#定义绘制K线图的函数 def pandas_candlestick_ohlc(stock_data, otherseries=None): # 设置绘图参数，主要是坐标轴 mondays = WeekdayLocator(MONDAY) alldays = DayLocator() dayFormatter = DateFormatter('%d') fig, ax = plt.subplots() fig.subplots_adjust(bottom=0.2) if stock_data.index[-1] - stock_data.index[0] < pd.Timedelta('730 days'): weekFormatter = DateFormatter('%b %d') ax.xaxis.set_major_locator(mondays) ax.xaxis.set_minor_locator(alldays) else: weekFormatter = DateFormatter('%b %d, %Y') ax.xaxis.set_major_formatter(weekFormatter) ax.grid(True) # 创建K线图 stock_array = np.array(stock_data.reset_index()[['date','open','high','low','close']]) stock_array[:,0] = date2num(stock_array[:,0]) candlestick_ohlc(ax, stock_array, colorup = "red", colordown="green", width=0.6) plt.title('厦门象屿', fontsize='9') # 可同时绘制其他折线图 if otherseries is not None: for each in otherseries: plt.plot(stock_data[each], label=each) plt.legend() ax.xaxis_date() ax.autoscale_view() plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right') plt.savefig(f'E:/应统案例大赛/附件1-股票交易数据/k线图a4.jpg') plt.show() stock_data.index.name='date' #日期为索引列 #对股票数据的列名重新命名 stock_data=stock_data[['open','high','low','close','chengjiaoe','zongshizhi','huanshoulv','shiyinglv','shijinglv']] data=stock_data.loc['2022-11-03':'2022-12-14'] #获取某个时间段内的时间序列数据 pandas_candlestick_ohlc(data)修改代码，使横坐标刻度小点

你可以在 `pandas_candlestick_ohlc()` 函数中添加一个参数 `figsize` 来控制绘图的大小，进而调整横坐标刻度的大小。例如：

import matplotlib.pyplot as plt
from matplotlib.dates import WeekdayLocator, DayLocator, DateFormatter, date2num
from mpl_finance import candlestick_ohlc

# 定义绘制 K 线图的函数
def pandas_candlestick_ohlc(stock_data, otherseries=None, figsize=(12, 6)):
    # 设置绘图参数，主要是坐标轴
    mondays = WeekdayLocator(MONDAY)
    alldays = DayLocator()
    dayFormatter = DateFormatter('%d')
    fig, ax = plt.subplots(figsize=figsize)
    fig.subplots_adjust(bottom=0.2)
    if stock_data.index[-1] - stock_data.index[0] < pd.Timedelta('730 days'):
        weekFormatter = DateFormatter('%b %d')
        ax.xaxis.set_major_locator(mondays)
        ax.xaxis.set_minor_locator(alldays)
    else:
        weekFormatter = DateFormatter('%b %d, %Y')
    ax.xaxis.set_major_formatter(weekFormatter)
    ax.grid(True)

    # 创建 K 线图
    stock_array = np.array(stock_data.reset_index()[['date','open','high','low','close']])
    stock_array[:,0] = date2num(stock_array[:,0])
    candlestick_ohlc(ax, stock_array, colorup = "red", colordown="green", width=0.6)
    plt.title('厦门象屿', fontsize='9')

    # 可同时绘制其他折线图
    if otherseries is not None:
        for each in otherseries:
            plt.plot(stock_data[each], label=each)
        plt.legend()

    ax.xaxis_date()
    ax.autoscale_view()
    plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')
    plt.savefig('E:/应统案例大赛/附件1-股票交易数据/k线图a4.jpg')
    plt.show()

stock_data.index.name = 'date' # 日期为索引列
# 对股票数据的列名重新命名
stock_data = stock_data[['open', 'high', 'low', 'close', 'chengjiaoe', 'zongshizhi', 'huanshoulv', 'shiyinglv', 'shijinglv']]
data = stock_data.loc['2022-11-03':'2022-12-14'] # 获取某个时间段内的时间序列数据
pandas_candlestick_ohlc(data, figsize=(8, 4)) # 修改 figsize 参数，使横坐标刻度小点

在这里，我们在 `pandas_candlestick_ohlc()` 函数中添加了一个参数 `figsize`，并将其传递到 `plt.subplots()` 方法中，用于控制绘图的大小。你可以根据需要调整 `figsize` 的值。