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hdemers/factorplot bug

Last active August 29, 2015 14:05
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factorplot bug
{
"metadata": {
"name": "",
"signature": "sha256:b558204f751fde84d16bf429dd76b6bd17dc0901bd51c746c7dbfc3e3a09f71e"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"import seaborn as sns\n",
"\n",
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df = pd.DataFrame({\n",
" 'x': [0, 0, 0, 0, 0, 0, 1, 1, 1],\n",
" 'y': [0.1, 0.2, 0.4, 0.5, 0.45, 0.6, 0.7, 0.8, 0.9],\n",
" 'category': ['a', 'a', 'a', 'b', 'b', 'b', 'b', 'b', 'b']\n",
"})"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sns.factorplot('x', 'y', 'category', df)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "low >= high",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-4-9102d0e92a30>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfactorplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'x'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'y'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'category'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m/home/hdemers/.virtualenvs/fwk-analytics/local/lib/python2.7/site-packages/seaborn/linearmodels.py\u001b[0m in \u001b[0;36mfactorplot\u001b[1;34m(x, y, hue, data, row, col, col_wrap, estimator, ci, n_boot, units, x_order, hue_order, col_order, row_order, kind, markers, linestyles, dodge, join, hline, size, aspect, palette, legend, legend_out, dropna, sharex, sharey, margin_titles)\u001b[0m\n\u001b[0;32m 873\u001b[0m kwargs.update(dict(dodge=dodge, join=join,\n\u001b[0;32m 874\u001b[0m markers=markers, linestyles=linestyles))\n\u001b[1;32m--> 875\u001b[1;33m \u001b[0mfacets\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap_dataframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpointplot\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 876\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 877\u001b[0m \u001b[1;31m# Draw legends and labels\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/hdemers/.virtualenvs/fwk-analytics/local/lib/python2.7/site-packages/seaborn/axisgrid.pyc\u001b[0m in \u001b[0;36mmap_dataframe\u001b[1;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[0;32m 373\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 374\u001b[0m \u001b[1;31m# Draw the plot\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 375\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_facet_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 376\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 377\u001b[0m \u001b[1;31m# Finalize the annotations and layout\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/hdemers/.virtualenvs/fwk-analytics/local/lib/python2.7/site-packages/seaborn/axisgrid.pyc\u001b[0m in \u001b[0;36m_facet_plot\u001b[1;34m(self, func, ax, plot_args, plot_kwargs)\u001b[0m\n\u001b[0;32m 391\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 392\u001b[0m \u001b[1;31m# Draw the plot\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 393\u001b[1;33m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mplot_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 394\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 395\u001b[0m \u001b[1;31m# Sort out the supporting information\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/hdemers/.virtualenvs/fwk-analytics/local/lib/python2.7/site-packages/seaborn/linearmodels.py\u001b[0m in \u001b[0;36mpointplot\u001b[1;34m(x, y, hue, data, estimator, hline, ci, n_boot, units, x_order, hue_order, markers, linestyles, dodge, dropna, color, palette, join, label, ax)\u001b[0m\n\u001b[0;32m 1001\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1002\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgca\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1003\u001b[1;33m \u001b[0mplotter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1004\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1005\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/hdemers/.virtualenvs/fwk-analytics/local/lib/python2.7/site-packages/seaborn/linearmodels.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, ax)\u001b[0m\n\u001b[0;32m 269\u001b[0m \u001b[1;34m\"\"\"Plot based on the stored value for kind of plot.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 270\u001b[0m \u001b[0mplotter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"plot\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 271\u001b[1;33m \u001b[0mplotter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 272\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 273\u001b[0m \u001b[1;31m# Set the plot attributes (these are shared across plot kinds\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/hdemers/.virtualenvs/fwk-analytics/local/lib/python2.7/site-packages/seaborn/linearmodels.py\u001b[0m in \u001b[0;36mpointplot\u001b[1;34m(self, ax)\u001b[0m\n\u001b[0;32m 333\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mpointplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 334\u001b[0m \u001b[1;34m\"\"\"Draw the plot with a point representation.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 335\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mpos\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mci\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestimate_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 336\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 337\u001b[0m \u001b[0mcolor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpalette\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx_palette\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpalette\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/hdemers/.virtualenvs/fwk-analytics/local/lib/python2.7/site-packages/seaborn/linearmodels.py\u001b[0m in \u001b[0;36mestimate_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 243\u001b[0m boots = algo.bootstrap(y_data, func=self.estimator,\n\u001b[0;32m 244\u001b[0m \u001b[0mn_boot\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_boot\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 245\u001b[1;33m units=unit_data)\n\u001b[0m\u001b[0;32m 246\u001b[0m \u001b[0mci\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mci\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mboots\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mci\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 247\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/hdemers/.virtualenvs/fwk-analytics/local/lib/python2.7/site-packages/seaborn/algorithms.pyc\u001b[0m in \u001b[0;36mbootstrap\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 72\u001b[0m \u001b[0mboot_dist\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 73\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_boot\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 74\u001b[1;33m \u001b[0mresampler\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 75\u001b[0m \u001b[0msample\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresampler\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 76\u001b[0m \u001b[0mboot_dist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0msample\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfunc_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/hdemers/.virtualenvs/fwk-analytics/local/lib/python2.7/site-packages/numpy/random/mtrand.so\u001b[0m in \u001b[0;36mmtrand.RandomState.randint (numpy/random/mtrand/mtrand.c:6981)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;31mValueError\u001b[0m: low >= high"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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6J/0Xn7tuTU0b2xXwpD5Hos2+gc8BrweORMTZiPh50bgj0XLPndPya/xp+j/18RT9F1yP\nZua5qplHoeXn+wvAOyLiSeBHwEOZ+duaiUcnIr4D/CdwZ0RciogHu940SZIkSZIkSZIkSZIkSZIk\nSZKk14T/BaWn25PDDCPNAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x4db9810>"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fix\n",
"Adding a check on y_data being empty, line 242 of linearmodels.py solve the problem and gives:\n",
"\n",
" if self.ci is not None and not y_data.empty:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sns.factorplot('x', 'y', 'category', df)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"<seaborn.axisgrid.FacetGrid at 0x4e548d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
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3gcYY43lpz6PkfKpugejJs8CkQeRhMv+ta4AyPApHT54FJg0KMcZVQFPac6j3\nDI/C0ZNngUlSQTA8CofP9JI0YBgehaMnzwKTpIKQ128SVCJHnwUGbCLzLLCbUp1IknLwzKNA5HoW\nWLpTSfkRQvg+8FTmx1AfQvhY2jNJkiRJkiRJkiRJkiRJkiRJkiRJkiRJGsxCCO8JIdSFECqyy1/J\n3vks6TiK0x5ASsv27dvfmThx4jbg7ydOnFgPfAH4g+3btx9KeTRJUqELIfxTCGFvCOGitGeRBgof\njKghLYQwHDiXzLfaTUt5HGnAMDw01H0NeAZ4H3B/CMFvb5R6wPDQkBVCuA6oBP4yxrgeuB34fgjB\n/19IkiRJkiRJkiRJkiRJkiRJkiRJkiRpcPr/4iuqGyE/9YMAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x4e54250>"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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