Fast analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Reviews.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "    .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>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>B001E4KFG0</td>\n",
       "      <td>A3SGXH7AUHU8GW</td>\n",
       "      <td>delmartian</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1303862400</td>\n",
       "      <td>Good Quality Dog Food</td>\n",
       "      <td>I have bought several of the Vitality canned d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>B00813GRG4</td>\n",
       "      <td>A1D87F6ZCVE5NK</td>\n",
       "      <td>dll pa</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1346976000</td>\n",
       "      <td>Not as Advertised</td>\n",
       "      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>B000LQOCH0</td>\n",
       "      <td>ABXLMWJIXXAIN</td>\n",
       "      <td>Natalia Corres \"Natalia Corres\"</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1219017600</td>\n",
       "      <td>\"Delight\" says it all</td>\n",
       "      <td>This is a confection that has been around a fe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>B000UA0QIQ</td>\n",
       "      <td>A395BORC6FGVXV</td>\n",
       "      <td>Karl</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1307923200</td>\n",
       "      <td>Cough Medicine</td>\n",
       "      <td>If you are looking for the secret ingredient i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>B006K2ZZ7K</td>\n",
       "      <td>A1UQRSCLF8GW1T</td>\n",
       "      <td>Michael D. Bigham \"M. Wassir\"</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1350777600</td>\n",
       "      <td>Great taffy</td>\n",
       "      <td>Great taffy at a great price.  There was a wid...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568449</th>\n",
       "      <td>568450</td>\n",
       "      <td>B001EO7N10</td>\n",
       "      <td>A28KG5XORO54AY</td>\n",
       "      <td>Lettie D. Carter</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1299628800</td>\n",
       "      <td>Will not do without</td>\n",
       "      <td>Great for sesame chicken..this is a good if no...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568450</th>\n",
       "      <td>568451</td>\n",
       "      <td>B003S1WTCU</td>\n",
       "      <td>A3I8AFVPEE8KI5</td>\n",
       "      <td>R. Sawyer</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1331251200</td>\n",
       "      <td>disappointed</td>\n",
       "      <td>I'm disappointed with the flavor. The chocolat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568451</th>\n",
       "      <td>568452</td>\n",
       "      <td>B004I613EE</td>\n",
       "      <td>A121AA1GQV751Z</td>\n",
       "      <td>pksd \"pk_007\"</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1329782400</td>\n",
       "      <td>Perfect for our maltipoo</td>\n",
       "      <td>These stars are small, so you can give 10-15 o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568452</th>\n",
       "      <td>568453</td>\n",
       "      <td>B004I613EE</td>\n",
       "      <td>A3IBEVCTXKNOH</td>\n",
       "      <td>Kathy A. Welch \"katwel\"</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1331596800</td>\n",
       "      <td>Favorite Training and reward treat</td>\n",
       "      <td>These are the BEST treats for training and rew...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568453</th>\n",
       "      <td>568454</td>\n",
       "      <td>B001LR2CU2</td>\n",
       "      <td>A3LGQPJCZVL9UC</td>\n",
       "      <td>srfell17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1338422400</td>\n",
       "      <td>Great Honey</td>\n",
       "      <td>I am very satisfied ,product is as advertised,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>568454 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Id   ProductId          UserId                      ProfileName  \\\n",
       "0            1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian   \n",
       "1            2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n",
       "2            3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n",
       "3            4  B000UA0QIQ  A395BORC6FGVXV                             Karl   \n",
       "4            5  B006K2ZZ7K  A1UQRSCLF8GW1T    Michael D. Bigham \"M. Wassir\"   \n",
       "...        ...         ...             ...                              ...   \n",
       "568449  568450  B001EO7N10  A28KG5XORO54AY                 Lettie D. Carter   \n",
       "568450  568451  B003S1WTCU  A3I8AFVPEE8KI5                        R. Sawyer   \n",
       "568451  568452  B004I613EE  A121AA1GQV751Z                    pksd \"pk_007\"   \n",
       "568452  568453  B004I613EE   A3IBEVCTXKNOH          Kathy A. Welch \"katwel\"   \n",
       "568453  568454  B001LR2CU2  A3LGQPJCZVL9UC                         srfell17   \n",
       "\n",
       "        HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
       "0                          1                       1      5  1303862400   \n",
       "1                          0                       0      1  1346976000   \n",
       "2                          1                       1      4  1219017600   \n",
       "3                          3                       3      2  1307923200   \n",
       "4                          0                       0      5  1350777600   \n",
       "...                      ...                     ...    ...         ...   \n",
