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提交 8d95595f 编写于 作者: Kwan的解忧杂货铺@新空间代码工作室's avatar Kwan的解忧杂货铺@新空间代码工作室 🐭
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fix:可视化整理

上级 88ede8e5
展开全部 隐藏空白更改
内联 并排
Showing with 140 addition and 911 deletion
05_数据容器/13_字典的常用操作.py
浏览文件 @ 8d95595f
......@@ -34,4 +34,3 @@ for key in my_dict:
# 统计字典内的元素数量, len()函数
num = len(my_dict)
print(f"字典中的元素数量有：{num}个")
07_文件操作/01_文件的读取.py
浏览文件 @ 8d95595f
......@@ -5,7 +5,7 @@
# 打开文件
import time
f = open("D:/测试.txt", "r", encoding="UTF-8")
f = open("/Users/qinyingjie/Downloads/改签合同系统操作指引.pdf", "r", encoding="UTF-8")
print(type(f))
# 读取文件 - read()
# print(f"读取10个字节的结果：{f.read(10)}")
......@@ -31,8 +31,11 @@ print("-----------------------------------------------")
# f.close()
# time.sleep(500000)
# with open 语法操作文件
with open("D:/测试.txt", "r", encoding="UTF-8") as f:
row = 0
with open("/Users/qinyingjie/Downloads/资料/第1-12章资料/资料/地图数据/疫情.txt", "r", encoding="UTF-8") as f:
for line in f:
print(f"每一行数据是：{line}")
row += 1
print(f"第{row}行数据是：{line}")
f.close()
time.sleep(500000)
\ No newline at end of file
time.sleep(500000)
08_异常_模块_包/04_模块的导入.py
浏览文件 @ 8d95595f
......@@ -4,18 +4,6 @@
from time import sleep
# 使用import导入time模块使用sleep功能（函数）
# import time # 导入Python内置的time模块（time.py这个代码文件）
# print("你好")
......@@ -41,6 +29,7 @@ from time import sleep
# print("我好")
from time import sleep as sl
print("你好")
sl(5)
print("我好")
08_异常_模块_包/05_自定义模块.py
浏览文件 @ 8d95595f
......@@ -9,13 +9,13 @@
# 导入不同模块的同名功能
# from my_module1 import test
from my_package import my_module1
# from my_module2 import test
# test(1, 2)
# __main__变量
# from my_module1 import test
# __all__变量
from my_module1 import *
test_a(1, 2)
# test_a(1, 2)
# test_b(2, 1)
09_可视化案例/01_json数据格式.py
浏览文件 @ 8d95595f
......@@ -2,13 +2,14 @@
演示JSON数据和Python字典的相互转换
"""
import json
# 准备列表，列表内每一个元素都是字典，将其转换为JSON
data = [{"name": "张大山", "age": 11}, {"name": "王大锤", "age": 13}, {"name": "赵小虎", "age": 16}]
json_str = json.dumps(data, ensure_ascii=False)
print(type(json_str))
print(json_str)
# 准备字典，将字典转换为JSON
d = {"name":"周杰轮", "addr":"台北"}
d = {"name": "周杰轮", "addr": "台北"}
json_str = json.dumps(d, ensure_ascii=False)
print(type(json_str))
print(json_str)
......
