diff --git a/analyses/2019_03_Aimaanhasan__issue#36_array_extension.ipynb b/analyses/2019_03_Aimaanhasan__issue#36_array_extension.ipynb
index 54adc8a..da05558 100644
--- a/analyses/2019_03_Aimaanhasan__issue#36_array_extension.ipynb
+++ b/analyses/2019_03_Aimaanhasan__issue#36_array_extension.ipynb
@@ -8,6 +8,84 @@
     "This notebook have used the existing examples of string processing which @birdsarah has produced, present in 'analyses/issue_36.ipynb' to analyse it using **array extensions**"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fletcher Array Extensions\n",
+    "Source: [fletcher docs](https://fletcher.readthedocs.io/en/latest/)\n",
+    "\n",
+    "Fletcher provides a generic implementation of the ExtensionDtype and ExtensionArray interfaces of Pandas for columns backed by Apache Arrow. By using it you can use any data type available in Apache Arrow natively in Pandas. Most prominently, fletcher provides native String und List types.\n",
+    "\n",
+    "In addition to bringing an alternative memory backend to NumPy, fletcher also provides high-performance operations on the new column types. It will either use the native implementation of an algorithm if provided in pyarrow or otherwise provide an implementation by itself using Numba.\n",
+    "\n",
+    "An example of usage is shown below\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 3 entries, 0 to 2\n",
+      "Data columns (total 1 columns):\n",
+      "str_column    2 non-null fletcher[string]\n",
+      "dtypes: fletcher[string](1)\n",
+      "memory usage: 108.0 bytes\n"
+     ]
+    }
+   ],
+   "source": [
+    "import fletcher as fr\n",
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.DataFrame({\n",
+    "    'str_column': fr.FletcherArray(['Test', None, 'Strings'])\n",
+    "})\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Analysis #1\n",
+    "### Comparing time to calculate count of unique values and nunique method usage \n",
+    "Calculating count of unique values not using fletcher - Wall time = 567ms. See <a href='#analysis-1.1'>Analysis-1.1</a> for code and processing <br>\n",
+    "Calculating count of unique values using fletcher - Wall time = 0ns. See <a href='#analysis-1.2'>Analysis-1.2</a> for code and processing <br>\n",
+    "The nunique method doesn't work using Fletcher Array thus  \n",
+    "\n",
+    "\n",
+    "\n",
+    "### Findings\n",
+    "Array extensions has decreased the Wall time and has increased efficiency"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Analysis #2\n",
+    "### .str in DASK with Fletcher Array Extension is not supported\n",
+    "It gives the above error because DASK doesn't allow access to str with Fletcher Array Extension. See <a href='#analysis-1.1'>Analysis-1.1</a> for code and processing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Conclusion\n",
+    "\n",
+    "1. Array extension makes string processing faster for some cases\n",
+    "1. Needs extra methods and operations for DASK methods to work correctly\n",
+    "1. Some methods are not supported for Fletcher Array type objects"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -21,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 1,
    "metadata": {
     "scrolled": true
    },
@@ -34,8 +112,8 @@
        "<td style=\"vertical-align: top; border: 0px solid white\">\n",
        "<h3>Client</h3>\n",
        "<ul>\n",
-       "  <li><b>Scheduler: </b>tcp://127.0.0.1:60056\n",
-       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:60057/status' target='_blank'>http://127.0.0.1:60057/status</a>\n",
+       "  <li><b>Scheduler: </b>tcp://127.0.0.1:56669\n",
+       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a>\n",
        "</ul>\n",
        "</td>\n",
        "<td style=\"vertical-align: top; border: 0px solid white\">\n",
@@ -43,17 +121,17 @@
        "<ul>\n",
        "  <li><b>Workers: </b>4</li>\n",
        "  <li><b>Cores: </b>4</li>\n",
-       "  <li><b>Memory: </b>4.16 GB</li>\n",
+       "  <li><b>Memory: </b>3.98 GB</li>\n",
        "</ul>\n",
        "</td>\n",
        "</tr>\n",
        "</table>"
       ],
       "text/plain": [
-       "<Client: scheduler='tcp://127.0.0.1:60056' processes=4 cores=4>"
+       "<Client: scheduler='tcp://127.0.0.1:56669' processes=4 cores=4>"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -67,7 +145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -166,19 +244,26 @@
        "4  https://ajax.googleapis.com/ajax/libs/webfont/...  "
       ]
      },
-     "execution_count": 27,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "df = dd.read_parquet(\n",
-    "    \"C:\\\\Users\\\\Ayman Hasan\\\\Desktop\\\\Outreachy\\\\sample\\\\part-00000-34d9b361-ea79-42eb-82ee-9c9f9259c339-c000.snappy.parquet\", engine='pyarrow',\n",
+    "    \"C:\\\\Users\\\\hamza\\\\Downloads\\\\safe_dataset.sample (1)\\\\sample\\\\part-00000-34d9b361-ea79-42eb-82ee-9c9f9259c339-c000.snappy.parquet\", engine='pyarrow',\n",
     "    columns=['argument_0', 'func_name', 'symbol', 'location', 'script_url']\n",
     ")\n",
     "df.head()\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -193,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -245,7 +330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -382,7 +467,7 @@
        "4       ajax.googleapis.com||webfont.js||ra/<  "
       ]
      },
-     "execution_count": 29,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -397,9 +482,188 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dfpd = df.compute() #Converting to Pandas Dataframe\n",
+    "\n",
+    "\n",
+    "for i in dfpd.columns:\n",
+    "    dfpd[i] = fr.FletcherArray(dfpd[i])\n",
+    "dfpd = dd.from_pandas(dfpd, npartitions=2)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Analysis - 1.1\n",
