花下猫说：公众号没有留言功能，真是不方便。上周，尝试性地推送了一篇英文文章，单从后台数据来看，效果还不错。这周继续尝试推送一篇。大家有什么想法，可以后台与我交流。
Pandas是Python数据科学生态中重要的基础成员，功能强大，用法灵活，简单记录之。
libconhash is a consistent hashing library which can be compiled both on Windows and Linux platforms, with the following features:
“Xgboost，LightGBM，Catboost，HistGradient。”
在最基本的层面上，Pandas 对象可以认为是 NumPy 结构化数组的增强版本，其中行和列用标签而不是简单的整数索引来标识。我们将在本章的过程中看到，Pandas 在基本数据结构之上提供了许多有用的工具，方法和功能，但几乎所有后续内容都需要了解这些结构是什么。因此，在我们继续之前，让我们介绍这三个基本的 Pandas 数据结构：Series，DataFrame和Index。
SpannableStringBuilder有append，insert, setSpan , removeSpan方法
前提是实现NSCopying协议的copyWithZone:方法，否则会导致出现找不到selector的崩溃。**unrecognized selector sent to instance**
使用官方迁移方案解决（一个很深的坑，网上有写方案是只是用低版本的，大家最好去官方获取最新的迁移方式。）
Computer users take it for granted that their systems can do more than one thing at a time. They assume that they can continue to work in a word processor, while other applications download files, manage the print queue, and stream audio. Even a single application is often expected to do more than one thing at a time. For example, that streaming audio application must simultaneously read the digital audio off the network, decompress it, manage playback, and update its display. Even the word processor should always be ready to respond to keyboard and mouse events, no matter how busy it is reformatting text or updating the display. Software that can do such things is known as concurrent software.
LINQ via C# Recently I am giving a series of talk on LINQ. the name “LINQ via C#” is copied from “ CLR via C# ”, one of my favorite books. Currently part 1 – 8 are finished, and the entire series should be 10 parts. The contents are: Introducing LINQ What
String、StringBuffer、StringBuilder是常用的字符序列，从源码上对比下，三者的区别
写一般是对比如1000个series 同时进行采集，所以他是纵向的。磁盘随机写效率低、SSD 写放大问题 → 顺序写 和 批量写 是必然选择 （sequential and batched writes are the ideal write pattern for spinning disks and SSDs alike. A simple rule to stick）
其他升级方式可以来这里看：Updating GitLab installed with the Omnibus GitLab package
本文主要研究一下Elasticsearch RestClient的NodeSelector
elasticsearch-7.0.1/client/rest/src/main/java/org/elasticsearch/client/NodeSelector.java
1.2.6: Sets 集合 集合是不同散列对象的无序集合。 Sets are unordered collections of distinct hashable objects. 但是，对象是
Nicolas Fränkel is a Developer Advocate with 15+ years experience consulting for many different customers, in a wide range of contexts (such as telecoms, banking, insurances, large retail and public sector). Usually working on Java/Java EE and Spring technologies, but with focused interests like Rich Internet Applications, Testing, CI/CD and DevOps. Currently working for Exoscale. Also double as a teacher in universities and higher education schools, a trainer and triples as a book author.
Git 支持在不同操作上执行的钩子。这些钩子在服务器上运行，可用于根据存储库的状态强制执行特定的提交策略或执行其他任务。
安装并使用PandasPandas对象简介Pandas的Series对象Series是广义的Numpy数组Series是特殊的字典创建Series对象Pandas的DataFrame对象DataFrame是广义的Numpy数组DataFrame是特殊的字典创建DataFrame对象Pandas的Index对象将Index看作不可变数组将Index看作有序集合
1、强大的hashlib，提供了用于加密相关的操作，代替了md5模块和sha模块，主要提供 SHA1, SHA224, SHA256, SHA384, SHA512 ，MD5 算法
Demystifying Mutability and References in Rust
列表是任何类型的对象的可变序列。 Lists are mutable sequences of objects of any type. 它们通常用于存储同质项目。 And they’re typically used to store homogeneous items. 列表是序列的一种类型，就像字符串一样，但它们确实有区别。 Lists are one type of sequence, just like strings but they do have their differences. 如果我们比较字符串和列表，一个区别是字符串是单个字符的序列， If we compare a string and a list, one difference is that strings are sequences of individual characters, 而列表是任何类型Python对象的序列。 whereas lists are sequences of any type of Python objects. 字符串和列表之间的另一个区别是字符串是不可变的，而列表是可变的。 Another difference between strings and lists is that strings are immutable, whereas lists are mutable. 除了这两个区别之外，字符串和列表当然也有自己的方法。 In addition to these two differences, strings and lists, of course,come with their own methods. 通常情况下，列表只包含一种类型的对象，尽管这不是严格的要求。 It is common practice for a list to hold objects of just one type,although this is not strictly a requirement. 让我们尝试几个简单的列表来测试它们。 Let’s try a couple of simple lists to experiment with them. 让我们构造一个简单的数字列表，以进一步了解列表。 Let’s construct a simple list of numbers to learn a little bit more about lists. 所以我要构造一个数字列表。 So I’m going to construct a list of numbers. 我要称之为数字。 I’m going to call it numbers. 我将使用数字2、4、6和8。 And I’ll use numbers 2, 4, 6, and 8. 假设我想提取或访问列表中的第一个元素。 Imagine I wanted to extract, or access, the first element of my list. 我要做的第一件事是键入列表的名称，然后我需要方括号。 The first thing for me to do is type the name of the list,then I need my square brackets. 现在请记住，在Python中，索引从零开始。 Now remember, in Python, indexes start at zero. 因此，为了能够查看该列表的第一个元素，我需要将其放入索引0，位置0。 So for me to be able to look at the first element of that list,I need to put in index 0, position 0. 在这里，Python告诉我第一个对象，即位于位置0的对象，是数字2。 Here, Python tells me that the first object, meaning the object located at position 0, is number 2. 如果我将索引更改为1，Python将给我第二个对象。 If I change the index to 1, Python gives me the second object. 现在，如果我想知道列表上最后一个对象是什么，我可以从右到左计算位置。 Now if I wanted to find out what is the very last object on my list,I can count positions from right to left. 这意味着我必须使用负指数。 And
