m0_62488776

python 使用API并将获取到的数据可视化的基本方法（详细）

本文代码大部分取自《Python编程：从入门到实践》中第17章，如有疑问还请参考原书。

什么是API

API（应用程序编程接口，Application Programming Interface）是一些预先定义的函数，目的是提供应用程序与开发人员基于某软件或硬件得以访问一组例程的能力，而又无需访问源码，或理解内部工作机制的细节。

其实不用管什么是API，因为我们使用API大部分也就是为了其中的数据，所以在这里API大概可以简单理解为：为一些发送过来的请求给予相应数据的东西。

使用Web API

Web API是网站的一部分，包含了广泛的功能。网络应用通过API接口，可以实现存储服务、消息服务、计算服务等能力，利用这些能力可以进行开发出强大功能的web应用。

在本文我们将编写一个程序，它将会自动下载github网站上星级最高的python项目的信息，并对这些信息简单可视化。

使用API请求调用数据

github的API可以通过API调用来请求各种信息，API调用可以用以下示例来理解，可以在复制以下内容到浏览器地址栏并按下回车键访问：

https://api.github.com/search/repositories?q=language:python&sort=stars

就在刚才，我们进行了一次API调用，返回的应该是类似于这样的纯文字界面（和这里的不完全一样是正常的，因为这里的数据是实时更新的）（如果没有结果可以多次尝试或是过一会再尝试，也可以找一个网络更好的地方来尝试）：

我们可以仔细研究一下在地址栏输入的类似于链接的内容，它可以分为四个部分：

第一部分（https://api.github.com/）是将请求发送到github网站的API调用的部分。
第二部分（search/repositories）是让API搜索github上所有的仓库（repository）。
第三部分（?q=language:python&sort=stars）还要分开来说：
- “?”代表我们要传递一个实参
- “q”表示查询，而等号让我们开始指定查询（q=）
- 我们查询的内容（language:python）是获取主要语言为python的仓库的信息
- 最后一部分（&sort=stars）指定将仓库按照stars这个标准排序

然而，了解这些不是最重要的，最重要的是通过这个API请求返回的内容，也就是前面的图片中的内容，这些就是根据我们的API调用请求获取的相应数据。之后我们的程序也将会以此为基础进行编写分析。

对于这十分长的数据，我们可以先看第二行和第三行，在我这里它们分别是：

  "total_count": 9180657,
  "incomplete_results": true,

这分别表示：github上有9180657个python项目、github的API返回的结果是不完整的（本来正常来说应该是false的，这里为true可能是因为实在是太多了……）

我第二天访问时：
  "total_count": 11395116,
  "incomplete_results": false,
可以看到这里是false，说明这次的请求返回了完整的结果。各位如果没有得到完整的结果可以隔一天在网好的地方再试试。

其他的可以看看，我们后面使用程序对其进行一些分析。

安装requests

如果已经安装了pip库，可以直接输入类似的命令直接安装requests库：

pip install requests

没有使用过pip可以去别的博客看看怎么装，这边建议使用anaconda，下载与管理各种库都很方便。

处理API响应

我们看看这个程序，它是用来执行API调用并对获得的数据做一定的处理的：

import requests  # ①


# 执行API调用并储存响应
url = 'https://api.github.com/search/repositories?q=language:python&sort=stars'  # ②
r = requests.get(url)  # ③
print('Status code:', r.status_code)  # ④

# 将API响应存储在一个变量中
response_dict = r.json()  # ⑤

# 处理结果
print(response_dict.keys())  # ⑥

首先在①处我们导入requests库，然后在②③处我们存储并访问API调用的URL，把包含返回数据的响应对象存在r中。之后在④处我们打印了响应对象的status_code，来看我们是否正确获取到了数据（状态码200表示正常，别的状态码可以看看这个网站：HTTP Cats）。

在⑤处我们使用json()方法将原本是JSON格式的信息转化为python的字典格式（本来所有数据python只识别为一个字符串，使用完这个方法后就可以按照字典的方法来方便的调用里面的值），转化成字典的数据存在了response_dict中。最后在⑥处字典所有的键被打印。

代码运行情况如下：

Status code: 200
dict_keys(['total_count', 'incomplete_results', 'items'])

可以看到状态为200，因此是请求成功的。还可以看到该字典只有三个键： 'total_count'、'incomplete_results'和'items'。

处理字典

在上面我们将数据转换为字典存储，下一步需要做的就是处理这个字典中的数据。

下面这个程序主要是对字典中的数据进行一些大体的输出，我们可以用这种方式来确认我们收到了期望的数据，方便进行进一步的处理。

import requests


# 执行API调用并储存响应
url = 'https://api.github.com/search/repositories?q=language:python&sort=stars'
r = requests.get(url)
print('Status code:', r.status_code)

# 将API响应存储在一个变量中
response_dict = r.json()
print('Total repositories:', response_dict['total_count'])  # ①

# 探索有关仓库的信息
repo_dicts = response_dict['items']  # ②
print('Repositories returned:', len(repo_dicts))

# 研究第一个仓库
repo_dict = repo_dicts[0]  # ③
print('\nKeys:', len(repo_dict))  # ④
for key in sorted(repo_dict.keys()):  # ⑤
    print(key)

前面几行和之前的程序都一样，我们从新的代码开始看。

①处我们打印了字典中'total_count'对应的值，它是说的github总共有多少个python仓库。

②处我们将字典中'items'对应的数据存在repo_dicts里面，这里的数据其实是一个列表，列表中有很多字典，每个字典包含一个python仓库的有关信息。在下一行我们打印了这个列表的长度，这个长度就是列表中字典的个数，也就是API调用后返回的仓库的个数。

为了进一步了解返回的每个仓库的信息，在③处我们取repo_dicts中的第一个字典存在repo_dict中。接下来在④处我们打印了字典所包含的键的个数，在⑤处我们按照字母顺序通过循环打印出该字典所有的键。

下面为程序的输出：

Status code: 200
Total repositories: 11395116
Repositories returned: 30

Keys: 80
allow_forking
archive_url
archived
assignees_url
blobs_url
branches_url
clone_url
collaborators_url
comments_url
commits_url
compare_url
contents_url
contributors_url
created_at
default_branch
deployments_url
description
disabled
downloads_url
events_url
fork
forks
forks_count
forks_url
full_name
git_commits_url
git_refs_url
git_tags_url
git_url
has_discussions
has_downloads
has_issues
has_pages
has_projects
has_wiki
homepage
hooks_url
html_url
id
is_template
issue_comment_url
issue_events_url
issues_url
keys_url
labels_url
language
languages_url
license
merges_url
milestones_url
mirror_url
name
node_id
notifications_url
open_issues
open_issues_count
owner
private
pulls_url
pushed_at
releases_url
score
size
ssh_url
stargazers_count
stargazers_url
statuses_url
subscribers_url
subscription_url
svn_url
tags_url
teams_url
topics
trees_url
updated_at
url
visibility
watchers
watchers_count
web_commit_signoff_required

这样，我们对数据的内容就有了更清晰的了解了。

通过上面的输出我们看到，一个仓库的字典有80个键，通过查看键的内容可以大致了解到每个键的作用（如果想要准确直到每个键的意思，那就需要阅读文档或是使用一些代码来查看）。

下面运行以下代码来提取部分键相关的值：

import requests


# 执行API调用并储存响应
url = 'https://api.github.com/search/repositories?q=language:python&sort=stars'
r = requests.get(url)
print('Status code:', r.status_code)

# 将API响应存储在一个变量中
response_dict = r.json()
print('Total repositories:', response_dict['total_count'])

# 探索有关仓库的信息
repo_dicts = response_dict['items']
print('Repositories returned:', len(repo_dicts))

# 研究第一个仓库
repo_dict = repo_dicts[0]

print('\n第一个仓库的部分信息：')
print('名称：', repo_dict['name'])  # ①
print('所有者：', repo_dict['owner']['login'])  # ②
print('Stars(获得的星星数量)：', repo_dict['stargazers_count'])  # ③
print('仓库的地址：', repo_dict['html_url'])
print('创建时间：', repo_dict['created_at'])  # ④
print('修改时间：', repo_dict['updated_at'])  # ⑤
print('对该仓库的介绍：', repo_dict['description'])

程序打印的东西前面都有相关解释，没什么好说的，我们直接看看结果吧：

Status code: 200
Total repositories: 8700023
Repositories returned: 30

第一个仓库的部分信息：
名称： nupic
所有者： numenta
Stars(获得的星星数量)： 6308
仓库的地址： https://github.com/numenta/nupic
创建时间： 2013-04-05T23:14:27Z
修改时间： 2023-01-30T08:03:47Z
对该仓库的介绍： Numenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM), a theory of intelligence based strictly on the neuroscience of the neocortex.