+    "<a id='analysis-1.1'></a>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Wall time: 418 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "n_unique_aggs = df.agg.nunique().compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1261"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n_unique_aggs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Wall time: 0 ns\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Using different approach\n",
+    "%time\n",
+    "n_unique_aggs_from_fr = len(dfpd.agg.unique().compute())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1261"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n_unique_aggs_from_fr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Analysis - 1.2\n",
+    "<a id='analysis-1.2'></a>\n",
+    "See <a href='#analysis-1.1'>Analysis-1.1</a> for different approach <br>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "data type not understood",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-10-7e79a33ce033>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mn_unique_aggs_from_fr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdfpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0magg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnunique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;31m#The above command returns an error\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\core.py\u001b[0m in \u001b[0;36mnunique\u001b[1;34m(self, split_every)\u001b[0m\n\u001b[0;32m   2068\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mderived_from\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2069\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mnunique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msplit_every\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2070\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop_duplicates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msplit_every\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msplit_every\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2071\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2072\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mderived_from\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\core.py\u001b[0m in \u001b[0;36mdrop_duplicates\u001b[1;34m(self, split_every, split_out, **kwargs)\u001b[0m\n\u001b[0;32m    418\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdrop_duplicates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msplit_every\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msplit_out\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    419\u001b[0m         \u001b[1;31m# Let pandas error on bad inputs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 420\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_meta_nonempty\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop_duplicates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    421\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;34m'subset'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'subset'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    422\u001b[0m             \u001b[0msplit_out_setup\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msplit_out_on_cols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\core.py\u001b[0m in \u001b[0;36m_meta_nonempty\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    287\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_meta_nonempty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    288\u001b[0m         \u001b[1;34m\"\"\" A non-empty version of `_meta` with fake data.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 289\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mmeta_nonempty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_meta\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    290\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    291\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\dask\\utils.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, arg, *args, **kwargs)\u001b[0m\n\u001b[0;32m    411\u001b[0m         \"\"\"\n\u001b[0;32m    412\u001b[0m         \u001b[0mmeth\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 413\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mmeth\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    414\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    415\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\utils.py\u001b[0m in \u001b[0;36m_nonempty_series\u001b[1;34m(s, idx)\u001b[0m\n\u001b[0;32m    445\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    446\u001b[0m         \u001b[0mentry\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_scalar_from_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 447\u001b[1;33m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mentry\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mentry\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    448\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    449\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0midx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mTypeError\u001b[0m: data type not understood"
+     ]
+    }
+   ],
+   "source": [
+    "n_unique_aggs_from_fr = dfpd.agg.nunique()\n",
+    "#The above command returns an error\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Looking for strings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = dd.read_parquet(\n",
+    "    'C:\\\\Users\\\\Ayman Hasan\\\\Desktop\\\\Outreachy\\\\sample\\\\part-00000-34d9b361-ea79-42eb-82ee-9c9f9259c339-c000.snappy.parquet', engine='pyarrow',\n",
+    "    columns=['argument_0', 'script_url']\n",
+    ")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\distributed\\worker.py:2788: UserWarning: Large object of size 3.66 MB detected in task graph: \n",
+      "  (                           argument_0             ...  9 columns], 5)\n",
+      "Consider scattering large objects ahead of time\n",
+      "with client.scatter to reduce scheduler burden and \n",
+      "keep data on workers\n",
+      "\n",
