Hi Pearl Li,

This is Binh, who has contacted your team a coupe days ago. I want to update information about troubleshooting mentioned above.  In the previous emails, my computed Hessian elements stop at 7th iteration.

Now, everything is much better as it stops at **31st diagonal element** as shown in the listing below. This time I used Matlab 2017a, there is no error in related to file path as we all start in the dir containing Main.m file. But, it should be noticed that the GitHub mode_in file in save/ directory needs to be replaced with the new one (replaced by the file generated in your last run 3 days ago) as it is likely to be an invalid file (due to transmission errors I supposed).

I tried to resolve this problem and report here again when I have a better solution to run the package smoothly. Sorry for previously disturbing emails to Marco and the team.

Best,

//--------------------------------------------------------------------//
Improvement on iteration 5 =        0.000000000
improvement < crit termination
smallest step still improving too slow, reversed gradient
max percentage change in the parameters,         0.0000000000
Re-computing Hessian

 Hessian Element: ( 1  1)
 Value Used: 4823135415.022876
 Hessian Element: ( 2  2)
 Value Used: 4920088832.446079
 Hessian Element: ( 3  3)
 Value Used: 685237244.196764
 Hessian Element: ( 5  5)
 Value Used: 4585345073.186686
 Hessian Element: ( 6  6)
 Value Used: 4507850947.544953
 Hessian Element: ( 7  7)
 Value Used: 5367882035.438800
 Hessian Element: ( 8  8)
 Value Used: 19130507370.740013
 Hessian Element: ( 9  9)
 Value Used: 5280981824.059217
 Hessian Element: (10 10)
 Value Used: 9899497593.766720
 Hessian Element: (11 11)
 Value Used: 4813025824.356885
 Hessian Element: (12 12)
 Value Used: 17130394263.002731
 Hessian Element: (13 13)
 Value Used: 5553571475.503223
 Hessian Element: (14 14)
 Value Used: 3882211121.039061
 Hessian Element: (15 15)
 Value Used: 9254314911.245480
 Hessian Element: (17 17)
 Value Used: 4957663669.322371
 Hessian Element: (18 18)
 Value Used: 6167340437.493320
 Hessian Element: (20 20)
 Value Used: 5385854714.748625
 Hessian Element: (21 21)
 Value Used: 5725108467.083120
 Hessian Element: (23 23)
 Value Used: 4729912869.559853
 Hessian Element: (24 24)
 Value Used: 1.307257
 Hessian Element: (25 25)
 Value Used: 4665305787.208392
 Hessian Element: (26 26)
 Value Used: 4456030944.546660
 Hessian Element: (27 27)
 Value Used: 6157581015.059493
 Hessian Element: (28 28)
 Value Used: 51799402106.712769
 Hessian Element: (29 29)
 Value Used: 5765296726.746206
 Hessian Element: (30 30)
 Value Used: 39134346536.299751
 Hessian Element: (31 31)Error using hessizero (line 56)
negative diagonal in hessian

Error in gibb (line 120)
    hessian = hessizero('objfcndsge',[params,para_mask],1,...

Error in Main (line 37)
gibb


Looks like this commit (part of #187) has merge conflicts from recent changes to the head branch - I'll update it for you. (It'll take me a couple of minutes.)


fix bug


C'est surtout parce qu'on a tous les deux d'autres choses à faire ^^.
Et l'application marche très bien comme ça. Je pense que c'est plus rentable pour toi de chercher un moyen de la porter sur d'autres plates-formes plutôt que de tout refaire dans un autre langage.


title: Tugas 1 Ca-IRK


Err, I meant to say 0. Ok, I though 0 was the top. But why does the seared tank have -1 then? 0 would mean that it's the bottom of the faucet block, not the bottom of the underlying block, right?


Should this be `-h` too?


A little too bright / crimson for my taste. Also, the thicker and more visible trail is at odds with the relatively sparse stains blood leaves behind. If we're going to increase the trail like this, we probably need to increase the staining. I'm not fully sold on this commit.

Rocket trail looks good. Explosion smoke not quite right imo. Decays too quickly.


Congrats!


this change makes the test flaky. My uneducated guess would be that the sort might not be working, and it only passes when the documents are inserted in the right way?


<item id="26578" article="a" name="blade of carving (overcharged)"> in XML


페북. 웹브라우즈,쇼핑앱 이미지로딩중 앱 종료 픽스


changes accept


Good! How the hell did they tag on something that doesn't build?


And, it is convenient if you post here the true values of the posterior modes. It helps us validate the re-optimized modes or at least we can adapt code to put it in directly. Thanks in advance!


It's not, you can see I fixed it in a follow up commit.


Yes it is the rigth way thanks for pointing it out


[Your benchmark job](https://github.com/JuliaLang/julia/commit/8b2cac4215976c656c5a3337edd4f36eb1c8aa51#commitcomment-22290596) has completed - possible performance regressions were detected. A full report can be found [here](https://github.com/JuliaCI/BaseBenchmarkReports/blob/7d181fdc634283f227c719fb90f384b00d0c03bb/8b2cac4_vs_4158b5e/report.md). cc @jrevels


Okay, my bad.


Pour l'instant j'ai moins le temps ouais. Mais maintenir une application avec deux solutions différente c'est trop chronophage. Chaque fonctionnalité doit être codée deux fois... Après ça donne naissance à des choses de [ce genre](https://github.com/mysql/mysql-workbench/tree/master/frontend). Vive le musée des horreurs. ^^


Add test with bad behaviour


Wrap this logic with some new 1.2 feature


你有我的电子邮件和电话啊。我没有换过


Adicionando teste


@est77 this adds support got generic float[2], with elements suffixed X and Y. It seems to work fine, any pitfalls i might be missing?


Puzzle `81-882850d7` disappeared in [`src/main/java/com/amihaiemil/versioneye/Repository.java`](https://github.com/decorators-squad/versioneye-api/blob/master/src/main/java/com/amihaiemil/versioneye/Repository.java), that's why I closed #88.


May I request a release for this? I don't like to put a permanent git source since it doesn't follow semver. This greatly improved both CI and local debugging experience.


"Due to the life Assets Store lifecycle"  - typo?  Did you mean more like:  "Due to the manual approval process at the Unity Asset Store for each client update it is generally easier..." etc.


Looks like this commit (part of #187) has merge conflicts from recent changes to the head branch - I'll update it for you. (It'll take me a couple of minutes.)


Asterisk C code uses only Tabs for indentation. Spaces are not allowed.


Yep; that one-liner is probably good to put in the documentation too.


@est77 this adds generic float[2] with elements suffix X,Y and resp. short names. Any pitfalls i might be missing?


![](https://github.trello.services/images/mini-trello-icon.png) [equals()](https://trello.com/c/iPO1rWHf/117-equals)


you are forgot change option name in using code
`String extensions = command.getOptionValue("e");`


This is **obviously** wrong. Read the comment above and take a look at the code below. It will take one random peer with ``insecure=port`` and ignore all the others.


Bwahaha. Maybe Lwt should pause here for a while, so we can savor the moment :) cc @persianturtle


Hey @ishitatsuyuki,

I'm currently looking for a better solution (using tns cli), cause this cleans the metadata only when you are building with webpack. If you build with webpack and then try to build with `tns build android/ios`, you'll hit the same error.


@Keno any ideas?


`my $p` forsvant..
skal det være `$patron->track_login` ?


Got to avoid that fifthglyph :+1:


@ritwickdsouza @EdoPut I already deployed this change in 3 production instances.

This change is the first step that will make possible to implement #70 (backward conversion), but it should also simplify development for the forward conversion (from NetJSON to native router configuration).

It is especially handy for you @EdoPut because you can create an "intermediate data" structure that represents the AirOS configuration format and then use only one jinja template to render that data structure (like is now for OpenWRT, notice that there's only one renderer class and one jinja template for OpenWRT now).

It will be less easy for @ritwickdsouza because he has to deal with several configuration formats, which will require to write more renderers and jinja2 templates.

I will try to explain in detail this change via email as soon as possible.


Hi, am I missing something? I can't seem to find this file inside this repo+directory...


@JohnAZoidberg please review


This commit break develop branch for previous setups (and only for admin user) as no entry to user_module table exists for that user...

I'm not familiar enough with the code to know what this addition does or solves?


:D


Congrats!


Imo revert and then the performance fixes can be iterated on in a new PR?


try this:

```
return make_optional<real_time>(real_clock::from_time_t(tt) + nanoseconds(n);
```


I guess `length_of_EXPONENT` should be compared with 32.  `length_of_EXPONENT` counts bytes, not bits.


changed theme


Bluemix toolchain: An unknown tool deployed [testRepo-Traceability](https://github.com/idsdev/testRepo-Traceability/releases/tag/deployed-2-env-ibm-yp-us-south-user-ibm.com-prod-20170526-122015 "deployed-2-env-ibm-yp-us-south-user-ibm.com-prod-20170526-122015") to [prod](https://console.ng.bluemix.net/apps/6e3bc311-c83d-4cd1-a457-99d9a5f20f19?env_id=ibm:yp:us-south "ibm:yp:us-south:user@ibm.com:prod"), including this commit


Bluemix toolchain: An unknown tool deployed [testRepo-Traceability](https://github.com/idsdev/testRepo-Traceability/releases/tag/deployed-2-env-ibm-yp-us-south-user-ibm.com-prod-20170526-122022 "deployed-2-env-ibm-yp-us-south-user-ibm.com-prod-20170526-122022") to [prod](https://console.ng.bluemix.net/apps/6e3bc311-c83d-4cd1-a457-99d9a5f20f19?env_id=ibm:yp:us-south "ibm:yp:us-south:user@ibm.com:prod"), including this commit


Bluemix toolchain: An unknown tool deployed [testRepo-Traceability](https://github.com/idsdev/testRepo-Traceability/releases/tag/deployed-2-env-Prod-20170526-122026 "deployed-2-env-Prod-20170526-122026") to [Prod](https://console.ng.bluemix.net/apps/6e3bc311-c83d-4cd1-a457-99d9a5f20f19?env_id=ibm:yp:us-south "There was a problem retrieving the space name"), including this commit


Looks like this commit (part of #187) has merge conflicts from recent changes to the head branch - I'll update it for you. (It'll take me a couple of minutes.)


Bluemix toolchain: An unknown tool deployed [testRepo-Traceability](https://github.com/idsdev/testRepo-Traceability/releases/tag/deployed-2-env-ibm-yp-us-south-user-ibm.com-prod-20170526-122035 "deployed-2-env-ibm-yp-us-south-user-ibm.com-prod-20170526-122035") to [prod](https://console.ng.bluemix.net/apps/6e3bc311-c83d-4cd1-a457-99d9a5f20f19?env_id=ibm:yp:us-south "ibm:yp:us-south:user@ibm.com:prod"), including this commit


add mid


fdfgfd


`fields_to_omit` should be 'FIELDS_TO_OMIT'. Constants in all caps is the Ruby idiom.


i like programming


LGTM


good


Bluemix [toolchain](https://console.bluemix.net/devops/toolchains/0008b03a-c0ab-47e8-90fe-ccbdaed0e7c7 "simple-toolchain-20170526121525019"): [Delivery Pipeline](https://console.bluemix.net/devops/pipelines/790af48f-98e7-48fc-99b7-3a73aea5e2f2 "simple-toolchain-20170526121525019") deployed [urFurniture](https://github.com/kanian/urFurniture/releases/tag/deploy-ibm-yp-us-south-urFurniture-urFurnitureDev-20170526-122653 "deploy-ibm-yp-us-south-urFurniture-urFurnitureDev-20170526-122653") to [urFurnitureDev](https://console.ng.bluemix.net/apps/fd187879-adb8-425f-a6f4-b837927511ee?env_id=ibm:yp:us-south "ibm:yp:us-south:urFurniture:urFurnitureDev"), including this commit


"you are offered employment with organisation" is ungrammatical. Presumably what is mean is the organisation resulting from the amalgamation.


No :)


Decided to go ahead with the API change to return None; 8279c48e52e56f910af6cf8a389e57639644f484


@jails you forgot create release tag : `git tag` :p


Fixed in 13e80d954243c58fed5a8558d7a5cd2098874762


Hello!
As I understood, the **.run()** function checking, is it time to callback users timer function or not.
But what will be happen if main **loop()** duration is more, than timer interval?


One more place: https://github.com/ckeditor/ckeditor-presets/blob/61bb5af931b7b9e3664a466cf62011784ceed805/build.sh#L134


should that be "missing scope **or** EventType auth failure" ?


@jails I've just created the tag at https://github.com/kahlan/kahlan/releases/tag/3.1.15


Should be `#{eoutvar}`?


This is waiting review from Matt, I'll delete this branch till he's looked at it (I pushed to the wrong git remote).


