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- ValueError Traceback (most recent call last)
- <ipython-input-58-e0ac8c5bf9eb> in <module>
- ----> 1 sns.lmplot(x='TDS', y='Li', data=df_sub, hue='Group Location', legend=True)
- 2 plt.show()
- C:ProgramDataAnaconda3libsite-packagesseabornregression.py in lmplot(x, y, data, hue, col, row, palette, col_wrap, height, 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, size)
- 587 scatter_kws=scatter_kws, line_kws=line_kws,
- 588 )
- --> 589 facets.map_dataframe(regplot, x, y, **regplot_kws)
- 590
- 591 # Add a legend
- C:ProgramDataAnaconda3libsite-packagesseabornaxisgrid.py in map_dataframe(self, func, *args, **kwargs)
- 818
- 819 # Draw the plot
- --> 820 self._facet_plot(func, ax, args, kwargs)
- 821
- 822 # Finalize the annotations and layout
- C:ProgramDataAnaconda3libsite-packagesseabornaxisgrid.py in _facet_plot(self, func, ax, plot_args, plot_kwargs)
- 836
- 837 # Draw the plot
- --> 838 func(*plot_args, **plot_kwargs)
- 839
- 840 # Sort out the supporting information
- C:ProgramDataAnaconda3libsite-packagesseabornregression.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)
- 787 scatter_kws["marker"] = marker
- 788 line_kws = {} if line_kws is None else copy.copy(line_kws)
- --> 789 plotter.plot(ax, scatter_kws, line_kws)
- 790 return ax
- 791
- C:ProgramDataAnaconda3libsite-packagesseabornregression.py in plot(self, ax, scatter_kws, line_kws)
- 342 self.scatterplot(ax, scatter_kws)
- 343 if self.fit_reg:
- --> 344 self.lineplot(ax, line_kws)
- 345
- 346 # Label the axes
- C:ProgramDataAnaconda3libsite-packagesseabornregression.py in lineplot(self, ax, kws)
- 387
- 388 # Fit the regression model
- --> 389 grid, yhat, err_bands = self.fit_regression(ax)
- 390
- 391 # Get set default aesthetics
- C:ProgramDataAnaconda3libsite-packagesseabornregression.py in fit_regression(self, ax, x_range, grid)
- 206 yhat, yhat_boots = self.fit_logx(grid)
- 207 else:
- --> 208 yhat, yhat_boots = self.fit_fast(grid)
- 209
- 210 # Compute the confidence interval at each grid point
- C:ProgramDataAnaconda3libsite-packagesseabornregression.py in fit_fast(self, grid)
- 228
- 229 beta_boots = algo.bootstrap(X, y, func=reg_func,
- --> 230 n_boot=self.n_boot, units=self.units).T
- 231 yhat_boots = grid.dot(beta_boots).T
- 232 return yhat, yhat_boots
- C:ProgramDataAnaconda3libsite-packagesseabornalgorithms.py in bootstrap(*args, **kwargs)
- 84 boot_dist = []
- 85 for i in range(int(n_boot)):
- ---> 86 resampler = rs.randint(0, n, n)
- 87 sample = [a.take(resampler, axis=0) for a in args]
- 88 boot_dist.append(f(*sample, **func_kwargs))
- mtrand.pyx in mtrand.RandomState.randint()
- ValueError: low >= high
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