TestCodes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   0    1\n",
      "0         1352500101  582\n",
      "1         1352500107  600\n",
      "2         1352500113  586\n",
      "3         1352500120  596\n",
      "4         1352500132  581\n",
      "5         1352500138  597\n",
      "6         1352500144  592\n",
      "7         1352500150  587\n",
      "8         1352500156  574\n",
      "9         1352500168  577\n",
      "10        1352500175  581\n",
      "11        1352500181  587\n",
      "12        1352500199  577\n",
      "13        1352500211  584\n",
      "14        1352500218  587\n",
      "15        1352500224  592\n",
      "16        1352500230  587\n",
      "17        1352500236  585\n",
      "18        1352500242  583\n",
      "19        1352500248  593\n",
      "20        1352500255  583\n",
      "21        1352500261  590\n",
      "22        1352500267  582\n",
      "23        1352500273  591\n",
      "24        1352500279  597\n",
      "25        1352500285  612\n",
      "26        1352500291  587\n",
      "27        1352500297  590\n",
      "28        1352500304  579\n",
      "29        1352500310  585\n",
      "...              ...  ...\n",
      "10239402  1420438369  203\n",
      "10239403  1420438375  206\n",
      "10239404  1420438381  203\n",
      "10239405  1420438387  205\n",
      "10239406  1420438393  202\n",
      "10239407  1420438399  203\n",
      "10239408  1420438405  205\n",
      "10239409  1420438411  203\n",
      "10239410  1420438417  206\n",
      "10239411  1420438424  203\n",
      "10239412  1420438430  206\n",
      "10239413  1420438436  204\n",
      "10239414  1420438442  204\n",
      "10239415  1420438448  204\n",
      "10239416  1420438454  203\n",
      "10239417  1420438460  204\n",
      "10239418  1420438466  203\n",
      "10239419  1420438472  202\n",
      "10239420  1420438479  203\n",
      "10239421  1420438485  202\n",
      "10239422  1420438491  207\n",
      "10239423  1420438497  202\n",
      "10239424  1420438503  203\n",
      "10239425  1420438509  204\n",
      "10239426  1420438515  203\n",
      "10239427  1420438521  201\n",
      "10239428  1420438527  201\n",
      "10239429  1420438534  201\n",
      "10239430  1420438540  202\n",
      "10239431  1420438546  203\n",
      "\n",
      "[10239432 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd    \n",
    "\n",
    "with open('/home/louis/Desktop/Energy/channel_1.dat','r') as f:\n",
    "    next(f) # skip first row\n",
    "    df = pd.DataFrame(l.rstrip().split() for l in f)\n",
    "\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       0\n",
      "0         1352500101 582\n",
      "1         1352500107 600\n",
      "2         1352500113 586\n",
      "3         1352500120 596\n",
      "4         1352500132 581\n",
      "5         1352500138 597\n",
      "6         1352500144 592\n",
      "7         1352500150 587\n",
      "8         1352500156 574\n",
      "9         1352500168 577\n",
      "10        1352500175 581\n",
      "11        1352500181 587\n",
      "12        1352500199 577\n",
      "13        1352500211 584\n",
      "14        1352500218 587\n",
      "15        1352500224 592\n",
      "16        1352500230 587\n",
      "17        1352500236 585\n",
      "18        1352500242 583\n",
      "19        1352500248 593\n",
      "20        1352500255 583\n",
      "21        1352500261 590\n",
      "22        1352500267 582\n",
      "23        1352500273 591\n",
      "24        1352500279 597\n",
      "25        1352500285 612\n",
      "26        1352500291 587\n",
      "27        1352500297 590\n",
      "28        1352500304 579\n",
      "29        1352500310 585\n",
      "...                  ...\n",
      "10239402  1420438369 203\n",
      "10239403  1420438375 206\n",
      "10239404  1420438381 203\n",
      "10239405  1420438387 205\n",
      "10239406  1420438393 202\n",
      "10239407  1420438399 203\n",
      "10239408  1420438405 205\n",
      "10239409  1420438411 203\n",
      "10239410  1420438417 206\n",
      "10239411  1420438424 203\n",
      "10239412  1420438430 206\n",
      "10239413  1420438436 204\n",
      "10239414  1420438442 204\n",
      "10239415  1420438448 204\n",
      "10239416  1420438454 203\n",
      "10239417  1420438460 204\n",
      "10239418  1420438466 203\n",
      "10239419  1420438472 202\n",
      "10239420  1420438479 203\n",
      "10239421  1420438485 202\n",
      "10239422  1420438491 207\n",
      "10239423  1420438497 202\n",
      "10239424  1420438503 203\n",
      "10239425  1420438509 204\n",
      "10239426  1420438515 203\n",
      "10239427  1420438521 201\n",
      "10239428  1420438527 201\n",
      "10239429  1420438534 201\n",
      "10239430  1420438540 202\n",
      "10239431  1420438546 203\n",
      "\n",
      "[10239432 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('/home/louis/Desktop/Energy/channel_1.dat',delimiter = ',', sep='\\s+', header=None, skiprows=1)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# test program for concatenation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "No axis named 2 for object type <class 'type'>",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-e97acd9d4f83>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m frame = pd.concat(list_, axis = 2\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m                   , ignore_index = True)\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[1;32m    226\u001b[0m                        \u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m                        \u001b[0mverify_integrity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverify_integrity\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 228\u001b[0;31m                        copy=copy, sort=sort)\n\u001b[0m\u001b[1;32m    229\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    230\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001b[0m\n\u001b[1;32m    325\u001b[0m             \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 327\u001b[0;31m             \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    328\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    329\u001b[0m         \u001b[0;31m# Need to flip BlockManager axis in the DataFrame special case\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_axis_number\u001b[0;34m(cls, axis)\u001b[0m\n\u001b[1;32m    359\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    360\u001b[0m         raise ValueError('No axis named {0} for object type {1}'\n\u001b[0;32m--> 361\u001b[0;31m                          .format(axis, type(cls)))\n\u001b[0m\u001b[1;32m    362\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    363\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: No axis named 2 for object type <class 'type'>"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "import pandas as pd\n",
