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mdiephuis/seaborn.ipynb

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seaborn.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"from pylab import rcParams\n",
"import sys\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"panda: 0.21.1\n",
"numpy: 1.13.3\n",
"sns 0.8.1\n",
"matplotlib version: 2.1.1\n",
"matplotlib backend: module://ipykernel.pylab.backend_inline\n",
"system: sys.version_info(major=2, minor=7, micro=12, releaselevel='final', serial=0)\n"
]
}
],
"source": [
"print(\"panda: %s\" % pd.__version__)\n",
"print(\"numpy: %s\" % np.__version__)\n",
"print(\"sns %s\" % sns.__version__)\n",
"print(\"matplotlib version: %s\" % matplotlib.__version__)\n",
"print(\"matplotlib backend: %s\" %matplotlib.get_backend())\n",
"print(\"system: %s\" % sys.version_info)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-0.562779</td>\n",
" <td>0.892263</td>\n",
" <td>-0.717172</td>\n",
" <td>-1.270343</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.046977</td>\n",
" <td>1.525139</td>\n",
" <td>0.092790</td>\n",
" <td>0.520384</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.076561</td>\n",
" <td>-0.437879</td>\n",
" <td>-0.593081</td>\n",
" <td>-0.534126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.568639</td>\n",
" <td>-1.784226</td>\n",
" <td>-0.361622</td>\n",
" <td>1.063413</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.546620</td>\n",
" <td>-0.654990</td>\n",
" <td>0.518164</td>\n",
" <td>-1.047701</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"0 -0.562779 0.892263 -0.717172 -1.270343\n",
"1 0.046977 1.525139 0.092790 0.520384\n",
"2 0.076561 -0.437879 -0.593081 -0.534126\n",
"3 1.568639 -1.784226 -0.361622 1.063413\n",
"4 0.546620 -0.654990 0.518164 -1.047701"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Generate some data\n",
"test = pd.DataFrame(np.random.randn(10, 4), columns=['A', 'B', 'C', 'D'])\n",
"test.head()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# Simple threshold to turn A into a label either 0 or 1\n",
"test['A'] = test['A'].apply(lambda x: 0 if x < 0 else 1)\n",
"# Cast A to str\n",
"test['A'] = test['A'].astype(str)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0.892263</td>\n",
" <td>-0.717172</td>\n",
" <td>-1.270343</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1.525139</td>\n",
" <td>0.092790</td>\n",
" <td>0.520384</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>-0.437879</td>\n",
" <td>-0.593081</td>\n",
" <td>-0.534126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>-1.784226</td>\n",
" <td>-0.361622</td>\n",
" <td>1.063413</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>-0.654990</td>\n",
" <td>0.518164</td>\n",
" <td>-1.047701</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"0 0 0.892263 -0.717172 -1.270343\n",
"1 1 1.525139 0.092790 0.520384\n",
"2 1 -0.437879 -0.593081 -0.534126\n",
"3 1 -1.784226 -0.361622 1.063413\n",
"4 1 -0.654990 0.518164 -1.047701"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<type 'str'>\n",
"object\n"
]
}
],
"source": [
"# Check type\n",
"print(type(test.iloc[0, 0]))\n",
"print(test['A'].dtype)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"scrolled": false
},
"outputs": [
{
"ename": "ValueError",
"evalue": "setting an array element with a sequence",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-15-ac02f34cb9f4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Gives an error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpairplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"A\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/home/diephuis/virtualenv/tf/local/lib/python2.7/site-packages/seaborn/axisgrid.pyc\u001b[0m in \u001b[0;36mpairplot\u001b[0;34m(data, hue, hue_order, palette, vars, x_vars, y_vars, kind, diag_kind, markers, size, aspect, dropna, plot_kws, diag_kws, grid_kws)\u001b[0m\n\u001b[1;32m 2058\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msquare_grid\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2059\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdiag_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"hist\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2060\u001b[0;31m \u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_diag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mdiag_kws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2061\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mdiag_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"kde\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2062\u001b[0m \u001b[0mdiag_kws\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"legend\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/diephuis/virtualenv/tf/local/lib/python2.7/site-packages/seaborn/axisgrid.pyc\u001b[0m in \u001b[0;36mmap_diag\u001b[0;34m(self, func, **kwargs)\u001b[0m\n\u001b[1;32m 1363\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1364\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1365\u001b[0;31m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhisttype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"barstacked\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1367\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/diephuis/virtualenv/tf/local/lib/python2.7/site-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mhist\u001b[0;34m(x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, normed, hold, data, **kwargs)\u001b[0m\n\u001b[1;32m 3023\u001b[0m \u001b[0mhisttype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhisttype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malign\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malign\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3024\u001b[0m \u001b[0mrwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3025\u001b[0;31m stacked=stacked, normed=normed, data=data, **kwargs)\n\u001b[0m\u001b[1;32m 3026\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3027\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/diephuis/virtualenv/tf/local/lib/python2.7/site-packages/matplotlib/__init__.pyc\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1715\u001b[0m warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m 1716\u001b[0m RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1717\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1718\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1719\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/diephuis/virtualenv/tf/local/lib/python2.7/site-packages/matplotlib/axes/_axes.pyc\u001b[0m in \u001b[0;36mhist\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 6096\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6097\u001b[0m \u001b[0;31m# process the unit information\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6098\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_process_unit_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6099\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_xunits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6100\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mbin_range\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/diephuis/virtualenv/tf/local/lib/python2.7/site-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36m_process_unit_info\u001b[0;34m(self, xdata, ydata, kwargs)\u001b[0m\n\u001b[1;32m 1987\u001b[0m \u001b[0;31m# we only need to update if there is nothing set yet.