It looks correct. I think that you want to name the child1 and child2 attributes simply x and y.
They get the main part of the attribute name (mayaAttributeName) when they are added to the parent attribute.
You can use both x & y for short and long names.


Bluemix [toolchain](https://console.bluemix.net/devops/toolchains/0008b03a-c0ab-47e8-90fe-ccbdaed0e7c7 "simple-toolchain-20170526121525019"): [Delivery Pipeline](https://console.bluemix.net/devops/pipelines/790af48f-98e7-48fc-99b7-3a73aea5e2f2 "simple-toolchain-20170526121525019") deployed [urFurniture](https://github.com/kanian/urFurniture/releases/tag/deploy-ibm-yp-us-south-urFurniture-urFurnitureDev-20170526-123444 "deploy-ibm-yp-us-south-urFurniture-urFurnitureDev-20170526-123444") to [urFurnitureDev](https://console.ng.bluemix.net/apps/66e24c1d-d0cb-4e6b-9712-d7b53edb7328?env_id=ibm:yp:us-south "ibm:yp:us-south:urFurniture:urFurnitureDev"), including this commit


I tried that, but it gives a syntax error at runtime.  I could put it in the criteria model if we really wanted to stick to naming conventions.


I couldn't understand your intention.

Do you mean `compiler.post_cmd = ["#{eoutvar}"]`? It doesn't change anything and `compiler.post_cmd = [#{eoutvar}]` will be syntax error.


Looks like this commit (part of #187) has merge conflicts from recent changes to the head branch - I'll update it for you. (It'll take me a couple of minutes.)


config-dev


It's not hard to add `trim`, but I think that it's redundant. One need to keep a reasonable balance between tolerance for the incoming data and code complexity...

Let's do it that way: if you'll encounter the case of `X-Forwarder-For` header in form of "`ip1 , ip2 , ...`", we'll add trim. But for now let's keep it as is.


Ah, it may affect for non-String input.


Bluemix [toolchain](https://console.bluemix.net/devops/toolchains/0008b03a-c0ab-47e8-90fe-ccbdaed0e7c7 "simple-toolchain-20170526121525019"): [Delivery Pipeline](https://console.bluemix.net/devops/pipelines/790af48f-98e7-48fc-99b7-3a73aea5e2f2 "simple-toolchain-20170526121525019") deployed [urFurniture](https://github.com/kanian/urFurniture/releases/tag/deploy-ibm-yp-us-south-urFurniture-urFurnitureDev-20170526-123700 "deploy-ibm-yp-us-south-urFurniture-urFurnitureDev-20170526-123700") to [urFurnitureDev](https://console.ng.bluemix.net/apps/0afb7222-44e1-42b1-b70e-45fd30ef2f7b?env_id=ibm:yp:us-south "ibm:yp:us-south:urFurniture:urFurnitureDev"), including this commit


Commit de apertura de repositorio.
En este momento esta vacio


换个环境比较好吧


Oh, my wrong. I intended `"#{eoutvar}"` but `"#{eoutvar}"` and `eoutvar` are completely the same.


With this commit it would appear machine shortname **ti99_4ev** is no longer needed?  It does not show a screen anymore nor does it appear to emulate startup (no beeps heard) unless I give it slot options which can be done on other machines.


Bluemix [toolchain](https://console.bluemix.net/devops/toolchains/0008b03a-c0ab-47e8-90fe-ccbdaed0e7c7 "urFurniture"): [Delivery Pipeline](https://console.bluemix.net/devops/pipelines/790af48f-98e7-48fc-99b7-3a73aea5e2f2 "simple-toolchain-20170526121525019") deployed [urFurniture](https://github.com/kanian/urFurniture/releases/tag/deploy-ibm-yp-us-south-urFurniture-urFurnitureDev-20170526-124114 "deploy-ibm-yp-us-south-urFurniture-urFurnitureDev-20170526-124114") to [urFurnitureDev](https://console.ng.bluemix.net/apps/983216f4-a135-466c-a231-f59a39646806?env_id=ibm:yp:us-south "ibm:yp:us-south:urFurniture:urFurnitureDev"), including this commit


Bluemix toolchain: An unknown tool deployed [testRepo-Traceability](https://github.com/idsdev/testRepo-Traceability/releases/tag/deployed-2-env-ibm-yp-us-south-user-ibm.com-prod-20170526-124122 "deployed-2-env-ibm-yp-us-south-user-ibm.com-prod-20170526-124122") to [prod](https://console.ng.bluemix.net/apps/6e3bc311-c83d-4cd1-a457-99d9a5f20f19?env_id=ibm:yp:us-south "ibm:yp:us-south:user@ibm.com:prod"), including this commit


Bluemix toolchain: An unknown tool deployed [testRepo-Traceability](https://github.com/idsdev/testRepo-Traceability/releases/tag/deployed-2-env-ibm-yp-us-south-user-ibm.com-prod-20170526-124129 "deployed-2-env-ibm-yp-us-south-user-ibm.com-prod-20170526-124129") to [prod](https://console.ng.bluemix.net/apps/6e3bc311-c83d-4cd1-a457-99d9a5f20f19?env_id=ibm:yp:us-south "ibm:yp:us-south:user@ibm.com:prod"), including this commit


Bluemix toolchain: An unknown tool deployed [testRepo-Traceability](https://github.com/idsdev/testRepo-Traceability/releases/tag/deployed-2-env-Prod-20170526-124133 "deployed-2-env-Prod-20170526-124133") to [Prod](https://console.ng.bluemix.net/apps/6e3bc311-c83d-4cd1-a457-99d9a5f20f19?env_id=ibm:yp:us-south "There was a problem retrieving the space name"), including this commit


Bluemix toolchain: An unknown tool deployed [testRepo-Traceability](https://github.com/idsdev/testRepo-Traceability/releases/tag/deployed-2-env-ibm-yp-us-south-user-ibm.com-prod-20170526-124142 "deployed-2-env-ibm-yp-us-south-user-ibm.com-prod-20170526-124142") to [prod](https://console.ng.bluemix.net/apps/6e3bc311-c83d-4cd1-a457-99d9a5f20f19?env_id=ibm:yp:us-south "ibm:yp:us-south:user@ibm.com:prod"), including this commit


I see. Since it'll be `Array#join`ed later, `eoutvar` and `"#{eoutvar}"` will behave in the same way. (Of course passing non-String eoutvar is unintentional regardless of it.)

I'll keep current behavior like before https://github.com/ruby/ruby/commit/af91b4235416af1ec499afafcf4524d93437607b.


That's better behavior than before.  The same thing is done in models/Model.js and models/AccessControlledModel.js.


Bluemix [toolchain](https://console.bluemix.net/devops/toolchains/0008b03a-c0ab-47e8-90fe-ccbdaed0e7c7 "urFurniture"): [Delivery Pipeline](https://console.bluemix.net/devops/pipelines/790af48f-98e7-48fc-99b7-3a73aea5e2f2 "simple-toolchain-20170526121525019") deployed [urFurniture](https://github.com/kanian/urFurniture/releases/tag/deploy-ibm-yp-us-south-urFurniture-urFurnitureDev-20170526-124418 "deploy-ibm-yp-us-south-urFurniture-urFurnitureDev-20170526-124418") to [urFurnitureDev](https://console.ng.bluemix.net/apps/453e6039-4e78-47be-98c7-07a7e358975a?env_id=ibm:yp:us-south "ibm:yp:us-south:urFurniture:urFurnitureDev"), including this commit


Ahh, got it. Please ignore my comment, then.

--
Dan Kohn <mailto:dan@linuxfoundation.org>
Executive Director, Cloud Native Computing Foundation <https://cncf.io>
tel:+1-415-233-1000

> On May 26, 2017, at 08:34, Jason Dossett <notifications@github.com> wrote:
>
> I tried that, but it gives a syntax error at runtime. I could put it in the criteria model if we really wanted to stick to naming conventions.
>
> —
> You are receiving this because you commented.
> Reply to this email directly, view it on GitHub, or mute the thread.
>


@mkresin
Is this correct for ipq806x?
I think that it made the power LED in R7800 to disappear.

ipq806x R7800, r4212-abbfcc8525

Looking at the paths, /proc/device-tree/base does not exist but /proc/device-tree has the roughly the same contents as /sys/firmware/devicetree/base/ :

```
root@LEDE:~# ls /sys/firmware/devicetree/base/
#address-cells    compatible        interrupt-parent  reserved-memory
#size-cells       cpu-pmu           memory            sfpb-mutex
aliases           cpus              memory@0          smem
chosen            firmware          model             soc
clock-controller  gpio-keys         name              thermal-zones
clocks            gpio-leds         qcom,pvs

root@LEDE:~# ls /proc/device-tree/base
ls: /proc/device-tree/base: No such file or directory

root@LEDE:~# ls /proc/device-tree/
#address-cells    clock-controller  cpus              interrupt-parent  name              smem
#size-cells       clocks            firmware          memory            qcom,pvs          soc
aliases           compatible        gpio-keys         memory@0          reserved-memory   thermal-zones
chosen            cpu-pmu           gpio-leds         model             sfpb-mutex

```


от проекта к проекту не забывай менять )


$ roslaunch turtle_war war_field.launch
$ cd (YOUR-ROS-WORKSPACE)/src/turtle_war/src
$ python makeCandy.py
でワールド上に4秒に1個ずつボールが出現する様子が見られると思います。
実行後20秒経つとボールが全て消えて、プログラムが終了します。


Looks like this commit (part of #187) has merge conflicts from recent changes to the head branch - I'll update it for you. (It'll take me a couple of minutes.)


Shouldn't we also redirect old /articles/* links, at least temporarily? For example the [csproj](https://docs.microsoft.com/en-us/dotnet/articles/core/tools/csproj) article now shows a 404 page, even though it is the first result Google and Bing return.

Any user that doesn't check this repo won't know why most links don't work.


Could you please backport it to 1.3.X also? Because it is relevant to #45 (which is fixed there)


Let's talk about why using the date from the user's computer will cause problems. Let's do that on a call.


для сайта в гх есть специальное место )


табуляция


enum class Shape(val shapeInt: Int) {
  RECTANGLE,
  OVAL,
  LINE,
  RING;

  companion object {
    private val VALS = values()
    fun fromInt(shapeInt: Int) = VALS.first { it.shapeInt == shapeInt }
    fun fromIntOrNull(shapeInt: Int) = VALS.firstOrNull { it.shapeInt == shapeInt }
  }
}


main-header


Any reason why you define this on the fly here and use the `sizeof(struct bpf_lpm_trie_key) + sizeof(union v6addr)` in the map definition? Might be worth defining this once.


Really minimalistic! Nice ;-)


Here you have no protection from situation when each philosopher takes his left fork and cannot take right one


No, it's okay.


I'm fine with merging this like this to get started but this should be converted to using the base `Map` type defined in `pkg/bpf`


это не блок


Yes, for one iteration it should work. For numerous iterations it should hang at some moment. I'd suspect this moment to become earlier for 4+ cores in CPU.


second..


Commit tras el push de mi virtual ubuntu
Con éxito


The renderer offsets to the location of the block so 0 would be the bottom, and since 0 is the bottom of the faucet block, anything smaller is below that. I'm just using the same functionality for that variable as before.


@Johnson-Fan Make sure there is not any mm-tools is running.


hey man! Thanks for reaching out. I'm trying it out and fixing whatever i
can. From what i see you left it in mid of upgrading stuff. i was tangled
in lot of dependency stuff. but man cool project. Is there a working
version?. could you point me to a commit where it was working fine. May be
i can better understand the project if i can watch a working version of it.