可以看到，在写本文的时候，github上用python的项目中星星最多的是nupic，所有者为用户numenta，有6308个星星……等等，不对劲，这里的仓库总数是8700023，而我们之前成功请求后得到的仓库数量为11395116，少了很多！我又尝试运行了几次程序都没有达到这个数，最后……

Status code: 403

这其实是后面要说的，大致就是短时间太多次申请会让api拒绝响应你的请求，github这里过一分钟再尝试就好了……

又试了很多次，算了……不想试了……反正就大概是这么个意思，我期待的输出应该是这样的（手动输入）：

Status code: 200
Total repositories: 11395116
Repositories returned: 30

第一个仓库的部分信息：
名称： public-apis
所有者： public-apis
Stars(获得的星星数量)： 226768
仓库的地址： https://github.com/public-apis/public-apis
创建时间： 2016-03-20T23:49:42Z
修改时间： 2023-02-04T02:31:43Z
对该仓库的介绍： A collective list of free APIs

概述最受欢迎的仓库

在输出仓库的地方写一个循环来打印返回值中的每个仓库的信息：

import requests


# 执行API调用并储存响应
url = 'https://api.github.com/search/repositories?q=language:python&sort=stars'
r = requests.get(url)
print('Status code:', r.status_code)

# 将API响应存储在一个变量中
response_dict = r.json()
print('Total repositories:', response_dict['total_count'])

# 探索有关仓库的信息
repo_dicts = response_dict['items']
print('Repositories returned:', len(repo_dicts))

print('\n每个仓库的部分信息：')
for repo_dict in repo_dicts:
    print('\n名称：', repo_dict['name'])
    print('所有者：', repo_dict['owner']['login'])
    print('Stars(获得的星星数量)：', repo_dict['stargazers_count'])
    print('仓库的地址：', repo_dict['html_url'])
    print('创建时间：', repo_dict['created_at'])
    print('修改时间：', repo_dict['updated_at'])
    print('对该仓库的介绍：', repo_dict['description'])

我的结果如下：

Status code: 200
Total repositories: 8936401
Repositories returned: 30

每个仓库的部分信息：

名称： Python-100-Days
所有者： jackfrued
Stars(获得的星星数量)： 130140
仓库的地址： https://github.com/jackfrued/Python-100-Days
创建时间： 2018-03-01T16:05:52Z
修改时间： 2023-02-04T04:42:12Z
对该仓库的介绍： Python - 100天从新手到大师

名称： youtube-dl
所有者： ytdl-org
Stars(获得的星星数量)： 117069
仓库的地址： https://github.com/ytdl-org/youtube-dl
创建时间： 2010-10-31T14:35:07Z
修改时间： 2023-02-04T04:38:02Z
对该仓库的介绍： Command-line program to download videos from YouTube.com and other video sites

名称： ansible
所有者： ansible
Stars(获得的星星数量)： 56151
仓库的地址： https://github.com/ansible/ansible
创建时间： 2012-03-06T14:58:02Z
修改时间： 2023-02-04T04:26:28Z
对该仓库的介绍： Ansible is a radically simple IT automation platform that makes your applications and systems easier to deploy and maintain. Automate everything from code deployment to network configuration to cloud management, in a language that approaches plain English, using SSH, with no agents to install on remote systems. https://docs.ansible.com.

名称： big-list-of-naughty-strings
所有者： minimaxir
Stars(获得的星星数量)： 44514
仓库的地址： https://github.com/minimaxir/big-list-of-naughty-strings
创建时间： 2015-08-08T20:57:20Z
修改时间： 2023-02-04T05:18:08Z
对该仓库的介绍： The Big List of Naughty Strings is a list of strings which have a high probability of causing issues when used as user-input data.

名称： pandas
所有者： pandas-dev
Stars(获得的星星数量)： 36763
仓库的地址： https://github.com/pandas-dev/pandas
创建时间： 2010-08-24T01:37:33Z
修改时间： 2023-02-04T05:13:53Z
对该仓库的介绍： Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

名称： XX-Net
所有者： XX-net
Stars(获得的星星数量)： 31648
仓库的地址： https://github.com/XX-net/XX-Net
创建时间： 2015-01-15T09:35:51Z
修改时间： 2023-02-04T05:06:54Z
对该仓库的介绍： A proxy tool to bypass GFW.

名称： HanLP
所有者： hankcs
Stars(获得的星星数量)： 27921
仓库的地址： https://github.com/hankcs/HanLP
创建时间： 2014-10-09T06:36:16Z
修改时间： 2023-02-04T00:00:02Z
对该仓库的介绍： 中文分词 词性标注 命名实体识别 依存句法分析 成分句法分析 语义依存分析 语义角色标注 指代消解 风格转换 语义相似度 新词发现 关键词短语提取 自动摘要 文本分类聚类 拼音简繁转换 自然语言处理

名称： MockingBird
所有者： babysor
Stars(获得的星星数量)： 26073
仓库的地址： https://github.com/babysor/MockingBird
创建时间： 2021-08-07T03:53:39Z
修改时间： 2023-02-04T05:06:38Z
对该仓库的介绍： AI拟声: 5秒内克隆您的声音并生成任意语音内容 Clone a voice in 5 seconds to generate arbitrary speech in real-time

名称： ray
所有者： ray-project
Stars(获得的星星数量)： 23914
仓库的地址： https://github.com/ray-project/ray
创建时间： 2016-10-25T19:38:30Z
修改时间： 2023-02-04T05:25:06Z
对该仓库的介绍： Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for accelerating ML workloads.

名称： python-fire
所有者： google
Stars(获得的星星数量)： 23772
仓库的地址： https://github.com/google/python-fire
创建时间： 2017-02-21T21:35:07Z
修改时间： 2023-02-03T15:16:43Z
对该仓库的介绍： Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.

名称： CheatSheetSeries
所有者： OWASP
Stars(获得的星星数量)： 22857
仓库的地址： https://github.com/OWASP/CheatSheetSeries
创建时间： 2018-12-21T14:26:43Z
修改时间： 2023-02-04T05:09:55Z
对该仓库的介绍： The OWASP Cheat Sheet Series was created to provide a concise collection of high value information on specific application security topics.

名称： ItChat
所有者： littlecodersh
Stars(获得的星星数量)： 22807
仓库的地址： https://github.com/littlecodersh/ItChat
创建时间： 2016-01-19T07:49:48Z
修改时间： 2023-02-04T04:32:41Z
对该仓库的介绍： A complete and graceful API for Wechat. 微信个人号接口、微信机器人及命令行微信，三十行即可自定义个人号机器人。

名称： numpy
所有者： numpy
Stars(获得的星星数量)： 22606
仓库的地址： https://github.com/numpy/numpy
创建时间： 2010-09-13T23:02:39Z
修改时间： 2023-02-04T03:08:47Z
对该仓库的介绍： The fundamental package for scientific computing with Python.

名称： hosts
所有者： StevenBlack
Stars(获得的星星数量)： 22306
仓库的地址： https://github.com/StevenBlack/hosts
创建时间： 2012-04-12T20:22:50Z
修改时间： 2023-02-04T03:38:55Z
对该仓库的介绍：  Consolidating and extending hosts files from several well-curated sources. Optionally pick extensions for porn, social media, and other categories.

