NMEAparser-FIK.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NMEA 0183 Data Parser with Python Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime, timedelta\n",
    "from IPython.display import Image as ImageDisp\n",
    "from pandas import DataFrame\n",
    "import string\n",
    "import os\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline --no-import-all\n",
    "#%matplotlib notebook\n",
    "#%matplotlib qt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Logfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fto = 'DATALOG2.TXT' #  File to Open\n",
    "l=[]\n",
    "l.extend(range(0,22))\n",
    "df = pd.read_csv(fto, sep=',', header=None, names=l, comment='*')\n",
    "df.drop(df[df[0]=='$GPTXT'].index, inplace=True) # Remove unused sentences\n",
    "df.drop(df[df[0]=='$GPRMC'].index, inplace=True)\n",
    "df.drop(df[df[0]=='$GPVTG'].index, inplace=True)\n",
    "df.drop(df[df[0]=='$GPGLL'].index, inplace=True)\n",
    "df.drop(df[df[0]=='$GPGSA'].index, inplace=True)\n",
    "\n",
    "# Convert strings to numbers\n",
    "df = df.apply(pd.to_numeric, errors='ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Concatenate GGA and GSV lines to one table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1 = df.loc[df[0]=='$GPGGA'] # Parse GGA sentences\n",
    "df2 = df.loc[df[2]==1]   # Parse GSV sentences\n",
    "df3 = df.loc[df[2]==2]\n",
    "df4 = df.loc[df[2]==3]\n",
    "df1 = df1.reset_index()\n",
    "df2 = df2.reset_index()\n",
    "df3 = df3.reset_index()\n",
    "df4 = df4.reset_index()\n",
    "df5 = pd.concat([df1,df2,df3,df4], axis=1,  ignore_index=True)\n",
    "# Convert NMEA time to pandas time\n",
    "df5[2] = pd.to_datetime(df5[2], format='%H%M%S.%f').dt.time\n",
    "#df5=df5.rename(columns = {2:'time'})#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filter minimal elevation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>82</th>\n",
       "      <th>83</th>\n",
       "      <th>84</th>\n",
       "      <th>85</th>\n",
       "      <th>86</th>\n",
       "      <th>87</th>\n",
       "      <th>88</th>\n",
       "      <th>89</th>\n",
       "      <th>90</th>\n",
       "      <th>91</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 92 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 92 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MINELEV = 20\n",
    "#df5[29][df5[27]<MINELEV] = 0\n",
    "#df['FirstName'][df.ID == 103] = \"Matt\"\n",
    "df5[df5[27] < MINELEV]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate average SNR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df5['avgsnr'] = df5[range(29,42,4) + range(50,63,4) + range(71,84,4)].mean(axis=1)\n",
    "#df5.ix[10][22:]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot SNR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7febb9884710>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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DYgh2US9YkPo6XaJ+O4rkEQRBEERuIcscP3l1LfY3tKNXUR6evWky8l2OmPtxbi68oqZr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\nAAAAtBAKeQAAAADQQv4fDQIKBPybDgEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7febb9a26e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams.update({'font.size': 11})\n",
    "\n",
    "\n",
    "df5.plot(x=2,y='avgsnr',figsize=(15,5))#,xticks=['10:30:00','11:00:00','11:30:00','12:00:00','12:30:00','13:00:00'])\n",
    "plt.grid(b=True, which='major', color='grey', linestyle='--')\n",
    "#plt.grid(b=True, which='minor', color='grey', linestyle='-')\n",