+      "    future = client.submit(func, big_data)    # bad\n",
+      "\n",
+      "    big_future = client.scatter(big_data)     # good\n",
+      "    future = client.submit(func, big_future)  # good\n",
+      "  % (format_bytes(len(b)), s))\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -534,7 +798,7 @@
        "4       ajax.googleapis.com||webfont.js||ra/<  "
       ]
      },
-     "execution_count": 30,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -551,474 +815,86 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Wall time: 567 ms\n"
+      "5\n",
+      "Wall time: 439 ms\n"
      ]
     }
    ],
    "source": [
     "%%time\n",
-    "n_unique_aggs = df.agg.nunique().compute()"
+    "print(df[df.argument_0.str.contains('modernizr')].script_url.nunique().compute())\n",
+    "#print(df[df.argument_0.str.contains('modernizr')].head())"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 32,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1261"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "n_unique_aggs"
+    "## Analysis - 2\n",
+    "<a id='analysis-2'></a>\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
-     "ename": "TypeError",
-     "evalue": "data type not understood",
+     "ename": "AttributeError",
+     "evalue": "Can only use .str accessor with object dtype",
      "output_type": "error",
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-33-7e79a33ce033>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mn_unique_aggs_from_fr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdfpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0magg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnunique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;31m#The above command returns an error\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\core.py\u001b[0m in \u001b[0;36mnunique\u001b[1;34m(self, split_every)\u001b[0m\n\u001b[0;32m   2068\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mderived_from\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2069\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mnunique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msplit_every\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2070\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop_duplicates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msplit_every\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msplit_every\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2071\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2072\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mderived_from\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\core.py\u001b[0m in \u001b[0;36mdrop_duplicates\u001b[1;34m(self, split_every, split_out, **kwargs)\u001b[0m\n\u001b[0;32m    418\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdrop_duplicates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msplit_every\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msplit_out\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    419\u001b[0m         \u001b[1;31m# Let pandas error on bad inputs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 420\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_meta_nonempty\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop_duplicates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    421\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;34m'subset'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'subset'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    422\u001b[0m             \u001b[0msplit_out_setup\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msplit_out_on_cols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\core.py\u001b[0m in \u001b[0;36m_meta_nonempty\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    287\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_meta_nonempty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    288\u001b[0m         \u001b[1;34m\"\"\" A non-empty version of `_meta` with fake data.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 289\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mmeta_nonempty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_meta\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    290\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    291\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\dask\\utils.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, arg, *args, **kwargs)\u001b[0m\n\u001b[0;32m    411\u001b[0m         \"\"\"\n\u001b[0;32m    412\u001b[0m         \u001b[0mmeth\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 413\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mmeth\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    414\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    415\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\utils.py\u001b[0m in \u001b[0;36m_nonempty_series\u001b[1;34m(s, idx)\u001b[0m\n\u001b[0;32m    445\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    446\u001b[0m         \u001b[0mentry\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_scalar_from_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 447\u001b[1;33m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mentry\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mentry\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    448\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    449\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0midx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mTypeError\u001b[0m: data type not understood"
