jupyter_english/tutorials/dask_objects_and_little_dask_ml_tutorial_iknyazeva.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "<img src=\"../../img/ods_stickers.jpg\" />\n",
    "    \n",
    "## [mlcourse.ai](https://mlcourse.ai) – Open Machine Learning Course \n",
    "### <center> Author: Irina Knyazeva, ODS Slack nickname : iknyazeva\n",
    "    \n",
    "## <center> Tutorial\n",
    "### <center> \"HANDLE DIFFERENT DATASET WITH DASK AND TRYING A LITTLE DASK ML\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## WHY DO I NEED DASK?\n",
    "\n",
    "Dask provides high-level Array, Bag, and DataFrame collections that mimic NumPy, lists, and Pandas but can operate in parallel on datasets that don’t fit into main memory. Dask’s high-level collections are alternatives to NumPy and Pandas for large datasets.\n",
    "\n",
    "## YOU DEFINITELY NEED DASK  IF\n",
    "if problem size close to limits of RAM, but fits to disk\n",
    "\n",
    "\n",
    "## Reading list\n",
    "This notebook based mainly  based on this three sources\n",
    "\n",
    "- [One more tutorial from analytics vidhya](https://www.analyticsvidhya.com/blog/2018/08/dask-big-datasets-machine_learning-python/)\n",
    "- [taken from towardsdatascience](https://towardsdatascience.com/trying-out-dask-dataframes-in-python-for-fast-data-analysis-in-parallel-aa960c18a915)\n",
    "\n",
    "- [DataCamp course](https://campus.datacamp.com/courses/parallel-computing-with-dask/)\n",
    "\n",
    "- [Dask documentation](https://docs.dask.org/en/latest/) \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import psutil, os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from dask import delayed\n",
    "import gc\n",
    "import time\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's  write a little function for tracking memory that takes python process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def memory_footprint():\n",
    "    mem = psutil.Process(os.getpid()).memory_info().rss\n",
    "    return (mem / 1024 ** 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory used before is 77.39 MB\n"
     ]
    }
   ],
   "source": [
    "before = memory_footprint()\n",
    "print(f'Memory used before is {round(before,2)} MB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory used after is 127.43 MB\n"
     ]
    }
   ],
   "source": [
    "N = (1024 ** 2) // 8\n",
    "x = np.random.randn(50*N)\n",
    "after = memory_footprint()\n",
    "print(f'Memory used after is {round(after,2)} MB')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Computes, but doesn't bind result to a variable allocate extra memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Extra memory obtained after computation 177.43 MB\n"
     ]
    }
   ],
   "source": [
    "x ** 2\n",
    "after1 = memory_footprint()\n",
    "print(f' Extra memory obtained after computation {round(after1,2)} MB')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dask arrays\n",
    "\n",
    "Dask Array implements a subset of the NumPy ndarray interface using blocked algorithms, cutting up the large array into many small arrays. This lets us compute on arrays larger than memory using all of our cores. We coordinate these blocked algorithms using Dask graphs.[dask array documentation](http://docs.dask.org/en/latest/array.html)\n",
    "\n",
    "\n",
    "\n",
    "<center>\n",
    "<img src=\"http://docs.dask.org/en/latest/_images/dask-array-black-text.svg\" />\n",
    "In dask there is three main structures: dask array (based on numpy array), dask dataframe (based on pandas dataframe)  and dask bags (for unstructured data as text)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dask arrays require little memory: 2.984375\n"
     ]
    }
   ],
   "source": [
    "import dask.array as da\n",
    "y = da.from_array(x, chunks=len(x)//4)\n",
    "print('Dask arrays require little memory:', memory_footprint()-after1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compute mean value of this numpy array \n",
      "\n",
      "Elapsed time for compute mean of numpy array (ms): 4\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "t_start = time.time()\n",
    "x.mean()\n",
    "t_end = time.time()\n",
    "print('Compute mean value of this numpy array \\n')\n",
    "print('Elapsed time for compute mean of numpy array (ms):', round((t_end - t_start) * 1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compute the same with dask \n",
      "\n",
      "Elapsed time for compute mean of dask array (ms): 21\n"
     ]
    }
   ],
   "source": [
    "t_start = time.time()\n",
    "y.mean().compute()\n",
    "t_end = time.time()\n",
    "print('Compute the same with dask \\n')\n",
    "print('Elapsed time for compute mean of dask array (ms):', round((t_end - t_start) * 1000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Actually, this example will never be used in practice, because if your numpy already in memory, any partitioning will always raise computational time. But if you need to process data from HDF5, NetCDF or bulk of numpy files from disk it could be extremely useful"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Delayed operations with dask\n",
    "\n",
    "But dask could be useful for small data with delayed computation. It could easily parallelize computation. Let's see the example with our previous numpy array   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time for compute complex functions with numpy array (ms): 426\n"
     ]
    }
   ],
   "source": [
    "def f(z):\n",
    "    return np.sqrt(z + 4)\n",
    "def g(y):\n",
    "    return y - 3\n",
    "def h(x):\n",
    "    return x ** 2\n",
    "\n",
    "time_start = time.time()\n",
    "x = np.random.randn(50*N)\n",
    "y=h(x);z=g(x); w=f(z+y);\n",
    "time_end = time.time()\n",
    "print('Elapsed time for compute complex functions with numpy array (ms):', round((time_end - time_start) * 1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After we get dask delayed object Delayed('f-10fe1849-e5f7-4f12-97df-e728a4123d43')\n",
      "Elapsed time for compute complex functions with numpy array with dask delayed (ms): 98\n"
     ]
    }
   ],
   "source": [
    "y = delayed(h)(x)\n",
    "z = delayed(g)(x)\n",
    "w = delayed(f)(z+y)\n",
    "print('After we get dask delayed object', w)\n",
    "time_start = time.time()\n",
    "w.compute()\n",
    "time_end = time.time()\n",
    "print('Elapsed time for compute complex functions with numpy array with dask delayed (ms):', round((time_end - time_start) * 1000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is easily understood why computation time decreased with the computational graph. Let's do this with the second way of introducing delay functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@delayed\n",
    "def f(z):\n",
    "    return np.sqrt(z + 4)\n",
    "@delayed\n",
    "def g(y):\n",
    "    return y - 3\n",
    "@delayed\n",
    "def h(x):\n",
    "    return x ** 2\n",
    "\n",
    "y = h(x); z = g(x)\n",
    "w = f(z+y)\n",
    "w.visualize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dask dataframe \n",
    "\n",
    "Dask DataFrames coordinate many Pandas DataFrames/Series arranged along the index. A Dask DataFrame is partitioned row-wise, grouping rows by index value for efficiency. These Pandas objects may live on disk or on other machines.\n",
    "(See documentation)[http://docs.dask.org/en/latest/dataframe.html]\n",
    "\n",
    "<center>\n",
    "<img src=\"http://docs.dask.org/en/latest/_images/dask-dataframe.svg\" width=\"40%\" height=\"40% />\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dask.dataframe as dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's return to start of our ML journey\n",
      "\n",
      "Load olympic dataset \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Let\\'s return to start of our ML journey\\n')\n",
    "print('Load olympic dataset \\n')\n",
    "PATH = '../../data/athlete_events.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Team</th>\n",
       "      <th>NOC</th>\n",
       "      <th>Games</th>\n",
       "      <th>Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>City</th>\n",
       "      <th>Sport</th>\n",
       "      <th>Event</th>\n",
       "      <th>Medal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A Dijiang</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>1992 Summer</td>\n",
       "      <td>1992</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>Basketball</td>\n",
       "      <td>Basketball Men's Basketball</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>A Lamusi</td>\n",
       "      <td>M</td>\n",
       "      <td>23.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>2012 Summer</td>\n",
       "      <td>2012</td>\n",
       "      <td>Summer</td>\n",
       "      <td>London</td>\n",
       "      <td>Judo</td>\n",
       "      <td>Judo Men's Extra-Lightweight</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Gunnar Nielsen Aaby</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1920 Summer</td>\n",
       "      <td>1920</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Antwerpen</td>\n",
       "      <td>Football</td>\n",
       "      <td>Football Men's Football</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Edgar Lindenau Aabye</td>\n",
       "      <td>M</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denmark/Sweden</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1900 Summer</td>\n",
       "      <td>1900</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Paris</td>\n",
       "      <td>Tug-Of-War</td>\n",
       "      <td>Tug-Of-War Men's Tug-Of-War</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Christine Jacoba Aaftink</td>\n",
       "      <td>F</td>\n",
       "      <td>21.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NED</td>\n",
       "      <td>1988 Winter</td>\n",
       "      <td>1988</td>\n",
       "      <td>Winter</td>\n",
       "      <td>Calgary</td>\n",
       "      <td>Speed Skating</td>\n",
       "      <td>Speed Skating Women's 500 metres</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID                      Name Sex   Age  Height  Weight            Team  \\\n",
       "0   1                 A Dijiang   M  24.0   180.0    80.0           China   \n",
       "1   2                  A Lamusi   M  23.0   170.0    60.0           China   \n",
       "2   3       Gunnar Nielsen Aaby   M  24.0     NaN     NaN         Denmark   \n",
       "3   4      Edgar Lindenau Aabye   M  34.0     NaN     NaN  Denmark/Sweden   \n",
       "4   5  Christine Jacoba Aaftink   F  21.0   185.0    82.0     Netherlands   \n",
       "\n",
       "   NOC        Games  Year  Season       City          Sport  \\\n",
       "0  CHN  1992 Summer  1992  Summer  Barcelona     Basketball   \n",
       "1  CHN  2012 Summer  2012  Summer     London           Judo   \n",
       "2  DEN  1920 Summer  1920  Summer  Antwerpen       Football   \n",
       "3  DEN  1900 Summer  1900  Summer      Paris     Tug-Of-War   \n",
       "4  NED  1988 Winter  1988  Winter    Calgary  Speed Skating   \n",
       "\n",
       "                              Event Medal  \n",
       "0       Basketball Men's Basketball   NaN  \n",
       "1      Judo Men's Extra-Lightweight   NaN  \n",
       "2           Football Men's Football   NaN  \n",
       "3       Tug-Of-War Men's Tug-Of-War  Gold  \n",
       "4  Speed Skating Women's 500 metres   NaN  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(PATH)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dask do not allocate memory after creation: -5.16015625\n"
     ]
    }
   ],
   "source": [
    "m1=memory_footprint()\n",
    "dask_df = dd.read_csv(PATH)\n",
    "m2 = memory_footprint()\n",
    "print('Dask do not allocate memory after creation:', m2-m1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "But we could see data as in pandas dataframe:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Team</th>\n",
       "      <th>NOC</th>\n",
       "      <th>Games</th>\n",
       "      <th>Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>City</th>\n",
       "      <th>Sport</th>\n",
       "      <th>Event</th>\n",
       "      <th>Medal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A Dijiang</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>1992 Summer</td>\n",
       "      <td>1992</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>Basketball</td>\n",
       "      <td>Basketball Men's Basketball</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>A Lamusi</td>\n",
       "      <td>M</td>\n",
       "      <td>23.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>2012 Summer</td>\n",
       "      <td>2012</td>\n",
       "      <td>Summer</td>\n",
       "      <td>London</td>\n",
       "      <td>Judo</td>\n",
       "      <td>Judo Men's Extra-Lightweight</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Gunnar Nielsen Aaby</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1920 Summer</td>\n",
       "      <td>1920</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Antwerpen</td>\n",
       "      <td>Football</td>\n",
       "      <td>Football Men's Football</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Edgar Lindenau Aabye</td>\n",
       "      <td>M</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denmark/Sweden</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1900 Summer</td>\n",
       "      <td>1900</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Paris</td>\n",
       "      <td>Tug-Of-War</td>\n",
       "      <td>Tug-Of-War Men's Tug-Of-War</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Christine Jacoba Aaftink</td>\n",
       "      <td>F</td>\n",
       "      <td>21.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NED</td>\n",
       "      <td>1988 Winter</td>\n",
       "      <td>1988</td>\n",
       "      <td>Winter</td>\n",
       "      <td>Calgary</td>\n",
       "      <td>Speed Skating</td>\n",
       "      <td>Speed Skating Women's 500 metres</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID                      Name Sex   Age  Height  Weight            Team  \\\n",
       "0   1                 A Dijiang   M  24.0   180.0    80.0           China   \n",
       "1   2                  A Lamusi   M  23.0   170.0    60.0           China   \n",
       "2   3       Gunnar Nielsen Aaby   M  24.0     NaN     NaN         Denmark   \n",
       "3   4      Edgar Lindenau Aabye   M  34.0     NaN     NaN  Denmark/Sweden   \n",
       "4   5  Christine Jacoba Aaftink   F  21.0   185.0    82.0     Netherlands   \n",
       "\n",
       "   NOC        Games  Year  Season       City          Sport  \\\n",
       "0  CHN  1992 Summer  1992  Summer  Barcelona     Basketball   \n",
       "1  CHN  2012 Summer  2012  Summer     London           Judo   \n",
       "2  DEN  1920 Summer  1920  Summer  Antwerpen       Football   \n",
       "3  DEN  1900 Summer  1900  Summer      Paris     Tug-Of-War   \n",
       "4  NED  1988 Winter  1988  Winter    Calgary  Speed Skating   \n",
       "\n",
       "                              Event Medal  \n",
       "0       Basketball Men's Basketball   NaN  \n",