java.util.stream public interface Collector<T, A, R> A mutable reduction operation that accumulates input elements into a mutable result container, optionally transforming the accumulated result into a final representation after all input elements have been processed. Reduction operations can be performed either sequentially or in parallel. Examples of mutable reduction operations include: accumulating elements into a Collection; concatenating strings using a StringBuilder; computing summary information about elements such as sum, min, max, or average; computing "pivot table" summaries such as "maximum valued transaction by seller", etc. The class Collectors provides implementations of many common mutable reductions. A Collector is specified by four functions that work together to accumulate entries into a mutable result container, and optionally perform a final transform on the result. They are: creation of a new result container (supplier()) incorporating a new data element into a result container (accumulator()) combining two result containers into one (combiner()) performing an optional final transform on the container (finisher()) Collectors also have a set of characteristics, such as Collector.Characteristics.CONCURRENT, that provide hints that can be used by a reduction implementation to provide better performance. A sequential implementation of a reduction using a collector would create a single result container using the supplier function, and invoke the accumulator function once for each input element. A parallel implementation would partition the input, create a result container for each partition, accumulate the contents of each partition into a subresult for that partition, and then use the combiner function to merge the subresults into a combined result. To ensure that sequential and parallel executions produce equivalent results, the collector functions must satisfy an identity and an associativity constraints. The identity constraint says that for any partially accumulated result, combi
Prometheus内部主要分为三大块，Retrieval是负责定时去暴露的目标页面上去抓取采样指标数据，Storage是负责将采样数据写磁盘，PromQL是Prometheus提供的查询语言模块
Objects是自jdk1.7起新增的工具类，这个类由一些实用的静态方法组成，这些方法可以方便我们平时的开发，例如对象比较、获取对象的hash码等。
A member function should be marked const unless it changes the object's observable state. This gives a more precise statement of design intent, better readability, more errors caught by the compiler, and sometimes more optimization opportunities.
新媒体管家 关键时刻，第一时间送达！ 本文有些零碎，总题来说，包括两个问题：（1）可变对象（最常见的是list dict）被意外修改的问题，（2）对参数（parameter）的检查问题。这两个问题，本质都是因为动态语言（动态类型语言）的特性造成了，动态语言的好处就不细说了，本文是要讨论因为动态－－这种灵活性带来的一些问题。 什么是动态语言（Dynamic Programming language）呢，是相对于静态语言而言，将很多静态语言编译（compilation）时期所做的事情推迟到运行时，在运行时修改代
索引对象Index Series和DataFrame中的索引都是Index对象 示例代码： print(type(ser_obj.index)) print(type(df_obj2.index)) print(df_obj2.index) 运行结果： <class 'pandas.indexes.range.RangeIndex'> <class 'pandas.indexes.numeric.Int64Index'> Int64Index([0, 1, 2, 3], dtype='int64')
一个Dataframe就是一张表格，Series表示的是一维数组，Dataframe则是一个二维数组，可以类比成一张excel的spreadsheet。也可以把 Dataframe当做一组Series的集合。
扩展Django的用户系统有几个方法： 1.在自定义Model中使用OneToOneField的方式来扩展，实现一个User Profile。 这种方式在1.5之前是推荐的，在User也有一个默认的get_profile方法来获取这个profile。 这种方式的好处是1.5以前的版本默认支持，并且对Django的影响最小，坏处主要是获取资料的时候需要一次join表。 示例代码如下： class UserProfile(models.Model): user = models.OneToOneFi
当一个请求连接进来时，django会创建一个HttpRequest对象来封装和保存所有请求相关的信息，并且会根据请求路由载入匹配的视图函数。每个请求的视图函数都会返回一个HttpResponse。
Implementations are in .ml files, interfaces are in .mli files. Comments can be nested, between delimiters (*...*) Integers: 123, 1_000, 0x4533, 0o773, 0b1010101 Chars: 'a', '\255', '\xFF', '\n' Floats: 0.1, -1.234e-34
It is often appropriate to reuse a single object instead of creating a new functionally equivalent object each time it is needed. Reuse can be both faster and more stylish. An object can always be reused if it is immutable (Item 15).