    "\n",
    "path =r'/home/louis/Desktop/Energy (copy)/' # use your path\n",
    "allFiles = glob.glob(path + \"/*.dat\")\n",
    "list_ = []\n",
    "\n",
    "for file_ in allFiles:\n",
    "    df = pd.read_csv(file_, delimiter = ' ', index_col=None, header=None)\n",
    "    list_.append(df)\n",
    "\n",
    "frame = pd.concat(list_, axis = 1  , ignore_index = True)\n",
    "print(frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           TS  heater          TS  kettle          TS  microwave  \\\n",
      "0  1352500098      10  1352500098       1  1352500098          1   \n",
      "1  1352500104      20  1352500104       2  1352500104          2   \n",
      "2  1352500110      30  1352500110       3  1352500110          3   \n",
      "3  1352500116      40  1352500116       4  1352500116          0   \n",
      "4  1352500122       0  1352500122       5  1352500122          0   \n",
      "5  1352500128       0  1352500128       6  1352500128          0   \n",
      "6  1352500134       0  1352500134       7  1352500134          0   \n",
      "7  1352500140       0  1352500140       8  1352500140          0   \n",
      "8  1352500146      80  1352500146       9  1352500146          9   \n",
      "\n",
      "             TS    pop  \n",
      "0  1.352500e+09  100.0  \n",
      "1  1.352500e+09  200.0  \n",
      "2  1.352500e+09  300.0  \n",
      "3  1.352500e+09  400.0  \n",
      "4  1.352500e+09  500.0  \n",
      "5  1.352500e+09  600.0  \n",
      "6           NaN    NaN  \n",
      "7           NaN    NaN  \n",
      "8           NaN    NaN  \n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "import pandas as pd\n",
    "\n",
    "path =r'/home/louis/Desktop/Energy (copy)/' # use your path\n",
    "allFiles = glob.glob(path + \"/*.dat\")\n",
    "\n",
    "list_ = []\n",
    "appl = ['heater','kettle','microwave', 'pop']\n",
    "i = 0\n",
    "for file_ in allFiles:\n",
    "    headers = ['TS', appl[i]]\n",
    "    df = pd.read_csv(file_, delimiter = ' ', index_col=None, names=headers )  \n",
    "    list_.append(df)\n",
    "    i = i+1\n",
    "    \n",
    "frame = pd.concat(list_, axis = 1, ignore_index = None)\n",
    "print(frame)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df1 = pd.read_csv('/home/louis/Desktop/Energy (copy)/channel_1.dat',delimiter = ' ', sep='\\s+', names = ['TS','Kettle'])\n",
    "df2 = pd.read_csv('/home/louis/Desktop/Energy (copy)/channel_5.dat', delimiter = ' ', index_col=None, names = ['TS','Microwave'])  \n",
    "df3 = pd.read_csv('/home/louis/Desktop/Energy (copy)/channel_6.dat', delimiter = ' ', index_col=None, names = ['TS','Lpatop'])  \n",
    "df4 = pd.read_csv('/home/louis/Desktop/Energy (copy)/channel_8.dat', delimiter = ' ', index_col=None, names = ['TS','TV'])  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           TS  Kettle  Microwave  Lpatop  TV\n",
      "0  1352500098      10      100.0       0   1\n",
      "1  1352500104      20      200.0       0   2\n",
      "2  1352500110      30        NaN       0   3\n",
      "3  1352500116      40      400.0       0   0\n",
      "4  1352500122       0      500.0       0   0\n",
      "5  1352500128       0      600.0       0   0\n",
      "6  1352500134       0        NaN       0   0\n",
      "7  1352500140       0        NaN       0   0\n",
      "8  1352500146      80        NaN       0   9\n"
     ]
    }
   ],
   "source": [
    "#frame1 = df1.set_index('TS').join(df2.set_index('TS'))\n",
    "frame1 = df1.join(df2.set_index('TS'), on='TS').join(df3.set_index('TS'), on='TS').join(df4.set_index('TS'), on='TS')\n",
    "#frame2 = frame1.join(df3.set_index('TS'), on='TS')\n",
    "#frame1 = df1.join(df4.set_index('TS'), on='TS')\n",
    "\n",
    "\n",
    "print(frame1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           TS  Kettle  Microwave  Lpatop     TV\n",
      "0  1352500098    10.0        1.0     1.0  100.0\n",
      "1  1352500104    20.0        2.0     2.0  200.0\n",
      "2  1352500110    30.0        3.0     3.0    NaN\n",
      "3  1352500114     NaN        NaN     NaN  300.0\n",
      "4  1352500116    40.0        4.0     0.0  400.0\n",
      "5  1352500122     0.0        5.0     0.0  500.0\n",
      "6  1352500128     0.0        6.0     0.0  600.0\n",
      "7  1352500134     0.0        7.0     0.0    NaN\n",
      "8  1352500140     0.0        8.0     0.0    NaN\n",
      "9  1352500146    80.0        9.0     9.0    NaN\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "import pandas as pd\n",
    "df1 = pd.read_csv('/home/louis/Desktop/Energy (copy)/channel_1.dat',delimiter = ' ', sep='\\s+', names = ['TS','Kettle'])\n",
    "df2 = pd.read_csv('/home/louis/Desktop/Energy (copy)/channel_5.dat', delimiter = ' ', index_col=None, names = ['TS','Microwave'])  \n",
    "\n",
    "path =r'/home/louis/Desktop/Energy (copy)/' # use your path\n",
    "allFiles = glob.glob(path + \"/*.dat\")\n",
    "appliances = ['Kettle', 'Microwave', 'Lpatop', 'TV']\n",
    "i = 0\n",
    "frame1 = pd.DataFrame(columns=['TS']) \n",
    "\n",
    "for file_ in allFiles:\n",
    "    df = pd.read_csv(file_,delimiter = ' ', names = ['TS', appliances[i]])\n",
    "    #frame1 = df.join(df.set_index('TS'), on='TS')\n",
    "    frame1 = frame1.merge(df, on='TS', how='outer', sort =True) \n",
    "    i = i+1\n",
    "    \n",
    "#rame1 = df1.set_index('TS').join(df2.set_index('TS'))\n",
    "    \n",
    "    \n",
    "print(frame1)\n",
    "#print(frame1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# End"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "list_.append(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               0             1             2\n",
      "0  1352500104 20  1352500104 0  1352500104 2\n",
      "1  1352500110 30  1352500110 0  1352500110 3\n",
      "2  1352500116 40  1352500116 0  1352500116 4\n",
      "3   1352500122 0  1352500122 0  1352500122 5\n",
      "4   1352500128 0  1352500128 0  1352500128 6\n",
      "5   1352500134 0  1352500134 0  1352500128 6\n",
      "6   1352500140 0  1352500140 0  1352500128 6\n",
      "7  1352500146 80  1352500146 0  1352500128 6\n"
     ]
    }
   ],
   "source": [
    "frame1 = frame.fillna(method='ffill')\n",
    "print(frame1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               0    1             2    3             4    5\n",
