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August 20, 2013 23:04
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Solving x axis overprinting on Pandas/Matplotlib bar charts
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{ | |
"metadata": { | |
"name": "TemperatureBarChart" | |
}, | |
"nbformat": 3, | |
"nbformat_minor": 0, | |
"worksheets": [ | |
{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"<h3>Solving x axis overprinting on Pandas/Matplotlib bar charts</h3>\n", | |
"<p>If creating a histogram based on \"binning\" a continuous variable such as temperature or distance, it would be inappropriate to use a line chart since the y-axis \"counts of occurrences\" would be meaningless without representing the bin width.</p>\n", | |
"<p>First, we grab some temperature data, compute a histogram, and store the histogram data into a new data frame called dfh.</p>" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"from pandas import *\n", | |
"df = read_csv(\"http://image.guardian.co.uk/sys-files/Guardian/documents/2009/12/08/\" +\n", | |
" \"us.csv?guni=Data:in%20body%20link\")\n", | |
"h=histogram(df.ix[map(lambda x: x.strip()==\"CHARLESTON\",df[\"Station\"]),6],bins=arange(3.4,16.2,0.2))\n", | |
"dfh = DataFrame(zip(h[1],h[0]),columns=[\"temperature\",\"count\"])" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 110 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"<p>A straightforward plot() of dfh results in overprinted x axis labels.</p>\n", | |
"<p>Note: this example is somewhat contrived in that we could have just called df.hist() (on the original un-histogrammed data) instead of dfh.plot(), and Pandas/Matplotlib does a fine job when plotting with hist(). So the assumption where this is relevant is that the data you have has already been histogrammed and the original un-histogrammed data is unavailable to you.</p>" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"dfh.plot(kind=\"bar\",x=\"temperature\",title=\"Jan. ave. temperatures for Charleston, SC, 1823-2009\")" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "pyout", | |
"prompt_number": 111, | |
"text": [ | |
"<matplotlib.axes.AxesSubplot at 0x109d5ab50>" | |
] | |
}, | |
{ | |
"output_type": "display_data", | |
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3RU1yuSskEonwkstdjZrfnDRsTX3XLb+qTXNcbWOa5zhbW02G+rOGzTQpRUX5\nqPlXPR0A/bndPDBW35vzmDJNC2vYzQxL11YN0bDFbLr0Xd3YaVuTJY0zYzpYw2YYhrECWMM2I/+m\n1ceaJr85adiabYavw9Y2lyWNs7XVZKg/a9gMwzCMVrCG3cxgDZs1bMa8YQ2bYRjGCmAN24z8WcPW\n3p81bOPHtMaaDPVnDZthGIbRCtawmxmsYbOGzZg3rGEzDMNYAaxhm5E/a9ja+7OGbfyY1liTof6s\nYTMMwzBawRp2M4M1bNawGfOGNWyGYRgrgDVsM/JnDVt7f9awjR/TGmsy1J81bIZhGEYrWMNuZrCG\nzRo2Y94YRcO+f/8+nnzySURERCAyMhJHjx7Vu0CGYRjm4eg9YU+ZMgUDBgzA2bNn8dtvvyEiIkJt\n2+auRZljTZaurbKGbfyY1liTof6m1rBttWpVj4KCAhw8eBBr1qypCWJrCycnJ31CMQzDMFqil4Z9\n6tQpPPvss4iMjMSvv/6KhIQELF26FA4ODn8FZg3bLGENmzVsxrzRNHfqdYVdVVWFkydP4l//+hc6\nduyIqVOnYv78+ZgzZ45Ku9TUVCgUCgCAs7Mz4uLikJiYCOCvfwF4u2m3/0J1W6z9gAGDUVZWLLSx\nt5eitLRIq3wODrIGvt9/v9Vo9f+1nVjPlghx/vKXy11VvulcJnPBli3fNnr+jIwMrftrrPHjbfPb\nzsjIwOrVqwFAmC/VQnpw8+ZNUigUwvbBgwdp4MCBKm3qhk5PTxeNI2bX1maof3OtCQABRED6nz/V\nv0+6tNUljyF9EotriE2Xfurir+vY6TJ+fDybzr+pzlF16HXT0dvbG35+fsjMzAQA7NmzB1FRUfqE\nYhiGYbRE73XYv/76K/7+97+joqICQUFBWLVqlcqNR9awzRNdNGxD9G5jaeXG0LC1rbUpNWxLv9fA\n6I+