       "568449                     0                       0      5  1299628800   \n",
       "568450                     0                       0      2  1331251200   \n",
       "568451                     2                       2      5  1329782400   \n",
       "568452                     1                       1      5  1331596800   \n",
       "568453                     0                       0      5  1338422400   \n",
       "\n",
       "                                   Summary  \\\n",
       "0                    Good Quality Dog Food   \n",
       "1                        Not as Advertised   \n",
       "2                    \"Delight\" says it all   \n",
       "3                           Cough Medicine   \n",
       "4                              Great taffy   \n",
       "...                                    ...   \n",
       "568449                 Will not do without   \n",
       "568450                        disappointed   \n",
       "568451            Perfect for our maltipoo   \n",
       "568452  Favorite Training and reward treat   \n",
       "568453                         Great Honey   \n",
       "\n",
       "                                                     Text  \n",
       "0       I have bought several of the Vitality canned d...  \n",
       "1       Product arrived labeled as Jumbo Salted Peanut...  \n",
       "2       This is a confection that has been around a fe...  \n",
       "3       If you are looking for the secret ingredient i...  \n",
       "4       Great taffy at a great price.  There was a wid...  \n",
       "...                                                   ...  \n",
       "568449  Great for sesame chicken..this is a good if no...  \n",
       "568450  I'm disappointed with the flavor. The chocolat...  \n",
       "568451  These stars are small, so you can give 10-15 o...  \n",
       "568452  These are the BEST treats for training and rew...  \n",
       "568453  I am very satisfied ,product is as advertised,...  \n",
       "\n",
       "[568454 rows x 10 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing the loading times\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.76 s ± 78 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "import pandas as pd\n",
    "pd.read_csv(\"Reviews.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.27 s ± 72 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "import csv\n",
    "with open('Reviews.csv', encoding=\"utf-8\") as csvfile:\n",
    "        reader = csv.reader(csvfile)\n",
    "        data = [row for row in reader]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experiment 1: Looking for chocolate\n",
    "Goal of this experiment is to get the reviewscores for all reviews containg the word Chocolate (or any other words) in a list."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Python standard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('Reviews.csv', encoding=\"utf-8\") as csvfile:\n",
    "        import csv\n",
    "        reader = csv.reader(csvfile)\n",
    "        data = [row for row in reader]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def python_standard_choc_1(data):\n",
    "    import csv\n",
    "\n",
    "    def parse_row_chocolate(row, word):\n",
    "        if word in row[9].lower():\n",
    "            return row[6]\n",
    "\n",
    "    total = []\n",
    "        \n",
    "    for row in data:\n",
    "        result = parse_row_chocolate(row, \"chocolate\")\n",
    "        if result:\n",
    "            total.append((result))\n",
    "    return len(total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "559 ms ± 2.67 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "python_standard_choc_1(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def python_standard_choc_2(data):\n",
    "    import csv\n",
    "\n",
    "    def parse_row_chocolate(row, word):\n",
    "        if word in row[9].lower():\n",
    "            return row[6]\n",
    "\n",
    "    return len([x for x in map(lambda row: parse_row_chocolate(row, \"chocolate\"), data) if x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "607 ms ± 8.81 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "python_standard_choc_2(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def python_standard_choc_3(data):\n",
    "    import csv\n",
    "    return len([row[6] for row in data if \"chocolate\" in row[9].lower()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "487 ms ± 2.51 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "python_standard_choc_3(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df_load = pd.read_csv(\"Reviews.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pandas_choc_1(df):\n",
    "    return len(list(df[df.Text.str.contains(\"chocolate\", case=False, regex=False, na=False)].Score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "854 ms ± 3.17 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "pandas_choc_1(df_load)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pandas_choc_2(df):\n",
    "    total = []\n",
    "    for index, row in df.iterrows():\n",
    "        if \"chocolate\" in row.Text.lower():\n",
    "            total.append(row[\"Score\"])\n",
    "    return len(total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56.7 s ± 506 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "pandas_choc_2(df_load)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pandas_choc_3(df):    \n",
    "    total = []\n",
    "    for row in df.itertuples():\n",
    "        if \"chocolate\" in row.Text.lower():\n",