       "1      Judo Men's Extra-Lightweight   NaN  \n",
       "2           Football Men's Football   NaN  \n",
       "3       Tug-Of-War Men's Tug-Of-War  Gold  \n",
       "4  Speed Skating Women's 500 metres   NaN  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('But we could see data as in pandas dataframe:')\n",
    "dask_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We can do many operation the same way as in pandas, but without loading all data in memory \n",
      " \n"
     ]
    }
   ],
   "source": [
    "# building delayed  computation\n",
    "print('We can do many operation the same way as in pandas, but without loading all data in memory \\n ')\n",
    "sex_distr = dask_df.loc[dask_df['Games'].str.contains('1996')].groupby('Sex')['Age'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here we done selecting and aggregation exactly the same way as we did in pandas \n",
      "\n",
      "But there is not any computation, we create dask structure \\ n\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Dask Series Structure:\n",
       "npartitions=1\n",
       "    float64\n",
       "        ...\n",
       "Name: Age, dtype: float64\n",
       "Dask Name: series-groupby-min-agg, 8 tasks"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Here we done selecting and aggregation exactly the same way as we did in pandas \\n')\n",
    "print('But there is not any computation, we create dask structure \\ n')\n",
    "sex_distr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computation is time consuming, but we remember that we dont't need to load data in memory for this computation \n",
      "\n",
      "Sex\n",
      "F    12.0\n",
      "M    14.0\n",
      "Name: Age, dtype: float64\n",
      "CPU times: user 665 ms, sys: 82.8 ms, total: 748 ms\n",
      "Wall time: 746 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print('Computation is time consuming, but we remember that we dont\\'t need to load all data in memory for this computation \\n')\n",
    "print(sex_distr.compute())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pandas of course more effective \n",
      "\n",
      "Sex\n",
      "F    12.0\n",
      "M    14.0\n",
      "Name: Age, dtype: float64\n",
      "CPU times: user 156 ms, sys: 3.07 ms, total: 159 ms\n",
      "Wall time: 158 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print('Pandas of course more effective \\n')\n",
    "print(df.loc[df['Games'].str.contains('1996')].groupby('Sex')['Age'].min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compatibility with Pandas API\n",
    "- Unavailable in dask.dataframe:\n",
    "    * some unsupported file formats (e.g., .xls, .zip,...)\n",
    "    * sorting\n",
    "- Available in dask.dataframe:\n",
    "    * indexing, selection, & reindexing\n",
    "    * aggregations: .sum(), .mean(), .std(), .min(), .max() etc.\n",
    "    * grouping with .groupby()\n",
    "    * datetime conversion with dd.to_datetime()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read collections of files to dask dataframe\n",
    "For example I've taken Alica Project.  Capstone_user_identification archive [link](https://drive.google.com/open?id=1AU3M_mFPofbfhFQa_Bktozq_vFREkWJA) (~7 Mb, unziped data ~60 Mb).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH_TO_DATA = '../../data/capstone_user_identification'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We can load all files in single dataframe \n",
      "\n",
      "Your dont't need this in Alica project, just an example \n",
      " \n"
     ]
    }
   ],
   "source": [
    "print('We can load all files in single dataframe \\n')\n",
    "print('Your dont\\'t need this in Alica project, just an example \\n ')\n",
    "user10dask = dd.read_csv(os.path.join(PATH_TO_DATA, \n",
    "                                       '10users/*.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We can look at the data\n",
      "Dask DataFrame Structure:\n",
      "               timestamp    site\n",
      "npartitions=10                  \n",
      "                  object  object\n",
      "                     ...     ...\n",
      "...                  ...     ...\n",
      "                     ...     ...\n",
      "                     ...     ...\n",
      "Dask Name: from-delayed, 30 tasks\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>site</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5327</th>\n",
       "      <td>2014-03-26 15:43:56</td>\n",
       "      <td>www.google.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5328</th>\n",
       "      <td>2014-03-26 15:43:57</td>\n",
       "      <td>plus.google.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5329</th>\n",
       "      <td>2014-03-26 15:43:57</td>\n",
       "      <td>mail.google.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5330</th>\n",
       "      <td>2014-03-26 15:43:58</td>\n",
       "      <td>accounts.google.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5331</th>\n",
       "      <td>2014-03-26 15:43:58</td>\n",
       "      <td>accounts.youtube.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                timestamp                  site\n",
       "5327  2014-03-26 15:43:56        www.google.com\n",
       "5328  2014-03-26 15:43:57       plus.google.com\n",
       "5329  2014-03-26 15:43:57       mail.google.com\n",
       "5330  2014-03-26 15:43:58   accounts.google.com\n",
       "5331  2014-03-26 15:43:58  accounts.youtube.com"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('We can look at the data')\n",
    "print(user10dask)\n",
    "user10dask.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's see what happens if we want to count all sites (it could seen as a one more way for dictionary creation) \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Let\\'s see what happens if we want to count all sites (it could seen as a one more way for dictionary creation) \\n')\n",
    "count_sites = user10dask.groupby('site')['site'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If we visualize this structure we'll see the picture of computation \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('If we visualize this structure we\\'ll see the picture of computation \\n')\n",
    "count_sites.visualize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 196 ms, sys: 43.8 ms, total: 240 ms\n",
      "Wall time: 177 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "site\n",
       "s.youtube.com                           8300\n",
       "www.google.fr                           7813\n",
       "www.google.com                          5441\n",
       "mail.google.com                         4158\n",
       "www.facebook.com                        4141\n",
       "apis.google.com                         3758\n",
       "r3---sn-gxo5uxg-jqbe.googlevideo.com    3244\n",
       "r1---sn-gxo5uxg-jqbe.googlevideo.com    3094\n",
       "plus.google.com                         2630\n",
       "accounts.google.com                     2089\n",
       "r2---sn-gxo5uxg-jqbe.googlevideo.com    1939\n",
       "fr-mg42.mail.yahoo.com                  1868\n",
       "www.youtube.com                         1804\n",
       "r4---sn-gxo5uxg-jqbe.googlevideo.com    1702\n",
       "clients1.google.com                     1493\n",
       "download.jboss.org                      1441\n",
       "s-static.ak.facebook.com                1388\n",
       "static.ak.facebook.com                  1265\n",
       "i1.ytimg.com                            1232\n",
       "twitter.com                             1204\n",
       "Name: site, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "count_sites.compute().sort_values(ascending=False)[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## JSON Files into Dask Bags\n",
    "\n",
    "Dask Bag implements operations like map, filter, fold, and groupby on collections of Python objects. It does this in parallel with a small memory footprint using Python iterators. It is similar to a parallel version of PyToolz or a Pythonic version of the PySpark RDD.[Dask bag documentation](http://docs.dask.org/en/latest/bag-overview.html)\n",
    "\n",
    "\n",
    "Dask bags are often used to parallelize simple computations on unstructured or semi-structured data like text data, log files, JSON records, or user defined Python objects.\n",
    " \n",
    "Let's see example with our Medium data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dask.bag as db\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Path to our medium data \n",
      "\n",
      "../../data/kaggle_medium\n"
     ]
    }
   ],
   "source": [
    "print('Path to our medium data \\n')\n",
    "PATH = '../../data/kaggle_medium'\n",
    "print(PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wrap train json to dask bag format \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dask.bag<bag-fro..., npartitions=1>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Wrap train json to dask bag format \\n')\n",
    "items = db.read_text(os.path.join(PATH,'train.json'))\n",
    "items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's look at one example \n",
      "\n",
      "('{\"_id\": \"https://medium.com/policy/medium-terms-of-service-9db0094a1e0f\", \"_timestamp\": 1520035195.282891, \"_spider\": \"medium\", \"url\": \"https://medium.com/policy/medium-terms-of-service-9db0094a1e0f\", \"domain\": \"medium.com\", \"published\": {\"$date\": \"2012-08-13T22:54:53.510Z\"}, \"title\": \"Medium Terms of Service \\\\u2013 Medium Policy \\\\u2013 Medium\", \"content\": \"<div><header class=\\\\\"container u-maxWidth740\\\\\"><div class=\\\\\"uiScale uiScale-ui--regular uiScale-caption--regular postMetaHeader u-paddingBottom10 row\\\\\"><div class=\\\\\"col u-size12of12 js-postMetaLockup\\\\\"><div class=\\\\\"uiScale uiScale-ui--regular uiScale-caption--regular postMetaLockup postMetaLockup--authorWithBio u-flexCenter js-postMetaLockup\\\\\"><div class=\\\\\"u-flex0\\\\\"><a class=\\\\\"link u-baseColor--link avatar\\\\\" href=\\\\\"https://medium.com/@Medium?source=post_header_lockup\\\\\" data-action=\\\\\"show-user-card\\\\\" data-action-source=\\\\\"post_header_lockup\\\\\" data-action-value=\\\\\"504c7870fdb6\\\\\" data-action-type=\\\\\"hover\\\\\" data-user-id=\\\\\"504c7870fdb6\\\\\" dir=\\\\\"auto\\\\\"><div class=\\\\\"u-relative u-inlineBlock u-flex0\\\\\"><img src=\\\\\"https://cdn-images-1.medium.com/fit/c/120/120/1*6_fgYnisCa9V21mymySIvA.png\\\\\" class=\\\\\"avatar-image avatar-image--small\\\\\" alt=\\\\\"Go to the profile of Medium\\\\\"><div class=\\\\\"avatar-halo u-absolute u-textColorGreenNormal svgIcon\\\\\" style=\\\\\"width: calc(100% + 12px); height: calc(100% + 12px); top:-6px; left:-6px\\\\\"><svg viewbox=\\\\\"0 0 114 114\\\\\" xmlns=\\\\\"http://www.w3.org/2000/svg\\\\\"><path d=\\\\\"M7.66922967,32.092726 C17.0070768,13.6353618 35.9421928,1.75 57,1.75 C78.0578072,1.75 96.9929232,13.6353618 106.33077,32.092726 L107.66923,31.4155801 C98.0784505,12.4582656 78.6289015,0.25 57,0.25 C35.3710985,0.25 15.9215495,12.4582656 6.33077033,31.4155801 L7.66922967,32.092726 Z\\\\\"></path><path d=\\\\\"M106.33077,81.661427 C96.9929232,100.118791 78.0578072,112.004153 57,112.004153 C35.9421928,112.004153 17.0070768,100.118791 7.66922967,81.661427 L6.33077033,82.338573 C15.9215495,101.295887 35.3710985,113.504153 57,113.504153 C78.6289015,113.504153 98.0784505,101.295887 107.66923,82.338573 L106.33077,81.661427 Z\\\\\"></path></svg></div></div></a></div><div class=\\\\\"u-flex1 u-paddingLeft15 u-overflowHidden\\\\\"><div class=\\\\\"u-lineHeightTightest\\\\\"><a class=\\\\\"ds-link ds-link--styleSubtle ui-captionStrong u-inlineBlock link link--darken link--darker\\\\\" href=\\\\\"https://medium.com/@Medium?source=post_header_lockup\\\\\" data-action=\\\\\"show-user-card\\\\\" data-action-source=\\\\\"post_header_lockup\\\\\" data-action-value=\\\\\"504c7870fdb6\\\\\" data-action-type=\\\\\"hover\\\\\" data-user-id=\\\\\"504c7870fdb6\\\\\" dir=\\\\\"auto\\\\\">Medium</a><span class=\\\\\"followState js-followState\\\\\" data-user-id=\\\\\"504c7870fdb6\\\\\"></span></div><div class=\\\\\"ui-caption ui-xs-clamp2 postMetaInline\\\\\">Everyone\\\\u2019s stories and ideas</div><div class=\\\\\"ui-caption postMetaInline js-testPostMetaInlineSupplemental\\\\\"><time datetime=\\\\\"2012-08-13T22:54:53.510Z\\\\\">Aug 13, 2012</time><span class=\\\\\"middotDivider u-fontSize12\\\\\"></span><span class=\\\\\"readingTime\\\\\" title=\\\\\"5 min read\\\\\"></span></div></div></div></div></div></header><div class=\\\\\"postArticle-content js-postField js-notesSource js-trackedPost\\\\\" data-post-id=\\\\\"9db0094a1e0f\\\\\" data-source=\\\\\"post_page\\\\\" data-collection-id=\\\\\"675ebe56ac25\\\\\" data-tracking-context=\\\\\"postPage\\\\\"><section name=\\\\\"bb8c\\\\\" class=\\\\\"section section--body section--first section--last\\\\\"><div class=\\\\\"section-divider\\\\\"><hr class=\\\\\"section-divider\\\\\"></div><div class=\\\\\"section-content\\\\\"><div class=\\\\\"section-inner sectionLayout--insetColumn\\\\\"><h1 name=\\\\\"title\\\\\" id=\\\\\"title\\\\\" class=\\\\\"graf graf--h2 graf--leading graf--title\\\\\">Medium Terms of\\\\u00a0Service</h1><p name=\\\\\"571b\\\\\" id=\\\\\"571b\\\\\" class=\\\\\"graf graf--p graf-after--h2\\\\\"><strong class=\\\\\"markup--strong markup--p-strong\\\\\">Effective: March 7, 2016</strong></p><p name=\\\\\"c90b\\\\\" id=\\\\\"c90b\\\\\" class=\\\\\"graf graf--p graf-after--p\\\\\">These Terms of Service (\\\\u201cTerms\\\\u201d) are a contract between you and A Medium Corporation. They govern your use of Medium\\\\u2019s sites, services, mobile apps, products, and content (\\\\u201cServices\\\\u201d).</p><p name=\\\\\"238b\\\\\" id=\\\\\"238b\\\\\" class=\\\\\"graf graf--p graf-after--p\\\\\">By using Medium, you agree to these Terms. If you don\\\\u2019t agree to any of the Terms, you can\\\\u2019t use Medium.</p><p name=\\\\\"7769\\\\\" id=\\\\\"7769\\\\\" class=\\\\\"graf graf--p graf-after--p\\\\\">We can change these Terms at any time. We keep a <a href=\\\\\"https://github.com/Medium/medium-policy\\\\\" data-href=\\\\\"https://github.com/Medium/medium-policy\\\\\" class=\\\\\"markup--anchor markup--p-anchor\\\\\" rel=\\\\\"nofollow noopener\\\\\" target=\\\\\"_blank\\\\\">historical</a> record of all changes to our Terms on GitHub. If a change is material, we\\\\u2019ll let you know before they take effect. By using Medium on or after that effective date, you agree to the new Terms. If you don\\\\u2019t agree to them, you should delete your account before they take effect, otherwise your use of the site and content will be subject to the new Terms.</p><h4 name=\\\\\"8c81\\\\\" id=\\\\\"8c81\\\\\" class=\\\\\"graf graf--h4 graf-after--p\\\\\"><strong class=\\\\\"markup--strong markup--h4-strong\\\\\">Content rights &amp; responsibilities</strong></h4><p name=\\\\\"ac74\\\\\" id=\\\\\"ac74\\\\\" class=\\\\\"graf graf--p graf-after--h4\\\\\">You own the rights to the content you create and post on Medium.