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1988\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhave_units\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1989\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_units\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1990\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1991\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mydata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/diephuis/virtualenv/tf/local/lib/python2.7/site-packages/matplotlib/axis.pyc\u001b[0m in \u001b[0;36mupdate_units\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 1436\u001b[0m \u001b[0mneednew\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverter\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mconverter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1437\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconverter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1438\u001b[0;31m \u001b[0mdefault\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefault_units\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1439\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdefault\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munits\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1440\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_units\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdefault\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/diephuis/virtualenv/tf/local/lib/python2.7/site-packages/matplotlib/category.pyc\u001b[0m in \u001b[0;36mdefault_units\u001b[0;34m(data, axis)\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[0;31m# default_units->axis_info->convert\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munit_data\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munit_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mUnitData\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 73\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munit_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/home/diephuis/virtualenv/tf/local/lib/python2.7/site-packages/matplotlib/category.pyc\u001b[0m in \u001b[0;36mshim_array\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mLooseVersion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__version__\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mLooseVersion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'1.8.0'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mshim_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0municode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 22\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mshim_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: setting an array element with a sequence"
]
},
{
"ename": "AttributeError",
"evalue": "'NoneType' object has no attribute 'seq'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/home/diephuis/virtualenv/tf/local/lib/python2.7/site-packages/IPython/core/formatters.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 332\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 333\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 334\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 335\u001b[0m \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 336\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/home/diephuis/virtualenv/tf/local/lib/python2.7/site-packages/matplotlib/text.pyc\u001b[0m in \u001b[0;36m_get_xy_display\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 232\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_get_xy_display\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 233\u001b[0m \u001b[0;34m'get the (possibly unit converted) transformed x, y in display coords'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 234\u001b[0;31m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_unitless_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 235\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform_point\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/diephuis/virtualenv/tf/local/lib/python2.7/site-packages/matplotlib/text.pyc\u001b[0m in \u001b[0;36mget_unitless_position\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 853\u001b[0m \u001b[0;31m# This will get the position with all unit information stripped away.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 854\u001b[0m \u001b[0;31m# This is here for convienience since it is done in several locations.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 855\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_xunits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_x\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 856\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_yunits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 857\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/diephuis/virtualenv/tf/local/lib/python2.7/site-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mconvert_xunits\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 190\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 191\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_units\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 193\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mconvert_yunits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/diephuis/virtualenv/tf/local/lib/python2.7/site-packages/matplotlib/axis.pyc\u001b[0m in \u001b[0;36mconvert_units\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 1489\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1490\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1491\u001b[0;31m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1492\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1493\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/diephuis/virtualenv/tf/local/lib/python2.7/site-packages/matplotlib/category.pyc\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(value, unit, axis)\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munit_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m \u001b[0mvmap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munit_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munit_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'seq'"
]
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7fb59ab290>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Gives an error\n",