    "            total.append(row.Score)\n",
    "    return len(total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.42 s ± 16.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "pandas_choc_3(df_load)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pandas_choc_4(df):    \n",
    "    def parse_chocolate_col(col):\n",
    "        if \"chocolate\" in col.lower():\n",
    "            return True\n",
    "        return False\n",
    "    return len(list(df[df[\"Text\"].apply(lambda col: parse_chocolate_col(col))]['Score']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600 ms ± 8.14 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "pandas_choc_4(df_load)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pandas_choc_5(df):   \n",
    "    def parse_chocolate_col(col):\n",
    "        if \"chocolate\" in col.lower():\n",
    "            return True\n",
    "        return False\n",
    "    return len(list(df[df[\"Text\"].map(lambda col: parse_chocolate_col(col))]['Score']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "604 ms ± 3.76 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "pandas_choc_5(df_load)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pandas_choc_6(df):\n",
    "    df[\"succes\"] = df.Text.str.lower().str.contains(\"chocolate\")\n",
    "    return df.succes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.16 s ± 19.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "pandas_choc_6(df_load)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def numpy_choc_1(df):\n",
    "    import numpy as np\n",
    "    def testfunc(arr):\n",
    "        if \"chocolate\" in arr.lower():\n",
    "            return True\n",
    "        return False\n",
    "\n",
    "    tfunc = np.vectorize(testfunc)\n",
    "    return len(list(df[tfunc(df.Text)].Score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "517 ms ± 2.38 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "numpy_choc_1(df_load)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def numpy_choc_2(df):\n",
    "    import numpy as np\n",
    "    def testfunc(arr):\n",
    "        if \"chocolate\" in arr.lower():\n",
    "            return True\n",
    "        return False\n",
    "\n",
    "    tfunc = np.vectorize(testfunc)\n",
    "    return len(list(df[tfunc(df.Text.values)].Score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "516 ms ± 2.29 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "numpy_choc_2(df_load)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Numba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  9.  10.  11.  12.  13.  14.  15.  16.  17.  18.]\n",
      " [ 19.  20.  21.  22.  23.  24.  25.  26.  27.  28.]\n",
      " [ 29.  30.  31.  32.  33.  34.  35.  36.  37.  38.]\n",
      " [ 39.  40.  41.  42.  43.  44.  45.  46.  47.  48.]\n",
      " [ 49.  50.  51.  52.  53.  54.  55.  56.  57.  58.]\n",
      " [ 59.  60.  61.  62.  63.  64.  65.  66.  67.  68.]\n",
      " [ 69.  70.  71.  72.  73.  74.  75.  76.  77.  78.]\n",
      " [ 79.  80.  81.  82.  83.  84.  85.  86.  87.  88.]\n",
      " [ 89.  90.  91.  92.  93.  94.  95.  96.  97.  98.]\n",
      " [ 99. 100. 101. 102. 103. 104. 105. 106. 107. 108.]]\n"
     ]
    }
   ],
   "source": [
    "from numba import jit\n",
    "import numpy as np\n",
    "\n",
    "x = np.arange(100).reshape(10, 10)\n",
    "\n",
    "@jit(nopython=True) # Set \"nopython\" mode for best performance, equivalent to @njit\n",
    "def go_fast(a): # Function is compiled to machine code when called the first time\n",
    "    trace = 0\n",
    "    for i in range(a.shape[0]):   # Numba likes loops\n",
    "        trace += np.tanh(a[i, i]) # Numba likes NumPy functions\n",
    "    return a + trace              # Numba likes NumPy broadcasting\n",
    "\n",
    "print(go_fast(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numba import jit\n",
    "# import csv\n",
    "\n",
    "@jit\n",
    "def testfunc(arr):\n",
    "        if \"chocolate\" in arr.lower():\n",
    "            return True\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def numba_choc_1(df=None, df_bool=False):\n",
    "    import numpy as np\n",
    "    \n",
    "    tfunc = np.vectorize(testfunc)\n",
    "    return len(list(df[tfunc(df.Text.values)].Score))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.19 s ± 151 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "numba_choc_1(df_load)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext Cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "45\n"
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    },
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     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%cython --annotate\n",
    "cdef int a = 0\n",
    "for i in range(10):\n",
    "    a += i\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext Cython\n",
    "\n",
    "with open('Reviews.csv', encoding=\"utf-8\") as csvfile:\n",
    "        import csv\n",
    "        reader = csv.reader(csvfile)\n",
    "        data = [row for row in reader]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%cython\n",
    "cpdef str parse_row_chocolate(list row, str word):\n",
    "        if word in row[9].lower():\n",
    "            return row[6]\n",
    "\n",
    "cpdef int cython_standard_choc_1(list data):        \n",
    "    cdef list total = []\n",
    "        \n",
    "    for row in data:\n",
    "        result = parse_row_chocolate(row, \"chocolate\")\n",
    "        if result:\n",
    "            total.append((result))\n",