    "plt.xlabel('UT')\n",
    "plt.ylabel('SNR [dB]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0  1  2  3  4\n",
      "0  1  1  1  2  2\n",
      "1  1  2  3  4  5\n",
      "2  1  2  3  3  5\n",
      "1    15\n",
      "2    14\n",
      "dtype: int64\n",
      "1    9\n",
      "2    8\n",
      "dtype: int64\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame([[1,1,1,2,2],[1,2,3,4,5],[1,2,3,3,5]])\n",
    "print df\n",
    "print df[df[1]==2].sum(axis=1)\n",
    "print df[df[1]==2][[3,4]].sum(axis=1)\n",
    "print df[df[1]==2].count()[3]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          0       1         2           3    4           5    6    7    8  \\\n",
      "0       2.0  $GPGGA  10:09:29         NaN  NaN         NaN  NaN  0.0  0.0   \n",
      "1       9.0  $GPGGA  10:09:30         NaN  NaN         NaN  NaN  0.0  0.0   \n",
      "2      16.0  $GPGGA  10:09:44         NaN  NaN         NaN  NaN  0.0  0.0   \n",
      "3      26.0  $GPGGA  10:09:58         NaN  NaN         NaN  NaN  0.0  4.0   \n",
      "4      33.0  $GPGGA  10:10:12  5301.62081    N  1837.64690    E  1.0  6.0   \n",
      "5      42.0  $GPGGA  10:10:27  5301.62141    N  1837.65152    E  1.0  6.0   \n",
      "6      54.0  $GPGGA  10:10:42  5301.62196    N  1837.64803    E  1.0  6.0   \n",
      "7      66.0  $GPGGA  10:10:57  5301.62134    N  1837.64756    E  1.0  6.0   \n",
      "8      72.0  $GPGGA  10:11:11  5301.62222    N  1837.64671    E  1.0  6.0   \n",
      "9      84.0  $GPGGA  10:11:26  5301.62215    N  1837.64698    E  1.0  6.0   \n",
      "10     96.0  $GPGGA  10:11:41  5301.62259    N  1837.64640    E  1.0  8.0   \n",
      "11    102.0  $GPGGA  10:11:55  5301.62250    N  1837.64713    E  1.0  8.0   \n",
      "12    114.0  $GPGGA  10:12:10  5301.62235    N  1837.64735    E  1.0  8.0   \n",
      "13    125.0  $GPGGA  10:12:25  5301.62238    N  1837.64730    E  1.0  8.0   \n",
      "14    132.0  $GPGGA  10:12:39  5301.62231    N  1837.64702    E  1.0  8.0   \n",
      "15    144.0  $GPGGA  10:12:54  5301.62220    N  1837.64611    E  1.0  8.0   \n",
      "16    155.0  $GPGGA  10:13:09  5301.62238    N  1837.64618    E  1.0  8.0   \n",
      "17    162.0  $GPGGA  10:13:23  5301.62227    N  1837.64659    E  1.0  8.0   \n",
      "18    174.0  $GPGGA  10:13:38  5301.62248    N  1837.64630    E  1.0  8.0   \n",
      "19    185.0  $GPGGA  10:13:53  5301.62380    N  1837.64558    E  1.0  9.0   \n",
      "20    192.0  $GPGGA  10:14:07  5301.62379    N  1837.64486    E  1.0  9.0   \n",
      "21    204.0  $GPGGA  10:14:22  5301.62358    N  1837.64564    E  1.0  8.0   \n",
      "22    215.0  $GPGGA  10:14:37  5301.62258    N  1837.64550    E  1.0  9.0   \n",
      "23    222.0  $GPGGA  10:14:51  5301.62217    N  1837.64555    E  1.0  9.0   \n",
      "24    234.0  $GPGGA  10:15:03  5301.62243    N  1837.64534    E  1.0  9.0   \n",
      "25    245.0  $GPGGA  10:15:18  5301.62265    N  1837.64516    E  1.0  8.0   \n",
      "26    252.0  $GPGGA  10:15:32  5301.62248    N  1837.64552    E  1.0  9.0   \n",
      "27    263.0  $GPGGA  10:15:47  5301.62249    N  1837.64563    E  1.0  9.0   \n",
      "28    274.0  $GPGGA  10:16:02  5301.62184    N  1837.64619    E  1.0  9.0   \n",
      "29    285.0  $GPGGA  10:16:17  5301.62195    N  1837.64586    E  1.0  9.0   \n",
      "...     ...     ...       ...         ...  ...         ...  ...  ...  ...   \n",
      "1472    NaN     NaN       NaN         NaN  NaN         NaN  NaN  NaN  NaN   \n",
