让，

data = [23, 85, 72, 43, 52]labels = list('abcde')plt.style.use('ggplot')plt.rcParams['font.sans-serif'] = ['simhei'] #指定默认字体plt.rcParams['axes.unicode_minus'] = False #解决保存图像是负号'-'显示为方块的问题plt.bar(range(len(data)),data)plt.xticks(range(len(data.

数据获取路径：https://github.com/pyecharts/pyecharts-gallery/blob/master/Graph/weibo.jsonimport jsonfrom pyecharts import options as optsfrom pyecharts.charts import Graphwith open("weibo.json", "r", encoding="utf-8") as f: j = json.load(f) nodes.

存在问题：没有设置figure，图形很挤，很乱，x轴其实可以挑着显示几个，全部显示就会装不下。1.简单实现from matplotlib import pyplot as pltx=range(3)y=[4,5,6]plt.plot(x,y)plt.show()2.用with open（） as f： 读取并且提取出需要的数据然后画图import csvfilename = 'data/crimeRatesByState2005.csv'flag=0d..

from pyecharts.charts import Pieimport pandas as pdfrom pyecharts import options as opts# pd.read_csv() 将csv文件 以 二维的形式读入 且 第一行 默认 不算入vote = pd.read_csv("vote_result.csv")print(vote) # 二维形式输出print(vote.values)print(vote.values.tolist());c = (.

# 引入相关的库from pyecharts.charts import Lineimport pyecharts.options as optsimport pandas as pdfrom pyecharts.globals import ThemeType# 导入数据df = pd.read_csv('data/beijing_AQI_2018.csv')attr = df['Date'].tolist()v1 = df['AQI'].tolist()# 函数具体实现内容( .

from pyecharts import options as optsfrom pyecharts.charts import Treedata = [ { "children": [ {"name": "日本"}, { "children": [{"children": [{"name": "I"}], "name": "北京"}, {"name": "上海"}], .

主题（事件）随事件变化情况退化版：import pyecharts.options as optsfrom pyecharts.charts import ThemeRiverx_data = ["Chinese", "Math", "English", "Chemistry", "PE", "Art"]y_data = [ ["2015/11/08", 10, "Chinese"], ["2015/11/09", 10, "Chinese"], ["2015/11

速查表

连续型时间数据可视化pyechars 1.X#引入作图所需要的包from pyecharts.charts import Lineimport pyecharts.options as opts#输入阶梯数据data_x = ['1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009']data_y = [0..

import ggplot as gpimport pandas as pdimport numpy as npcrime = pd.read_csv("crimeRatesByState2005.csv")print(gp.ggplot(gp.aes(x='murder',y='burglary'),data=crime)+gp.geom_point( ) )# 去掉离群点后crime2 = crime[ crime.state!="United States" ]crime2 ..

plt.style.use('ggplot')plt.rcParams['font.sans-serif'] = ['simhei']plt.rcParams['axes.unicode_minus'] = Falseplt.figure(figsize=(20,10))plt.bar(city_count.index,city_count.values)plt.xlabel('City')plt.ylabel('Count')plt.title('星巴克分布城市Top10柱状图')p.

整体from pyecharts.charts import Bar #从 pyecharts 的 charts 库中导入 条形图from pyecharts import options as optsbar = ( Bar() # 实例化一个 Bar 类 的对象 并 设置其基本配置 #配置类型有 x轴，y轴，系列参数（如label标签），全局参数（如标题，xy轴的配置，图例） .add_xaxis() .add_yaxis() .set_seri.

1.数据的获取 http://datasets.flowingdata.com/对应的文件名字 http://datasets.flowingdata.com/hot-dog-contest-winners.csv2.gallary.pyecharts.org pyechart 库

import pandas as pdimport csvfilename = "hot-dog-contest-winners.csv"msg = pd.read_csv(filename)# print(msg.values)# print(msg)datax = []datay = []with open(filename) as f: reader = csv.reader(f) for datarow in reader: if re...

plot(x, y) # plot x and y using default line style and color plot(x, y, 'bo') # plot x and y using blue circle markers plot(y) # plot y using x as index array 0..N-1 plot(y, 'r+') # ditto, but with red plusses ...

from pyecharts.charts import Pieimport pandas as pdfrom pyecharts import options as opts# pd.read_csv() 将csv文件 以 二维的形式读入 且 第一行 默认 不算入vote = pd.read_csv("vote_result.csv")print(vote) # 二维形式输出print(vote.values)print(vote.values.tolist());c = (.

离散型时间数据可视化若数据存在子分类，且子分类相加有意义的话，可以使用它from pyecharts.charts import Barfrom pyecharts import options as optsimport csv# 读入文件 并且 从文件中 读取数据filename = "hot-dog-places.csv"datax = []datay = []with open(filename) as f: reader = csv.reader(f) .

import pandas as pdtmp = pd.read_csv(**.csv)print(tmp) 二维print(tmp.values) 列表套列表行 为 单位print(tmp.values.tolist())列表套列表 一行from pyecharts.charts import Pieimport pandas as pdfrom pyecharts import options as opts# pd.read_csv() 将csv文件 以 ...

from pyecharts.charts import Barfrom pyecharts import options as optsimport csv#离散型时间数据可视化filename = "hot-dog-contest-winners.csv"datax = []datay = []with open(filename) as f: reader = csv.reader(f) for datarow in reader: .

plt.style.use('ggplot')plt.figure(figsize=(20,10))plt.axis('off')plt.subplot(2,2,1)plt.subplot(2,2,4)#plt.subplot(221)#plt.subplot(224)plt.style.use('ggplot')plt.figure(figsize=(20,10))plt.axis('off')for idx in range(4): plt.subplot(..