    "    return len(total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "464 ms ± 6.22 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "cython_standard_choc_1(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%cython\n",
    "cpdef int cython_standard_choc_3(list data):\n",
    "    return len([row[6] for row in data if \"chocolate\" in row[9].lower()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "466 ms ± 3.37 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "cython_standard_choc_3(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%cython\n",
    "# Bint stands for boolean int\n",
    "cpdef bint testfunc(str arr):\n",
    "        if \"chocolate\" in arr.lower():\n",
    "            return True\n",
    "        return False\n",
    "\n",
    "cpdef int cython_numpy_choc_1(df):\n",
    "    import numpy as np\n",
    "    tfunc = np.vectorize(testfunc)\n",
    "    return len(list(df[tfunc(df.Text.values)].Score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "480 ms ± 11 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "cython_numpy_choc_1(df_load)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experiment 2: Liking what?\n",
    "\n",
    "Goal of the experiment is to calculate the std deviation of the ratings.\n",
    "- Convert string to int\n",
    "- Calculate the sum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Regular Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('Reviews.csv', encoding=\"utf-8\") as csvfile:\n",
    "    import csv\n",
    "    reader = csv.reader(csvfile)\n",
    "    next(reader)\n",
    "    data = [row for row in reader]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def python_standard_score_1(reader):\n",
    "    import math\n",
    "\n",
    "    total = []\n",
    "\n",
    "    for row in reader:\n",
    "        total.append(int(row[6]))\n",
    "\n",
    "    average = sum(total) / len(total)\n",
    "    total2 = []\n",
    "    for value in total:\n",
    "        total2.append((value - average) ** 2)\n",
    "\n",
    "    std_dev = math.sqrt((1 / (len(total2) -1) * sum(total2)))\n",
    "\n",
    "    return std_dev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "356 ms ± 1.94 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "python_standard_score_1(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def python_standard_score_2(reader):\n",
    "    import math\n",
    "\n",
    "    def calc_difference(value, average):\n",
    "        return (value - average) ** 2\n",
    "        \n",
    "    scores = [int(row[6]) for row in reader]\n",
    "    average = sum(scores) / len(scores)\n",
    "\n",
    "    deviations = list(map(lambda score: calc_difference(score, average), scores))       \n",
    "    return math.sqrt((1 / (len(deviations) - 1) * sum(deviations)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "431 ms ± 10.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "python_standard_score_2(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def python_standard_score_2B(reader):\n",
    "    import math\n",
    "\n",
    "    def calc_difference(value, average):\n",
    "        return (value - average) ** 2\n",
    "    \n",
    "    scores = [int(row[6]) for row in reader]\n",
    "    average = sum(scores) / len(scores)\n",
    "\n",
    "    deviations = map(lambda score: calc_difference(score, average), scores)      \n",
    "    return math.sqrt((1 / (len(scores) - 1) * sum(deviations)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "396 ms ± 14.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "python_standard_score_2B(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Interesting find\n",
    "def python_standard_score_3(reader):\n",
    "    import statistics\n",
    "        \n",
    "    scores = [int(row[6]) for row in reader]\n",
    "    return statistics.stdev(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.97 s ± 25.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "python_standard_score_3(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def python_standard_score_4(reader):\n",
    "    import math\n",
    "    \n",
    "    scores = [int(row[6]) for row in reader]\n",
    "    average = sum(scores) / len(scores)\n",
    "\n",
    "    deviations = [(value - average) ** 2 for value in scores]      \n",
    "    return math.sqrt((1 / (len(deviations) - 1) * sum(deviations)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313 ms ± 3.09 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "python_standard_score_4(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1',\n",
       " 'B001E4KFG0',\n",
       " 'A3SGXH7AUHU8GW',\n",
       " 'delmartian',\n",
       " '1',\n",
       " '1',\n",
       " '5',\n",
       " '1303862400',\n",
       " 'Good Quality Dog Food',\n",
       " 'I have bought several of the Vitality canned dog food products and have found them all to be of good quality. The product looks more like a stew than a processed meat and it smells better. My Labrador is finicky and she appreciates this product better than  most.']"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"Reviews.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pandas_score_1(df): \n",
    "    import math\n",
    "    \n",
    "    deviations = (df.Score - df.Score.mean()) ** 2     \n",
    "    return math.sqrt((1 / (len(deviations) - 1) * sum(deviations)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54.5 ms ± 408 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "pandas_score_1(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pandas_score_2(df): \n",
    "    import math\n",
    "    \n",
