【深度学习入门】深度学习四板斧其一 -- Python入门学习笔记2
-捕获异常:
·
七、可视化图表
-JSON:#字典或者嵌套字典的列表
JSON本质上是一个带有特定格式的字符串,功能是一种在各个编程语言中流通的数据格式,负责不同编程语言中的数据传递和交互。
import json
#准备列表,列表内每个元素都是字典,将其转换为JSON
data = [{"name":"apple","price":5},{"name":"peach","price":2},{"name":"banana","price":6}]
json_str = json.dumps(data,ensure_ascii=False)
print(type(json_str))
print(json_str)
#输出:
# <class 'str'>
# [{"name": "apple", "price": 5}, {"name": "peach", "price": 2}, {"name": "banana", "price": 6}]
#准备字典,将字典转换为JSON
d = {"name":"apple","price":5}
json_str1 = json.dumps(d,ensure_ascii=False)
print(type(json_str1))
print(json_str1)
#输出:
# <class 'str'>
# {"name": "apple", "price": 5}
#将JSON字符串转换为Python数据类型
s = '[{"name": "apple", "price": 5}, {"name": "peach", "price": 2}, {"name": "banana", "price": 6}]'
l = json.loads(s)
print(type(l))
print(l)
#输出:
# <class 'list'>
# [{'name': 'apple', 'price': 5}, {'name': 'peach', 'price': 2}, {'name': 'banana', 'price': 6}]
#将JSON字符串转换为Python数据类型
s = '{"name": "apple", "price": 5}'
l = json.loads(s)
print(type(l))
print(l)
#输出:
# <class 'dict'>
# {'name': 'apple', 'price': 5}
-pyecharts模块:
用于制作数据可视化效果图。
官网:点击这里跳转
画廊:点击这里跳转
-基础折线图:
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
line.add_xaxis(["苹果","橘子","香蕉"])
#给折线图对象添加y轴的数据
line.add_yaxis("价格",[2,3,5])
#设置全局配置项
line.set_global_opts(
title_opts=TitleOpts(title="水果价格",
pos_left="center",
pos_bottom="1%"),
legend_opts=LegendOpts(is_show=True),
toolbox_opts=ToolboxOpts(is_show=True),
visualmap_opts=VisualMapOpts(is_show=True)
)
#生成图像
line.render()
-数据处理:
json格式化工具:点击跳转
-应用折线图:
import json
from pyecharts.charts import Line
from pyecharts.options import TitleOpts,ToolboxOpts,LabelOpts
#处理数据
f_us = open("美国.txt","r",encoding="UTF-8")
us_data = f_us.read()
f_jp = open("日本.txt","r",encoding="UTF-8")
jp_data = f_jp.read()
f_in = open("印度.txt","r",encoding="UTF-8")
in_data = f_in.read()
#去掉不合JSON规范的开头
us_data = us_data.replace("jsonp_1629344292311_69436(","")
jp_data = jp_data.replace("jsonp_1629350871167_29498(","")
in_data = in_data.replace("jsonp_1629350745930_63180(","")
#去掉不合JSON规范的结尾
us_data = us_data[:-2]
jp_data = jp_data[:-2]
in_data = in_data[:-2]
#JSON转Python字典
us_dict = json.loads(us_data)
jp_dict = json.loads(jp_data)
in_dict = json.loads(in_data)
#获取trend key
us_trend_data = us_dict['data'][0]['trend']
jp_trend_data = jp_dict['data'][0]['trend']
in_trend_data = in_dict['data'][0]['trend']
#获取日期数据,用于x轴,取2020年(到314下标结束)
us_x_data = us_trend_data['updateDate'][:314]
jp_x_data = jp_trend_data['updateDate'][:314]
in_x_data = in_trend_data['updateDate'][:314]
#获取确认数据,用于y轴,取2020年
us_y_data = us_trend_data['list'][0]['data'][:314]
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()
#添加x轴数据
line.add_xaxis(us_x_data)
#添加y轴数据
line.add_yaxis("美国确诊人数",us_y_data,label_opts=LabelOpts(is_show=False))
line.add_yaxis("日本确诊人数",jp_y_data,label_opts=LabelOpts(is_show=False))
line.add_yaxis("印度确诊人数",in_y_data,label_opts=LabelOpts(is_show=False))
#设置全局配置项
line.set_global_opts(
title_opts=TitleOpts(title="2020年美日印三国确诊人数对比折线图",
pos_left="center",