      "0  1352500098 10  0.0  1352500098 0  0.0  1352500098 1  0.0\n",
      "1  1352500104 20  0.0  1352500104 0  0.0  1352500104 2  0.0\n",
      "2  1352500110 30  0.0  1352500110 0  0.0  1352500110 3  0.0\n",
      "3  1352500116 40  0.0  1352500116 0  0.0  1352500116 4  0.0\n",
      "4   1352500122 0  0.0  1352500122 0  0.0  1352500122 5  0.0\n",
      "5   1352500128 0  0.0  1352500128 0  0.0  1352500128 6  0.0\n",
      "6   1352500134 0  0.0  1352500134 0  0.0             0  0.0\n",
      "7   1352500140 0  0.0  1352500140 0  0.0             0  0.0\n",
      "8  1352500146 80  0.0  1352500146 0  0.0             0  0.0\n"
     ]
    }
   ],
   "source": [
    "frame2 = frame.fillna(0)\n",
    "print(frame2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4d44aaf3c8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "#f['Date'] = df['Date'].map(lambda x: datetime.strptime(str(x), '%Y/%m/%d %H:%M:%S.%f'))\n",
    "x = frame2[0]\n",
    "y = frame2[1]\n",
    "z = frame2[2]\n",
    "\n",
    "# plot\n",
    "plt.plot(x,y,z)\n",
    "# beautify the x-labels\n",
    "plt.gcf().autofmt_xdate()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Actual Concatenation of 6 channels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import pandas as pd\n",
    "\n",
    "path =r'/home/louis/Desktop/Energy/' # use your path\n",
    "allFiles = glob.glob(path + \"/*.dat\")\n",
    "\n",
    "list_ = []\n",
    "\n",
    "for file_ in allFiles:\n",
    "    df = pd.read_csv(file_,index_col=None, header=None)\n",
    "    list_.append(df)\n",
    "\n",
    "cframe = pd.concat(list_, axis = 1, ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     0               1             2             3  \\\n",
      "0         1352500098 1  1352500095 599  1352500098 1  1355523693 1   \n",
      "1         1352500104 1  1352500101 582  1352500104 1  1355523699 1   \n",
      "2         1352500110 1  1352500107 600  1352500110 1  1355523705 1   \n",
      "3         1352500116 1  1352500113 586  1352500116 1  1355523711 1   \n",
      "4         1352500122 1  1352500120 596  1352500122 1  1355523717 1   \n",
      "5         1352500128 1  1352500132 581  1352500128 1  1355523723 1   \n",
      "6         1352500134 1  1352500138 597  1352500134 1  1355523729 1   \n",
      "7         1352500140 1  1352500144 592  1352500140 1  1355523735 1   \n",
      "8         1352500146 1  1352500150 587  1352500146 1  1355523741 1   \n",
      "9         1352500152 1  1352500156 574  1352500152 1  1355523747 1   \n",
      "10        1352500158 1  1352500168 577  1352500158 1  1355523754 1   \n",
      "11        1352500164 1  1352500175 581  1352500164 1  1355523760 1   \n",
      "12        1352500170 1  1352500181 587  1352500170 1  1355523766 1   \n",
      "13        1352500176 1  1352500199 577  1352500176 1  1355523772 1   \n",
      "14        1352500182 1  1352500211 584  1352500182 1  1355523778 1   \n",
      "15        1352500188 1  1352500218 587  1352500188 1  1355523784 1   \n",
      "16        1352500194 1  1352500224 592  1352500194 1  1355523790 1   \n",
      "17        1352500200 1  1352500230 587  1352500200 1  1355523796 1   \n",
      "18        1352500206 1  1352500236 585  1352500206 1  1355523803 1   \n",
      "19        1352500212 1  1352500242 583  1352500212 1  1355523809 1   \n",
      "20        1352500218 1  1352500248 593  1352500218 1  1355523815 1   \n",
      "21        1352500224 1  1352500255 583  1352500224 1  1355523821 1   \n",
      "22        1352500230 1  1352500261 590  1352500230 1  1355523827 1   \n",
      "23        1352500236 1  1352500267 582  1352500236 1  1355523833 1   \n",
      "24        1352500242 1  1352500273 591  1352500242 1  1355523839 1   \n",
      "25        1352500248 1  1352500279 597  1352500249 1  1355523845 1   \n",
      "26        1352500255 1  1352500285 612  1352500255 1  1355523852 1   \n",
      "27        1352500261 1  1352500291 587  1352500261 1  1355523858 1   \n",
      "28        1352500267 1  1352500297 590  1352500267 1  1355523864 1   \n",
      "29        1352500273 1  1352500304 579  1352500273 1  1355523870 1   \n",
      "...                ...             ...           ...           ...   \n",
      "10239403           NaN  1420438369 203           NaN           NaN   \n",
      "10239404           NaN  1420438375 206           NaN           NaN   \n",
      "10239405           NaN  1420438381 203           NaN           NaN   \n",
      "10239406           NaN  1420438387 205           NaN           NaN   \n",
      "10239407           NaN  1420438393 202           NaN           NaN   \n",
      "10239408           NaN  1420438399 203           NaN           NaN   \n",
      "10239409           NaN  1420438405 205           NaN           NaN   \n",
      "10239410           NaN  1420438411 203           NaN           NaN   \n",
      "10239411           NaN  1420438417 206           NaN           NaN   \n",
      "10239412           NaN  1420438424 203           NaN           NaN   \n",
      "10239413           NaN  1420438430 206           NaN           NaN   \n",
      "10239414           NaN  1420438436 204           NaN           NaN   \n",
      "10239415           NaN  1420438442 204           NaN           NaN   \n",
      "10239416           NaN  1420438448 204           NaN           NaN   \n",
      "10239417           NaN  1420438454 203           NaN           NaN   \n",
      "10239418           NaN  1420438460 204           NaN           NaN   \n",
      "10239419           NaN  1420438466 203           NaN           NaN   \n",
      "10239420           NaN  1420438472 202           NaN           NaN   \n",
      "10239421           NaN  1420438479 203           NaN           NaN   \n",
      "10239422           NaN  1420438485 202           NaN           NaN   \n",
      "10239423           NaN  1420438491 207           NaN           NaN   \n",
      "10239424           NaN  1420438497 202           NaN           NaN   \n",
      "10239425           NaN  1420438503 203           NaN           NaN   \n",
      "10239426           NaN  1420438509 204           NaN           NaN   \n",
      "10239427           NaN  1420438515 203           NaN           NaN   \n",
      "10239428           NaN  1420438521 201           NaN           NaN   \n",
      "10239429           NaN  1420438527 201           NaN           NaN   \n",
      "10239430           NaN  1420438534 201           NaN           NaN   \n",
      "10239431           NaN  1420438540 202           NaN           NaN   \n",
      "10239432           NaN  1420438546 203           NaN           NaN   \n",