堆叠图的实现基础是 Bar 只是多了几个 add_yaxis()需要堆叠的时候需要加参数 stack=' ' 字符串名字相同就会堆叠到一块bar = ( Bar() .add_xaxis([1,2,3,4]) .add_yaxis('A',[5,6,7,8],stack="stack1") .add_yaxis('B',[9,10,11,12],stack="stack1") .add_yaxis('C',[13,14,15,16],stack="...

文本数据可视化import pyecharts.options as optsfrom pyecharts.charts import WordCloud data = [ ("数学", "48896"), ("英语", "81374"), ("物理", "81374"), ("化学", "76495"), ("生物", "76495"),]( WordCloud() .add(series_name="热点分析", dat.

import pandas as pdimport matplotlib.pyplot as pltimport seaborn as snscrime = pd.read_csv("crimeRatesByState2005.csv")crime2 = crime[crime.state != "United States"]crime2 = crime2[crime2.state != "District of Columbia"]crime2 = crime2.drop(['state'.

1.首先读取文件，观察数据发现只需要第一列和第三列filename = 'data/hot-dog-contest-winners.csv'df = pd.read_csv(filename)df.head()2.提取所需要的数据 并 用 pyecharts 作图pd.read_csv() 实现import pandas as pdfrom pyecharts.charts import Barimport pyecharts.options as optsfilena

连续型时间数据可视化pyecharts 1.X 版本#引入作图所需要的包from pyecharts.charts import Lineimport pyecharts.options as opts#输入阶梯数据data_x = ['1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009']data_y =.

import matplotlib.pyplot as pltimport pandas as pdimport numpy as npcrime = pd.read_csv("crimeRatesByState2005.csv")print(list(crime.murder))crime2 = crime[ crime.state != "United States"]crime2 = crime2[ crime.state != "District of Columbia"]p.

httpbin.org整体思路：# -*- codeing = utf-8 -*-# @Time : 11/30/2020 07:47 PM# @Author : Gyp# @File : spiderfrom bs4 import BeautifulSoupimport reimport urllib.request,urllib.errorimport sqlite3import xlwtdef main(): # 1 爬取网页 # 2 解析数据..

# css 选择器# t_list = bs.select('title')# t_list = bs.select('a') # Tag# t_list = bs.select('.mnav') # class# t_list = bs.select("#u1") # id# t_list = bs.select("a[class='cur-img']") #根据 a标签 的class 属性的取值来找对应的标签# t_list = bs.select("head>meta.

豆瓣 li 里面 每一个 div class='item' 的项# -*- codeing = utf-8 -*-# @Time : 12/05/2020 07:58 PM# @Author : Gyp# @File : retestimport re# 获取 详情链接findLink = re.compile(r'<a href="(.*?)">')# 获取 图片findImgsrc = re.compile(r'<img.*src="(.*?)".*/>'

BeautifulSoup 将 读取到的 html 进行解析，在内存中以树的形式存储，便于操作常用的操作有t_list = bs.find_all("a") 找到所有的a标签并返回成列表形式bs.title 返回一个标签以及其内部的所有东西bs.title.string 返回一个标签的内容bs.title.attrs 返回一个标签的属性bs.head.contents 将head里面的内容以 列表形式 返回# ...

import re# bs4 BeautifulSoup 可以解析 xml文件，jason文件，html文件from bs4 import BeautifulSoup#打开文件 并 放入内存file = open("./baidu.html","rb")html = file.read().decode("utf-8")# 以 字节形式 打开并读取了 html 则 html的类型是 bytes 字节形式# print("html: ",type(html))# 在内存中 建立了 .

python 中 常用的 调用 正则表达式 来 进行 字符串的 模式匹配的方法：re 库 ( search 只找 第一次 出现的 找到即返回 findall 找所有并且将结果 以列表的形式返回）re.findall(pattern,string) re.search(pattern,string) re.sub(pattern,to,string) pat =re.compile(pattern) pat.search(string...

# -*- codeing = utf-8 -*-# @Time : 11/30/2020 07:47 PM# @Author : Gyp# @File : spiderfrom bs4 import BeautifulSoupimport reimport urllib.request,urllib.errorimport sqlite3import xlwtdef main(): # 1 爬取网页 # 2 解析数据 # 3 保存数据 爬取网页 解析数.

获取一个指定网页内的所有内容# -*- codeing = utf-8 -*-# @Time : 11/24/2020 12:11 PM# @Author : Gyp# @File : testfrom bs4 import BeautifulSoupimport urllib.request,urllib.errorimport xlwtimport sqlite3# -----------------------------------------------------..

源自：百度 AI Studio 课程目录 Python数据结构 Python面向对象 Python JSON Python 异常处理 常见Linux命令 Python数据结构数字、字符串、列表、元祖、字典数字Python Number 数据类型用于存储数值。Python Number 数据类型用于存储数值，包括整型、长整型、浮点型、复数。（1）Python math 模块：Python 中数学运算常用的函数基本都在 math 模块

1.import pandas as pdimport numpy as npfrom pandas import DataFrame,Seriesimport jsonwith open(r'C:\Users\92448\Desktop\jupyter\material\源码、数据、表格\数据\eueo2012.json') as f: obj = f.read() result = json.loads(obj) df = DataFrame(result).

1.连接import pymysql#创建一个连接对象conn = pymysql.connect( host = 'localhost', user = 'root', passwd = '123456', db = 'mydb', )2.建表# 创建光标对象 通过光标对象进行操作cursor = conn.cursor()create = """ CREATE TABLE ch4ex9 ( .