Seaborn lmplot hue plotting error
1
$begingroup$
I am trying to colour the points on an lmplot by a categorical variable contained within a column of a DataFrame and I am getting the following error:
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
The categororical variable is a string.
seaborn
asked yesterday
P GriffinP Griffin
63
$endgroup$
|
show 1 more comment
1
$begingroup$
I am trying to colour the points on an lmplot by a categorical variable contained within a column of a DataFrame and I am getting the following error:
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
The categororical variable is a string.
seaborn
asked yesterday
P GriffinP Griffin
63
$endgroup$
$begingroup$
Could you share the code that caused the error, please? If not, would you alternatively be able to extract a simplified example that illustrates the problem?
$endgroup$
– mapto
yesterday
$begingroup$
Sure. It was a simple one liner, something like this:sns.lmplot(x='col1', y='col2', data=df, hue='col3')
$endgroup$
– P Griffin
yesterday
$begingroup$
If you remove the hue parameter, does it work?
$endgroup$
– Tasos
20 hours ago
$begingroup$
Yes, it works fine without the hue parameter.
$endgroup$
– P Griffin
18 hours ago
$begingroup$
Could it be because there are only one of some of the categorical variables?
$endgroup$
– P Griffin
17 hours ago
|
show 1 more comment
1
1
1
$begingroup$
I am trying to colour the points on an lmplot by a categorical variable contained within a column of a DataFrame and I am getting the following error:
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
The categororical variable is a string.
seaborn
asked yesterday
P GriffinP Griffin
63
$endgroup$
I am trying to colour the points on an lmplot by a categorical variable contained within a column of a DataFrame and I am getting the following error:
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
The categororical variable is a string.
seaborn
seaborn
asked yesterday
P GriffinP Griffin
63
asked yesterday
P GriffinP Griffin
63
edited yesterday
P Griffin
asked yesterday
P GriffinP Griffin
63
asked yesterday
P GriffinP Griffin
63
asked yesterday
P GriffinP Griffin
63
63
$begingroup$
Could you share the code that caused the error, please? If not, would you alternatively be able to extract a simplified example that illustrates the problem?
$endgroup$
– mapto
yesterday
$begingroup$
Sure. It was a simple one liner, something like this:sns.lmplot(x='col1', y='col2', data=df, hue='col3')
$endgroup$
– P Griffin
yesterday
$begingroup$
If you remove the hue parameter, does it work?
$endgroup$
– Tasos
20 hours ago
$begingroup$
Yes, it works fine without the hue parameter.
$endgroup$
– P Griffin
18 hours ago
$begingroup$
Could it be because there are only one of some of the categorical variables?
$endgroup$
– P Griffin
17 hours ago
|
show 1 more comment
$begingroup$
Could you share the code that caused the error, please? If not, would you alternatively be able to extract a simplified example that illustrates the problem?
$endgroup$
– mapto
yesterday
$begingroup$
Sure. It was a simple one liner, something like this:sns.lmplot(x='col1', y='col2', data=df, hue='col3')
$endgroup$
– P Griffin
yesterday
$begingroup$
If you remove the hue parameter, does it work?
$endgroup$
– Tasos
20 hours ago
$begingroup$
Yes, it works fine without the hue parameter.
$endgroup$
– P Griffin
18 hours ago
$begingroup$
Could it be because there are only one of some of the categorical variables?
$endgroup$
– P Griffin
17 hours ago
$begingroup$
Could you share the code that caused the error, please? If not, would you alternatively be able to extract a simplified example that illustrates the problem?
$endgroup$
– mapto
yesterday
$begingroup$
Could you share the code that caused the error, please? If not, would you alternatively be able to extract a simplified example that illustrates the problem?
$endgroup$
– mapto
yesterday
$begingroup$
Sure. It was a simple one liner, something like this:
sns.lmplot(x='col1', y='col2', data=df, hue='col3')$endgroup$
– P Griffin
yesterday
$begingroup$
Sure. It was a simple one liner, something like this:
sns.lmplot(x='col1', y='col2', data=df, hue='col3')$endgroup$
– P Griffin
yesterday
$begingroup$
If you remove the hue parameter, does it work?
$endgroup$
– Tasos
20 hours ago
$begingroup$
If you remove the hue parameter, does it work?
$endgroup$
– Tasos
20 hours ago
$begingroup$
Yes, it works fine without the hue parameter.
$endgroup$
– P Griffin
18 hours ago
$begingroup$
Yes, it works fine without the hue parameter.
$endgroup$
– P Griffin
18 hours ago
$begingroup$
Could it be because there are only one of some of the categorical variables?
$endgroup$
– P Griffin
17 hours ago
$begingroup$
Could it be because there are only one of some of the categorical variables?
$endgroup$
– P Griffin
17 hours ago
|
show 1 more comment
0
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$begingroup$
Could you share the code that caused the error, please? If not, would you alternatively be able to extract a simplified example that illustrates the problem?
$endgroup$
– mapto
yesterday
$begingroup$
Sure. It was a simple one liner, something like this:
sns.lmplot(x='col1', y='col2', data=df, hue='col3')$endgroup$
– P Griffin
yesterday
$begingroup$
If you remove the hue parameter, does it work?
$endgroup$
– Tasos
20 hours ago
$begingroup$
Yes, it works fine without the hue parameter.
$endgroup$
– P Griffin
18 hours ago
$begingroup$
Could it be because there are only one of some of the categorical variables?
$endgroup$
– P Griffin
17 hours ago