      "\n",
      "                       4             5  \n",
      "0          1355523692 85  1352500098 0  \n",
      "1          1355523698 85  1352500104 0  \n",
      "2          1355523704 84  1352500110 0  \n",
      "3          1355523710 85  1352500116 0  \n",
      "4          1355523716 85  1352500122 0  \n",
      "5          1355523722 85  1352500128 0  \n",
      "6          1355523728 85  1352500134 0  \n",
      "7          1355523734 85  1352500140 0  \n",
      "8          1355523740 85  1352500146 0  \n",
      "9          1355523746 86  1352500152 0  \n",
      "10         1355523753 86  1352500158 0  \n",
      "11         1355523759 86  1352500164 0  \n",
      "12         1355523765 85  1352500170 0  \n",
      "13         1355523771 85  1352500176 0  \n",
      "14         1355523777 85  1352500182 0  \n",
      "15         1355523783 85  1352500188 0  \n",
      "16         1355523789 85  1352500194 0  \n",
      "17         1355523795 85  1352500200 0  \n",
      "18         1355523802 86  1352500206 0  \n",
      "19        1355523808 103  1352500212 0  \n",
      "20         1355523814 86  1352500218 0  \n",
      "21        1355523820 103  1352500224 0  \n",
      "22         1355523826 86  1352500230 0  \n",
      "23         1355523832 85  1352500236 0  \n",
      "24         1355523838 86  1352500243 0  \n",
      "25        1355523844 103  1352500249 0  \n",
      "26        1355523851 103  1352500255 0  \n",
      "27        1355523857 103  1352500261 0  \n",
      "28         1355523863 86  1352500267 0  \n",
      "29         1355523869 86  1352500273 0  \n",
      "...                  ...           ...  \n",
      "10239403             NaN           NaN  \n",
      "10239404             NaN           NaN  \n",
      "10239405             NaN           NaN  \n",
      "10239406             NaN           NaN  \n",
      "10239407             NaN           NaN  \n",
      "10239408             NaN           NaN  \n",
      "10239409             NaN           NaN  \n",
      "10239410             NaN           NaN  \n",
      "10239411             NaN           NaN  \n",
      "10239412             NaN           NaN  \n",
      "10239413             NaN           NaN  \n",
      "10239414             NaN           NaN  \n",
      "10239415             NaN           NaN  \n",
      "10239416             NaN           NaN  \n",
      "10239417             NaN           NaN  \n",
      "10239418             NaN           NaN  \n",
      "10239419             NaN           NaN  \n",
      "10239420             NaN           NaN  \n",
      "10239421             NaN           NaN  \n",
      "10239422             NaN           NaN  \n",
      "10239423             NaN           NaN  \n",
      "10239424             NaN           NaN  \n",
      "10239425             NaN           NaN  \n",
      "10239426             NaN           NaN  \n",
      "10239427             NaN           NaN  \n",
      "10239428             NaN           NaN  \n",
      "10239429             NaN           NaN  \n",
      "10239430             NaN           NaN  \n",
      "10239431             NaN           NaN  \n",
      "10239432             NaN           NaN  \n",
      "\n",
      "[10239433 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "print(cframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "  <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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>1352500098 1</td>\n",
       "      <td>1352500095 599</td>\n",
       "      <td>1352500098 1</td>\n",
       "      <td>1355523693 1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1352500104 1</td>\n",
       "      <td>1352500101 582</td>\n",
       "      <td>1352500104 1</td>\n",
       "      <td>1355523699 1</td>\n",
       "      <td>1355523698 85</td>\n",
       "      <td>1352500104 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1352500110 1</td>\n",
       "      <td>1352500107 600</td>\n",
       "      <td>1352500110 1</td>\n",
       "      <td>1355523705 1</td>\n",
       "      <td>1355523704 84</td>\n",
       "      <td>1352500110 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1352500116 1</td>\n",
       "      <td>1352500113 586</td>\n",
       "      <td>1352500116 1</td>\n",
       "      <td>1355523711 1</td>\n",
       "      <td>1355523710 85</td>\n",
       "      <td>1352500116 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1352500122 1</td>\n",
       "      <td>1352500120 596</td>\n",
       "      <td>1352500122 1</td>\n",
       "      <td>1355523717 1</td>\n",
       "      <td>1355523716 85</td>\n",
       "      <td>1352500122 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1352500128 1</td>\n",
       "      <td>1352500132 581</td>\n",
       "      <td>1352500128 1</td>\n",
       "      <td>1355523723 1</td>\n",
       "      <td>1355523722 85</td>\n",
       "      <td>1352500128 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1352500134 1</td>\n",
       "      <td>1352500138 597</td>\n",
       "      <td>1352500134 1</td>\n",
       "      <td>1355523729 1</td>\n",
       "      <td>1355523728 85</td>\n",
       "      <td>1352500134 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1352500140 1</td>\n",
       "      <td>1352500144 592</td>\n",
       "      <td>1352500140 1</td>\n",
       "      <td>1355523735 1</td>\n",
       "      <td>1355523734 85</td>\n",
       "      <td>1352500140 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1352500146 1</td>\n",
       "      <td>1352500150 587</td>\n",
       "      <td>1352500146 1</td>\n",
       "      <td>1355523741 1</td>\n",
       "      <td>1355523740 85</td>\n",
       "      <td>1352500146 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1352500152 1</td>\n",
       "      <td>1352500156 574</td>\n",
       "      <td>1352500152 1</td>\n",
       "      <td>1355523747 1</td>\n",
       "      <td>1355523746 86</td>\n",
       "      <td>1352500152 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1352500158 1</td>\n",
       "      <td>1352500168 577</td>\n",
       "      <td>1352500158 1</td>\n",
       "      <td>1355523754 1</td>\n",
       "      <td>1355523753 86</td>\n",
       "      <td>1352500158 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1352500164 1</td>\n",
       "      <td>1352500175 581</td>\n",
       "      <td>1352500164 1</td>\n",
       "      <td>1355523760 1</td>\n",
       "      <td>1355523759 86</td>\n",
       "      <td>1352500164 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1352500170 1</td>\n",