      "1473    NaN     NaN       NaN         NaN  NaN         NaN  NaN  NaN  NaN   \n",
      "1474    NaN     NaN       NaN         NaN  NaN         NaN  NaN  NaN  NaN   \n",
      "1475    NaN     NaN       NaN         NaN  NaN         NaN  NaN  NaN  NaN   \n",
      "1476    NaN     NaN       NaN         NaN  NaN         NaN  NaN  NaN  NaN   \n",
      "1477    NaN     NaN       NaN         NaN  NaN         NaN  NaN  NaN  NaN   \n",
      "1478    NaN     NaN       NaN         NaN  NaN         NaN  NaN  NaN  NaN   \n",
      "1479    NaN     NaN       NaN         NaN  NaN         NaN  NaN  NaN  NaN   \n",
      "1480    NaN     NaN       NaN         NaN  NaN         NaN  NaN  NaN  NaN   \n",
      "1481    NaN     NaN       NaN         NaN  NaN         NaN  NaN  NaN  NaN   \n",
      "1482    NaN     NaN       NaN         NaN  NaN         NaN  NaN  NaN  NaN   \n",
      "1483    NaN     NaN       NaN         NaN  NaN         NaN  NaN  NaN  NaN   \n",
      "1484    NaN     NaN       NaN         NaN  NaN         NaN  NaN  NaN  NaN   \n",
      "1485    NaN     NaN       NaN         NaN  NaN         NaN  NaN  NaN  NaN   \n",
      "1486    NaN     NaN       NaN         NaN  NaN         NaN  NaN  NaN  NaN   \n",
      "1487    NaN     NaN       NaN         NaN  NaN         NaN  NaN  NaN  NaN   \n",
      "1488    NaN     NaN       NaN         NaN  NaN         NaN  NaN  NaN  NaN   \n",
      "1489    NaN     NaN       NaN         NaN  NaN         NaN  NaN  NaN  NaN   \n",
      "1490    NaN     NaN       NaN         NaN  NaN         NaN  NaN  NaN  NaN   \n",
      "1491    NaN     NaN       NaN         NaN  NaN         NaN  NaN  NaN  NaN   \n",
      "1492    NaN     NaN       NaN         NaN  NaN         NaN  NaN  NaN  NaN   \n",
      "1493    NaN     NaN       NaN         NaN  NaN         NaN  NaN  NaN  NaN   \n",
      "1494    NaN     NaN       NaN         NaN  NaN         NaN  NaN  NaN  NaN   \n",
      "1495    NaN     NaN       NaN         NaN  NaN         NaN  NaN  NaN  NaN   \n",
      "1496    NaN     NaN       NaN         NaN  NaN         NaN  NaN  NaN  NaN   \n",
      "1497    NaN     NaN       NaN         NaN  NaN         NaN  NaN  NaN  NaN   \n",
      "1498    NaN     NaN       NaN         NaN  NaN         NaN  NaN  NaN  NaN   \n",
      "1499    NaN     NaN       NaN         NaN  NaN         NaN  NaN  NaN  NaN   \n",
      "1500    NaN     NaN       NaN         NaN  NaN         NaN  NaN  NaN  NaN   \n",
      "1501    NaN     NaN       NaN         NaN  NaN         NaN  NaN  NaN  NaN   \n",
      "\n",
      "          9    ...        83     84    85    86    87     88    89  90  91  \\\n",
      "0     99.99    ...       NaN    NaN   NaN   NaN   NaN    NaN   NaN NaN NaN   \n",
      "1     99.99    ...       NaN    NaN   NaN   NaN   NaN    NaN   NaN NaN NaN   \n",
      "2     99.99    ...       NaN    NaN   NaN   NaN   NaN    NaN   NaN NaN NaN   \n",
      "3     29.10    ...       NaN    NaN   NaN   NaN   NaN    NaN   NaN NaN NaN   \n",
      "4      1.41    ...       NaN    NaN   NaN   NaN   NaN    NaN   NaN NaN NaN   \n",
      "5      1.41    ...       NaN    NaN   NaN   NaN   NaN    NaN   NaN NaN NaN   \n",
      "6      1.41    ...       NaN    NaN   NaN   NaN   NaN    NaN   NaN NaN NaN   \n",
      "7      1.41    ...       NaN    NaN   NaN   NaN   NaN    NaN   NaN NaN NaN   \n",
      "8      1.40    ...       NaN    NaN   NaN   NaN   NaN    NaN   NaN NaN NaN   \n",
