应用熊猫 df 中所有列的分布

您迭代点数据的方式有误。您组织数据框的方式使您很难找出迭代数据的适当方法，并且很容易遇到您遇到的那种错误。如果您的df组织方式使您可以轻松地迭代代表每个组A和B每次的数据子集，那就更好了。如果你从你的数据字典中分离出时间，下面是你d如何构建一个更容易使用的df：import pandas as pdtime = [1]d = ({    'A1_Y' : [5883.102906],                     'A1_X' : [3321.527705],     'A2_Y' : [5898.467202],                     'A2_X' : [3328.331657],    'A3_Y' : [5886.270552],                     'A3_X' : [3366.777169],                     'B1_Y' : [5897.925245],                     'B1_X' : [3297.143092],     'B2_Y' : [5905.137781],                     'B2_X' : [3321.167842],    'B3_Y' : [5888.291025],                     'B3_X' : [3347.263205],                                                                  'A1_Radius' : [10.3375199],      'A2_Radius' : [10.0171423],     'A3_Radius' : [11.42129333],                                       'B1_Radius' : [18.69514267],      'B2_Radius' : [10.65877044],     'B3_Radius' : [9.947025444],                           'A1_Scaling' : [0.0716513620],    'A2_Scaling' : [0.0056262380],     'A3_Scaling' : [0.0677243260,],                                     'B1_Scaling' : [0.0364290850],    'B2_Scaling' : [0.0585827450],       'B3_Scaling' : [0.0432806750],                                         'A1_Rotation' : [20.58078926],     'A2_Rotation' : [173.5056346],       'A3_Rotation' : [36.23648405],                                   'B1_Rotation' : [79.81849817],        'B2_Rotation' : [132.2437404],                           'B3_Rotation' : [44.28198078],                                     })# a list of tuples of the form ((time, group_id, point_id, value_label), value)tuples = [((t, k.split('_')[0][0], int(k.split('_')[0][1]), k.split('_')[1]), v[i]) for k,v in d.items() for i,t in enumerate(time)]df = pd.Series(dict(tuples)).unstack(-1)df.index.names = ['time', 'group', 'id']print(df)输出：                  Radius    Rotation   Scaling            X            Ytime group id                                                           1    A     1   10.337520   20.580789  0.071651  3321.527705  5883.102906           2   10.017142  173.505635  0.005626  3328.331657  5898.467202           3   11.421293   36.236484  0.067724  3366.777169  5886.270552     B     1   18.695143   79.818498  0.036429  3297.143092  5897.925245           2   10.658770  132.243740  0.058583  3321.167842  5905.137781           3    9.947025   44.281981  0.043281  3347.263205  5888.291025这将使迭代数据中的子集变得更加容易。以下是在每个时间点迭代每个组的子数据帧的方法：for time, tdf in df.groupby('time'):    for group, gdf in tdf.groupby('group'):        ...这是您上一个问题中我的代码的更新版本，它使用这个组织得更好的数据框在每个时间点创建您想要的图：for time,subdf in df.groupby('time'):    plotmvs(subdf)输出：下面是上述plotmvs函数的完整代码：import numpy as npimport pandas as pdfrom mpl_toolkits.axes_grid1 import make_axes_locatableimport matplotlib.pyplot as pltimport scipy.stats as stsdef datalimits(*data, pad=.15):    dmin,dmax = min(d.min() for d in data), max(d.max() for d in data)    spad = pad*(dmax - dmin)    return dmin - spad, dmax + spaddef rot(theta):    theta = np.deg2rad(theta)    return np.array([        [np.cos(theta), -np.sin(theta)],        [np.sin(theta), np.cos(theta)]    ])def getcov(radius=1, scale=1, theta=0):    cov = np.array([        [radius*(scale + 1), 0],        [0, radius/(scale + 1)]    ])    r = rot(theta)    return r @ cov @ r.Tdef mvpdf(x, y, xlim, ylim, radius=1, velocity=0, scale=0, theta=0):    """Creates a grid of data that represents the PDF of a multivariate gaussian.    