On Fri, May 26, 2017 at 4:44 PM, Webkadabra <notifications@github.com>
wrote:

> As a help pointer I should tlel that Brainfock is based on
> https://github.com/este/este so I think at some point i tried to update
> it to whatever is working with Este now, but failed cus I went hungry and
> had to do other stuff..
>
> —
> You are receiving this because you authored the thread.
> Reply to this email directly, view it on GitHub
> <https://github.com/thousif/OI/commit/5c737046c165a7734ed8e90cc9ff04056f369b6b#commitcomment-22292036>,
> or mute the thread
> <https://github.com/notifications/unsubscribe-auth/AEI8FcGC4-cQmVonqXliLumr6tCblkkjks5r9rQcgaJpZM4Nnd8B>
> .
>


OK, if you're not keeping up with the forum thread, we'll take that out. Sorry, I was trying to be helpful.


code for "Add news" part


Bluemix [toolchain](https://console.bluemix.net/devops/toolchains/a1c1f084-c18b-4ecb-99f8-9a9b22da21b5 "FB Face Recognition"): [Delivery Pipeline](https://console.bluemix.net/devops/pipelines/63e7d3d4-ea51-4079-91c8-7e11b1d3f83b "FB Face Recognition") deployed [FB-Face-Recognition](https://github.com/iris-bot/FB-Face-Recognition/releases/tag/deploy-ibm-yp-us-south-iris-bot-dev-20170526-125907 "deploy-ibm-yp-us-south-iris-bot-dev-20170526-125907") to [dev](https://console.ng.bluemix.net/apps/47bcbc3c-0115-4065-99e5-49b693b7caac?env_id=ibm:yp:us-south "ibm:yp:us-south:iris-bot:dev"), including this commit


**[unitTests_js_branches run failed](http://jenkins.wikia-prod:8080/job/unitTests_js_branches/41013/testReport/) for this commit**


sorry, I've just read your message (I'm @HZZ meeting as ARC)

can you confirm me that you are fine, now ?
or do you still need any feedback by myself ?
thanks
  mia

On Fri, May 26, 2017 at 2:14 PM, Kevin McDermott <notifications@github.com>
wrote:

> Hi, am I missing something? I can't seem to find this file inside this
> repo+directory...
>
> —
> You are receiving this because you were mentioned.
> Reply to this email directly, view it on GitHub
> <https://github.com/gomber/cmssw/commit/798ff106881701eb9262f7bb6f9f8d0aeb5b658a#commitcomment-22292749>,
> or mute the thread
> <https://github.com/notifications/unsubscribe-auth/AEt58839HbGSg4-2CAWZ1fBzaESmMUw_ks5r9sIkgaJpZM4NmBts>
> .
>


Looks like this commit (part of #187) has merge conflicts from recent changes to the head branch - I'll update it for you. (It'll take me a couple of minutes.)


You have to switch to `eplETS` here to get data from your plugin's reducer not from traffic view.


new helper


зачем так далеко?


It seems that now test checks exactly what is needed. But let me give a suggestion. `String.substring()` is always tricky to understand. I would rather firstly check `assertTrue(content.endWith("\r\n\r\n"))` and split lines after.


nn


I think this (and all parts in the second section with "den" should had "denGenericTriggerEventPSet", versus "num"

e.g. hltPhotonmonitoring.numGenericTriggerEventPSet.hltInputTag   = cms.InputTag( "TriggerResults::HLT" ) --> hltPhotonmonitoring.denGenericTriggerEventPSet.hltInputTag   = cms.InputTag( "TriggerResults::HLT" )

This could be causing the crashes...

Also, I assume L28 with the PF_Jet40*, etc, are supposed to be reference paths? If so, this should be in the denom again.


надо было назад сдвинуть под первый @RequestBody


On 26-5-2017 14:18, Chang Liu wrote:
> try this:
>
> |return make_optional<real_time>(real_clock::from_time_t(tt) +
> nanoseconds(n); |

close enough to get me going....

Turns out to be:
return boost::make_optional<real_time>(real_clock::from_time_t(tt) +
nanoseconds(n));

And that works.

--WjW


eigtl. *l

das gibt aber einen Error aus :/


Yes at one point it was working fine and I even had online demo and used it for a small team. But I think I messed up locking dependencies, so not sure it will work now, but this one release was kind of working https://github.com/Brainfock/Brainfock/releases/tag/0.28.0


consistency on quotes in this method!


Thank you.


thx and sorry 👍


Thanks a lot! I didn't notice that :)


@bartolomesoriano
For the focused layout. If you take the border texture out of the image control and place it into a separate control. This should fix both.


Completes issue #4.


Damned, I fixed that one before I pushed the commit... but in a different branch.


cool, i'll check this version out! Thanks!!


Can you expand on that?


output


:smile:


Also, as a vstring, should it be vstring("HLT_PFJet40_v*","HLT_PFJet60_v*","HLT_PFJet80_v*") and not one long string ?


See -ting-=session /form


Has this updated file been confirmed to work? It displays a "Hello World" page under "View", but no matter how I download it, Adobe Acrobat claims it's corrupted and unrepairable (just like the original file). I tried the "Download" button and the Zip file download (and extracted the file) and they both failed. I can download and view "sample.pdf", so I think my download method is OK, and the file is bad.


example


The `ATTR_CMN_MODTIME` code block should be before the `ATTR_CMN_ACCTIME` code block, otherwise the mtime and atime will be assigned to `times` in the wrong order.


to_s не обязателен, нужно просто ожидать строку


Hi Alicia -

This isn't working because your css is not specific enough. If you prefix your `.currentPage` class with the CSS that you are using to target the other links this works:
```
.left-nav .items ul li a.currentPage {
  background-color: yellow;
}
```


filed llvm bug: https://bugs.llvm.org//show_bug.cgi?id=33179


añadido datos nuevos


ie.: http://cmslxr.fnal.gov/source/CommonTools/TriggerUtils/src/GenericTriggerEventFlag.cc#0108


Looks like this commit (part of #187) has merge conflicts from recent changes to the head branch - I'll update it for you. (It'll take me a couple of minutes.)


Nice work, thank you


Достаточно просто вставлять станцию на предпоследнее место


Tole pa ne bo držalo, da sem jaz lastnik kode. Vidva sta lastnika kode, tako da prosim popravita. (Podobno drugje.)


@mistervee, just to see if I understand (sorry for being slow), this means creating another image in the focusedlayout and using the list-select-focus.png as the texture for that image? Image dimensions to be the same as the bookmark image? Does that mean no bordertexture for that new image? I know the dimensions are not quite right yet, I just want to understand how to do it.  Do I use HighlightColorVar as the colordiffuse for that new image? Do I leave it on the ListItem.Icon in the focusedlayout?


Próba adatbázisban frissítve ez a bug.
Regisztrációnál is átírtam.
_music_ esetén javítva.


A setting i'm currently always using is ``default_section = THIRDPARTY``. That way all packages not in stdlib or set via ``known_first_party`` are considered to be third party.


Why did you "eta expand" (this is a technical term) this definition?  In Haskell,

    reqF = SRS.require [reqIntro]
and

    reqF = \subSec -> SRS.require [reqIntro] subSec
are completely equivalent.  Yes, the first takes a bit to get used to, but you'll appreciate it when you do.


There's an off-by-one error here, causing a segmentation fault if 3 times are specified.

The index for the `tv_sec` and `tv_nsec` should be the same as far as I understand for each timestamp.

So `size` should only be incremented AFTER assigning `tv_sec` and `tv_nsec`.


Не нужно инициализировать переменные nil. Это просиходит автоматом. Просто используй их там, где нужно


should the knight_cant_be_obstructed method's output return 'false' if the name is 'Knight'?


Тут должно быть увеличение скорости. Т.е. если текущая скорость 50, а мы передаем 10, то скорость должна быть 60


Thanks for this fix, this really brought down our mysql activities for an hour. :+1:


@pablohpsilva Cool! Looking forward to it.  :)


Раздели на 2 метода - для добавления и для удаления


это также должны быть отдельные методы


@Baransu it didn't help. Should I modify actions maybe?


Please don't.   I understand that you find the expanded version clearer (now). Over time, that will change.


to_s необязателен


نوکرم ❤


Сделай так, чтобы метод принимал аргумент - тип поездов - и возвращал значения только для этого типа


Looks like this commit (part of #187) has merge conflicts from recent changes to the head branch - I'll update it for you. (It'll take me a couple of minutes.)


Посмотри на метод select


Yeah, right :)


это должно быть 3 разных метода: для текущей, следующей и предыдущей станций


should this be 2.1.0 since it's minor change not a bugfix


Hi, if catalina is started with `run` instead of `start`, it will not create the catalina.out file.


Not sure, but I think this might have broken drag-and-drop on android chrome. Since DnD support on different browsers seems to be pretty flaky, we might want to consider moving to our own DnD implementation... Of course, I would really like to avoid that. On the other hand, we already have something quite comparable with the time-adjust handles in timeline entries.. This wasn't even too complicated. Additional advantages with this solution would be that
1. the "Drop image here to load" popup wouldn't open
1. We aren't restricted to using DOMStrings for data transfer between drag source and drag sink


ex4, method getSuperSmartTranslation() not finished


Bluemix toolchain: An unknown tool deployed [testRepo-Traceability](https://github.com/idsdev/testRepo-Traceability/releases/tag/deployed-2-env-ibm-yp-us-south-user-ibm.com-prod-20170526-132611 "deployed-2-env-ibm-yp-us-south-user-ibm.com-prod-20170526-132611") to [prod](https://console.ng.bluemix.net/apps/6e3bc311-c83d-4cd1-a457-99d9a5f20f19?env_id=ibm:yp:us-south "ibm:yp:us-south:user@ibm.com:prod"), including this commit


probably `len(request.RegistryAuth) > 0`


Bluemix toolchain: An unknown tool deployed [testRepo-Traceability](https://github.com/idsdev/testRepo-Traceability/releases/tag/deployed-2-env-ibm-yp-us-south-user-ibm.com-prod-20170526-132619 "deployed-2-env-ibm-yp-us-south-user-ibm.com-prod-20170526-132619") to [prod](https://console.ng.bluemix.net/apps/6e3bc311-c83d-4cd1-a457-99d9a5f20f19?env_id=ibm:yp:us-south "ibm:yp:us-south:user@ibm.com:prod"), including this commit


Maybe write into the returned body that the `RegistryAuth` had a format issue - possibly with `err` returned? And logged


Bluemix toolchain: An unknown tool deployed [testRepo-Traceability](https://github.com/idsdev/testRepo-Traceability/releases/tag/deployed-2-env-Prod-20170526-132739 "deployed-2-env-Prod-20170526-132739") to [Prod](https://console.ng.bluemix.net/apps/6e3bc311-c83d-4cd1-a457-99d9a5f20f19?env_id=ibm:yp:us-south "There was a problem retrieving the space name"), including this commit


Bluemix toolchain: An unknown tool deployed [testRepo-Traceability](https://github.com/idsdev/testRepo-Traceability/releases/tag/deployed-2-env-ibm-yp-us-south-user-ibm.com-prod-20170526-132749 "deployed-2-env-ibm-yp-us-south-user-ibm.com-prod-20170526-132749") to [prod](https://console.ng.bluemix.net/apps/6e3bc311-c83d-4cd1-a457-99d9a5f20f19?env_id=ibm:yp:us-south "ibm:yp:us-south:user@ibm.com:prod"), including this commit


あ・・・ごめん・・・
全部小文字だね。
今後、気をつけます！


The original case studies version of glassbr didn't have an introduction of what **Instance Models** was, so I was asked to insert the generic introduction that most of the other examples consisted of (issue #219); an issue arose when SWHS, SSP and GamePhys all referenced `General Definitions` (which is a section that GlassBR doesn't contain).


Hi Anthony,

I contacted you to you debian.org email about a week ago. Would you be able to to get back to me this weekend? Sorry being so pushy, I'm just worried that the longer the delay, the more negative impact would be to my request, and I _really really_ don't want to fail this time. Thanks for helping!


Hi @Dragomir-Ivanov , the .idea folder and its files are not needed. It's there for a mistake of mine


修正ありがとうございます！


@anthonyfok


Curly braces around body of `if` even with only one statement.


See above


TeamCity Kotlin :: Kotlin Native mac [Build 3854](http://buildserver.labs.intellij.net/viewLog.html?buildId=18715943&buildTypeId=Kotlin_KotlinNativeMac) is now running


Looks like this commit (part of #187) has merge conflicts from recent changes to the head branch - I'll update it for you. (It'll take me a couple of minutes.)


The @bradmartin removed this files from the final version


At the time I had seen another function (in retrospect I realize it was probably an outdated one [Example\Drasil\SpecificSystemDescription.hs]) that employed an "eta expansion" (did I use the term correctly?) when it did not need to and I assumed it was just something we were doing for this project (clarity maybe?).  Thank you for telling me otherwise, I will remove them from OrganizationOfSRS when I get the chance.


Added note about companion object properties below.


Adding @mairaw

The redirects should be configured, but don't seem to be working yet.


Nice job!


Server Side Scripting language.


Any advice? :-)


- [ ] Did you mean "step2" here?


Updated with some new topics


Added note on this,


How is this different from `time()`?


Why not use hasPermission()?


You have to update action here which require changing action type which is string in `actionTypes.js`


What kind of complexity were you facing @barnabyalter? Maybe I can learn from this to improve the library, thanks.


Done


should take into account use_create_lots/use_existing_lots on the picking type


You don't even need `$date = false;`

```
if (!$date || isset($options['strict']) && true === $options['strict'] && 0 < DateTime::getLastErrors()['warning_count']) {
```


This checks a square, not radius.


The reason why I put it there is because the general rule says "don't use underscores in property names", and I'd like to explain clearly what the exceptions are. Moreover, we officially use the "backing property" term in the docs, and this is simply an example of that pattern.