09_可视化案例/02_pyecharts基础入门.py
浏览文件 @ 8d95595f
......@@ -4,6 +4,7 @@
# 导包
from pyecharts.charts import Line
from pyecharts.options import TitleOpts, LegendOpts, ToolboxOpts, VisualMapOpts
# 创建一个折线图对象
line = Line()
# 给折线图对象添加x轴的数据
......@@ -20,4 +21,4 @@ line.set_global_opts(
)
# 通过render方法，将代码生成为图像
line.render()
line.render("GDP展示.html")
09_可视化案例/03_折线图开发.py
浏览文件 @ 8d95595f
......@@ -6,14 +6,14 @@ from pyecharts.charts import Line
from pyecharts.options import TitleOpts, LabelOpts
# 处理数据
f_us = open("D:/美国.txt", "r", encoding="UTF-8")
us_data = f_us.read() # 美国的全部内容
f_us = open("/Users/qinyingjie/Documents/idea-workspace/study/python-demo/data/折线图数据/美国.txt", "r", encoding="UTF-8")
us_data = f_us.read() # 美国的全部内容
f_jp = open("D:/日本.txt", "r", encoding="UTF-8")
jp_data = f_jp.read() # 日本的全部内容
f_jp = open("/Users/qinyingjie/Documents/idea-workspace/study/python-demo/data/折线图数据/日本.txt", "r", encoding="UTF-8")
jp_data = f_jp.read() # 日本的全部内容
f_in = open("D:/印度.txt", "r", encoding="UTF-8")
in_data = f_in.read() # 印度的全部内容
f_in = open("/Users/qinyingjie/Documents/idea-workspace/study/python-demo/data/折线图数据/印度.txt", "r", encoding="UTF-8")
in_data = f_in.read() # 印度的全部内容
# 去掉不合JSON规范的开头
us_data = us_data.replace("jsonp_1629344292311_69436(", "")
......@@ -46,13 +46,13 @@ jp_y_data = jp_trend_data['list'][0]['data'][:314]
in_y_data = in_trend_data['list'][0]['data'][:314]
# 生成图表
line = Line() # 构建折线图对象
line = Line() # 构建折线图对象
# 添加x轴数据
line.add_xaxis(us_x_data) # x轴是公用的，所以使用一个国家的数据即可
line.add_xaxis(us_x_data) # x轴是公用的，所以使用一个国家的数据即可
# 添加y轴数据
line.add_yaxis("美国确诊人数", us_y_data, label_opts=LabelOpts(is_show=False)) # 添加美国的y轴数据
line.add_yaxis("日本确诊人数", jp_y_data, label_opts=LabelOpts(is_show=False)) # 添加日本的y轴数据
line.add_yaxis("印度确诊人数", in_y_data, label_opts=LabelOpts(is_show=False)) # 添加印度的y轴数据
line.add_yaxis("美国确诊人数", us_y_data, label_opts=LabelOpts(is_show=False)) # 添加美国的y轴数据
line.add_yaxis("日本确诊人数", jp_y_data, label_opts=LabelOpts(is_show=False)) # 添加日本的y轴数据
line.add_yaxis("印度确诊人数", in_y_data, label_opts=LabelOpts(is_show=False)) # 添加印度的y轴数据
# 设置全局选项
line.set_global_opts(
......@@ -61,7 +61,7 @@ line.set_global_opts(
)
# 调用render方法，生成图表
line.render()
line.render("折线图开发.html")
# 关闭文件对象
f_us.close()
f_jp.close()
......
09_可视化案例/04_地图可视化的基本使用.py
浏览文件 @ 8d95595f
......@@ -8,11 +8,11 @@ from pyecharts.options import VisualMapOpts
map = Map()
# 准备数据
data = [
("北京", 99),
("上海", 199),
("湖南", 299),
("北京", 1),
("上海", 11),
("湖南", 88),
("台湾", 399),
("广东", 499)
("广东", 200)
]
# 添加数据
map.add("测试地图", data, "china")
......@@ -31,4 +31,4 @@ map.set_global_opts(
)
# 绘图
map.render()
map.render("地图可视化.html")
09_可视化案例/05_全国疫情可视化地图开发.py
浏览文件 @ 8d95595f
......@@ -6,23 +6,22 @@ from pyecharts.charts import Map
from pyecharts.options import *
# 读取数据文件
f = open("D:/疫情.txt", "r", encoding="UTF-8")
data = f.read() # 全部数据
f = open("/Users/qinyingjie/Documents/idea-workspace/study/python-demo/data/地图数据/疫情.txt", "r", encoding="UTF-8")
data = f.read() # 全部数据
# 关闭文件
f.close()
# 取到各省数据
# 将字符串json转换为python的字典
data_dict = json.loads(data) # 基础数据字典
data_dict = json.loads(data) # 基础数据字典
# 从字典中取出省份的数据
province_data_list = data_dict["areaTree"][0]["children"]
# 组装每个省份和确诊人数为元组，并各个省的数据都封装入列表内
data_list = [] # 绘图需要用的数据列表
data_list = [] # 绘图需要用的数据列表