       "      <td>1352500181 587</td>\n",
       "      <td>1352500170 1</td>\n",
       "      <td>1355523766 1</td>\n",
       "      <td>1355523765 85</td>\n",
       "      <td>1352500170 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1352500176 1</td>\n",
       "      <td>1352500199 577</td>\n",
       "      <td>1352500176 1</td>\n",
       "      <td>1355523772 1</td>\n",
       "      <td>1355523771 85</td>\n",
       "      <td>1352500176 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1352500182 1</td>\n",
       "      <td>1352500211 584</td>\n",
       "      <td>1352500182 1</td>\n",
       "      <td>1355523778 1</td>\n",
       "      <td>1355523777 85</td>\n",
       "      <td>1352500182 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1352500188 1</td>\n",
       "      <td>1352500218 587</td>\n",
       "      <td>1352500188 1</td>\n",
       "      <td>1355523784 1</td>\n",
       "      <td>1355523783 85</td>\n",
       "      <td>1352500188 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1352500194 1</td>\n",
       "      <td>1352500224 592</td>\n",
       "      <td>1352500194 1</td>\n",
       "      <td>1355523790 1</td>\n",
       "      <td>1355523789 85</td>\n",
       "      <td>1352500194 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1352500200 1</td>\n",
       "      <td>1352500230 587</td>\n",
       "      <td>1352500200 1</td>\n",
       "      <td>1355523796 1</td>\n",
       "      <td>1355523795 85</td>\n",
       "      <td>1352500200 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1352500206 1</td>\n",
       "      <td>1352500236 585</td>\n",
       "      <td>1352500206 1</td>\n",
       "      <td>1355523803 1</td>\n",
       "      <td>1355523802 86</td>\n",
       "      <td>1352500206 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1352500212 1</td>\n",
       "      <td>1352500242 583</td>\n",
       "      <td>1352500212 1</td>\n",
       "      <td>1355523809 1</td>\n",
       "      <td>1355523808 103</td>\n",
       "      <td>1352500212 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1352500218 1</td>\n",
       "      <td>1352500248 593</td>\n",
       "      <td>1352500218 1</td>\n",
       "      <td>1355523815 1</td>\n",
       "      <td>1355523814 86</td>\n",
       "      <td>1352500218 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1352500224 1</td>\n",
       "      <td>1352500255 583</td>\n",
       "      <td>1352500224 1</td>\n",
       "      <td>1355523821 1</td>\n",
       "      <td>1355523820 103</td>\n",
       "      <td>1352500224 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1352500230 1</td>\n",
       "      <td>1352500261 590</td>\n",
       "      <td>1352500230 1</td>\n",
       "      <td>1355523827 1</td>\n",
       "      <td>1355523826 86</td>\n",
       "      <td>1352500230 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1352500236 1</td>\n",
       "      <td>1352500267 582</td>\n",
       "      <td>1352500236 1</td>\n",
       "      <td>1355523833 1</td>\n",
       "      <td>1355523832 85</td>\n",
       "      <td>1352500236 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1352500242 1</td>\n",
       "      <td>1352500273 591</td>\n",
       "      <td>1352500242 1</td>\n",
       "      <td>1355523839 1</td>\n",
       "      <td>1355523838 86</td>\n",
       "      <td>1352500243 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1352500248 1</td>\n",
       "      <td>1352500279 597</td>\n",
       "      <td>1352500249 1</td>\n",
       "      <td>1355523845 1</td>\n",
       "      <td>1355523844 103</td>\n",
       "      <td>1352500249 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1352500255 1</td>\n",
       "      <td>1352500285 612</td>\n",
       "      <td>1352500255 1</td>\n",
       "      <td>1355523852 1</td>\n",
       "      <td>1355523851 103</td>\n",
       "      <td>1352500255 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1352500261 1</td>\n",
       "      <td>1352500291 587</td>\n",
       "      <td>1352500261 1</td>\n",
       "      <td>1355523858 1</td>\n",
       "      <td>1355523857 103</td>\n",
       "      <td>1352500261 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1352500267 1</td>\n",
       "      <td>1352500297 590</td>\n",
       "      <td>1352500267 1</td>\n",
       "      <td>1355523864 1</td>\n",
       "      <td>1355523863 86</td>\n",
       "      <td>1352500267 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1352500273 1</td>\n",
       "      <td>1352500304 579</td>\n",
       "      <td>1352500273 1</td>\n",
       "      <td>1355523870 1</td>\n",
       "      <td>1355523869 86</td>\n",
       "      <td>1352500273 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239403</th>\n",
       "      <td>1420438564 1</td>\n",
       "      <td>1420438369 203</td>\n",
       "      <td>1420439138 1</td>\n",
       "      <td>1420438622 1</td>\n",
       "      <td>1420438603 81</td>\n",
       "      <td>1420439118 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239404</th>\n",
       "      <td>1420438564 1</td>\n",
       "      <td>1420438375 206</td>\n",
       "      <td>1420439138 1</td>\n",
       "      <td>1420438622 1</td>\n",
       "      <td>1420438603 81</td>\n",
       "      <td>1420439118 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239405</th>\n",
       "      <td>1420438564 1</td>\n",
       "      <td>1420438381 203</td>\n",
       "      <td>1420439138 1</td>\n",
       "      <td>1420438622 1</td>\n",
       "      <td>1420438603 81</td>\n",
       "      <td>1420439118 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239406</th>\n",
       "      <td>1420438564 1</td>\n",
       "      <td>1420438387 205</td>\n",
       "      <td>1420439138 1</td>\n",
       "      <td>1420438622 1</td>\n",
       "      <td>1420438603 81</td>\n",
       "      <td>1420439118 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239407</th>\n",
       "      <td>1420438564 1</td>\n",
       "      <td>1420438393 202</td>\n",
       "      <td>1420439138 1</td>\n",
       "      <td>1420438622 1</td>\n",
       "      <td>1420438603 81</td>\n",
       "      <td>1420439118 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239408</th>\n",
       "      <td>1420438564 1</td>\n",
       "      <td>1420438399 203</td>\n",
       "      <td>1420439138 1</td>\n",
       "      <td>1420438622 1</td>\n",
       "      <td>1420438603 81</td>\n",
       "      <td>1420439118 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239409</th>\n",
       "      <td>1420438564 1</td>\n",
       "      <td>1420438405 205</td>\n",