pos_bottom="1%"),
toolbox_opts=ToolboxOpts(is_show=True)
)
#生成图标
line.render()
#关闭文件
f_us.close()
f_jp.close()
f_in.close()
八、地图
-基础地图:
from pyecharts.charts import Map
from pyecharts.options import VisualMapOpts,ToolboxOpts
#准备地图对象
map = Map()
#准备数据
data = [
("北京市",99),
("上海市",199),
("湖南省",299),
("台湾省",399),
("广东省",499)
]
#添加数据
map.add("测试地图",data,"china")
#设置全局选项
map.set_global_opts(
toolbox_opts=ToolboxOpts(is_show=True),
visualmap_opts=VisualMapOpts(
is_show=True,
is_piecewise=True,
pieces=[
{"min":1,"max":9,"label":"1-9","color":"#CCFFFF"},
{"min":10,"max":99,"label":"10-99","color":"#FF6666"},
{"min":100,"max":500,"label":"100-500","color":"#990033"}
]
)
)
#绘图
map.render()
-全国疫情地图:
import json
from pyecharts.charts import Map
from pyecharts.options import *
#读取数据文件
f = open("疫情.txt","r",encoding="UTF-8")
data = f.read()
#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)
#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,并每个省的数据都封装入列表中
data_list = []
for province_data in province_data_list:
province_name = province_data["name"] + "省"
province_confirm = province_data["total"]["confirm"]
data_list.append((province_name,province_confirm))
#准备地图对象
map = Map()
#添加数据
map.add("各省份确诊人数",data_list,"china")
#设置全局选项
map.set_global_opts(
title_opts=TitleOpts(title="全国疫情地图"),
toolbox_opts=ToolboxOpts(is_show=True),
visualmap_opts=VisualMapOpts(
is_show=True,
is_piecewise=True,
pieces=[
{"min":1,"max":99,"label":"1-9人","color":"#CCFFFF"},
{"min":100,"max":999,"label":"100-999人","color":"#FFFF99"},
{"min":1000,"max":4999,"label":"1000-4999人","color":"#FF9966"},
{"min":5000,"max":9999,"label":"5000-9999人","color":"#FF6666"},
{"min":10000,"max":99999,"label":"10000-99999人","color":"#CC3333"},
{"min":100000,"label":"100000+","color":"#990033"}
]
)
)
#绘图
map.render()
-河南省疫情地图:
import json
from pyecharts.charts import Map
from pyecharts.options import *
#读取数据文件
f = open("疫情.txt","r",encoding="UTF-8")
data = f.read()
#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)
#取到河南省数据
cities_data_list = data_dict["areaTree"][0]["children"][3]["children"]
#组装每个省份和确诊人数为元组,并每个省的数据都封装入列表中
data_list = []
for cities_data in cities_data_list:
city_name = cities_data["name"] + "市"
city_confirm = cities_data["total"]["confirm"]
data_list.append((city_name,city_confirm))
#手动添加济源市的数据
data_list.append(("济源市",5))
#准备地图对象
map = Map()
#添加数据
map.add("河南省疫情分布",data_list,"河南")
#设置全局选项
map.set_global_opts(
title_opts=TitleOpts(title="河南省疫情地图"),
toolbox_opts=ToolboxOpts(is_show=True),
visualmap_opts=VisualMapOpts(
is_show=True,
is_piecewise=True,
pieces=[
{"min":1,"max":99,"label":"1-9人","color":"#CCFFFF"},
{"min":100,"max":999,"label":"100-999人","color":"#FFFF99"},
{"min":1000,"max":4999,"label":"1000-4999人","color":"#FF9966"},
{"min":5000,"max":9999,"label":"5000-9999人","color":"#FF6666"},
{"min":10000,"max":99999,"label":"10000-99999人","color":"#CC3333"},
{"min":100000,"label":"100000+","color":"#990033"}
]
)
)
#绘图
map.render()
九、柱状图
-基础柱状图:
from pyecharts.charts import Bar
from pyecharts.options import LabelOpts,ToolboxOpts