"_ = sns.pairplot(test, hue=\"A\")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<type 'str'>\n",
"object\n"
]
}
],
"source": [
"# Use apply, and check types\n",
"test['A'] = test['A'].apply(str)\n",
"print(type(test.iloc[0, 0]))\n",
"print(test['A'].dtype)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"scrolled": false
},
"outputs": [
{
"ename": "ValueError",
"evalue": "setting an array element with a sequence",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-17-3d92b2c43400>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Still gives an error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpairplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"A\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/home/diephuis/virtualenv/tf/local/lib/python2.7/site-packages/seaborn/axisgrid.pyc\u001b[0m in \u001b[0;36mpairplot\u001b[0;34m(data, hue, hue_order, palette, vars, x_vars, y_vars, kind, diag_kind, markers, size, aspect, dropna, plot_kws, diag_kws, grid_kws)\u001b[0m\n\u001b[1;32m 2058\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msquare_grid\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2059\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdiag_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"hist\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2060\u001b[0;31m \u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_diag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mdiag_kws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2061\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mdiag_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"kde\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2062\u001b[0m \u001b[0mdiag_kws\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"legend\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/diephuis/virtualenv/tf/local/lib/python2.7/site-packages/seaborn/axisgrid.pyc\u001b[0m in \u001b[0;36mmap_diag\u001b[0;34m(self, func, **kwargs)\u001b[0m\n\u001b[1;32m 1363\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1364\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1365\u001b[0;31m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhisttype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"barstacked\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1367\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/diephuis/virtualenv/tf/local/lib/python2.7/site-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mhist\u001b[0;34m(x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, normed, hold, data, **kwargs)\u001b[0m\n\u001b[1;32m 3023\u001b[0m \u001b[0mhisttype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhisttype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malign\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malign\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3024\u001b[0m \u001b[0mrwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3025\u001b[0;31m stacked=stacked, normed=normed, data=data, **kwargs)\n\u001b[0m\u001b[1;32m 3026\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3027\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/diephuis/virtualenv/tf/local/lib/python2.7/site-packages/matplotlib/__init__.pyc\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1715\u001b[0m warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m 1716\u001b[0m RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1717\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1718\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1719\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/diephuis/virtualenv/tf/local/lib/python2.7/site-packages/matplotlib/axes/_axes.pyc\u001b[0m in \u001b[0;36mhist\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 6096\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6097\u001b[0m \u001b[0;31m# process the unit information\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6098\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_process_unit_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6099\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_xunits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6100\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mbin_range\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/diephuis/virtualenv/tf/local/lib/python2.7/site-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36m_process_unit_info\u001b[0;34m(self, xdata, ydata, kwargs)\u001b[0m\n\u001b[1;32m 1987\u001b[0m \u001b[0;31m# we only need to update if there is nothing set yet.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1988\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhave_units\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1989\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_units\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1990\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1991\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mydata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/diephuis/virtualenv/tf/local/lib/python2.7/site-packages/matplotlib/axis.pyc\u001b[0m in \u001b[0;36mupdate_units\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 1436\u001b[0m \u001b[0mneednew\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverter\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mconverter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1437\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconverter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1438\u001b[0;31m \u001b[0mdefault\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefault_units\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1439\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdefault\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munits\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1440\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_units\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdefault\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/diephuis/virtualenv/tf/local/lib/python2.7/site-packages/matplotlib/category.pyc\u001b[0m in \u001b[0;36mdefault_units\u001b[0;34m(data, axis)\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[0;31m# default_units->axis_info->convert\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munit_data\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munit_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mUnitData\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 73\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munit_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/diephuis/virtualenv/tf/local/lib/python2.7/site-packages/matplotlib/category.