       "      <td>1420439138 1</td>\n",
       "      <td>1420438622 1</td>\n",
       "      <td>1420438603 81</td>\n",
       "      <td>1420439118 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239410</th>\n",
       "      <td>1420438564 1</td>\n",
       "      <td>1420438411 203</td>\n",
       "      <td>1420439138 1</td>\n",
       "      <td>1420438622 1</td>\n",
       "      <td>1420438603 81</td>\n",
       "      <td>1420439118 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239411</th>\n",
       "      <td>1420438564 1</td>\n",
       "      <td>1420438417 206</td>\n",
       "      <td>1420439138 1</td>\n",
       "      <td>1420438622 1</td>\n",
       "      <td>1420438603 81</td>\n",
       "      <td>1420439118 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239412</th>\n",
       "      <td>1420438564 1</td>\n",
       "      <td>1420438424 203</td>\n",
       "      <td>1420439138 1</td>\n",
       "      <td>1420438622 1</td>\n",
       "      <td>1420438603 81</td>\n",
       "      <td>1420439118 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239413</th>\n",
       "      <td>1420438564 1</td>\n",
       "      <td>1420438430 206</td>\n",
       "      <td>1420439138 1</td>\n",
       "      <td>1420438622 1</td>\n",
       "      <td>1420438603 81</td>\n",
       "      <td>1420439118 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239414</th>\n",
       "      <td>1420438564 1</td>\n",
       "      <td>1420438436 204</td>\n",
       "      <td>1420439138 1</td>\n",
       "      <td>1420438622 1</td>\n",
       "      <td>1420438603 81</td>\n",
       "      <td>1420439118 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239415</th>\n",
       "      <td>1420438564 1</td>\n",
       "      <td>1420438442 204</td>\n",
       "      <td>1420439138 1</td>\n",
       "      <td>1420438622 1</td>\n",
       "      <td>1420438603 81</td>\n",
       "      <td>1420439118 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239416</th>\n",
       "      <td>1420438564 1</td>\n",
       "      <td>1420438448 204</td>\n",
       "      <td>1420439138 1</td>\n",
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       "      <td>1420438603 81</td>\n",
       "      <td>1420439118 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239417</th>\n",
       "      <td>1420438564 1</td>\n",
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       "      <td>1420438603 81</td>\n",
       "      <td>1420439118 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239418</th>\n",
       "      <td>1420438564 1</td>\n",
       "      <td>1420438460 204</td>\n",
       "      <td>1420439138 1</td>\n",
       "      <td>1420438622 1</td>\n",
       "      <td>1420438603 81</td>\n",
       "      <td>1420439118 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239419</th>\n",
       "      <td>1420438564 1</td>\n",
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       "      <td>1420439138 1</td>\n",
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       "      <td>1420438603 81</td>\n",
       "      <td>1420439118 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239420</th>\n",
       "      <td>1420438564 1</td>\n",
       "      <td>1420438472 202</td>\n",
       "      <td>1420439138 1</td>\n",
       "      <td>1420438622 1</td>\n",
       "      <td>1420438603 81</td>\n",
       "      <td>1420439118 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239421</th>\n",
       "      <td>1420438564 1</td>\n",
       "      <td>1420438479 203</td>\n",
       "      <td>1420439138 1</td>\n",
       "      <td>1420438622 1</td>\n",
       "      <td>1420438603 81</td>\n",
       "      <td>1420439118 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239422</th>\n",
       "      <td>1420438564 1</td>\n",
       "      <td>1420438485 202</td>\n",
       "      <td>1420439138 1</td>\n",
       "      <td>1420438622 1</td>\n",
       "      <td>1420438603 81</td>\n",
       "      <td>1420439118 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239423</th>\n",
       "      <td>1420438564 1</td>\n",
       "      <td>1420438491 207</td>\n",
       "      <td>1420439138 1</td>\n",
       "      <td>1420438622 1</td>\n",
       "      <td>1420438603 81</td>\n",
       "      <td>1420439118 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239424</th>\n",
       "      <td>1420438564 1</td>\n",
       "      <td>1420438497 202</td>\n",
       "      <td>1420439138 1</td>\n",
       "      <td>1420438622 1</td>\n",
       "      <td>1420438603 81</td>\n",
       "      <td>1420439118 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239425</th>\n",
       "      <td>1420438564 1</td>\n",
       "      <td>1420438503 203</td>\n",
       "      <td>1420439138 1</td>\n",
       "      <td>1420438622 1</td>\n",
       "      <td>1420438603 81</td>\n",
       "      <td>1420439118 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239426</th>\n",
       "      <td>1420438564 1</td>\n",
       "      <td>1420438509 204</td>\n",
       "      <td>1420439138 1</td>\n",
       "      <td>1420438622 1</td>\n",
       "      <td>1420438603 81</td>\n",
       "      <td>1420439118 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239427</th>\n",
       "      <td>1420438564 1</td>\n",
       "      <td>1420438515 203</td>\n",
       "      <td>1420439138 1</td>\n",
       "      <td>1420438622 1</td>\n",
       "      <td>1420438603 81</td>\n",
       "      <td>1420439118 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239428</th>\n",
       "      <td>1420438564 1</td>\n",
       "      <td>1420438521 201</td>\n",
       "      <td>1420439138 1</td>\n",
       "      <td>1420438622 1</td>\n",
       "      <td>1420438603 81</td>\n",
       "      <td>1420439118 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239429</th>\n",
       "      <td>1420438564 1</td>\n",
       "      <td>1420438527 201</td>\n",
       "      <td>1420439138 1</td>\n",
       "      <td>1420438622 1</td>\n",
       "      <td>1420438603 81</td>\n",
       "      <td>1420439118 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239430</th>\n",
       "      <td>1420438564 1</td>\n",
       "      <td>1420438534 201</td>\n",
       "      <td>1420439138 1</td>\n",
       "      <td>1420438622 1</td>\n",
       "      <td>1420438603 81</td>\n",
       "      <td>1420439118 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239431</th>\n",
       "      <td>1420438564 1</td>\n",
       "      <td>1420438540 202</td>\n",
       "      <td>1420439138 1</td>\n",
       "      <td>1420438622 1</td>\n",
