The hue feature is not working when I am using pairplot.
Here is my data frame:
Here is the code that doesn't work:
sns.pairplot(activities, hue="Day")
If I remove the hue option it works. Also if I change the hue to a numerical column (such as Distance) it works, but it is not working with the Day column for some reason. Here's the error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/tmp/ipykernel_121/1783531066.py in <module>
----> 1 sns.pairplot(activities, hue="Day")
/opt/conda/lib/python3.7/site-packages/seaborn/_decorators.py in inner_f(*args, **kwargs)
44 )
45 kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 46 return f(**kwargs)
47 return inner_f
48
/opt/conda/lib/python3.7/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)
2020 elif diag_kind == "kde":
2021 diag_kws.setdefault("fill", True)
-> 2022 grid.map_diag(kdeplot, **diag_kws)
2023
2024 # Maybe plot on the off-diagonals
/opt/conda/lib/python3.7/site-packages/seaborn/axisgrid.py in map_diag(self, func, **kwargs)
1400 plot_kwargs.setdefault("hue_order", self._hue_order)
1401 plot_kwargs.setdefault("palette", self._orig_palette)
-> 1402 func(x=vector, **plot_kwargs)
1403 self._clean_axis(ax)
1404
/opt/conda/lib/python3.7/site-packages/seaborn/_decorators.py in inner_f(*args, **kwargs)
44 )
45 kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 46 return f(**kwargs)
47 return inner_f
48
/opt/conda/lib/python3.7/site-packages/seaborn/distributions.py in kdeplot(x, y, shade, vertical, kernel, bw, gridsize, cut, clip, legend, cumulative, shade_lowest, cbar, cbar_ax, cbar_kws, ax, weights, hue, palette, hue_order, hue_norm, multiple, common_norm, common_grid, levels, thresh, bw_method, bw_adjust, log_scale, color, fill, data, data2, **kwargs)
1733 legend=legend,
1734 estimate_kws=estimate_kws,
-> 1735 **plot_kws,
1736 )
1737
/opt/conda/lib/python3.7/site-packages/seaborn/distributions.py in plot_univariate_density(self, multiple, common_norm, common_grid, fill, legend, estimate_kws, **plot_kws)
914 common_grid,
915 estimate_kws,
--> 916 log_scale,
917 )
918
/opt/conda/lib/python3.7/site-packages/seaborn/distributions.py in _compute_univariate_density(self, data_variable, common_norm, common_grid, estimate_kws, log_scale)
314
315 # Estimate the density of observations at this level
--> 316 density, support = estimator(observations, weights=weights)
317
318 if log_scale:
/opt/conda/lib/python3.7/site-packages/seaborn/_statistics.py in __call__(self, x1, x2, weights)
185 """Fit and evaluate on univariate or bivariate data."""
186 if x2 is None:
--> 187 return self._eval_univariate(x1, weights)
188 else:
189 return self._eval_bivariate(x1, x2, weights)
/opt/conda/lib/python3.7/site-packages/seaborn/_statistics.py in _eval_univariate(self, x, weights)
144 support = self.support
145 if support is None:
--> 146 support = self.define_support(x, cache=False)
147
148 kde = self._fit(x, weights)
/opt/conda/lib/python3.7/site-packages/seaborn/_statistics.py in define_support(self, x1, x2, weights, cache)
117 """Create the evaluation grid for a given data set."""
118 if x2 is None:
--> 119 support = self._define_support_univariate(x1, weights)
120 else:
121 support = self._define_support_bivariate(x1, x2, weights)
/opt/conda/lib/python3.7/site-packages/seaborn/_statistics.py in _define_support_univariate(self, x, weights)
89 def _define_support_univariate(self, x, weights):
90 """Create a 1D grid of evaluation points."""
---> 91 kde = self._fit(x, weights)
92 bw = np.sqrt(kde.covariance.squeeze())
93 grid = self._define_support_grid(
/opt/conda/lib/python3.7/site-packages/seaborn/_statistics.py in _fit(self, fit_data, weights)
135 fit_kws["weights"] = weights
136
--> 137 kde = stats.gaussian_kde(fit_data, **fit_kws)
138 kde.set_bandwidth(kde.factor * self.bw_adjust)
139
/opt/conda/lib/python3.7/site-packages/scipy/stats/kde.py in __init__(self, dataset, bw_method, weights)
204 self._neff = 1/sum(self._weights**2)
205
--> 206 self.set_bandwidth(bw_method=bw_method)
207
208 def evaluate(self, points):
/opt/conda/lib/python3.7/site-packages/scipy/stats/kde.py in set_bandwidth(self, bw_method)
552 raise ValueError(msg)
553
--> 554 self._compute_covariance()
555
556 def _compute_covariance(self):
/opt/conda/lib/python3.7/site-packages/scipy/stats/kde.py in _compute_covariance(self)
564 bias=False,
565 aweights=self.weights))
--> 566 self._data_inv_cov = linalg.inv(self._data_covariance)
567
568 self.covariance = self._data_covariance * self.factor**2
/opt/conda/lib/python3.7/site-packages/scipy/linalg/basic.py in inv(a, overwrite_a, check_finite)
937
938 """
--> 939 a1 = _asarray_validated(a, check_finite=check_finite)
940 if len(a1.shape) != 2 or a1.shape[0] != a1.shape[1]:
941 raise ValueError('expected square matrix')
/opt/conda/lib/python3.7/site-packages/scipy/_lib/_util.py in _asarray_validated(a, check_finite, sparse_ok, objects_ok, mask_ok, as_inexact)
294 if not objects_ok:
295 if a.dtype is np.dtype('O'):
--> 296 raise ValueError('object arrays are not supported')
297 if as_inexact:
298 if not np.issubdtype(a.dtype, np.inexact):
ValueError: object arrays are not supported
Any ideas why hue isn't working?