muZM/ONPM4AmbJ2zGvDH5w58a3rBRb9fWZqh/c69Jl/XB2rbVNo8u/rrUZJhN3K5Ln7TNZYz3\nqbkfz9bYJzH4WSIMwzAWAksizQyWRFgSYcwbk0siDMMwjOGwhm1G/qxhG1YTa9jaxbTG49ka+yQG\nX2EzDMNYCKxhNzNYw2YNmzFvWMNmGIaxAljDNiN/1rANq4k1bO1iWuPxbI19EoOvsBmGYSwE1rCb\nGaxhs4bNmDesYTMMw1gBrGGbkT9r2IbVxBq2djGt8Xi2xj6JwVfYDMMwFgJr2M0M1rBZw2bMG9aw\nGYZhrAAHwQ7TAAAgAElEQVTWsM3InzVsw2piDVu7mNZ4PFtjn8TQ61vTGcbYiH2TeWFhnknzWzqm\nHlPGcFjDbmZYioZtqF6src3QmJakYbMubhmwhs0wDGMFsIZtRv6sYWvv31QatjH8Ta1hG1qTOR7P\n1nmONoSvsBmGYSwE1rCbGaxhs4bdmDGZxoc1bIZhGCuANWwz8mcNW3t/1rCNH1OXXKY+nq3zHG0I\nX2EzDMNYCKxhNzNYw2YNuzFjMo0Pa9gMwzBWAGvYZuTPGrb2/qxhGz+mLrlMfTxb5znaEL7CZhiG\nsRBYw25msIbNGnZjxmQaH9awGYZhrADWsM3InzVs7f1ZwzZ+TF1ymfp4ts5ztCF8hc0wDGMhsIbd\nzGANmzXsxozJND6sYTMMw1gBBk3YSqUS8fHxGDx4sMZ2zV2LMseaWMNu7Jjat2UN23L9LVrDXrp0\nKSIjI//8V4thGIYxJnpr2NevX0dqaipef/11fPDBB9i6datqYNawzRLWsFnDbsyYTOOjae7U+1vT\nX375ZSxcuBCFhYVq26SmpkKhUAAAnJ2dERcXh8TERAB//QvA2027/Req29q2V/dt4uLfMJ4BIFEl\nh1h96r+hvKG/uvr/2k6sZ0uEOE3v7+AgQ1lZsbBtby9FaWlRTct64y/mr278dNmuX6+6b1LXNt6Q\nIcO1Oh62bPlWr3qbw3ZGRgZWr14NAMJ8qRbSg61bt9LkyZOJiCg9PZ0GDRrUoE3d0Onp6aJxxOza\n2gz1b641ASCACEj/86f690msrSE2XWpS52+Mmkztb+j7ZMh7r0tNTRVTlz41tX9TnaPq0EvDPnz4\nMLZs2YLAwECkpKRg3759GDt2rD6hGIZhGC0xeB32/v37sWjRItawLQRDNewa9Nd7DclDFq5hq/MX\no6k0bF1qaqqYzR2jr8PmVSIMwzDGx+AJu2fPntiyZYvGNk25dtGUMS2pJl3W4hpjzbO2NRm65rnp\nYhrub8j7ZOh7r0tNTRWTz9GG8CcdGYZhLAR+lkgzgzVs1rD1rampYjZ3+FkiDMMwVgA/D9uM/FnD\n1t6fNWxtfFnDbmx/1rAZhmEYrWANu5nBGjZr2PrW1FQxmzusYTMMw1gBrGGbkT9r2Nr7s4atjS9r\n2I3tzxo2wzAMoxWsYTczWMNmDVvfmpoqZnOHNWyGYRgrgDVsM/JnDVt7f9awtfFlDbux/VnDZhiG\nYbSCNexmBmvYrGHrW1NTxWzusIbNMAxjBbCGbUb+rGFr788atja+rGE3tj9r2AzDMIxWsIbdxMjl\nrigqyhe2ZTIXFBbmGcVfrG3Ntqq++JddNSZr2OavYetyPJhawzb02G8uaJo7ecJuYgy9mdRUNw2N\nNTkaUidP2A3bGuN4MNaEbYwbqdaIyW86NnctylAN2bQatKH+4jFZwxaxGvQ+GxazKTVsY+jy1jhv\niMEaNsMwjIXAkkgTw5KI/nWyJMKSSHPA5JIIwzAMYzisYZvInzVsw/xZw27cmKxhm19NYvAVNsMw\njIXAGnYTwxq2/nWyhs0adnOANWyGYRgrgDVsE/mzhm2YP2vYjRuTNWzzq0kMvsJmGIaxEFjDbmJY\nw9a/TtawWcNuDrCGzTAMYwWwhm0if9awDfNnDbtxY7KGbX41icFX2AzDMBYCa9hNDGvY+tfJGjZr\n2M0B1rAZhmGsANawTeTPGrZh/qxhN25M1rDNryYx9Jqws7OzkZSUhKioKERHR2PZsmX6hGEYhmF0\nQC8N+9atW7h16xbi4uJQXFyMhIQEbN68GREREX8FZg1bFNaw9a+TNWzWsJsDja5he3t7Iy4uDgAg\nlUoRERGBnJwc/StkGIZhHoqtoQGysrLwyy+/oHPnzg32paamQqFQICsrC3FxcYiLi0NiYiKAGs3m\n1KlTmDp1qrBdS2JiIjIyMjBgwGCUlRULdnt7KUpLi7T2r/977TYALFmypEE9je0vVv9fLAEQJ2w5\nOMiEtjKZC7Zs+VZrf7Fvo96y5ds6bTPQENX8NW1OAWg4nvr4q/olCjHVjZ+2/obkb2ivbzONv9j7\nJ+Zf9xgBNB9P6o5n9fWrPx7r5tI2v67Hk9j5JHZ+63I+GsO/fozG8M/IyMD8+fPh7e0NhULRYGxU\nIAMoKiqihIQE2rRpU4N9dUOnp6eL+ovZ69oAEEAEpP/5E2rbNoatsf3F6lfXpxq7mO3h/ppjmsZf\nl/fO1DVZsr+lj7Oh51hT+zfVvKEOvddhV1ZWYtCgQejfv7/wV6QujaFhW7rmpb++Zx7aKmvY5u+v\nLqYY5jjOlnQ+NxWNrmETESZOnIjIyEjRyZphGIZpfPSasA8dOoR169YhPT0d8fHxiI+Px44dO9S2\nN8aaZV38Tb+e0hCbpfuLx+R12I3tLx7TUsbZ1Oeoec4bDdHrpmP37t1RXV2tjyvDMAyjJ2b9LBFL\n17xYw7ZcbdVS/NXFFMMcx9mSzuemgp8lwjAMYwVYxLNEWMO2ZH/xmJairVqOv3hMSxlnU5+j5jlv\nNISvsBmGYSwE1rCNCGvYlqutWoq/uphimOM4W9L53FSwhs0wDGMFsIbdBP6WrY0a6i8e01K0Vcvx\nF49pKeNs6nPUPOeNhvAVNsMwjIXAGrYRYQ3bcrVVS/FXF1MMcxxnSzqfmwrWsBmGYawA1rCbwN+y\ntVFD/cVjWoq2ajn+4jEtZZxNfY6a57zREL7CZhiGsRBYwzYirGFbrrZqKf7qYophjuNsSedzU8Ea\nNsMwjBXAGnYT+Fu2Nmqov3hMS9FWLcdfPKaljLOpz1HznDcawlfYDMMwloLab3s0EAAkk7kQakQr\nYZuI1NrFYgBU52V4udrmVtdWlz6J1a+uT6p2MZt6f+1iNq2/upiNO06NU5Ml+1v6OBvjfDbWOa6t\nv6H1axoXo950rKFueN1uPhjjJoUuMZvqJk1Df9PfzDLUn2tqvn3SJaYxzmd1+ZvKX5c+qcuvLkYT\nSSIZWtubSsO2dM3PcvyNEdNQf2PENLW/MWIa6q99zKa7T2SYv3qtWbu2uviLwRo2wzCMhcCSCEsi\nRvHnmppvn3SJyZKIWUoiDMMwjKGwhq13W3F/69MhDfU3RkxD/Y0R09T+xohpqL/2MVnDVmdXha+w\nGYZhLATWsFnDNoo/19R8+6RLTNawWcNmGIaxSljD1rutuL/16ZCG+hsjpqH+xohpan9jxDTUX/uY\nrGGrs6vCV9gMwzAWAmvYrGEbxZ9rar590iUma9isYTMMw1glrGHr3Vbc3/p0SEP9jRHTUH9jxDS1\nvzFiGuqvfUzWsNXZVeErbIZhGAuBNWzWsI3izzU13z7pEpM1bNawGYZhrBLWsPVuK+5vfTqkof7G\niGmovzFimtrfGDEN9dc+JmvY6uyq6D1h79ixA+Hh4QgJCcGCBQse0vqU1vZTp8TaivuLtdXWpktM\n8bba98kwm6X7c01N42/ZNWl/3hp+jhrir64mQ/ukfqxU0WvCViqVePHFF7Fjxw788ccf2LBhA86e\nPavB477W9vv3xdqK+4u11damS0zxttr3yTCbpftzTU3jb9k1aX/eGn6OGuKvriZD+6R+rFTRa8I+\nfvw4goODoVAo0LJlS4waNQrfffedPqEYhmEYLdFrwr5x4wb8/PyE7bZt2+LGjRsaPLK0tmdlibUV\n9xdrq61Nl5jibcX9tW/bVDFN7W+MmIb6GyOmqf2NEdNQf+1jan/eGn6OGuKvriZD+6R+rFTRa1nf\nN998gx07duCzzz4DAKxbtw7Hjh3DRx999FdgYVkfwzAMowvqpmVbfYL5+voiOztb2M7Ozkbbtm21\nSsgwDMPoh16SSIcOHXDhwgVkZWWhoqICX3zxBYYMGdLYtTEMwzB10OsK29bWFv/617+QnJwMpVKJ\niRMnIiIiorFrYxiGYepgtI+mMwzDMI1Li7S0tDRjJ9myZQvCwsJUbBcuXEB6ejqqqqrg5eUFACgq\nKsLp06fRunVrFBYW4vz587h16xZsbGwglUr1yvWwPPb29rh9+7bOubhPltmn2jwA4OLiAhsbG9Fc\nDGOONPoV9rfffivccJRIah6EMnnyZHh4eGD69OlITU3F2rVr8c4778Db2xuHDx+Gq6sr1qxZgxde\neAHu7u44deoU3N3dERkZCQC4fv067OzsQETIz8/HgAEDsGDBAqSnp4OI8Oqrr+L9998HESElJQXL\nly+HXC5HSUmJ2jyBgYE4ffo0nJyc0KJFC+Gm6cWLF5GbmwsHBwc8/vjjavMY2iexPC4uLvj2228x\nY8YMLFiwQBg/7pP+fRLL06NHD+zYsQP5+fnw9fXF0qVLhVxnzpxBUlISPDw8ANQsWY2KioK9vT06\nd+6s8kdix44d6Nevn7Ddv39/bNy4EU5OTigtLcX8+fPx008/wcbGBpMmTcLjjz+O9evX4/Dhw4iM\njMSjjz6K77//XlgSq20usTwnT55EWFgYgoODERISgj59+gi5XF1d0bp1a9y6dcuofRLLU3tvKycn\nh/v0kD5FRkZi0qRJaNmyJdTR6BO2ra0t+vXrJxzwRIRvvvkGtra2GDp0KFatWoUOHTpg586d6Nmz\nJ77//ns89thjyMnJwalTpzB8+HDMnz8fM2bMwOnTp4W47du3R3FxMX7++WesWLECK1euRGZmJvr1\n64cjR45gyJAhICKsXbsWY8eOBQCcPn1abZ6goCBERUWhvLwcly5dEvJ069YNI0aMwGeffYbx48er\nzWNon8TybNmyBeHh4XB0dMTw4cOF8eM+6d8nsTxubm6IiopCdXU1tm/fjtjYWJw6dQpff/011q5d\ni3v37mHRokUAgC+++AK7d+9Gu3btUFJSgqVLl2Lo0KEYPHgwDhw4gB49egg1bd++Hf3790eLFi3g\n5eUFR0dH/P7777h58yZyc3PRp08fFBcXY/jw4ViyZAmuXLmCGTNmCH+ExHKtWLECEokE+/fvF3KJ\n5XnyySfx/PPPIz8/H/Hx8XB2dkZxcTHs7Oywe/duKBQK/N///Z/R+iSWJzs7G//6179QUVGBbt26\n4ZdffuE+qenT8OHDsWfPHgDAmjVr1E2vADUyx48fp6SkJPr3v/9N1dXVRESkUCgoLi6OsrOziYgo\nMTGRSktLKTY2lqqqqigyMpJ8fHyIiCg4OJiIiNq3b68SNyYmhoKCgoTtffv2ka+vLyUkJFDbtm2F\nXHZ2dlrlqc0lloeIhFzq8hjaJ7E8QUFBtGLFCpJKpSrjx33Sv09ieYiIYmNjKTIykohIpU8VFRUq\nuaKioig3N5eCgoLoypUrlJCQQB9++CHFxcWRi4sL7du3jzIyMig9PZ1atGhBGRkZlJGRQfHx8URE\nFB0dLdTr4eFBlZWVQq6oqCiVPonl8vX1paeffpqCgoI05qnN1b59e6qsrBRyBQcH04MHD4Q6jNUn\nsTxERJGRkRQYGEhExH3S0Cciourq6ga56tPoT+vr2LEjdu/ejYqKCvTq1QvHjh0DAHz44YdITk7G\nW2+9haioKPTu3RvFxcUICgqCs7MzQkNDMXXqVMTFxSE0NBS2trY4fPgwDh06hI0bNyIrKwtJSUlC\nnqSkJHz//ffIz89HXl6ekMvV1VWrPIcOHYKbmxtu3ryJL774QshVUFCAxx57TPi3Rl0eQ/sklufb\nb7/FvHnz0LJlS5Xx4z7p3yexPGlpacjOzoaXlxdeeOEFIVdFRQVmzJgBf3//uhc0KCwsRIsWLaBQ\nKJCRkYEffvgBPXr0gL29Pd59913I5XIkJiaiVatWuHTpEnr27InY2Fj89NNPqK6uxpkzZ9CiRQuU\nlZWhoKAAAGBjY4MHDx6onDtiuSIjI3H9+nXcvn1bYx4AKC8vh62tLYqKioRcLVq0wOXLl1FdXW3U\nPonlAYDKykrhX3zuk/o+1drr56qPUVeJ3LhxA1OnTsXPP/+My5cv4/79+/jf//6HCxcuoLKyEq6u\nrsjLy0NMTAz+8Y9/YN68eTh8+DBatWoFJycn5ObmAqj5oI5MJsPw4cPRpUsXlRzXrl3DnDlz8Pbb\nbwu5Tp48qVWe8PBwdOrUCRkZGYKOWFpaigEDBmDatGkPzWNInx6W5/PPP1cZP+6T/n2qn8fPzw89\nevTAzp074ePjI+T67rvvcOHCBSQkJKBdu3YAau7J2Nvb47PPPkP//v0B1JywEydOxLp163Dt2jW8\n/PLL8PT0xObNm9GnTx8cPHgQHh4eOHnyJKRSKcrLy+Hl5YVZs2ZhyZIl6Nq1K3bv3o2SkhJ06NBB\neMyDplxr167Fk08+qTZPraxSWVkJR0dHTJs2DUuWLIG/vz/S09OhUCiEf9WN0SexPNnZ2di/fz8W\nLlyIF198UXiPuE8N+9S1a1ccPXoUY8eOxYwZM6AOXtbHMHVQKpU4fvw4bty4AYlEAltbWyQkJIh+\nkvfQoUPo3r07AGDbtm04fPgw5s2bh4KCAly5cgVVVVVo27YtysvLIZfL4erqikuXLuHEiRMIDw9H\ndHS0zrk05fH29kZWVlaDXCEhIXjw4IHR+1Q/j6+vL7y8vGBvbw9vb2/u00P6FB4ejtjYWM0HqEbB\npJH5z3/+o7VdW5uh/lxT8+2TLjEZxhzgrwhjGC0YOHCgVjZd2hrDn2uyvj7VxSiSyI8//ghXV1dE\nRkYiIyMDJ06cQHx8PFq1atXAbmtri759+zZo27t3byHe2LFj8d///rdBHjG7IbaDBw/i+PHjiImJ\nQd++fXWyaWr71VdfISkpCcOGDRPWY+7duxcdO3ZEWloa7OzshDWaLi4umDt3LgICAjS2VedfXl6O\nJUuWIDo6Wqhr2bJlGDZsmMojccVsurR98OABNm7cCF9fX2Et6cGDB1FaWoqUlBT0799fWF8aEhIC\nmUyGgIAAjW3V+UdGRqJXr17Ytm0brl+/DhsbG4SFhaFLly7YtWuXXrann34ad+/exbffftvALpfL\nGxxrOTk5aNOmzUNturQ1hj/XZH19qkujT9gzZ85Eeno6lEolkpKScODAAQwcOBDLly+HjY0NPD09\nBbudnR1OnToFJycnpKSk4MCBA7h//z7u3r0LLy8vBAUFAQD27dsHqVQKiUSCjh07Crl++OEHuLu7\nA4Bg19a2b98+2NnZ4dFHH8WWLVvw2Wef4d///jfu3buHgIAADBo0CO7u7mptw4YNw6JFizBr1izM\nnDlTo39OTg6Cg4MxePBgXL58GY6Ojti8eTPGjh2LM2fOwM3NTVijOWzYMHTr1g2bN2/GP/7xD7Vt\n1fn36dMHQM0Dup5++mmMGDECwcHBcHBwQFBQkEabh4cHnJyctGo7ZcoUKJVKlJaWCmtJb926hZs3\nb0KpVCIxMVFYXzp79mxUV1cjJiZGY1t1/h999BGuX7+OyZMnY/v27YiPj8eFCxdw+PBhPPXUUzh7\n9qxONmdnZ6xYsQJ+fn4YOnSoENPZ2RmbNm3Cxx9/rLIiiTF/7ty5A09Pz4fadGmrzt+kNLbGEhER\nQZWVlVRSUkJSqZTu379PRETh4eEUHR2tYo+IiKDCwkKKiooSbHFxcfTUU09RYGCgsJ7R29ubgoOD\nqU+fPirrKW1tbalPnz704Ycf6myrjZmRkUFERAkJCXTnzh2Ki4uj4uJiioqK0mgjqlmDXLuWVlPb\n8PBwwVa7HjM8PFyIUXeNZnh4uLAOWFNbdf5xcXEUExNDO3fupPHjx5O7uzvJZDJauXIlbdq0SaMt\nOTmZ/P396f79+w/1l0qltHr1asrLyxPWkkZHR1N1dTVFRUWprC+Njo6m6OholXWnYm3V+UdFRQnj\nXFJSQj169KCoqCi6fPkyxcbG6mwjIgoNDRXGudaen59Pzz//PLVq1YqcnZ3JxcWFgoODqXPnzhQS\nEiLYwsLCaMqUKTR16lQKCwvT2Fad/6uvvkr5+fmi51C/fv0azXb//n169dVXycfHh9avX69iCwkJ\nEWy19vj4eBo9erTGtmK2a9eu0cSJEyk+Pp7y8/MpNTWVoqKiaNiwYTR69Gihv6mpqRQaGkpBQUH0\n0ksvabSp8w8LC6MRI0bQuXPnKDc3l+7du0dt27alixcv0sWLFwVbQECAik1TWzGbp6en4Jefn08T\nJkyg6Oho6tmzJ926dYuISLAHBARQSkoK3bp1S6NNnX90dLTQVhN6Pa1PE3Z2drC1tYWtrS2CgoLg\n5OQEAGjVqhUACFdqTk5OsLOzg0wmU2l74sQJLF26FNu3b4dcLkd8fDxat26Nc+fOYenSpXj33Xex\ncOFCxMfHw9fXF/3798e2bdvQs2dPnWy1z4yIiYlBbm4ulEolPDw8oFQqhfWxmmx//rEDgIf6R0VF\n4YsvvoCtra2wHjMqKgrz5s2DnZ0doqOj8dNPP6Fjx44ICAjA+fPnAUBjW3X+Dx48gKOjI/r27Yu+\nffuioqIC4eHh2LlzJ/bs2YN79+6ptf3www9ITU1FUFAQ7t27p9E/ODgY27Ztw8svv4zKykoUFBSg\nuroaBQUFqKqqEtaXurm5QalUgohU1p2KtVXnXzuWQM1a1ZKSEkgkEvj4+KCyslJnW+1xWlFRoRJz\n5MiR6N27NwICAnDu3DlIJBL07NkTDg4OaNWqFc6fPw+JRIKbN28Ky7wOHjwILy8vtW3V+c+bNw/9\n+vXDxx9/LJw7td+Levz4cZw8eVInW629vm369Onw9/dHaWkpNmzYgG+++QZKpRLh4eGorKwUbOvX\nr8f48eNx48YNvPnmm1i5cqXatmK2vLw8DB8+HN9//z0eeeQRpKam4u2338Zjjz0Ge3t7REVFCfbM\nzEz4+Phg37592LVrl1qbOv81a9bg7t272L59u3AFfP36dYSHhwOAsFLkxo0bCA4Ohq2trcrqEbG2\nYrY7d+4gISEBEokESUlJ8PHxwdatW9G9e3c8++yz2Lx5M6ZNmwYfHx84OjqiY8eOePbZZ+Hm5qbW\nps5/69at2LRpk2BXi8bpXA86depEJSUlRESkVCoFe0JCAsXGxqrYO3XqRDdu3KD4+HiVtvn5+RQV\nFUVPPvkkTZ48mdq2bSvsy87ObmDX1xYQEEAKhYIUCgUFBgZSTk4OBQQEUEBAANnZ2Wm0ERH5+/uT\nnZ3dQ/3z8/MpJSWF7OzsqFOnTmRra0v+/v7k6elJvr6+gk2hUFCXLl1o8ODBFBgYqLGtOn9HR0c6\ndeqUynsSFxdHRETFxcUabbV2MVv9tvPmzaPAwEAKCQmhTz/9lCIiIqhDhw7UsmVLcnd3F2wTJ04k\nT09PcnV1pdDQUI1t1fl7e3uTt7c3TZw4kUJDQ2nFihW0ZMkSCg8PJy8vL51tRETvvPMOOTg4qMQM\nCQmh27dv06OPPir0MyQkROVnXbuYrX5bdf42NjZkb29PiYmJwgsAOTs7k42Njc62Wnt9m6OjIyUm\nJlLr1q2JiGju3Lnk