237篇历史文章合集

Bertha 。 2023-02-23 09:25 2阅读 0赞

大家好，我是朱小五

给大家整理了一下「凹凸数据」截止到2020年7月5日的历史文章合集。点击文章标题就可以跳转到相应文章，定期更新。

大家可以按目录依次查看，也可以搜索（手机端点击右上角3个点，再选择搜索页面内容即可，电脑端建议用网页打开，Ctrl+F即可）

# 爬虫、数据分析、可视化，一条龙服务！ #

## 数据分析案例！ ##

### 热点型小案例 ###

[Python爬取 201865 条《隐秘的角落》弹幕，发现看剧不如爬山？][Python_ 201865]

[用python爬取《龙岭迷窟》评论，看看比同系列鬼吹灯作品以及《盗墓笔记》好在哪里？][python]

[用python爬取4332条粽子数据进行分析，再送15盒粽子给大家][python_4332_15]

[在B站外，我看到了另一批“后浪”！][B]

[爬了爬周杰伦新歌《Mojito》的MV弹幕][Mojito_MV]

[用python爬取3万多条评论，看韩国人如何评价韩国电影《寄生虫》？][python_3]

[我用python分析了李子柒的辣酱真的好吃吗？][python 1]

[爬取6271家死亡公司数据，看十年创业公司消亡史][6271]

### 数据分析实战 ###

[一个公式三指标，电商分析的破局之道][Link 1]

[只会环比下降3%的数据分析师还有救吗？][3]

[用Python和Tableau对母婴商品销量进行数据分析（附Python源码及Tableau文件）][Python_Tableau_Python_Tableau]

[为何总给外卖打差评？我们来数据分析一下！][Link 2]

[用python分析1225万条淘宝数据，终于搞清楚了我的交易行为][python_1225]

[数据分析实战：利用python对心脏病数据集进行分析][python 2]

[Kaggle竞赛入门实战——机器学习预测房屋价格][Kaggle]

[利用Excel进行电商数据分析的流程是什么样的？][Excel]

[利用SQL进行数据分析 I 淘宝用户分析实操][SQL_ I]

[数据实战 | 预测二手车每年平均价值损失][Link 3]

[数据分析 | 9年间各省市平均收入分析][_ 9]

[数据分析实战之超市零售分析（附python代码）][python 3]

### 趣味型小案例 ###

[手把手带你爬天猫，获取杜蕾斯评论数据][Link 4]

[用Python分析一百单八将内部社交网络][Python]

[疫情过后重新开店？我用python帮表哥分析了一下商业数据][python 4]

[python爬取招聘网站数据，利用Tableau可视化交互大屏，指导你如何学习、找工作!][python_Tableau]

[用python分析了5000部票房，发现赚钱的电影都有这些特征~][python_5000]

[用python数据分析了北京积分落户名单，发现……][python 5]

[给剧荒的你：Python高分日剧刷剧指南][Python 1]

[用python爬取去哪儿网游记，给自己做一份疫情结束后的旅行指南][python 6]

[用Python爬取b站弹幕，看大家还会接受《爱情公寓5》吗？][Python_b_5]

[爬取3万景点，分析十一哪里人从众从人？][3 1]

[2000万条直播数据，揭秘斗鱼主播生存现状][2000]

[数说：这只程序员组建的乐队为何能进HOT5？][HOT5]

[44万条数据揭秘：如何成为网易云音乐评论区的网红段子手？][44]

[在北京看场雪为什么这么难？][Link 5]

[王思聪究竟上了多少次热搜？][Link 6]

[38年后的今天，用数据回顾什么是女排精神？][38]

[寒冬之下，971个互联网人总结了他们的2019][971_2019]

[用python对2019年二手房价格进行数据分析][python_2019]

[用python进行超全的疫情大屏展示][python 7]

[B站市值破百亿，你真的不考虑当个up主吗？| 附python源码][B_up_ _python]

[我花了21.7斤人民币，在深圳买一套三居室][21.7]

[我用加强版RFM模型，轻松扒出B站优质up主！（含数据+实战代码）][RFM_B_up]

## 爬虫！ ##

### 爬虫技巧 ###

[破解大众点评字体静态加密][Link 7]

[破解猫眼电影字体动态加密][Link 8]

[一篇长文带你在python里玩转Json数据][python_Json]