</p><p name=\\\\\"651b\\\\\" id=\\\\\"651b\\\\\" class=\\\\\"graf graf--p graf-after--p\\\\\">By posting content to Medium, you give us a nonexclusive license to publish it on Medium Services, including anything reasonably related to publishing it (like storing, displaying, reformatting, and distributing it). In consideration for Medium granting you access to and use of the Services, you agree that Medium may enable advertising on the Services, including in connection with the display of your content or other information. We may also use your content to promote Medium, including its products and content. We will never sell your content to third parties without your explicit permission.</p><p name=\\\\\"2584\\\\\" id=\\\\\"2584\\\\\" class=\\\\\"graf graf--p graf-after--p\\\\\">You\\\\u2019re responsible for the content you post. This means you assume all risks related to it, including someone else\\\\u2019s reliance on its accuracy, or claims relating to intellectual property or other legal rights.</p><p name=\\\\\"c207\\\\\" id=\\\\\"c207\\\\\" class=\\\\\"graf graf--p graf-after--p\\\\\">You\\\\u2019re welcome to post content on Medium that you\\\\u2019ve published elsewhere, as long as you have the rights you need to do so. By posting content to Medium, you represent that doing so doesn\\\\u2019t conflict with any other agreement you\\\\u2019ve made.</p><p name=\\\\\"0372\\\\\" id=\\\\\"0372\\\\\" class=\\\\\"graf graf--p graf-after--p\\\\\">By posting content you didn\\\\u2019t create to Medium, you are representing that you have the right to do so. For example, you are posting a work that\\\\u2019s in the public domain, used under license (including a free license, such as <a href=\\\\\"https://creativecommons.org/licenses/\\\\\" data-href=\\\\\"https://creativecommons.org/licenses/\\\\\" class=\\\\\"markup--anchor markup--p-anchor\\\\\" rel=\\\\\"nofollow noopener\\\\\" target=\\\\\"_blank\\\\\">Creative Commons</a>), or a fair use.</p><p name=\\\\\"0472\\\\\" id=\\\\\"0472\\\\\" class=\\\\\"graf graf--p graf-after--p\\\\\">We can remove any content you post for any reason.</p><p name=\\\\\"db2b\\\\\" id=\\\\\"db2b\\\\\" class=\\\\\"graf graf--p graf-after--p\\\\\">You can delete any of your posts, or your account, anytime. Processing the deletion may take a little time, but we\\\\u2019ll do it as quickly as possible. We may keep backup copies of your deleted post or account on our servers for up to 14 days after you delete it.</p><h4 name=\\\\\"baf1\\\\\" id=\\\\\"baf1\\\\\" class=\\\\\"graf graf--h4 graf-after--p\\\\\"><strong class=\\\\\"markup--strong markup--h4-strong\\\\\">Our content and\\\\u00a0services</strong></h4><p name=\\\\\"adc7\\\\\" id=\\\\\"adc7\\\\\" class=\\\\\"graf graf--p graf-after--h4\\\\\">We reserve all rights in Medium\\\\u2019s look and feel. Some parts of Medium are licensed under third-party open source licenses. We also make some of our own code available under open source licenses. As for other parts of Medium, you may not copy or adapt any portion of our code or visual design elements (including logos) without express written permission from Medium unless otherwise permitted by law.</p><p name=\\\\\"20e4\\\\\" id=\\\\\"20e4\\\\\" class=\\\\\"graf graf--p graf-after--p\\\\\">You may not do, or try to do, the following: (1) access or tamper with non-public areas of the Services, our computer systems, or the systems of our technical providers; (2) access or search the Services by any means other than the currently available, published interfaces (e.g., APIs) that we provide; (3) forge any TCP/IP packet header or any part of the header information in any email or posting, or in any way use the Services to send altered, deceptive, or false source-identifying information; or (4) interfere with, or disrupt, the access of any user, host, or network, including sending a virus, overloading, flooding, spamming, mail-bombing the Services, or by scripting the creation of content or accounts in such a manner as to interfere with or create an undue burden on the Services.</p><p name=\\\\\"f5dd\\\\\" id=\\\\\"f5dd\\\\\" class=\\\\\"graf graf--p graf-after--p\\\\\">Crawling the Services is allowed if done in accordance with the provisions of our robots.txt file, but scraping the Services is prohibited.</p><p name=\\\\\"71a8\\\\\" id=\\\\\"71a8\\\\\" class=\\\\\"graf graf--p graf-after--p\\\\\">We may change, terminate, or restrict access to any aspect of the service, at any time, without notice.</p><h4 name=\\\\\"12f1\\\\\" id=\\\\\"12f1\\\\\" class=\\\\\"graf graf--h4 graf-after--p\\\\\"><strong class=\\\\\"markup--strong markup--h4-strong\\\\\">No children</strong></h4><p name=\\\\\"2ce7\\\\\" id=\\\\\"2ce7\\\\\" class=\\\\\"graf graf--p graf-after--h4\\\\\">Medium is only for people 13 years old and over. By using Medium, you affirm that you are over 13. If we learn someone under 13 is using Medium, we\\\\u2019ll terminate their account.</p><h4 name=\\\\\"531c\\\\\" id=\\\\\"531c\\\\\" class=\\\\\"graf graf--h4 graf-after--p\\\\\"><strong class=\\\\\"markup--strong markup--h4-strong\\\\\">Security</strong></h4><p name=\\\\\"3155\\\\\" id=\\\\\"3155\\\\\" class=\\\\\"graf graf--p graf-after--h4\\\\\">If you find a security vulnerability on Medium, tell us. We have a <a href=\\\\\"https://medium.com/policy/medium-s-bug-bounty-disclosure-program-34b1c80764c2\\\\\" data-href=\\\\\"https://medium.com/policy/medium-s-bug-bounty-disclosure-program-34b1c80764c2\\\\\" class=\\\\\"markup--anchor markup--p-anchor\\\\\" target=\\\\\"_blank\\\\\">bug bounty disclosure program</a>.</p><h4 name=\\\\\"05cc\\\\\" id=\\\\\"05cc\\\\\" class=\\\\\"graf graf--h4 graf-after--p\\\\\"><strong class=\\\\\"markup--strong markup--h4-strong\\\\\">Incorporated rules and\\\\u00a0policies</strong></h4><p name=\\\\\"5207\\\\\" id=\\\\\"5207\\\\\" class=\\\\\"graf graf--p graf-after--h4\\\\\">By using the Services, you agree to let Medium collect and use information as detailed in our <a href=\\\\\"https://medium.com/p/f03bf92035c9\\\\\" data-href=\\\\\"https://medium.com/p/f03bf92035c9\\\\\" class=\\\\\"markup--anchor markup--p-anchor\\\\\" target=\\\\\"_blank\\\\\">Privacy Policy</a>. If you\\\\u2019re outside the United States, you consent to letting Medium transfer, store, and process your information (including your personal information and content) in and out of the United States.</p><p name=\\\\\"6230\\\\\" id=\\\\\"6230\\\\\" class=\\\\\"graf graf--p graf-after--p\\\\\">To enable a functioning community, we have <a href=\\\\\"https://medium.com/policy/medium-rules-30e5502c4eb4\\\\\" data-href=\\\\\"https://medium.com/policy/medium-rules-30e5502c4eb4\\\\\" class=\\\\\"markup--anchor markup--p-anchor\\\\\" target=\\\\\"_blank\\\\\">Rules</a>. To ensure usernames are distributed and used fairly, we have a <a href=\\\\\"https://medium.com/@Medium/medium-username-policy-7054a77fb04f\\\\\" data-href=\\\\\"https://medium.com/@Medium/medium-username-policy-7054a77fb04f\\\\\" class=\\\\\"markup--anchor markup--p-anchor\\\\\" target=\\\\\"_blank\\\\\">Username Policy</a>. Under our <a href=\\\\\"https://medium.com/policy/mediums-copyright-and-dmca-policy-d126f73695\\\\\" data-href=\\\\\"https://medium.com/policy/mediums-copyright-and-dmca-policy-d126f73695\\\\\" class=\\\\\"markup--anchor markup--p-anchor\\\\\" target=\\\\\"_blank\\\\\">DMCA Policy</a>, we\\\\u2019ll remove material after receiving a valid takedown notice. Under our <a href=\\\\\"https://medium.com/policy/mediums-trademark-policy-e3bb53df59a7\\\\\" data-href=\\\\\"https://medium.com/policy/mediums-trademark-policy-e3bb53df59a7\\\\\" class=\\\\\"markup--anchor markup--p-anchor\\\\\" target=\\\\\"_blank\\\\\">Trademark Policy</a>, we\\\\u2019ll investigate any use of another\\\\u2019s trademark and respond appropriately.</p><p name=\\\\\"21ad\\\\\" id=\\\\\"21ad\\\\\" class=\\\\\"graf graf--p graf-after--p\\\\\">By using Medium, you agree to follow these Rules and Policies. If you don\\\\u2019t, we may remove content, or suspend or delete your account.</p><h4 name=\\\\\"a2a2\\\\\" id=\\\\\"a2a2\\\\\" class=\\\\\"graf graf--h4 graf-after--p\\\\\"><strong class=\\\\\"markup--strong markup--h4-strong\\\\\">Miscellaneous</strong></h4><p name=\\\\\"b7da\\\\\" id=\\\\\"b7da\\\\\" class=\\\\\"graf graf--p graf-after--h4\\\\\"><em class=\\\\\"markup--em markup--p-em\\\\\">Disclaimer of warranty.</em> Medium provides the Services to you as is. You use them at your own risk and discretion. That means they don\\\\u2019t come with any warranty. None express, none implied. No implied warranty of merchantability, fitness for a particular purpose, availability, security, title or non-infringement.</p><p name=\\\\\"7073\\\\\" id=\\\\\"7073\\\\\" class=\\\\\"graf graf--p graf-after--p\\\\\"><em class=\\\\\"markup--em markup--p-em\\\\\">Limitation of Liability</em>. Medium won\\\\u2019t be liable to you for any damages that arise from your using the Services. This includes if the Services are hacked or unavailable. This includes all types of damages (indirect, incidental, consequential, special or exemplary). And it includes all kinds of legal claims, such as breach of contract, breach of warranty, tort, or any other loss.</p><p name=\\\\\"3d70\\\\\" id=\\\\\"3d70\\\\\" class=\\\\\"graf graf--p graf-after--p\\\\\"><em class=\\\\\"markup--em markup--p-em\\\\\">No waiver.</em> If Medium doesn\\\\u2019t exercise a particular right under these Terms, that doesn\\\\u2019t waive it.</p><p name=\\\\\"ab04\\\\\" id=\\\\\"ab04\\\\\" class=\\\\\"graf graf--p graf-after--p\\\\\"><em class=\\\\\"markup--em markup--p-em\\\\\">Severability</em>. If any provision of these terms is found invalid by a court of competent jurisdiction, you agree that the court should try to give effect to the parties\\\\u2019 intentions as reflected in the provision and that other provisions of the Terms will remain in full effect.</p><p name=\\\\\"bde8\\\\\" id=\\\\\"bde8\\\\\" class=\\\\\"graf graf--p graf-after--p\\\\\"><em class=\\\\\"markup--em markup--p-em\\\\\">Choice of law and jurisdiction.</em> These Terms are governed by California law, without reference to its conflict of laws provisions. You agree that any suit arising from the Services must take place in a court located in San Francisco, California.</p><p name=\\\\\"bbb3\\\\\" id=\\\\\"bbb3\\\\\" class=\\\\\"graf graf--p graf-after--p\\\\\"><em class=\\\\\"markup--em markup--p-em\\\\\">Entire agreement.</em> These Terms (including any document incorporated by reference into them) are the whole agreement between Medium and you concerning the Services.</p><p name=\\\\\"dbf1\\\\\" id=\\\\\"dbf1\\\\\" class=\\\\\"graf graf--p graf-after--p\\\\\"><em class=\\\\\"markup--em markup--p-em\\\\\">Government use.</em> If you\\\\u2019re \\\\u200busing \\\\u200bMedium for the U.S. Government, <a href=\\\\\"https://medium.com/@Medium/amendment-to-medium-terms-of-service-applicable-to-u-s-government-users-fccb00db67d7\\\\\" data-href=\\\\\"https://medium.com/@Medium/amendment-to-medium-terms-of-service-applicable-to-u-s-government-users-fccb00db67d7\\\\\" class=\\\\\"markup--anchor markup--p-anchor\\\\\" target=\\\\\"_blank\\\\\">this Amendment</a> to \\\\u200bMedium\\\\u2019s Terms of Service \\\\u200bapplies to you\\\\u200b.</p><p name=\\\\\"3318\\\\\" id=\\\\\"3318\\\\\" class=\\\\\"graf graf--p graf-after--p graf--trailing\\\\\">Questions? Let us know at <a href=\\\\\"mailto:%20legal@medium.com\\\\\" data-href=\\\\\"mailto:%20legal@medium.com\\\\\" class=\\\\\"markup--anchor markup--p-anchor\\\\\" target=\\\\\"_blank\\\\\">legal@medium.com</a>.</p></div></div></section></div><footer class=\\\\\"u-paddingTop10\\\\\"><div class=\\\\\"container u-maxWidth740\\\\\"><div class=\\\\\"row\\\\\"><div class=\\\\\"col u-size12of12\\\\\"></div></div><div class=\\\\\"row\\\\\"><div class=\\\\\"col u-size12of12 js-postTags\\\\\"><div class=\\\\\"u-paddingBottom10\\\\\"><ul class=\\\\\"tags tags--postTags tags--borderless\\\\\"><li><a class=\\\\\"link u-baseColor--link\\\\\" href=\\\\\"https://medium.com/tag/terms-and-conditions?source=post\\\\\" data-action-source=\\\\\"post\\\\\">Terms And Conditions</a></li><li><a class=\\\\\"link u-baseColor--link\\\\\" href=\\\\\"https://medium.com/tag/terms?source=post\\\\\" data-action-source=\\\\\"post\\\\\">Terms</a></li><li><a class=\\\\\"link u-baseColor--link\\\\\" href=\\\\\"https://medium.com/tag/medium?source=post\\\\\" data-action-source=\\\\\"post\\\\\">Medium</a></li></ul></div></div></div><section class=\\\\\"uiScale uiScale-ui--small uiScale-caption--regular u-borderTopLightest u-marginTop10 u-paddingTop20\\\\\"><div class=\\\\\"ui-h3 u-textColorDarker u-fontSize22\\\\\">One clap, two clap, three clap, forty?</div><p class=\\\\\"ui-body u-marginBottom20 u-textColorDark u-fontSize16\\\\\">By clapping more or less, you can signal to us which stories really stand out.</p></section><div class=\\\\\"postActions js-postActionsFooter\\\\\"><div class=\\\\\"u-flexCenter\\\\\"><div class=\\\\\"u-flex1\\\\\"><div class=\\\\\"multirecommend js-actionMultirecommend u-flexCenter u-width60\\\\\" data-post-id=\\\\\"9db0094a1e0f\\\\\" data-is-icon-29px=\\\\\"true\\\\\" data-is-circle=\\\\\"true\\\\\" data-has-recommend-list=\\\\\"true\\\\\" data-source=\\\\\"post_actions_footer-----9db0094a1e0f---------------------clap_footer\\\\\"><div class=\\\\\"u-relative u-foreground\\\\\"><div class=\\\\\"clapUndo u-width60 u-round u-height32 u-absolute u-borderBox u-paddingRight5 u-transition--transform200Spring u-background--brandSageLighter js-clapUndo\\\\\" style=\\\\\"top: 14px; padding: 2px;\\\\\"></div></div><span class=\\\\\"u-textAlignCenter u-relative u-background js-actionMultirecommendCount u-marginLeft10\\\\\"></span></div></div><div class=\\\\\"buttonSet u-flex0\\\\\"></div></div></div></div><div class=\\\\\"u-maxWidth740 u-paddingTop20 u-marginTop20 u-borderTopLightest container u-paddingBottom20 u-xs-paddingBottom10 js-postAttributionFooterContainer\\\\\"><div class=\\\\\"row js-postFooterInfo\\\\\"><div class=\\\\\"col u-size6of12 u-xs-size12of12\\\\\"><li class=\\\\\"uiScale uiScale-ui--small uiScale-caption--regular u-block u-paddingBottom18 js-cardUser\\\\\"><div class=\\\\\"u-marginLeft20 u-floatRight\\\\\"><span class=\\\\\"followState js-followState\\\\\" data-user-id=\\\\\"504c7870fdb6\\\\\"></span></div><div class=\\\\\"u-tableCell\\\\\"><a class=\\\\\"link u-baseColor--link avatar\\\\\" href=\\\\\"https://medium.com/@Medium?source=footer_card\\\\\" title=\\\\\"Go to the profile of Medium\\\\\" aria-label=\\\\\"Go to the profile of Medium\\\\\" data-action-source=\\\\\"footer_card\\\\\" data-user-id=\\\\\"504c7870fdb6\\\\\" dir=\\\\\"auto\\\\\"><div class=\\\\\"u-relative u-inlineBlock u-flex0\\\\\"><img src=\\\\\"https://cdn-images-1.medium.com/fit/c/120/120/1*6_fgYnisCa9V21mymySIvA.png\\\\\" class=\\\\\"avatar-image avatar-image--small\\\\\" alt=\\\\\"Go to the profile of Medium\\\\\"><div class=\\\\\"avatar-halo u-absolute u-textColorGreenNormal svgIcon\\\\\" style=\\\\\"width: calc(100% + 12px); height: calc(100% + 12px); top:-6px; left:-6px\\\\\"><svg viewbox=\\\\\"0 0 114 114\\\\\" xmlns=\\\\\"http://www.w3.org/2000/svg\\\\\"><path d=\\\\\"M7.66922967,32.092726 C17.0070768,13.6353618 35.9421928,1.75 57,1.75 C78.0578072,1.75 96.9929232,13.6353618 106.33077,32.092726 L107.66923,31.4155801 C98.0784505,12.4582656 78.6289015,0.25 57,0.25 C35.3710985,0.25 15.9215495,12.4582656 6.33077033,31.4155801 L7.66922967,32.092726 Z\\\\\"></path><path d=\\\\\"M106.33077,81.661427 C96.9929232,100.118791 