merge

java8 merge合并两个map,key相同value相加

大兔子大兔子 提交于 2020-02-08 18:44:11
Map<String,Integer> map1= new HashMap<String, Integer>(); map1.put("one",1); map1.put("two",2); map1.put("three",3); Map<String,Integer> map2= new HashMap<String,Integer>(); map2.put("one",1); map2.put("two",2); //map1合并到map2中 map1.forEach((key,value) -> map2.merge(key,value,Integer::sum)); System.out.println(JSON.toJSONString(map2)); 结果:{"one":2,"three":3,"two":4} 来源: CSDN 作者: df331009 链接: https://blog.csdn.net/df331009/article/details/104225770

MERGE - UPDATE column values separately, based on logic in WHEN MATCHED block

我与影子孤独终老i 提交于 2020-02-08 10:05:08
问题 Earlier today, I asked this question and got the answer I was looking for. Now I have a follow-up question: What I want: I want the MERGE to compare each column value, per row, in the target table against the corresponding value in the source table, and make any updates based on the logic separated by OR in the WHEN MATCHED AND block. I am afraid that the code I've written (pictured below) will make the updates listed in the THEN UPDATE SET block if any of the logic separated by OR in the

归并排序(merge sort)

六月ゝ 毕业季﹏ 提交于 2020-02-07 23:09:38
1.归 ListNode* sortList(ListNode* head) { if (head == nullptr || head->next == nullptr) return head; // 1.将待排序序列分为两部分 ListNode* pre = nullptr, *slow = head, *fast = head; while(fast != nullptr && fast->next != nullptr) { pre = slow; slow = slow->next; fast = fast->next->next; } pre->next = nullptr; // 2.对两个子部分再次进行划分 ListNode* l1 = sortList(head); ListNode* l2 = sortList(slow); // 3.合并 return merge(l1, l2); } 2.并 ListNode* merge(ListNode* l1, ListNode* l2) { ListNode* p = nullptr; if(l1->val < l2->val) { p = l1; l1 = l1->next; } else { p = l2; l2 = l2->next; } ListNode* l = p; while(l1 !=

Simultaneously Aggregating Overlapping Ranges (A Rectangle Problem)

喜你入骨 提交于 2020-02-06 23:59:44
问题 My Problem Consider a set of data with two intervals. For instance, consider a student schedule of classes. Each record has a begin and end date, and each class has a period start time and a period end time. But this schedule is not 'normalized' in the sense that some records overlap. So if you search for records encompassing a given date and period for a student, you might get multiple matches. Here's a contrived example. I represent the dates as integers to simplify the problem: declare

pandas(5)

左心房为你撑大大i 提交于 2020-02-06 03:41:26
文章目录 合并数据集: Concat与Append操作 知识回顾: NumPy数组的合并 通过pd.concat实现简易合并 Pandas 0.18版中的函数签名 合并数据集: 合并与连接 数据连接的类型 设置数据合并的键 设置数据连接的集合操作规则 重复列名: suffixes参数 《Python数据科学手册》读书笔记 合并数据集: Concat与Append操作 这既包括将 两个不同的数据集非常简单地拼接在一起, 也包括用数据库那样的连接 (join) 与合并(merge) 操作处理有重叠字段的数据集。 Series 与 DataFrame 都具备这类操作, Pandas 的函数与方法让数据合并变得快 速简单。 先来用 pd.concat 函数演示一个 Series 与 DataFrame 的简单合并操 作。 首先导入 Pandas 和 NumPy: import pandas as pd import numpy as np 简单起见, 定义一个能够创建 DataFrame 某种形式的函数, 后面将会 用到: def make_df ( cols , ind ) : """一个简单的DataFrame""" data = { c : [ str ( c ) + str ( i ) for i in ind ] for c in cols } return pd .

