Import_Export_Data.ipynb

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    "### Import and Export Data with Python\n",
    "\n",
    "See additional information on working with data in [Begin Python](https://github.com/APMonitor/begin_python) and [Data Science](https://github.com/APMonitor/data_science) Courses.\n",
    "\n",
    "#### Method 1: Pandas"
   ]
  },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>T1</th>\n",
       "      <th>T2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.93</td>\n",
       "      <td>19.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.93</td>\n",
       "      <td>19.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.96</td>\n",
       "      <td>19.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.93</td>\n",
       "      <td>19.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.93</td>\n",
       "      <td>19.29</td>\n",
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      "text/plain": [
       "   Time   Q1   Q2     T1     T2\n",
       "0     0  0.0  0.0  19.93  19.25\n",
       "1     1  0.0  0.0  19.93  19.29\n",
       "2     2  0.0  0.0  19.96  19.29\n",
       "3     3  0.0  0.0  19.93  19.29\n",
       "4     4  0.0  0.0  19.93  19.29"
      ]
     },
     "execution_count": 1,
     "metadata": {},
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   ],
   "source": [
    "import pandas as pd\n",
    "url = 'http://apmonitor.com/pdc/uploads/Main/tclab_data2.txt'\n",
    "data = pd.read_csv(url)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv('file.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 2: Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0.     0.     0.     0.    19.93  19.25]\n",
      " [  1.     1.     0.     0.    19.93  19.29]\n",
      " [  2.     2.     0.     0.    19.96  19.29]\n",
      " ...\n",
      " [598.   598.    50.    80.    68.82  50.64]\n",
      " [599.   599.    50.    80.    68.92  50.42]\n",
      " [600.   600.    50.    80.    68.75  50.51]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "data = np.loadtxt('file.csv',delimiter=',',skiprows=1)\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt('file2.csv',data,delimiter=',',\\\n",
    "           comments='',header='Time,Q1,Q2,T1,T2')"
   ]
  }
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