78.0578072,112.004153 57,112.004153 C35.9421928,112.004153 17.0070768,100.118791 7.66922967,81.661427 L6.33077033,82.338573 C15.9215495,101.295887 35.3710985,113.504153 57,113.504153 C78.6289015,113.504153 98.0784505,101.295887 107.66923,82.338573 L106.33077,81.661427 Z\\\\\"></path></svg></div></div></a></div><div class=\\\\\"u-tableCell u-verticalAlignMiddle u-breakWord u-paddingLeft15\\\\\"><h3 class=\\\\\"ui-h3 u-fontSize18 u-lineHeightTighter\\\\\"><a class=\\\\\"link link--primary u-accentColor--hoverTextNormal\\\\\" href=\\\\\"https://medium.com/@Medium\\\\\" property=\\\\\"cc:attributionName\\\\\" title=\\\\\"Go to the profile of Medium\\\\\" aria-label=\\\\\"Go to the profile of Medium\\\\\" rel=\\\\\"author cc:attributionUrl\\\\\" data-user-id=\\\\\"504c7870fdb6\\\\\" dir=\\\\\"auto\\\\\">Medium</a></h3><div class=\\\\\"ui-caption u-textColorGreenNormal u-fontSize13 u-tintSpectrum u-accentColor--textNormal u-marginBottom7\\\\\">Medium member since Aug 2017</div><p class=\\\\\"ui-body u-fontSize14 u-lineHeightBaseSans u-textColorDark u-marginBottom4\\\\\">Everyone\\\\u2019s stories and ideas</p></div></li></div><div class=\\\\\"col u-size6of12 u-xs-size12of12 u-xs-marginTop30\\\\\"><li class=\\\\\"uiScale uiScale-ui--small uiScale-caption--regular u-block u-paddingBottom18 js-cardCollection\\\\\"><div class=\\\\\"u-marginLeft20 u-floatRight\\\\\"></div><div class=\\\\\"u-tableCell \\\\\"><a class=\\\\\"link u-baseColor--link avatar avatar--roundedRectangle\\\\\" href=\\\\\"https://medium.com/policy?source=footer_card\\\\\" title=\\\\\"Go to Medium Policy\\\\\" aria-label=\\\\\"Go to Medium Policy\\\\\" data-action-source=\\\\\"footer_card\\\\\"><img src=\\\\\"https://cdn-images-1.medium.com/fit/c/120/120/1*6_fgYnisCa9V21mymySIvA.png\\\\\" class=\\\\\"avatar-image u-size60x60\\\\\" alt=\\\\\"Medium Policy\\\\\"></a></div><div class=\\\\\"u-tableCell u-verticalAlignMiddle u-breakWord u-paddingLeft15\\\\\"><h3 class=\\\\\"ui-h3 u-fontSize18 u-lineHeightTighter u-marginBottom4\\\\\"><a class=\\\\\"link link--primary u-accentColor--hoverTextNormal\\\\\" href=\\\\\"https://medium.com/policy?source=footer_card\\\\\" rel=\\\\\"collection\\\\\" data-action-source=\\\\\"footer_card\\\\\">Medium Policy</a></h3><p class=\\\\\"ui-body u-fontSize14 u-lineHeightBaseSans u-textColorDark u-marginBottom4\\\\\">The Fine Print</p><div class=\\\\\"buttonSet\\\\\"></div></div></li></div></div></div><div class=\\\\\"js-postFooterPlacements\\\\\"></div><div class=\\\\\"u-padding0 u-clearfix u-backgroundGrayLightest u-print-hide supplementalPostContent js-responsesWrapper\\\\\"></div><div class=\\\\\"supplementalPostContent js-heroPromo\\\\\"></div></footer></div>\", \"author\": {\"name\": null, \"url\": \"https://medium.com/@Medium\", \"twitter\": \"@Medium\"}, \"image_url\": null, \"tags\": [], \"link_tags\": {\"canonical\": \"https://medium.com/policy/medium-terms-of-service-9db0094a1e0f\", \"publisher\": \"https://plus.google.com/103654360130207659246\", \"author\": \"https://medium.com/@Medium\", \"search\": \"/osd.xml\", \"alternate\": \"android-app://com.medium.reader/https/medium.com/p/9db0094a1e0f\", \"stylesheet\": \"https://cdn-static-1.medium.com/_/fp/css/main-branding-base.Ch8g7KPCoGXbtKfJaVXo_w.css\", \"icon\": \"https://cdn-static-1.medium.com/_/fp/icons/favicon-rebrand-medium.3Y6xpZ-0FSdWDnPM3hSBIA.ico\", \"apple-touch-icon\": \"https://cdn-images-1.medium.com/fit/c/120/120/1*6_fgYnisCa9V21mymySIvA.png\", \"mask-icon\": \"https://cdn-static-1.medium.com/_/fp/icons/monogram-mask.KPLCSFEZviQN0jQ7veN2RQ.svg\"}, \"meta_tags\": {\"viewport\": \"width=device-width, initial-scale=1\", \"title\": \"Medium Terms of Service \\\\u2013 Medium Policy \\\\u2013 Medium\", \"referrer\": \"unsafe-url\", \"description\": \"These Terms of Service (\\\\u201cTerms\\\\u201d) are a contract between you and A Medium Corporation. They govern your use of Medium\\\\u2019s sites, services, mobile apps, products, and content (\\\\u201cServices\\\\u201d). By using\\\\u2026\", \"theme-color\": \"#000000\", \"og:title\": \"Medium Terms of Service \\\\u2013 Medium Policy \\\\u2013 Medium\", \"og:url\": \"https://medium.com/policy/medium-terms-of-service-9db0094a1e0f\", \"fb:app_id\": \"542599432471018\", \"og:description\": \"These Terms of Service (\\\\u201cTerms\\\\u201d) are a contract between you and A Medium Corporation. They govern your use of Medium\\\\u2019s sites, services, mobile apps, products, and content (\\\\u201cServices\\\\u201d). By using\\\\u2026\", \"twitter:description\": \"These Terms of Service (\\\\u201cTerms\\\\u201d) are a contract between you and A Medium Corporation. They govern your use of Medium\\\\u2019s sites, services, mobile apps, products, and content (\\\\u201cServices\\\\u201d). By using\\\\u2026\", \"author\": \"Medium\", \"og:type\": \"article\", \"twitter:card\": \"summary\", \"article:publisher\": \"https://www.facebook.com/medium\", \"article:author\": \"https://medium.com/@Medium\", \"robots\": \"index, follow\", \"article:published_time\": \"2012-08-13T22:54:53.510Z\", \"twitter:creator\": \"@Medium\", \"twitter:site\": \"@Medium\", \"og:site_name\": \"Medium\", \"twitter:label1\": \"Reading time\", \"twitter:data1\": \"5 min read\", \"twitter:app:name:iphone\": \"Medium\", \"twitter:app:id:iphone\": \"828256236\", \"twitter:app:url:iphone\": \"medium://p/9db0094a1e0f\", \"al:ios:app_name\": \"Medium\", \"al:ios:app_store_id\": \"828256236\", \"al:android:package\": \"com.medium.reader\", \"al:android:app_name\": \"Medium\", \"al:ios:url\": \"medium://p/9db0094a1e0f\", \"al:android:url\": \"medium://p/9db0094a1e0f\", \"al:web:url\": \"https://medium.com/policy/medium-terms-of-service-9db0094a1e0f\"}}\\n',)\n",
      "CPU times: user 16.9 ms, sys: 26.1 ms, total: 43 ms\n",
      "Wall time: 42.7 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print('Let\\'s look at one example \\n')\n",
    "print(items.take(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We can parse date with json library and get dict like object \n",
      "\n",
      "<class 'dask.bag.core.Bag'>\n"
     ]
    }
   ],
   "source": [
    "print('We can parse date with json library and get dict like object \\n')\n",
    "dict_items = items.map(json.loads)\n",
    "print(type(dict_items))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'_id': 'https://medium.com/policy/medium-terms-of-service-9db0094a1e0f',\n",
       "  '_timestamp': 1520035195.282891,\n",
       "  '_spider': 'medium',\n",
       "  'url': 'https://medium.com/policy/medium-terms-of-service-9db0094a1e0f',\n",
       "  'domain': 'medium.com',\n",
       "  'published': {'$date': '2012-08-13T22:54:53.510Z'},\n",
       "  'title': 'Medium Terms of Service – Medium Policy – Medium',\n",
       "  'content': '<div><header class=\"container u-maxWidth740\"><div class=\"uiScale uiScale-ui--regular uiScale-caption--regular postMetaHeader u-paddingBottom10 row\"><div class=\"col u-size12of12 js-postMetaLockup\"><div class=\"uiScale uiScale-ui--regular uiScale-caption--regular postMetaLockup postMetaLockup--authorWithBio u-flexCenter js-postMetaLockup\"><div class=\"u-flex0\"><a class=\"link u-baseColor--link avatar\" href=\"https://medium.com/@Medium?source=post_header_lockup\" data-action=\"show-user-card\" data-action-source=\"post_header_lockup\" data-action-value=\"504c7870fdb6\" data-action-type=\"hover\" data-user-id=\"504c7870fdb6\" dir=\"auto\"><div class=\"u-relative u-inlineBlock u-flex0\"><img src=\"https://cdn-images-1.medium.com/fit/c/120/120/1*6_fgYnisCa9V21mymySIvA.png\" class=\"avatar-image avatar-image--small\" alt=\"Go to the profile of Medium\"><div class=\"avatar-halo u-absolute u-textColorGreenNormal svgIcon\" style=\"width: calc(100% + 12px); height: calc(100% + 12px); top:-6px; left:-6px\"><svg viewbox=\"0 0 114 114\" xmlns=\"http://www.w3.org/2000/svg\"><path d=\"M7.66922967,32.092726 C17.0070768,13.6353618 35.9421928,1.75 57,1.75 C78.0578072,1.75 96.9929232,13.6353618 106.33077,32.092726 L107.66923,31.4155801 C98.0784505,12.4582656 78.6289015,0.25 57,0.25 C35.3710985,0.25 15.9215495,12.4582656 6.33077033,31.4155801 L7.66922967,32.092726 Z\"></path><path d=\"M106.33077,81.661427 C96.9929232,100.118791 78.0578072,112.004153 57,112.004153 C35.9421928,112.004153 17.0070768,100.118791 7.66922967,81.661427 L6.33077033,82.338573 C15.9215495,101.295887 35.3710985,113.504153 57,113.504153 C78.6289015,113.504153 98.0784505,101.295887 107.66923,82.338573 L106.33077,81.661427 Z\"></path></svg></div></div></a></div><div class=\"u-flex1 u-paddingLeft15 u-overflowHidden\"><div class=\"u-lineHeightTightest\"><a class=\"ds-link ds-link--styleSubtle ui-captionStrong u-inlineBlock link link--darken link--darker\" href=\"https://medium.com/@Medium?source=post_header_lockup\" data-action=\"show-user-card\" data-action-source=\"post_header_lockup\" data-action-value=\"504c7870fdb6\" data-action-type=\"hover\" data-user-id=\"504c7870fdb6\" dir=\"auto\">Medium</a><span class=\"followState js-followState\" data-user-id=\"504c7870fdb6\"></span></div><div class=\"ui-caption ui-xs-clamp2 postMetaInline\">Everyone’s stories and ideas</div><div class=\"ui-caption postMetaInline js-testPostMetaInlineSupplemental\"><time datetime=\"2012-08-13T22:54:53.510Z\">Aug 13, 2012</time><span class=\"middotDivider u-fontSize12\"></span><span class=\"readingTime\" title=\"5 min read\"></span></div></div></div></div></div></header><div class=\"postArticle-content js-postField js-notesSource js-trackedPost\" data-post-id=\"9db0094a1e0f\" data-source=\"post_page\" data-collection-id=\"675ebe56ac25\" data-tracking-context=\"postPage\"><section name=\"bb8c\" class=\"section section--body section--first section--last\"><div class=\"section-divider\"><hr class=\"section-divider\"></div><div class=\"section-content\"><div class=\"section-inner sectionLayout--insetColumn\"><h1 name=\"title\" id=\"title\" class=\"graf graf--h2 graf--leading graf--title\">Medium Terms of\\xa0Service</h1><p name=\"571b\" id=\"571b\" class=\"graf graf--p graf-after--h2\"><strong class=\"markup--strong markup--p-strong\">Effective: March 7, 2016</strong></p><p name=\"c90b\" id=\"c90b\" class=\"graf graf--p graf-after--p\">These Terms of Service (“Terms”) are a contract between you and A Medium Corporation. They govern your use of Medium’s sites, services, mobile apps, products, and content (“Services”).</p><p name=\"238b\" id=\"238b\" class=\"graf graf--p graf-after--p\">By using Medium, you agree to these Terms. If you don’t agree to any of the Terms, you can’t use Medium.</p><p name=\"7769\" id=\"7769\" class=\"graf graf--p graf-after--p\">We can change these Terms at any time. We keep a <a href=\"https://github.com/Medium/medium-policy\" data-href=\"https://github.com/Medium/medium-policy\" class=\"markup--anchor markup--p-anchor\" rel=\"nofollow noopener\" target=\"_blank\">historical</a> record of all changes to our Terms on GitHub. If a change is material, we’ll let you know before they take effect. By using Medium on or after that effective date, you agree to the new Terms. If you don’t agree to them, you should delete your account before they take effect, otherwise your use of the site and content will be subject to the new Terms.</p><h4 name=\"8c81\" id=\"8c81\" class=\"graf graf--h4 graf-after--p\"><strong class=\"markup--strong markup--h4-strong\">Content rights &amp; responsibilities</strong></h4><p name=\"ac74\" id=\"ac74\" class=\"graf graf--p graf-after--h4\">You own the rights to the content you create and post on Medium.</p><p name=\"651b\" id=\"651b\" class=\"graf graf--p graf-after--p\">By posting content to Medium, you give us a nonexclusive license to publish it on Medium Services, including anything reasonably related to publishing it (like storing, displaying, reformatting, and distributing it). In consideration for Medium granting you access to and use of the Services, you agree that Medium may enable advertising on the Services, including in connection with the display of your content or other information. We may also use your content to promote Medium, including its products and content. We will never sell your content to third parties without your explicit permission.</p><p name=\"2584\" id=\"2584\" class=\"graf graf--p graf-after--p\">You’re responsible for the content you post. This means you assume all risks related to it, including someone else’s reliance on its accuracy, or claims relating to intellectual property or other legal rights.</p><p name=\"c207\" id=\"c207\" class=\"graf graf--p graf-after--p\">You’re welcome to post content on Medium that you’ve published elsewhere, as long as you have the rights you need to do so. By posting content to Medium, you represent that doing so doesn’t conflict with any other agreement you’ve made.</p><p name=\"0372\" id=\"0372\" class=\"graf graf--p graf-after--p\">By posting content you didn’t create to Medium, you are representing that you have the right to do so. For example, you are posting a work that’s in the public domain, used under license (including a free license, such as <a href=\"https://creativecommons.org/licenses/\" data-href=\"https://creativecommons.org/licenses/\" class=\"markup--anchor markup--p-anchor\" rel=\"nofollow noopener\" target=\"_blank\">Creative Commons</a>), or a fair use.</p><p name=\"0472\" id=\"0472\" class=\"graf graf--p graf-after--p\">We can remove any content you post for any reason.</p><p name=\"db2b\" id=\"db2b\" class=\"graf graf--p graf-after--p\">You can delete any of your posts, or your account, anytime. Processing the deletion may take a little time, but we’ll do it as quickly as possible. We may keep backup copies of your deleted post or account on our servers for up to 14 days after you delete it.</p><h4 name=\"baf1\" id=\"baf1\" class=\"graf graf--h4 graf-after--p\"><strong class=\"markup--strong markup--h4-strong\">Our content and\\xa0services</strong></h4><p name=\"adc7\" id=\"adc7\" class=\"graf graf--p graf-after--h4\">We reserve all rights in Medium’s look and feel. Some parts of Medium are licensed under third-party open source licenses. We also make some of our own code available under open source licenses. As for other parts of Medium, you may not copy or adapt any portion of our code or visual design elements (including logos) without express written permission from Medium unless otherwise permitted by law.</p><p name=\"20e4\" id=\"20e4\" class=\"graf graf--p graf-after--p\">You may not do, or try to do, the following: (1) access or tamper with non-public areas of the Services, our computer systems, or the systems of our technical providers; (2) access or search the Services by any means other than the currently available, published interfaces (e.g., APIs) that we provide; (3) forge any TCP/IP packet header or any part of the header information in any email or posting, or in any way use the Services to send altered, deceptive, or false source-identifying information; or (4) interfere with, or disrupt, the access of any user, host, or network, including sending a virus, overloading, flooding, spamming, mail-bombing the Services, or by scripting the creation of content or accounts in such a manner as to interfere with or create an undue burden on the Services.