4OBAd+/eFd7XuXPnUteuXSkqKkqwaWr7MJufn58QIzY2Vjjva+2127GxsRpt\n6vwXLVpEycnJFBoaKrRzcXGh5ORk+vXXXwWbQqEQ2ta1i7UVs9nZ2Qm/t2/fXvj0bFxcnPDfWa29\ndizat2+v0abOv24eTTT6hF1WViZqz87Opt9++61B27t37zaw17Vt3bqVZs6c2SCemN0QW11KSkro\n8uXLetk0tT116hT98ssv9NNPP9HNmzeJqObfy/o2dXZtbOfOnWtQj7Y2XdteuXKFcnNziYjo4sWL\ntHHjRvr+++8b2E6dOqV1W3X+p0+fpq+++orOnj0r5DfEJmbv06cPLViwQOXf0h49elD//v2pe/fu\ngu3mzZsUHBxMwcHBD22rzt/T05O6dOmiUk9kZCSdP39e5QJFW1ut3cvLS8UWHh5OSqVSpa2Pjw9F\nRESQv7+/YFu1ahXZ2dmpTJbq2orZ/Pz8KDIykvz9/WnWrFmCvX379sLHrWvttZNSdHS0Rps6f6Ia\nCUYul9PUqVOpoKCAFAoFXbt2jZ588kkVW23b+nZtbC1atKDFixfTokWLKCAgQJhYfX19ydvbW8Xu\n6+tLixcvJm9vb402df611D5yQR1Nug6bYcyZ3NxceuWVVwRd2tnZmYKDg6ljx46CBu3s7ExhYWH0\n4osv0ksvvfTQtur8hwwZQkeOHFHJ/+WXX9LZs2dp06ZNOttq7R999JGKbfr06bRr1y6VttOnT6d3\n331XeB5MLU888QS1adOmgX/9tmK2N954g7755psGMSdPnkxDhgxRsb3xxht08uRJeuKJJzTa1PkT\nEWVmZtITTzxBmzdvpk6dOpGnp6ewT8ymzq7J5ujoSGlpacLr9u3bREQ0bdo0at++vYp99uzZKnZ1\nNnX+REQ5OTk0ZsyYBn2tC0/YDKMFK1eu1MqmS1t1/rWyTWPZTO1v7JpKSkqE/8hrx1TMpktbdf5N\n1Sd18ITNMFpQX35QZ9OlrTH8uSbr61Nd+FkiDPMnMTExDWwXLlwAUPNBobofRrpw4YKorX5bY/hz\nTer9KyoqEBUVpdJWzFa/rbY2Y/WplvPnzwurl0TROJ0zTDPC09OTTp48SVeuXBFe7u7utG3bNvLw\n8Ghgd3d3f2hbY/hzTdbXp9pX3efAi9Ho67AZxlIZOHAgiouLER8fL9gGDx4MuVyO3r17Q6FQqNiv\nXbvWwFa/rTH8uSbr61MtPXv2bGCrC0siDMMwFgI/rY9hGMZC4AmbYRjGQuAJm2EYxkLgCZsxGQUF\nBfjkk09MXcZDWbJkCcrKykxdBsPwhM2Yjvz8fJWn1ZkKqvkAmdr9S5cuRWlpqU4xa58uyDCNCU/Y\njMl47bXXcOnSJcTHx2PGjBlYtGgROnXqhNjYWKSlpQEAsrKyEB4ejvHjxyMsLAyjR4/Grl270K1b\nN4SGhuKnn34CAKSlpWHMmDHo2rUrQkND8fnnnwt5Fi5cKBo3LCwM48aNQ0xMDLKzszF58mR07NgR\n0dHRQrtly5YhJycHSUlJwrcgSaVSIfbXX3+N8ePHAwBSU1Px3HPP4ZFHHsGrr76KS5cuoX///ujQ\noSav6HcAAAMFSURBVAN69OghPDaXYfSmCT6PwDCiZGVlCU9i27lzJ02aNImIah6/O2jQIDpw4ABd\nuXKFbG1t6cyZM1RdXU0JCQk0YcIEIiL67rvvaOjQoURENHv2bIqLi6Py8nK6d+8e+fn5UU5Ojsa4\nNjY2dOzYMaGevLw8IiKqqqqixMREOn36NBHVPKaz9gmCRERSqVT4/euvv6bU1FQiIho3bhwNHjxY\nePpar1696MKFC0REdPToUerVq1cjjyDT3OAPzjAmg+rIELt27cKuXbuED62UlJTg4sWL8PPzQ2Bg\noPDR4KioKOFr0KKjo5GVlQUAkEgkePzxx9GqVSu0atUKSUlJOH78OA4ePKg2bkBAADp16iTU8MUX\nX+Czzz5DVVUVbt68iT/++EP048PqkEgkGDFiBCQSCYqLi3HkyBGMGDFC2K/xI8cMowU8YTNmw8yZ\nMzFp0iQVW1ZWlvBtRQBgY2MDOzs74feqqiq18SQSica4jo6OwvaVK1ewePFinDhxAk5OThg/fjzK\ny8s1xgXQ4Gakg4MDAKC6uhrOzs745Zdf1NbHMLrCGjZjMmQyGYqKigAAffv2xcqVK4Wv8Lpx4wbu\n3r2rdSwiwnfffYcHDx4gNzcXGRkZ6NSpE5KTk7WKW1hYCEdHR8jlcty+fRs//PCDSp2FhYXCtpeX\nF86dO4fq6mps2rRJZQKvRS6XIzAwEF9//bVQ32+//aZ1fxhGDL7CZkyGm5sbunXrhpiYGPTv3x9P\nP/00unTpAqBmkly3bh0kEkmDCbHudu3vEokE7du3R1JSEu7du4e33noL3t7e8Pb2xtmzZx8aNzY2\nFvHx8QgPD4efnx+6d+8u7Js0aRL69esHX19f7N27F/Pnz8egQYPg4eGBDh06CH8M6te2fv16PP/8\n85g7dy4qKyuRkpKC9u3bN+IIMs0NfpYIYxW8/fbbkEqlmDZtmqlLYRijwZIIYzWISRMMY03wFTbD\nMIyFwFfYDMMwFgJP2AzDMBYCT9gMwzAWAk/YDMMwFgJP2AzDMBbC/wN7wpwTYE0/kwAAAABJRU5E\nrkJggg==\n" | |
} | |
], | |
"prompt_number": 111 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"<p>The solution is to call set_xticklabels and give it a list of the labels we want, putting in \"\" for the labels we want to skip. The code below achieves it in a single line of code.</p>" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"ax=dfh.plot(kind=\"bar\",x=\"temperature\",title=\"Jan. ave. temperatures for Charleston, SC, 1823-2009\")\n", | |