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<timed eval>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\properties.pyx\u001b[0m in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\core.py\u001b[0m in \u001b[0;36mstr\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1892\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1893\u001b[0m         \u001b[1;34m\"\"\" Namespace for string methods \"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1894\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mStringAccessor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1895\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1896\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__dir__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\accessor.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, series)\u001b[0m\n\u001b[0;32m     34\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     35\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Accessor cannot be initialized'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 36\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     37\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_series\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mseries\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     38\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\accessor.py\u001b[0m in \u001b[0;36m_validate\u001b[1;34m(self, series)\u001b[0m\n\u001b[0;32m    114\u001b[0m                 \u001b[0mis_categorical_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    115\u001b[0m                 series.cat.categories.dtype == 'object')):\n\u001b[1;32m--> 116\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Can only use .str accessor with object dtype\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    117\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    118\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mderived_from\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mStringMethods\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mAttributeError\u001b[0m: Can only use .str accessor with object dtype"
      ]
     }
    ],
    "source": [
-    "n_unique_aggs_from_fr = dfpd.agg.nunique()\n",
-    "#The above command returns an error\n"
+    "%%time\n",
+    "print(len(dfpd[dfpd.argument_0.str.contains('modernizr')].script_url.unique().compute())) #contains is not supported with fletcher array extension"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 34,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Wall time: 0 ns\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Using different approach\n",
-    "%time\n",
-    "n_unique_aggs_from_fr = len(dfpd.agg.unique().compute())\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1261"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "n_unique_aggs_from_fr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Analysis #1\n",
-    "### Comparing time to calculate count of unique values \n",
-    "Calculating count of unique values not using fletcher - Wall time = 567ms <br>\n",
-    "Calculating count of unique values using fletcher - Wall time = 0ns\n",
-    "\n",
-    "### Findings\n",
-    "Array extensions has decreased the Wall time and has increased efficiency"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Looking for strings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "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>argument_0</th>\n",
-       "      <th>script_url</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td></td>\n",
-       "      <td>https://staticxx.facebook.com/connect/xd_arbit...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td></td>\n",
-       "      <td>https://staticxx.facebook.com/connect/xd_arbit...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td></td>\n",
-       "      <td>https://staticxx.facebook.com/connect/xd_arbit...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td></td>\n",
-       "      <td>https://staticxx.facebook.com/connect/xd_arbit...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td></td>\n",
-       "      <td>https://ajax.googleapis.com/ajax/libs/webfont/...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  argument_0                                         script_url\n",
-       "0             https://staticxx.facebook.com/connect/xd_arbit...\n",
-       "1             https://staticxx.facebook.com/connect/xd_arbit...\n",
-       "2             https://staticxx.facebook.com/connect/xd_arbit...\n",
-       "3             https://staticxx.facebook.com/connect/xd_arbit...\n",
-       "4             https://ajax.googleapis.com/ajax/libs/webfont/..."
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = dd.read_parquet(\n",
-    "    'C:\\\\Users\\\\Ayman Hasan\\\\Desktop\\\\Outreachy\\\\sample\\\\part-00000-34d9b361-ea79-42eb-82ee-9c9f9259c339-c000.snappy.parquet', engine='pyarrow',\n",
-    "    columns=['argument_0', 'script_url']\n",
-    ")\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "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>argument_0</th>\n",
-       "      <th>script_url</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td></td>\n",
-       "      <td>https://staticxx.facebook.com/connect/xd_arbit...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td></td>\n",
-       "      <td>https://staticxx.facebook.com/connect/xd_arbit...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td></td>\n",
-       "      <td>https://staticxx.facebook.com/connect/xd_arbit...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td></td>\n",
-       "      <td>https://staticxx.facebook.com/connect/xd_arbit...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td></td>\n",
-       "      <td>https://ajax.googleapis.com/ajax/libs/webfont/...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  argument_0                                         script_url\n",
-       "0             https://staticxx.facebook.com/connect/xd_arbit...\n",
-       "1             https://staticxx.facebook.com/connect/xd_arbit...\n",
-       "2             https://staticxx.facebook.com/connect/xd_arbit...\n",
-       "3             https://staticxx.facebook.com/connect/xd_arbit...\n",
-       "4             https://ajax.googleapis.com/ajax/libs/webfont/..."