</p><p name=\"f5dd\" id=\"f5dd\" class=\"graf graf--p graf-after--p\">Crawling the Services is allowed if done in accordance with the provisions of our robots.txt file, but scraping the Services is prohibited.</p><p name=\"71a8\" id=\"71a8\" class=\"graf graf--p graf-after--p\">We may change, terminate, or restrict access to any aspect of the service, at any time, without notice.</p><h4 name=\"12f1\" id=\"12f1\" class=\"graf graf--h4 graf-after--p\"><strong class=\"markup--strong markup--h4-strong\">No children</strong></h4><p name=\"2ce7\" id=\"2ce7\" class=\"graf graf--p graf-after--h4\">Medium is only for people 13 years old and over. By using Medium, you affirm that you are over 13. If we learn someone under 13 is using Medium, we’ll terminate their account.</p><h4 name=\"531c\" id=\"531c\" class=\"graf graf--h4 graf-after--p\"><strong class=\"markup--strong markup--h4-strong\">Security</strong></h4><p name=\"3155\" id=\"3155\" class=\"graf graf--p graf-after--h4\">If you find a security vulnerability on Medium, tell us. We have a <a href=\"https://medium.com/policy/medium-s-bug-bounty-disclosure-program-34b1c80764c2\" data-href=\"https://medium.com/policy/medium-s-bug-bounty-disclosure-program-34b1c80764c2\" class=\"markup--anchor markup--p-anchor\" target=\"_blank\">bug bounty disclosure program</a>.</p><h4 name=\"05cc\" id=\"05cc\" class=\"graf graf--h4 graf-after--p\"><strong class=\"markup--strong markup--h4-strong\">Incorporated rules and\\xa0policies</strong></h4><p name=\"5207\" id=\"5207\" class=\"graf graf--p graf-after--h4\">By using the Services, you agree to let Medium collect and use information as detailed in our <a href=\"https://medium.com/p/f03bf92035c9\" data-href=\"https://medium.com/p/f03bf92035c9\" class=\"markup--anchor markup--p-anchor\" target=\"_blank\">Privacy Policy</a>. If you’re outside the United States, you consent to letting Medium transfer, store, and process your information (including your personal information and content) in and out of the United States.</p><p name=\"6230\" id=\"6230\" class=\"graf graf--p graf-after--p\">To enable a functioning community, we have <a href=\"https://medium.com/policy/medium-rules-30e5502c4eb4\" data-href=\"https://medium.com/policy/medium-rules-30e5502c4eb4\" class=\"markup--anchor markup--p-anchor\" target=\"_blank\">Rules</a>. To ensure usernames are distributed and used fairly, we have a <a href=\"https://medium.com/@Medium/medium-username-policy-7054a77fb04f\" data-href=\"https://medium.com/@Medium/medium-username-policy-7054a77fb04f\" class=\"markup--anchor markup--p-anchor\" target=\"_blank\">Username Policy</a>. Under our <a href=\"https://medium.com/policy/mediums-copyright-and-dmca-policy-d126f73695\" data-href=\"https://medium.com/policy/mediums-copyright-and-dmca-policy-d126f73695\" class=\"markup--anchor markup--p-anchor\" target=\"_blank\">DMCA Policy</a>, we’ll remove material after receiving a valid takedown notice. Under our <a href=\"https://medium.com/policy/mediums-trademark-policy-e3bb53df59a7\" data-href=\"https://medium.com/policy/mediums-trademark-policy-e3bb53df59a7\" class=\"markup--anchor markup--p-anchor\" target=\"_blank\">Trademark Policy</a>, we’ll investigate any use of another’s trademark and respond appropriately.</p><p name=\"21ad\" id=\"21ad\" class=\"graf graf--p graf-after--p\">By using Medium, you agree to follow these Rules and Policies. If you don’t, we may remove content, or suspend or delete your account.</p><h4 name=\"a2a2\" id=\"a2a2\" class=\"graf graf--h4 graf-after--p\"><strong class=\"markup--strong markup--h4-strong\">Miscellaneous</strong></h4><p name=\"b7da\" id=\"b7da\" class=\"graf graf--p graf-after--h4\"><em class=\"markup--em markup--p-em\">Disclaimer of warranty.</em> Medium provides the Services to you as is. You use them at your own risk and discretion. That means they don’t come with any warranty. None express, none implied. No implied warranty of merchantability, fitness for a particular purpose, availability, security, title or non-infringement.</p><p name=\"7073\" id=\"7073\" class=\"graf graf--p graf-after--p\"><em class=\"markup--em markup--p-em\">Limitation of Liability</em>. Medium won’t be liable to you for any damages that arise from your using the Services. This includes if the Services are hacked or unavailable. This includes all types of damages (indirect, incidental, consequential, special or exemplary). And it includes all kinds of legal claims, such as breach of contract, breach of warranty, tort, or any other loss.</p><p name=\"3d70\" id=\"3d70\" class=\"graf graf--p graf-after--p\"><em class=\"markup--em markup--p-em\">No waiver.</em> If Medium doesn’t exercise a particular right under these Terms, that doesn’t waive it.</p><p name=\"ab04\" id=\"ab04\" class=\"graf graf--p graf-after--p\"><em class=\"markup--em markup--p-em\">Severability</em>. If any provision of these terms is found invalid by a court of competent jurisdiction, you agree that the court should try to give effect to the parties’ intentions as reflected in the provision and that other provisions of the Terms will remain in full effect.</p><p name=\"bde8\" id=\"bde8\" class=\"graf graf--p graf-after--p\"><em class=\"markup--em markup--p-em\">Choice of law and jurisdiction.</em> These Terms are governed by California law, without reference to its conflict of laws provisions. You agree that any suit arising from the Services must take place in a court located in San Francisco, California.</p><p name=\"bbb3\" id=\"bbb3\" class=\"graf graf--p graf-after--p\"><em class=\"markup--em markup--p-em\">Entire agreement.</em> These Terms (including any document incorporated by reference into them) are the whole agreement between Medium and you concerning the Services.</p><p name=\"dbf1\" id=\"dbf1\" class=\"graf graf--p graf-after--p\"><em class=\"markup--em markup--p-em\">Government use.</em> If you’re \\u200busing \\u200bMedium for the U.S. Government, <a href=\"https://medium.com/@Medium/amendment-to-medium-terms-of-service-applicable-to-u-s-government-users-fccb00db67d7\" data-href=\"https://medium.com/@Medium/amendment-to-medium-terms-of-service-applicable-to-u-s-government-users-fccb00db67d7\" class=\"markup--anchor markup--p-anchor\" target=\"_blank\">this Amendment</a> to \\u200bMedium’s Terms of Service \\u200bapplies to you\\u200b.</p><p name=\"3318\" id=\"3318\" class=\"graf graf--p graf-after--p graf--trailing\">Questions? Let us know at <a href=\"mailto:%20legal@medium.com\" data-href=\"mailto:%20legal@medium.com\" class=\"markup--anchor markup--p-anchor\" target=\"_blank\">legal@medium.com</a>.</p></div></div></section></div><footer class=\"u-paddingTop10\"><div class=\"container u-maxWidth740\"><div class=\"row\"><div class=\"col u-size12of12\"></div></div><div class=\"row\"><div class=\"col u-size12of12 js-postTags\"><div class=\"u-paddingBottom10\"><ul class=\"tags tags--postTags tags--borderless\"><li><a class=\"link u-baseColor--link\" href=\"https://medium.com/tag/terms-and-conditions?source=post\" data-action-source=\"post\">Terms And Conditions</a></li><li><a class=\"link u-baseColor--link\" href=\"https://medium.com/tag/terms?source=post\" data-action-source=\"post\">Terms</a></li><li><a class=\"link u-baseColor--link\" href=\"https://medium.com/tag/medium?source=post\" data-action-source=\"post\">Medium</a></li></ul></div></div></div><section class=\"uiScale uiScale-ui--small uiScale-caption--regular u-borderTopLightest u-marginTop10 u-paddingTop20\"><div class=\"ui-h3 u-textColorDarker u-fontSize22\">One clap, two clap, three clap, forty?</div><p class=\"ui-body u-marginBottom20 u-textColorDark u-fontSize16\">By clapping more or less, you can signal to us which stories really stand out.</p></section><div class=\"postActions js-postActionsFooter\"><div class=\"u-flexCenter\"><div class=\"u-flex1\"><div class=\"multirecommend js-actionMultirecommend u-flexCenter u-width60\" data-post-id=\"9db0094a1e0f\" data-is-icon-29px=\"true\" data-is-circle=\"true\" data-has-recommend-list=\"true\" data-source=\"post_actions_footer-----9db0094a1e0f---------------------clap_footer\"><div class=\"u-relative u-foreground\"><div class=\"clapUndo u-width60 u-round u-height32 u-absolute u-borderBox u-paddingRight5 u-transition--transform200Spring u-background--brandSageLighter js-clapUndo\" style=\"top: 14px; 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       "  'author': {'name': None,\n",
       "   'url': 'https://medium.com/@Medium',\n",
       "   'twitter': '@Medium'},\n",
       "  'image_url': None,\n",
       "  'tags': [],\n",
       "  'link_tags': {'canonical': 'https://medium.com/policy/medium-terms-of-service-9db0094a1e0f',\n",
       "   'publisher': 'https://plus.google.com/103654360130207659246',\n",
       "   'author': 'https://medium.com/@Medium',\n",
       "   'search': '/osd.xml',\n",
       "   'alternate': 'android-app://com.medium.reader/https/medium.com/p/9db0094a1e0f',\n",
       "   'stylesheet': 'https://cdn-static-1.medium.com/_/fp/css/main-branding-base.Ch8g7KPCoGXbtKfJaVXo_w.css',\n",
       "   'icon': 'https://cdn-static-1.medium.com/_/fp/icons/favicon-rebrand-medium.3Y6xpZ-0FSdWDnPM3hSBIA.ico',\n",
       "   'apple-touch-icon': 'https://cdn-images-1.medium.com/fit/c/120/120/1*6_fgYnisCa9V21mymySIvA.png',\n",
       "   'mask-icon': 'https://cdn-static-1.medium.com/_/fp/icons/monogram-mask.KPLCSFEZviQN0jQ7veN2RQ.svg'},\n",
       "  'meta_tags': {'viewport': 'width=device-width, initial-scale=1',\n",
       "   'title': 'Medium Terms of Service – Medium Policy – Medium',\n",
       "   'referrer': 'unsafe-url',\n",
       "   'description': 'These Terms of Service (“Terms”) are a contract between you and A Medium Corporation. They govern your use of Medium’s sites, services, mobile apps, products, and content (“Services”). By using…',\n",
       "   'theme-color': '#000000',\n",
       "   'og:title': 'Medium Terms of Service – Medium Policy – Medium',\n",
       "   'og:url': 'https://medium.com/policy/medium-terms-of-service-9db0094a1e0f',\n",
       "   'fb:app_id': '542599432471018',\n",
       "   'og:description': 'These Terms of Service (“Terms”) are a contract between you and A Medium Corporation. They govern your use of Medium’s sites, services, mobile apps, products, and content (“Services”). By using…',\n",
       "   'twitter:description': 'These Terms of Service (“Terms”) are a contract between you and A Medium Corporation. They govern your use of Medium’s sites, services, mobile apps, products, and content (“Services”). By using…',\n",
       "   'author': 'Medium',\n",
       "   'og:type': 'article',\n",
       "   'twitter:card': 'summary',\n",
       "   'article:publisher': 'https://www.facebook.com/medium',\n",
       "   'article:author': 'https://medium.com/@Medium',\n",
       "   'robots': 'index, follow',\n",
       "   'article:published_time': '2012-08-13T22:54:53.510Z',\n",
       "   'twitter:creator': '@Medium',\n",
       "   'twitter:site': '@Medium',\n",
       "   'og:site_name': 'Medium',\n",
       "   'twitter:label1': 'Reading time',\n",
       "   'twitter:data1': '5 min read',\n",
       "   'twitter:app:name:iphone': 'Medium',\n",
       "   'twitter:app:id:iphone': '828256236',\n",
       "   'twitter:app:url:iphone': 'medium://p/9db0094a1e0f',\n",
       "   'al:ios:app_name': 'Medium',\n",
       "   'al:ios:app_store_id': '828256236',\n",
       "   'al:android:package': 'com.medium.reader',\n",
       "   'al:android:app_name': 'Medium',\n",
       "   'al:ios:url': 'medium://p/9db0094a1e0f',\n",
       "   'al:android:url': 'medium://p/9db0094a1e0f',\n",
       "   'al:web:url': 'https://medium.com/policy/medium-terms-of-service-9db0094a1e0f'}},)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_items.take(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We can take any key from all records \n",
      "\n",
      "With take method we received tuple of objects \n",
      "\n",
      "('Medium Terms of Service – Medium Policy – Medium', 'Amendment to Medium Terms of Service Applicable to U.S. Government Users', '走入山與海之間：閩東大刀會和兩岸走私 – Yun-Chen Chien（簡韻真） – Medium')\n"
     ]
    }
   ],
   "source": [
    "print('We can take any key from all records \\n')\n",
    "title_bag  = dict_items.pluck('title')\n",
    "print('With take method we received tuple of objects \\n')\n",
    "print(title_bag.take(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can write any function for processing data and apply it with map function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_title(text):\n",
    "    \n",
    "    import string\n",
    "    cut_set = set(string.punctuation)\n",
    "    cut_set.update(['”','—','…', \"“\",'⌘','❤','+','®','➜','¬','–'])\n",
    "    text = text.translate(text.maketrans(''.join(cut_set),\" \" * len(cut_set)))\n",
    "    text = text.lower()\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "title_bag  = dict_items.pluck('title').map(clean_title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('medium terms of service   medium policy   medium',\n",
       " 'amendment to medium terms of service applicable to u s  government users',\n",
       " '走入山與海之間：閩東大刀會和兩岸走私   yun chen chien（簡韻真）   medium')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "title_bag.take(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Process  meta_tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "meta_tags_bag  = dict_items.pluck('meta_tags')\n",
    "test_meta = meta_tags_bag.take(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'viewport': 'width=device-width, initial-scale=1',\n",
       " 'title': 'Amendment to Medium Terms of Service Applicable to U.S. Government Users',\n",
       " 'referrer': 'origin',\n",
       " 'description': 'This agreement (“Amendment”) is an amendment to Medium’s Terms. It is between Medium and the U.S. Government and applies to the use of Medium Services by the Government. The reason for this Amendment…',\n",
       " 'theme-color': '#000000',\n",
       " 'og:title': 'Amendment to Medium Terms of Service Applicable to U.S. Government Users',\n",
       " 'og:url': 'https://medium.com/policy/amendment-to-medium-terms-of-service-applicable-to-u-s-government-users-fccb00db67d7',\n",
       " 'fb:app_id': '542599432471018',\n",
       " 'og:description': 'This agreement (“Amendment”) is an amendment to Medium’s Terms. It is between Medium and the U.S. Government and applies to the use of…',\n",
       " 'twitter:description': 'This agreement (“Amendment”) is an amendment to Medium’s Terms. It is between Medium and the U.S. Government and applies to the use of…',\n",
       " 'author': 'Medium',\n",
       " 'og:type': 'article',\n",
       " 'twitter:card': 'summary',\n",
       " 'article:publisher': 'https://www.facebook.com/medium',\n",
       " 'article:author': 'https://medium.com/@Medium',\n",
       " 'robots': 'noindex, follow',\n",
       " 'article:published_time': '2015-08-03T07:44:50.331Z',\n",
       " 'twitter:creator': '@Medium',\n",
       " 'twitter:site': '@Medium',\n",
       " 'og:site_name': 'Medium',\n",
       " 'twitter:label1': 'Reading time',\n",
       " 'twitter:data1': '7 min read',\n",
       " 'twitter:app:name:iphone': 'Medium',\n",
       " 'twitter:app:id:iphone': '828256236',\n",
       " 'twitter:app:url:iphone': 'medium://p/fccb00db67d7',\n",
       " 'al:ios:app_name': 'Medium',\n",
       " 'al:ios:app_store_id': '828256236',\n",
       " 'al:android:package': 'com.medium.reader',\n",
       " 'al:android:app_name': 'Medium',\n",
       " 'al:ios:url': 'medium://p/fccb00db67d7',\n",
       " 'al:android:url': 'medium://p/fccb00db67d7',\n",
       " 'al:web:url': 'https://medium.com/policy/amendment-to-medium-terms-of-service-applicable-to-u-s-government-users-fccb00db67d7'}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_meta[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_meta_tags(meta):\n",
    "    author = meta['author'].strip()\n",
    "    min_reads = int(meta['twitter:data1'].split()[0])\n",
    "    return {'author':author, 'min_reads':min_reads}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "meta_tags_bag= meta_tags_bag.map(clean_meta_tags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'author': 'Medium', 'min_reads': 5},)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_tags_bag.take(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine all together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 779 µs, sys: 248 µs, total: 1.03 ms\n",
      "Wall time: 1.03 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#content_bag = dict_items.pluck('content').map(clean_content)\n",
    "title_bag  = dict_items.pluck('title').map(clean_title)\n",
    "published_bag  = dict_items.pluck('published').map(lambda x: x['$date'])\n",
    "meta_bag = dict_items.pluck('meta_tags').map(clean_meta_tags)\n",
    "domain_bag = dict_items.pluck('domain')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "@delayed\n",
    "def combine_to_df(list_dict):\n",
    "    \n",
    "    list_df = [pd.DataFrame(dict_) for dict_ in list_dict]\n",
    "    return pd.concat(list_df, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined = combine_to_df([published_bag, meta_bag, domain_bag])\n",
    "combined.visualize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 59.9s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>published</th>\n",
       "      <th>Author</th>\n",
       "      <th>min_reads</th>\n",
       "      <th>domain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-08-13T22:54:53.510Z</td>\n",
       "      <td>Medium</td>\n",
       "      <td>5</td>\n",
       "      <td>medium.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-08-03T07:44:50.331Z</td>\n",
       "      <td>Medium</td>\n",
       "      <td>7</td>\n",
       "      <td>medium.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-02-05T13:08:17.410Z</td>\n",
       "      <td>Yun-Chen Chien（簡韻真）</td>\n",
       "      <td>2</td>\n",
       "      <td>medium.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-05-06T08:16:30.776Z</td>\n",
       "      <td>Vaibhav Khulbe</td>\n",
       "      <td>3</td>\n",
       "      <td>medium.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-06-04T14:46:25.772Z</td>\n",
       "      <td>Vaibhav Khulbe</td>\n",
       "      <td>4</td>\n",
       "      <td>medium.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  published               Author  min_reads      domain\n",
       "0  2012-08-13T22:54:53.510Z               Medium          5  medium.com\n",
       "1  2015-08-03T07:44:50.331Z               Medium          7  medium.com\n",
       "2  2017-02-05T13:08:17.410Z  Yun-Chen Chien（簡韻真）          2  medium.com\n",
       "3  2017-05-06T08:16:30.776Z       Vaibhav Khulbe          3  medium.com\n",
       "4  2017-06-04T14:46:25.772Z       Vaibhav Khulbe          4  medium.com"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It takes time, around a minute \n",
    "from dask.diagnostics import ProgressBar\n",
    "with ProgressBar():\n",
    "    df = combined.compute()\n",
    "df.columns = ['published', 'Author','min_reads','domain']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We can create dask dataframe from pandas \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('We can create dask dataframe from pandas \\n')\n",
    "dd_no_content = dd.from_pandas(df, npartitions=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><strong>Dask DataFrame Structure:</strong></div>\n",
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>published</th>\n",
       "      <th>Author</th>\n",
       "      <th>min_reads</th>\n",
       "      <th>domain</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>npartitions=4</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15579</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31158</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46737</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62312</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<div>Dask Name: from_pandas, 4 tasks</div>"
      ],
      "text/plain": [
       "Dask DataFrame Structure:\n",
       "              published  Author min_reads  domain\n",
       "npartitions=4                                    \n",
       "0                object  object     int64  object\n",
       "15579               ...     ...       ...     ...\n",
       "31158               ...     ...       ...     ...\n",
       "46737               ...     ...       ...     ...\n",
       "62312               ...     ...       ...     ...\n",
       "Dask Name: from_pandas, 4 tasks"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd_no_content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transform published column to datetime as we did with pandas, it will by slightly slowly than in pandas \n",
      "\n",
      "CPU times: user 277 ms, sys: 2.14 ms, total: 279 ms\n",
      "Wall time: 277 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print('Transform published column to datetime as we did with pandas, it will by slightly slowly than in pandas \\n')\n",
    "df['published'] = pd.to_datetime(df.published, format='%Y-%m-%dT%H:%M:%S.%fZ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transform published column to datetime  with pandas, \n",
      "\n",
      "CPU times: user 273 ms, sys: 6.49 ms, total: 279 ms\n",
      "Wall time: 274 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print('Transform published column to datetime  with pandas, \\n')\n",
    "dd_no_content['published'] = dd.to_datetime(dd_no_content.published, format='%Y-%m-%dT%H:%M:%S.%fZ').compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>published</th>\n",
       "      <th>Author</th>\n",
       "      <th>min_reads</th>\n",
       "      <th>domain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-08-13 22:54:53.510</td>\n",
       "      <td>Medium</td>\n",
       "      <td>5</td>\n",
       "      <td>medium.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-08-03 07:44:50.331</td>\n",
       "      <td>Medium</td>\n",
       "      <td>7</td>\n",
       "      <td>medium.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-02-05 13:08:17.410</td>\n",
       "      <td>Yun-Chen Chien（簡韻真）</td>\n",
       "      <td>2</td>\n",
       "      <td>medium.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-05-06 08:16:30.776</td>\n",
       "      <td>Vaibhav Khulbe</td>\n",
       "      <td>3</td>\n",
       "      <td>medium.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-06-04 14:46:25.772</td>\n",
       "      <td>Vaibhav Khulbe</td>\n",
       "      <td>4</td>\n",
       "      <td>medium.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                published               Author  min_reads      domain\n",
       "0 2012-08-13 22:54:53.510               Medium          5  medium.com\n",
       "1 2015-08-03 07:44:50.331               Medium          7  medium.com\n",
       "2 2017-02-05 13:08:17.410  Yun-Chen Chien（簡韻真）          2  medium.com\n",
       "3 2017-05-06 08:16:30.776       Vaibhav Khulbe          3  medium.com\n",
       "4 2017-06-04 14:46:25.772       Vaibhav Khulbe          4  medium.com"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd_no_content.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We can apply function with mixed transformation to dask dataframe written for pandas df without changes \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('We can apply function with mixed transformation to dask dataframe written for pandas df without changes \\n')\n",
    "def additional_time_features_df(df, to_cat_cols = ['Author','domain', 'month', 'year', 'day_of_week']):\n",
    "    \n",
    "    df['month'] = df['published'].apply(lambda ts: ts.month)\n",
    "    df['year'] = df['published'].apply(lambda ts: ts.year)\n",
    "    hour = df['published'].apply(lambda ts: ts.hour)\n",
    "    df['hour'] = hour\n",
    "    df['morning'] = ((hour >= 7) & (hour <= 11)).astype('float64')\n",
    "    df['day'] = ((hour >= 12) & (hour <= 18)).astype('int')\n",
    "    df['evening'] = ((hour >= 19) & (hour <= 23)).astype('int')\n",
    "    df['night'] = ((hour >= 0) & (hour <= 6)).astype('int')\n",
    "    df['sin_hour'] = np.sin(2*np.pi*df['hour']/24)\n",
    "    df['cos_hour'] = np.cos(2*np.pi*df['hour']/24)\n",
    "    df = df.drop([\"hour\"], axis=1)\n",
    "    day_of_week = df['published'].dt.dayofweek.astype('int')\n",
    "    df['day_of_week']=day_of_week\n",
    "    df['weekend'] = (day_of_week >= 5).astype('int')\n",
    "    # turn to categorical \n",
    "    df[to_cat_cols] = df[to_cat_cols].astype('category')\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 694 ms, sys: 15.2 ms, total: 709 ms\n",
      "Wall time: 707 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_medium_train = additional_time_features_df(df.copy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "dd_medium_train = additional_time_features_df(dd_no_content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 884 ms, sys: 52.9 ms, total: 937 ms\n",
      "Wall time: 861 ms\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>published</th>\n",
       "      <th>Author</th>\n",
       "      <th>min_reads</th>\n",
       "      <th>domain</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>morning</th>\n",
       "      <th>day</th>\n",
       "      <th>evening</th>\n",
       "      <th>night</th>\n",
       "      <th>sin_hour</th>\n",
       "      <th>cos_hour</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-08-13 22:54:53.510</td>\n",
       "      <td>Medium</td>\n",
       "      <td>5</td>\n",
       "      <td>medium.com</td>\n",
       "      <td>8</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>8.660254e-01</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-08-03 07:44:50.331</td>\n",
       "      <td>Medium</td>\n",
       "      <td>7</td>\n",
       "      <td>medium.com</td>\n",
       "      <td>8</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.659258e-01</td>\n",
       "      <td>-2.588190e-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-02-05 13:08:17.410</td>\n",
       "      <td>Yun-Chen Chien（簡韻真）</td>\n",
       "      <td>2</td>\n",
       "      <td>medium.com</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.588190e-01</td>\n",
       "      <td>-9.659258e-01</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-05-06 08:16:30.776</td>\n",
       "      <td>Vaibhav Khulbe</td>\n",
       "      <td>3</td>\n",
       "      <td>medium.com</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-06-04 14:46:25.772</td>\n",
       "      <td>Vaibhav Khulbe</td>\n",
       "      <td>4</td>\n",
       "      <td>medium.com</td>\n",
       "      <td>6</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017-04-02 16:21:15.171</td>\n",
       "      <td>Kate Reed Petty</td>\n",
       "      <td>7</td>\n",
       "      <td>medium.com</td>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-08-15 04:16:02.103</td>\n",
       "      <td>exedre</td>\n",
       "      <td>12</td>\n",
       "      <td>medium.com</td>\n",
       "      <td>8</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2015-01-14 21:31:07.568</td>\n",
       "      <td>Raghav Haran</td>\n",
       "      <td>5</td>\n",
       "      <td>medium.com</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-7.071068e-01</td>\n",
       "      <td>7.071068e-01</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014-02-11 04:11:54.771</td>\n",
       "      <td>Francine Lee</td>\n",
       "      <td>4</td>\n",
       "      <td>medium.com</td>\n",
       "      <td>2</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015-10-25 02:58:05.551</td>\n",
       "      <td>Raghav Haran</td>\n",
       "      <td>8</td>\n",
       "      <td>medium.com</td>\n",
       "      <td>10</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2016-08-15 15:31:13.601</td>\n",
       "      <td>E²</td>\n",
       "      <td>4</td>\n",
       "      <td>medium.com</td>\n",
       "      <td>8</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-7.071068e-01</td>\n",
       "      <td>-7.071068e-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2016-08-09 21:01:06.303</td>\n",
       "      <td>One Month</td>\n",
       "      <td>9</td>\n",
       "      <td>medium.com</td>\n",
       "      <td>8</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-7.071068e-01</td>\n",
       "      <td>7.071068e-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2016-09-08 15:47:57.336</td>\n",
       "      <td>Frank DeGeorge</td>\n",
       "      <td>7</td>\n",
       "      <td>hackernoon.com</td>\n",
       "      <td>9</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-7.071068e-01</td>\n",
       "      <td>-7.071068e-01</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2016-09-30 18:05:35.950</td>\n",
       "      <td>Gregório Jung</td>\n",
       "      <td>8</td>\n",
       "      <td>medium.com</td>\n",
       "      <td>9</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.836970e-16</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2017-06-27 15:49:22.909</td>\n",
       "      <td>Stephen Hays</td>\n",
       "      <td>7</td>\n",
       "      <td>hackernoon.com</td>\n",
       "      <td>6</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-7.071068e-01</td>\n",
       "      <td>-7.071068e-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015-07-13 06:52:44.618</td>\n",
       "      <td>Andy Raskin</td>\n",
       "      <td>5</td>\n",
       "      <td>medium.com</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>6.123234e-17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2017-05-01 13:22:43.785</td>\n",
       "      <td>Stephen Hays</td>\n",
       "      <td>8</td>\n",
       "      <td>hackernoon.com</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.588190e-01</td>\n",
       "      <td>-9.659258e-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2016-08-31 17:11:24.263</td>\n",
       "      <td>Andy Raskin</td>\n",
       "      <td>7</td>\n",
       "      <td>medium.com</td>\n",
       "      <td>8</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-9.659258e-01</td>\n",
       "      <td>-2.588190e-01</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2017-06-30 07:55:55.103</td>\n",
       "      <td>Mohit Mamoria</td>\n",
       "      <td>16</td>\n",
       "      <td>hackernoon.com</td>\n",
       "      <td>6</td>\n",
       "      <td>2017</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.659258e-01</td>\n",