名称： locust
所有者： locustio
Stars(获得的星星数量)： 20615
仓库的地址： https://github.com/locustio/locust
创建时间： 2011-02-17T11:08:03Z
修改时间： 2023-02-03T20:58:10Z
对该仓库的介绍： Scalable load testing tool written in Python

名称： vnpy
所有者： vnpy
Stars(获得的星星数量)： 19911
仓库的地址： https://github.com/vnpy/vnpy
创建时间： 2015-03-02T03:36:58Z
修改时间： 2023-02-04T02:15:08Z
对该仓库的介绍： 基于Python的开源量化交易平台开发框架

名称： pytorch-CycleGAN-and-pix2pix
所有者： junyanz
Stars(获得的星星数量)： 19125
仓库的地址： https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
创建时间： 2017-04-18T10:33:05Z
修改时间： 2023-02-03T17:37:48Z
对该仓库的介绍： Image-to-Image Translation in PyTorch

名称： magenta
所有者： magenta
Stars(获得的星星数量)： 18175
仓库的地址： https://github.com/magenta/magenta
创建时间： 2016-05-05T20:10:40Z
修改时间： 2023-02-03T17:52:22Z
对该仓库的介绍： Magenta: Music and Art Generation with Machine Intelligence

名称： dash
所有者： plotly
Stars(获得的星星数量)： 18032
仓库的地址： https://github.com/plotly/dash
创建时间： 2015-04-10T01:53:08Z
修改时间： 2023-02-04T00:49:38Z
对该仓库的介绍： Data Apps & Dashboards for Python. No JavaScript Required.

名称： matplotlib
所有者： matplotlib
Stars(获得的星星数量)： 16818
仓库的地址： https://github.com/matplotlib/matplotlib
创建时间： 2011-02-19T03:17:12Z
修改时间： 2023-02-03T17:03:16Z
对该仓库的介绍： matplotlib: plotting with Python

名称： recommenders
所有者： microsoft
Stars(获得的星星数量)： 14983
仓库的地址： https://github.com/microsoft/recommenders
创建时间： 2018-09-19T10:06:07Z
修改时间： 2023-02-04T04:46:02Z
对该仓库的介绍： Best Practices on Recommendation Systems

名称： wagtail
所有者： wagtail
Stars(获得的星星数量)： 14078
仓库的地址： https://github.com/wagtail/wagtail
创建时间： 2014-02-03T12:41:59Z
修改时间： 2023-02-03T21:49:53Z
对该仓库的介绍： A Django content management system focused on flexibility and user experience

名称： loguru
所有者： Delgan
Stars(获得的星星数量)： 14008
仓库的地址： https://github.com/Delgan/loguru
创建时间： 2017-08-15T17:22:32Z
修改时间： 2023-02-04T05:04:55Z
对该仓库的介绍： Python logging made (stupidly) simple

名称： gensim
所有者： RaRe-Technologies
Stars(获得的星星数量)： 13919
仓库的地址： https://github.com/RaRe-Technologies/gensim
创建时间： 2011-02-10T07:43:04Z
修改时间： 2023-02-04T04:55:00Z
对该仓库的介绍： Topic Modelling for Humans

名称： wechat_jump_game
所有者： wangshub
Stars(获得的星星数量)： 13862
仓库的地址： https://github.com/wangshub/wechat_jump_game
创建时间： 2017-12-29T02:00:19Z
修改时间： 2023-02-03T09:26:56Z
对该仓库的介绍： 微信《跳一跳》Python 辅助

名称： mackup
所有者： lra
Stars(获得的星星数量)： 12900
仓库的地址： https://github.com/lra/mackup
创建时间： 2013-04-06T19:22:54Z
修改时间： 2023-02-04T00:53:49Z
对该仓库的介绍： Keep your application settings in sync (OS X/Linux)

名称： awesome-oss-alternatives
所有者： RunaCapital
Stars(获得的星星数量)： 12857
仓库的地址： https://github.com/RunaCapital/awesome-oss-alternatives
创建时间： 2021-11-15T09:38:58Z
修改时间： 2023-02-04T03:47:32Z
对该仓库的介绍： Awesome list of open-source startup alternatives to well-known SaaS products 

名称： searx
所有者： searx
Stars(获得的星星数量)： 12502
仓库的地址： https://github.com/searx/searx
创建时间： 2013-10-15T16:20:51Z
修改时间： 2023-02-04T03:51:34Z
对该仓库的介绍： Privacy-respecting metasearch engine

名称： python-mini-projects
所有者： Python-World
Stars(获得的星星数量)： 12175
仓库的地址： https://github.com/Python-World/python-mini-projects
创建时间： 2020-06-21T05:18:42Z
修改时间： 2023-02-04T05:26:30Z
对该仓库的介绍： A collection of simple python mini projects to enhance your python skills

名称： learn_python3_spider
所有者： wistbean
Stars(获得的星星数量)： 11581
仓库的地址： https://github.com/wistbean/learn_python3_spider
创建时间： 2019-04-02T20:19:54Z
修改时间： 2023-02-04T02:29:11Z
对该仓库的介绍： python爬虫教程系列、从0到1学习python爬虫，包括浏览器抓包，手机APP抓包，如 fiddler、mitmproxy，各种爬虫涉及的模块的使用，如：requests、beautifulSoup、selenium、appium、scrapy等，以及IP代理，验证码识别，Mysql，MongoDB数据库的python使用，多线程多进程爬虫的使用，css 爬虫加密逆向破解，JS爬虫逆向，分布式爬虫，爬虫项目实战实例等

因为没有覆盖所有的仓库（8936401/11395116），所以看到的不是最多的，如果是正确的星星最多的仓库在我运行的时间点应该是这样：

Status code: 200
Total repositories: 11395116
Repositories returned: 30

每个仓库的部分信息：

名称： public-apis
所有者： public-apis
Stars(获得的星星数量)： 226768
仓库的地址： https://github.com/public-apis/public-apis
创建时间： 2016-03-20T23:49:42Z
修改时间： 2023-02-04T02:31:43Z
对该仓库的介绍： A collective list of free APIs

名称： system-design-primer
所有者： donnemartin
Stars(获得的星星数量)： 210360
仓库的地址： https://github.com/donnemartin/system-design-primer
创建时间： 2017-02-26T16:15:28Z
修改时间： 2023-02-04T02:43:56Z
对该仓库的介绍： Learn how to design large-scale systems. Prep for the system design interview.  Includes Anki flashcards.

名称： awesome-python
所有者： vinta
Stars(获得的星星数量)： 155592
仓库的地址： https://github.com/vinta/awesome-python
创建时间： 2014-06-27T21:00:06Z
修改时间： 2023-02-04T01:46:21Z
对该仓库的介绍： A curated list of awesome Python frameworks, libraries, software and resources

名称： Python
所有者： TheAlgorithms
Stars(获得的星星数量)： 151895
仓库的地址： https://github.com/TheAlgorithms/Python
创建时间： 2016-07-16T09:44:01Z
修改时间： 2023-02-04T02:33:17Z
对该仓库的介绍： All Algorithms implemented in Python

名称： Python-100-Days
所有者： jackfrued
Stars(获得的星星数量)： 130135
仓库的地址： https://github.com/jackfrued/Python-100-Days
创建时间： 2018-03-01T16:05:52Z
修改时间： 2023-02-04T02:41:16Z
对该仓库的介绍： Python - 100天从新手到大师

名称： youtube-dl
所有者： ytdl-org
Stars(获得的星星数量)： 117067
仓库的地址： https://github.com/ytdl-org/youtube-dl
创建时间： 2010-10-31T14:35:07Z
修改时间： 2023-02-04T02:23:47Z
对该仓库的介绍： Command-line program to download videos from YouTube.com and other video sites

名称： transformers
所有者： huggingface
Stars(获得的星星数量)： 79609
仓库的地址： https://github.com/huggingface/transformers
创建时间： 2018-10-29T13:56:00Z
修改时间： 2023-02-04T02:21:56Z
对该仓库的介绍：  Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

名称： thefuck
所有者： nvbn
Stars(获得的星星数量)： 75566
仓库的地址： https://github.com/nvbn/thefuck
创建时间： 2015-04-08T15:08:04Z
修改时间： 2023-02-04T00:37:06Z
对该仓库的介绍： Magnificent app which corrects your previous console command.