       "      <td>1420438603 81</td>\n",
       "      <td>1420439118 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239432</th>\n",
       "      <td>1420438564 1</td>\n",
       "      <td>1420438546 203</td>\n",
       "      <td>1420439138 1</td>\n",
       "      <td>1420438622 1</td>\n",
       "      <td>1420438603 81</td>\n",
       "      <td>1420439118 0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10239433 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     0               1             2             3  \\\n",
       "0         1352500098 1  1352500095 599  1352500098 1  1355523693 1   \n",
       "1         1352500104 1  1352500101 582  1352500104 1  1355523699 1   \n",
       "2         1352500110 1  1352500107 600  1352500110 1  1355523705 1   \n",
       "3         1352500116 1  1352500113 586  1352500116 1  1355523711 1   \n",
       "4         1352500122 1  1352500120 596  1352500122 1  1355523717 1   \n",
       "5         1352500128 1  1352500132 581  1352500128 1  1355523723 1   \n",
       "6         1352500134 1  1352500138 597  1352500134 1  1355523729 1   \n",
       "7         1352500140 1  1352500144 592  1352500140 1  1355523735 1   \n",
       "8         1352500146 1  1352500150 587  1352500146 1  1355523741 1   \n",
       "9         1352500152 1  1352500156 574  1352500152 1  1355523747 1   \n",
       "10        1352500158 1  1352500168 577  1352500158 1  1355523754 1   \n",
       "11        1352500164 1  1352500175 581  1352500164 1  1355523760 1   \n",
       "12        1352500170 1  1352500181 587  1352500170 1  1355523766 1   \n",
       "13        1352500176 1  1352500199 577  1352500176 1  1355523772 1   \n",
       "14        1352500182 1  1352500211 584  1352500182 1  1355523778 1   \n",
       "15        1352500188 1  1352500218 587  1352500188 1  1355523784 1   \n",
       "16        1352500194 1  1352500224 592  1352500194 1  1355523790 1   \n",
       "17        1352500200 1  1352500230 587  1352500200 1  1355523796 1   \n",
       "18        1352500206 1  1352500236 585  1352500206 1  1355523803 1   \n",
       "19        1352500212 1  1352500242 583  1352500212 1  1355523809 1   \n",
       "20        1352500218 1  1352500248 593  1352500218 1  1355523815 1   \n",
       "21        1352500224 1  1352500255 583  1352500224 1  1355523821 1   \n",
       "22        1352500230 1  1352500261 590  1352500230 1  1355523827 1   \n",
       "23        1352500236 1  1352500267 582  1352500236 1  1355523833 1   \n",
       "24        1352500242 1  1352500273 591  1352500242 1  1355523839 1   \n",
       "25        1352500248 1  1352500279 597  1352500249 1  1355523845 1   \n",
       "26        1352500255 1  1352500285 612  1352500255 1  1355523852 1   \n",
       "27        1352500261 1  1352500291 587  1352500261 1  1355523858 1   \n",
       "28        1352500267 1  1352500297 590  1352500267 1  1355523864 1   \n",
       "29        1352500273 1  1352500304 579  1352500273 1  1355523870 1   \n",
       "...                ...             ...           ...           ...   \n",
       "10239403  1420438564 1  1420438369 203  1420439138 1  1420438622 1   \n",
       "10239404  1420438564 1  1420438375 206  1420439138 1  1420438622 1   \n",
       "10239405  1420438564 1  1420438381 203  1420439138 1  1420438622 1   \n",
       "10239406  1420438564 1  1420438387 205  1420439138 1  1420438622 1   \n",
       "10239407  1420438564 1  1420438393 202  1420439138 1  1420438622 1   \n",
       "10239408  1420438564 1  1420438399 203  1420439138 1  1420438622 1   \n",
       "10239409  1420438564 1  1420438405 205  1420439138 1  1420438622 1   \n",
       "10239410  1420438564 1  1420438411 203  1420439138 1  1420438622 1   \n",
       "10239411  1420438564 1  1420438417 206  1420439138 1  1420438622 1   \n",
       "10239412  1420438564 1  1420438424 203  1420439138 1  1420438622 1   \n",
       "10239413  1420438564 1  1420438430 206  1420439138 1  1420438622 1   \n",
       "10239414  1420438564 1  1420438436 204  1420439138 1  1420438622 1   \n",
       "10239415  1420438564 1  1420438442 204  1420439138 1  1420438622 1   \n",
       "10239416  1420438564 1  1420438448 204  1420439138 1  1420438622 1   \n",
       "10239417  1420438564 1  1420438454 203  1420439138 1  1420438622 1   \n",
       "10239418  1420438564 1  1420438460 204  1420439138 1  1420438622 1   \n",
       "10239419  1420438564 1  1420438466 203  1420439138 1  1420438622 1   \n",
       "10239420  1420438564 1  1420438472 202  1420439138 1  1420438622 1   \n",
       "10239421  1420438564 1  1420438479 203  1420439138 1  1420438622 1   \n",
       "10239422  1420438564 1  1420438485 202  1420439138 1  1420438622 1   \n",
       "10239423  1420438564 1  1420438491 207  1420439138 1  1420438622 1   \n",
       "10239424  1420438564 1  1420438497 202  1420439138 1  1420438622 1   \n",
       "10239425  1420438564 1  1420438503 203  1420439138 1  1420438622 1   \n",
       "10239426  1420438564 1  1420438509 204  1420439138 1  1420438622 1   \n",
       "10239427  1420438564 1  1420438515 203  1420439138 1  1420438622 1   \n",
       "10239428  1420438564 1  1420438521 201  1420439138 1  1420438622 1   \n",
       "10239429  1420438564 1  1420438527 201  1420439138 1  1420438622 1   \n",
       "10239430  1420438564 1  1420438534 201  1420439138 1  1420438622 1   \n",
       "10239431  1420438564 1  1420438540 202  1420439138 1  1420438622 1   \n",
       "10239432  1420438564 1  1420438546 203  1420439138 1  1420438622 1   \n",
       "\n",
       "                       4             5  \n",
       "0          1355523692 85  1352500098 0  \n",
       "1          1355523698 85  1352500104 0  \n",
       "2          1355523704 84  1352500110 0  \n",
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       "5          1355523722 85  1352500128 0  \n",
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       "7          1355523734 85  1352500140 0  \n",
       "8          1355523740 85  1352500146 0  \n",
       "9          1355523746 86  1352500152 0  \n",
       "10         1355523753 86  1352500158 0  \n",
       "11         1355523759 86  1352500164 0  \n",
       "12         1355523765 85  1352500170 0  \n",
       "13         1355523771 85  1352500176 0  \n",
       "14         1355523777 85  1352500182 0  \n",
       "15         1355523783 85  1352500188 0  \n",
       "16         1355523789 85  1352500194 0  \n",
       "17         1355523795 85  1352500200 0  \n",
       "18         1355523802 86  1352500206 0  \n",
       "19        1355523808 103  1352500212 0  \n",
       "20         1355523814 86  1352500218 0  \n",