       "      <td>-2.588190e-01</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2016-12-13 23:29:35.556</td>\n",
       "      <td>Oscar Boyson</td>\n",
       "      <td>6</td>\n",
       "      <td>medium.com</td>\n",
       "      <td>12</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.588190e-01</td>\n",
       "      <td>9.659258e-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2016-01-27 22:19:05.027</td>\n",
       "      <td>Brian Verne</td>\n",
       "      <td>5</td>\n",
       "      <td>hackernoon.com</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2016-12-14 01:15:02.122</td>\n",
       "      <td>Morgan Courtney</td>\n",
       "      <td>11</td>\n",
       "      <td>hackernoon.com</td>\n",
       "      <td>12</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.588190e-01</td>\n",
       "      <td>9.659258e-01</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2016-09-05 22:02:40.326</td>\n",
       "      <td>Jarrett Carter Sr.</td>\n",
       "      <td>4</td>\n",
       "      <td>medium.com</td>\n",
       "      <td>9</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2016-12-13 17:59:40.527</td>\n",
       "      <td>thrace</td>\n",
       "      <td>8</td>\n",
       "      <td>medium.com</td>\n",
       "      <td>12</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-9.659258e-01</td>\n",
       "      <td>-2.588190e-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2017-05-02 17:28:39.120</td>\n",
       "      <td>JakeElman</td>\n",
       "      <td>8</td>\n",
       "      <td>medium.com</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-9.659258e-01</td>\n",
       "      <td>-2.588190e-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2016-08-30 23:43:24.940</td>\n",
       "      <td>Hanna Fogel</td>\n",
       "      <td>2</td>\n",
       "      <td>medium.com</td>\n",
       "      <td>8</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.588190e-01</td>\n",
       "      <td>9.659258e-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2017-04-26 02:50:29.511</td>\n",
       "      <td>Asaeda</td>\n",
       "      <td>9</td>\n",
       "      <td>medium.com</td>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2016-06-18 06:54:10.331</td>\n",
       "      <td>Dr. Syed Jamal Hasan</td>\n",
       "      <td>11</td>\n",
       "      <td>medium.com</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>6.123234e-17</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2016-05-17 17:52:00.960</td>\n",
       "      <td>tiffany jernigan</td>\n",
       "      <td>7</td>\n",
       "      <td>medium.com</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-9.659258e-01</td>\n",
       "      <td>-2.588190e-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2017-04-17 16:29:28.306</td>\n",
       "      <td>Richy Chacon</td>\n",
       "      <td>4</td>\n",
       "      <td>medium.com</td>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62283</th>\n",
       "      <td>2017-06-26 20:07:57.240</td>\n",
       "      <td>Jacqueline Bashaw</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62284</th>\n",
       "      <td>2017-05-23 23:24:15.931</td>\n",
       "      <td>Lily Herman</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.588190e-01</td>\n",
       "      <td>9.659258e-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62285</th>\n",
       "      <td>2017-02-02 17:52:00.430</td>\n",
       "      <td>Angel Powell</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-9.659258e-01</td>\n",
       "      <td>-2.588190e-01</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62286</th>\n",
       "      <td>2017-06-14 15:17:18.712</td>\n",
       "      <td>Lily Herman</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-7.071068e-01</td>\n",
       "      <td>-7.071068e-01</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62287</th>\n",
       "      <td>2014-11-12 19:00:00.000</td>\n",
       "      <td>The Hairpin</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-9.659258e-01</td>\n",
       "      <td>2.588190e-01</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62288</th>\n",
       "      <td>2014-03-18 10:55:54.000</td>\n",
       "      <td>The Billfold</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>2014</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62289</th>\n",
       "      <td>2012-05-03 19:00:03.000</td>\n",
       "      <td>The Awl</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-9.659258e-01</td>\n",
       "      <td>2.588190e-01</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62290</th>\n",
       "      <td>2015-11-02 06:12:22.782</td>\n",
       "      <td>Josh Fruhlinger</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>6.123234e-17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62291</th>\n",
       "      <td>2012-11-02 00:00:54.000</td>\n",
       "      <td>The Awl</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62292</th>\n",
       "      <td>2012-11-29 20:00:42.000</td>\n",
       "      <td>David Roth</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62293</th>\n",
       "      <td>2012-11-28 18:00:10.000</td>\n",
       "      <td>The Awl</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.836970e-16</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62294</th>\n",
       "      <td>2016-06-09 16:19:34.121</td>\n",
       "      <td>Cecília Olliveira</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62295</th>\n",
       "      <td>2016-06-23 17:39:16.171</td>\n",
       "      <td>Amy Hawman</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-9.659258e-01</td>\n",
       "      <td>-2.588190e-01</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62296</th>\n",
       "      <td>2016-08-23 00:33:48.276</td>\n",
       "      <td>Orlando Trott</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62297</th>\n",
       "      <td>2015-07-20 15:16:40.169</td>\n",
       "      <td>Transifex</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-7.071068e-01</td>\n",
       "      <td>-7.071068e-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62298</th>\n",
       "      <td>2015-12-31 22:06:54.772</td>\n",
       "      <td>LA BioMed</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62299</th>\n",
       "      <td>2017-01-05 16:19:59.807</td>\n",
       "      <td>Jessica Chen Riolfi</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62300</th>\n",
       "      <td>2016-03-21 18:48:18.079</td>\n",
       "      <td>Pierre @ L’Escapadou</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.836970e-16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62301</th>\n",
       "      <td>2017-02-07 18:34:31.427</td>\n",
       "      <td>Nick Troiano</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.836970e-16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62302</th>\n",
       "      <td>2016-06-29 02:49:57.853</td>\n",
       "      <td>Amanda L.</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62303</th>\n",
       "      <td>2016-10-04 12:22:51.674</td>\n",
       "      <td>Mayank Agarwal</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62304</th>\n",
       "      <td>2016-10-10 04:17:03.477</td>\n",
       "      <td>Mayank Agarwal</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62305</th>\n",
       "      <td>2016-10-21 06:30:55.281</td>\n",
       "      <td>Mayank Agarwal</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>6.123234e-17</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62306</th>\n",
       "      <td>2017-05-23 04:37:28.709</td>\n",
       "      <td>Randi Gloss</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62307</th>\n",
       "      <td>2016-04-05 23:01:22.486</td>\n",
       "      <td>Heather Nann</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.588190e-01</td>\n",
       "      <td>9.659258e-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62308</th>\n",
       "      <td>2016-01-28 01:03:08.798</td>\n",
       "      <td>Heather Nann</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.588190e-01</td>\n",
       "      <td>9.659258e-01</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62309</th>\n",
       "      <td>2016-01-14 13:28:30.277</td>\n",
       "      <td>Heather Nann</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.588190e-01</td>\n",
       "      <td>-9.659258e-01</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62310</th>\n",
       "      <td>2016-03-06 06:51:45.307</td>\n",
       "      <td>Heather Nann</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>6.123234e-17</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62311</th>\n",
       "      <td>2017-01-15 17:45:22.836</td>\n",
       "      <td>Nick Todorov</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-9.659258e-01</td>\n",
       "      <td>-2.588190e-01</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62312</th>\n",
       "      <td>2016-01-25 03:20:33.005</td>\n",
       "      <td>Heather Nann</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.071068e-01</td>\n",
       "      <td>7.071068e-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62313 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    published                Author  min_reads  \\\n",
       "0     2012-08-13 22:54:53.510                Medium          5   \n",
       "1     2015-08-03 07:44:50.331                Medium          7   \n",
       "2     2017-02-05 13:08:17.410   Yun-Chen Chien（簡韻真）          2   \n",
       "3     2017-05-06 08:16:30.776        Vaibhav Khulbe          3   \n",
       "4     2017-06-04 14:46:25.772        Vaibhav Khulbe          4   \n",
       "5     2017-04-02 16:21:15.171       Kate Reed Petty          7   \n",
       "6     2016-08-15 04:16:02.103                exedre         12   \n",
       "7     2015-01-14 21:31:07.568          Raghav Haran          5   \n",
       "8     2014-02-11 04:11:54.771          Francine Lee          4   \n",
       "9     2015-10-25 02:58:05.551          Raghav Haran          8   \n",
       "10    2016-08-15 15:31:13.601                    E²          4   \n",
       "11    2016-08-09 21:01:06.303             One Month          9   \n",
       "12    2016-09-08 15:47:57.336        Frank DeGeorge          7   \n",
       "13    2016-09-30 18:05:35.950         Gregório Jung          8   \n",
       "14    2017-06-27 15:49:22.909          Stephen Hays          7   \n",
       "15    2015-07-13 06:52:44.618           Andy Raskin          5   \n",
       "16    2017-05-01 13:22:43.785          Stephen Hays          8   \n",
       "17    2016-08-31 17:11:24.263           Andy Raskin          7   \n",
       "18    2017-06-30 07:55:55.103         Mohit Mamoria         16   \n",
       "19    2016-12-13 23:29:35.556          Oscar Boyson          6   \n",
       "20    2016-01-27 22:19:05.027           Brian Verne          5   \n",
       "21    2016-12-14 01:15:02.122       Morgan Courtney         11   \n",
       "22    2016-09-05 22:02:40.326    Jarrett Carter Sr.          4   \n",
       "23    2016-12-13 17:59:40.527                thrace          8   \n",
       "24    2017-05-02 17:28:39.120             JakeElman          8   \n",
       "25    2016-08-30 23:43:24.940           Hanna Fogel          2   \n",
       "26    2017-04-26 02:50:29.511                Asaeda          9   \n",
       "27    2016-06-18 06:54:10.331  Dr. Syed Jamal Hasan         11   \n",
       "28    2016-05-17 17:52:00.960      tiffany jernigan          7   \n",
       "29    2017-04-17 16:29:28.306          Richy Chacon          4   \n",
       "...                       ...                   ...        ...   \n",
       "62283 2017-06-26 20:07:57.240     Jacqueline Bashaw          4   \n",
       "62284 2017-05-23 23:24:15.931           Lily Herman          5   \n",
       "62285 2017-02-02 17:52:00.430          Angel Powell          5   \n",
       "62286 2017-06-14 15:17:18.712           Lily Herman          6   \n",
       "62287 2014-11-12 19:00:00.000           The Hairpin         12   \n",
       "62288 2014-03-18 10:55:54.000          The Billfold         14   \n",
       "62289 2012-05-03 19:00:03.000               The Awl          4   \n",
       "62290 2015-11-02 06:12:22.782       Josh Fruhlinger         18   \n",
       "62291 2012-11-02 00:00:54.000               The Awl          9   \n",
       "62292 2012-11-29 20:00:42.000            David Roth          6   \n",
       "62293 2012-11-28 18:00:10.000               The Awl          8   \n",
       "62294 2016-06-09 16:19:34.121     Cecília Olliveira          3   \n",
       "62295 2016-06-23 17:39:16.171            Amy Hawman          8   \n",
       "62296 2016-08-23 00:33:48.276         Orlando Trott          5   \n",
       "62297 2015-07-20 15:16:40.169             Transifex          6   \n",
       "62298 2015-12-31 22:06:54.772             LA BioMed          3   \n",
       "62299 2017-01-05 16:19:59.807   Jessica Chen Riolfi          7   \n",
       "62300 2016-03-21 18:48:18.079  Pierre @ L’Escapadou          7   \n",
       "62301 2017-02-07 18:34:31.427          Nick Troiano          6   \n",
       "62302 2016-06-29 02:49:57.853             Amanda L.          9   \n",
       "62303 2016-10-04 12:22:51.674        Mayank Agarwal          4   \n",
       "62304 2016-10-10 04:17:03.477        Mayank Agarwal          9   \n",
       "62305 2016-10-21 06:30:55.281        Mayank Agarwal          5   \n",
       "62306 2017-05-23 04:37:28.709           Randi Gloss          7   \n",
       "62307 2016-04-05 23:01:22.486          Heather Nann          3   \n",
       "62308 2016-01-28 01:03:08.798          Heather Nann          4   \n",
       "62309 2016-01-14 13:28:30.277          Heather Nann          5   \n",
       "62310 2016-03-06 06:51:45.307          Heather Nann          3   \n",
       "62311 2017-01-15 17:45:22.836          Nick Todorov          7   \n",
       "62312 2016-01-25 03:20:33.005          Heather Nann          5   \n",
       "\n",
       "               domain month  year  morning  day  evening  night      sin_hour  \\\n",
       "0          medium.com     8  2012      0.0    0        1      0 -5.000000e-01   \n",
       "1          medium.com     8  2015      1.0    0        0      0  9.659258e-01   \n",
       "2          medium.com     2  2017      0.0    1        0      0 -2.588190e-01   \n",
       "3          medium.com     5  2017      1.0    0        0      0  8.660254e-01   \n",
       "4          medium.com     6  2017      0.0    1        0      0 -5.000000e-01   \n",
       "5          medium.com     4  2017      0.0    1        0      0 -8.660254e-01   \n",
       "6          medium.com     8  2016      0.0    0        0      1  8.660254e-01   \n",
       "7          medium.com     1  2015      0.0    0        1      0 -7.071068e-01   \n",
       "8          medium.com     2  2014      0.0    0        0      1  8.660254e-01   \n",
       "9          medium.com    10  2015      0.0    0        0      1  5.000000e-01   \n",
       "10         medium.com     8  2016      0.0    1        0      0 -7.071068e-01   \n",
       "11         medium.com     8  2016      0.0    0        1      0 -7.071068e-01   \n",