"r=ax.set_xticklabels(map(lambda x: 3.4+x/5.0 if (x+2)%5==0 else \"\", range(65)))" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"png": 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J7GEzsoI9bMbeYQ+bYRjGDmAP24b0tliTsXr2sE2f39Hfe0fpkxjOkrZi7AaV\nqjVKS4uEZaXSEyUlhVasyHKYq+9icRnGHLCH7WDI3Vs1xsMWa9On77rGTmpNchpnxnqwh80wDGMH\nsIdtQ3rL+mOWyW+LHrY+HrQ+HralPHRr7ztyrslYvbU9bD7DZhiGkQnsYTsY7GGzh83YNuxhMwzD\n2AHsYduQnj1s6Xr2sM0f0x5rMlbPHjbDMAwjCfawHQz2sNnDZmwb9rAZhmHsAPawbUjPHrZ0PXvY\n5o9pjzUZq2cPm2EYhpEEe9gOBnvY7GEztg172AzDMHYAe9g2pGcPW7qePWzzx7THmozVs4fNMAzD\nSII9bAeDPWz2sBnbxiwe9q1bt/DUU08hPDwcEREROHz4sMEFMgzDMPfG4Al76tSpGDhwIM6cOYOT\nJ08iPDxc57aO7kXZYk1y91bZwzZ/THusyVi9tT1sg77Tsbi4GAcOHMC6devqgjg7w93d3ZBQDMMw\njEQM8rBPnDiBZ599FhEREfjll18QFxeHFStWwMXF5a/A7GHbJOxhs4fN2DZNzZ0GnWHX1NTg+PHj\nePfdd9GtWzdMmzYNqampmD9/vtZ2ycnJUKvVAAAPDw/ExMQgPj4ewF//AvCyZZf/QntZbPuBA4eg\noqJM2KZVKzeUl5dKyufiomyk/eabbWar/6/l+AZt8RDnL73Yt56np39p8vyZmZmS+2uu8eNl21vO\nzMzE2rVrAUCYL3VCBnDlyhVSq9XC8oEDB2jQoEFa29QPnZGRIRpHrF1qm7F6R60JAAFEQMafP3W/\nT/psq08eY/okFteYNn36qY9e37HTZ/x4f7ae3lLHqC4Muujo6+uLgIAAZGVlAQD27t2LyMhIQ0Ix\nDMMwEjH4PuxffvkFTz/9NKqrq9GhQwesWbNG68Ije9i2iT4etjF+t7m8cnN42FJrtaSHLfdrDYzh\nNDV38gdnHAyesHnCZmwbqz/8qfEFG93tUtuM1Tt6TfrcHyx1W6l59NHrU5NxbeLt+vRJai5zvE+O\nvj/bY5/E4GeJMAzDyAS2RBwMtkTYEmFsG6tbIgzDMIzxsIdtQ3r2sI2riT1saTHtcX+2xz6JwWfY\nDMMwMoE9bAeDPWz2sBnbhj1shmEYO4A9bBvSs4dtXE3sYUuLaY/7sz32SQw+w2YYhpEJ7GE7GOxh\ns4fN2DbsYTMMw9gB7GHbkJ49bONqYg9bWkx73J/tsU9i8Bk2wzCMTGAP28FgD5s9bMa2YQ+bYRjG\nDmAP24aw+zLxAAAXq0lEQVT07GEbVxN72NJi2uP+bI99EsOgb01nGHMj9k3mJSWFVs0vd6w9pozx\nsIftYMjFwzbWL5baZmxMOXnY7IvLA/awGYZh7AD2sG1Izx62dL2lPGxz6K3tYRtbky3uz/Z5jDaG\nz7AZhmFkAnvYDgZ72OxhmzImY3rYw2YYhrED2MO2IT172NL17GGbP6Y+uay9P9vnMdoYPsNmGIaR\nCexhOxjsYbOHbcqYjOlhD5thGMYOYA/bhvTsYUvXs4dt/pj65LL2/myfx2hj+AybYRhGJrCH7WCw\nh80etiljMqaHPWyGYRg7gD1sG9Kzhy1dzx62+WPqk8va+7N9HqON4TNshmEYmcAetoPBHjZ72KaM\nyZge9rAZhmHsAKMmbI1Gg9jYWAwZMqTJ7Rzdi7LFmtjDNnVM6duyhy1fvaw97BUrViAiIuLPf7UY\nhmEYc2Kwh3358mUkJyfj5ZdfxltvvYVt27ZpB2YP2yZhD5s9bFPGZEyPWTzsf/3rX1iyZAmcnNgG\nZxiGsQTOhoi2b9+Otm3bIjY2tknvJTk5GWq1GtnZ2YiJiUFMTAzi4+MB1Hk2J06cwLRp04Tlu8TH\nxwvL9X+/u2wK/fLlyxvVY229WH9Mrf+L5QBihKWmxrO+56lStUZpaZGwrFR6AoBo21+6v2oUq19X\nTDG9rvrrtj0BYJpI3fHQJhONMb/exUWJiooyYW2rVm4oLy+t27LB+Ivp746fMftjw/rFxr6kpFDy\n/jx06AhJ+0N6+peiemsfD/rqxcbDWH1mZiZSU1Ph6+sLtVqNJiEDmDNnDrVv357UajX5+vqSi4sL\nJSUlaW1TP3RGRoZoHLF2qW3G6h21JgAEEAEZf/7U/T6JbWtMmz416dKboyZr6419n4x57/WpyVIx\n9emTpfWWOkZ1YfR92N9++y2WLl3KHrZMMNbDrsNwv9eYPCRzD1uXXgxLedj61GSpmI6O2e/D5rtE\nGIZhzI/RE3avXr2Qnp7e5DaOfj+lLdZk7H3Yxt6zLLUme7wP29gxMcd7r09NlorJx2hj+BYPhmEY\nmcDPEnEw2MNmD9vQmiwV09HhZ4kwDMPYAfw8bBvSs4ctXc8ethQte9im1rOHzTAMw0iCPWwHgz1s\n9rANrclSMR0d9rAZhmHsAPawbUjPHrZ0PXvYUrTsYZtazx42wzAMIwn2sB0M9rDZwza0JkvFdHTY\nw2YYhrED2MO2IT172NL17GFL0bKHbWo9e9gMwzCMJNjDdjDYw2YP29CaLBXT0WEPm2EYxg5gD9uG\n9OxhS9ezhy1Fyx62qfXsYTMMwzCSYA/bwuj6lmpz6MW2rVvW9hf/ateOyR627XvY+uwP1vawjd33\nHYWm5k6esC2MsReTLHXR0FyTozF18oTdeFtz7A/mmrDNcSHVHrH6RUdH96KM9ZCt60EbqxePyR62\nSKtR77NxMS3pYZvDl7fHeUMM9rAZhmFkAlsiFoYtEcPrZEuELRFHwOqWCMMwDGM87GFbSc8etnF6\n9rBNG5M9bNurSQw+w2YYhpEJ7GFbGPawDa+TPWz2sB0B9rAZhmHsAPawraRnD9s4PXvYpo3JHrbt\n1SQGn2EzDMPIBPawLQx72IbXyR42e9iOAHvYDMMwdgB72FbSs4dtnJ49bNPGZA/b9moSg8+wGYZh\nZAJ72BaGPWzD62QPmz1sR4A9bIZhGDuAPWwr6dnDNk7PHrZpY7KHbXs1iWHQhJ2bm4vevXsjMjIS\nUVFRSEtLMyQMwzAMowcGedhXr17F1atXERMTg7KyMsTFxeGrr75CeHj4X4HZwxaFPWzD62QPmz1s\nR8DkHravry9iYmIAAG5ubggPD0d+fr7hFTIMwzD3xNnYANnZ2fj555/x4