名称： django
所有者： django
Stars(获得的星星数量)： 68545
仓库的地址： https://github.com/django/django
创建时间： 2012-04-28T02:47:18Z
修改时间： 2023-02-04T02:42:34Z
对该仓库的介绍： The Web framework for perfectionists with deadlines.

名称： HelloGitHub
所有者： 521xueweihan
Stars(获得的星星数量)： 64515
仓库的地址： https://github.com/521xueweihan/HelloGitHub
创建时间： 2016-05-04T06:24:11Z
修改时间： 2023-02-04T02:27:04Z
对该仓库的介绍： :octocat: 分享 GitHub 上有趣、入门级的开源项目。Share interesting, entry-level open source projects on GitHub.

名称： flask
所有者： pallets
Stars(获得的星星数量)： 61772
仓库的地址： https://github.com/pallets/flask
创建时间： 2010-04-06T11:11:59Z
修改时间： 2023-02-04T01:54:01Z
对该仓库的介绍： The Python micro framework for building web applications.

名称： core
所有者： home-assistant
Stars(获得的星星数量)： 57748
仓库的地址： https://github.com/home-assistant/core
创建时间： 2013-09-17T07:29:48Z
修改时间： 2023-02-04T01:46:06Z
对该仓库的介绍： :house_with_garden: Open source home automation that puts local control and privacy first.

名称： awesome-machine-learning
所有者： josephmisiti
Stars(获得的星星数量)： 57619
仓库的地址： https://github.com/josephmisiti/awesome-machine-learning
创建时间： 2014-07-15T19:11:19Z
修改时间： 2023-02-04T02:29:31Z
对该仓库的介绍： A curated list of awesome Machine Learning frameworks, libraries and software.

名称： keras
所有者： keras-team
Stars(获得的星星数量)： 57220
仓库的地址： https://github.com/keras-team/keras
创建时间： 2015-03-28T00:35:42Z
修改时间： 2023-02-04T00:03:05Z
对该仓库的介绍： Deep Learning for humans

名称： ansible
所有者： ansible
Stars(获得的星星数量)： 56150
仓库的地址： https://github.com/ansible/ansible
创建时间： 2012-03-06T14:58:02Z
修改时间： 2023-02-04T01:27:03Z
对该仓库的介绍： Ansible is a radically simple IT automation platform that makes your applications and systems easier to deploy and maintain. Automate everything from code deployment to network configuration to cloud management, in a language that approaches plain English, using SSH, with no agents to install on remote systems. https://docs.ansible.com.

名称： fastapi
所有者： tiangolo
Stars(获得的星星数量)： 54108
仓库的地址： https://github.com/tiangolo/fastapi
创建时间： 2018-12-08T08:21:47Z
修改时间： 2023-02-04T02:24:07Z
对该仓库的介绍： FastAPI framework, high performance, easy to learn, fast to code, ready for production

名称： scikit-learn
所有者： scikit-learn
Stars(获得的星星数量)： 52782
仓库的地址： https://github.com/scikit-learn/scikit-learn
创建时间： 2010-08-17T09:43:38Z
修改时间： 2023-02-03T21:56:14Z
对该仓库的介绍： scikit-learn: machine learning in Python

名称： cpython
所有者： python
Stars(获得的星星数量)： 50441
仓库的地址： https://github.com/python/cpython
创建时间： 2017-02-10T19:23:51Z
修改时间： 2023-02-03T23:30:32Z
对该仓库的介绍： The Python programming language

名称： manim
所有者： 3b1b
Stars(获得的星星数量)： 49445
仓库的地址： https://github.com/3b1b/manim
创建时间： 2015-03-22T18:50:58Z
修改时间： 2023-02-04T01:28:40Z
对该仓库的介绍： Animation engine for explanatory math videos

名称： requests
所有者： psf
Stars(获得的星星数量)： 48990
仓库的地址： https://github.com/psf/requests
创建时间： 2011-02-13T18:38:17Z
修改时间： 2023-02-03T19:39:24Z
对该仓库的介绍： A simple, yet elegant, HTTP library.

名称： face_recognition
所有者： ageitgey
Stars(获得的星星数量)： 47188
仓库的地址： https://github.com/ageitgey/face_recognition
创建时间： 2017-03-03T21:52:39Z
修改时间： 2023-02-04T00:17:39Z
对该仓库的介绍： The world's simplest facial recognition api for Python and the command line

名称： you-get
所有者： soimort
Stars(获得的星星数量)： 46618
仓库的地址： https://github.com/soimort/you-get
创建时间： 2012-08-20T15:53:36Z
修改时间： 2023-02-03T15:46:21Z
对该仓库的介绍： :arrow_double_down: Dumb downloader that scrapes the web