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dfpd = df.compute() #Converting to Pandas Dataframe\n",
-    "\n",
-    "\n",
-    "for i in dfpd.columns:\n",
-    "    dfpd[i] = fr.FletcherArray(dfpd[i])\n",
-    "dfpd = dd.from_pandas(dfpd, npartitions=2)\n",
-    "dfpd.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5\n",
-      "Wall time: 603 ms\n",
-      "Compiler : 258 ms\n"
-     ]
-    }
-   ],
    "source": [
-    "%%time\n",
-    "print(df[df.argument_0.str.contains('modernizr')].script_url.nunique().compute())\n",
-    "#print(df[df.argument_0.str.contains('modernizr')].head())"
+    "#### Splitting symbol"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
-     "ename": "AttributeError",
-     "evalue": "Can only use .str accessor with object dtype",
+     "ename": "OSError",
+     "evalue": "Passed non-file path: C:\\Users\\Ayman Hasan\\Desktop\\Outreachy\\sample\\part-00000-34d9b361-ea79-42eb-82ee-9c9f9259c339-c000.snappy.parquet",
      "output_type": "error",
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[1;32m<timed eval>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\properties.pyx\u001b[0m in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\core.py\u001b[0m in \u001b[0;36mstr\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1892\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1893\u001b[0m         \u001b[1;34m\"\"\" Namespace for string methods \"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1894\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mStringAccessor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1895\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1896\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__dir__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\accessor.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, series)\u001b[0m\n\u001b[0;32m     34\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     35\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Accessor cannot be initialized'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 36\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     37\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_series\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mseries\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     38\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\accessor.py\u001b[0m in \u001b[0;36m_validate\u001b[1;34m(self, series)\u001b[0m\n\u001b[0;32m    114\u001b[0m                 \u001b[0mis_categorical_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    115\u001b[0m                 series.cat.categories.dtype == 'object')):\n\u001b[1;32m--> 116\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Can only use .str accessor with object dtype\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    117\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    118\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mderived_from\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mStringMethods\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mAttributeError\u001b[0m: Can only use .str accessor with object dtype"
+      "\u001b[1;31mOSError\u001b[0m                                   Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-14-42b03c87c6f2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m df = dd.read_parquet(\n\u001b[0;32m      2\u001b[0m     \u001b[1;34m'C:\\\\Users\\\\Ayman Hasan\\\\Desktop\\\\Outreachy\\\\sample\\\\part-00000-34d9b361-ea79-42eb-82ee-9c9f9259c339-c000.snappy.parquet'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'pyarrow'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'argument_0'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'func_name'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'symbol'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'location'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'script_url'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m )\n\u001b[0;32m      5\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\io\\parquet.py\u001b[0m in \u001b[0;36mread_parquet\u001b[1;34m(path, columns, filters, categories, index, storage_options, engine, infer_divisions)\u001b[0m\n\u001b[0;32m   1152\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1153\u001b[0m     return read(fs, fs_token, paths, columns=columns, filters=filters,\n\u001b[1;32m-> 1154\u001b[1;33m                 categories=categories, index=index, infer_divisions=infer_divisions)\n\u001b[0m\u001b[0;32m   1155\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1156\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\io\\parquet.py\u001b[0m in \u001b[0;36m_read_pyarrow\u001b[1;34m(fs, fs_token, paths, columns, filters, categories, index, infer_divisions)\u001b[0m\n\u001b[0;32m    673\u001b[0m         \u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    674\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 675\u001b[1;33m     \u001b[0mdataset\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mParquetDataset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpaths\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilesystem\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mget_pyarrow_filesystem\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    676\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mdataset\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpartitions\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    677\u001b[0m         partitions = [n for n in dataset.partitions.partition_names\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pyarrow\\parquet.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, path_or_paths, filesystem, schema, metadata, split_row_groups, validate_schema, filters, metadata_nthreads)\u001b[0m\n\u001b[0;32m    888\u001b[0m          \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcommon_metadata_path\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    889\u001b[0m          \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmetadata_path\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_make_manifest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 890\u001b[1;33m             path_or_paths, self.fs, metadata_nthreads=metadata_nthreads)\n\u001b[0m\u001b[0;32m    891\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    892\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcommon_metadata_path\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pyarrow\\parquet.py\u001b[0m in \u001b[0;36m_make_manifest\u001b[1;34m(path_or_paths, fs, pathsep, metadata_nthreads)\u001b[0m\n\u001b[0;32m   1063\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mfs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misfile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1064\u001b[0m                 raise IOError('Passed non-file path: {0}'\n\u001b[1;32m-> 1065\u001b[1;33m                               .format(path))\n\u001b[0m\u001b[0;32m   1066\u001b[0m             \u001b[0mpiece\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mParquetDatasetPiece\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1067\u001b[0m             \u001b[0mpieces\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpiece\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mOSError\u001b[0m: Passed non-file path: C:\\Users\\Ayman Hasan\\Desktop\\Outreachy\\sample\\part-00000-34d9b361-ea79-42eb-82ee-9c9f9259c339-c000.snappy.parquet"