gradle配置项packagingOptions

可紊 提交于 2020-02-05 11:41:08
版权声明:本文为延成原创文章,转载请标明出处 packagingOptions常见的设置项有exclude、pickFirst、doNotStrip、merge。 1. exclude,过滤掉某些文件或者目录不添加到APK中,作用于APK,不能过滤aar和jar中的内容。 比如: packagingOptions { exclude 'META-INF/**' exclude 'lib/arm64-v8a/libmediaplayer.so' } 2. pickFirst,匹配到多个相同文件,只提取第一个,作用于APK,不能过滤aar和jar中的文件。 比如: packagingOptions { pickFirst "lib/armeabi-v7a/libaaa.so" pickFirst "lib/armeabi-v7a/libbbb.so" } 3. doNotStrip,可以设置某些动态库不被优化压缩。 比如: packagingOptions { doNotStrip "*/armeabi/*.so" doNotStrip "*/armeabi-v7a/*.so" } 4. merge,将匹配的文件都添加到APK中,和pickFirst有些相反,会合并所有文件。 比如: packagingOptions { merge '**/LICENSE.txt' merge '**

Using two dataframes to calculate final value pandas

妖精的绣舞 提交于 2020-02-05 00:24:17
问题 Currently, I have two dataframes where I am merging on 'KEY'. My first dataframe contains a KEY and the original price of a product. My second dataframe collects information for each time a person makes a payment. I need to create a final calculated column in df1 which shows the remaining balance. The remaining balance is calculated by subtracting payment_price from the original_price. The only caveat is that only certain price_codes reflect a payment (13, 14 and 15). I'm not sure if the best

git相关

假如想象 提交于 2020-02-04 19:21:24
进入到想要用git管理的project目录下 1.git init 意即该目录会被git监视一切的变动 同时生成一个.git文件夹下面存放了管理该project的一切必要信息 2.git add <filename>,相应的文件会被git跟踪更改 3.git commit -m 'some commit message' 4.merge all changes to git repository将所有git跟踪的信息都归入到git repository 5.总起来看git监视的目录下的文件有下面几种状态 untracked file changed but not being staged 对以上两种file 进行 git add操作都会将文件变为changed to be commited状态 changed to be commited 7.我们通过git status查看当前目录下,待处理的文件的状态。 8. git remote -v 查看关联的远端仓库 9. github上新建一个全新的库时,在本地执行git push <remote> <branch>的时候,会将本地的所有文件都上传到github.如果远端库不是一个全新的库,那么需要现在本地执行git pull 将远端和本地文件合并,然后再push才会成功

c# - Best approach for a bulk update/merge on a table with high data volumne, but small change set

喜夏-厌秋 提交于 2020-02-04 02:01:19
问题 I have a table that has 200K+ rows, that I need to update on daily basis. My console app generates all this data on a daily run and needs to update the table with the results. The situation is that I'm operating on a slow and network, and at each run there is less than 0.1% rows to update/insert/delete, so clearly there's space to optimize. The table is simple - key column, plus 2 nvarchar columns. So my question is - what's the best approach in this particular case? I could always plow it

[CF1290C] Prefix Enlightenment:并查集

牧云@^-^@ 提交于 2020-02-03 18:38:33
题意 题目链接 给定一个 n n n 位 01 串 S S S 。 给定 k k k 个集合 A 1 , A 2 , ⋯   , A k A_1, A_2, \cdots, A_k A 1 ​ , A 2 ​ , ⋯ , A k ​ ,任意三个集合满足 A i ∩ A j ∩ A k = ∅ A_i\cap A_j\cap A_k=\empty A i ​ ∩ A j ​ ∩ A k ​ = ∅ 。 一次操作被定义为选择一个集合 A i A_i A i ​ ,对集合中的每个元素 x x x 执行 S x ← S x ⊕ 1 S_x\leftarrow S_x\oplus 1 S x ​ ← S x ​ ⊕ 1 。保证能通过若干次操作使得 S S S 变为全 1 串。 定义 m i m_i m i ​ 为使 S S S 的前 i i i 个位置都变成 1 的最小操作次数,我们需要求出 m 1 , m 2 , ⋯   , m n m_1, m_2, \cdots, m_n m 1 ​ , m 2 ​ , ⋯ , m n ​ 。 数据范围: 1 ≤ n , k ≤ 3 × 1 0 5 1\le n,k\le 3\times 10^5 1 ≤ n , k ≤ 3 × 1 0 5 。 题解 首先注意到题目竟然没有给 ∑ ∣ A i ∣ \sum |A_i| ∑ ∣ A i ​ ∣ 的数据范围