名称： funNLP
所有者： fighting41love
Stars(获得的星星数量)： 46364
仓库的地址： https://github.com/fighting41love/funNLP
创建时间： 2018-08-21T11:20:39Z
修改时间： 2023-02-03T19:00:22Z
对该仓库的介绍： 中英文敏感词、语言检测、中外手机/电话归属地/运营商查询、名字推断性别、手机号抽取、身份证抽取、邮箱抽取、中日文人名库、中文缩写库、拆字词典、词汇情感值、停用词、反动词表、暴恐词表、繁简体转换、英文模拟中文发音、汪峰歌词生成器、职业名称词库、同义词库、反义词库、否定词库、汽车品牌词库、汽车零件词库、连续英文切割、各种中文词向量、公司名字大全、古诗词库、IT词库、财经词库、成语词库、地名词库、历史名人词库、诗词词库、医学词库、饮食词库、法律词库、汽车词库、动物词库、中文聊天语料、中文谣言数据、百度中文问答数据集、句子相似度匹配算法集合、bert资源、文本生成&摘要相关工具、cocoNLP信息抽取工具、国内电话号码正则匹配、清华大学XLORE:中英文跨语言百科知识图谱、清华大学人工智能技术系列报告、自然语言生成、NLU太难了系列、自动对联数据及机器人、用户名黑名单列表、罪名法务名词及分类模型、微信公众号语料、cs224n深度学习自然语言处理课程、中文手写汉字识别、中文自然语言处理 语料/数据集、变量命名神器、分词语料库+代码、任务型对话英文数据集、ASR 语音数据集 + 基于深度学习的中文语音识别系统、笑声检测器、Microsoft多语言数字/单位/如日期时间识别包、中华新华字典数据库及api(包括常用歇后语、成语、词语和汉字)、文档图谱自动生成、SpaCy 中文模型、Common Voice语音识别数据集新版、神经网络关系抽取、基于bert的命名实体识别、关键词(Keyphrase)抽取包pke、基于医疗领域知识图谱的问答系统、基于依存句法与语义角色标注的事件三元组抽取、依存句法分析4万句高质量标注数据、cnocr：用来做中文OCR的Python3包、中文人物关系知识图谱项目、中文nlp竞赛项目及代码汇总、中文字符数据、speech-aligner: 从“人声语音”及其“语言文本”产生音素级别时间对齐标注的工具、AmpliGraph: 知识图谱表示学习(Python)库：知识图谱概念链接预测、Scattertext 文本可视化(python)、语言/知识表示工具：BERT & ERNIE、中文对比英文自然语言处理NLP的区别综述、Synonyms中文近义词工具包、HarvestText领域自适应文本挖掘工具（新词发现-情感分析-实体链接等）、word2word：(Python)方便易用的多语言词-词对集：62种语言/3,564个多语言对、语音识别语料生成工具：从具有音频/字幕的在线视频创建自动语音识别(ASR)语料库、构建医疗实体识别的模型（包含词典和语料标注）、单文档非监督的关键词抽取、Kashgari中使用gpt-2语言模型、开源的金融投资数据提取工具、文本自动摘要库TextTeaser: 仅支持英文、人民日报语料处理工具集、一些关于自然语言的基本模型、基于14W歌曲知识库的问答尝试--功能包括歌词接龙and已知歌词找歌曲以及歌曲歌手歌词三角关系的问答、基于Siamese bilstm模型的相似句子判定模型并提供训练数据集和测试数据集、用Transformer编解码模型实现的根据Hacker News文章标题自动生成评论、用BERT进行序列标记和文本分类的模板代码、LitBank：NLP数据集——支持自然语言处理和计算人文学科任务的100部带标记英文小说语料、百度开源的基准信息抽取系统、虚假新闻数据集、Facebook: LAMA语言模型分析，提供Transformer-XL/BERT/ELMo/GPT预训练语言模型的统一访问接口、CommonsenseQA：面向常识的英文QA挑战、中文知识图谱资料、数据及工具、各大公司内部里大牛分享的技术文档 PDF 或者 PPT、自然语言生成SQL语句（英文）、中文NLP数据增强（EDA）工具、英文NLP数据增强工具 、基于医药知识图谱的智能问答系统、京东商品知识图谱、基于mongodb存储的军事领域知识图谱问答项目、基于远监督的中文关系抽取、语音情感分析、中文ULMFiT-情感分析-文本分类-语料及模型、一个拍照做题程序、世界各国大规模人名库、一个利用有趣中文语料库 qingyun 训练出来的中文聊天机器人、中文聊天机器人seqGAN、省市区镇行政区划数据带拼音标注、教育行业新闻语料库包含自动文摘功能、开放了对话机器人-知识图谱-语义理解-自然语言处理工具及数据、中文知识图谱：基于百度百科中文页面-抽取三元组信息-构建中文知识图谱、masr: 中文语音识别-提供预训练模型-高识别率、Python音频数据增广库、中文全词覆盖BERT及两份阅读理解数据、ConvLab：开源多域端到端对话系统平台、中文自然语言处理数据集、基于最新版本rasa搭建的对话系统、基于TensorFlow和BERT的管道式实体及关系抽取、一个小型的证券知识图谱/知识库、复盘所有NLP比赛的TOP方案、OpenCLaP：多领域开源中文预训练语言模型仓库、UER：基于不同语料+编码器+目标任务的中文预训练模型仓库、中文自然语言处理向量合集、基于金融-司法领域(兼有闲聊性质)的聊天机器人、g2pC：基于上下文的汉语读音自动标记模块、Zincbase 知识图谱构建工具包、诗歌质量评价/细粒度情感诗歌语料库、快速转化「中文数字」和「阿拉伯数字」、百度知道问答语料库、基于知识图谱的问答系统、jieba_fast 加速版的jieba、正则表达式教程、中文阅读理解数据集、基于BERT等最新语言模型的抽取式摘要提取、Python利用深度学习进行文本摘要的综合指南、知识图谱深度学习相关资料整理、维基大规模平行文本语料、StanfordNLP 0.2.0：纯Python版自然语言处理包、NeuralNLP-NeuralClassifier：腾讯开源深度学习文本分类工具、端到端的封闭域对话系统、中文命名实体识别：NeuroNER vs. BertNER、新闻事件线索抽取、2019年百度的三元组抽取比赛：“科学空间队”源码、基于依存句法的开放域文本知识三元组抽取和知识库构建、中文的GPT2训练代码、ML-NLP - 机器学习(Machine Learning)NLP面试中常考到的知识点和代码实现、nlp4han:中文自然语言处理工具集(断句/分词/词性标注/组块/句法分析/语义分析/NER/N元语法/HMM/代词消解/情感分析/拼写检查、XLM：Facebook的跨语言预训练语言模型、用基于BERT的微调和特征提取方法来进行知识图谱百度百科人物词条属性抽取、中文自然语言处理相关的开放任务-数据集-当前最佳结果、CoupletAI - 基于CNN+Bi-LSTM+Attention 的自动对对联系统、抽象知识图谱、MiningZhiDaoQACorpus - 580万百度知道问答数据挖掘项目、brat rapid annotation tool: 序列标注工具、大规模中文知识图谱数据：1.4亿实体、数据增强在机器翻译及其他nlp任务中的应用及效果、allennlp阅读理解:支持多种数据和模型、PDF表格数据提取工具 、 Graphbrain：AI开源软件库和科研工具，目的是促进自动意义提取和文本理解以及知识的探索和推断、简历自动筛选系统、基于命名实体识别的简历自动摘要、中文语言理解测评基准，包括代表性的数据集&基准模型&语料库&排行榜、树洞 OCR 文字识别 、从包含表格的扫描图片中识别表格和文字、语声迁移、Python口语自然语言处理工具集(英文)、 similarity：相似度计算工具包，java编写、海量中文预训练ALBERT模型 、Transformers 2.0 、基于大规模音频数据集Audioset的音频增强 、Poplar：网页版自然语言标注工具、图片文字去除，可用于漫画翻译 、186种语言的数字叫法库、Amazon发布基于知识的人-人开放领域对话数据集 、中文文本纠错模块代码、繁简体转换 、 Python实现的多种文本可读性评价指标、类似于人名/地名/组织机构名的命名体识别数据集 、东南大学《知识图谱》研究生课程(资料)、. 英文拼写检查库 、 wwsearch是企业微信后台自研的全文检索引擎、CHAMELEON：深度学习新闻推荐系统元架构 、 8篇论文梳理BERT相关模型进展与反思、DocSearch：免费文档搜索引擎、 LIDA：轻量交互式对话标注工具 、aili - the fastest in-memory index in the East 东半球最快并发索引 、知识图谱车音工作项目、自然语言生成资源大全 、中日韩分词库mecab的Python接口库、中文文本摘要/关键词提取、汉字字符特征提取器 (featurizer)，提取汉字的特征（发音特征、字形特征）用做深度学习的特征、中文生成任务基准测评 、中文缩写数据集、中文任务基准测评 - 代表性的数据集-基准(预训练)模型-语料库-baseline-工具包-排行榜、PySS3：面向可解释AI的SS3文本分类器机器可视化工具 、中文NLP数据集列表、COPE - 格律诗编辑程序、doccano：基于网页的开源协同多语言文本标注工具 、PreNLP：自然语言预处理库、简单的简历解析器，用来从简历中提取关键信息、用于中文闲聊的GPT2模型：GPT2-chitchat、基于检索聊天机器人多轮响应选择相关资源列表(Leaderboards、Datasets、Papers)、(Colab)抽象文本摘要实现集锦(教程 、词语拼音数据、高效模糊搜索工具、NLP数据增广资源集、微软对话机器人框架 、 GitHub Typo Corpus：大规模GitHub多语言拼写错误/语法错误数据集、TextCluster：短文本聚类预处理模块 Short text cluster、面向语音识别的中文文本规范化、BLINK：最先进的实体链接库、BertPunc：基于BERT的最先进标点修复模型、Tokenizer：快速、可定制的文本词条化库、中文语言理解测评基准，包括代表性的数据集、基准(预训练)模型、语料库、排行榜、spaCy 医学文本挖掘与信息提取 、 NLP任务示例项目代码集、 python拼写检查库、chatbot-list - 行业内关于智能客服、聊天机器人的应用和架构、算法分享和介绍、语音质量评价指标(MOSNet, BSSEval, STOI, PESQ, SRMR)、 用138GB语料训练的法文RoBERTa预训练语言模型 、BERT-NER-Pytorch：三种不同模式的BERT中文NER实验、无道词典 - 有道词典的命令行版本，支持英汉互查和在线查询、2019年NLP亮点回顾、 Chinese medical dialogue data 中文医疗对话数据集 、最好的汉字数字(中文数字)-阿拉伯数字转换工具、 基于百科知识库的中文词语多词义/义项获取与特定句子词语语义消歧、awesome-nlp-sentiment-analysis - 情感分析、情绪原因识别、评价对象和评价词抽取、LineFlow：面向所有深度学习框架的NLP数据高效加载器、中文医学NLP公开资源整理 、MedQuAD：(英文)医学问答数据集、将自然语言数字串解析转换为整数和浮点数、Transfer Learning in Natural Language Processing (NLP) 、面向语音识别的中文/英文发音辞典、Tokenizers：注重性能与多功能性的最先进分词器、CLUENER 细粒度命名实体识别 Fine Grained Named Entity Recognition、 基于BERT的中文命名实体识别、中文谣言数据库、NLP数据集/基准任务大列表、nlp相关的一些论文及代码, 包括主题模型、词向量(Word Embedding)、命名实体识别(NER)、文本分类(Text Classificatin)、文本生成(Text Generation)、文本相似性(Text Similarity)计算等，涉及到各种与nlp相关的算法，基于keras和tensorflow 、Python文本挖掘/NLP实战示例、 Blackstone：面向非结构化法律文本的spaCy pipeline和NLP模型通过同义词替换实现文本“变脸” 、中文 预训练 ELECTREA 模型: 基于对抗学习 pretrain Chinese Model 、albert-chinese-ner - 用预训练语言模型ALBERT做中文NER 、基于GPT2的特定主题文本生成/文本增广、开源预训练语言模型合集、多语言句向量包、编码、标记和实现：一种可控高效的文本生成方法、 英文脏话大列表 、attnvis：GPT2、BERT等transformer语言模型注意力交互可视化、CoVoST：Facebook发布的多语种语音-文本翻译语料库，包括11种语言(法语、德语、荷兰语、俄语、西班牙语、意大利语、土耳其语、波斯语、瑞典语、蒙古语和中文)的语音、文字转录及英文译文、Jiagu自然语言处理工具 - 以BiLSTM等模型为基础，提供知识图谱关系抽取 中文分词 词性标注 命名实体识别 情感分析 新词发现 关键词 文本摘要 文本聚类等功能、用unet实现对文档表格的自动检测，表格重建、NLP事件提取文献资源列表 、 金融领域自然语言处理研究资源大列表、CLUEDatasetSearch - 中英文NLP数据集：搜索所有中文NLP数据集，附常用英文NLP数据集 、medical_NER - 中文医学知识图谱命名实体识别 、(哈佛)讲因果推理的免费书、知识图谱相关学习资料/数据集/工具资源大列表、Forte：灵活强大的自然语言处理pipeline工具集 、Python字符串相似性算法库、PyLaia：面向手写文档分析的深度学习工具包、TextFooler：针对文本分类/推理的对抗文本生成模块、Haystack：灵活、强大的可扩展问答(QA)框架、中文关键短语抽取工具