      ]
     }
    ],
-   "source": [
-    "%%time\n",
-    "print(len(dfpd[dfpd.argument_0.str.contains('modernizr')].script_url.unique().compute())) #contains is not supported with fletcher array extension"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Analysis #2\n",
-    "### .str in DASK with Fletcher Array Extension is not supported\n",
-    "It gives the above error because DASK doesn't allow access to str with Fletcher Array Extension"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Conclusion\n",
-    "\n",
-    "1. Array extension makes string processing faster for some cases\n",
-    "1. Needs extra methods and operations for DASK methods to work correctly\n",
-    "1. Some methods are not supported for Fletcher Array type objects"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Splitting symbol"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "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>argument_0</th>\n",
-       "      <th>func_name</th>\n",
-       "      <th>symbol</th>\n",
-       "      <th>location</th>\n",
-       "      <th>script_url</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td></td>\n",
-       "      <td>a/&lt;</td>\n",
-       "      <td>window.name</td>\n",
-       "      <td>https://staticxx.facebook.com/connect/xd_arbit...</td>\n",
-       "      <td>https://staticxx.facebook.com/connect/xd_arbit...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td></td>\n",
-       "      <td>a/&lt;</td>\n",
-       "      <td>window.name</td>\n",
-       "      <td>https://staticxx.facebook.com/connect/xd_arbit...</td>\n",
-       "      <td>https://staticxx.facebook.com/connect/xd_arbit...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td></td>\n",
-       "      <td>A</td>\n",
-       "      <td>window.document.cookie</td>\n",
-       "      <td>https://staticxx.facebook.com/connect/xd_arbit...</td>\n",
-       "      <td>https://staticxx.facebook.com/connect/xd_arbit...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td></td>\n",
-       "      <td>x</td>\n",
-       "      <td>window.navigator.userAgent</td>\n",
-       "      <td>https://staticxx.facebook.com/connect/xd_arbit...</td>\n",
-       "      <td>https://staticxx.facebook.com/connect/xd_arbit...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td></td>\n",
-       "      <td>ra/&lt;</td>\n",
-       "      <td>window.navigator.userAgent</td>\n",
-       "      <td>https://cas.us.criteo.com/delivery/r/afr.php?d...</td>\n",
-       "      <td>https://ajax.googleapis.com/ajax/libs/webfont/...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  argument_0 func_name                      symbol  \\\n",
-       "0                  a/<                 window.name   \n",
-       "1                  a/<                 window.name   \n",
-       "2                    A      window.document.cookie   \n",
-       "3                    x  window.navigator.userAgent   \n",
-       "4                 ra/<  window.navigator.userAgent   \n",
-       "\n",
-       "                                            location  \\\n",
-       "0  https://staticxx.facebook.com/connect/xd_arbit...   \n",
-       "1  https://staticxx.facebook.com/connect/xd_arbit...   \n",
-       "2  https://staticxx.facebook.com/connect/xd_arbit...   \n",
-       "3  https://staticxx.facebook.com/connect/xd_arbit...   \n",
-       "4  https://cas.us.criteo.com/delivery/r/afr.php?d...   \n",
-       "\n",
-       "                                          script_url  \n",
-       "0  https://staticxx.facebook.com/connect/xd_arbit...  \n",
-       "1  https://staticxx.facebook.com/connect/xd_arbit...  \n",
-       "2  https://staticxx.facebook.com/connect/xd_arbit...  \n",
-       "3  https://staticxx.facebook.com/connect/xd_arbit...  \n",
-       "4  https://ajax.googleapis.com/ajax/libs/webfont/...  "
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "df = dd.read_parquet(\n",
     "    'C:\\\\Users\\\\Ayman Hasan\\\\Desktop\\\\Outreachy\\\\sample\\\\part-00000-34d9b361-ea79-42eb-82ee-9c9f9259c339-c000.snappy.parquet', engine='pyarrow',\n",
@@ -1029,110 +905,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
    "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>argument_0</th>\n",
-       "      <th>func_name</th>\n",
-       "      <th>symbol</th>\n",
-       "      <th>location</th>\n",
-       "      <th>script_url</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td></td>\n",
-       "      <td>a/&lt;</td>\n",
-       "      <td>window.name</td>\n",
-       "      <td>https://staticxx.facebook.com/connect/xd_arbit...</td>\n",
-       "      <td>https://staticxx.facebook.com/connect/xd_arbit...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td></td>\n",
-       "      <td>a/&lt;</td>\n",
-       "      <td>window.name</td>\n",
-       "      <td>https://staticxx.facebook.com/connect/xd_arbit...</td>\n",
-       "      <td>https://staticxx.facebook.com/connect/xd_arbit...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td></td>\n",
-       "      <td>A</td>\n",
-       "      <td>window.document.cookie</td>\n",
-       "      <td>https://staticxx.facebook.com/connect/xd_arbit...</td>\n",
-       "      <td>https://staticxx.facebook.com/connect/xd_arbit...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td></td>\n",
-       "      <td>x</td>\n",
-       "      <td>window.navigator.userAgent</td>\n",
-       "      <td>https://staticxx.facebook.com/connect/xd_arbit...</td>\n",
-       "      <td>https://staticxx.facebook.com/connect/xd_arbit...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td></td>\n",