先来看看速度优化对比(这里用了 Django的 DebugToolbar库来查看状态)
I had a programming interview recently, a phone-screen in which we used a collaborative text editor.
With px.bar, each row of the DataFrame is represented as a rectangular mark.
今天在联盟的群里看到有小伙伴在讨论lambda，小编特地找个一篇文章给大家普及下这方面的知识。 C++11 新增了很多特性，lambda 表达式是其中之一，如果你想了解的 C++11 完整特性，建议去看看C++标准。本文作为 5 月的最后一篇博客，将介绍 C++11 的 lambda 表达式。 很多语言都提供了 lambda 表达式，如 Python，Java 8。lambda 表达式可以方便地构造匿名函数，如果你的代码里面存在大量的小函数，而这些函数一般只被调用一次，那么不妨将他们重构成 lambda 表
<matplotlib.axes._subplots.AxesSubplot at 0x1a260c38d0>
在Python中，对象按可变属性可以分为可变对象和不可变对象两种。理解这两种对象的差异对于编写高效且易于维护的代码至关重要。本文将介绍Python中的可变对象和不可变对象，以及在使用它们时需要注意的事项。
在创建字典表语句中使用“layout”来指定字典的类型，目前扩展字典支持7种类型，分别为flat、hashed、range_hashed、cache、complex_key_hashed、complex_key_cache、ip_trie，不同的字典类型决定了数据在内存中以何种结构组织和存储。
If an object’s value can be modified, the object is said to be mutable. If the value cannot be modified,the object is said to be immutable.
本系列参考自「Python Data Science Handbook」第三章，旨在对 Pandas 库的使用方法进行归纳与总结。
Pandas是一个强大的分析结构化数据的工具集，基于NumPy构建，提供了 高级数据结构 和 数据操作工具，它是使Python成为强大而高效的数据分析环境的重要因素之一。
在源代码的开头，是作者引入的一些包，有标准的，也有第三方的。像sha256，hex这些标准包是为了sha-256编码用的，其他还有启动http服务，打印日志的log，并发控制的sync，时间戳的time。
golang的垃圾回收算法跟java一样，都是根可达算法。代码中main0函数里a和b是互相引用，但是a和b没有外部引用。因此a和b会被当成垃圾被回收掉。而析构函数的调用不是有序的，所以B和C都有可能，答案选D。让我们看看答案是什么，如下：
1. stl map is an associative array where keys are stored in sorted order using balanced trees. while hash_map is a hashed associated container, where keys are not stored in an ordered way. key, value pair is stored using a hashed function. 2. insertion and lookup takes ologn time in map, also performance would degrade as the key size increases. mainly balance operations on large key ranges would kill performance. while lookup is very efficient o(1) in hash_map. 3. map is useful where you want to store keys in sorted order, hash_map is used where keys order is not important and lookup is very efficient. 4. one more difference is map has the important property that inserting a new element into a map does not invalidate iterators that point to existing elements. erasing an element from a map also does not invalidate any iterators. performance would mostly be o(lgn) due to the implementation of a balanced tree. for map custom objects you would need at the minimum the following operators to store data in a map "<" ">" "==" and of course the other stuff for deep copy.
有什么方法可以将列转换为适当的类型？例如，上面的例子，如何将列2和3转为浮点数？有没有办法将数据转换为DataFrame格式时指定类型？或者是创建DataFrame，然后通过某种方法更改每列的类型？理想情况下，希望以动态的方式做到这一点，因为可以有数百个列，明确指定哪些列是哪种类型太麻烦。可以假定每列都包含相同类型的值。
特质 (Traits) 用于在类 (Class)之间共享接口 (Interface)和字段 (Fields)。类似Java8的接口。 类和对象 (Objects)可以继承Trait，但Trait不能被实例化，因此特质没有参数。
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