for province_data in province_data_list:
province_name = province_data["name"] # 省份名称
province_confirm = province_data["total"]["confirm"] # 确诊人数
province_name = province_data["name"] # 省份名称
province_confirm = province_data["total"]["confirm"] # 确诊人数
data_list.append((province_name, province_confirm))
# 创建地图对象
map = Map()
# 添加数据
......@@ -31,8 +30,8 @@ map.add("各省份确诊人数", data_list, "china")
map.set_global_opts(
title_opts=TitleOpts(title="全国疫情地图"),
visualmap_opts=VisualMapOpts(
is_show=True, # 是否显示
is_piecewise=True, # 是否分段
is_show=True, # 是否显示
is_piecewise=True, # 是否分段
pieces=[
{"min": 1, "max": 99, "lable": "1~99人", "color": "#CCFFFF"},
{"min": 100, "max": 999, "lable": "100~9999人", "color": "#FFFF99"},
......
09_可视化案例/06_省级疫情可视化地图开发.py
浏览文件 @ 8d95595f
......@@ -6,7 +6,7 @@ from pyecharts.charts import Map
from pyecharts.options import *
# 读取文件
f = open("D:/疫情.txt", "r", encoding="UTF-8")
f = open("/Users/qinyingjie/Documents/idea-workspace/study/python-demo/data/地图数据/疫情.txt", "r", encoding="UTF-8")
data = f.read()
# 关闭文件
f.close()
......@@ -33,8 +33,8 @@ map.add("河南省疫情分布", data_list, "河南")
map.set_global_opts(
title_opts=TitleOpts(title="河南省疫情地图"),
visualmap_opts=VisualMapOpts(
is_show=True, # 是否显示
is_piecewise=True, # 是否分段
is_show=True, # 是否显示
is_piecewise=True, # 是否分段
pieces=[
{"min": 1, "max": 99, "lable": "1~99人", "color": "#CCFFFF"},
{"min": 100, "max": 999, "lable": "100~9999人", "color": "#FFFF99"},
......
09_可视化案例/07_基础柱状图开发.py
浏览文件 @ 8d95595f
......@@ -3,17 +3,14 @@
"""
from pyecharts.charts import Bar
from pyecharts.options import LabelOpts
# 使用Bar构建基础柱状图
bar = Bar()
# 添加x轴的数据
bar.add_xaxis(["中国", "美国", "英国"])
# 添加y轴数据
# 添加y轴数据 设置数值标签在右侧
bar.add_yaxis("GDP", [30, 20, 10], label_opts=LabelOpts(position="right"))
# 反转x和y轴
bar.reversal_axis()
# 绘图
bar.render("基础柱状图.html")
# 反转x轴和y轴
# 设置数值标签在右侧
\ No newline at end of file
09_可视化案例/08_基础时间线柱状图开发.py
浏览文件 @ 8d95595f
......@@ -37,4 +37,3 @@ timeline.add_schema(
# 绘图是用时间线对象绘图，而不是bar对象了
timeline.render("基础时间线柱状图.html")
09_可视化案例/10_GDP动态柱状图开发.py
浏览文件 @ 8d95595f
......@@ -6,7 +6,8 @@ from pyecharts.options import *
from pyecharts.globals import ThemeType
# 读取数据
f = open("D:/1960-2019全球GDP数据.csv", "r", encoding="GB2312")
f = open("/Users/qinyingjie/Documents/idea-workspace/study/python-demo/data/动态柱状图数据/1960-2019全球GDP数据.csv", "r",
encoding="GB2312")
data_lines = f.readlines()
# 关闭文件
f.close()
......@@ -18,9 +19,9 @@ data_lines.pop(0)
# 先定义一个字典对象
data_dict = {}
for line in data_lines:
year = int(line.split(",")[0]) # 年份
country = line.split(",")[1] # 国家
gdp = float(line.split(",")[2]) # gdp数据
year = int(line.split(",")[0]) # 年份
country = line.split(",")[1] # 国家
gdp = float(line.split(",")[2]) # gdp数据
# 如何判断字典里面有没有指定的key呢？
try:
data_dict[year].append([country, gdp])
......@@ -40,8 +41,8 @@ for year in sorted_year_list:
x_data = []
y_data = []
for country_gdp in year_data:
x_data.append(country_gdp[0]) # x轴添加国家
y_data.append(country_gdp[1] / 100000000) # y轴添加gdp数据
x_data.append(country_gdp[0]) # x轴添加国家
y_data.append(country_gdp[1] / 100000000) # y轴添加gdp数据
# 构建柱状图
bar = Bar()
......@@ -57,7 +58,6 @@ for year in sorted_year_list:
)
timeline.add(bar, str(year))
# for循环每一年的数据，基于每一年的数据，创建每一年的bar对象
# 在for中，将每一年的bar对象添加到时间线中
......