-       "      <td>ra/&lt;</td>\n",
-       "      <td>window.navigator.userAgent</td>\n",
-       "      <td>https://cas.us.criteo.com/delivery/r/afr.php?d...</td>\n",
-       "      <td>https://ajax.googleapis.com/ajax/libs/webfont/...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  argument_0 func_name                      symbol  \\\n",
-       "0                  a/<                 window.name   \n",
-       "1                  a/<                 window.name   \n",
-       "2                    A      window.document.cookie   \n",
-       "3                    x  window.navigator.userAgent   \n",
-       "4                 ra/<  window.navigator.userAgent   \n",
-       "\n",
-       "                                            location  \\\n",
-       "0  https://staticxx.facebook.com/connect/xd_arbit...   \n",
-       "1  https://staticxx.facebook.com/connect/xd_arbit...   \n",
-       "2  https://staticxx.facebook.com/connect/xd_arbit...   \n",
-       "3  https://staticxx.facebook.com/connect/xd_arbit...   \n",
-       "4  https://cas.us.criteo.com/delivery/r/afr.php?d...   \n",
-       "\n",
-       "                                          script_url  \n",
-       "0  https://staticxx.facebook.com/connect/xd_arbit...  \n",
-       "1  https://staticxx.facebook.com/connect/xd_arbit...  \n",
-       "2  https://staticxx.facebook.com/connect/xd_arbit...  \n",
-       "3  https://staticxx.facebook.com/connect/xd_arbit...  \n",
-       "4  https://ajax.googleapis.com/ajax/libs/webfont/...  "
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "dfpd = df.compute() #Converting to Pandas Dataframe\n",
     "\n",
@@ -1145,22 +920,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'DataFrame' object has no attribute 'symbol_parts'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-46-5545e22d8c09>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'symbol_2'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msymbol_parts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mdfpd\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'symbol_0'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdfpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msymbol_parts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\core.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2531\u001b[0m             return new_dd_object(merge(self.dask, dsk), name,\n\u001b[0;32m   2532\u001b[0m                                  meta, self.divisions)\n\u001b[1;32m-> 2533\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"'DataFrame' object has no attribute %r\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2534\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2535\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__dir__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'symbol_parts'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "df['symbol_parts'] = df.symbol.str.split('.')\n",
     "df['symbol_0'] = df.symbol_parts.str.get(0)\n",
@@ -1172,25 +934,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "Can only use .str accessor with object dtype",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-47-26e6793c788c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdfpd\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'symbol_parts'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdfpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msymbol\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'.'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mdfpd\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'symbol_0'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdfpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msymbol_parts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mdfpd\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'symbol_1'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msymbol_parts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\properties.pyx\u001b[0m in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\core.py\u001b[0m in \u001b[0;36mstr\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1892\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1893\u001b[0m         \u001b[1;34m\"\"\" Namespace for string methods \"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1894\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mStringAccessor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1895\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1896\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__dir__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\accessor.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, series)\u001b[0m\n\u001b[0;32m     34\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     35\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Accessor cannot be initialized'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 36\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     37\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_series\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mseries\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     38\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\accessor.py\u001b[0m in \u001b[0;36m_validate\u001b[1;34m(self, series)\u001b[0m\n\u001b[0;32m    114\u001b[0m                 \u001b[0mis_categorical_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    115\u001b[0m                 series.cat.categories.dtype == 'object')):\n\u001b[1;32m--> 116\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Can only use .str accessor with object dtype\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    117\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    118\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mderived_from\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mStringMethods\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mAttributeError\u001b[0m: Can only use .str accessor with object dtype"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "dfpd['symbol_parts'] = dfpd.symbol.str.split('.')\n",
     "dfpd['symbol_0'] = dfpd.symbol_parts.str.get(0)\n",
@@ -1201,18 +947,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5\n",
-      "Wall time: 367 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "print(df[df.symbol_1 == 'fillText'].func_name.nunique().compute())"
@@ -1222,6 +959,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -1229,6 +973,18 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
   }
  },
  "nbformat": 4,