    "    deviations = (df.Score - df.Score.mean()) ** 2     \n",
    "    return math.sqrt((1 / (len(deviations) - 1) * deviations.sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.49 ms ± 162 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "pandas_score_2(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pandas_score_3(df):\n",
    "    return df.Score.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.33 ms ± 157 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "pandas_score_3(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def numpy_score_1(df):\n",
    "    return df.Score.values.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.4 ms ± 49.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "numpy_score_1(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def numpy_score_2(df): \n",
    "    import math\n",
    "    \n",
    "    deviations = (df.Score.values - df.Score.values.mean()) ** 2     \n",
    "    return math.sqrt((1 / (len(deviations) - 1) * deviations.sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.36 ms ± 35.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "numpy_score_2(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Numba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('Reviews.csv', encoding=\"utf-8\") as csvfile:\n",
    "    import csv\n",
    "    reader = csv.reader(csvfile)\n",
    "    next(reader)\n",
    "    data = [row for row in reader]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numba import jit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "@jit\n",
    "def numba_score_1(reader):\n",
    "    \n",
    "    scores = [int(row[6]) for row in reader]\n",
    "    average = sum(scores) / len(scores)\n",
    "\n",
    "    deviations = [(value - average) ** 2 for value in scores]      \n",
    "    return math.sqrt((1 / (len(deviations) - 1) * sum(deviations)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-4-f96871e94303>:3: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"numba_score_1\" failed type inference due to: \u001b[1mUntyped global name 'sum':\u001b[0m \u001b[1m\u001b[1mcannot determine Numba type of <class 'builtin_function_or_method'>\u001b[0m\n",
      "\u001b[1m\n",
      "File \"<ipython-input-4-f96871e94303>\", line 7:\u001b[0m\n",
      "\u001b[1mdef numba_score_1(reader):\n",
      "    <source elided>\n",
      "    scores = [int(row[6]) for row in reader]\n",
      "\u001b[1m    average = sum(scores) / len(scores)\n",
      "\u001b[0m    \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0m\n",
      "  @jit\n",
      "c:\\users\\svw-b\\envs\\big_data\\lib\\site-packages\\numba\\object_mode_passes.py:178: NumbaWarning: \u001b[1mFunction \"numba_score_1\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"<ipython-input-4-f96871e94303>\", line 4:\u001b[0m\n",
      "\u001b[1m@jit\n",
      "\u001b[1mdef numba_score_1(reader):\n",
      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  state.func_ir.loc))\n",
      "c:\\users\\svw-b\\envs\\big_data\\lib\\site-packages\\numba\\object_mode_passes.py:188: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit http://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"<ipython-input-4-f96871e94303>\", line 4:\u001b[0m\n",
      "\u001b[1m@jit\n",
      "\u001b[1mdef numba_score_1(reader):\n",
      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  state.func_ir.loc))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "505 ms ± 3.56 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "numba_score_1(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "@jit\n",
    "def numba_score_2(scores):\n",
    "    average = sum(scores) / len(scores)\n",
    "\n",
    "    deviations = [(value - average) ** 2 for value in scores]      \n",
    "    return math.sqrt((1 / (len(deviations) - 1) * sum(deviations)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-4-357b6f70b1c5>:3: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"numba_score_2\" failed type inference due to: \u001b[1mUntyped global name 'sum':\u001b[0m \u001b[1m\u001b[1mcannot determine Numba type of <class 'builtin_function_or_method'>\u001b[0m\n",
      "\u001b[1m\n",
      "File \"<ipython-input-4-357b6f70b1c5>\", line 5:\u001b[0m\n",
      "\u001b[1mdef numba_score_2(scores):\n",
      "\u001b[1m    average = sum(scores) / len(scores)\n",
      "\u001b[0m    \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0m\n",
      "  @jit\n",
      "c:\\users\\svw-b\\envs\\big_data\\lib\\site-packages\\numba\\object_mode_passes.py:178: NumbaWarning: \u001b[1mFunction \"numba_score_2\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"<ipython-input-4-357b6f70b1c5>\", line 4:\u001b[0m\n",
      "\u001b[1m@jit\n",
      "\u001b[1mdef numba_score_2(scores):\n",
      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  state.func_ir.loc))\n",
      "c:\\users\\svw-b\\envs\\big_data\\lib\\site-packages\\numba\\object_mode_passes.py:188: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit http://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"<ipython-input-4-357b6f70b1c5>\", line 4:\u001b[0m\n",
      "\u001b[1m@jit\n",
      "\u001b[1mdef numba_score_2(scores):\n",
      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  state.func_ir.loc))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.85 s ± 58.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "numba_score_2(df.Score.values.astype(int))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext Cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%cython\n",
    "cpdef float cython_standard_score_1(list reader):\n",