[收藏这些API，获取网易云音乐数据超轻松][API]

[开开心心爬APP，结果一坑连一坑][APP]

[一些常用的爬虫技巧总结][Link 9]

[除了Selenium，介绍一款新的网页自动化工具来爬虫][Selenium]

### 爬虫案例 ###

[一不小心，我爬取了100万条微博评论][100]

[手把手带你爬天猫，获取杜蕾斯评论数据][Link 4]

[二手房价格及信息爬取][Link 10]

[爬取公众号全部文章，就是这么简单（方法1）][1]

[我早晚要把这公众号爬出花儿来！（方法2）][2]

[手把手教你爬取天堂网1920\*1080大图片][1920_1080]

[Python3 网络爬虫：视频下载，那些事儿！][Python3]

[对不起，我把APP也给爬了][APP 1]

[手把手教你利用Python网络爬虫获取链家网的房产信息][Python 2]

[爬虫&可视化 | 三分钟带你看遍世界大学排行][Link 11]

## 数据可视化！ ##

[数据可视化 | 数据可视化的基本流程总结][Link 12]

### Excel可视化 ###

[酷炫的 动态可视化 交互大屏，用Excel就能做!][_ _Excel]

[火爆全网的酷炫图表，只用Excel就能做！][Excel 1]

### python可视化 ###

#### Matplotlib ####

[Matplotlib绘制漫威英雄战力图，带你飞起来！][Matplotlib]

[25个常用Matplotlib图的Python代码，干货收藏！][25_Matplotlib_Python]

[数据可视化 | 16个 matplotlib 绘图实用小技巧][_ 16_ matplotlib]

#### pyecharts ####

[超详细Pyecharts 1.x 教程，让你的图表动起来～][Pyecharts 1.x]

[如何把pyecharts的炫酷延续到PPT里！？][pyecharts_PPT]

[好看的 BI 大屏 Pyecharts 也可以做][BI _ Pyecharts]

[酷炫的迁徙图和轮播图，用pyecharts轻松做出来][pyecharts]

[想搞懂pyecharts可视化？10分钟你信不信？][pyecharts_10]

#### 其他python可视化 ####

[用Python演绎5种常见可视化视图][Python_5]

[Python绘图还在用Matplotlib？安利一款“手绘”可视化神器！][Python_Matplotlib]

[Python的Web框架Flask + Vue 生成漂亮的词云][Python_Web_Flask _ Vue]

[这里有8个流行的Python可视化工具包，你喜欢哪个？][8_Python]

[用Python制作酷炫词云图，原来这么简单！][Python 3]

[比 Excel 更强大，Python 的可视化库 Altair 入门][Excel _Python _ Altair]

[5个常用的大数据可视化分析工具][5]

[5 种 非传统 的可视化图，也是用Python画的][5 _ _ _Python]

[用python如何画出好看的地图][python 8]

[不让数据动起来，那PPT多没意思啊][PPT]

[如何用 Python 将 Excel 表格转成可视化图形？][Python _ Excel]

[一款高颜值的词云包让我拍案叫绝][Link 13]

### Tableau可视化 ###

[类比MySQL，学习Tableau][MySQL_Tableau]

[利用Tableau绘制辐射堆叠图，炫酷易上手][Tableau]

[Tableau实战：银行信贷客户可视化分析][Tableau 1]

[python做RFM代码太复杂，使用Tableau多简单！][python_RFM_Tableau]

[如何在Tableau中创建雷达图？（不修改数据源）][Tableau 2]

### 其他工具可视化 ###

[火爆全网的动态曲线图是怎么做的？][Link 14]

### 图形详解 ###

[画了1000次折线图后，我总结出一个套路……][1000]

[数据可视化｜用散点图进行数据分析][Link 15]

[数据可视化｜用箱线图进行对比分析][Link 16]

[数据可视化｜用雷达图进行对比分析][Link 17]

[瀑布图有什么用？用python怎么画？][python 9]

### 可视化作品 ###

[用数据可视化的形式，展示新冠疫情全国殉职医护人员名单。][Link 18]

[分析全球1085例新冠肺炎患者数据，我们得出这些结论][1085]

[31个惊艳的数据可视化作品，让你感受“数据之美”！][31]

## 办公自动化 ##

[利用Python实现报表自动化][Python 4]

[教你用Python拆分表格并发送邮件][Python 5]