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 102\u001b[0m \"\"\"\n\u001b[1;32m 103\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 104\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_seq_locs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/diephuis/virtualenv/tf/local/lib/python2.7/site-packages/matplotlib/category.pyc\u001b[0m in \u001b[0;36m_set_seq_locs\u001b[0;34m(self, data, value)\u001b[0m\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 111\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_set_seq_locs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 112\u001b[0;31m \u001b[0mstrdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshim_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 113\u001b[0m \u001b[0mnew_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0md\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstrdata\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0md\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseq\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mns\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnew_s\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/diephuis/virtualenv/tf/local/lib/python2.7/site-packages/matplotlib/category.pyc\u001b[0m in \u001b[0;36mshim_array\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mLooseVersion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__version__\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mLooseVersion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'1.8.0'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mshim_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0municode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 22\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mshim_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: setting an array element with a sequence"
]
},
{
"ename": "AttributeError",
"evalue": "'NoneType' object has no attribute 'seq'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m/home/diephuis/virtualenv/tf/local/lib/python2.7/site-packages/matplotlib/text.pyc\u001b[0m in \u001b[0;36m_get_xy_display\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 232\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_get_xy_display\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 233\u001b[0m \u001b[0;34m'get the (possibly unit converted) transformed x, y in display coords'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 234\u001b[0;31m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_unitless_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 235\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform_point\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/diephuis/virtualenv/tf/local/lib/python2.7/site-packages/matplotlib/text.pyc\u001b[0m in \u001b[0;36mget_unitless_position\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 853\u001b[0m \u001b[0;31m# This will get the position with all unit information stripped away.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 854\u001b[0m \u001b[0;31m# This is here for convienience since it is done in several locations.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 855\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_xunits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_x\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 856\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_yunits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 857\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/diephuis/virtualenv/tf/local/lib/python2.7/site-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mconvert_xunits\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 190\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 191\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_units\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 193\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mconvert_yunits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/diephuis/virtualenv/tf/local/lib/python2.7/site-packages/matplotlib/axis.pyc\u001b[0m in \u001b[0;36mconvert_units\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 1489\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1490\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1491\u001b[0;31m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1492\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1493\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/diephuis/virtualenv/tf/local/lib/python2.7/site-packages/matplotlib/category.pyc\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(value, unit, axis)\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munit_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m \u001b[0mvmap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munit_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munit_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'seq'"
]
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7f1d7c1390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Still gives an error\n",
"_ = sns.pairplot(test, hue=\"A\")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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AAADWi5AEAAAQg5AEAAAQg5AEAAAQg5AEAAAQg5CURmEoXTgneaMNw6QrAoDmYc5DSiUa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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7f1d850210>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Works\n",
"_ = sns.pairplot(test, hue=\"A\", vars=[\"B\", \"C\", \"D\"])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>zero</td>\n",
" <td>0.892263</td>\n",
" <td>-0.717172</td>\n",
" <td>-1.270343</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>one</td>\n",
" <td>1.525139</td>\n",
" <td>0.092790</td>\n",
" <td>0.520384</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>one</td>\n",
" <td>-0.437879</td>\n",
" <td>-0.593081</td>\n",
" <td>-0.534126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>one</td>\n",
" <td>-1.784226</td>\n",
" <td>-0.361622</td>\n",
" <td>1.063413</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>one</td>\n",
" <td>-0.654990</td>\n",
" <td>0.518164</td>\n",
" <td>-1.047701</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"0 zero 0.892263 -0.717172 -1.270343\n",
"1 one 1.525139 0.092790 0.520384\n",
"2 one -0.437879 -0.593081 -0.534126\n",
"3 one -1.784226 -0.361622 1.063413\n",
"4 one -0.654990 0.518164 -1.047701"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Define a map, to map the zero and one strings to new strings that don't contain numbers.\n",
"custom_map = {'0': 'zero', '1': 'one'}\n",
"test['A'] = test['A'].map(custom_map)\n",
"test.head()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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EMVa5LPKSCoxXLou8NL2GDFevvfZa0iUAi8JYRZYwXoHFachwBQAAkBTCFQAAQIwIVwAA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\nApnwfXt9y+sAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7f1d91f390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Works\n",
"_ = sns.pairplot(test, hue=\"A\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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