Pyecharts html交互可视化_我用pyecharts做了很多可以交互的图,html格式的

1. Package

from pyecharts import options as opts
from pyecharts.charts import Bar, Line,Page
from pyecharts.faker import Faker

2. Bar function

def bar_store() -> Bar:
    
    bar = (
        Bar()
        .add_xaxis(year_time)
        .add_yaxis(sample_col[0], sample_value[0],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[1], sample_value[1],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[2], sample_value[2],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[3], sample_value[3],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[4], sample_value[4],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[5], sample_value[5],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[6], sample_value[6],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[7], sample_value[7],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[8], sample_value[8],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[9], sample_value[9],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[10], sample_value[10],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[11], sample_value[11],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[12], sample_value[12],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[13], sample_value[13],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[14], sample_value[14],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[15], sample_value[15],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[16], sample_value[16],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[17], sample_value[17],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[18], sample_value[18],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[19], sample_value[19],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[20], sample_value[20],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[21], sample_value[21],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[22], sample_value[22],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[23], sample_value[23],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[24], sample_value[24],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[25], sample_value[25],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[26], sample_value[26],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[27], sample_value[27],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[28], sample_value[28],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[29], sample_value[29],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[30], sample_value[30],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[31], sample_value[31],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[32], sample_value[32],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[33], sample_value[33],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[34], sample_value[34],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[35], sample_value[35],stack="stack1",category_gap="50%")
        .add_yaxis(sample_col[36], sample_value[36],stack="stack1",category_gap="50%")
        .extend_axis(
            yaxis=opts.AxisOpts(
                axislabel_opts=opts.LabelOpts(formatter="{value}"), interval=50000
            )
        )
        .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
        .set_global_opts(
            title_opts=opts.TitleOpts(title="{0}柱状图".format(title)),
            yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value}")),
            legend_opts=opts.LegendOpts(type_="scroll", pos_left="90%", orient="vertical"),
    
        )
    )
 
#     line = Line().add_xaxis(week3_data['weeks'].to_list()).add_yaxis("消费金额",v3, yaxis_index=1).set_series_opts(label_opts=opts.LabelOpts(is_show=False))
#     bar.overlap(line)    
    return bar

3. 应用

# store 切片 
chnl_code = int(chnl_code_ls[i])
title = chnl_dict[chnl_code_ls[i]]
print(chnl_code,title)
#sample 获取单独一家店数据，这家店每个月每个类型的销售额
df_sample = df_cates_d[df_cates_d['chnl_code'] == chnl_code]
df_sample = df_sample[['month', 'material_class_desc_large',  'month_cate_sales']]
#pivot dataframe转向，类别为columns，month为index
df_sample = df_sample.pivot(index = 'month',columns = 'material_class_desc_large',values = 'month_cate_sales').reset_index()
df_sample = df_sample.sort_values(by='month')
# fillna
df_sample = df_sample.fillna(0)
# fillcate （以防每个店类型不同）
sample_col = df_sample.columns.tolist()
na_cate = list(set(cates_ls).difference(set(sample_col))) 
for cate_name in na_cate:
    df_sample[cate_name] = 0
print(len(df_sample.columns)== 38)
 
# 开店月
year_time = df_sample['month'].to_list()
sample_value = [] # 对应值
sample_col = [] #列名称
for cols in cates_ls:
    try:
        temp = df_sample[cols].to_list()
        sample_col.append(cols)
        sample_value.append(temp)
    except:
        continue
if  len(sample_col) == 37:      
    # bar chart
    bar = bar_store()
    bar.render('./store_cate/{0}分类型销量堆叠柱状图.html'.format(title))