名称： scrapy
所有者： scrapy
Stars(获得的星星数量)： 46054
仓库的地址： https://github.com/scrapy/scrapy
创建时间： 2010-02-22T02:01:14Z
修改时间： 2023-02-03T22:25:40Z
对该仓库的介绍： Scrapy, a fast high-level web crawling & scraping framework for Python.

名称： localstack
所有者： localstack
Stars(获得的星星数量)： 45737
仓库的地址： https://github.com/localstack/localstack
创建时间： 2016-10-25T23:48:03Z
修改时间： 2023-02-03T23:49:00Z
对该仓库的介绍：   A fully functional local AWS cloud stack. Develop and test your cloud & Serverless apps offline!

名称： PayloadsAllTheThings
所有者： swisskyrepo
Stars(获得的星星数量)： 44923
仓库的地址： https://github.com/swisskyrepo/PayloadsAllTheThings
创建时间： 2016-10-18T07:29:07Z
修改时间： 2023-02-04T01:24:27Z
对该仓库的介绍： A list of useful payloads and bypass for Web Application Security and Pentest/CTF

名称： big-list-of-naughty-strings
所有者： minimaxir
Stars(获得的星星数量)： 44512
仓库的地址： https://github.com/minimaxir/big-list-of-naughty-strings
创建时间： 2015-08-08T20:57:20Z
修改时间： 2023-02-04T01:53:46Z
对该仓库的介绍： The Big List of Naughty Strings is a list of strings which have a high probability of causing issues when used as user-input data.

名称： faceswap
所有者： deepfakes
Stars(获得的星星数量)： 43376
仓库的地址： https://github.com/deepfakes/faceswap
创建时间： 2017-12-19T09:44:13Z
修改时间： 2023-02-03T23:00:41Z
对该仓库的介绍： Deepfakes Software For All

名称： rich
所有者： Textualize
Stars(获得的星星数量)： 41876
仓库的地址： https://github.com/Textualize/rich
创建时间： 2019-11-10T15:28:09Z
修改时间： 2023-02-04T02:08:40Z
对该仓库的介绍： Rich is a Python library for rich text and beautiful formatting in the terminal.

名称： devops-exercises
所有者： bregman-arie
Stars(获得的星星数量)： 39308
仓库的地址： https://github.com/bregman-arie/devops-exercises
创建时间： 2019-10-03T17:31:21Z
修改时间： 2023-02-04T02:29:29Z
对该仓库的介绍： Linux, Jenkins, AWS, SRE, Prometheus, Docker, Python, Ansible, Git, Kubernetes, Terraform, OpenStack, SQL, NoSQL, Azure, GCP, DNS, Elastic, Network, Virtualization. DevOps Interview Questions

不必过于在意这个，成功运行了就可以了。上面或是你运行得到的会有一些有趣的项目，可以稍微看看。

查看API的访问限制

前面有过这种情况，就是在运行程序多次后会出现403状态，这是因为大多数API有一个速率限制，也就是你在特定时间内可以执行的请求数存在限制。

对于了解github的API的速率限制，可以在浏览器中输入来查看：

https://api.github.com/rate_limit

结果类似如下：

{
    "resources": {
        "core": {
            "limit": 60,
            "remaining": 60,
            "reset": 1675493245,
            "used": 0,
            "resource": "core"
        },
        "graphql": {
            "limit": 0,
            "remaining": 0,
            "reset": 1675493245,
            "used": 0,
            "resource": "graphql"
        },
        "integration_manifest": {
            "limit": 5000,
            "remaining": 5000,
            "reset": 1675493245,
            "used": 0,
            "resource": "integration_manifest"
        },
        "search": {  # ①
            "limit": 10,  # ②
            "remaining": 10,  # ③
            "reset": 1675489705,
            "used": 0,
            "resource": "search"
        }
    },
    "rate": {
        "limit": 60,
        "remaining": 60,
        "reset": 1675493245,
        "used": 0,
        "resource": "core"
    }
}

我们只看搜索API的限制，也就是①处里面的内容。②处告诉我们，每分钟最多10次请求，而我们这一分钟还可以使用10次（③处）。达到限制后，就只能等待次数重置。

很多API都要求注册后获得API密钥才可以调用，github目前还没有这样的要求，但是获得API密钥后，限制将会变宽，请求次数将会增加很多。

使用pygal可视化数据

代码如下：

import requests
import pygal
from pygal.style import LightColorizedStyle as LCS, LightenStyle as LS


# 执行API调用并储存响应
url = 'https://api.github.com/search/repositories?q=language:python&sort=stars'
r = requests.get(url)
print('Status code:', r.status_code)

# 将API响应存储在一个变量中
response_dict = r.json()
print('Total repositories:', response_dict['total_count'])

# 探索有关仓库的信息
repo_dicts = response_dict['items']

names, stars = [], []  # ①
for repo_dict in repo_dicts:
    names.append(repo_dict['name'])  # ②
    stars.append(repo_dict['stargazers_count'])

# 可视化
my_style = LS('#333366', base_style=LCS)  # ③
chart = pygal.Bar(style=my_style, x_label_rotation=45, show_legend=False)  # ④
chart.title = 'GitHub上星星最多的python项目'
chart.x_labels = names

chart.add('', stars)  # ⑤
chart.render_to_file('python_repos.svg')

我们首先创建了names和stars两个列表（①），然后一一将对应的信息储存在对应的列表中（②）。

③处我们定义了一种样式（'#333366'是将基础颜色设定为深蓝色，并且使用了LightColorizedStyle类作为基本风格），在④处创建条形图（首先将③处的样式传递进去（style=my_style），然后让x轴上的标签全部旋转45°（x_label_rotation=45），最后因为只有一组数据所以设置了不显示图例（show_legend=False））。下一行是设置了表格的标题，再下一行是设置了x轴上的标签为names列表中的值。最后在⑤处我们对这个条形图添加数据（传递的空字符串是该组数据的名称，因为只有一组数据，所以随意了）。

