# 绘制每个股票的K线图 fig, ax = plt.subplots() for name, group in groups: title_dict = {"title": name} mpf.plot(group, type='candle', mav=(5, 10, 20), volume=True, show_nontrading=True, title=title_dict)TypeError: Expect data.index as DatetimeIndex修改代码

在这个代码中，你需要将每个股票的数据的索引转换为`DatetimeIndex`类型，然后再调用`mpf.plot`函数进行绘图。可以在`for`循环中对每个股票的数据进行索引转换，具体代码如下：

import pandas as pd
import mplfinance as mpf
import matplotlib.pyplot as plt

for name, group in groups:
    # 将索引转换为DatetimeIndex类型
    group.index = pd.to_datetime(group.index)
    title_dict = {"title": name}
    mpf.plot(group, type='candle', mav=(5, 10, 20), volume=True, show_nontrading=True, title=title_dict)
    plt.show()  # 显示每个股票的K线图

在这个代码中，我们使用`pd.to_datetime`函数将每个股票的数据的索引转换为`DatetimeIndex`类型，然后再将修改后的数据传递给`mpf.plot`函数进行绘图，并使用`plt.show()`函数显示每个股票的K线图。这样，你就可以避免出现“Expect data.index as DatetimeIndex”这个错误了。

相关问题

import matplotlib.pyplot as plt import pandas as pd plt.rcParams['font.family'] = 'Fangsong' # 指定使用宋体字体 # 读取数据 df = pd.read_excel('200马力拖拉机明细.xlsx') fig= plt.figure(figsize=(1000, 1000), dpi=300) # 绘制箱线图 fig, ax = plt.subplots() bp = df.boxplot(column='sale', by=['FactoryName', 'JiJXH'], ax=ax,rot=90, grid=False) # 修改横轴标签 xtick_labels = [label.get_text() for label in ax.get_xticklabels()] new_xtick_labels = [label.replace(' ', '\n') for label in xtick_labels] ax.set_xticklabels(new_xtick_labels) # 绘制折线图 median_dict = df.groupby(['FactoryName', 'JiJXH'])['sale'].median().to_dict() for i, label in enumerate(xtick_labels): factory_jijxh = tuple(label.split('\n')) median = median_dict.get(factory_jijxh, None) if median is not None: ax.plot(i+1, median, marker='-', color='red') # 显示图形 plt.show() 怎样使图变大

可以调整 `figsize` 参数来改变图的大小，例如将宽度设为 20，高度设为 10：

fig= plt.figure(figsize=(20, 10), dpi=300)

另外，如果图显示不全，可以调整 `plt.subplots_adjust()` 来控制子图之间的间距，例如：

fig, ax = plt.subplots()
plt.subplots_adjust(left=0.1, right=0.9, bottom=0.2, top=0.9, wspace=0.3, hspace=0.3)
bp = df.boxplot(column='sale', by=['FactoryName', 'JiJXH'], ax=ax,rot=90, grid=False)

其中，`left` 和 `right` 控制左右边距，`bottom` 和 `top` 控制上下边距，`wspace` 和 `hspace` 控制子图之间的宽度和高度间距。

fig, ax = plt.subplots() ax.plot(new_name_lst, x_list, label='velocity_x') ax.plot(new_name_lst, y_list, label='velocity_y') ax.plot(new_name_lst, list3, label='velocity') # leg = ax.legend(loc='upper left', fancybox=True, shadow=True) leg.get_frame().set_alpha(0.5) lines = [l for l in ax.lines] lined = dict() for legline, origline in zip(leg.get_lines(), lines): legline.set_picker(True) lined[legline] = origline def onpick(event): legline = event.artist origline = lined[legline] visible = not origline.get_visible() origline.set_visible(visible) if visible: legline.set_alpha(1.0) else: legline.set_alpha(0.2) fig.canvas.draw() fig.canvas.mpl_connect('pick_event', onpick) plt.show()这个代码加一个跟随鼠标的虚线

你可以尝试添加以下代码来实现跟随鼠标的虚线：

import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots()

# 假设你已经有了名为 new_name_lst、x_list、y_list 和 list3 的变量
ax.plot(new_name_lst, x_list, label='velocity_x')
ax.plot(new_name_lst, y_list, label='velocity_y')
ax.plot(new_name_lst, list3, label='velocity')

leg = ax.legend(loc='upper left', fancybox=True, shadow=True)
leg.get_frame().set_alpha(0.5)
lines = [l for l in ax.lines]
lined = dict()
for legline, origline in zip(leg.get_lines(), lines):
    legline.set_picker(True)
    lined[legline] = origline

def onpick(event):
    legline = event.artist
    origline = lined[legline]
    visible = not origline.get_visible()
    origline.set_visible(visible)
    if visible:
        legline.set_alpha(1.0)
    else:
        legline.set_alpha(0.2)
    fig.canvas.draw()

    # 添加跟随鼠标的虚线
    if isinstance(event.artist, plt.Line2D):
        xdata = event.artist.get_xdata()
        ydata = event.artist.get_ydata()
        x = event.mouseevent.xdata
        y = event.mouseevent.ydata
        idx = np.searchsorted(xdata, x)
        if idx < len(xdata) and abs(x - xdata[idx]) > abs(x - xdata[idx-1]):
            idx -= 1
        if idx >= len(xdata) or idx == 0:
            return
        x = xdata[idx]
        y = ydata[idx]
        if not hasattr(ax, 'vline'):
            ax.vline = ax.axvline(x=x, color='k', linestyle='--')
        else:
            ax.vline.set_xdata([x, x])
            ax.vline.set_ydata([min(y, ax.get_ylim()[1]), max(y, ax.get_ylim()[0])])

fig.canvas.mpl_connect('pick_event', onpick)
plt.show()

这个代码会在图形中添加一个跟随鼠标的垂直虚线，当鼠标拾取到某个线条时，虚线会跟随鼠标移动到该线条上。