09_可视化案例/全国疫情地图.html 已删除 100644 → 0
浏览文件 @ 88ede8e5
<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8">
<title>Awesome-pyecharts</title>
<script type="text/javascript" src="https://assets.pyecharts.org/assets/echarts.min.js"></script>
<script type="text/javascript" src="https://assets.pyecharts.org/assets/maps/china.js"></script>
</head>
<body>
<div id="345a3b147c254be2baae8b48c56b038f" class="chart-container" style="width:900px; height:500px;"></div>
<script>
var chart_345a3b147c254be2baae8b48c56b038f = echarts.init(
document.getElementById('345a3b147c254be2baae8b48c56b038f'), 'white', {renderer: 'canvas'});
var option_345a3b147c254be2baae8b48c56b038f = {
"animation": true,
"animationThreshold": 2000,
"animationDuration": 1000,
"animationEasing": "cubicOut",
"animationDelay": 0,
"animationDurationUpdate": 300,
"animationEasingUpdate": "cubicOut",
"animationDelayUpdate": 0,
"color": [
"#c23531",
"#2f4554",
"#61a0a8",
"#d48265",
"#749f83",
"#ca8622",
"#bda29a",
"#6e7074",
"#546570",
"#c4ccd3",
"#f05b72",
"#ef5b9c",
"#f47920",
"#905a3d",
"#fab27b",
"#2a5caa",
"#444693",
"#726930",
"#b2d235",
"#6d8346",
"#ac6767",
"#1d953f",
"#6950a1",
"#918597"
],
"series": [
{
"type": "map",
"name": "\u5404\u7701\u4efd\u786e\u8bca\u4eba\u6570",
"label": {
"show": true,
"position": "top",
"margin": 8
},
"mapType": "china",
"data": [
{
"name": "\u53f0\u6e7e",
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},
{
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{
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{
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{
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{
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"value": 603
},
{
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"value": 63
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{
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"value": 199
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{
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"value": 255
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{
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"value": 190
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{
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"value": 410
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{
"name": "\u5409\u6797",
"value": 574
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{
"name": "\u9ed1\u9f99\u6c5f",
"value": 1613
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{
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"value": 77
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{
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"value": 18
},
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"value": 937
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{
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"value": 147
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"value": 1
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"value": 1008
},
{
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"value": 1317
},
{
"name": "\u65b0\u7586",
"value": 980
}
],
"roam": true,
"aspectScale": 0.75,
"nameProperty": "name",
"selectedMode": false,
"zoom": 1,
"mapValueCalculation": "sum",
"showLegendSymbol": true,
