Why Identical Data Produces Different Pyecharts Maps and How to Fix It

Hi, I am a Python enthusiast.

Introduction

In a Python group a fan asked about a puzzling Pyecharts visualization where two datasets, datas and datas2, were identical but produced different HTML maps.

Idea

Below is the original code that reads an Excel file, creates the two datasets, and renders a map.

import pandas as pd
from pyecharts import options as opts
from pyecharts.charts import Map
import operator as op
import time

df_tb = pd.read_excel('./data.xlsx')

locations = [location for location in df_tb['地区']]
values = [value for value in df_tb['2016年']]
datas = list(zip(locations, values))

print(datas)
for data in datas:
    print(data)
print(type(datas))

map = (
    Map()
        .add('gdp', [location for location in datas], 'china')
        .set_global_opts(
            title_opts=opts.TitleOpts(title='各省贫困县分布图'),
            visualmap_opts=opts.VisualMapOpts(max_=150)
        )
)
map.render('各省贫困县分布图.html')

The map generated from datas shows colors and values, while the one from datas2 appears without colors or data.

Inspecting the two lists reveals that the numbers in datas are of type int, whereas those in datas2 are numpy.int64. The latter cannot be rendered in HTML, causing the visual discrepancy.

Solution

Convert the NumPy integers to native Python ints before building the map.

def func(m):
    a = []
    for i in range(0, 35):
        b = (df_tb['地区'][i], int(df_tb[m][i]))
        a.append(b)
    return a

After applying this change and re‑running the program, the map displays correctly with colors and values.

Conclusion

The issue was caused by a data‑type mismatch; converting numpy.int64 to int resolves the problem, ensuring consistent visual output in Pyecharts maps.