This check is unreliable. Anyone could create a sign with such text, and this bypasses the permission checking.


if move.has_tracking != 'none' and move.picking_type_id.use...


@wjwithagen i am really sorry for breaking the build on FreeBSD. and practically reverting your work. i will loop you in next time whenever possible. but in the meanwhile, maybe we (you) could bisect and fin out the commit that fails the build so we can fix it?

@badone ^


Thank you for the additional information Maryyam.  A question that might arise is whether GlassBR should have a General Definitions section.  In my opinion, it should not.  The role of the general definitions is related to general concepts that are important, but not part of the fundamental concepts (theoretical models).  For instance, for SWHS a theoretical model is conservation of thermal energy.  A general definition is Newton's law of cooling.  GlassBR just doesn't have anything in this category.  That is, there is no need to bridge, or fill in additional information, between the theory and the instanced models.  From a physics point of view, GlassBR is a simpler problem than SWHS.


bravo


duplicated in button_validate?


Why do you decided to exclude 7.1 version?


I think we should cover the JSON Feed specs with a few more fields wrapped around if statements so that it does not generate empty attributes for nothing.

I'll send you a PR later :)


not like this hahahaha 🗡


Added


> 2017.05.25 Qin shangyao
### Series and DataFrame

```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

s = pd.Series([1,3,5,np.nan,6.0,8])
s
```

    0    1.0
    1    3.0
    2    5.0
    3    NaN
    4    6.0
    5    8.0
    dtype: float64

```python
date = pd.date_range('20130101', periods=6)
date
```

    DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
                   '2013-01-05', '2013-01-06'],
                  dtype='datetime64[ns]', freq='D')

```python
df = pd.DataFrame(np.random.randn(6,4),index=date,columns=list("abcd"))
df
```

      a
      b
      c
      d

      2013-01-01
      -2.476804
      -0.716176
      1.765384
      -0.263695

      2013-01-02
      -0.883719
      1.416166
      -0.508578
      0.829370

      2013-01-03
      -0.630924
      -1.071192
      0.378221
      0.631995

      2013-01-04
      -0.026111
      -0.045274
      -1.047580
      1.150283

      2013-01-05
      2.609569
      -1.097223
      -1.901388
      1.166126

      2013-01-06
      2.570729
      -1.163833
      0.516856
      0.516148

#### 如果参数是一个 dict，每个 dict 的 value 会被转化成一个 Series

```python
df2 = pd.DataFrame({ 'A' : 1.,
   ....:                      'B' : pd.Timestamp('20130102'),
   ....:                      'C' : pd.Series(1,index=list(range(4)),dtype='float32'),
   ....:                      'D' : np.array([3] * 4,dtype='int32'),
   ....:                      'E' : pd.Categorical(["test","train","test","train"]),
   ....:                      'F' : 'foo' })

df2
```

      A
      B
      C
      D
      E
      F

      0
      1.0
      2013-01-02
      1.0
      3
      test
      foo

      1
      1.0
      2013-01-02
      1.0
      3
      train
      foo

      2
      1.0
      2013-01-02
      1.0
      3
      test
      foo

      3
      1.0
      2013-01-02
      1.0
      3
      train
      foo

#### the types of each column

```python
df2.dtypes
```

    A           float64
    B    datetime64[ns]
    C           float32
    D             int32
    E          category
    F            object
    dtype: object

#### 你可以认为，DataFrame 是由 Series 组成的

```python
df2.A
```

    0    1.0
    1    1.0
    2    1.0
    3    1.0
    Name: A, dtype: float64

### Extract data imformation

```python
df.head(3)
df.tail(3)
```

      a
      b
      c
      d

      2013-01-01
      -2.476804
      -0.716176
      1.765384
      -0.263695

      2013-01-02
      -0.883719
      1.416166
      -0.508578
      0.829370

      2013-01-03
      -0.630924
      -1.071192
      0.378221
      0.631995

      a
      b
      c
      d

      2013-01-04
      -0.026111
      -0.045274
      -1.047580
      1.150283

      2013-01-05
      2.609569
      -1.097223
      -1.901388
      1.166126

      2013-01-06
      2.570729
      -1.163833
      0.516856
      0.516148

#### descibe,index columns values

```python
df.index
df.columns
df.values
df.describe()
```

    DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
                   '2013-01-05', '2013-01-06'],
                  dtype='datetime64[ns]', freq='D')

    Index(['a', 'b', 'c', 'd'], dtype='object')

    array([[-2.47680427, -0.71617564,  1.76538371, -0.26369535],
           [-0.88371866,  1.41616597, -0.50857791,  0.82936977],
           [-0.63092415, -1.07119237,  0.37822062,  0.63199538],
           [-0.02611075, -0.04527356, -1.04757957,  1.15028296],
           [ 2.60956923, -1.09722282, -1.90138791,  1.16612596],
           [ 2.57072902, -1.16383299,  0.51685629,  0.51614821]])

      a
      b
      c
      d

      count
      6.000000
      6.000000
      6.000000
      6.000000

      mean
      0.193790
      -0.446255
      -0.132847
      0.671704

      std
      2.025213
      1.003143
      1.295549
      0.528979

      min
      -2.476804
      -1.163833
      -1.901388
      -0.263695

      25%
      -0.820520
      -1.090715
      -0.912829
      0.545110

      50%
      -0.328517
      -0.893684
      -0.065179
      0.730683

      75%
      1.921519
      -0.212999
      0.482197
      1.070055

      max
      2.609569
      1.416166
      1.765384
      1.166126

#### 和 numpy 一样，可以方便的得到转置

```python
df.T
```

      2013-01-01 00:00:00
      2013-01-02 00:00:00
      2013-01-03 00:00:00
      2013-01-04 00:00:00
      2013-01-05 00:00:00
      2013-01-06 00:00:00

      a
      -2.476804
      -0.883719
      -0.630924
      -0.026111
      2.609569
      2.570729

      b
      -0.716176
      1.416166
      -1.071192
      -0.045274
      -1.097223
      -1.163833

      c
      1.765384
      -0.508578
      0.378221
      -1.047580
      -1.901388
      0.516856

      d
      -0.263695
      0.829370
      0.631995
      1.150283
      1.166126
      0.516148

#### 对 axis 按照 index 排序（axis=1 是指第二个维度，即：列）

```python
df.sort_index(axis=1,ascending=False)
```

      d
      c
      b
      a

      2013-01-01
      -0.263695
      1.765384
      -0.716176
      -2.476804

      2013-01-02
      0.829370
      -0.508578
      1.416166
      -0.883719

      2013-01-03
      0.631995
      0.378221
      -1.071192
      -0.630924

      2013-01-04
      1.150283
      -1.047580
      -0.045274
      -0.026111

      2013-01-05
      1.166126
      -1.901388
      -1.097223
      2.609569

      2013-01-06
      0.516148
      0.516856
      -1.163833
      2.570729

#### sort by values

```python
df.sort_values(by="a")
```

      a
      b
      c
      d

      2013-01-01
      -2.476804
      -0.716176
      1.765384
      -0.263695

      2013-01-02
      -0.883719
      1.416166
      -0.508578
      0.829370

      2013-01-03
      -0.630924
      -1.071192
      0.378221
      0.631995

      2013-01-04
      -0.026111
      -0.045274
      -1.047580
      1.150283

      2013-01-06
      2.570729
      -1.163833
      0.516856
      0.516148

      2013-01-05
      2.609569
      -1.097223
      -1.901388
      1.166126

#### abtain index and column

```python
df["a"]
```

    2013-01-01   -2.476804
    2013-01-02   -0.883719
    2013-01-03   -0.630924
    2013-01-04   -0.026111
    2013-01-05    2.609569
    2013-01-06    2.570729
    Freq: D, Name: a, dtype: float64

```python
df[0:2]
```

      a
      b
      c
      d

      2013-01-01
      -2.476804
      -0.716176
      1.765384
      -0.263695

      2013-01-02
      -0.883719
      1.416166
      -0.508578
      0.829370

```python
df["20130101":"20130104"]
```

      a
      b
      c
      d

      2013-01-01
      -2.476804
      -0.716176
      1.765384
      -0.263695

      2013-01-02
      -0.883719
      1.416166
      -0.508578
      0.829370

      2013-01-03
      -0.630924
      -1.071192
      0.378221
      0.631995

      2013-01-04
      -0.026111
      -0.045274
      -1.047580
      1.150283

```python
df.loc[date[1:3]]
```

      a
      b
      c
      d

      2013-01-02
      -0.883719
      1.416166
      -0.508578
      0.829370

      2013-01-03
      -0.630924
      -1.071192
      0.378221
      0.631995

```python
df.loc[:,['a','b']]
```

      a
      b

      2013-01-01
      -2.476804
      -0.716176

      2013-01-02
      -0.883719
      1.416166

      2013-01-03
      -0.630924
      -1.071192

      2013-01-04
      -0.026111
      -0.045274

      2013-01-05
      2.609569
      -1.097223

      2013-01-06
      2.570729
      -1.163833

```python
df.loc['20130101':'20130104',["a","b"]]
```

      a
      b

      2013-01-01
      -2.476804
      -0.716176

      2013-01-02
      -0.883719
      1.416166

      2013-01-03
      -0.630924
      -1.071192

      2013-01-04
      -0.026111
      -0.045274

#### only one row selected

```python
df.loc['20130104',["a","b"]]
```

    a   -0.026111
    b   -0.045274
    Name: 2013-01-04 00:00:00, dtype: float64

```python
df.loc[date[0],'a']
```

    -2.4768042737290275

#### 这种情况通常用 at ，速度更快

```python
df.at[date[1],"d"]
```

    0.82936976776458216

### iloc select row and column by index

```python
df.iloc[2:5,0:2]
```

      a
      b

      2013-01-03
      -0.630924
      -1.071192

      2013-01-04
      -0.026111
      -0.045274

      2013-01-05
      2.609569
      -1.097223

```python
df.iloc[[0,1,4],[0,3]]
```

      a
      d

      2013-01-01
      -2.476804
      -0.263695

      2013-01-02
      -0.883719
      0.829370

      2013-01-05
      2.609569
      1.166126

```python
df.iloc[[1,4],0:3]
```

      a
      b
      c

      2013-01-02
      -0.883719
      1.416166
      -0.508578

      2013-01-05
      2.609569
      -1.097223
      -1.901388

 ### 布尔值下标

```python
df[df.a > 0]
```

      a
      b
      c
      d

      2013-01-05
      2.609569
      -1.097223
      -1.901388
      1.166126

      2013-01-06
      2.570729
      -1.163833
      0.516856
      0.516148

### 没有填充的值等于 NaN

```python
df[df>0]
```

      a
      b
      c
      d

      2013-01-01
      NaN
      NaN
      1.765384
      NaN

      2013-01-02
      NaN
      1.416166
      NaN
      0.829370

      2013-01-03
      NaN
      NaN
      0.378221
      0.631995

      2013-01-04
      NaN
      NaN
      NaN
      1.150283

      2013-01-05
      2.609569
      NaN
      NaN
      1.166126

      2013-01-06
      2.570729
      NaN
      0.516856
      0.516148

### isin() 函数：是否在集合中

```python
df3 = df.copy()
df3["e"] = ['one', 'one','two','three','four','three']
df3
```

      a
      b
      c
      d
      e

      2013-01-01
      -2.476804
      -0.716176
      1.765384
      -0.263695
      one

      2013-01-02
      -0.883719
      1.416166
      -0.508578
      0.829370
      one

      2013-01-03
      -0.630924
      -1.071192
      0.378221
      0.631995
      two

      2013-01-04
      -0.026111
      -0.045274
      -1.047580
      1.150283
      three

      2013-01-05
      2.609569
      -1.097223
      -1.901388
      1.166126
      four

      2013-01-06
      2.570729
      -1.163833
      0.516856
      0.516148
      three

```python
df3[df3['e'].isin(["one","three"])]
```

      a
      b
      c
      d
      e

      2013-01-01
      -2.476804
      -0.716176
      1.765384
      -0.263695
      one

      2013-01-02
      -0.883719
      1.416166
      -0.508578
      0.829370
      one

      2013-01-04
      -0.026111
      -0.045274
      -1.047580
      1.150283
      three

      2013-01-06
      2.570729
      -1.163833
      0.516856
      0.516148
      three

### Setting

为 DataFrame 增加新的列，按 index 对应

```python
s1 = pd.Series([1,2,3,4,5,6],index = pd.date_range("20130102",periods=6))
s1
df["f"] = s1
df
```

