AKuederle/pandas_xlim_issue.ipynb

## pandas_xlim_issue.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>I</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.000000</th>\n",
       "      <td>-0.004425</td>\n",
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       "    <tr>\n",
       "      <th>0.002441</th>\n",
       "      <td>-0.003018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.004883</th>\n",
       "      <td>-0.002408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.007324</th>\n",
       "      <td>-0.001544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.009766</th>\n",
       "      <td>-0.000931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.012207</th>\n",
       "      <td>-0.000494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.014648</th>\n",
       "      <td>-0.000220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.017090</th>\n",
       "      <td>0.000177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.019531</th>\n",
       "      <td>0.000436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.021973</th>\n",
       "      <td>0.000702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.024414</th>\n",
       "      <td>0.000888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.026855</th>\n",
       "      <td>0.001254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.029297</th>\n",
       "      <td>0.001349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.031738</th>\n",
       "      <td>0.001483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.034180</th>\n",
       "      <td>0.001782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.036621</th>\n",
       "      <td>0.002014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.039062</th>\n",
       "      <td>0.002142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.041504</th>\n",
       "      <td>0.002252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.043945</th>\n",
       "      <td>0.002502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.046387</th>\n",
       "      <td>0.002539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.048828</th>\n",
       "      <td>0.002930</td>\n",
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       "    <tr>\n",
       "      <th>0.051270</th>\n",
       "      <td>0.003015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.053711</th>\n",
       "      <td>0.003149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.056152</th>\n",
       "      <td>0.003064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.058594</th>\n",
       "      <td>0.003268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.061035</th>\n",
       "      <td>0.003284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.063477</th>\n",
       "      <td>0.003662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.065918</th>\n",
       "      <td>0.003696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.068359</th>\n",
       "      <td>0.003857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.070801</th>\n",
       "      <td>0.003937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.073242</th>\n",
       "      <td>0.000735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.070801</th>\n",
       "      <td>0.000995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.068359</th>\n",
       "      <td>0.001517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.065918</th>\n",
       "      <td>0.001694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.063477</th>\n",
       "      <td>0.001608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.061035</th>\n",
       "      <td>0.002039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.058594</th>\n",
       "      <td>0.002426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.056152</th>\n",
       "      <td>0.002585</td>\n",
