python数据分析pyecharts【饼状图、直方图、词云、地图】

目录

饼状图 

直方图

词云

地图

饼状图 

from pyecharts.charts import Pie
from pyecharts import options as opts
 
data = {'神农架林区': 2.6016,
        '恩施州': 3.0729,
        '十堰市': 3.4300,
        '宜昌市': 3.4555,
        '襄阳市': 4.0543,
        '咸宁市': 4.1145,
        '荆门市': 4.1777,
        '潜江市': 4.2574,
        '黄冈市': 4.4093,
        '黄石市': 4.4914,
        '随州市': 4.6480,
        '鄂州市': 4.8873,
        '荆州市': 4.9619,
        '仙桃市': 5.0019,
        '天门市': 5.0204,
        '孝感市': 5.0245,
        '武汉市': 5.3657}
x_data = [i for i in data.keys()]
y_data = [i for i in data.values()]
pie = Pie()
pie.width = "1500px"
pie.add("", [list(z) for z in zip(x_data, y_data)], radius=["40%", "75%"])
pie.set_global_opts(
    title_opts=opts.TitleOpts(title="2023年1~9月17个城市地表水环境质量状况排名", pos_bottom="0px", pos_left="36.5%"))
# b为x轴，c为y轴，d为百分比
pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b} {d}%"))
pie.render("饼状图.html")

直方图

import pyecharts.options as opts
from pyecharts.charts import Bar
 
data = {'神农架林区': 2.6016,
        '恩施州': 3.0729,
        '十堰市': 3.4300,
        '宜昌市': 3.4555,
        '襄阳市': 4.0543,
        '咸宁市': 4.1145,
        '荆门市': 4.1777,
        '潜江市': 4.2574,
        '黄冈市': 4.4093,
        '黄石市': 4.4914,
        '随州市': 4.6480,
        '鄂州市': 4.8873,
        '荆州市': 4.9619,
        '仙桃市': 5.0019,
        '天门市': 5.0204,
        '孝感市': 5.0245,
        '武汉市': 5.3657}
x_data = [i for i in data.keys()]
y_data = [i for i in data.values()]
bar = Bar()
bar.width = '1500px'
bar.add_yaxis("2023年1~9月17个城市地表水环境质量状况排名", y_data, color='#85C1E9')
bar.add_xaxis(x_data)
# bar.set_global_opts(xaxis_opts=opts.AxisOpts(name="城市"),yaxis_opts=opts.AxisOpts(name="CWQI"))
# bar.set_global_opts(xaxis_opts=opts.AxisOpts(name="城市", axislabel_opts={"rotate":45}),yaxis_opts=opts.AxisOpts(name="CWQI"))
bar.set_global_opts(xaxis_opts=opts.AxisOpts(name="城市", axislabel_opts={"interval": "0"}),
                    yaxis_opts=opts.AxisOpts(name="CWQI"))
bar.render("直方图.html")

词云

from pyecharts.charts import WordCloud
from collections import Counter
import jieba
 
# 读取网页并过滤
with open("网页内容.txt", encoding="utf8") as f:
    asd = f.read()
# 分词后-》过滤-》计算频率
asds = jieba.lcut(asd)
wordCount = []
for asd in asds:
    if (len(asd) > 1):
        wordCount.append(asd)
word_counter = Counter(wordCount)
words_list = word_counter.most_common(10000)
print(words_list)
wc = WordCloud()
wc.width = "1500px"
wc.add("", data_pair=words_list)
wc.render("词云.html")

 地图

from pyecharts.charts import Map
from pyecharts import options as opts
data = {'神农架林区': 2.6016,
#
        '恩施土家族苗族自治州': 3.0729,
        '十堰市': 3.4300,
        '宜昌市': 3.4555,
        '襄阳市': 4.0543,
        '咸宁市': 4.1145,
        '荆门市': 4.1777,
        '潜江市': 4.2574,
        '黄冈市': 4.4093,
        '黄石市': 4.4914,
        '随州市': 4.6480,
        '鄂州市': 4.8873,
        '荆州市': 4.9619,
        '仙桃市': 5.0019,
        '天门市': 5.0204,
        '孝感市': 5.0245,
        '武汉市': 5.3657}
x_data = [i for i in data.keys()]
y_data = [i for i in data.values()]
map=Map()
map.width="1500px"
map.height="650px"
map.add("",[list(z) for z in zip(x_data,y_data)],"湖北")
# map.set_global_opts(visualmap_opts=opts.VisualMapOpts(is_show=True))
map.set_global_opts(visualmap_opts=opts.VisualMapOpts(is_show=True,max_=max(y_data),min_=(min(y_data))))
map.render(path="地图.html")