python - 在 pandas 绘图中包含每个数据点的标签

Question

假设我们有一个包含价格和数量的 DataFrame（想想金融）。

用该价格点的数量标记每个价格点的最佳方式是什么？

                  Price   Volume
2013-04-10 04:46  1300      19
2013-04-10 04:47  1305      20
2013-04-10 04:48  1302       6
2013-04-10 04:49  1301      10

Answer 1

这是一种可能的实现

我已导入以下内容：

import pandas as pd
import datetime as dt
import matplotlib.pyplot as plt

现在我们可以重新创建数据

ind = pd.date_range(start=dt.datetime(2013, 4, 10, 4, 46),
                      periods=4, freq='Min')

data = pd.DataFrame([[1200, 19], [1302, 20], [1302, 6], [1301, 10]],
                    index=ind, columns=['Price', 'Volume'])

现在我将定义 annotate_plot 函数。文档字符串应该有足够的信息来弄清楚它在做什么。

def annotate_plot(frame, plot_col, label_col, **kwargs):
    """
    Annotate the plot of a given DataFrame using one of its columns

    Should be called right after a DataFrame or series plot method,
    before telling matplotlib to show the plot.

    Parameters
    ----------
    frame : pandas.DataFrame

    plot_col : str
        The string identifying the column of frame that was plotted

    label_col : str
        The string identifying the column of frame to be used as label

    kwargs:
        Other key-word args that should be passed to plt.annotate

    Returns
    -------
    None

    Notes
    -----
    After calling this function you should call plt.show() to get the
    results. This function only adds the annotations, it doesn't show
    them.
    """
    import matplotlib.pyplot as plt  # Make sure we have pyplot as plt

    for label, x, y in zip(frame[label_col], frame.index, frame[plot_col]):
        plt.annotate(label, xy=(x, y), **kwargs)

这个函数现在可以用来做一个带有标签的基本图

data.Price.plot(marker='*')
annotate_plot(data, 'Price', 'Volume')
plt.show()

您还可以通过 annotate_plot 函数直接传递给 plt.annotate() 的任意参数。请注意，这些论点中的大多数都取自这个答案。

bbox = dict(boxstyle='round,pad=0.5', fc='green', alpha=0.3)
ha = 'right'
va = 'bottom'
arrowprops = dict(arrowstyle='->', connectionstyle='arc3,rad=0')
xytext = (-20, 20)
textcoords = 'offset points'

data.Price.plot(marker='*')
annotate_plot(data, 'Price', 'Volume', bbox=bbox, ha=ha, va=va, 
              xytext=xytext, textcoords=textcoords)

plt.show()