我在尝试在Jupyter中绘制一个lmplot时遇到了麻烦。
下面你可以看到我的数据的第一行和其他数据也一样。
Year_of_Release Platform_General Platform counts Global_Sales(M#) GS_Amount/Game
1994.0 Sony_Playstation PS 1 1.27 1.270000
1996.0 Sony_Playstation PS 5 17.48 3.496000
1997.0 Sony_Playstation PS 12 30.89 2.574167
1998.0 Sony_Playstation PS 22 43.08 1.958182
1999.0 Sony_Playstation PS 25 49.02 1.960800
以下是绘制图表的代码，但
# Make a custom palette with platform colors
pal = dict(Sony_Playstation="#6495ED",Microsoft_Xbox="#F08080",Nintendo="Green")
# Show the survival proability as a function of platforms
g = sns.lmplot(x="Year_of_Release", y="GS_Amount/Game", col="Platform_General", hue="Platform_General",
data=vgs_df_pf_grouped,palette=pal, y_jitter=.02, logistic=True)
# Use more informative axis labels than are provided by default
g.set_axis_labels("Year of Release", "Sales per Game Released (M #)")
当我在下面运行此代码时出现长错误按摩，由于我是数据分析的新手，我无法理解数据或代码有什么问题。
我很感激这方面的一些帮助。谢谢。
ValueError Traceback (most recent call last)
<ipython-input-68-c78b6ba34d96> in <module>()
5 # Show the survival proability as a function of age and sex
6 g = sns.lmplot(x="Year_of_Release", y="GS_Amount/Game",col="Platform_General", hue="Platform_General",
----> 7 data=vgs_df_pf_grouped,palette=pal, y_jitter=.02, logistic=True)
8
9 # Use more informative axis labels than are provided by default
~/anaconda3/lib/python3.6/site-packages/seaborn/regression.py in lmplot(x, y, data, hue, col, row, palette, col_wrap, size, aspect, markers, sharex, sharey, hue_order, col_order, row_order, legend, legend_out, x_estimator, x_bins, x_ci, scatter, fit_reg, ci, n_boot, units, order, logistic, lowess, robust, logx, x_partial, y_partial, truncate, x_jitter, y_jitter, scatter_kws, line_kws)
588 scatter_kws=scatter_kws, line_kws=line_kws,
589 )
--> 590 facets.map_dataframe(regplot, x, y, **regplot_kws)
591
592 # Add a legend
~/anaconda3/lib/python3.6/site-packages/seaborn/axisgrid.py in map_dataframe(self, func, *args, **kwargs)
795
796 # Draw the plot
--> 797 self._facet_plot(func, ax, args, kwargs)
798
799 # Finalize the annotations and layout
~/anaconda3/lib/python3.6/site-packages/seaborn/axisgrid.py in _facet_plot(self, func, ax, plot_args, plot_kwargs)
813
814 # Draw the plot
--> 815 func(*plot_args, **plot_kwargs)
816
817 # Sort out the supporting information
~/anaconda3/lib/python3.6/site-packages/seaborn/regression.py in regplot(x, y, data, x_estimator, x_bins, x_ci, scatter, fit_reg, ci, n_boot, units, order, logistic, lowess, robust, logx, x_partial, y_partial, truncate, dropna, x_jitter, y_jitter, label, color, marker, scatter_kws, line_kws, ax)
788 scatter_kws["marker"] = marker
789 line_kws = {} if line_kws is None else copy.copy(line_kws)
--> 790 plotter.plot(ax, scatter_kws, line_kws)
791 return ax
792
~/anaconda3/lib/python3.6/site-packages/seaborn/regression.py in plot(self, ax, scatter_kws, line_kws)
340 self.scatterplot(ax, scatter_kws)
341 if self.fit_reg:
--> 342 self.lineplot(ax, line_kws)
343
344 # Label the axes
~/anaconda3/lib/python3.6/site-packages/seaborn/regression.py in lineplot(self, ax, kws)
385
386 # Fit the regression model
--> 387 grid, yhat, err_bands = self.fit_regression(ax)
388
389 # Get set default aesthetics
~/anaconda3/lib/python3.6/site-packages/seaborn/regression.py in fit_regression(self, ax, x_range, grid)
198 from statsmodels.genmod.families import Binomial
199 yhat, yhat_boots = self.fit_statsmodels(grid, GLM,
--> 200 family=Binomial())
201 elif self.lowess:
202 ci = None
~/anaconda3/lib/python3.6/site-packages/seaborn/regression.py in fit_statsmodels(self, grid, model, **kwargs)
258 return yhat
259
--> 260 yhat = reg_func(X, y)
261 if self.ci is None:
262 return yhat, None
~/anaconda3/lib/python3.6/site-packages/seaborn/regression.py in reg_func(_x, _y)
252 def reg_func(_x, _y):
253 try:
--> 254 yhat = model(_y, _x, **kwargs).fit().predict(grid)
255 except glm.PerfectSeparationError:
256 yhat = np.empty(len(grid))
~/anaconda3/lib/python3.6/site-packages/statsmodels/genmod/generalized_linear_model.py in fit(self, start_params, maxiter, method, tol, scale, cov_type, cov_kwds, use_t, full_output, disp, max_start_irls, **kwargs)
901 return self._fit_irls(start_params=start_params, maxiter=maxiter,
902 tol=tol, scale=scale, cov_type=cov_type,
--> 903 cov_kwds=cov_kwds, use_t=use_t, **kwargs)
904 else:
905 return self._fit_gradient(start_params=start_params,
~/anaconda3/lib/python3.6/site-packages/statsmodels/genmod/generalized_linear_model.py in _fit_irls(self, start_params, maxiter, tol, scale, cov_type, cov_kwds, use_t, **kwargs)
977 dev = self.family.deviance(self.endog, mu, self.freq_weights)
978 if np.isnan(dev):
--> 979 raise ValueError("The first guess on the deviance function "
980 "returned a nan. This could be a boundary "
981 " problem and should be reported.")
ValueError: The first guess on the deviance function returned a nan. This could be a boundary problem and should be reported.