    2013-01-02    1
    2013-01-03    2
    2013-01-04    3
    2013-01-05    4
    2013-01-06    5
    2013-01-07    6
    Freq: D, dtype: int64

      a
      b
      c
      d
      f

      2013-01-01
      -2.476804
      -0.716176
      1.765384
      -0.263695
      NaN

      2013-01-02
      -0.883719
      1.416166
      -0.508578
      0.829370
      1.0

      2013-01-03
      -0.630924
      -1.071192
      0.378221
      0.631995
      2.0

      2013-01-04
      -0.026111
      -0.045274
      -1.047580
      1.150283
      3.0

      2013-01-05
      2.609569
      -1.097223
      -1.901388
      1.166126
      4.0

      2013-01-06
      2.570729
      -1.163833
      0.516856
      0.516148
      5.0

### 通过 label 设置

```python
df =df.iloc[0:5,0:5]
df
df.at[date[0],"g"] = 0
df
df.iat[0,4] = 0
df
```

      a
      b
      c
      d
      f

      2013-01-01
      -2.476804
      0.000000
      1.765384
      -0.263695
      NaN

      2013-01-02
      -0.883719
      1.416166
      -0.508578
      0.829370
      1.0

      2013-01-03
      -0.630924
      -1.071192
      0.378221
      0.631995
      2.0

      2013-01-04
      -0.026111
      -0.045274
      -1.047580
      1.150283
      3.0

      2013-01-05
      2.609569
      -1.097223
      -1.901388
      1.166126
      4.0

      a
      b
      c
      d
      f
      g

      2013-01-01
      -2.476804
      0.000000
      1.765384
      -0.263695
      NaN
      0.0

      2013-01-02
      -0.883719
      1.416166
      -0.508578
      0.829370
      1.0
      NaN

      2013-01-03
      -0.630924
      -1.071192
      0.378221
      0.631995
      2.0
      NaN

      2013-01-04
      -0.026111
      -0.045274
      -1.047580
      1.150283
      3.0
      NaN

      2013-01-05
      2.609569
      -1.097223
      -1.901388
      1.166126
      4.0
      NaN

      a
      b
      c
      d
      f
      g

      2013-01-01
      -2.476804
      0.000000
      1.765384
      -0.263695
      0.0
      0.0

      2013-01-02
      -0.883719
      1.416166
      -0.508578
      0.829370
      1.0
      NaN

      2013-01-03
      -0.630924
      -1.071192
      0.378221
      0.631995
      2.0
      NaN

      2013-01-04
      -0.026111
      -0.045274
      -1.047580
      1.150283
      3.0
      NaN

      2013-01-05
      2.609569
      -1.097223
      -1.901388
      1.166126
      4.0
      NaN

### 用 numpy 数组设置

```python
df.loc[:,'D'] = np.array([5] * len(df))
df
```

      a
      b
      c
      d
      f
      g
      D

      2013-01-01
      -2.476804
      0.000000
      1.765384
      -0.263695
      0.0
      0.0
      5

      2013-01-02
      -0.883719
      1.416166
      -0.508578
      0.829370
      1.0
      NaN
      5

      2013-01-03
      -0.630924
      -1.071192
      0.378221
      0.631995
      2.0
      NaN
      5

      2013-01-04
      -0.026111
      -0.045274
      -1.047580
      1.150283
      3.0
      NaN
      5

      2013-01-05
      2.609569
      -1.097223
      -1.901388
      1.166126
      4.0
      NaN
      5

```python
df[df>0] = -df
df
```

      a
      b
      c
      d
      f
      g
      D

      2013-01-01
      -2.476804
      0.000000
      -1.765384
      -0.263695
      0.0
      0.0
      -5

      2013-01-02
      -0.883719
      -1.416166
      -0.508578
      -0.829370
      -1.0
      NaN
      -5

      2013-01-03
      -0.630924
      -1.071192
      -0.378221
      -0.631995
      -2.0
      NaN
      -5

      2013-01-04
      -0.026111
      -0.045274
      -1.047580
      -1.150283
      -3.0
      NaN
      -5

      2013-01-05
      -2.609569
      -1.097223
      -1.901388
      -1.166126
      -4.0
      NaN
      -5

### 2017-05-25___________________________________________________________________________________________
___________________________________________________________________________________________

```python
import pandas as pd
import numpy as np
dates = pd.date_range('20130101', periods=6)
dates
df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=list("abcd"))
df

```

    DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
                   '2013-01-05', '2013-01-06'],
                  dtype='datetime64[ns]', freq='D')

      a
      b
      c
      d

      2013-01-01
      -0.511371
      0.090467
      -0.539142
      0.225816

      2013-01-02
      -0.459897
      0.643272
      0.771382
      -0.295121

      2013-01-03
      -0.910942
      -0.203966
      0.395766
      -0.396847

      2013-01-04
      -0.319435
      -0.847182
      1.666120
      -0.081185

      2013-01-05
      -0.206759
      1.218271
      1.662933
      0.362833

      2013-01-06
      -0.755261
      -1.295831
      0.251587
      -0.725112

```python
df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ['E'])
df1

```

      a
      b
      c
      d
      E

      2013-01-01
      -0.602699
      -0.284765
      0.598670
      1.302064
      NaN

      2013-01-02
      -0.505329
      0.351779
      -0.661491
      0.729336
      NaN

      2013-01-03
      -1.299002
      0.965935
      -2.046690
      0.956991
      NaN

      2013-01-04
      0.210309
      -0.185296
      -0.691416
      0.129877
      NaN

```python
df1.loc[dates[0]:dates[1],'E'] = 1
df1
```

      a
      b
      c
      d
      E

      2013-01-01
      -0.602699
      -0.284765
      0.598670
      1.302064
      1.0

      2013-01-02
      -0.505329
      0.351779
      -0.661491
      0.729336
      1.0

      2013-01-03
      -1.299002
      0.965935
      -2.046690
      0.956991
      NaN

      2013-01-04
      0.210309
      -0.185296
      -0.691416
      0.129877
      NaN

### 丢弃有 NaN 的行

```python
df1.dropna()

```

      a
      b
      c
      d
      E

      2013-01-01
      -0.602699
      -0.284765
      0.598670
      1.302064
      1.0

      2013-01-02
      -0.505329
      0.351779
      -0.661491
      0.729336
      1.0

### 填充缺失值

```python
 df1.fillna(value=5)
```

      a
      b
      c
      d
      E

      2013-01-01
      -0.602699
      -0.284765
      0.598670
      1.302064
      1.0

      2013-01-02
      -0.505329
      0.351779
      -0.661491
      0.729336
      1.0

      2013-01-03
      -1.299002
      0.965935
      -2.046690
      0.956991
      5.0

      2013-01-04
      0.210309
      -0.185296
      -0.691416
      0.129877
      5.0

### 获取布尔值的 mask：哪些值是 NaN

```python
pd.isnull(df1)
```

      a
      b
      c
      d
      E

      2013-01-01
      False
      False
      False
      False
      False

      2013-01-02
      False
      False
      False
      False
      False

      2013-01-03
      False
      False
      False
      False
      True

      2013-01-04
      False
      False
      False
      False
      True

# 操作
> 通常，操作都会把 NaN 排除在外

```python
df

```

      a
      b
      c
      d

      2013-01-01
      -0.602699
      -0.284765
      0.598670
      1.302064

      2013-01-02
      -0.505329
      0.351779
      -0.661491
      0.729336

      2013-01-03
      -1.299002
      0.965935
      -2.046690
      0.956991

      2013-01-04
      0.210309
      -0.185296
      -0.691416
      0.129877

      2013-01-05
      1.125516
      -1.427224
      0.346131
      -0.911883

      2013-01-06
      2.005626
      -0.058209
      -0.539457
      0.863572

```python
df.mean()

```

    a    0.155737
    b   -0.106297
    c   -0.499042
    d    0.511659
    dtype: float64

```python
df.mean(1)
```

    2013-01-01    0.253318
    2013-01-02   -0.021426
    2013-01-03   -0.355692
    2013-01-04   -0.134132
    2013-01-05   -0.216865
    2013-01-06    0.567883
    Freq: D, dtype: float64

```python
df.apply(np.cumsum)  #sun by column (sequence)
```

      a
      b
      c
      d

      2013-01-01
      -0.602699
      -0.284765
      0.598670
      1.302064

      2013-01-02
      -1.108028
      0.067013
      -0.062821
      2.031400

      2013-01-03
      -2.407031
      1.032948
      -2.109511
      2.988391

      2013-01-04
      -2.196722
      0.847652
      -2.800927
      3.118268

      2013-01-05
      -1.071206
      -0.579571
      -2.454797
      2.206385

      2013-01-06
      0.934420
      -0.637780
      -2.994253
      3.069957

```python
df.apply(lambda x: x.max() - x.min())
```

    a    3.304629
    b    2.393159
    c    2.645361
    d    2.213947
    dtype: float64

# 直方图

```python
s = pd.Series(np.random.randint(0, 7, size=10))
s

```

    0    4
    1    4
    2    4
    3    6
    4    0
    5    1
    6    1
    7    4
    8    2
    9    2
    dtype: int32

```python
s.value_counts()
```

    4    4
    2    2
    1    2
    6    1
    0    1
    dtype: int64

### Series 自带了很多字符串处理函数，在 str 属性中，下面是一个例子

```python
s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])
s.str.lower()
```

    0       a
    1       b
    2       c
    3    aaba
    4    baca
    5     NaN
    6    caba
    7     dog
    8     cat
    dtype: object

# Merge

### Concat

#### 简单地按行拼接

```python
df = pd.DataFrame(np.random.randn(10, 4))
df

```

      0
      1
      2
      3

      0
      0.198220
      -0.113350
      -0.333208
      0.927895

      1
      -0.760095
      0.647052
      1.909611
      -0.550962

      2
      -1.086743
      -0.076392
      0.067433
      0.384251

      3
      -1.001007
      0.536725
      -0.253654
      -0.836611

      4
      -1.227877
      1.979616
      2.225039
      0.293663

      5
      0.121842
      0.533299
      0.824241
      -0.715254

      6
      -1.818999
      1.111292
      1.247154
      0.059203

      7
      -1.127814
      1.092636
      1.703457
      -1.597345

      8
      0.070651
      -0.418590
      0.505904
      0.172837

      9
      1.629082
      -0.614038
      -1.108976
      0.507900

```python
# break it into pieces
pieces = [df[3:7], df[:3], df[7:]]
pieces
```

    [          0         1         2         3
     3 -1.001007  0.536725 -0.253654 -0.836611
     4 -1.227877  1.979616  2.225039  0.293663
     5  0.121842  0.533299  0.824241 -0.715254
     6 -1.818999  1.111292  1.247154  0.059203,
               0         1         2         3
     0  0.198220 -0.113350 -0.333208  0.927895
     1 -0.760095  0.647052  1.909611 -0.550962
     2 -1.086743 -0.076392  0.067433  0.384251,
               0         1         2         3
     7 -1.127814  1.092636  1.703457 -1.597345
     8  0.070651 -0.418590  0.505904  0.172837
     9  1.629082 -0.614038 -1.108976  0.507900]

```python
pd.concat(pieces)
```

      0
      1
      2
      3

      3
      -1.001007
      0.536725
      -0.253654
      -0.836611

      4
      -1.227877
      1.979616
      2.225039
      0.293663

      5
      0.121842
      0.533299
      0.824241
      -0.715254

      6
      -1.818999
      1.111292
      1.247154
      0.059203

      0
      0.198220
      -0.113350
      -0.333208
      0.927895

      1
      -0.760095
      0.647052
      1.909611
      -0.550962

      2
      -1.086743
      -0.076392
      0.067433
      0.384251

      7
      -1.127814
      1.092636
      1.703457
      -1.597345

      8
      0.070651
      -0.418590
      0.505904
      0.172837

      9
      1.629082
      -0.614038
      -1.108976
      0.507900

# join

```python
left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})
left
```

      key
      lval

      0
      foo
      1

      1
      foo
      2

```python
right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})
right
#type(right)
```

      key
      rval

      0
      foo
      4

      1
      foo
      5

```python
pd.merge(left, right, on='key')
```

      key
      lval
      rval

      0
      foo
      1
      4

      1
      foo
      1
      5

      2
      foo
      2
      4

      3
      foo
      2
      5

# Append

### 向 DataFrame 增加新的数据行

```python
df = pd.DataFrame(np.random.randn(8, 4), columns=['A','B','C','D'])
df
```

      A
      B
      C
      D

      0
      -0.825989
      -0.930949
      0.446269
      1.016605

      1
      0.088570
      0.466926
      1.059724
      -0.114125

      2
      -1.295749
      -0.058309
      -0.283798
      0.481738

      3
      0.528254
      0.459432
      -2.225993
      0.315637

      4
      0.158093
      -0.887665
      2.956332
      1.064774

      5
      -0.728375
      0.025109
      -0.798117
      0.887267

      6
      0.485368
      0.078010
      1.276471
      -0.501533

      7
      1.938066
      0.922048
      0.200092
      1.312887

```python
s = df.iloc[3]
s
```