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       "    <tr>\n",
       "      <th>-0.053711</th>\n",
       "      <td>0.002582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.051270</th>\n",
       "      <td>0.002997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.048828</th>\n",
       "      <td>0.003391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.046387</th>\n",
       "      <td>0.003445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.043945</th>\n",
       "      <td>0.003653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.041504</th>\n",
       "      <td>0.004019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.039062</th>\n",
       "      <td>0.004129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.036621</th>\n",
       "      <td>0.004065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.034180</th>\n",
       "      <td>0.004523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.031738</th>\n",
       "      <td>0.004800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.029297</th>\n",
       "      <td>0.005118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.026855</th>\n",
       "      <td>0.005154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.024414</th>\n",
       "      <td>0.005441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.021973</th>\n",
       "      <td>0.005652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.019531</th>\n",
       "      <td>0.005713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.017090</th>\n",
       "      <td>0.005975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.014648</th>\n",
       "      <td>0.006259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.012207</th>\n",
       "      <td>0.006375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.009766</th>\n",
       "      <td>0.006555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.007324</th>\n",
       "      <td>0.006882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.004883</th>\n",
       "      <td>0.007031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.002441</th>\n",
       "      <td>0.007092</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>820 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  I\n",
       "U                  \n",
       " 0.000000 -0.004425\n",
       " 0.002441 -0.003018\n",
       " 0.004883 -0.002408\n",
       " 0.007324 -0.001544\n",
       " 0.009766 -0.000931\n",
       " 0.012207 -0.000494\n",
       " 0.014648 -0.000220\n",
       " 0.017090  0.000177\n",
       " 0.019531  0.000436\n",
       " 0.021973  0.000702\n",
       " 0.024414  0.000888\n",
       " 0.026855  0.001254\n",
       " 0.029297  0.001349\n",
       " 0.031738  0.001483\n",
       " 0.034180  0.001782\n",
       " 0.036621  0.002014\n",
       " 0.039062  0.002142\n",
       " 0.041504  0.002252\n",
       " 0.043945  0.002502\n",
       " 0.046387  0.002539\n",
       " 0.048828  0.002930\n",
       " 0.051270  0.003015\n",
       " 0.053711  0.003149\n",
       " 0.056152  0.003064\n",
       " 0.058594  0.003268\n",
       " 0.061035  0.003284\n",
       " 0.063477  0.003662\n",
       " 0.065918  0.003696\n",
       " 0.068359  0.003857\n",
       " 0.070801  0.003937\n",
       "...             ...\n",
       "-0.073242  0.000735\n",
       "-0.070801  0.000995\n",
       "-0.068359  0.001517\n",
       "-0.065918  0.001694\n",
       "-0.063477  0.001608\n",
       "-0.061035  0.002039\n",
       "-0.058594  0.002426\n",
       "-0.056152  0.002585\n",
       "-0.053711  0.002582\n",
       "-0.051270  0.002997\n",
       "-0.048828  0.003391\n",
       "-0.046387  0.003445\n",
       "-0.043945  0.003653\n",
       "-0.041504  0.004019\n",
       "-0.039062  0.004129\n",
       "-0.036621  0.004065\n",
       "-0.034180  0.004523\n",
       "-0.031738  0.004800\n",
       "-0.029297  0.005118\n",