[真香还是假香，Python处理分析128张Excel表格竟然不到3秒？][Python_128_Excel_3]

[刚学完python自动化系列文章，就接了一单任务][python 10]

[这97页pdf >= 20篇python自动化办公文章][97_pdf _ 20_python]

[每天重复这样的Excel操作，写个python脚本多省事！][Excel_python]

# 业务范围：Python、SQL、Excel、Tableau #

## Python ##

### 通过小故事学python ###

[我用python破解了同事的加密压缩包！][python 11]

[表弟的数学题，我竟然用了python才解出来][python 12]

[我用python算出了同事的身份证号码！][python 13]

[宁可用python倒读红楼梦，也不向拼多多低头][python 14]

[我用python帮朋友做了张图，结果][python 15]

### 手撕python ###

[墙裂推荐！B站上的Python学习资源][B_Python]

#### Numpy ####

[这一招将 Numpy 加速 700 倍！！！][Numpy _ 700]

[100道测试题，轻松玩转python的Numpy模块！][100_python_Numpy]

[手撕numpy(一)：简单说明和创建数组的不同方式][numpy]

[手撕numpy(二)：各种特性和简单操作][numpy 1]

[手撕numpy(三)：切片和索引详解][numpy 2]

[手撕numpy(四)：数组的广播机制、数组元素的底层存储][numpy 3]

[手撕numpy(五)：一招帮你彻底搞懂axis=0和axis=1][numpy_axis_0_axis_1]

#### pandas ####

[超全的pandas数据分析常用函数总结：上篇][pandas]

[超全的pandas数据分析常用函数总结：下篇][pandas 1]

[Pandas进阶修炼120题｜完整版][Pandas_120]

[金牌厨师Pandas：听说Excel处理数据分行快得很？][Pandas_Excel]

[13个Pandas实用技巧，有点香 ！][13_Pandas_]

[让数据分析更简单的Panda技巧：万能转格式、轻松合并、数据压缩...][Panda_...]

[pandas读取表格后的常用数据处理操作][pandas 2]

[pandas：数据离散化与离散化数据的后期处理(one-hot)][pandas_one-hot]

[ix | pandas读取表格后的行列取值改值操作][ix _ pandas]

[对比Excel，学习pandas数据透视表][Excel_pandas]

[对比MySQL学习Pandas的groupby分组聚合][MySQL_Pandas_groupby]

[Series(一)：Series的创建方式和常用属性说明][Series_Series]

[Series(二)：Series的元素获取方式][Series_Series 1]

[Series(三)：Series和ndarray对比学习][Series_Series_ndarray]

[Series(四)：Series和ndarray在运算时的异同][Series_Series_ndarray 1]

[Series(五)：Series的增、删、改、查][Series_Series 2]

[DataFrame(1)：DataFrame结构的详细介绍][DataFrame_1_DataFrame]

未完待续

#### matplotlib ####

[matplotlib绘图的核心原理讲解][matplotlib]

[matplotlib绘图技巧详解(一)][matplotlib 1]

[matplotlib绘图技巧详解(二)][matplotlib 2]

[matplotlib绘图技巧详解(三)][matplotlib 3]

[matplotlib相关图形绘制(一)][matplotlib 4]

[matplotlib相关图形绘制(二)][matplotlib 5]

[数据可视化 | 16个 matplotlib 绘图实用小技巧][_ 16_ matplotlib]

[Matplotlib绘制漫威英雄战力图，带你飞起来！][Matplotlib]

[25个常用Matplotlib图的Python代码，干货收藏！][25_Matplotlib_Python]

### python其他技巧 ###

[python中的时间处理大总结][python 16]

[如何用python将中文日期转换为数字日期 | 答疑][python_ _]

[上来就情感分析？还是先用python去去重吧！][python 17]

[python格式化输出：%s和format()用法比较][python_s_format]

[python格式化输出：f-string格式化输出][python_f-string]

[青铜和黄金选手分别怎么玩转python列表？][python 18]

[Python22个构造函数法-助力数据挖掘与分析][Python22_-]

[Python初学者常犯的5个错误][Python_5 1]

### Python编辑器 ###

[Python 最强编辑器详细使用指南！][Python 6]

[10个必会的 PyCharm 技巧][10_ PyCharm]

[向Excel说再见，神级编辑器统一表格与Python][Excel_Python]