程序最后得到一个后缀是.svg的文件，在浏览器中打开它就可以看到交互式图像了。

我这里运行同样是没有获得全部的数据……所以我用之前得到的数据来构图，结果如下：

鼠标移动到对应的条上就可以看到具体的星星数量。（还是前几个项目的星星最多呀）

改进pygal图表

虽然上边这个就很好了，不过我们还可以加一点我们自己的设置来修饰这个表格，修改后的程序如下：

import requests
import pygal
from pygal.style import LightColorizedStyle as LCS, LightenStyle as LS


# 执行API调用并储存响应
url = 'https://api.github.com/search/repositories?q=language:python&sort=stars'
r = requests.get(url)
print('Status code:', r.status_code)

# 将API响应存储在一个变量中
response_dict = r.json()
print('Total repositories:', response_dict['total_count'])

# 探索有关仓库的信息
repo_dicts = response_dict['items']

names, stars = [], []
for repo_dict in repo_dicts:
    names.append(repo_dict['name'])
    stars.append(repo_dict['stargazers_count'])

# 可视化
my_style = LS('#333366', base_style=LCS)
# 设置标题、副标签、主标签字体大小
my_style.title_font_size = 24
my_style.label_font_size = 16
my_style.major_label_font_size = 20

my_config = pygal.Config()
my_config.x_label_rotation = 45  # 让标签绕x轴旋转45度
my_config.show_legend = False  # 隐藏图例
my_config.truncate_label = 15  # 将较长的项目名缩短为15个字符(如果鼠标移动到被省略的项目名上，还可以显示完整的项目名)
my_config.show_y_guides = False  # 隐藏图表中的水平线
my_config.width = 1000  # 自定义图标宽度

chart = pygal.Bar(my_config, style=my_style)
chart.title = 'GitHub上星星最多的python项目'
chart.x_labels = names

chart.add('', stars)
chart.render_to_file('python_repos.svg')

运行结果如下：

这里和书上的代码有一些不同，想知道的话具体可以看这里。

添加自定义工具提示

工具提示就是当鼠标移动到条形框中后显示的交互式信息，在我们之前的图表中这里显示的是项目名字和星星数目。我们可以自定义一个工具提示，让其还显示项目对应的描述。

这其实很简单，我们可以先用下面这个程序举个例子：

import pygal
from pygal.style import LightColorizedStyle as LCS, LightenStyle as LS

my_style = LS('#333366', base_style=LCS)
chart = pygal.Bar(style=my_style, x_label_rotation=45, show_legend=False)

chart.title = 'Python Projects'
chart.x_labels = ['httpie', 'django', 'flask']

# 创建一个包含三个字典的列表
plot_dicts = [
    {'value': 16101, 'label': 'Description of httpie.'},  # 每个字典包含两个键：value和label
    {'value': 15028, 'label': 'Description of django.'},
    {'value': 14798, 'label': 'Description of flask.'},
    ]

chart.add('', plot_dicts)  # 将此特定列表使用add()方法加入图表中
chart.render_to_file('bar_descriptions.svg')

我们可以看到，pygal接收了列表plot_dicts中的'value'值作为条的高度，同时接收'label'的值到了提示中。

它运行的结果是：

所以说，在add()方法中传入由字典组成的列表，字典中带有'label'键的话，可以在'label'的值中自定义提示，pygal将添加到工具提示中。

由此，我们可以将代码更改如下：

import requests
import pygal
from pygal.style import LightColorizedStyle as LCS, LightenStyle as LS


# 执行API调用并储存响应
url = 'https://api.github.com/search/repositories?q=language:python&sort=stars'
r = requests.get(url)
print('Status code:', r.status_code)

# 将API响应存储在一个变量中
response_dict = r.json()
print('Total repositories:', response_dict['total_count'])

# 探索有关仓库的信息
repo_dicts = response_dict['items']

names, plot_dicts = [], []
for repo_dict in repo_dicts:
    names.append(repo_dict['name'])
    # 每次新建一个字典存入列表
    plot_dict = {
        'value': repo_dict['stargazers_count'],
        'label': repo_dict['description'],
    }
    plot_dicts.append(plot_dict)

# 可视化
my_style = LS('#333366', base_style=LCS)
my_style.title_font_size = 24
my_style.label_font_size = 16
my_style.major_label_font_size = 20

my_config = pygal.Config()
my_config.x_label_rotation = 45
my_config.show_legend = False
my_config.truncate_label = 15
my_config.show_y_guides = False
my_config.width = 1000

chart = pygal.Bar(my_config, style=my_style)
chart.title = 'GitHub上星星最多的python项目'
chart.x_labels = names

chart.add('', plot_dicts)  # 将包含字典的列表传入add()方法
chart.render_to_file('python_repos.svg')

这里将之前的stars列表改成了plot_dicts列表，其中的元素为每一次传入的plot_dict字典，里面有'value'键对应的星星数量和'label'键对应的项目描述。生成的图表如下：

添加可点击链接

pygal可以很简单的为图标的每条添加网站的链接，点击即可跳转到相应界面。和上面添加自定义提示类似，我们只需要在plot_dict字典中再加一个'xlink'键，其值为对应的链接即可。

具体代码如下：

import requests
import pygal
from pygal.style import LightColorizedStyle as LCS, LightenStyle as LS


# 执行API调用并储存响应
url = 'https://api.github.com/search/repositories?q=language:python&sort=stars'
r = requests.get(url)
print('Status code:', r.status_code)

# 将API响应存储在一个变量中
response_dict = r.json()
print('Total repositories:', response_dict['total_count'])

# 探索有关仓库的信息
repo_dicts = response_dict['items']

names, plot_dicts = [], []
for repo_dict in repo_dicts:
    names.append(repo_dict['name'])
    plot_dict = {
        'value': repo_dict['stargazers_count'],
        'label': repo_dict['description'],
        'xlink': repo_dict['html_url'],  # 就加了个这个
    }
    plot_dicts.append(plot_dict)

# 可视化
my_style = LS('#333366', base_style=LCS)
my_style.title_font_size = 24
my_style.label_font_size = 16
my_style.major_label_font_size = 20

my_config = pygal.Config()
my_config.x_label_rotation = 45
my_config.show_legend = False
my_config.truncate_label = 15
my_config.show_y_guides = False
my_config.width = 1000

chart = pygal.Bar(my_config, style=my_style)
chart.title = 'GitHub上星星最多的python项目'
chart.x_labels = names

chart.add('', plot_dicts)
chart.render_to_file('python_repos.svg')