IMPNlqXnJwMtVqN7Oxs\nxMTEICYmBvHx8QDqPJsTJ05g2rRpwvJd4uPjkZmZiYEDh6Ciokxob9XKDeXlpZL1DX+/uwwAy5cv\nb1SPqfVi9f/FcgAxwpKLi1LYVqn0RHr6l5L1Yt9GnZ7+Zb1tM9EY7fx125wA0Hg8DdFr6+KFmLrG\nT6remPyN2xu2WUcv9v6J6evvI0DT+5Ou/Vl3/br3x/q5pObXd38SO57Ejm99jkdz6BvGMIU+MzMT\nqamp8PX1hVqtbjQ2WpARlJaWUlxcHG3ZsqXRuvqhMzIyRPVi7fXbABBABGT8+RM6tzVFm6n1YvXr\n6lNdu1jbvfVNx7SOXp/3zto1yVkv93E29hiztN5S84YuDL4P+86dOxg8eDAGDBgg/BWpjyk8bLl7\nXob7e7bhrbKHbft6XTHFsMVxltPxbClM7mETESZPnoyIiAjRyZphGIYxPQZN2AcPHsSGDRuQkZGB\n2NhYxMbGYufOnTq3N8c9y/rorX8/pTFtcteLx+T7sE2tF48pl3G29jFqm/NGYwy66PjII4+gtrbW\nECnDMAxjIDb9LBG5e17sYcvXW5WLXldMMWxxnOV0PFsKfpYIwzCMHSCLZ4mwhy1nvXhMuXir8tGL\nx5TLOFv7GLXNeaMxfIbNMAwjE9jDNiPsYcvXW5WLXldMMWxxnOV0PFsK9rAZhmHsAPawLaCXtzdq\nrF48ply8VfnoxWPKZZytfYza5rzRGD7DZhiGkQnsYZsR9rDl663KRa8rphi2OM5yOp4tBXvYDMMw\ndgB72BbQy9sbNVYvHlMu3qp89OIx5TLO1j5GbXPeaAyfYTMMw8gE9rDNCHvY8vVW5aLXFVMMWxxn\nOR3PloI9bIZhGDuAPWwL6OXtjRqrF48pF29VPnrxmHIZZ2sfo7Y5bzSGz7AZhmHkgs5vezQSAKRU\nehLqTCthmYh0tovFAKjey/hypebWta0+fRKrX1eftNvF2nTrpcW0rF5XTNOOk2lqkrNe7uNsjuPZ\nXMe4VL2x9Tc1Lma96FhH/fD6XXwwx0UKfWJa6iJNY731L2YZq+eaHLdP+sQ0x/GsK7+l9Pr0SVd+\nXTEsZIlkSm63lIctd89PPnpzxDRWb46Y1tabI6axeukxLXedyDi9bq9Z2rb66MVgD5thGEYmsCXC\nlohZ9FyT4/ZJn5hsidikJcIwDMMYC3vYBm8rrrc/H9JYvTliGqs3R0xr680R01i99JjsYetq14bP\nsBmGYWQCe9jsYZtFzzU5bp/0ickeNnvYDMMwdgl72AZvK663Px/SWL05YhqrN0dMa+vNEdNYvfSY\n7GHrateGz7AZhmFkAnvY7GGbRc81OW6f9InJHjZ72AzDMHYJe9gGbyuutz8f0li9OWIaqzdHTGvr\nzRHTWL30mOxh62rXhs+wGYZhZAJ72Oxhm0XPNTlun/SJyR42e9gMwzB2CXvYBm8rrrc/H9JYvTli\nGqs3R0xr680R01i99JjsYetq18bgCXvnzp3o1KkTOnbsiMWLF99j6xOS20+cENtWXC+2rdQ2fWKK\nbyu9T8a1yV3PNVlGL++apB+3xh+jxuh11WRsn3SPlTYGTdgajQYvvvgidu7cidOnT2PTpk04c+ZM\nE4pbkttv3RLbVlwvtq3UNn1iim8rvU/GtcldzzVZRi/vmqQft8Yfo8boddVkbJ90j5U2Bk3YR48e\nRUhICNRqNZo3b47Ro0dj69athoRiGIZhJGLQhJ2Xl4eAgABhuX379sjLy2tCkS25PTtbbFtxvdi2\nUtv0iSm+rbhe+raWimltvTliGqs3R0xr680R01i99JjSj1vjj1Fj9LpqMrZPusdKG4Nu6/vf//6H\nnTt34qOPPgIAbNiwAUeOHME777zzV2Dhtj6GYRhGH3RNy86GBPP390dubq6wnJubi/bt20tKyDAM\nwxiGQZZI165d8ccffyA7OxvV1dX49NNPMXToUFPXxjAMw9TDoDNsZ2dnvPvuu0hISIBGo8HkyZMR\nHh5u6toYhmGYepjto+kMwzCMaTHoDNsSFBQU4PLly1AoFPD394ePj4/sc3Gf5JOLYWwRm5uwf/75\nZ0yZMgW3bt0SLmRevnwZHh4eWLlyJR544AHZ5eI+ySfXmTNnsHXrVuE21fbt22Po0KFmsfwslcve\n8lgylyX7JAmyMTp37kyHDx9u1H7o0CHq3LmzLHNxn+SRKzU1lbp06UKLFi2i9evX0/r162nhwoXU\npUsXWrhwocnyWDKXveWxZC5L9kkqNjdhh4SE6FzXoUMHWebiPskjV0hICFVXVzdqr6qqMkufLJHL\n3vJYMpcl+yQVm7NEBgwYgIEDB2LChAkICAgAESE3NxeffPIJ+vfvL8tc3Cd55GrWrBny8vKgVqu1\n2vPz89GsWTOT5bFkLnvLY8lcluyTVGzyLpFvvvkG6enpgm/k7++PoUOHYuDAgbLNxX2y/Vw7d+7E\niy++iJCQEOHRC7m5ufjjjz/w7rvvYsCAAbLLZW95LJnLkn2Sik1O2AxjLTQaDY4ePYq8vDzhbpSu\nXbvC2dn0/4xaKpe95bFkLkv2SRJWMWIM5P3337e7XNwn+eRiGGvDXxHGMBIYNGiQ3eWytzyWzGXJ\nPtVHFpbI+PHj8cknn5g9z4EDB3D06FFER0ejX79+Jot7+PBhhIeHw93dHeXl5UhNTcXx48cRGRmJ\nOXPmwMPDw2S50tLSMHz4cK3H35qDqqoqbN68Gf7+/ujbty82btyIH374AREREfjHP/6BFi1amDTf\n+fPn8eWXX+Ly5ctwcnJCWFgYxowZA5VKZdI8usjPz0e7du3sKpe95bFkLkv2qT42N2EPGTIECoX2\nNw/v378fffr0gUKhQHp6uslyde/eHUePHgUAfPTRR/jvf/+L4cOHY/fu3Rg8eDDmzJljkjwRERE4\nefIknJ2d8Y9//AOurq546qmnsHfvXpw8eRJffvmlSfIAgLu7O1xcXNChQweMGTMGI0eOhLe3t8ni\n32XMmDHQaDQoLy+Hh4cHysrKMGLECOzduxcAsG7dOpPlWrFiBbZv345evXrh66+/RmxsLDw8PLBl\nyxasXLkSvXv3NlkuRp5cu3YNbdu2tXYZ5seqhowIMTExNGbMGNq/fz9lZmZSRkYG+fr6UmZmJmVm\nZpo8113i4uLo2rVrRERUVlZGkZGRJsvTqVMn4ffY2Fitdab+kElMTAxpNBratWsXTZw4kdq0aUMJ\nCQm0du1aKikpMVmeqKgoIiK6c+cOeXt70507d4iIqLa2VlhnKiIjI6mmpoaIiG7fvk2PPfYYERHl\n5ORQly5dTJanqKiIZs2aRWFhYeTh4UGenp4UFhZGs2bNoqKiIpPluRf9+/c3Waxbt27RrFmzaOzY\nsbRx40atdVOmTDFZnkuXLtHkyZOFsUpOTqbIyEgaN24cFRQUmCwPEdHNmze1Xjdu3KCgoCBh2VTs\n2LFD+L2oqIgmTZpEUVFRlJiYSFevXjVZHn2wOQ/72LFjiIuLwxtvvAGVSoX4+Hi0bNkSvXr1Qq9e\nvUyaS6PRoLCwEDdv3oRGoxHORF1dXU16FTgyMhKrV68GAHTp0gU//vgjACArK8vk1gEAODk5oV+/\nfli9ejXy8vIwZcoU7NixA8HBwSbLUVtbi6qqKpSWlqKiogLFxcUAgMrKStTW1posD1D3ZRh37twR\n4t++fRsAEBgYKLSbglGjRsHT0xOZmZkoLCxEYWEhMjIy4OHhgVGjRpksDwAcP35c9PXTTz/h559/\nNlmeiRMnAgCefPJJbNq0CU8++SQqKysBAIcOHTJZnuTkZHTp0gXu7u546KGHEBYWhm+++Qbdu3fH\nlClTTJYHANq0aYO4uDjh1bVrV+Tl5Qm/m4r6/2FPnz4dfn5+2LZtG7p164Znn33WZHn0wip/JiSQ\nm5tLTz31FD3//PPUvn17s+QICgoitVpNarWagoODKT8/n4iISkpKTH7mNn78eAoODqbu3buTs7Mz\nqdVqevTRR+nEiRMmy0Ok/V9DQ8rKykyWZ+HChRQcHEyhoaH0wQcfUHh4OE2ePJkiIyNp8eLFJstD\nRLR8+XKKioqiyZMnU2hoKK1atYqIiAoKCujRRx81WZ6OHTsatM4QnJycKD4+XvTVsmVLk+Vp+B/c\nggULqGfPnnT9+vUm9xV9qX+8BAQE6FxnCpYuXUoJCQn0yy+/CG1qtdqkOYi0j6XOnTtTbW2t1rI1\nsNkJ+y7btm2jOXPmWDTn7du36cKFCyaPe+vWLfr555/pxx9/pCtXrpg8PhHR2bNnzRJXjIsXLwr/\ngp47d442b95s8j9Ad/n111/p888/pzNnzpglPhFR3759afHixVr/7l65coVSU1Pp8ccfN2muiIgI\n+v3330XXmfIEpVOnTqTRaLTa1qxZQxERERQYGGiyPPUnsLlz52qtM7VFRlRnwTz11FM0bdo0Ki4u\nNsuE7e/vT8uWLaOlS5dSUFCQ1oQdHR1t8nxSsPkJm2Esxc2bN2nmzJmCh+3h4UFhYWE0c+ZMk3qj\nRESfffaZzj8+W7ZsMVmeGTNm0O7duxu179ixo8lntOjLK6+8InqNJCsri5588kmT5WnIV199Rd27\nd6e2bduaPPa8efMoJSVFeN314vPz8ykpKcnk+aTAEzbDSGD16tUWy3XX8uE80rh9+zadPHmSiCz3\nPllq7Bpic7f1MYwtEhAQoPXF0/aQy97yWDKXJftUH5t7Wh/DWIvo6Gid6woKCmSZy97y3CvXtWvX\nLJLH1H2SCk/YDPMn165dw86dO+Hp6dloXc+ePWWZy97yWDKXJfskFZ6wGeZPBg0ahLKyMsTGxjZa\nZ+rPAFgql73lsWQuS/ZJKuxhMwzDyASb+6QjwzAMIw5P2AzDMDKBJ2zGahQXF+O9996zdhn3ZPny\n5aioqLB2GQzDEzZjPYqKirBy5UprlwGq+wCZzvUrVqxAeXm5XjE1Go2xZTFMI3jCZqzG7Nmzcf78\necTGxuLf//43li5diu7du6NLly5ISUkBAGRnZ6NTp06YOHEiwsLCMHbsWOzevRsPP/wwQkNDhScf\npqSkICkpCT179kRoaCg+/vhjIc+SJUtE44aFhWHChAmIjo5Gbm4unn/+eXTr1g1RUVHCdmlpacjP\nz0fv3r3x+OOPAwDc3NyE2F988YXwRLzk5GQ899xzeOihhzBr1iycP38eAwYMQNeuXfHYY4/h999/\nN/OIMnaPVT5fyTBElJ2dLTwYaNeuXfTMM88QEZFGo6HBgwfTd999RxcvXiRnZ2c6deoU1dbWUlxc\nHE2aNImIiLZu3UrDhg0jorrnPsTExFBlZSXduHGDAgICKD8/v8m4Tk5OdOTIEaGewsJCIiKqqamh\n+Ph4+vXXX4mo7klw9Z8l4ubmJvz+xRdfUHJyMhERTZgwgYYMGSI8JKhPnz70xx9/EBHR4cOHqU+f\nPiYeQcbR4PuwGatB9WyI3bt3Y/fu3cI9r7dv38a5c+cQEBCA4OBgREZGAqh7tnjfvn0BAFFRUcjO\nzgZQ98zsJ554Avfddx/uu+8+9O7dG0ePHsWBAwd0xg0KCkL37t2FGj799FN89NFHqKmpwZUrV3D6\n9GlERUVJ7o9CocDIkSOhUChQVlaGQ4cOYeTIkcL66upqwwaKYf6EJ2zGZpgzZw6eeeYZrbbs7Gzc\nd999wrKTk5PwpQ9OTk6oqanRGU+hUDQZ19XVVVi+ePEili1bhmPHjsHd3R0TJ04UHvSvKy6ARhcj\nXVxcANR9wYOHh4dJv4yAYdjDZqyGUqlEaWkpAAjfkHP322Ty8vJw/fp1ybGICFu3bkVVVRVu3ryJ\nzMxMdO/eHQkJCZLilpSUwNXVFSqVCgUFBdixY4dWnSUlJcKyj48Pzp49i9raWmzZskVrAr+LSqVC\ncHAwvvjiC6G+kydPSu4Pw4jBZ9iM1fDy8sLDDz+M6OhoDBgwAGPGjEGPHj0A1E2SGzZsgEKhaDQh\n1l+++7tCoUDnzp3Ru3dv3LhxA6+99hp8fX3h6+uLM2fO3DNuly5dEBsbi06dOiEgIACPPPKIsO6Z\nZ55B//794e/vj3379iE1NRWDBw+Gt7c3unbtKvwxaFjbxo0bMWXKFCxYsAB37txBYmIiOnfubMIR\nZBwN/mg6Yxf85z//gZubG6ZPn27tUhjGbLAlwtgNYtYEw9gTfIbNMAwjE/gMm2EYRibwhM0wDCMT\neMJmGIaRCTxhMwzDyASesBmGYWQCT9gMwzAy4f8DRGD+YBKvFT8AAAAASUVORK5CYII=\n" | |
} | |
], | |
"prompt_number": 112 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 112 | |
} | |
], | |
"metadata": {} | |
} | |
] | |
} |
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