       "-0.026855  0.005154\n",
       "-0.024414  0.005441\n",
       "-0.021973  0.005652\n",
       "-0.019531  0.005713\n",
       "-0.017090  0.005975\n",
       "-0.014648  0.006259\n",
       "-0.012207  0.006375\n",
       "-0.009766  0.006555\n",
       "-0.007324  0.006882\n",
       "-0.004883  0.007031\n",
       "-0.002441  0.007092\n",
       "\n",
       "[820 rows x 1 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Leervoltagram = pd.read_csv(\"./Messdaten/Referenz_2_20mV_s.txt\",index_col=0, sep=\";\", decimal=\",\")\n",
    "Leervoltagram.rename(columns={\"WE(1).Current (A)\":\"I\"}, inplace=True)\n",
    "Leervoltagram.index.names = [\"U\"]\n",
    "Leervoltagram.I = Leervoltagram.I * 10**6\n",
    "Leervoltagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8c78470>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1f80BepxDA1i1ahVnzkwPgV4G6WmWczSaa4zFrAVM/PKY+RdIt0Eac+5jzik05p5no5eK\n150HTJvI6qEGp06frcnc37NedjLXGEt/7M3WavXqVZw6NfEzU/v73kOjOb/v9Q+LWt8zP1RmhSvx\nAzK6sR7TWZNOJdbDD5WRJM3IEJCkghkCklQwQ0CSCmYISFLB5v0S0Yh4KXA38CrgEHBNZj4zQ7u/\nA34beCozX9Nvf0nS4NQ5E/gQcH9mXgp8o1qeyWeA0Rr9JUkDUicErgbuqr6+C3jbTI0y8wHg6fn2\nlyQNTp0QuDAzj1dfHwcuXOT+kqSauj4nEBH3A2tm2HRL+0JmtiJi3m89rttfkjQ/XUMgMzfPti0i\njkfEmsx8MiJeATzV577n07/RbA73uZtznzXpZD2msyadrMdZdS4H7QOurb6+FvjKIveXJNVUJwQ+\nAWyOiB8AV1bLRMRFEfHVyUYR8QXg34FLI+JwRPxht/6SpMWz0u4iKklaQL5jWJIKZghIUsEMAUkq\n2KJ+vGREjAK7gCFgT2bunKHNJ4GrgOeA6zLz4W59u92DKCI+DLwHOA38cWbeN9AHWEMf92JasDpE\nxDeZeB/If1fDb87MHw/mEfZnUPWo1n8J+FXgs5l5U9tYvwJ8FngBcG9m3jyoxzcfS1STb7JMjxEY\naE02A38FnA/8H/BnmfmvVZ9lfZz0a9HOBCJiCNjNxH2ENgDbIuLyKW3eAlySmeuBG4Dbeug74z2I\nImIDsLVqPwp8KiKW85nPnPdSWsA6PP+RvMAfZObG6t+y+eFmQPUA/gf4CPCnM+zzNuD66vhbX/3i\nWE6WoibL+RiBwdVkHPidzLyCiZewf65tyOV+nPRlMX8pbgLGMvNQZp4E9gJbprR5/n5CmfkgcEFE\nrJmj72z3INoCfCEzT2bmIWCsGme56uVeSgtVh9e2jbkAHys+EAOpR2Y+l5nfAv63faDqDYvDmfmd\natXfz7LPpbSoNWmzXI8RGFxNvpuZT1brHwN+PiLOWyHHSV8WMwTWAofblo9U63ppc1GXvrPdg+ii\nql23/S0nvdxLqVsN+6nDRW3Ld0XEwxHxkTqTH4BB1WPS1NdGr6WzTkdZfsfLYtdk0nI9RmDwNQH4\nPeA/qgBZCcdJXxbzOYFe35DQy18djZnG6+EeREv6pogFuBfT1HXzrcOkd2TmsYj4BeBLEfGuzPzc\nnL0WyDKsx5JbhjVZ0mMElrYmEfHLVG9s7WvSK8hihsBRYKRteYTORJ2pzcVVm/NmWH+0+nq2exDN\nNNZRltAC3Iup22Pquw6Zeaz6/2cR8Q9MnDYv2g/4EtVjNker/jONtWiWWU2W/Bip9r0kNYmIi4F7\ngHdl5o/axlry42QhLebloANMPImyLiLOZ+LJyn1T2uwD3g0QEa8DnqlO1br1ne0eRPuA34+I8yPi\n1cB6YPI63nLUy72UFqwOETEUES8DiIjzgLcC31vgx1THoOoxqeOMMzOfAJ6NiNdWT5y/a5Z9LqVF\nrckKOEZgQDWJiAuArwIfzMxvTw60Qo6TvixaCGTmKWAHsJ+JJ1ruzsyDEbE9IrZXbe4FHo+IMeB2\n4MZufauhZ7wHUWY+Bnyxav/PwI2ZuZwvCcx5L6YFrsMLgK9FxCPAw0xcM71jMR5ojwZSj2qMQ8Ct\nwHUxcT+ry6pNNwJ7gB8y8UTi1wb6CPu32DX5OZb3MQKDq8kO4JeAP6+eD3l4MhBZ/sdJX7x3kCQV\nbDm/bl6SNGCGgCQVzBCQpIIZApJUMENAkgpmCEhSwQwBaZ6qNx/9aIb1Z5ZiPtJ8GAKSVDBDQJIK\nZghIUsEMAWn+pl37r24q5r1YtGIYAtL8PQ384pR1L6/WSyuCISDNU2aeAH4YEb/btvoG4P4lmpLU\nN+8iKtUQEeuZ+ODxlwHnA48A78/Mny7pxKQeGQKSVDAvB0lSwQwBSSqYISBJBTMEJKlghoAkFcwQ\nkKSCGQKSVDBDQJIK9v9ZVDNNZxI4ZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8c45080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Leervoltagram.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x8b0b208>]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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crXWVUuoS4DFgVTjrionl8frZe7yN4/U9HK7rpLnTee4xC1BZkk1ZQQZVJTnM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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8ab50b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Leervoltagram.index, Leervoltagram.I)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.4, 1)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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d3Q4XL/z5BFs+qAegzJbBl6+ei6rIle6kGBdRQCilrMCjwNVAE7BTKfWq1rp2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8c5d0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Leervoltagram.plot()\n",
    "plt.xlim([-0.4,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}