      "9      1.40    ...      29.0  172.0  30.0   NaN   NaN    NaN   NaN NaN NaN   \n",
      "10     1.14    ...      22.0  220.0  36.0  39.0  29.0  172.0  36.0 NaN NaN   \n",
      "11     1.14    ...      29.0  172.0  37.0   NaN   NaN    NaN   NaN NaN NaN   \n",
      "12     1.14    ...      29.0  172.0  37.0   NaN   NaN    NaN   NaN NaN NaN   \n",
      "13     1.14    ...      29.0  172.0  33.0   NaN   NaN    NaN   NaN NaN NaN   \n",
      "14     1.14    ...      29.0  172.0  32.0   NaN   NaN    NaN   NaN NaN NaN   \n",
      "15     1.14    ...      29.0  172.0  35.0   NaN   NaN    NaN   NaN NaN NaN   \n",
      "16     1.14    ...      22.0  220.0  39.0   NaN   NaN    NaN   NaN NaN NaN   \n",
      "17     1.14    ...      22.0  220.0  39.0   NaN   NaN    NaN   NaN NaN NaN   \n",
      "18     1.14    ...      22.0  220.0  28.0   NaN   NaN    NaN   NaN NaN NaN   \n",
      "19     0.92    ...      22.0  220.0  27.0   NaN   NaN    NaN   NaN NaN NaN   \n",
      "20     0.92    ...      29.0  172.0  35.0   NaN   NaN    NaN   NaN NaN NaN   \n",
      "21     0.96    ...      29.0  172.0  32.0   NaN   NaN    NaN   NaN NaN NaN   \n",
      "22     0.96    ...      29.0  172.0  31.0   NaN   NaN    NaN   NaN NaN NaN   \n",
      "23     1.08    ...      29.0  172.0  34.0   NaN   NaN    NaN   NaN NaN NaN   \n",
      "24     0.96    ...      29.0  172.0  33.0   NaN   NaN    NaN   NaN NaN NaN   \n",
      "25     1.13    ...      29.0  172.0  36.0   NaN   NaN    NaN   NaN NaN NaN   \n",
      "26     0.92    ...      29.0  172.0  34.0   NaN   NaN    NaN   NaN NaN NaN   \n",
      "27     0.96    ...       NaN    NaN   NaN   NaN   NaN    NaN   NaN NaN NaN   \n",
      "28     0.92    ...      29.0  172.0  40.0   NaN   NaN    NaN   NaN NaN NaN   \n",
      "29     0.92    ...      29.0  172.0  38.0   NaN   NaN    NaN   NaN NaN NaN   \n",
      "...     ...    ...       ...    ...   ...   ...   ...    ...   ...  ..  ..   \n",
      "1472    NaN    ...      71.0   69.0   NaN  31.0  40.0  236.0   NaN NaN NaN   \n",
      "1473    NaN    ...      71.0   69.0   NaN  31.0  40.0  236.0   NaN NaN NaN   \n",
      "1474    NaN    ...      71.0   69.0   NaN  31.0  40.0  236.0   NaN NaN NaN   \n",
      "1475    NaN    ...      71.0   69.0   NaN  31.0  39.0  236.0   NaN NaN NaN   \n",
      "1476    NaN    ...      71.0   69.0   NaN  31.0  39.0  236.0   NaN NaN NaN   \n",
      "1477    NaN    ...      71.0   69.0   NaN  31.0  39.0  236.0   NaN NaN NaN   \n",
      "1478    NaN    ...      71.0   69.0   NaN  31.0  39.0  236.0   NaN NaN NaN   \n",
      "1479    NaN    ...      71.0   69.0   NaN  31.0  39.0  236.0   NaN NaN NaN   \n",
      "1480    NaN    ...      71.0   69.0   NaN  31.0  39.0  236.0   NaN NaN NaN   \n",
      "1481    NaN    ...      71.0   69.0   NaN  31.0  39.0  236.0   NaN NaN NaN   \n",
      "1482    NaN    ...      71.0   69.0   NaN  31.0  39.0  236.0   NaN NaN NaN   \n",
      "1483    NaN    ...      70.0   70.0   NaN  31.0  39.0  235.0   NaN NaN NaN   \n",
      "1484    NaN    ...      70.0   70.0   NaN  31.0  39.0  235.0   NaN NaN NaN   \n",
      "1485    NaN    ...      70.0   70.0   NaN  31.0  39.0  235.0   NaN NaN NaN   \n",
      "1486    NaN    ...      70.0   70.0   NaN  31.0  39.0  235.0   NaN NaN NaN   \n",
      "1487    NaN    ...      70.0   70.0  12.0  31.0  39.0  235.0   NaN NaN NaN   \n",
      "1488    NaN    ...      70.0   70.0  12.0  31.0  39.0  235.0   NaN NaN NaN   \n",
      "1489    NaN    ...      70.0   70.0   NaN  31.0  39.0  235.0   NaN NaN NaN   \n",