    "    import math\n",
    "\n",
    "    cdef list total = []\n",
    "\n",
    "    for row in reader:\n",
    "        total.append(int(row[6]))\n",
    "\n",
    "    cdef float average = sum(total) / len(total)\n",
    "    cdef list total2 = []\n",
    "    for value in total:\n",
    "        total2.append((value - average) ** 2)\n",
    "\n",
    "    cdef float std_dev = math.sqrt((1 / (len(total2) -1) * sum(total2)))\n",
    "\n",
    "    return std_dev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "221 ms ± 2.31 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "cython_standard_score_1(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%cython\n",
    "cpdef float cython_pandas_score_2(df): \n",
    "    import math\n",
    "    \n",
    "    deviations = (df.Score - df.Score.mean()) ** 2     \n",
    "    return math.sqrt((1 / (len(deviations) - 1) * deviations.sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.41 ms ± 267 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "cython_pandas_score_2(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%cython\n",
    "cpdef float cython_numpy_score_2(df): \n",
    "    import math\n",
    "    \n",
    "    deviations = (df.Score.values - df.Score.values.mean()) ** 2     \n",
    "    return math.sqrt((1 / (len(deviations) - 1) * deviations.sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.42 ms ± 104 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "cython_numpy_score_2(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%cython\n",
    "cpdef float cython_numpy_score_1(df):\n",
    "    return df.Score.values.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.54 ms ± 188 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "cython_numpy_score_1(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experiment 3: Sorting it out (text)\n",
    "Sort the summaries of the review on alphabetically ascending"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Regular Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('Reviews.csv', encoding=\"utf-8\") as csvfile:\n",
    "    import csv\n",
    "    reader = csv.reader(csvfile)\n",
    "    next(reader)\n",
    "    data = [row for row in reader]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def python_standard_sorting_1(reader):\n",
    "    summaries = []\n",
    "    for row in reader:\n",
    "        summaries.append(row[8])\n",
    "    return sorted(summaries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "456 ms ± 5.35 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "python_standard_sorting_1(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def python_standard_sorting_2(reader):\n",
    "    return sorted([row[8] for row in reader])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "425 ms ± 1.47 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "python_standard_sorting_2(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def qsort(inlist):\n",
    "#     if inlist == []: \n",
    "#         return []\n",
    "#     else:\n",
    "#         pivot = inlist[0]\n",
    "#         lesser = qsort([x for x in inlist[1:] if x < pivot])\n",
    "#         greater = qsort([x for x in inlist[1:] if x >= pivot])\n",
    "#         return lesser + [pivot] + greater"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "# qsort([row[8] for row in data])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"Reviews.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pandas_sorting_1(df):\n",
    "    return df.Summary.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "945 ms ± 6.93 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "pandas_sorting_1(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pandas_sorting_2(df):\n",
    "    return sorted(list(df.Summary.astype(str)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "396 ms ± 8.21 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "pandas_sorting_2(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pandas_sorting_3(df):\n",
    "    return sorted(df.Summary.astype(str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "386 ms ± 8.58 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "pandas_sorting_3(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def numpy_sorting_1(df):\n",
    "    import numpy as np\n",
    "    return np.sort(df.Summary.astype(str).values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "732 ms ± 3.24 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "numpy_sorting_1(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def numpy_sorting_2(df):\n",
    "    return sorted(df.Summary.astype(str).values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "353 ms ± 10.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "numpy_sorting_2(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Numba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numba import jit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# From https://github.com/lprakash/Sorting-Algorithms/blob/master/sorts.ipynb\n",
    "@jit\n",
    "def quicksort(a, s, e):\n",
    "    #print(a, s, e)\n",
    "    #print(a.__repr__)\n",
    "    if (e-s)==0:\n",
    "        return \n",
    "    pivot = a[e-1]\n",
    "    p1 = s\n",
    "    p2 = e - 1\n",
    "    while (p1 != p2):\n",
    "        if (a[p1] > pivot):\n",
    "            a[p2] = a[p1]\n",
    "            a[p1] = a[p2-1]\n",
    "            a[p2-1] = pivot\n",
    "            p2 = p2 -1\n",
    "        else: \n",
    "            p1+=1\n",
    "    quicksort(a, s, p2)\n",