[漫画 | 写 Python 到底用什么编辑器好？][_ _ Python]

[Python 编辑神器 Jupyter ，推出首款官方可视化 Debug 工具！][Python _ Jupyter _ Debug]

[PyCharm 2020.1 稳定版发布][PyCharm 2020.1]

### python其他骚气应用 ###

[武大学生用python敲出樱花开放 | 附源码][python_ _ 1]

[用Python生成自己专属的手机春节壁纸][Python 7]

[如何用python制作动态二维码，来哄女朋友开心？][python 19]

[用python来分析：基金抄底成功的概率有多大？][python 20]

[这个 Python 项目很骚气！一秒生成可爱像素风图片！][Python 8]

[用python重新定义【2019十大网络流行语】][python_2019 1]

[天猫双11数据过于完美？我们用python来看看][11_python]

[python盘点：知乎沙雕问题排行榜][python 21]

[\#6.1\# 用python画出你的童年回忆][6.1_ _python]

[别再问我怎么Python打包成exe了！][Python_exe]

[90行python让微信地球转起来，太酷了吧！][90_python]

[20行代码教你用python给证件照换底色][20_python]

## 统计学 ##

### 统计学基础 ###

[推荐收藏 | 统计学常用的数据分析方法大总结！][Link 19]

[统计学5个基本概念，你知道多少？][5 1]

### python x 统计学 ###

[用python重温统计学基础：描述性统计分析][python 22]

[用python重温统计学基础：离散型概率分布][python 23]

[利用python回顾统计学中的基础概念（全）][python 24]

## SQL ##

### SQL知识 ###

[SQL语句+语法 I 数据分析面试必备！][SQL_ I 1]

[来了，SQL在线模拟器][SQL]

[1000 行 MySQL 学习笔记，值得收藏！][1000 _ MySQL]

[别一直用 Select \* 了][Select _]

[8种常被忽视的SQL错误用法，你有没有踩过坑？][8_SQL]

[一款SQL自动检查神器！][SQL 1]

[写好SQL很有必要][SQL 2]

[利用SQL进行数据分析 I 淘宝用户分析实操][SQL_ I]

[Hive SQL50道练习题][Hive SQL50]

[几个SQL在线刷题的网站][SQL 3]

### python x SQL ###

[通过cursor游标讲解，带你初步搞懂python操作mysql数据库][cursor_python_mysql]

[干货！python与MySQL数据库的交互实战][python_MySQL]

## EXCEL ##

[数据分析常用的Excel函数合集（上）][Excel 2]

[数据分析常用的Excel函数合集（下）][Excel 3]

[酷炫的 动态可视化 交互大屏，用Excel就能做!][_ _Excel]

[34个常用Excel小技巧，助你玩转数据！][34_Excel]

[利用Excel进行电商数据分析的流程是什么样的？][Excel]

[最值得收藏的7个高效Excel图表操作技巧！][7_Excel]

[python吊打Excel？屁！那是你不会用！][python_Excel]

[看了我做的年度报表，老板大呼NB！][NB]

## Tableau ##

[类比MySQL，学习Tableau][MySQL_Tableau]

[Tableau实战：银行信贷客户可视化分析][Tableau 1]

[python做RFM代码太复杂，使用Tableau多简单！][python_RFM_Tableau]

# 数据分析师的自我修养 #

## 数据分析师应该了解的业务知识 ##

[没有食材，数据分析师如何做饭？][Link 20]

[不懂数据分析框架，你拿头去分析？][Link 21]

[事件、留存、漏斗分析到底是个啥？][Link 22]

[手把手教你做用户画像：3种标签类型、8大系统模块][3_8]

[做数据分析应该了解的——留存率模型][Link 23]

[数据分析报告，就该这么写][Link 24]

[用户画像到底是怎么回事？看一个生活中的案例][Link 25]

[用户留存分析案例 | 以京东、淘宝、饿了么为例！][Link 26]

[保姆级教学：手把手教你如何实现同期群分析][Link 27]

[数据分析师必知必会：AB测试项目复盘（附PPT、python源码）][AB_PPT_python]

[百度内部PPT流出：数据分析的道与术][PPT 1]

[10大经典数据分析模型，你知道几个？][10]

## 数据分析师的求职之路 ##

[我这个菜狗，是如何入门数据分析的？][Link 28]

[回顾我的第一份数据分析工作][Link 29]

