot img1License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def _ragged_getitem(rt_input, key_list):
              """Helper for indexing and slicing ragged tensors with __getitem__().
              Extracts the specified piece of the `rt_input`.  See
              `RaggedTensor.__getitem__` for examples and restrictions.
            Returns the handle to the replicated variable .
            pythondot img2Lines of Code : 77dot img2License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def get_replicated_var_handle(self,
                                            name: Text,
                                            handle_id: Text,
                                            vars_: Union[List[core_types.Tensor],
            Returns the implicit gradient of a function .
            pythondot img3Lines of Code : 77dot img3License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def implicit_val_and_grad(f):
              """Returns a function which differentiates f with respect to variables.
              The wrapped function returns the value and the gradient of f when called with
              the same arguments. The gradient is with respect to all trainab  
            Pandas: New column adding values of different columns with strings and numbers
            Pythondot img4Lines of Code : 20dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            def played_from_start(entry):
                entry = str(entry)  # Without this, np.nan is a float.
                if entry == 'nan' or entry == '':
                    return 0
                if entry.startswith('Bench'):
                    return 0
                if entry == 'Starting':
                    return 9
            I don't understand how the second bracket works
            Pythondot img5Lines of Code : 22dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            y = np.array([1,2,1,3])
            array([True, False, True, False])
            x = np.array([[1,2],[3,4],[5,6],[7,8]])
                array([[1, 2],
                       [3, 4],
                       [5, 6],
                       [7, 8]])
            Invalid syntax in python_data['division'].append(int(split[3]))
            Pythondot img6Lines of Code : 4dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            data["age"].append(int(split[2]) <- here
            Pygame rect.inflate function does not seem to work
            Pythondot img7Lines of Code : 4dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            rect = pygame.Rect(block.x+scx, block.y+scy, 10, 10)
            rect.inflate_ip(zoom, zoom) 
            pygame.draw.rect(screen, block.color, rect)
            copy iconCopy
            df.loc[df.jpgs.apply(lambda x: "123.jpg" not in x)]
            FastAPI - How to get the response body in Middleware
            Pythondot img9Lines of Code : 22dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            from starlette.concurrency import iterate_in_threadpool
            async def some_middleware(request: Request, call_next):
                response = await call_next(request)
                response_body = [chunk async for chunk in response.body_it
            Another way of generating a list of special elements in python
            Pythondot img10Lines of Code : 5dot img10License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            >>> from itertools import product
            >>> a = [''.join(p) for p in product('01', repeat=3)]
            >>> a
            ['000', '001', '010', '011', '100', '101', '110', '111']

            Community Discussions


            Why is `np.sum(range(N))` very slow?
            Asked 2022-Mar-29 at 14:31

            I saw a video about speed of loops in python, where it was explained that doing sum(range(N)) is much faster than manually looping through range and adding the variables together, since the former runs in C due to built-in functions being used, while in the latter the summation is done in (slow) python. I was curious what happens when adding numpy to the mix. As I expected np.sum(np.arange(N)) is the fastest, but sum(np.arange(N)) and np.sum(range(N)) are even slower than doing the naive for loop.

            Why is this?

            Here's the script I used to test, some comments about the supposed cause of slowing done where I know (taken mostly from the video) and the results I got on my machine (python 3.10.0, numpy 1.21.2):

            updated script:



            Answered 2021-Oct-16 at 17:42

            From the cpython source code for sum sum initially seems to attempt a fast path that assumes all inputs are the same type. If that fails it will just iterate:



            Why is the return type of std::rand() not an unsigned int?
            Asked 2022-Mar-28 at 15:04

            std::rand said,

            int rand();
            Returns a pseudo-random integral value between ​0​ and RAND_MAX (​0​ and RAND_MAX included).

            Since it is guaranteed that a non-negative integer will be returned, why the return type is signed?

            I am not talking about if we should use it here. Is it a historical issue or some bad design?



            Answered 2022-Mar-02 at 02:12

            There is much debate about unsigned. Without going too much into subjective territory, consider the following: What matters is not whether the value returned from rand() cannot be negative. What matters is that rand() returns a value of a certain type and that type determines what you can do with that value. rand() never returns a negative value, but does it make sense to apply operations on the value that make the value negative? Certainly yes. For example you might want to do:



            Repeat values of an array on both the axes
            Asked 2022-Mar-26 at 04:54

            Say I have this array:



            Answered 2022-Jan-09 at 16:18

            You'd have to use np.repeat twice here.



