Erro ao utilizar kind='reg'

Ocorreu um erro ao utilizar o kind='reg' que não consigo identificar o que possa ser a causa. Ao final ele não plota com um reta, conforme ocorre na aula.

TypeError                                 Traceback (most recent call last)
<ipython-input-33-a19ff08c362a> in <module>
----> 1 ax = sns.pairplot(dados, y_vars='consumo', x_vars=['temp_min','temp_media', 'temp_max', 'chuva', 'fds'], kind="reg")
      2 ax.fig.suptitle('Dispersão entre as Variáveis', fontsize=20, y=1.10)

D:\Anaconda_3\lib\site-packages\seaborn\axisgrid.py in pairplot(data, hue, hue_order, palette, vars, x_vars, y_vars, kind, diag_kind, markers, height, aspect, corner, dropna, plot_kws, diag_kws, grid_kws, size)
   2132     elif kind == "reg":
   2133         from .regression import regplot  # Avoid circular import
-> 2134         plotter(regplot, **plot_kws)
   2135 
   2136     # Add a legend

D:\Anaconda_3\lib\site-packages\seaborn\axisgrid.py in map(self, func, **kwargs)
   1363         row_indices, col_indices = np.indices(self.axes.shape)
   1364         indices = zip(row_indices.flat, col_indices.flat)
-> 1365         self._map_bivariate(func, indices, **kwargs)
   1366         return self
   1367 

D:\Anaconda_3\lib\site-packages\seaborn\axisgrid.py in _map_bivariate(self, func, indices, **kwargs)
   1504             y_var = self.y_vars[i]
   1505             ax = self.axes[i, j]
-> 1506             self._plot_bivariate(x_var, y_var, ax, func, kw_color, **kws)
   1507         self._add_axis_labels()
   1508 

D:\Anaconda_3\lib\site-packages\seaborn\axisgrid.py in _plot_bivariate(self, x_var, y_var, ax, func, kw_color, **kwargs)
   1534             color = self.palette[k] if kw_color is None else kw_color
   1535 
-> 1536             func(x, y, label=label_k, color=color, **kwargs)
   1537 
   1538         self._clean_axis(ax)

D:\Anaconda_3\lib\site-packages\seaborn\regression.py in regplot(x, y, data, x_estimator, x_bins, x_ci, scatter, fit_reg, ci, n_boot, units, seed, order, logistic, lowess, robust, logx, x_partial, y_partial, truncate, dropna, x_jitter, y_jitter, label, color, marker, scatter_kws, line_kws, ax)
    816     scatter_kws["marker"] = marker
    817     line_kws = {} if line_kws is None else copy.copy(line_kws)
--> 818     plotter.plot(ax, scatter_kws, line_kws)
    819     return ax
    820 

D:\Anaconda_3\lib\site-packages\seaborn\regression.py in plot(self, ax, scatter_kws, line_kws)
    363 
    364         if self.fit_reg:
--> 365             self.lineplot(ax, line_kws)
    366 
    367         # Label the axes

D:\Anaconda_3\lib\site-packages\seaborn\regression.py in lineplot(self, ax, kws)
    406         """Draw the model."""
    407         # Fit the regression model
--> 408         grid, yhat, err_bands = self.fit_regression(ax)
    409         edges = grid[0], grid[-1]
    410 

D:\Anaconda_3\lib\site-packages\seaborn\regression.py in fit_regression(self, ax, x_range, grid)
    214             yhat, yhat_boots = self.fit_logx(grid)
    215         else:
--> 216             yhat, yhat_boots = self.fit_fast(grid)
    217 
    218         # Compute the confidence interval at each grid point

D:\Anaconda_3\lib\site-packages\seaborn\regression.py in fit_fast(self, grid)
    239                                     n_boot=self.n_boot,
    240                                     units=self.units,
--> 241                                     seed=self.seed).T
    242         yhat_boots = grid.dot(beta_boots).T
    243         return yhat, yhat_boots

D:\Anaconda_3\lib\site-packages\seaborn\algorithms.py in bootstrap(*args, **kwargs)
     83     for i in range(int(n_boot)):
     84         resampler = integers(0, n, n)
---> 85         sample = [a.take(resampler, axis=0) for a in args]
     86         boot_dist.append(f(*sample, **func_kwargs))
     87     return np.array(boot_dist)

D:\Anaconda_3\lib\site-packages\seaborn\algorithms.py in <listcomp>(.0)
     83     for i in range(int(n_boot)):
     84         resampler = integers(0, n, n)
---> 85         sample = [a.take(resampler, axis=0) for a in args]
     86         boot_dist.append(f(*sample, **func_kwargs))
     87     return np.array(boot_dist)

TypeError: Cannot cast array data from dtype('int64') to dtype('int32') according to the rule 'safe'