diff --git a/lab_pokemon.ipynb b/lab_pokemon.ipynb index 0eddbe9..5c6d17c 100644 --- a/lab_pokemon.ipynb +++ b/lab_pokemon.ipynb @@ -3727,52 +3727,58 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 1, "id": "6f934273-b829-4bc2-a7f4-a27a3fc44a99", "metadata": {}, "outputs": [ { - "ename": "ValueError", - "evalue": "min() iterable argument is empty", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[68], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcountplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpeople\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mincome\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msexual_orientation\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-KngG6AWe-py3.12/lib/python3.12/site-packages/seaborn/_decorators.py:46\u001b[0m, in \u001b[0;36m_deprecate_positional_args..inner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 36\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 37\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPass the following variable\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m as \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124mkeyword arg\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 38\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFrom version 0.12, the only valid positional argument \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[38;5;167;01mFutureWarning\u001b[39;00m\n\u001b[1;32m 44\u001b[0m )\n\u001b[1;32m 45\u001b[0m kwargs\u001b[38;5;241m.\u001b[39mupdate({k: arg \u001b[38;5;28;01mfor\u001b[39;00m k, arg \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(sig\u001b[38;5;241m.\u001b[39mparameters, args)})\n\u001b[0;32m---> 46\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-KngG6AWe-py3.12/lib/python3.12/site-packages/seaborn/categorical.py:3598\u001b[0m, in \u001b[0;36mcountplot\u001b[0;34m(x, y, hue, data, order, hue_order, orient, color, palette, saturation, dodge, ax, **kwargs)\u001b[0m\n\u001b[1;32m 3595\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m x \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 3596\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot pass values for both `x` and `y`\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 3598\u001b[0m plotter \u001b[38;5;241m=\u001b[39m \u001b[43m_CountPlotter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3599\u001b[0m \u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue_order\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3600\u001b[0m \u001b[43m \u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mci\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_boot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munits\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mseed\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3601\u001b[0m \u001b[43m \u001b[49m\u001b[43morient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpalette\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msaturation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3602\u001b[0m \u001b[43m \u001b[49m\u001b[43merrcolor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrwidth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcapsize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdodge\u001b[49m\n\u001b[1;32m 3603\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3605\u001b[0m plotter\u001b[38;5;241m.\u001b[39mvalue_label \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcount\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 3607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-KngG6AWe-py3.12/lib/python3.12/site-packages/seaborn/categorical.py:1586\u001b[0m, in \u001b[0;36m_BarPlotter.__init__\u001b[0;34m(self, x, y, hue, data, order, hue_order, estimator, ci, n_boot, units, seed, orient, color, palette, saturation, errcolor, errwidth, capsize, dodge)\u001b[0m\n\u001b[1;32m 1583\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Initialize the plotter.\"\"\"\u001b[39;00m\n\u001b[1;32m 1584\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestablish_variables(x, y, hue, data, orient,\n\u001b[1;32m 1585\u001b[0m order, hue_order, units)\n\u001b[0;32m-> 1586\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mestablish_colors\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcolor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpalette\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msaturation\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1587\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimate_statistic(estimator, ci, n_boot, seed)\n\u001b[1;32m 1589\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdodge \u001b[38;5;241m=\u001b[39m dodge\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-KngG6AWe-py3.12/lib/python3.12/site-packages/seaborn/categorical.py:319\u001b[0m, in \u001b[0;36m_CategoricalPlotter.establish_colors\u001b[0;34m(self, color, palette, saturation)\u001b[0m\n\u001b[1;32m 317\u001b[0m \u001b[38;5;66;03m# Determine the gray color to use for the lines framing the plot\u001b[39;00m\n\u001b[1;32m 318\u001b[0m light_vals \u001b[38;5;241m=\u001b[39m [colorsys\u001b[38;5;241m.\u001b[39mrgb_to_hls(\u001b[38;5;241m*\u001b[39mc)[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m c \u001b[38;5;129;01min\u001b[39;00m rgb_colors]\n\u001b[0;32m--> 319\u001b[0m lum \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mmin\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mlight_vals\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m.6\u001b[39m\n\u001b[1;32m 320\u001b[0m gray \u001b[38;5;241m=\u001b[39m mpl\u001b[38;5;241m.\u001b[39mcolors\u001b[38;5;241m.