    "    quicksort(a, p2+1, e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def quicksortwrapper(a):\n",
    "    quicksort(a, 0, len(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\svw-b\\envs\\big_data\\lib\\site-packages\\numba\\ir_utils.py:2041: NumbaPendingDeprecationWarning: \u001b[1m\n",
      "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'a' of function 'quicksort'.\n",
      "\n",
      "For more information visit http://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n",
      "\u001b[1m\n",
      "File \"<ipython-input-3-e903ddbe35be>\", line 3:\u001b[0m\n",
      "\u001b[1m@jit\n",
      "\u001b[1mdef quicksort(a, s, e):\n",
      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.27 s ± 7.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "a = list([row[8] for row in data])\n",
    "quicksort(a, 0, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext Cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%cython\n",
    "cpdef list python_standard_sorting_2(list reader):\n",
    "    return sorted([row[8] for row in reader])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "390 ms ± 2.01 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "python_standard_sorting_2(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experiment 3B: Sorting it out (int)\n",
    "Sort the scores of the reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Regular Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('Reviews.csv', encoding=\"utf-8\") as csvfile:\n",
    "    import csv\n",
    "    reader = csv.reader(csvfile)\n",
    "    next(reader)\n",
    "    data = [row for row in reader]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def python_standard_sorting_1(reader):\n",
    "    summaries = []\n",
    "    for row in reader:\n",
    "        summaries.append(row[6])\n",
    "    return sorted(summaries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128 ms ± 3.77 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "python_standard_sorting_1(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def python_standard_sorting_1b(reader):\n",
    "    summaries = []\n",
    "    for row in reader:\n",
    "        summaries.append(int(row[6]))\n",
    "    return sorted(summaries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 223 ms\n"
     ]
    },
    {
     "data": {
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       " 1,\n",
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       " 1,\n",
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       " 1,\n",
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       " 1,\n",
       " 1,\n",
       " 1,\n",
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       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
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       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
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       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
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       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " ...]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "python_standard_sorting_1b(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def python_standard_sorting_2(reader):\n",
    "    return sorted([row[6] for row in reader])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "105 ms ± 1.67 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "python_standard_sorting_2(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def qsort(inlist):\n",
    "#     if inlist == []: \n",
    "#         return []\n",
    "#     else:\n",
    "#         pivot = inlist[0]\n",
    "#         lesser = qsort([x for x in inlist[1:] if x < pivot])\n",
    "#         greater = qsort([x for x in inlist[1:] if x >= pivot])\n",
    "#         return lesser + [pivot] + greater"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "# qsort([int(row[6]) for row in data])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"Reviews.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pandas_sorting_1(df):\n",
    "    return df.Score.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37.6 ms ± 489 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "pandas_sorting_1(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pandas_sorting_2(df):\n",
    "    return sorted(list(df.Score.astype(int)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80 ms ± 407 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "pandas_sorting_2(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pandas_sorting_3(df):\n",
    "    return sorted(df.Score.astype(int))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "77.6 ms ± 1.5 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "pandas_sorting_3(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def numpy_sorting_1(df):\n",
    "    import numpy as np\n",
    "    return np.sort(df.Score.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.1 ms ± 64 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "numpy_sorting_1(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def numpy_sorting_2(df):\n",
    "    return sorted(df.Score.astype(str).values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "391 ms ± 6.63 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "numpy_sorting_2(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Numba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numba import jit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# From https://github.com/lprakash/Sorting-Algorithms/blob/master/sorts.ipynb\n",
    "@jit\n",
