如何轻松解决Python mplfinance库最新版本的中文显示乱码问题

# -*- coding: utf-8 -*- # @Time : 2021/11/7 20:40 # @Author : zhaozhuang # 导入 efinance 如果没有安装则需要通过执行命令: pip install efinance 来安装 import efinance as ef import mplfinance as mpf import pandas as pd # import matplotlib.pyplot as plt pd.set_option('display.max_rows', 50000) pd.set_option('display.max_columns', 50000) pd.set_option('display.width', 2000) # plt.rcParams['font.sans-serif'] = ['SimHei'] # plt.rcParams['axes.unicode_minus'] = False # 解决mplfinance绘制输出中文乱码 # s = mpf.make_mpf_style(rc={'font.family': 'SimHei'}) def get_kchar(df: pd.DataFrame, code: str, name: str): date_index = df['日期'] date_index = pd.to_datetime(date_index) data = df[['开盘', '最高', '最低', '收盘', '成交量']] data = data.rename(columns={'开盘': 'Open', '收盘': 'Close', '最高': 'High', '最低': 'Low', '成交量': 'Volume'}) data.index = date_index my_color = mpf.make_marketcolors(up='red', down='green') my_style = mpf.make_mpf_style(marketcolors=my_color, rc={'font.family': 'SimHei'}) add_plot = [ mpf.make_addplot(turnover_rate, scatter=True, marker='^', color='red'), mpf.make_addplot(turnover_rate, color='red'), mpf.make_addplot(ma_turnover_rate, color='blue'), ] mpf.plot(data, type='candle', style=my_style, figscale=2, addplot=add_plot, mav=(5, 20, 30), ylabel='price', volume=True, title=f'\n\n\n {code} {name} K_line') # mpf.plot(data, type='candle', style=my_style, figscale=2, addplot=add_plot, mav=(5, 20, 30), volume=True, # title=f'\n\n\n {code} {name} K_line') paras = { 'stock_code': '002626', 'begin': '20210130', 'end': '20251105', 'freq': 101 } stock_code = paras['stock_code'] beg = paras['begin'] end = paras['end'] freq = paras['freq'] # 获取最新一个交易日的分钟级别股票行情数据 df = ef.stock.get_quote_history(stock_codes=stock_code, beg=beg, end=end, klt=freq) # 将数据存储到 csv 文件中 # df.to_excel(f'{stock_code}_{freq}.xlsx', encoding='utf-8-sig', index=None) print(f'股票: {stock_code} 的行情数据已存储到文件: {stock_code}_{freq}.xlsx 中！') # 获取一支股票的量比 name = df['股票名称'].values[0] vol_ma = df['成交量'].rolling(window=5).mean().values vol_raito = df['成交量'].values / df['成交量'].rolling(window=5).mean().values turnover_rate = df['换手率'].values ma_turnover_rate = df['换手率'].rolling(window=21).mean().values # print(vol_ma) # print(vol_raito) get_kchar(df, paras['stock_code'], name)