\u001b[39mrgb2hex((lum, lum, lum))\n\u001b[1;32m 322\u001b[0m \u001b[38;5;66;03m# Assign object attributes\u001b[39;00m\n", - "\u001b[0;31mValueError\u001b[0m: min() iterable argument is empty" + "name": "stdout", + "output_type": "stream", + "text": [ + "[5 8 6 7 4 2 3 1]\n", + "['other' 'heterosexual' 'bisexual' 'homosexual']\n" ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "sns.countplot(data=people, x=\"income\", hue=\"sexual_orientation\")" + "import seaborn as sns\n", + "import pandas as pd\n", + "\n", + "people = pd.read_csv(\"brfss_2020.csv\")\n", + "\n", + "\n", + "\n", + "\n", + "sns.countplot(data=people, x=\"income\", hue=\"sexual_orientation\")\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 67, + "execution_count": null, "id": "8b4b852b-402c-45d4-b3bb-840e47b249ed", "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "min() iterable argument is empty", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[67], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbarplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpeople\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msexual_orientation\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mage\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mincome\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcol\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mincome_group\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-KngG6AWe-py3.12/lib/python3.12/site-packages/seaborn/_decorators.py:46\u001b[0m, in \u001b[0;36m_deprecate_positional_args..inner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 36\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 37\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPass the following variable\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m as \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124mkeyword arg\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 38\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFrom version 0.12, the only valid positional argument \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[38;5;167;01mFutureWarning\u001b[39;00m\n\u001b[1;32m 44\u001b[0m )\n\u001b[1;32m 45\u001b[0m kwargs\u001b[38;5;241m.\u001b[39mupdate({k: arg \u001b[38;5;28;01mfor\u001b[39;00m k, arg \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(sig\u001b[38;5;241m.\u001b[39mparameters, args)})\n\u001b[0;32m---> 46\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-KngG6AWe-py3.12/lib/python3.12/site-packages/seaborn/categorical.py:3182\u001b[0m, in \u001b[0;36mbarplot\u001b[0;34m(x, y, hue, data, order, hue_order, estimator, ci, n_boot, units, seed, orient, color, palette, saturation, errcolor, errwidth, capsize, dodge, ax, **kwargs)\u001b[0m\n\u001b[1;32m 3169\u001b[0m \u001b[38;5;129m@_deprecate_positional_args\u001b[39m\n\u001b[1;32m 3170\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mbarplot\u001b[39m(\n\u001b[1;32m 3171\u001b[0m \u001b[38;5;241m*\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3179\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 3180\u001b[0m ):\n\u001b[0;32m-> 3182\u001b[0m plotter \u001b[38;5;241m=\u001b[39m \u001b[43m_BarPlotter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue_order\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3183\u001b[0m \u001b[43m \u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mci\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_boot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munits\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mseed\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3184\u001b[0m \u001b[43m \u001b[49m\u001b[43morient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpalette\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msaturation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3185\u001b[0m \u001b[43m \u001b[49m\u001b[43merrcolor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrwidth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcapsize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdodge\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3187\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 3188\u001b[0m ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mgca()\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-KngG6AWe-py3.12/lib/python3.12/site-packages/seaborn/categorical.py:1586\u001b[0m, in \u001b[0;36m_BarPlotter.__init__\u001b[0;34m(self, x, y, hue, data, order, hue_order, estimator, ci, n_boot, units, seed, orient, color, palette, saturation, errcolor, errwidth, capsize, dodge)\u001b[0m\n\u001b[1;32m 1583\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Initialize the plotter.\"\"\"\u001b[39;00m\n\u001b[1;32m 1584\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestablish_variables(x, y, hue, data, orient,\n\u001b[1;32m 1585\u001b[0m order, hue_order, units)\n\u001b[0;32m-> 1586\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mestablish_colors\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcolor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpalette\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msaturation\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1587\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimate_statistic(estimator, ci, n_boot, seed)\n\u001b[1;32m 1589\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdodge \u001b[38;5;241m=\u001b[39m dodge\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-KngG6AWe-py3.12/lib/python3.12/site-packages/seaborn/categorical.py:319\u001b[0m, in \u001b[0;36m_CategoricalPlotter.establish_colors\u001b[0;34m(self, color, palette, saturation)\u001b[0m\n\u001b[1;32m 317\u001b[0m \u001b[38;5;66;03m# Determine the gray color to use for the lines framing the plot\u001b[39;00m\n\u001b[1;32m 318\u001b[0m light_vals \u001b[38;5;241m=\u001b[39m [colorsys\u001b[38;5;241m.\u001b[39mrgb_to_hls(\u001b[38;5;241m*\u001b[39mc)[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m c \u001b[38;5;129;01min\u001b[39;00m rgb_colors]\n\u001b[0;32m--> 319\u001b[0m lum \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mmin\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mlight_vals\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m.6\u001b[39m\n\u001b[1;32m 320\u001b[0m gray \u001b[38;5;241m=\u001b[39m mpl\u001b[38;5;241m.\u001b[39mcolors\u001b[38;5;241m.\u001b[39mrgb2hex((lum, lum, lum))\n\u001b[1;32m 322\u001b[0m \u001b[38;5;66;03m# Assign object attributes\u001b[39;00m\n", - "\u001b[0;31mValueError\u001b[0m: min() iterable argument is empty" - ] - } - ], + "outputs": [], "source": [] }, { @@ -3782,6 +3788,14 @@ "metadata": {}, "outputs": [], "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "631c1296-e35d-4fab-aa60-1d936297d924", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": {