            Error: [PrivateRoute] is not a component. All component children of must be a or
            Asked 2022-Mar-24 at 16:08

            I'm using React Router v6 and am creating private routes for my application.

            In file PrivateRoute.js, I've the code



            Answered 2021-Nov-12 at 21:20

            I ran into the same issue today and came up with the following solution based on this very helpful article by Andrew Luca

            In PrivateRoute.js:



            Keras AttributeError: 'Sequential' object has no attribute 'predict_classes'
            Asked 2022-Mar-23 at 04:30

            Im attempting to find model performance metrics (F1 score, accuracy, recall) following this guide

            This exact code was working a few months ago but now returning all sorts of errors, very confusing since i havent changed one character of this code. Maybe a package update has changed things?

            I fit the sequential model with, then used model.evaluate to find test accuracy. Now i am attempting to use model.predict_classes to make class predictions (model is a multi-class classifier). Code shown below:



            Answered 2021-Aug-19 at 03:49

            This function were removed in TensorFlow version 2.6. According to the keras in rstudio reference

            update to



            How does Java know which overloaded method to call with lambda expressions? (Supplier, Consumer, Callable, ...)
            Asked 2022-Mar-17 at 08:29

            First off, I have no idea how to decently phrase the question, so this is up for suggestions.

            Lets say we have following overloaded methods:



            Answered 2022-Mar-17 at 08:29

            It all makes sense and has a simple pattern besides () -> null being a Callable I think. The Runnable is clearly different from the Supplier/Callable as it has no input and output values. The difference between Callable and Supplier is that with the Callable you have to handle exceptions.

            The reason that () -> null is a Callable without an exception is the return type of your definition Callable. It requires you to return the reference to some object. The only possible reference to return for Void is null. This means that the lambda () -> null is exactly what your definition demands. It would also work for your Supplier example if you would remove the Callable definition. However, it uses Callable over Supplier as the Callable has the exact type.

            Callable is chosen over Supplier as it is more specific (as a comment already suggested). The Java Docs state that it chooses the most specific type if possible:

            Type inference is a Java compiler's ability to look at each method invocation and corresponding declaration to determine the type argument (or arguments) that make the invocation applicable. The inference algorithm determines the types of the arguments and, if available, the type that the result is being assigned, or returned. Finally, the inference algorithm tries to find the most specific type that works with all of the arguments.



            Understanding color scales in ggplot2
            Asked 2022-Feb-03 at 17:47

            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.



            Answered 2022-Feb-01 at 18:14

            This 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.

            Overall Categorization

            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:

            1. 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 1 and 2, there can exist something that would have a theoretical value of 1.5. 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.

            2. The Colorspace. The color palette used to draw onto the plot. By default, ggplot2 uses (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.

            3. 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().

            Continuous Scales

            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):



            Difference between NA_real_ and NaN
            Asked 2022-Jan-19 at 13:02

            When I use .Internal(inspect()) to NA_real_ and NaN, it returns,



            Answered 2021-Dec-24 at 10:45

            NA 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.



            ellipsis ... as function in substitute?
            Asked 2022-Jan-13 at 06:04

            I'm having trouble understanding how/why parentheses work where they otherwise should not work®.



            Answered 2022-Jan-09 at 16:14

            Note: When referring to documentation and source code, I provide links to an unofficial GitHub mirror of R's official Subversion repository. The links are bound to commit 97b6424 in the GitHub repo, which maps to revision 81461 in the Subversion repo (the latest at the time of this edit).

            substitute is a "special" whose arguments are not evaluated (doc).



            Why does Math.min() return -0 from [+0, 0, -0]
            Asked 2021-Dec-23 at 08:22

            I know (-0 === 0) comes out to be true. I am curious to know why -0 < 0 happens?

            When I run this code in stackoverflow execution context, it returns 0.



            Answered 2021-Dec-22 at 14:17

            This is a specialty of Math.min, as specified:

   Math.min ( ...args )


            1. For each element number of coerced, do

            a. If number is NaN, return NaN.

            b. If number is -0𝔽 and lowest is +0𝔽, set lowest to -0𝔽.

            c. If number < lowest, set lowest to number.

            1. Return lowest.

            Note that in most cases, +0 and -0 are treated equally, also in the ToString conversion, thus (-0).toString() evaluates to "0". That you can observe the difference in the browser console is an implementation detail of the browser.


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install returns

            You are also required to configure mypy correctly and install our plugin to fix this existing issue:. We also recommend to use the same mypy settings we use. Make sure you know how to get started, check out our docs! Try our demo.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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