Pandas 返回一个返回与输入组长度相同的数组的函数

对不起，标题不好。请编辑以使其有意义。

下面有很多代码。别担心。这只是一个最小的例子。

我想要做的是按标签对数据进行分组，应用我的函数（检查给定标签的坐标是在椭圆内部还是外部）。这将返回一个与数据长度相同的真/假数组。-1如果它在椭圆之外，我想将标签更改为。

玩弄apply和transform我能得到的最远是

label
1    [True, True, False, True, False, False, True, ...
2    [False, False, True, True, False, False, True,...
dtype: object

但是如何将其转换回原始数据帧，并将标签设置为 -1 为False遇到的每个？

底部的注释位显示了它在没有标签的情况下是如何工作的。

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import patches
import pandas as pd

def _plot_ellipse(xdata, ydata, n_std, ax = None, return_ax = False, **kwargs):
    """
    Parameters
    ----------
    xdata : array-like
    ydata : array-like
    n_std : scalar
        Number of sigmas (e.g. 2 for 95% confidence interval)
    ax : ax to plot on
    return_ax : bool
        Returns axis for plot
    return_inside : bool
        Returns a list of True/False for inside/outside ellipse
    **kwargs
        Passed to matplotlib.patches.Ellipse. Color, alpha, etc..

    Returns
    -------
    Ellipse with the correct orientation, given the data


    Example
    -------
    x = np.random.randn(100)
    y = 0.1 * x + np.random.randn(100)

    fig, ax = plt.subplots()

    ax, in_out = _plot_ellipse(x, y, n_std = 2, ax = ax, alpha = 0.5, return_ax = True)
    ax.scatter(x, y, c = in_out)
    plt.show()

    """

    def _eigsorted(cov):
        vals, vecs = np.linalg.eigh(cov)
        order = vals.argsort()[::-1]
        return vals[order], vecs[:, order]

    points = np.stack([xdata, ydata], axis = 1) # Combine points to 2-column matrix
    center = points.mean(axis = 0)      # Calculate mean for every column (x,y)

    # Calculate covariance matrix for coordinates (how correlated they are)
    cov = np.cov(points, rowvar = False)  # rowvar = False because there are 2 variables, not nrows variables

    vals, vecs = _eigsorted(cov)

    angle = np.degrees(np.arctan2(*vecs[:,0][::-1]))
    width, height = 2 * n_std * np.sqrt(vals)

    in_out = _is_in_ellipse(xdata = xdata, ydata = ydata, center = center, width = width, height = height, angle = angle)

    if return_ax:
        ellip = patches.Ellipse(xy = center, width = width, height = height, angle = angle, **kwargs)
        if ax is None:
            ax = plt.gca()
        ax.add_artist(ellip)
        return ax, in_out
    else:
        return in_out

def _is_in_ellipse(xdata, ydata, center, width, height, angle):
    """
    Determines whether points are in ellipse, given the parameters of the ellipse

    Parameters
    ----------
    xdata : array-like
    ydata : array-lie
    center : array-like, tuple
        center of the ellipse as (x,y)
    width : scalar
    height : scalar
    angle : scalar
        angle in degrees

    Returns
    -------
    List of True/False, depending on points being inside/outside of the ellipse
    """

    cos_angle = np.cos(np.radians(180-angle))
    sin_angle = np.sin(np.radians(180-angle))

    xc = xdata - center[0]
    yc = ydata - center[1]

    xct = xc * cos_angle - yc * sin_angle
    yct = xc * sin_angle + yc * cos_angle

    rad_cc = (xct**2/(width/2)**2) + (yct**2/(height/2)**2)

    in_ellipse = []
    for r in rad_cc:
        in_ellipse.append(True) if r <= 1. else in_ellipse.append(False)

    return in_ellipse


# For a single label
# x = np.random.normal(0, 1, 100)
# y = np.random.normal(0, 1, 100)
# labels = [1] * len(x)
#
# df = pd.DataFrame({"x" : x, "y" : y, "label" : labels})
#
# ax, in_out = _plot_ellipse(df.x, df.y, 2, return_ax = True, alpha = 0.5)
# ax.scatter(df.x, df.y, c = in_out)
# plt.show()


# For multiple labels
x = np.random.normal(0, 1, 100)
y = np.random.normal(0, 1, 100)
labels1 = [1] * 50
labels2 = [2] * 50
labels = labels1 + labels2

df = pd.DataFrame({"x" : x, "y" : y, "label" : labels})

df = df.groupby("label").apply(lambda group: _plot_ellipse(xdata = group["x"], ydata = group["y"], n_std = 1, return_ax = False))

print(df)