[我在滴滴数据分析岗实习了8个月][8]

[「双非」研究生数据分析春招心路历程。][Link 30]

[如何看待2020校招数据分析岗位招聘情况？][2020]

## 数据分析师的自我修养 ##

[数据岗位以后再也没有数据分析师！][Link 31]

[不要过度迷信数据分析！][Link 32]

[讲一下数据分析思维][Link 33]

[披着数据分析师的外衣，干着鱼龙混杂的活][Link 34]

[面试官眼中的数据分析师][Link 35]

[数据分析师要去数据部门还是业务部门？][Link 36]

[数据分析师正在被攻击][Link 37]

[用数据分析思维识别不靠谱流言!][Link 38]

[一种高效且通用的数据分析思维][Link 39]

[我是业务，我投诉了数据团队][Link 40]

[1份优秀的数据分析报告，8步就能搞定！][1_8]

[干货 | 常见数据分析规范][Link 41]

[一套数据分析的实用方法论。][Link 42]

[一位数据分析师的成长日记][Link 43]

## 数据分析师面试题 ##

[「2020」PDD数据分析笔试题 | 附解答][2020_PDD_ _]

[总结了28道数据分析经典面试题][28]

[我拿到了腾讯最爱考的数据分析面试题][Link 44]

[SQL笔试 I 经典44题及答案解析~][SQL_ I _44]

[数据分析师笔试题 | 真题实战②][Link 45]

[数据分析师笔试题 | 真题实战③][Link 46]

[数据分析师笔试题 | 真题实战④][Link 47]

[一套题 | 助你轻松准备数据分析岗面试][Link 48]

## 数据分析报告 ##

[女性消费特征分析报告][Link 49]

[2020年抖音用户画像报告][2020 1]

[2020政府工作报告 | 一图全读懂][2020_ _]

[数据会说话 | 疫情期间大学生上网课效果如何？][Link 50]

[618全网销售数据分析报告][618]

![format_png][]

