Python基础之pyecharts创建可视化图表之折线图
我们先看下最终实现的效果
①、效果一：2020年印美日新冠累计确诊人数
2020年是新冠疫情爆发的一年, 随着疫情的爆发, 国内外确诊人数成了大家关心的热点,
相信大家都有看过类似的疫情报告. 本案例对印度美国日本三个国家确诊人数的进行了
可视化处理, 形成了可视化的疫情确诊人数报告。
②、效果二：全国疫情地图可视化
③、效果三：动态GDP增长图
由于我们的数据来源于txt文件，需要解析Json，我们先学下Python中Json
1、json格式化数据
在Python中使用json需要导入 json包
实例：
# 导入 json包
import json
# 准备符合格式json格式要求的python数据
data = [{"name": "老王", "age": 16}, {"name": "张三", "age": 20}]
# 通过 json.dumps(data) 方法把python数据转化为了 json数据
data = json.dumps(data)
print(type(data))
# 通过 json.loads(data) 方法把json数据转化为了 python数据
data = json.loads(data)
print(type(data))
2、pyecharts模块介绍
Echarts 是个由百度开源的数据可视化，凭借着良好的交互性，精巧的图表设计，得到了众多开发者的认可. 而 Python 是门富有表达力的语言，很适合用于数据处理. 当数据分析遇上数据可视化时pyecharts 诞生了。
github：https://github.com/pyecharts/pyecharts
2.1、pyecharts模块安装
通过命令"pip install pyecharts"安装模块
3、pyecharts入门
代码：
"""
构建基础折线图
"""
# 导入Line包
from pyecharts.charts import Line
# 得到折线图对象
line = Line()
# 添加X轴数据
line.add_xaxis(["中国", "美国", "英国"])
# 添加Y轴数据
line.add_yaxis("GDP", [50, 40, 30])
# 生成图表
line.render()
pyecharts模块中有很多的配置选项, 常用到2个类别的选项:
全局配置选项
系列配置选项
4、生成日本、美国、印度2020年新冠确诊人数折线图
4.1、数据
日本：
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4.2、配置说明
4.3、代码+效果
代码：
import json
from pyecharts.charts import Line
import pyecharts.options as opts
"""
日本数据获取
"""
f = open("../data/折线图数据/日本.txt", "r", encoding="UTF-8")
data = f.read()
f.close()
# 删除jsonp_1629350871167_29498(
data = data.replace("jsonp_1629350871167_29498(", "")
# 删除结尾 ");"
data = data[:-2]
# 数据格式符合json格式后,对数据进行转化
data = json.loads(data)
# 获取日本的疫情数据
data = data["data"][0]['trend']
# x1_data存放日期数据
japan_x1_data = data['updateDate']
# y1_data存放人数数据
japan_y1_data = data['list'][0]["data"]
# 获取2020年的数据
japan_x1_data = data['updateDate'][:314]
# 获取2020年的数据
japan_y1_data = data['list'][0]["data"][:314]
"""
美国数据获取
"""
f = open("../data/折线图数据/美国.txt", "r", encoding="UTF-8")
data = f.read()
f.close()
# 删除jsonp_1629350871167_29498(
data = data.replace("jsonp_1629344292311_69436(", "")
# 删除结尾 ");"
data = data[:-2]
# 数据格式符合json格式后,对数据进行转化
data = json.loads(data)
# 获取印度的疫情数据
data = data["data"][0]['trend']
# x1_data存放日期数据
us_x1_data = data['updateDate']
# y1_data存放人数数据
us_y1_data = data['list'][0]["data"]
# 获取2020年的数据
us_x1_data = data['updateDate'][:314]
# 获取2020年的数据
us_y1_data = data['list'][0]["data"][:314]
"""
印度数据获取
"""
f = open("../data/折线图数据/印度.txt", "r", encoding="UTF-8")
data = f.read()
f.close()
# 删除jsonp_1629350871167_29498(
data = data.replace("jsonp_1629350745930_63180(", "")
# 删除结尾 ");"
data = data[:-2]
# 数据格式符合json格式后,对数据进行转化
data = json.loads(data)
# 获取印度的疫情数据
data = data["data"][0]['trend']
# x1_data存放日期数据
india_x1_data = data['updateDate']
# y1_data存放人数数据
india_y1_data = data['list'][0]["data"]
# 获取2020年的数据
india_x1_data = data['updateDate'][:314]
# 获取2020年的数据
india_y1_data = data['list'][0]["data"][:314]
# 绘制折线图
line = (
Line(init_opts=opts.InitOpts(width="1000px", height="600px"))
.add_xaxis(xaxis_data=japan_x1_data[0:-1:2]) # x轴数据 三个国家都一致，所以就公用了
.add_yaxis( # 配置Y轴
series_name="日本确诊人数", # 设置图例别名
y_axis=japan_y1_data[0:-1], # 设置Y周数据
symbol_size=10, # 设置点大小
label_opts=opts.LabelOpts(is_show=True), # 标签设置项：显示标签
linestyle_opts=opts.LineStyleOpts(width=2) # 线条宽度和样式
)
.add_yaxis( # 配置Y轴
series_name="美国确诊人数", # 设置图例别名
y_axis=us_y1_data[0:-1], # 设置Y周数据
symbol_size=10, # 设置点大小
label_opts=opts.LabelOpts(is_show=True), # 标签设置项：显示标签
linestyle_opts=opts.LineStyleOpts(width=2) # 线条宽度和样式
)
.add_yaxis( # 配置Y轴
series_name="印度确诊人数", # 设置图例别名
y_axis=india_y1_data[0:-1], # 设置Y周数据
symbol_size=10, # 设置点大小
label_opts=opts.LabelOpts(is_show=True), # 标签设置项：显示标签
linestyle_opts=opts.LineStyleOpts(width=2) # 线条宽度和样式
)
.set_global_opts(
title_opts=opts.TitleOpts(title="日本、美国、印度2020年新冠确认人数折线图", pos_left="center", pos_bottom="1%")
)
)
line.render()
5、下篇文章
下篇文章我们继续讲解全国疫情地图可视化