"emphasis": {}
}
],
"legend": [
{
"data": [
"\u5404\u7701\u4efd\u786e\u8bca\u4eba\u6570"
],
"selected": {
"\u5404\u7701\u4efd\u786e\u8bca\u4eba\u6570": true
},
"show": true,
"padding": 5,
"itemGap": 10,
"itemWidth": 25,
"itemHeight": 14
}
],
"tooltip": {
"show": true,
"trigger": "item",
"triggerOn": "mousemove|click",
"axisPointer": {
"type": "line"
},
"showContent": true,
"alwaysShowContent": false,
"showDelay": 0,
"hideDelay": 100,
"textStyle": {
"fontSize": 14
},
"borderWidth": 0,
"padding": 5
},
"title": [
{
"text": "\u5168\u56fd\u75ab\u60c5\u5730\u56fe",
"padding": 5,
"itemGap": 10
}
],
"visualMap": {
"show": true,
"type": "piecewise",
"min": 0,
"max": 100,
"inRange": {
"color": [
"#50a3ba",
"#eac763",
"#d94e5d"
]
},
"calculable": true,
"inverse": false,
"splitNumber": 5,
"orient": "vertical",
"showLabel": true,
"itemWidth": 20,
"itemHeight": 14,
"borderWidth": 0,
"pieces": [
{
"min": 1,
"max": 99,
"lable": "1~99\u4eba",
"color": "#CCFFFF"
},
{
"min": 100,
"max": 999,
"lable": "100~9999\u4eba",
"color": "#FFFF99"
},
{
"min": 1000,
"max": 4999,
"lable": "1000~4999\u4eba",
"color": "#FF9966"
},
{
"min": 5000,
"max": 9999,
"lable": "5000~99999\u4eba",
"color": "#FF6666"
},
{
"min": 10000,
"max": 99999,
"lable": "10000~99999\u4eba",
"color": "#CC3333"
},
{
"min": 100000,
"lable": "100000+",
"color": "#990033"
}
]
}
};
chart_345a3b147c254be2baae8b48c56b038f.setOption(option_345a3b147c254be2baae8b48c56b038f);
</script>
</body>
</html>
09_可视化案例/基础时间线柱状图.html
浏览文件 @ 8d95595f
......@@ -3,15 +3,15 @@
<head>
<meta charset="UTF-8">
<title>Awesome-pyecharts</title>
<script type="text/javascript" src="https://assets.pyecharts.org/assets/echarts.min.js"></script>
<script type="text/javascript" src="https://assets.pyecharts.org/assets/v5/echarts.min.js"></script>
</head>
<body>
<div id="ab78bbd0ab244ae1bb4babaa3734a166" class="chart-container" style="width:900px; height:500px;"></div>
<body >
<div id="99c442b784b24f4a93768fd4a50956ea" class="chart-container" style="width:900px; height:500px; "></div>
<script>
var chart_ab78bbd0ab244ae1bb4babaa3734a166 = echarts.init(
document.getElementById('ab78bbd0ab244ae1bb4babaa3734a166'), 'light', {renderer: 'canvas'});
var option_ab78bbd0ab244ae1bb4babaa3734a166 = {
var chart_99c442b784b24f4a93768fd4a50956ea = echarts.init(
document.getElementById('99c442b784b24f4a93768fd4a50956ea'), 'light', {renderer: 'canvas'});
var option_99c442b784b24f4a93768fd4a50956ea = {
"baseOption": {
"series": [
{
......@@ -23,7 +23,10 @@
60,
60
],
"realtimeSort": false,
"showBackground": false,
"stackStrategy": "samesign",
"cursor": "pointer",
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09_可视化案例/基础柱状图.html 已删除 100644 → 0
浏览文件 @ 88ede8e5
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09_可视化案例/河南省疫情地图.html 已删除 100644 → 0
浏览文件 @ 88ede8e5
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data/动态柱状图数据/1960-2019全球GDP数据.csv 0 → 100644
浏览文件 @ 8d95595f
此差异已折叠。
data/地图数据/疫情.txt 0 → 100644
浏览文件 @ 8d95595f
此差异已折叠。
data/折线图数据/印度.txt 0 → 100644
浏览文件 @ 8d95595f
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data/折线图数据/日本.txt 0 → 100644
浏览文件 @ 8d95595f
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data/折线图数据/美国.txt 0 → 100644
浏览文件 @ 8d95595f
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