Introducción a la biblioteca de visualización de datos de Python pyecharts
Directorio de artículos
Herramientas de dibujo:
Utilice la biblioteca de pyecharts de código abierto de Baidu
Puede consultar su documento
oficial pyecharts documento oficial
Preprocesamiento de datos
Instalación del módulo
instalación de pip
pyecharts
Módulo de importación
import pandas as pd
df = pd.read_excel('taobao.xlsx')
Deduplicación
# 删除行完全一样的值
df.drop_duplicates(inplace=True)
# 删除列重复的值
df.drop_duplicates(subset=['列名','列名'])
Procesamiento de ubicación geográfica
location_list = []
for location in df['location']:
location = location.split(' ')[0]
location_list.append(location)
df['location'] = location_list
Procesar ventas
sales_list = []
for sale in df['sales']:
sale = sale[:-3].replace('+', '')
if '万' in sale:
sale = int(float(sale.replace('万', '')) * 10000)
sales_list.append(sale)
df['sales'] = sales_list
Hacer un gráfico
### Importar módulos
import jieba
import pandas as pd
from pyecharts import options as opts
from pyecharts.globals import ThemeType
from pyecharts.globals import SymbolType
from pyecharts.charts import Pie, Bar, Map, WordCloud, Page
2.1 Nube de palabras
Dos métodos:
pyechartsNube de palabras generada incorporadawordcloudEl módulo genera nube de palabras (recomendado
método uno:
stop_words_txt = 'stop_words.txt'
# 载入停用词,即过滤词
jieba.analyse.set_stop_words(stop_words_txt)
# TextRank 关键词抽取，只获取固定词性
# topK为返回权重最大的关键词，默认值为20
# withWeight为返回权重值，默认为False
keywords_count_list = jieba.analyse.textrank(' '.join(df1.comment), topK=100, withWeight=True)
print(keywords_count_list)
word_cloud = (
WordCloud()
.add("", keywords_count_list, word_size_range=[5, 50],
shape=SymbolType.TRIANGLE,
)
.set_global_opts(title_opts=opts.TitleOpts(title="这里输入标题"))
)
# 这句话是渲染成一个html文件到当前文件夹下面
# word_cloud.render('WordCloud.html')
Método 2: (recomendado, se puede personalizar
instalación de pip
wordcloud
import jieba
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
from wordcloud import WordCloud
# 打开文本
# text = open('1.txt',encoding='utf-8').read()
# 中文分词
text = ' '.join(jieba.cut(text))
# 生成对象
mask = np.array(Image.open("input_picture"))
wc = WordCloud(mask=mask,font_path='C:\Windows\Fonts\SimHei.ttf',mode='RGBA').generate(text)
# 显示词云
# plt.imshow(wc, interpolation='bilinear')
# plt.axis("off")
# plt.show()
# 保存到文件
wc.to_file('output_picture')
2.2 Histograma
Histograma general:
bar = (
Bar()
.add_xaxis(Faker.days_attrs)
.add_yaxis("商家A", Faker.days_values)
.set_global_opts(
title_opts=opts.TitleOpts(title="Bar-DataZoom（slider+inside）"),
)
# .render("bar_datazoom_both.html")
)
Histograma horizontal:
.reversal_axis()
.set_series_opts(label_opts=opts.LabelOpts(position="right"))
Histograma del control deslizante:
datazoom_opts=[opts.DataZoomOpts()]
2.3 Gráfico circular
Los datos provienen de:standard_goods_comments.xlsx
Utilice la taza para mostrar aquí
[('B', 1909), ('C', 810), ('A', 696), ('D', 259)]
Taza de visualización de múltiples imágenes:
from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.commons.utils import JsCode
fn = """
function(params) {
if(params.name == 'other')
return '\\n\\n\\n' + params.name + ' : ' + params.value + '%';
return params.name + ' : ' + params.value + '%';
}
"""
def new_label_opts():
return opts.LabelOpts(formatter=JsCode(fn), position="center")
pie = (
Pie()
.add(
"",
[['A_cup', round(696/total_cup, 2)*100],['other',round(1 - 696/total_cup, 2)*100]],
center=["20%", "30%"],
radius=[60, 80],
label_opts=new_label_opts(),
)
.add(
"",
[['B_cup', round(1909/total_cup, 2)*100],['other',round(1 - 1909/total_cup, 2)*100]],
center=["55%", "30%"],
radius=[60, 80],
label_opts=new_label_opts(),
)
.add(
"",
[['C_cup', round(810/total_cup, 2)*100],['other',round(1 - 810/total_cup, 2)*100]],
center=["20%", "70%"],
radius=[60, 80],
label_opts=new_label_opts(),
)
.add(
"",
[['D_cup', round(259/total_cup * 100, 1)],['other',round(1 - 259/total_cup, 2)*100]],
center=["55%", "70%"],
radius=[60, 80],
label_opts=new_label_opts(),
)
.set_global_opts(
title_opts=opts.TitleOpts(title="Cup-多饼图"),
legend_opts=opts.LegendOpts(
type_="scroll", pos_top="20%", pos_left="80%", orient="vertical"
),
)
# .render("mutiple_pie.html")
)
2.3.1 Diagrama de rosas
Exhibición de epidemia:
from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.faker import Faker
v = Faker.choose()
pie = (
Pie()
.add(
"",
[list(z) for z in zip(v, list(range(10,80,10)))],
radius=["30%", "75%"],
center=["25%", "50%"],
rosetype="radius",
label_opts=opts.LabelOpts(is_show=False),
)
.add(
"",
[list(z) for z in zip(v,list(range(10,80,10))[::-1])],
radius=["30%", "75%"],
center=["75%", "50%"],
rosetype="area",
)
.set_global_opts(title_opts=opts.TitleOpts(title="Pie-玫瑰图示例"))
)
2.4 Mapa
from pyecharts import options as opts
from pyecharts.charts import Map
from pyecharts.faker import Faker
map = (
Map()
.add("店铺数量",[['广东',100],['广西',100],['湖南',19,]], "china")
.set_global_opts(
title_opts=opts.TitleOpts(title="商家店铺地址分布图"),
visualmap_opts=opts.VisualMapOpts(max_=200),
)
)
2.5 Diagrama de waterpolo
el clima:
from pyecharts import options as opts
from pyecharts.charts import Liquid
liquid = (
Liquid()
.add("lq", [0.45,0.5])
# 第一个值为显示的值，第二个值为水的分量
.set_global_opts(title_opts=opts.TitleOpts(title="今日湿度"))
.render("liquid_base.html")
)
Gráfico integrado
Integración de varios gráficos
Page.save_resize_html('page_draggable_layout.html',cfg_file= 'chart_config.json')
Documentos de referencia:
- Domine rápidamente las operaciones básicas de pyecharts gráficos de uso común en 5 minutos
- documento oficial de pyecharts
Lectura recomendada: