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January 3, 2018 11:39
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import pandas as pd\n", | |
| "import io\n", | |
| "%matplotlib inline" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": { | |
| "collapsed": true | |
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| "outputs": [], | |
| "source": [ | |
| "data = ',#67,#85,#94\\n2010-08-16,0,30,0\\n2010-08-18,0,2,0\\n2011-03-07,0,32,0\\n2012-04-27,1,0,0\\n2016-11-07,31,0,0\\n2016-11-11,32,0,0\\n2017-05-21,0,0,2\\n2017-06-21,0,0,2\\n2017-07-18,0,0,2\\n'" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "filelike = io.StringIO(data)\n", | |
| "occurances_df = pd.read_csv(filelike,index_col=0)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "outputs": [ | |
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| " <th></th>\n", | |
| " <th>#67</th>\n", | |
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| " <th>2010-08-16</th>\n", | |
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| " <th>2011-03-07</th>\n", | |
| " <td>0</td>\n", | |
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| " <th>2012-04-27</th>\n", | |
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| " <th>2016-11-11</th>\n", | |
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| " #67 #85 #94\n", | |
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| "occurances_df" | |
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| "'0.22.0'" | |
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| "execution_count": 5, | |
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| "pd.__version__" | |
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| "execution_count": 6, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0x2d7b5321e10>" | |
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sQIN7ukNEnFhh1+E1zmJmZv3gmaJmZiXhQjczKwkXuplZSbjQzcxKwoVuZlYSLnQzs5Jw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\nJeFCNzMrif8PDMc+AJ7SQKIAAAAASUVORK5CYII=\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x2d7bab73b00>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "occurances_df.plot(kind='bar',align=\"center\")" | |
| ] | |
| }, | |
| { | |
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| "execution_count": null, | |
| "metadata": { | |
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| }, | |
| "outputs": [], | |
| "source": [] | |
| } | |
| ], | |
| "metadata": { | |
| "anaconda-cloud": {}, | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "language": "python", | |
| "name": "python3" | |
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| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
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| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
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| "c5af4cb5894f43dea5e3ec5a0469fdbf": { | |
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