I am using the code below that I took from the Seaborn documentation as it is. Running this code results in an error.
AttributeError: module 'pandas' has no attribute 'Panel'
I am wondering if there is a way around this problem without reverting to a previous version of Pandas. Can anyone help?
tips = sns.load_dataset("tips")
tips["big_tip"] = (tips.tip / tips.total_bill) > .15
sns.lmplot(x="total_bill", y="big_tip", data=tips,
logistic=True, y_jitter=.03);
The version info as well as the complete error message are as follows:
pandas : 1.3.5 seaborn: '0.11.2'
--------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-4-2a96c34ef86c> in <module>
2 tips["big_tip"] = (tips.tip / tips.total_bill) > .15
3 sns.lmplot(x="total_bill", y="big_tip", data=tips,
----> 4 logistic=True, y_jitter=.03);
~/anaconda3/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
~/anaconda3/lib/python3.7/site-packages/seaborn/regression.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, seed, order, logistic, lowess, robust, logx, x_partial, y_partial, truncate, x_jitter, y_jitter, scatter_kws, line_kws, facet_kws, size)
643 scatter_kws=scatter_kws, line_kws=line_kws,
644 )
--> 645 facets.map_dataframe(regplot, x=x, y=y, **regplot_kws)
646 facets.set_axis_labels(x, y)
647
~/anaconda3/lib/python3.7/site-packages/seaborn/axisgrid.py in map_dataframe(self, func, *args, **kwargs)
775
776 # Draw the plot
--> 777 self._facet_plot(func, ax, args, kwargs)
778
779 # For axis labels, prefer to use positional args for backcompat
~/anaconda3/lib/python3.7/site-packages/seaborn/axisgrid.py in _facet_plot(self, func, ax, plot_args, plot_kwargs)
804 plot_args = []
805 plot_kwargs["ax"] = ax
--> 806 func(*plot_args, **plot_kwargs)
807
808 # Sort out the supporting information
~/anaconda3/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
~/anaconda3/lib/python3.7/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)
861 scatter_kws["marker"] = marker
862 line_kws = {} if line_kws is None else copy.copy(line_kws)
--> 863 plotter.plot(ax, scatter_kws, line_kws)
864 return ax
865
~/anaconda3/lib/python3.7/site-packages/seaborn/regression.py in plot(self, ax, scatter_kws, line_kws)
368
369 if self.fit_reg:
--> 370 self.lineplot(ax, line_kws)
371
372 # Label the axes
~/anaconda3/lib/python3.7/site-packages/seaborn/regression.py in lineplot(self, ax, kws)
411 """Draw the model."""
412 # Fit the regression model
--> 413 grid, yhat, err_bands = self.fit_regression(ax)
414 edges = grid[0], grid[-1]
415
~/anaconda3/lib/python3.7/site-packages/seaborn/regression.py in fit_regression(self, ax, x_range, grid)
209 from statsmodels.genmod.families import Binomial
210 yhat, yhat_boots = self.fit_statsmodels(grid, GLM,
--> 211 family=Binomial())
212 elif self.lowess:
213 ci = None
~/anaconda3/lib/python3.7/site-packages/seaborn/regression.py in fit_statsmodels(self, grid, model, **kwargs)
279 return yhat
280
--> 281 yhat = reg_func(X, y)
282 if self.ci is None:
283 return yhat, None
~/anaconda3/lib/python3.7/site-packages/seaborn/regression.py in reg_func(_x, _y)
273 def reg_func(_x, _y):
274 try:
--> 275 yhat = model(_y, _x, **kwargs).fit().predict(grid)
276 except glm.PerfectSeparationError:
277 yhat = np.empty(len(grid))
~/anaconda3/lib/python3.7/site-packages/statsmodels/genmod/generalized_linear_model.py in __init__(self, endog, exog, family, offset, exposure, freq_weights, var_weights, missing, **kwargs)
289 offset=offset, exposure=exposure,
290 freq_weights=freq_weights,
--> 291 var_weights=var_weights, **kwargs)
292 self._check_inputs(family, self.offset, self.exposure, self.endog,
293 self.freq_weights, self.var_weights)
~/anaconda3/lib/python3.7/site-packages/statsmodels/base/model.py in __init__(self, endog, exog, **kwargs)
214
215 def __init__(self, endog, exog=None, **kwargs):
--> 216 super(LikelihoodModel, self).__init__(endog, exog, **kwargs)
217 self.initialize()
218
~/anaconda3/lib/python3.7/site-packages/statsmodels/base/model.py in __init__(self, endog, exog, **kwargs)
66 hasconst = kwargs.pop('hasconst', None)
67 self.data = self._handle_data(endog, exog, missing, hasconst,
---> 68 **kwargs)
69 self.k_constant = self.data.k_constant
70 self.exog = self.data.exog
~/anaconda3/lib/python3.7/site-packages/statsmodels/base/model.py in _handle_data(self, endog, exog, missing, hasconst, **kwargs)
89
90 def _handle_data(self, endog, exog, missing, hasconst, **kwargs):
---> 91 data = handle_data(endog, exog, missing, hasconst, **kwargs)
92 # kwargs arrays could have changed, easier to just attach here
93 for key in kwargs:
~/anaconda3/lib/python3.7/site-packages/statsmodels/base/data.py in handle_data(endog, exog, missing, hasconst, **kwargs)
631 exog = np.asarray(exog)
632
--> 633 klass = handle_data_class_factory(endog, exog)
634 return klass(endog, exog=exog, missing=missing, hasconst=hasconst,
635 **kwargs)
~/anaconda3/lib/python3.7/site-packages/statsmodels/base/data.py in handle_data_class_factory(endog, exog)
611 if data_util._is_using_ndarray_type(endog, exog):
612 klass = ModelData
--> 613 elif data_util._is_using_pandas(endog, exog):
614 klass = PandasData
615 elif data_util._is_using_patsy(endog, exog):
~/anaconda3/lib/python3.7/site-packages/statsmodels/tools/data.py in _is_using_pandas(endog, exog)
99
100 def _is_using_pandas(endog, exog):
--> 101 from statsmodels.compat.pandas import data_klasses as klasses
102 return (isinstance(endog, klasses) or isinstance(exog, klasses))
103
~/anaconda3/lib/python3.7/site-packages/statsmodels/compat/pandas.py in <module>
21 except ImportError:
22 from pandas.tseries import frequencies
---> 23 data_klasses = (pandas.Series, pandas.DataFrame, pandas.Panel)
24 else:
25 try:
~/anaconda3/lib/python3.7/site-packages/pandas/__init__.py in __getattr__(name)
242 return _SparseArray
243
--> 244 raise AttributeError(f"module 'pandas' has no attribute '{name}'")
245
246
AttributeError: module 'pandas' has no attribute 'Panel'
you are using the latest version of pandas library where Panal is removed from pandas version 0.25 and onward