#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轴的数据 - 设置数值标签在右侧
bar.add_yaxis("GDP",[30,20,10],label_opts=LabelOpts(position="right"))
bar.set_global_opts(toolbox_opts=ToolboxOpts(is_show=True))
#反转x轴和y轴
bar.reversal_axis()
#绘图
bar.render()
-基础时间线柱状图:
from pyecharts.charts import Bar,Timeline
from pyecharts.options import LabelOpts,ToolboxOpts
from pyecharts.globals import ThemeType
#使用Bar构建基础柱状图
bar1 = Bar()
bar1.add_xaxis(["中国","美国","英国"])
bar1.add_yaxis("GDP",[30,20,10],label_opts=LabelOpts(position="right"))
bar1.set_global_opts(toolbox_opts=ToolboxOpts(is_show=True))
bar1.reversal_axis()
bar2 = Bar()
bar2.add_xaxis(["中国","美国","英国"])
bar2.add_yaxis("GDP",[50,50,20],label_opts=LabelOpts(position="right"))
bar2.reversal_axis()
bar3 = Bar()
bar3.add_xaxis(["中国","美国","英国"])
bar3.add_yaxis("GDP",[80,80,30],label_opts=LabelOpts(position="right"))
bar3.reversal_axis()
#构建时间线对象 - 主题设置
timeline = Timeline({"theme":ThemeType.LIGHT})
#在时间线内添加柱状图对象
timeline.add(bar1,"点1")
timeline.add(bar2,"点2")
timeline.add(bar3,"点3")
#自动播放设置
timeline.add_schema(
play_interval=1000,
is_timeline_show=True,
is_auto_play=True,
is_loop_play=True
)
#绘图
timeline.render()
-列表的sort方法:
my_list = [["a",33],["b",55],["c",11]]
#排序,基于带明函数
def choose_sort_key(element):
return element[1]
my_list.sort(key=choose_sort_key,reverse=True)
print(my_list)
#输出:[['b', 55], ['a', 33], ['c', 11]]
#排序,基于lambda匿名函数
my_list.sort(key=lambda element:element[1],reverse=True)
print(my_list)
#输出:[['b', 55], ['a', 33], ['c', 11]]
-动态GDP柱状图绘制:
from pyecharts.charts import Bar,Timeline
from pyecharts.options import *
from pyecharts.globals import ThemeType
#读取数据
f = open("1960-2019全球GDP数据.csv","r",encoding="GB2312")
data_lines = f.readlines()
f.close()
#删除第一条数据
data_lines.pop(0)
#将数据转换为字典存储,格式为:
#{年份:[[国家,gdp],[国家,gdp],......],年份:[[国家,gdp],[国家,gdp],......],......}
data_dict = {}
for line in data_lines:
year = int(line.split(",")[0])
country = line.split(",")[1]
gdp = float(line.split(",")[2])
#判断字典里面有没有指定key
try:
data_dict[year].append([country,gdp])
except KeyError:
data_dict[year] = []
data_dict[year].append([country, gdp])
#创建时间线对象
timeline = Timeline({"theme":ThemeType.LIGHT})
#排序年份
sort_year_list = sorted(data_dict.keys())
for year in sort_year_list:
data_dict[year].sort(key = lambda element: element[1],reverse=True)
#取出本年份前8名
year_data = data_dict[year][0:8]
x_data = []
y_data = []
for country_gdp in year_data:
x_data.append(country_gdp[0])
y_data.append(country_gdp[1] / 100000000)
#构建柱状图
bar = Bar()
x_data.reverse()
y_data.reverse()
bar.add_xaxis(x_data)
bar.add_yaxis("GDP(亿)",y_data,label_opts=LabelOpts(position="right"))
bar.reversal_axis()
#设置每一年的图表的标题
bar.set_global_opts(
title_opts=TitleOpts(title=f"{year}年全球前8GDP数据"),
toolbox_opts=ToolboxOpts(is_show=True)
)
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对象添加进时间线中
#设置时间线自动播放
timeline.add_schema(
play_interval=1000,
is_timeline_show=True,
is_auto_play=True,
is_loop_play=False
)
#绘图
timeline.render()
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