[Python_ 201865]: https://blog.csdn.net/zhuxiao5/article/details/106990963
[python]: https://blog.csdn.net/zhuxiao5/article/details/105554909
[python_4332_15]: https://blog.csdn.net/zhuxiao5/article/details/106846664
[B]: https://blog.csdn.net/zhuxiao5/article/details/105942320
[Mojito_MV]: https://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d3dd9c93ab4cfc143bbaaee4dac6089cf79d4bde75ebcc67e968e5e062d5f18b85bf73e04&idx=1&mid=2247489629&scene=21&sn=d953f6a9f74a0b1b6955229503f88094#wechat_redirect
[python_3]: https://blog.csdn.net/zhuxiao5/article/details/104634280
[python 1]: http://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d26ebc93aaffd9ac4f39ecccb7a5417fca025ea3208d22b34ca2e25db4d34de9f90708a0d&idx=2&mid=2247484271&scene=21&sn=3ff6ffad125e9ff74ace13b68fc0f03f#wechat_redirect
[6271]: https://blog.csdn.net/zhuxiao5/article/details/103268297
[Link 1]: https://blog.csdn.net/zhuxiao5/article/details/106880424
[3]: https://blog.csdn.net/zhuxiao5/article/details/105609027
[Python_Tableau_Python_Tableau]: https://blog.csdn.net/zhuxiao5/article/details/105721777
[Link 2]: https://blog.csdn.net/zhuxiao5/article/details/105760515
[python_1225]: https://blog.csdn.net/zhuxiao5/article/details/105154479
[python 2]: https://blog.csdn.net/zhuxiao5/article/details/104403839
[Kaggle]: https://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d3fefc93ab6f9c00ffbae8d2a42e245e044ef3edeb115d57cc8c9837f74c6587e260ed78b&idx=2&mid=2247490155&scene=21&sn=ce6ee86acc1a207b437966929a1ea096#wechat_redirect
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[_ 9]: https://blog.csdn.net/zhuxiao5/article/details/104687948
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[Link 4]: https://blog.csdn.net/zhuxiao5/article/details/105132474
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[python_Tableau]: https://blog.csdn.net/zhuxiao5/article/details/104981247
[python_5000]: http://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d22fdc93aabeb8cb4fd77bb37b8c90176b5eb69868cc83814aef17732b2ba19bdc853a33e&idx=1&mid=2247485305&scene=21&sn=2b64527d17dee97eab843814648b2632#wechat_redirect
[python 5]: https://blog.csdn.net/zhuxiao5/article/details/103900860
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[Python_b_5]: https://blog.csdn.net/zhuxiao5/article/details/102618211
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[Link 5]: https://blog.csdn.net/zhuxiao5/article/details/103360227
[Link 6]: https://blog.csdn.net/zhuxiao5/article/details/103249119
[38]: https://blog.csdn.net/zhuxiao5/article/details/103094664
[971_2019]: http://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d26f4c93aafe264d4992d98d726f41915f1867ec6ac8af638f9f5996bbb40ac2b678c0bb1&idx=1&mid=2247484272&scene=21&sn=9993988a62030022c76a80603fa8d824#wechat_redirect
[python_2019]: https://blog.csdn.net/zhuxiao5/article/details/103607686
[python 7]: http://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d21d0c93aa8c62f33fd5845ab3d0d150c8338dca2ae58b8973552a3b97d49187a79c1d8f6&idx=2&mid=2247484500&scene=21&sn=6c97be6a03c15bf555e2bac4116c9be5#wechat_redirect
[B_up_ _python]: https://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d2f36c93aa620ab30d00e4839ac417a545726604b9309c9dcd1ac8d754174dca0310458ab&idx=3&mid=2247486130&scene=21&sn=e8d7812a1eb9c48b109c6eb2a6981dde#wechat_redirect
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[Link 7]: http://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d2620c93aaf36bb715fc6ad1b91ed78e09cf5ef1f867eba789b725e4308582f0926e38525&idx=1&mid=2247484324&scene=21&sn=73c115c0d47825a0901866a5f9ec1404#wechat_redirect
[Link 8]: http://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d2624c93aaf32d8cf4c9c32dea9374db6cde1d76e2ee6a62d6f1a6983ebbef8691f9388ca&idx=1&mid=2247484320&scene=21&sn=254eafb498b266cd3e6145127e4fad8c#wechat_redirect
[python_Json]: https://blog.csdn.net/zhuxiao5/article/details/102982834
[API]: https://blog.csdn.net/zhuxiao5/article/details/102982833
[APP]: https://blog.csdn.net/zhuxiao5/article/details/106450030
[Link 9]: https://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d377ec93abe68fc06455d65cec97d87cfaf47662b126075cc6d4db40935c3ce8719bc61a6&idx=2&mid=2247488250&scene=21&sn=4ad361c9620632d0cb067742e545ff30#wechat_redirect
[Selenium]: https://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d3d58c93ab44ea3ca0abd779635ea7b7151f82dadf4c484033fbcb3dc5c6dc2facf21f1f8&idx=2&mid=2247489756&scene=21&sn=b581e90e8c9b8d156f3efc0756791fcb#wechat_redirect