    A    0.528254
    B    0.459432
    C   -2.225993
    D    0.315637
    Name: 3, dtype: float64

```python
df.append(s, ignore_index=True)
```

      A
      B
      C
      D

      0
      -0.825989
      -0.930949
      0.446269
      1.016605

      1
      0.088570
      0.466926
      1.059724
      -0.114125

      2
      -1.295749
      -0.058309
      -0.283798
      0.481738

      3
      0.528254
      0.459432
      -2.225993
      0.315637

      4
      0.158093
      -0.887665
      2.956332
      1.064774

      5
      -0.728375
      0.025109
      -0.798117
      0.887267

      6
      0.485368
      0.078010
      1.276471
      -0.501533

      7
      1.938066
      0.922048
      0.200092
      1.312887

      8
      0.528254
      0.459432
      -2.225993
      0.315637

# Grouping

### 和 SQL 中的 GROUP BY 类似，包括以下这几步：

#### 根据某些规则，把数据分组
#### 对每组应用一个聚集函数，把结果放在一个数据结构中
#### 准备一下测试用的数据集

```python
df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
   ....:                           'foo', 'bar', 'foo', 'foo'],
   ....:                    'B' : ['one', 'one', 'two', 'three',
   ....:                           'two', 'two', 'one', 'three'],
   ....:                    'C' : np.random.randn(8),
   ....:                    'D' : np.random.randn(8)})
df
```

      A
      B
      C
      D

      0
      foo
      one
      -0.775319
      1.355852

      1
      bar
      one
      1.823183
      0.170446

      2
      foo
      two
      0.420823
      0.453347

      3
      bar
      three
      -0.481126
      -0.663604

      4
      foo
      two
      -0.072094
      -1.082077

      5
      bar
      two
      1.275680
      -1.942532

      6
      foo
      one
      0.491159
      0.051445

      7
      foo
      three
      -0.057878
      0.415029

做 Group 操作并对每组求和

```python
df.groupby("B").max()
```

      A
      C
      D

      B

      one
      foo
      1.823183
      1.355852

      three
      foo
      -0.057878
      0.415029

      two
      foo
      1.275680
      0.453347

```python
df.groupby("A").sum()
```

      C
      D

      A

      bar
      2.617737
      -2.435690

      foo
      0.006691
      1.193596

可以对两列进行 Group by 并求和

```python
df.groupby(["A","B"]).mean()
```

      C
      D

      A
      B

      bar
      one
      1.823183
      0.170446

      three
      -0.481126
      -0.663604

      two
      1.275680
      -1.942532

      foo
      one
      -0.142080
      0.703649

      three
      -0.057878
      0.415029

      two
      0.174365
      -0.314365

# Reshape

### Stack 层叠

准备一下数据

```python
tuples = list(zip(*[['bar', 'bar', 'baz', 'baz',
   ....:                      'foo', 'foo', 'qux', 'qux'],
   ....:                     ['one', 'two', 'one', 'two',
   ....:                      'one', 'two', 'one', 'two']]))
tuples
```

    [('bar', 'one'),
     ('bar', 'two'),
     ('baz', 'one'),
     ('baz', 'two'),
     ('foo', 'one'),
     ('foo', 'two'),
     ('qux', 'one'),
     ('qux', 'two')]

```python
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
index
```

    MultiIndex(levels=[['bar', 'baz', 'foo', 'qux'], ['one', 'two']],
               labels=[[0, 0, 1, 1, 2, 2, 3, 3], [0, 1, 0, 1, 0, 1, 0, 1]],
               names=['first', 'second'])

```python
df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])
df
```

      A
      B

      first
      second

      bar
      one
      0.331881
      -0.481361

      two
      0.763976
      -0.380786

      baz
      one
      0.380942
      0.386760

      two
      -2.061579
      0.040525

      foo
      one
      -0.070928
      0.012881

      two
      -0.084449
      1.150036

      qux
      one
      -0.543624
      -0.030466

      two
      0.259531
      0.112799

```python
df2 = df[:4]
df2
```

      A
      B

      first
      second

      bar
      one
      0.331881
      -0.481361

      two
      0.763976
      -0.380786

      baz
      one
      0.380942
      0.386760

      two
      -2.061579
      0.040525

####  stack() 把 DataFrame 的列“压缩”到 index 里去

```python
stacked = df2.stack()
stacked
```

    first  second
    bar    one     A    0.331881
                   B   -0.481361
           two     A    0.763976
                   B   -0.380786
    baz    one     A    0.380942
                   B    0.386760
           two     A   -2.061579
                   B    0.040525
    dtype: float64

#### 反之，只要是 MultiIndex 都可以用 unstack() 恢复出列，默认把最后一个 index 解开

```python
stacked.unstack()
```

      A
      B

      first
      second

      bar
      one
      0.331881
      -0.481361

      two
      0.763976
      -0.380786

      baz
      one
      0.380942
      0.386760

      two
      -2.061579
      0.040525

```python
stacked.unstack(1)
```

      second
      one
      two

      first

      bar
      A
      0.331881
      0.763976

      B
      -0.481361
      -0.380786

      baz
      A
      0.380942
      -2.061579

      B
      0.386760
      0.040525

```python
stacked.unstack(0)
```

      first
      bar
      baz

      second

      one
      A
      0.331881
      0.380942

      B
      -0.481361
      0.386760

      two
      A
      0.763976
      -2.061579

      B
      -0.380786
      0.040525

# Pivot Table 旋转

```python
df = pd.DataFrame({'A' : ['one', 'one', 'two', 'three'] * 3,
   .....:                    'B' : ['A', 'B', 'C'] * 4,
   .....:                    'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,
   .....:                    'D' : np.random.randn(12),
   .....:                    'E' : np.random.randn(12)})
   .....:
df
```

      A
      B
      C
      D
      E

      0
      one
      A
      foo
      -1.459025
      -0.758426

      1
      one
      B
      foo
      -0.444686
      0.764198

      2
      two
      C
      foo
      0.073439
      -0.516841

      3
      three
      A
      bar
      -1.286879
      0.143409

      4
      one
      B
      bar
      -1.278215
      0.480630

      5
      one
      C
      bar
      1.125577
      -1.442182

      6
      two
      A
      foo
      -1.623573
      -0.899026

      7
      three
      B
      foo
      -1.654890
      0.845784

      8
      one
      C
      foo
      -1.046331
      1.506097

      9
      one
      A
      bar
      -0.925142
      2.655543

      10
      two
      B
      bar
      -0.676643
      0.882354

      11
      three
      C
      bar
      -1.217325
      -0.966007

#### pivot 是把原来的数据(values)作为新表的行(index)、列(columns)

```python
pd.pivot_table(df, values='E', index=['A', 'B'], columns=['C'])
```

      C
      bar
      foo

      A
      B

      one
      A
      2.655543
      -0.758426

      B
      0.480630
      0.764198

      C
      -1.442182
      1.506097

      three
      A
      0.143409
      NaN

      B
      NaN
      0.845784

      C
      -0.966007
      NaN

      two
      A
      NaN
      -0.899026

      B
      0.882354
      NaN

      C
      NaN
      -0.516841

# 时间序列

#### pandas 的时间序列功能在金融应用中很有用。

### resample 功能：

```python
rng = pd.date_range('1/1/2012', periods=100, freq='S')

```

    DatetimeIndex(['2012-01-01 00:00:00', '2012-01-01 00:00:01',
                   '2012-01-01 00:00:02', '2012-01-01 00:00:03',
                   '2012-01-01 00:00:04', '2012-01-01 00:00:05',
                   '2012-01-01 00:00:06', '2012-01-01 00:00:07',
                   '2012-01-01 00:00:08', '2012-01-01 00:00:09',
                   '2012-01-01 00:00:10', '2012-01-01 00:00:11',
                   '2012-01-01 00:00:12', '2012-01-01 00:00:13',
                   '2012-01-01 00:00:14', '2012-01-01 00:00:15',
                   '2012-01-01 00:00:16', '2012-01-01 00:00:17',
                   '2012-01-01 00:00:18', '2012-01-01 00:00:19',
                   '2012-01-01 00:00:20', '2012-01-01 00:00:21',
                   '2012-01-01 00:00:22', '2012-01-01 00:00:23',
                   '2012-01-01 00:00:24', '2012-01-01 00:00:25',
                   '2012-01-01 00:00:26', '2012-01-01 00:00:27',
                   '2012-01-01 00:00:28', '2012-01-01 00:00:29',
                   '2012-01-01 00:00:30', '2012-01-01 00:00:31',
                   '2012-01-01 00:00:32', '2012-01-01 00:00:33',
                   '2012-01-01 00:00:34', '2012-01-01 00:00:35',
                   '2012-01-01 00:00:36', '2012-01-01 00:00:37',
                   '2012-01-01 00:00:38', '2012-01-01 00:00:39',
                   '2012-01-01 00:00:40', '2012-01-01 00:00:41',
                   '2012-01-01 00:00:42', '2012-01-01 00:00:43',
                   '2012-01-01 00:00:44', '2012-01-01 00:00:45',
                   '2012-01-01 00:00:46', '2012-01-01 00:00:47',
                   '2012-01-01 00:00:48', '2012-01-01 00:00:49',
                   '2012-01-01 00:00:50', '2012-01-01 00:00:51',
                   '2012-01-01 00:00:52', '2012-01-01 00:00:53',
                   '2012-01-01 00:00:54', '2012-01-01 00:00:55',
                   '2012-01-01 00:00:56', '2012-01-01 00:00:57',
                   '2012-01-01 00:00:58', '2012-01-01 00:00:59',
                   '2012-01-01 00:01:00', '2012-01-01 00:01:01',
                   '2012-01-01 00:01:02', '2012-01-01 00:01:03',
                   '2012-01-01 00:01:04', '2012-01-01 00:01:05',
                   '2012-01-01 00:01:06', '2012-01-01 00:01:07',
                   '2012-01-01 00:01:08', '2012-01-01 00:01:09',
                   '2012-01-01 00:01:10', '2012-01-01 00:01:11',
                   '2012-01-01 00:01:12', '2012-01-01 00:01:13',
                   '2012-01-01 00:01:14', '2012-01-01 00:01:15',
                   '2012-01-01 00:01:16', '2012-01-01 00:01:17',
                   '2012-01-01 00:01:18', '2012-01-01 00:01:19',
                   '2012-01-01 00:01:20', '2012-01-01 00:01:21',
                   '2012-01-01 00:01:22', '2012-01-01 00:01:23',
                   '2012-01-01 00:01:24', '2012-01-01 00:01:25',
                   '2012-01-01 00:01:26', '2012-01-01 00:01:27',
                   '2012-01-01 00:01:28', '2012-01-01 00:01:29',
                   '2012-01-01 00:01:30', '2012-01-01 00:01:31',
                   '2012-01-01 00:01:32', '2012-01-01 00:01:33',
                   '2012-01-01 00:01:34', '2012-01-01 00:01:35',
                   '2012-01-01 00:01:36', '2012-01-01 00:01:37',
                   '2012-01-01 00:01:38', '2012-01-01 00:01:39'],
                  dtype='datetime64[ns]', freq='S')

```python
ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)
ts.resample("T").sum()
```

    2012-01-01 00:00:00    15181
    2012-01-01 00:01:00     7709
    Freq: T, dtype: int32

### 时区表示

```python
rng = pd.date_range('3/6/2012 00:00', periods=5, freq='D')
ts = pd.Series(np.random.randn(len(rng)), index=rng)
ts

```

    2012-03-06   -1.684170
    2012-03-07   -1.135531
    2012-03-08    0.847895
    2012-03-09    0.495021
    2012-03-10   -0.742937
    Freq: D, dtype: float64

```python
ts_utc = ts.tz_localize('UTC')
ts_utc
```

    2012-03-06 00:00:00+00:00   -1.684170
    2012-03-07 00:00:00+00:00   -1.135531
    2012-03-08 00:00:00+00:00    0.847895
    2012-03-09 00:00:00+00:00    0.495021
    2012-03-10 00:00:00+00:00   -0.742937
    Freq: D, dtype: float64

```python
#时区转换
ts_utc.tz_convert('US/Eastern')

```

    2012-03-05 19:00:00-05:00   -1.684170
    2012-03-06 19:00:00-05:00   -1.135531
    2012-03-07 19:00:00-05:00    0.847895
    2012-03-08 19:00:00-05:00    0.495021
    2012-03-09 19:00:00-05:00   -0.742937
    Freq: D, dtype: float64