       "12     hackernoon.com     9  2016      0.0    1        0      0 -7.071068e-01   \n",
       "13         medium.com     9  2016      0.0    1        0      0 -1.000000e+00   \n",
       "14     hackernoon.com     6  2017      0.0    1        0      0 -7.071068e-01   \n",
       "15         medium.com     7  2015      0.0    0        0      1  1.000000e+00   \n",
       "16     hackernoon.com     5  2017      0.0    1        0      0 -2.588190e-01   \n",
       "17         medium.com     8  2016      0.0    1        0      0 -9.659258e-01   \n",
       "18     hackernoon.com     6  2017      1.0    0        0      0  9.659258e-01   \n",
       "19         medium.com    12  2016      0.0    0        1      0 -2.588190e-01   \n",
       "20     hackernoon.com     1  2016      0.0    0        1      0 -5.000000e-01   \n",
       "21     hackernoon.com    12  2016      0.0    0        0      1  2.588190e-01   \n",
       "22         medium.com     9  2016      0.0    0        1      0 -5.000000e-01   \n",
       "23         medium.com    12  2016      0.0    1        0      0 -9.659258e-01   \n",
       "24         medium.com     5  2017      0.0    1        0      0 -9.659258e-01   \n",
       "25         medium.com     8  2016      0.0    0        1      0 -2.588190e-01   \n",
       "26         medium.com     4  2017      0.0    0        0      1  5.000000e-01   \n",
       "27         medium.com     6  2016      0.0    0        0      1  1.000000e+00   \n",
       "28         medium.com     5  2016      0.0    1        0      0 -9.659258e-01   \n",
       "29         medium.com     4  2017      0.0    1        0      0 -8.660254e-01   \n",
       "...               ...   ...   ...      ...  ...      ...    ...           ...   \n",
       "62283             NaN     6  2017      0.0    0        1      0 -8.660254e-01   \n",
       "62284             NaN     5  2017      0.0    0        1      0 -2.588190e-01   \n",
       "62285             NaN     2  2017      0.0    1        0      0 -9.659258e-01   \n",
       "62286             NaN     6  2017      0.0    1        0      0 -7.071068e-01   \n",
       "62287             NaN    11  2014      0.0    0        1      0 -9.659258e-01   \n",
       "62288             NaN     3  2014      1.0    0        0      0  5.000000e-01   \n",
       "62289             NaN     5  2012      0.0    0        1      0 -9.659258e-01   \n",
       "62290             NaN    11  2015      0.0    0        0      1  1.000000e+00   \n",
       "62291             NaN    11  2012      0.0    0        0      1  0.000000e+00   \n",
       "62292             NaN    11  2012      0.0    0        1      0 -8.660254e-01   \n",
       "62293             NaN    11  2012      0.0    1        0      0 -1.000000e+00   \n",
       "62294             NaN     6  2016      0.0    1        0      0 -8.660254e-01   \n",
       "62295             NaN     6  2016      0.0    1        0      0 -9.659258e-01   \n",
       "62296             NaN     8  2016      0.0    0        0      1  0.000000e+00   \n",
       "62297             NaN     7  2015      0.0    1        0      0 -7.071068e-01   \n",
       "62298             NaN    12  2015      0.0    0        1      0 -5.000000e-01   \n",
       "62299             NaN     1  2017      0.0    1        0      0 -8.660254e-01   \n",
       "62300             NaN     3  2016      0.0    1        0      0 -1.000000e+00   \n",
       "62301             NaN     2  2017      0.0    1        0      0 -1.000000e+00   \n",
       "62302             NaN     6  2016      0.0    0        0      1  5.000000e-01   \n",
       "62303             NaN    10  2016      0.0    1        0      0  1.224647e-16   \n",
       "62304             NaN    10  2016      0.0    0        0      1  8.660254e-01   \n",
       "62305             NaN    10  2016      0.0    0        0      1  1.000000e+00   \n",
       "62306             NaN     5  2017      0.0    0        0      1  8.660254e-01   \n",
       "62307             NaN     4  2016      0.0    0        1      0 -2.588190e-01   \n",
       "62308             NaN     1  2016      0.0    0        0      1  2.588190e-01   \n",
       "62309             NaN     1  2016      0.0    1        0      0 -2.588190e-01   \n",
       "62310             NaN     3  2016      0.0    0        0      1  1.000000e+00   \n",
       "62311             NaN     1  2017      0.0    1        0      0 -9.659258e-01   \n",
       "62312             NaN     1  2016      0.0    0        0      1  7.071068e-01   \n",
       "\n",
       "           cos_hour day_of_week  weekend  \n",
       "0      8.660254e-01           0        0  \n",
       "1     -2.588190e-01           0        0  \n",
       "2     -9.659258e-01           6        1  \n",
       "3     -5.000000e-01           5        1  \n",
       "4     -8.660254e-01           6        1  \n",
       "5     -5.000000e-01           6        1  \n",
       "6      5.000000e-01           0        0  \n",
       "7      7.071068e-01           2        0  \n",
       "8      5.000000e-01           1        0  \n",
       "9      8.660254e-01           6        1  \n",
       "10    -7.071068e-01           0        0  \n",
       "11     7.071068e-01           1        0  \n",
       "12    -7.071068e-01           3        0  \n",
       "13    -1.836970e-16           4        0  \n",
       "14    -7.071068e-01           1        0  \n",
       "15     6.123234e-17           0        0  \n",
       "16    -9.659258e-01           0        0  \n",
       "17    -2.588190e-01           2        0  \n",
       "18    -2.588190e-01           4        0  \n",
       "19     9.659258e-01           1        0  \n",
       "20     8.660254e-01           2        0  \n",
       "21     9.659258e-01           2        0  \n",
       "22     8.660254e-01           0        0  \n",
       "23    -2.588190e-01           1        0  \n",
       "24    -2.588190e-01           1        0  \n",
       "25     9.659258e-01           1        0  \n",
       "26     8.660254e-01           2        0  \n",
       "27     6.123234e-17           5        1  \n",
       "28    -2.588190e-01           1        0  \n",
       "29    -5.000000e-01           0        0  \n",
       "...             ...         ...      ...  \n",
       "62283  5.000000e-01           0        0  \n",
       "62284  9.659258e-01           1        0  \n",
       "62285 -2.588190e-01           3        0  \n",
       "62286 -7.071068e-01           2        0  \n",
       "62287  2.588190e-01           2        0  \n",
       "62288 -8.660254e-01           1        0  \n",
       "62289  2.588190e-01           3        0  \n",
       "62290  6.123234e-17           0        0  \n",
       "62291  1.000000e+00           4        0  \n",
       "62292  5.000000e-01           3        0  \n",
       "62293 -1.836970e-16           2        0  \n",
       "62294 -5.000000e-01           3        0  \n",
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       "62312  7.071068e-01           0        0  \n",
       "\n",
       "[62313 rows x 14 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "dd_medium_train.compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dask ML\n",
    "\n",
    "Dask ML provides scalable machine learning algorithms in python which are compatible with scikit-learn. Let us first understand how scikit-learn handles the computations and then we will look at how Dask performs these operations differently. See dask-ml tutorials: [Examples from dask ml](http://ml.dask.org/examples.html)\n",
    "\n",
    "You need to install dask-ml at first \n",
    "\n",
    "There are two main parts in dask ml:\n",
    "    - approaches to handle big datasets \n",
    "    - approaches to handle big models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handle big model with dask distributed\n",
    "The biggest model from our course was a random forest on text data in the week with Random Forest assignment. Below I just reproduce part of our assignment, but  I reduced nrows and max features in Count vectorizer, but you can check with original parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    3060\n",
       "0    1940\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Download data\n",
    "df = pd.read_csv(\"../../data/movie_reviews_train.csv\", nrows=5000)\n",
    "\n",
    "# Split data to train and test\n",
    "X_text = df[\"text\"]\n",
    "y_text = df[\"label\"]\n",
    "\n",
    "# Classes counts\n",
    "df.label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold,GridSearchCV\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "# Split on 3 folds\n",
    "skf = StratifiedKFold(n_splits=3, shuffle=True, random_state=17)\n",
    "\n",
    "# In Pipeline we will modify the text and train logistic regression\n",
    "classifier = Pipeline([\n",
    "    ('vectorizer', CountVectorizer(max_features=500, ngram_range=(1, 3))),\n",
    "    ('clf', LogisticRegression(random_state=17))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8.34 s, sys: 139 ms, total: 8.47 s\n",
      "Wall time: 8.47 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "parameters = {'clf__C': (0.1, 1, 10, 100)}\n",
    "grid_search = GridSearchCV(classifier, parameters, scoring ='roc_auc', cv=skf)\n",
    "grid_search = grid_search.fit(X_text, y_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7042233630808542"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replace joblib with dask\n",
    "\n",
    "In this approach all we need to do is replace joblib to dask distributed. We need to initialize distributed client, and change backend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time for grid_search with joblib replace (s): 5\n",
      "CPU times: user 1.39 s, sys: 142 ms, total: 1.53 s\n",
      "Wall time: 5.87 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.externals import joblib\n",
    "from dask.distributed import Client\n",
    "client = Client()\n",
    "parameters = {'clf__C': (0.1, 1, 10, 100)}\n",
    "grid_search = GridSearchCV(classifier, parameters, scoring ='roc_auc', cv=skf)\n",
    "\n",
    "t_start = time.time()\n",
    "\n",
    "with joblib.parallel_backend('dask'):\n",
    "    grid_search.fit(X_text, y_text)\n",
    "t_end = time.time()\n",
    "print('Elapsed time for grid_search with joblib replace (s):', round((t_end - t_start)))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7042233630808542"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replace Grid search with dask\n",
    "Parallel to Gridsearch CV in sklearn, Dask provides a library called Dask-search CV (Dask-search CV is now included in Dask ML). It merges steps so that there are less repetitions. Below are the installation steps for Dask-search. We need to install it separately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip3 install dask-searchcv\n",
    "import dask_searchcv as dcv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use a pipelines in dask grid search, and according the documentation we should use dask with pipelines with many opeations which could be parallelized, especially included feature union, but I've tried and get an error as a result... Anyway time consuming operations as CountVectorizer couldn't be parallelized, so here gridsearch from dask  only for classifier [documentation](https://dask-searchcv.readthedocs.io/en/latest/). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 762 ms, sys: 30.8 ms, total: 793 ms\n",
      "Wall time: 788 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "vect = CountVectorizer(max_features=500, ngram_range=(1, 3))\n",
    "Xvect = vect.fit_transform(X_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time for grid_search (without time spended to vectorization) 0 (s):\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression()\n",
    "parameters = {'C': (0.1, 1, 10, 100)}\n",
    "t_start = time.time()\n",
    "grid_search = dcv.GridSearchCV(lr, parameters, scoring ='roc_auc', cv=skf)\n",
    "grid_search.fit(Xvect, y_text)\n",
    "t_end = time.time()\n",
    "print(f'Elapsed time for grid_search (without time spended to vectorization) {round((t_end - t_start))} (s):')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7020017187686919"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I tried to see how good dask will be with random forest with original parameters, but sometimes this raise en error get \"(OSError: [Errno 24] Too many open files) after execution, and I couldn't fix it....\"  Sometimes it works ok, for small data it works in most cases, but if you re-run this notebook several times there is a big chance to get such an error. So, I believe that dask-ml very usefull, but for know I definitely don't know how it should be used properly. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time for dask grid_search for Random Forest 3 (s):\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(random_state=17)\n",
    "min_samples_leaf = [1, 2, 3]\n",
    "max_features = [0.3, 0.5, 0.7]\n",
    "max_depth = [None]\n",
    "\n",
    "parameters = {'max_features': max_features,\n",
    "              'min_samples_leaf': min_samples_leaf,\n",
    "              'max_depth': max_depth}\n",
    "grid_search = dcv.GridSearchCV(rf, parameters,  scoring ='roc_auc', cv=skf)\n",
    "t_start = time.time()\n",
    "grid_search.fit(Xvect, y_text)\n",
    "t_end = time.time()\n",
    "print(f'Elapsed time for dask grid_search for Random Forest {round((t_end - t_start))} (s):')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handle model with big data\n",
    "There are number of models rewritten in dask, which could take dask object (huge arrays) and compute models on them. You could read more in dask documentation. Below an example with KMeans, but also there are dask version of linear models, processing functions. The notation is very similar to scikit-learn, and it should be easy to use. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dask_ml import datasets\n",
    "from dask_ml.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dask.array<concatenate, shape=(10000000, 2), dtype=float64, chunksize=(1000000, 2)>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, y = datasets.make_blobs(n_samples=10000000,\n",
    "                                   chunks=1000000,\n",
    "                                   random_state=0,\n",
    "                                   centers=3)\n",
    "# Persist will give you back a lazy dask.delayed object \n",
    "X = X.persist()\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='full', copy_x=True, init='k-means||', init_max_iter=2,\n",
       "    max_iter=300, n_clusters=3, n_jobs=1, oversampling_factor=10,\n",
       "    precompute_distances='auto', random_state=None, tol=0.0001)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km = KMeans(n_clusters=3, init_max_iter=2, oversampling_factor=10)\n",
    "km.fit(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Actually I read the article about dask couple of days ago and I've decided that task with tutorial a good way to get acquainted with the library. So I ask  you not to be very strict if  I misunderstood something:))"
   ]
  }
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