apply

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I have a UIImage that is a small symbol that is all black. The UIImage is getting set in a custom UIButton subclass I have. Is it possible to have the image to apply the tintColor to it, so instead of the black image it changes colors to whatever the tintColor is? I'm just trying to avoid creating new assets. // here I want defaultImageName (that is black) to use the tintColor (that is white) [self setImage:[UIImage imageNamed:defaultImageName] forState:UIControlStateNormal]; 回答1: If you are just supporting iOS 7 you can use tintColor and

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I really hope someone can advise. I have 2 branches master | design Working in design I did a stash and switched to master, made some adjustments. Switched back to design and did a stash apply only to lose all my changes in the design branch. I am hoping all my work is within a stash as I have not cleared or removed these. If I do a stash list I get 4 results: stash@{0}: WIP on design: f2c0c72... Adjust Password Recover Email stash@{1}: WIP on design: f2c0c72... Adjust Password Recover Email stash@{2}: WIP on design: eb65635... Email Adjust

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 由 翻译 强力驱动 问题: On a concrete problem, say I have a DataFrame DF word tag count 0 a S 30 1 the S 20 2 a T 60 3 an T 5 4 the T 10 I want to find, for every "word", the "tag" that has the most "count" . So the return would be something like word tag count 1 the S 20 2 a T 60 3 an T 5 I don't care about the count column or if the order/Index is original or messed up. Returning a dictionary { 'the' : 'S' , ...} is just fine. I hope I can do DF . groupby ([ 'word' ]). agg ( lambda x : x [ 'tag' ][ x [ 'count' ]. argmax () ] ) but it doesn't work. I can

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I am trying to use drop_duplicates on a column of a dataframe , A len ['1', '2'] 2 ['1', '2'] 2 ['3'] 1 ['4', '5'] 2 ['4', '5'] 2 The result dataframe should look like A len ['1', '2'] 2 ['3'] 1 ['4', '5'] 2 I have tried df.drop_duplicates('A', inplace=True) , but got error, unhashable type: 'numpy.ndarray' I have also converted A to lists and sets using df['A'].apply(list) and df['A'].apply(set) , and then using drop_duplicates , but all failed with unhashable type: 'set' and 'list' . I am wondering how to resolve the issue. 回答1: You need

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: is there any prepared function in python to apply a filter (for example Butterworth filter) to a given signal? I looking for such a function in 'scipy.signal' but I haven't find any useful functions more than filter design ones. actually I want this function to convolve a filter with the signal. 回答1: Yes! There are two: scipy.signal.filtfilt scipy.signal.lfilter There are also methods for convolution ( convolve and fftconvolve ), but these are probably not appropriate for your application because it involves IIR filters. Full code sample: b,

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I am trying to apply changes I stashed earlier with git stash pop and get the message: Cannot apply to a dirty working tree, please stage your changes Any suggestion on how to deal with that? 回答1: When I have to apply stashed changes to a dirty working copy, e.g. pop more than one changeset from the stash, I use the following: $ git stash show -p | git apply -3 && git stash drop Basically it creates a patch pipes that to the apply command if there are any conflicts they will need to be resolved via 3-way merge if apply (or merge) succeeded

Consider this pandas example where I'm calculating column C by multiplying A with B and a float if a certain condition is fulfilled using apply with a lambda function: import pandas as pd df = pd.DataFrame({'A':[1,2,3,4,5,6,7,8,9],'B':[9,8,7,6,5,4,3,2,1]}) df['C'] = df.apply(lambda x: x.A if x.B > 5 else 0.1*x.A*x.B, axis=1) The expected result would be: A B C 0 1 9 1.0 1 2 8 2.0 2 3 7 3.0 3 4 6 4.0 4 5 5 2.5 5 6 4 2.4 6 7 3 2.1 7 8 2 1.6 8 9 1 0.9 The problem is that this code is slow and I need to do this operation on a dataframe with around 56 million rows. The %timeit -result of the above

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: What is the rule/process when a function is called with pandas apply() through lambda vs. not? Examples below. Without lambda apparently, the entire series ( df[column name] ) is passed to the "test" function which throws an error trying to do a boolean operation on a series. If the same function is called via lambda it works. Iteration over each row with each passed as "x" and the df[ column name ] returns a single value for that column in the current row. It's like lambda is removing a dimension. Anyone have an explanation or point to the

Seems easy enough, i want to call a function with array of arguments. Sure, i can say func.apply(this, ['some', 'arguments']); but that will change the value of this inside func . Any idea how to do this without changing it? s4y You cannot, because of the way this works in JavaScript . Read on: Going by the "Entering An Execution Context" section of the ECMAScript spec: when you call a function, the value of this is determined by what's to it's left (called the activation object ) . Let's create a function called steve, and put him in an object: function steve(){} var obj = { method: steve };

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I have a very small .i file basically with this information: %module (directors="1") tu %include "typemaps.i" %include "enums.swg" %header %{ #include <my_header.h> %} %apply (char *STRING, size_t LENGTH) { (char* msg_buf, int buf_len) }; When generating java bindings there are no warnings but when generating c# bindings I get this: SWIG: Warning 453: Can't apply (char *STRING,size_t LENGTH). No typemaps are defined Can anyone advise on what might be the problem. The .i file is identical for java and c#. 文章来源: SWIG: Warning 453: Can't apply