[100]: http://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d207bc93aa96d645b6eeed2f14d267990d74e03746eb2d5bd78c5509f879124cd7fc2c579&idx=1&mid=2247484927&scene=21&sn=b7fe1716f260e5825e08805e9225fd72#wechat_redirect
[Link 10]: https://blog.csdn.net/zhuxiao5/article/details/103607679
[1]: https://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&idx=1&mid=2247486112&scene=21&sn=99684a062eeeef694dd303ccbc239625#wechat_redirect
[2]: https://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d3db4c93ab4a2d516f30511ba06d1d08c5a451bd94b28b03d5240add08ecd420c2e7a1092&idx=1&mid=2247489584&scene=21&sn=ebc5adf26f6aa122525aeb92a521751c#wechat_redirect
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[Python3]: https://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d29d9c93aa0cfd6345dbfc971a0747d082169f1899ba3e8e290c7887058deff2ed9eaebb4&idx=2&mid=2247486557&scene=21&sn=8cf123b4998fefe804df11c66c3c9b1a#wechat_redirect
[APP 1]: https://blog.csdn.net/zhuxiao5/article/details/106168890
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[Link 11]: https://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d3cecc93ab5fa38cd7306b789b93a9823dfb116e54e8a48f496c7f116e84084eb25fec8b6&idx=2&mid=2247489896&scene=21&sn=149801597164375d15232e90c02c0f87#wechat_redirect
[Link 12]: http://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d2de6c93aa4f0784ad2fbe62b8ffbc21056c1e7dc38948cec3291f80980c92e37fff42a3a&idx=4&mid=2247485538&scene=21&sn=236904980fdbfcff9ba84e3770d29d75#wechat_redirect
[_ _Excel]: https://blog.csdn.net/zhuxiao5/article/details/105571317
[Excel 1]: http://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d206bc93aa97d3cf655283fff263e51328b0b1a65c6686f85a58cfc92a106a28c4b1a945b&idx=1&mid=2247484911&scene=21&sn=19480e9f16895a567e4380687a49525a#wechat_redirect
[Matplotlib]: http://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d2053c93aa94510b00c9f9b09128838968c34b6b3e7c324f61ea9715eb998359c91289c2a&idx=2&mid=2247484887&scene=21&sn=0b3d455c12bff238d2e7a132e8e87281#wechat_redirect
[25_Matplotlib_Python]: http://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d26eac93aaffcf9c3fad5ac8d4f2a8daed725f242f53aaf46e0babe64d8d8445f0a7f929a&idx=2&mid=2247484270&scene=21&sn=8094b8237887c7991f27b40308a5657d#wechat_redirect
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[Pyecharts 1.x]: http://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d2d1bc93aa40d2d36f83f6cd3c9d16c472851cb39d51dd0be80a9905d074fa7fa3fcf0c59&idx=2&mid=2247485599&scene=21&sn=70ec5fe8af596a4a0386c3c970111aec#wechat_redirect
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[BI _ Pyecharts]: https://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d3db4c93ab4a2609b9e8f25358d5fe199b92e2c3952c1fde0dbbb9eefd8f7b5560718a38c&idx=3&mid=2247489584&scene=21&sn=71c8c8259517ae9bf1036c8a22a779d3#wechat_redirect
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[Python_5]: http://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d2fb6c93aa6a0c3da2cc23cdf8341533dae2a0ba6f7d5b7b1aa4657905fa5bd6e1223990a&idx=2&mid=2247486002&scene=21&sn=f1d5548cbbf81ea100df3551cea5b86d#wechat_redirect
[Python_Matplotlib]: http://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d2604c93aaf129aeb1d8e896661180257254e5c47cb9b718b5bb67ec60fab1f614ba301f3&idx=2&mid=2247484288&scene=21&sn=e991250a25fa94675b1dd030945084fc#wechat_redirect
[Python_Web_Flask _ Vue]: http://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d20ccc93aa9dae1928445520e88532aea71ffb0a803e3c6f541a5effd4c695ea56d5b0c90&idx=2&mid=2247484744&scene=21&sn=8da7e47a495722b8ba1b999665a26400#wechat_redirect
[8_Python]: http://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d21c0c93aa8d6edc8d7e20bd09039076727ba02fa86aba0ef6d2310177be498599a84a254&idx=1&mid=2247484484&scene=21&sn=edea95ec9ee4cdf96bc4b275ed57b20f#wechat_redirect
[Python 3]: http://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d26e9c93aafff10d0c9b4b89bcfbb99195d1030a96e15f5ea4088de2f19599fec293fa672&idx=1&mid=2247484269&scene=21&sn=16a8061db2e799efb64cce2a4f96888e#wechat_redirect
[Excel _Python _ Altair]: http://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d21a4c93aa8b2ba2a024a4f948eaca1a8a55184c3ff34539a2a453cede85ac59503aab6f3&idx=1&mid=2247484448&scene=21&sn=2a4568511f5208ff61017a35b59e4480#wechat_redirect
[5]: https://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d2961c93aa0770ea61b656cbfc3234f561d2a9ffdd0a0f0e769e74661c2d0af19a56f1ccb&idx=4&mid=2247486693&scene=21&sn=bd3a8511859389d8ed7f56619e74b7a4#wechat_redirect
