fpraktikum/GL/scripts/Untitled1.ipynb

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   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "data = pd.read_excel(r'./../messungen/messungen.xlsx', sheet_name='A3a')\n",
    "data = data.sort_values(by=['Lcm'])"
   ]
  },
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   "source": [
    "%matplotlib qt5\n",
    "plt.errorbar(data['Lcm'], data['mean'], yerr=data['sttdev'],\n",
    "             xerr=.5, linestyle='none', marker='x', markersize=10,\n",
    "             ecolor='red', elinewidth=1, barsabove=True,)\n",
    "plt.xlabel('L [cm]')\n",
    "plt.ylabel('P [mWatt]')\n",
    "plt.grid()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.991\n",
       "1    1.010\n",
       "2    0.990\n",
       "4    0.825\n",
       "3    0.313\n",
       "Name: mean, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "data['mean']"
   ]
  },
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