### 从 Timestamp index 转换成 TimePeriod

```python
rng = pd.date_range('1/1/2012', periods=5, freq='M')
ts = pd.Series(np.random.randn(len(rng)), index=rng)
ts
ts.to_period()
ts.to_period().to_timestamp()
```

    2012-01-31    1.167916
    2012-02-29   -0.944750
    2012-03-31   -1.353469
    2012-04-30   -0.220746
    2012-05-31   -0.357615
    Freq: M, dtype: float64

    2012-01    1.167916
    2012-02   -0.944750
    2012-03   -1.353469
    2012-04   -0.220746
    2012-05   -0.357615
    Freq: M, dtype: float64

    2012-01-01    1.167916
    2012-02-01   -0.944750
    2012-03-01   -1.353469
    2012-04-01   -0.220746
    2012-05-01   -0.357615
    Freq: MS, dtype: float64

# 类别

```python
df = pd.DataFrame({"id":[1,2,3,4,5,6], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']})
df
```

      id
      raw_grade

      0
      1
      a

      1
      2
      b

      2
      3
      b

      3
      4
      a

      4
      5
      a

      5
      6
      e

```python
#把上述第二列文本类型转化成类别
df["grade"] = df["raw_grade"].astype("category")
df.grade # or df['grade']
df
```

    0    a
    1    b
    2    b
    3    a
    4    a
    5    e
    Name: grade, dtype: category
    Categories (3, object): [a, b, e]

      id
      raw_grade
      grade

      0
      1
      a
      a

      1
      2
      b
      b

      2
      3
      b
      b

      3
      4
      a
      a

      4
      5
      a
      a

      5
      6
      e
      e

### 类别可以 inplace 地赋值：（只是改一下对应的字符串嘛，类别是用 Index 对象存储的）

```python
df["grade"].cat.categories = ["very good", "good", "very bad"]
df
```

      id
      raw_grade
      grade

      0
      1
      a
      very good

      1
      2
      b
      good

      2
      3
      b
      good

      3
      4
      a
      very good

      4
      5
      a
      very good

      5
      6
      e
      very bad

### 修改类别时，如果有新的类别，会自动加进去

```python
df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", "medium", "good", "very good"])
df['grade']
```

    0    very good
    1         good
    2         good
    3    very good
    4    very good
    5     very bad
    Name: grade, dtype: category
    Categories (5, object): [very bad, bad, medium, good, very good]

```python
#做 group by 的时候，空的类别也会被呈现出来

df.groupby("grade").size()
df
```

    grade
    very bad     1
    bad          0
    medium       0
    good         2
    very good    3
    dtype: int64

      id
      raw_grade
      grade

      0
      1
      a
      very good

      1
      2
      b
      good

      2
      3
      b
      good

      3
      4
      a
      very good

      4
      5
      a
      very good

      5
      6
      e
      very bad

# **对于 DataFrame，可以直接 plot**

```python
df2 = pd.DataFrame(np.random.randn(100, 4),columns=['A', 'B', 'C', 'D'])
df2
```

      A
      B
      C
      D

      0
      -0.370685
      -0.154518
      -0.271449
      -0.885060

      1
      -0.243143
      0.365756
      0.048412
      -0.580112

      2
      1.124348
      -0.728610
      -1.536214
      -0.553055

      3
      1.578256
      0.447072
      -0.179933
      0.349233

      4
      0.239953
      0.314130
      0.999452
      0.069708

      5
      0.082091
      -0.118935
      0.441708
      0.067978

      6
      1.091403
      -0.252562
      0.866390
      0.074122

      7
      -0.449079
      0.180126
      -0.220658
      -0.428368

      8
      0.665899
      -1.615098
      2.119522
      0.051510

      9
      0.030922
      -0.496331
      -0.311393
      1.114837

      10
      -1.034900
      0.036954
      -1.223412
      0.788893

      11
      0.043814
      -0.515007
      0.168072
      -0.583951

      12
      2.147344
      -0.685654
      -0.902062
      1.180635

      13
      -0.081746
      -0.984048
      -0.263919
      0.392202

      14
      -0.218320
      -0.862620
      -1.300287
      2.150485

      15
      0.689651
      0.554502
      0.669934
      -0.487478

      16
      -0.134602
      0.564416
      -0.504190
      0.203005

      17
      0.390121
      1.428538
      0.885747
      -0.920177

      18
      -1.369989
      -1.271622
      -0.054291
      0.428196

      19
      1.160963
      -1.313064
      0.850245
      0.297977

      20
      0.741412
      1.012128
      0.011564
      -0.645272

      21
      0.770491
      -0.497547
      0.579313
      1.159999

      22
      0.928572
      -0.820314
      -1.180193
      1.406279

      23
      0.666230
      -0.135030
      -0.723418
      0.147707

      24
      -0.943594
      0.169585
      -0.396945
      -0.344790

      25
      -0.565793
      0.182098
      -0.689279
      1.595866

      26
      0.860313
      0.207596
      0.051473
      -1.400945

      27
      0.577284
      0.705074
      -0.866381
      0.735285

      28
      -0.026657
      0.137143
      0.376712
      -1.437817

      29
      -0.513783
      1.659109
      0.926064
      -1.452439

      ...
      ...
      ...
      ...
      ...

      70
      1.567620
      0.112571
      0.296566
      1.880676

      71
      0.328096
      0.138384
      -1.086756
      -0.937968

      72
      -0.997334
      2.807080
      -1.340475
      0.284261

      73
      -0.995872
      1.188866
      -0.231619
      -0.234302

      74
      -0.232942
      -0.230950
      0.187815
      0.900796

      75
      0.191307
      1.085525
      -1.394069
      0.818234

      76
      0.724546
      0.706317
      0.220072
      -0.336109

      77
      -0.990668
      -0.027119
      -2.421378
      -0.119679

      78
      -0.689280
      0.495900
      -0.548844
      0.022780

      79
      0.068734
      -0.206847
      0.555043
      0.939891

      80
      0.301460
      0.340575
      -1.338764
      0.442644

      81
      -0.577242
      -0.506678
      0.111737
      -0.800262

      82
      -0.682550
      -1.292298
      -0.857854
      1.472022

      83
      -0.565005
      0.721060
      -0.373054
      -0.151424

      84
      0.894394
      -0.153244
      0.551335
      -0.800476

      85
      0.287139
      -0.030197
      1.499702
      -0.461086

      86
      -0.863743
      0.798126
      1.875427
      0.312478

      87
      0.593485
      0.206480
      0.464924
      -0.735877

      88
      0.769589
      -0.496035
      -0.958488
      -1.588026

      89
      0.223335
      1.089245
      0.886892
      0.550559

      90
      -0.290650
      0.669631
      0.334951
      -0.529269

      91
      1.599340
      0.010133
      1.259110
      0.368818

      92
      -1.231712
      0.411729
      -0.001522
      0.054912

      93
      -0.783944
      0.576312
      0.298449
      1.120112

      94
      -0.119034
      0.462312
      0.484350
      1.100151

      95
      0.223333
      -0.501618
      2.214140
      -2.343834

      96
      1.584619
      -0.480723
      -0.765888
      -0.946113

      97
      -0.560680
      0.286095
      -0.172715
      -0.941293

      98
      2.148963
      1.764289
      0.491088
      0.715644

      99
      0.777417
      1.053557
      -0.569696
      0.761845

100 rows × 4 columns

```python
f = df2.cumsum()
f
```

      A
      B
      C
      D

      0
      -0.370685
      -0.154518
      -0.271449
      -0.885060

      1
      -0.613829
      0.211238
      -0.223038
      -1.465172

      2
      0.510519
      -0.517372
      -1.759252
      -2.018226

      3
      2.088775
      -0.070300
      -1.939185
      -1.668993

      4
      2.328728
      0.243830
      -0.939733
      -1.599284

      5
      2.410819
      0.124895
      -0.498025
      -1.531307

      6
      3.502222
      -0.127667
      0.368365
      -1.457185

      7
      3.053143
      0.052459
      0.147707
      -1.885553

      8
      3.719042
      -1.562639
      2.267229
      -1.834043

      9
      3.749964
      -2.058970
      1.955836
      -0.719206

      10
      2.715063
      -2.022016
      0.732424
      0.069687

      11
      2.758878
      -2.537023
      0.900497
      -0.514264

      12
      4.906222
      -3.222677
      -0.001565
      0.666371

      13
      4.824476
      -4.206724
      -0.265484
      1.058573

      14
      4.606156
      -5.069344
      -1.565771
      3.209059

      15
      5.295807
      -4.514842
      -0.895837
      2.721581

      16
      5.161206
      -3.950426
      -1.400027
      2.924587

      17
      5.551327
      -2.521888
      -0.514280
      2.004410

      18
      4.181338
      -3.793510
      -0.568571
      2.432606

      19
      5.342301
      -5.106574
      0.281674
      2.730583

      20
      6.083713
      -4.094446
      0.293238
      2.085310

      21
      6.854204
      -4.591993
      0.872551
      3.245309

      22
      7.782776
      -5.412307
      -0.307642
      4.651588

      23
      8.449005
      -5.547337
      -1.031060
      4.799295

      24
      7.505412
      -5.377751
      -1.428005
      4.454505

      25
      6.939619
      -5.195653
      -2.117284
      6.050372

      26
      7.799932
      -4.988057
      -2.065811
      4.649427

      27
      8.377216
      -4.282983
      -2.932193
      5.384712

      28
      8.350559
      -4.145841
      -2.555481
      3.946894

      29
      7.836776
      -2.486732
      -1.629417
      2.494456

      ...
      ...
      ...
      ...
      ...

      70
      8.074164
      -6.502464
      -5.080041
      5.234228

      71
      8.402260
      -6.364081
      -6.166798
      4.296261

      72
      7.404926
      -3.557001
      -7.507272
      4.580521

      73
      6.409054
      -2.368134
      -7.738892
      4.346219

      74
      6.176112
      -2.599084
      -7.551077
      5.247016

      75
      6.367419
      -1.513559
      -8.945146
      6.065249

      76
      7.091966
      -0.807242
      -8.725073
      5.729140

      77
      6.101297
      -0.834361
      -11.146452
      5.609461

      78
      5.412018
      -0.338461
      -11.695296
      5.632242

      79
      5.480752
      -0.545308
      -11.140252
      6.572132

      80
      5.782213
      -0.204733
      -12.479017
      7.014777

      81
      5.204971
      -0.711411
      -12.367280
      6.214515

      82
      4.522421
      -2.003709
      -13.225134
      7.686538

      83
      3.957415
      -1.282650
      -13.598188
      7.535113

      84
      4.851810
      -1.435894
      -13.046853
      6.734638

      85
      5.138949
      -1.466090
      -11.547151
      6.273552

      86
      4.275206
      -0.667964
      -9.671724
      6.586030

      87
      4.868691
      -0.461484
      -9.206800
      5.850153

      88
      5.638280
      -0.957520
      -10.165287
      4.262127

      89
      5.861615
      0.131726
      -9.278396
      4.812685

      90
      5.570966
      0.801357
      -8.943444
      4.283416

      91
      7.170306
      0.811490
      -7.684335
      4.652234

      92
      5.938594
      1.223220
      -7.685857
      4.707145

      93
      5.154650
      1.799532
      -7.387408
      5.827257

      94
      5.035616
      2.261844
      -6.903059
      6.927408

      95
      5.258949
      1.760226
      -4.688919
      4.583574

      96
      6.843568
      1.279502
      -5.454807
      3.637461

      97
      6.282888
      1.565597
      -5.627522
      2.696169

      98
      8.431851
      3.329886
      -5.136435
      3.411813

      99
      9.209268
      4.383443
      -5.706130
      4.173658

100 rows × 4 columns

```python
import matplotlib.pyplot as plt
plt.figure()
f.plot()
plt.legend(loc='best')
plt.show()
```

    <matplotlib.figure.Figure at 0x9f286d8>

    <matplotlib.axes._subplots.AxesSubplot at 0x9f53cc0>

    <matplotlib.legend.Legend at 0x90b1048>

    <matplotlib.figure.Figure at 0x9f286d8>

![png](output_125_4.png)

# 读取、写入数据

## CSV

```python
df.to_csv('first.csv')#write
```

```python
pd.read_csv("first.csv",index_col=0)
```

      id
      raw_grade
      grade

      0
      1
      a
      very good

      1
      2
      b
      good

      2
      3
      b
      good

      3
      4
      a
      very good

      4
      5
      a
      very good

      5
      6
      e
      very bad

# HDF5

```python
#df.to_hdf('foo.h5','df') 略
#pd.read_hdf('foo.h5','df')
```

# Excel

```python
df2.to_excel('myexcel.xlsx',sheet_name="1")
```

```python
pd.read_excel("myexcel.xlsx","1",index_col= 0,na_values=["NA"])
```