x, y: The center of the returned PDF    (xy)lim: The extent of the returned PDF    radius: The PDF will be dilated by this factor    scale: The PDF be stretched by a factor of (scale + 1) in the x direction, and squashed by a factor of 1/(scale + 1) in the y direction    theta: The PDF will be rotated by this many degrees    returns: X, Y, PDF. X and Y hold the coordinates of the PDF.    """    # create the coordinate grids    X,Y = np.meshgrid(np.linspace(*xlim), np.linspace(*ylim))    # stack them into the format expected by the multivariate pdf    XY = np.stack([X, Y], 2)    # displace xy by half the velocity    x,y = rot(theta) @ (velocity/2, 0) + (x, y)    # get the covariance matrix with the appropriate transforms    cov = getcov(radius=radius, scale=scale, theta=theta)    # generate the data grid that represents the PDF    PDF = sts.multivariate_normal([x, y], cov).pdf(XY)    return X, Y, PDFdef mvpdfs(xs, ys, xlim, ylim, radius=None, velocity=None, scale=None, theta=None):    PDFs = []    for i,(x,y) in enumerate(zip(xs,ys)):        kwargs = {            'radius': radius[i] if radius is not None else 1,            'velocity': velocity[i] if velocity is not None else 0,            'scale': scale[i] if scale is not None else 0,            'theta': theta[i] if theta is not None else 0,            'xlim': xlim,            'ylim': ylim        }        X, Y, PDF = mvpdf(x, y, **kwargs)        PDFs.append(PDF)    return X, Y, np.sum(PDFs, axis=0)def plotmvs(df, xlim=None, ylim=None, fig=None, ax=None):    """Plot an xy point with an appropriately tranformed 2D gaussian around it.    Also plots other related data like the reference point.    """    if xlim is None: xlim = datalimits(df['X'])    if ylim is None: ylim = datalimits(df['Y'])    if fig is None:        fig = plt.figure(figsize=(8,8))        ax = fig.gca()    elif ax is None:        ax = fig.gca()    PDFs = []    for (group,gdf),color in zip(df.groupby('group'), ('red', 'blue')):        # plot the xy points of each group        ax.plot(*gdf[['X','Y']].values.T, '.', c=color)        # fetch the PDFs of the 2D gaussian for each group        kwargs = {            'radius': gdf['Radius'].values if 'Radius' in gdf else None,            'velocity': gdf['Velocity'].values if 'Velocity' in gdf else None,            'scale': gdf['Scaling'].values if 'Scaling' in gdf else None,            'theta': gdf['Rotation'].values if 'Rotation' in gdf else None,            'xlim': xlim,            'ylim': ylim        }        X, Y, PDF = mvpdfs(gdf['X'].values, gdf['Y'].values, **kwargs)        PDFs.append(PDF)    # create the PDF for all points from the difference of the sums of the 2D Gaussians from group A and group B    PDF = PDFs[0] - PDFs[1]    # normalize PDF by shifting and scaling, so that the smallest value is 0 and the largest is 1    normPDF = PDF - PDF.min()    normPDF = normPDF/normPDF.max()    # plot and label the contour lines of the 2D gaussian    cs = ax.contour(X, Y, normPDF, levels=6, colors='w', alpha=.5)    ax.clabel(cs, fmt='%.3f', fontsize=12)    # plot the filled contours of the 2D gaussian. Set levels high for smooth contours    cfs = ax.contourf(X, Y, normPDF, levels=50, cmap='viridis')    # create the colorbar and ensure that it goes from 0 -> 1    divider = make_axes_locatable(ax)    cax = divider.append_axes("right", size="5%", pad=0.1)    cbar = fig.colorbar(cfs, ax=ax, cax=cax)    cbar.set_ticks([0, .2, .4, .6, .8, 1])    # ensure that x vs y scaling doesn't disrupt the transforms applied to the 2D gaussian    ax.set_aspect('equal', 'box')    return fig, ax

应用熊猫 df 中所有列的分布

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