       "21        1355523820 103  1352500224 0  \n",
       "22         1355523826 86  1352500230 0  \n",
       "23         1355523832 85  1352500236 0  \n",
       "24         1355523838 86  1352500243 0  \n",
       "25        1355523844 103  1352500249 0  \n",
       "26        1355523851 103  1352500255 0  \n",
       "27        1355523857 103  1352500261 0  \n",
       "28         1355523863 86  1352500267 0  \n",
       "29         1355523869 86  1352500273 0  \n",
       "...                  ...           ...  \n",
       "10239403   1420438603 81  1420439118 0  \n",
       "10239404   1420438603 81  1420439118 0  \n",
       "10239405   1420438603 81  1420439118 0  \n",
       "10239406   1420438603 81  1420439118 0  \n",
       "10239407   1420438603 81  1420439118 0  \n",
       "10239408   1420438603 81  1420439118 0  \n",
       "10239409   1420438603 81  1420439118 0  \n",
       "10239410   1420438603 81  1420439118 0  \n",
       "10239411   1420438603 81  1420439118 0  \n",
       "10239412   1420438603 81  1420439118 0  \n",
       "10239413   1420438603 81  1420439118 0  \n",
       "10239414   1420438603 81  1420439118 0  \n",
       "10239415   1420438603 81  1420439118 0  \n",
       "10239416   1420438603 81  1420439118 0  \n",
       "10239417   1420438603 81  1420439118 0  \n",
       "10239418   1420438603 81  1420439118 0  \n",
       "10239419   1420438603 81  1420439118 0  \n",
       "10239420   1420438603 81  1420439118 0  \n",
       "10239421   1420438603 81  1420439118 0  \n",
       "10239422   1420438603 81  1420439118 0  \n",
       "10239423   1420438603 81  1420439118 0  \n",
       "10239424   1420438603 81  1420439118 0  \n",
       "10239425   1420438603 81  1420439118 0  \n",
       "10239426   1420438603 81  1420439118 0  \n",
       "10239427   1420438603 81  1420439118 0  \n",
       "10239428   1420438603 81  1420439118 0  \n",
       "10239429   1420438603 81  1420439118 0  \n",
       "10239430   1420438603 81  1420439118 0  \n",
       "10239431   1420438603 81  1420439118 0  \n",
       "10239432   1420438603 81  1420439118 0  \n",
       "\n",
       "[10239433 rows x 6 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cframe.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "MemoryError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-f9a268c565fe>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mfillna\u001b[0;34m(self, value, method, axis, inplace, limit, downcast, **kwargs)\u001b[0m\n\u001b[1;32m   4032\u001b[0m                      \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4033\u001b[0m                                   \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlimit\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4034\u001b[0;31m                                   downcast=downcast, **kwargs)\n\u001b[0m\u001b[1;32m   4035\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4036\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mAppender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_shared_docs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'replace'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0m_shared_doc_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mfillna\u001b[0;34m(self, value, method, axis, inplace, limit, downcast)\u001b[0m\n\u001b[1;32m   6121\u001b[0m                 new_data = self._data.fillna(value=value, limit=limit,\n\u001b[1;32m   6122\u001b[0m                                              \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6123\u001b[0;31m                                              downcast=downcast)\n\u001b[0m\u001b[1;32m   6124\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6125\u001b[0m                 \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnotna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mfillna\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    523\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    524\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfillna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 525\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'fillna'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    526\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    527\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdowncast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)\u001b[0m\n\u001b[1;32m    393\u001b[0m                                             copy=align_copy)\n\u001b[1;32m    394\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 395\u001b[0;31m             \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    396\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    397\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mfillna\u001b[0;34m(self, value, limit, inplace, downcast)\u001b[0m\n\u001b[1;32m    388\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    389\u001b[0m             \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_try_coerce_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 390\u001b[0;31m             \u001b[0mblocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mputmask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    391\u001b[0m             blocks = [b.make_block(values=self._try_coerce_result(b.values))\n\u001b[1;32m    392\u001b[0m                       for b in blocks]\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mputmask\u001b[0;34m(self, mask, new, align, inplace, axis, transpose)\u001b[0m\n\u001b[1;32m    947\u001b[0m         \"\"\"\n\u001b[1;32m    948\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 949\u001b[0;31m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    950\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    951\u001b[0m         \u001b[0mnew\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'values'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mMemoryError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "cframe.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import pandas as pd\n",
    "\n",
    "path =r'/home/louis/Desktop/Energy (copy)/' # use your path\n",
    "allFiles = glob.glob(path + \"/*.dat\")\n",
    "\n",
    "list_ = []\n",
    "appl = ['heater','kettle','microwave', 'pop']\n",
    "i = 0\n",
    "for file_ in allFiles:\n",
    "    headers = ['TS', appl[i]]\n",
    "    df = pd.read_csv(file_, delimiter = ' ', index_col=None, names=headers)  \n",
    "    list_.append(df)\n",
    "    i = i+1\n",
    "    \n",
    "frame = pd.concat(list_, axis = 1, ignore_index = None)\n",
    "print(frame)\n"
   ]
  }
 ],
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