      A
      B
      C
      D

      0
      -0.370685
      -0.154518
      -0.271449
      -0.885060

      1
      -0.243143
      0.365756
      0.048412
      -0.580112

      2
      1.124348
      -0.728610
      -1.536214
      -0.553055

      3
      1.578256
      0.447072
      -0.179933
      0.349233

      4
      0.239953
      0.314130
      0.999452
      0.069708

      5
      0.082091
      -0.118935
      0.441708
      0.067978

      6
      1.091403
      -0.252562
      0.866390
      0.074122

      7
      -0.449079
      0.180126
      -0.220658
      -0.428368

      8
      0.665899
      -1.615098
      2.119522
      0.051510

      9
      0.030922
      -0.496331
      -0.311393
      1.114837

      10
      -1.034900
      0.036954
      -1.223412
      0.788893

      11
      0.043814
      -0.515007
      0.168072
      -0.583951

      12
      2.147344
      -0.685654
      -0.902062
      1.180635

      13
      -0.081746
      -0.984048
      -0.263919
      0.392202

      14
      -0.218320
      -0.862620
      -1.300287
      2.150485

      15
      0.689651
      0.554502
      0.669934
      -0.487478

      16
      -0.134602
      0.564416
      -0.504190
      0.203005

      17
      0.390121
      1.428538
      0.885747
      -0.920177

      18
      -1.369989
      -1.271622
      -0.054291
      0.428196

      19
      1.160963
      -1.313064
      0.850245
      0.297977

      20
      0.741412
      1.012128
      0.011564
      -0.645272

      21
      0.770491
      -0.497547
      0.579313
      1.159999

      22
      0.928572
      -0.820314
      -1.180193
      1.406279

      23
      0.666230
      -0.135030
      -0.723418
      0.147707

      24
      -0.943594
      0.169585
      -0.396945
      -0.344790

      25
      -0.565793
      0.182098
      -0.689279
      1.595866

      26
      0.860313
      0.207596
      0.051473
      -1.400945

      27
      0.577284
      0.705074
      -0.866381
      0.735285

      28
      -0.026657
      0.137143
      0.376712
      -1.437817

      29
      -0.513783
      1.659109
      0.926064
      -1.452439

      ...
      ...
      ...
      ...
      ...

      70
      1.567620
      0.112571
      0.296566
      1.880676

      71
      0.328096
      0.138384
      -1.086756
      -0.937968

      72
      -0.997334
      2.807080
      -1.340475
      0.284261

      73
      -0.995872
      1.188866
      -0.231619
      -0.234302

      74
      -0.232942
      -0.230950
      0.187815
      0.900796

      75
      0.191307
      1.085525
      -1.394069
      0.818234

      76
      0.724546
      0.706317
      0.220072
      -0.336109

      77
      -0.990668
      -0.027119
      -2.421378
      -0.119679

      78
      -0.689280
      0.495900
      -0.548844
      0.022780

      79
      0.068734
      -0.206847
      0.555043
      0.939891

      80
      0.301460
      0.340575
      -1.338764
      0.442644

      81
      -0.577242
      -0.506678
      0.111737
      -0.800262

      82
      -0.682550
      -1.292298
      -0.857854
      1.472022

      83
      -0.565005
      0.721060
      -0.373054
      -0.151424

      84
      0.894394
      -0.153244
      0.551335
      -0.800476

      85
      0.287139
      -0.030197
      1.499702
      -0.461086

      86
      -0.863743
      0.798126
      1.875427
      0.312478

      87
      0.593485
      0.206480
      0.464924
      -0.735877

      88
      0.769589
      -0.496035
      -0.958488
      -1.588026

      89
      0.223335
      1.089245
      0.886892
      0.550559

      90
      -0.290650
      0.669631
      0.334951
      -0.529269

      91
      1.599340
      0.010133
      1.259110
      0.368818

      92
      -1.231712
      0.411729
      -0.001522
      0.054912

      93
      -0.783944
      0.576312
      0.298449
      1.120112

      94
      -0.119034
      0.462312
      0.484350
      1.100151

      95
      0.223333
      -0.501618
      2.214140
      -2.343834

      96
      1.584619
      -0.480723
      -0.765888
      -0.946113

      97
      -0.560680
      0.286095
      -0.172715
      -0.941293

      98
      2.148963
      1.764289
      0.491088
      0.715644

      99
      0.777417
      1.053557
      -0.569696
      0.761845

100 rows × 4 columns


Looks like this commit (part of #187) has merge conflicts from recent changes to the head branch - I'll update it for you. (It'll take me a couple of minutes.)


My problem is different, I’ll work on a test case…

From: Ed Hager [mailto:notifications@github.com]
Sent: Thursday, May 25, 2017 7:05 PM
To: SitePen/dgrid <dgrid@noreply.github.com>
Cc: eQuorumRayNewman <ray.newman@equorum.com>; Mention <mention@noreply.github.com>
Subject: Re: [SitePen/dgrid] ColumnHider: scale icon size down in browsers with small scrollbars. Closes #1328 (27f0000)

@eQuorumRayNewman <https://github.com/equorumraynewman> , I created ticket #1371 <https://github.com/SitePen/dgrid/issues/1371> . If your problem is different, can you please provide a test case? Thanks!

—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub <https://github.com/SitePen/dgrid/commit/27f000000c4fb4a72f7421e3e977464a7e4b568f#commitcomment-22286334> , or mute the thread <https://github.com/notifications/unsubscribe-auth/AIB9rGAU5h3zx-pPUDVI55G9UCBZhCH-ks5r9gkNgaJpZM4Nleqx> .


@tchaikov @badone

No worries, I'm a bit frustrated, but then we fix it and get done with it.
I'm almost back at 78% thru the compilation. So give or take some more glitches, it'll get done. :)


I think we should recommend to use `until` in this case.


方方，那这样的话 this.props.onSignUp.call(null, user) 可以写成 this.props.onSignUp(user) 吗?


Violates PSR-9.


Added secondary constructors. I don't think there can be a general rule for where nested classes should be; they should be close to the code that uses them.


Yes I know. Hence my "Did I break that in 1.8?". Oh well, at that point there were no other fluid tanks, guess I can at least test it now ;D


Yes, this is subjective. I don't want to spell out any rule on margins or wrapping requirements.


This is a first draft for #6


Desgraciadamente, la tabla EMAIL no existe


mktime() returns the time in seconds for example 282882 and time() returns it as timestamp i think


Pra validação vermelhinha funcionar tens que importar o arquivo js/validator @hirobs


Did i really write ass? i meant add ofc


cha


I think you want the export to be res-config


Não esquece de alterar o nome doo botão :joy: @hirobs


Зачем хранить имя и тип отдельно, когда у тебя есть сразо **объект**? В объекте есть и имя, и тип, и методы, к которым можно обращаться. Вот и храни массив объектов. Забудь о существовании атрибутов отдельно от объектов, оперируй только объектами.


Nice!


Зачем нужна эта инстанс-переменная? Почему локальной не хватило?


I am using 3.5, and the minimum required version is 3.0.2. You are right, the variable name was changed after version 3.6. We should find a way to either make this backward compatible or enforce newer CMake versions.


@hirobs Temos que abrir o modal de criação de Cadastro quando clica em editar, acho que se não colcoarmos o professor vai reclamar :/


I prefer separating the logic of 'strict' option, it's more readable.


Which means that`inModelF` needs to be parametrized so that it can omit that [which is what @niazim3 said!].

Right now it can probably be done by changing the `inModelF` function to have some `Maybe` parameters that correspond to General Definition.  But a better solution would be to do for this the same as was done for the introduction paragraph for the reference section (i.e. create a little language of parametrizations).


Looks like this commit (part of #187) has merge conflicts from recent changes to the head branch - I'll update it for you. (It'll take me a couple of minutes.)


Скобки здесь не нужны. И надо проверять, что кол-во вагонов не не равно нулю, а больше нуля


the solution about this issue is this

```
  public static void runOnUiThreadAndBlock(final Runnable r) {
        if (getActivity() == null) {
            throw new RuntimeException("Cannot run on UI thread because getActivity() is null.  This generally means we are running inside a service in the background so UI access is disabled.");
        }

        final boolean[] completed = new boolean[1];
        getActivity().runOnUiThread(new Runnable() {
            @Override
            public void run() {
                try {
                    r.run();
                } catch(Throwable t) {
                    com.codename1.io.Log.e(t);
                }
                completed[0] = true; //Please add this delete line
                synchronized(completed) {
                    completed[0] = true; // and this . I tested and it work
                    completed.notify();
                }
            }
        });
        Display.getInstance().invokeAndBlock(new Runnable() {
            @Override
            public void run() {
                synchronized(completed) {
                    while(!completed[0]) {
                        try {
                            completed.wait();
                        } catch(InterruptedException err) {}
                    }
                }
            }
        });
    }
```


require('http') module


Silly me! It should not have been `EXPERT=yes` but `I_KNOW_WHAT_I_DO_AND_WONT_ASK_FOR_SUPPORT=agreed`.


Сделай так, чтобы метод принимал аргумент - тип поезда - и возвращал данные только для этого типа


Above and beyond requirements.  I like it!


Посмотри на метод select


This could be written as

  map (makeRef . Definition . Theory) [im1, im2, im3]


## test


Would this be better generated?  You know the symbol (as it is the abbreviation for `Instance Model`), and you know the number (from the line below).


Раздели на 2 метода: один для добавления вагонов, другой - для удаления. У каждого метода должна быть одна, четко обозначенная обязанность


Fair, I'd consider making it a private method instead.
I'm just not a big fan of overwriting variables after they've been created.


update controller index file: only comment added


Не имя, а объект надо возвращать. Имя - это всего лишь атрибут объекта, а у него еще могут быть другие методы и атрибуты


I do not think that it is a 5 elo gain at LTC.  My guess is that it was lucky to pass fast.  No convincing evidence that it is really better at LTC than STC and the only way to check if there is a significant difference is not to use SPRT but to use fixed number of games both for STC and LTC,


You end up using this code pattern 4 times - factor it out.  Also, the previous line comments apply.


Давай понятные имена аргументам, например, month и days


Не нужно тут предзаполнять массив, пусть это полностью автоматом происходит


If only `name` is provided, and `last_name` is null, this returns `name` ended in an empty space. This will break the tests.

We do this in backend, for inspiration:
`name.present? || last_name.present? ? [name, last_name].select(&:present?).join(' ')`


@wjwithagen You can fix them and test them better. Again, sorry for the
inconvenience caused.


Looks like this commit (part of #187) has merge conflicts from recent changes to the head branch - I'll update it for you. (It'll take me a couple of minutes.)


this commit broke the compilation of rntester. xcode complains about `"folly::stringPrintf(char const*, ...)", referenced from:` im guessing `JSCHelpers.h`


Зачем тут each? эта задача решается без each, только с while


You are deserve to be called "study bully"


项目开发过程中出现了问题，google API没有在电脑安装，所以该程序暂时搁置


The 'core data' here is the list `[t1NewtonSL, t2NewtonTL, r3NewtonLUG, t4ChaslesThm, t5NewtonSLR]`.  It should exist on its own -- probably moved into the system information data-structure.  Then the rest of the information should be derived from that.


@javache


Не нужно вычислять последний элемент, нужно в массив поместить все числа меньше 100. И, кстати, а где массив чисел? Вывод на экран- это не массив


程序代码由于引入的api自己电脑上没安装，所以开发暂时搁置，是个无用项目


You are deserve to be called "study bully."


`zipWith itemRefToSent s8_row_t1 (...)`  is the idiom you are looking for here.  BTW, not need for `()` around `itermRefToSent`.


Пустой массив принято задавать как []


Why was this function removed?


cambiando titulo


Используй << для добавления в массив, именно так принято по стилю


Dear @McpeBooster,
I am sorry to inform you that your submitted release, "KnockbackFFA" (v1.0.0), for the project [KnockbackFFA-McpeBooster](https://poggit.pmmp.io/ci/McpeBooster/KnockbackFFA/KnockbackFFA-McpeBooster) on 2017-05-25 11:27:53 has been rejected.

This plugin is barely customizable. This plugin is suitable for your personal server, but it could hardly be useful for other servers under their own circumstances.
For example, not every server manages their arenas by worlds.

Please resolve the above-listed issues and submit the updated plugin again.


We can always add an extra dialog if `EXPERT=yes` with text like "WIP targets are provided only for internal use. Any issues or forum threads for these targets will be closed or removed immediately without further discussion"


cambio en html titulo


Можно обойтись без переменных, задава начальные 2 значения массива и работая дальше с его элементами