      "1490    NaN    ...      69.0   70.0   NaN  31.0  39.0  235.0   NaN NaN NaN   \n",
      "1491    NaN    ...      69.0   70.0   NaN  31.0  39.0  235.0   NaN NaN NaN   \n",
      "1492    NaN    ...      69.0   70.0   NaN  31.0  39.0  235.0   NaN NaN NaN   \n",
      "1493    NaN    ...      69.0   70.0   NaN  31.0  38.0  235.0   NaN NaN NaN   \n",
      "1494    NaN    ...      69.0   70.0   NaN  31.0  38.0  235.0   NaN NaN NaN   \n",
      "1495    NaN    ...      69.0   70.0   NaN  31.0  38.0  235.0   NaN NaN NaN   \n",
      "1496    NaN    ...      69.0   70.0   NaN  31.0  38.0  234.0   NaN NaN NaN   \n",
      "1497    NaN    ...      69.0   70.0   NaN  31.0  38.0  234.0   NaN NaN NaN   \n",
      "1498    NaN    ...      69.0   70.0   NaN  31.0  38.0  234.0   NaN NaN NaN   \n",
      "1499    NaN    ...      69.0   70.0   NaN  31.0  38.0  234.0  20.0 NaN NaN   \n",
      "1500    NaN    ...      69.0   71.0   NaN  31.0  38.0  234.0   NaN NaN NaN   \n",
      "1501    NaN    ...      69.0   71.0   NaN  31.0  38.0  234.0   NaN NaN NaN   \n",
      "\n",
      "         avgsnr  \n",
      "0     29.200000  \n",
      "1     27.250000  \n",
      "2     27.250000  \n",
      "3     32.833333  \n",
      "4     32.166667  \n",
      "5     31.285714  \n",
      "6     32.250000  \n",
      "7     28.375000  \n",
      "8     27.375000  \n",
      "9     29.000000  \n",
      "10    30.222222  \n",
      "11    30.000000  \n",
      "12    31.000000  \n",
      "13    30.250000  \n",
      "14    29.555556  \n",
      "15    31.555556  \n",
      "16    29.888889  \n",
      "17    30.111111  \n",
      "18    27.333333  \n",
      "19    28.444444  \n",
      "20    29.111111  \n",
      "21    27.222222  \n",
      "22    28.666667  \n",
      "23    28.666667  \n",
      "24    27.333333  \n",
      "25    27.555556  \n",
      "26    22.000000  \n",
      "27    21.250000  \n",
      "28    22.333333  \n",
      "29    21.777778  \n",
      "...         ...  \n",
      "1472  39.333333  \n",
      "1473  39.333333  \n",
      "1474  39.333333  \n",
      "1475  39.666667  \n",
      "1476  39.666667  \n",
      "1477  39.666667  \n",
      "1478  39.666667  \n",
      "1479  39.666667  \n",
      "1480  39.666667  \n",
      "1481  39.666667  \n",
      "1482  39.666667  \n",
      "1483  39.333333  \n",
      "1484  39.333333  \n",
      "1485  39.333333  \n",
      "1486  39.333333  \n",
      "1487  39.333333  \n",
      "1488  39.333333  \n",
      "1489  39.666667  \n",
      "1490  39.333333  \n",
      "1491  39.333333  \n",
      "1492  39.333333  \n",
      "1493  39.333333  \n",
      "1494  39.333333  \n",
      "1495  39.333333  \n",
      "1496  39.333333  \n",
      "1497  39.333333  \n",
      "1498  39.333333  \n",
      "1499  39.333333  \n",
      "1500  39.666667  \n",
      "1501  39.666667  \n",
      "\n",
      "[1502 rows x 93 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7febb90ca690>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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AAADAFxWX1+i+tzYqLSFSV5+W6HQcn9FkebPWbpB0xDFMa23aIe9PbsFcAAAAANqp+9/e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\nkgaAyZskSZIkDYD/AWLa+hoU4UuqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7febb971e510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print df5\n",
    "#dfx = df5.loc[df[0]=='$GPGGA']\n",
    "dfx[100:1000].plot(x=2,y=8,figsize=(15,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}