运行后的图表如下：

可以看到工具提示的右下角有一个Link图标，而且此时我们的鼠标已经变成了点击状态，单击就可以在新的标签页打开这个项目的github界面。

至此，本文就全部结束了。我还有一点想要补充，这其实也是一个网络爬虫教程，因为简单的爬虫也就这样。

我将我当时请求api完全成功的数据放在下边，可以和你收集到的数据形成一个参考。

{
  "total_count": 11395116,
  "incomplete_results": false,
  "items": [
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      "full_name": "public-apis/public-apis",
      "private": false,
      "owner": {
        "login": "public-apis",
        "id": 51121562,
        "node_id": "MDEyOk9yZ2FuaXphdGlvbjUxMTIxNTYy",
        "avatar_url": "https://avatars.githubusercontent.com/u/51121562?v=4",
        "gravatar_id": "",
        "url": "https://api.github.com/users/public-apis",
        "html_url": "https://github.com/public-apis",
        "followers_url": "https://api.github.com/users/public-apis/followers",
        "following_url": "https://api.github.com/users/public-apis/following{/other_user}",
        "gists_url": "https://api.github.com/users/public-apis/gists{/gist_id}",
        "starred_url": "https://api.github.com/users/public-apis/starred{/owner}{/repo}",
        "subscriptions_url": "https://api.github.com/users/public-apis/subscriptions",
        "organizations_url": "https://api.github.com/users/public-apis/orgs",
        "repos_url": "https://api.github.com/users/public-apis/repos",
        "events_url": "https://api.github.com/users/public-apis/events{/privacy}",
        "received_events_url": "https://api.github.com/users/public-apis/received_events",
        "type": "Organization",
        "site_admin": false
      },
      "html_url": "https://github.com/public-apis/public-apis",
      "description": "A collective list of free APIs",
      "fork": false,
      "url": "https://api.github.com/repos/public-apis/public-apis",
      "forks_url": "https://api.github.com/repos/public-apis/public-apis/forks",
      "keys_url": "https://api.github.com/repos/public-apis/public-apis/keys{/key_id}",
      "collaborators_url": "https://api.github.com/repos/public-apis/public-apis/collaborators{/collaborator}",
      "teams_url": "https://api.github.com/repos/public-apis/public-apis/teams",
      "hooks_url": "https://api.github.com/repos/public-apis/public-apis/hooks",
      "issue_events_url": "https://api.github.com/repos/public-apis/public-apis/issues/events{/number}",
      "events_url": "https://api.github.com/repos/public-apis/public-apis/events",
      "assignees_url": "https://api.github.com/repos/public-apis/public-apis/assignees{/user}",
      "branches_url": "https://api.github.com/repos/public-apis/public-apis/branches{/branch}",
      "tags_url": "https://api.github.com/repos/public-apis/public-apis/tags",
      "blobs_url": "https://api.github.com/repos/public-apis/public-apis/git/blobs{/sha}",
      "git_tags_url": "https://api.github.com/repos/public-apis/public-apis/git/tags{/sha}",
      "git_refs_url": "https://api.github.com/repos/public-apis/public-apis/git/refs{/sha}",
      "trees_url": "https://api.github.com/repos/public-apis/public-apis/git/trees{/sha}",
      "statuses_url": "https://api.github.com/repos/public-apis/public-apis/statuses/{sha}",
      "languages_url": "https://api.github.com/repos/public-apis/public-apis/languages",
      "stargazers_url": "https://api.github.com/repos/public-apis/public-apis/stargazers",
      "contributors_url": "https://api.github.com/repos/public-apis/public-apis/contributors",
      "subscribers_url": "https://api.github.com/repos/public-apis/public-apis/subscribers",
      "subscription_url": "https://api.github.com/repos/public-apis/public-apis/subscription",
      "commits_url": "https://api.github.com/repos/public-apis/public-apis/commits{/sha}",
      "git_commits_url": "https://api.github.com/repos/public-apis/public-apis/git/commits{/sha}",
      "comments_url": "https://api.github.com/repos/public-apis/public-apis/comments{/number}",
      "issue_comment_url": "https://api.github.com/repos/public-apis/public-apis/issues/comments{/number}",
      "contents_url": "https://api.github.com/repos/public-apis/public-apis/contents/{+path}",
      "compare_url": "https://api.github.com/repos/public-apis/public-apis/compare/{base}...{head}",
      "merges_url": "https://api.github.com/repos/public-apis/public-apis/merges",
      "archive_url": "https://api.github.com/repos/public-apis/public-apis/{archive_format}{/ref}",
      "downloads_url": "https://api.github.com/repos/public-apis/public-apis/downloads",
      "issues_url": "https://api.github.com/repos/public-apis/public-apis/issues{/number}",
      "pulls_url": "https://api.github.com/repos/public-apis/public-apis/pulls{/number}",
      "milestones_url": "https://api.github.com/repos/public-apis/public-apis/milestones{/number}",
      "notifications_url": "https://api.github.com/repos/public-apis/public-apis/notifications{?since,all,participating}",
      "labels_url": "https://api.github.com/repos/public-apis/public-apis/labels{/name}",
      "releases_url": "https://api.github.com/repos/public-apis/public-apis/releases{/id}",
      "deployments_url": "https://api.github.com/repos/public-apis/public-apis/deployments",
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      "updated_at": "2023-02-04T02:31:43Z",
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      "git_url": "git://github.com/public-apis/public-apis.git",
      "ssh_url": "[email protected]:public-apis/public-apis.git",
      "clone_url": "https://github.com/public-apis/public-apis.git",
      "svn_url": "https://github.com/public-apis/public-apis",
      "homepage": "http://public-apis.org",
      "size": 4948,
      "stargazers_count": 226768,
      "watchers_count": 226768,
      "language": "Python",
      "has_issues": true,
      "has_projects": false,
      "has_downloads": true,
      "has_wiki": false,
      "has_pages": false,
      "has_discussions": false,
      "forks_count": 25799,
      "mirror_url": null,
      "archived": false,
      "disabled": false,
      "open_issues_count": 130,
      "license": {
        "key": "mit",
        "name": "MIT License",
        "spdx_id": "MIT",
        "url": "https://api.github.com/licenses/mit",
        "node_id": "MDc6TGljZW5zZTEz"
      },
      "allow_forking": true,
      "is_template": false,
      "web_commit_signoff_required": false,
      "topics": [
        "api",
        "apis",
        "dataset",
        "development",
        "free",
        "list",
        "lists",
        "open-source",
        "public",
        "public-api",
        "public-apis",
        "resources",
        "software"
      ],
      "visibility": "public",
      "forks": 25799,
      "open_issues": 130,
      "watchers": 226768,
      "default_branch": "master",
      "score": 1.0
    },
    {
      "id": 83222441,
      "node_id": "MDEwOlJlcG9zaXRvcnk4MzIyMjQ0MQ==",
      "name": "system-design-primer",
      "full_name": "donnemartin/system-design-primer",
      "private": false,
      "owner": {
        "login": "donnemartin",
        "id": 5458997,
        "node_id": "MDQ6VXNlcjU0NTg5OTc=",
        "avatar_url": "https://avatars.githubusercontent.com/u/5458997?v=4",
        "gravatar_id": "",
        "url": "https://api.github.com/users/donnemartin",
        "html_url": "https://github.com/donnemartin",
        "followers_url": "https://api.github.com/users/donnemartin/followers",
        "following_url": "https://api.github.com/users/donnemartin/following{/other_user}",
        "gists_url": "https://api.github.com/users/donnemartin/gists{/gist_id}",
        "starred_url": "https://api.github.com/users/donnemartin/starred{/owner}{/repo}",
        "subscriptions_url": "https://api.github.com/users/donnemartin/subscriptions",
        "organizations_url": "https://api.github.com/users/donnemartin/orgs",
        "repos_url": "https://api.github.com/users/donnemartin/repos",
        "events_url": "https://api.github.com/users/donnemartin/events{/privacy}",
        "received_events_url": "https://api.github.com/users/donnemartin/received_events",
        "type": "User",
        "site_admin": false
      },
      "html_url": "https://github.com/donnemartin/system-design-primer",
      "description": "Learn how to design large-scale systems. Prep for the system design interview.  Includes Anki flashcards.",
      "fork": false,
      "url": "https://api.github.com/repos/donnemartin/system-design-primer",
      "forks_url": "https://api.github.com/repos/donnemartin/system-design-primer/forks",
      "keys_url": "https://api.github.com/repos/donnemartin/system-design-primer/keys{/key_id}",
      "collaborators_url": "https://api.github.com/repos/donnemartin/system-design-primer/collaborators{/collaborator}",
      "teams_url": "https://api.github.com/repos/donnemartin/system-design-primer/teams",
      "hooks_url": "https://api.github.com/repos/donnemartin/system-design-primer/hooks",
      "issue_events_url": "https://api.github.com/repos/donnemartin/system-design-primer/issues/events{/number}",
      "events_url": "https://api.github.com/repos/donnemartin/system-design-primer/events",
      "assignees_url": "https://api.github.com/repos/donnemartin/system-design-primer/assignees{/user}",
      "branches_url": "https://api.github.com/repos/donnemartin/system-design-primer/branches{/branch}",
      "tags_url": "https://api.github.com/repos/donnemartin/system-design-primer/tags",
      "blobs_url": "https://api.github.com/repos/donnemartin/system-design-primer/git/blobs{/sha}",
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      "languages_url": "https://api.github.com/repos/donnemartin/system-design-primer/languages",
      "stargazers_url": "https://api.github.com/repos/donnemartin/system-design-primer/stargazers",
      "contributors_url": "https://api.github.com/repos/donnemartin/system-design-primer/contributors",
      "subscribers_url": "https://api.github.com/repos/donnemartin/system-design-primer/subscribers",
      "subscription_url": "https://api.github.com/repos/donnemartin/system-design-primer/subscription",
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      "has_issues": true,
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