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[python 8]: https://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d3140c93ab856c2f7f1eb2d4b20c437454ff5c483715c6535f34ff581165190f7b5045f9d&idx=3&mid=2247488708&scene=21&sn=bb4e93f159c943a72992b610d128c964#wechat_redirect
[PPT]: https://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d337dc93aba6ba9a2734a23e263ec1f0c295bbaffec9f82540c071d19d053a7521f8d0e68&idx=1&mid=2247489273&scene=21&sn=3ad801e5faac17ea1edeb0f04fd1484e#wechat_redirect
[Python _ Excel]: https://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d337dc93aba6b619927f855916adfd1a718082a727b1e42523b7a1fd970ff40117bb3113f&idx=2&mid=2247489273&scene=21&sn=0edb51a52a3024c4c3e636a18b0bbf47#wechat_redirect
[Link 13]: https://blog.csdn.net/zhuxiao5/article/details/107096681
[MySQL_Tableau]: https://blog.csdn.net/zhuxiao5/article/details/104810115
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[python 12]: https://blog.csdn.net/zhuxiao5/article/details/103675871
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[python 15]: https://blog.csdn.net/zhuxiao5/article/details/102982832
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[numpy 2]: https://blog.csdn.net/zhuxiao5/article/details/104912759
[numpy 3]: https://blog.csdn.net/zhuxiao5/article/details/104935804
[numpy_axis_0_axis_1]: https://blog.csdn.net/zhuxiao5/article/details/104935801
[pandas]: https://blog.csdn.net/zhuxiao5/article/details/105236792
[pandas 1]: https://blog.csdn.net/zhuxiao5/article/details/105260039
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[pandas 2]: https://blog.csdn.net/zhuxiao5/article/details/104853568
[pandas_one-hot]: https://blog.csdn.net/zhuxiao5/article/details/104832576
[ix _ pandas]: https://blog.csdn.net/zhuxiao5/article/details/104832577
[Excel_pandas]: https://blog.csdn.net/zhuxiao5/article/details/104421780
[MySQL_Pandas_groupby]: https://blog.csdn.net/zhuxiao5/article/details/104284545
[Series_Series]: https://blog.csdn.net/zhuxiao5/article/details/104958408
[Series_Series 1]: https://blog.csdn.net/zhuxiao5/article/details/104958407
[Series_Series_ndarray]: https://blog.csdn.net/zhuxiao5/article/details/105040068
[Series_Series_ndarray 1]: https://blog.csdn.net/zhuxiao5/article/details/105213282
[Series_Series 2]: https://blog.csdn.net/zhuxiao5/article/details/105213283
[DataFrame_1_DataFrame]: https://blog.csdn.net/zhuxiao5/article/details/105283683
[matplotlib]: https://blog.csdn.net/zhuxiao5/article/details/104368441
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[matplotlib 3]: https://blog.csdn.net/zhuxiao5/article/details/104529032
[matplotlib 4]: https://blog.csdn.net/zhuxiao5/article/details/104645698
[matplotlib 5]: https://blog.csdn.net/zhuxiao5/article/details/104645697
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[python 17]: https://blog.csdn.net/zhuxiao5/article/details/105085837
[python_s_format]: https://blog.csdn.net/zhuxiao5/article/details/104026422
[python_f-string]: https://blog.csdn.net/zhuxiao5/article/details/105631975
[python 18]: https://blog.csdn.net/zhuxiao5/article/details/104111885
[Python22_-]: https://blog.csdn.net/zhuxiao5/article/details/103790117
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[Python 7]: https://blog.csdn.net/zhuxiao5/article/details/104067217
[python 19]: https://blog.csdn.net/zhuxiao5/article/details/105283682
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[11_python]: https://blog.csdn.net/zhuxiao5/article/details/103047042
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[python 24]: https://blog.csdn.net/zhuxiao5/article/details/104624320
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[Hive SQL50]: http://mp.weixin.qq.com/s?__biz=MzU5Nzg5ODQ3NQ%3D%3D&chksm=fe4d2c02c93aa514af073f9a6506a2b17e7f04104e184cf56cc88462edebcd9c897771766f7c&idx=2&mid=2247485830&scene=21&sn=0de743d77bd72332ba2026ba20176e17#wechat_redirect
[SQL 3]: https://blog.csdn.net/zhuxiao5/article/details/106152811
[cursor_python_mysql]: https://blog.csdn.net/zhuxiao5/article/details/103900861
[python_MySQL]: https://blog.csdn.net/zhuxiao5/article/details/103918026
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[python_Excel]: https://blog.csdn.net/zhuxiao5/article/details/106088996
[NB]: https://blog.csdn.net/zhuxiao5/article/details/106485395
[Link 20]: https://blog.csdn.net/zhuxiao5/article/details/105502036
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[Link 27]: https://blog.csdn.net/zhuxiao5/article/details/105898172
[AB_PPT_python]: https://blog.csdn.net/zhuxiao5/article/details/106132477
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