    "def quicksort(a, s, e):\n",
    "    #print(a, s, e)\n",
    "    #print(a.__repr__)\n",
    "    if (e-s)==0:\n",
    "        return \n",
    "    pivot = a[e-1]\n",
    "    p1 = s\n",
    "    p2 = e - 1\n",
    "    while (p1 != p2):\n",
    "        if (a[p1] > pivot):\n",
    "            a[p2] = a[p1]\n",
    "            a[p1] = a[p2-1]\n",
    "            a[p2-1] = pivot\n",
    "            p2 = p2 -1\n",
    "        else: \n",
    "            p1+=1\n",
    "    quicksort(a, s, p2)\n",
    "    quicksort(a, p2+1, e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "@jit\n",
    "def sort(l):\n",
    "    return sorted(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-6-026d6d4df67e>:3: NumbaPendingDeprecationWarning: \u001b[1m\u001b[1m\n",
      "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'iterable' of function 'list_constructor.<locals>.list_impl'.\n",
      "\n",
      "For more information visit http://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n",
      "\u001b[1m\n",
      "File \"..\\..\\..\\users\\svw-b\\envs\\big_data\\lib\\site-packages\\numba\\targets\\listobj.py\", line 464:\u001b[0m\n",
      "\u001b[1m\n",
      "\u001b[1m    def list_impl(iterable):\n",
      "\u001b[0m    \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0m\n",
      "  return sorted(l)\n",
      "<ipython-input-6-026d6d4df67e>:3: NumbaPendingDeprecationWarning: \u001b[1m\u001b[1m\n",
      "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'iterable' of function 'ol_sorted.<locals>.impl'.\n",
      "\n",
      "For more information visit http://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n",
      "\u001b[1m\n",
      "File \"..\\..\\..\\users\\svw-b\\envs\\big_data\\lib\\site-packages\\numba\\targets\\listobj.py\", line 1108:\u001b[0m\n",
      "\u001b[1m\n",
      "\u001b[1m    def impl(iterable, key=None, reverse=False):\n",
      "\u001b[0m    \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0m\n",
      "  return sorted(l)\n",
      "c:\\users\\svw-b\\envs\\big_data\\lib\\site-packages\\numba\\ir_utils.py:2041: NumbaPendingDeprecationWarning: \u001b[1m\n",
      "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'l' of function 'sort'.\n",
      "\n",
      "For more information visit http://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n",
      "\u001b[1m\n",
      "File \"<ipython-input-6-026d6d4df67e>\", line 2:\u001b[0m\n",
      "\u001b[1m@jit\n",
      "\u001b[1mdef sort(l):\n",
      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.54 s ± 22.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "a = list([int(row[6]) for row in data])\n",
    "sort(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def quicksortwrapper(a):\n",
    "    quicksort(a, 0, len(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\svw-b\\envs\\big_data\\lib\\site-packages\\numba\\ir_utils.py:2041: NumbaPendingDeprecationWarning: \u001b[1m\n",
      "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'a' of function 'quicksort'.\n",
      "\n",
      "For more information visit http://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n",
      "\u001b[1m\n",
      "File \"<ipython-input-3-e903ddbe35be>\", line 3:\u001b[0m\n",
      "\u001b[1m@jit\n",
      "\u001b[1mdef quicksort(a, s, e):\n",
      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.29 s ± 50.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "a = list([row[6] for row in data])\n",
    "quicksort(a, 0, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext Cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%cython\n",
    "cpdef list cython_standard_sorting_1(list reader):\n",
    "    return sorted([row[6] for row in reader])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78.9 ms ± 557 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "cython_standard_sorting_1(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extra / Testing area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "listt = [random.randint(0, 100) for x in range(100000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 15 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "86"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "new_list = []\n",
    "for x in listt:\n",
    "    new_list.append(x + 1)\n",
    "new_list[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 5.99 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "86"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "[x + 1 for x in listt][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def addition(n):\n",
    "    return n + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 9.99 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "86"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "list(map(lambda x: x + 1, listt))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.29 s ± 58.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "import csv\n",
    "with open('Reviews.csv', encoding=\"utf-8\") as csvfile:\n",
    "        reader = csv.reader(csvfile)\n",
    "        for x in reader:\n",
    "            pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.68 s ± 82.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "import pandas as pd\n",
    "pd.read_csv(\"Reviews.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "test = open('Reviews.csv', encoding=\"utf8\")    \n",
    "reader = csv.reader(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extra_test(reader):\n",
    "    def parse_row_chocolate(row, word):\n",
    "        if word in row[9].lower():\n",
    "            return row[6]\n",
    "\n",
    "    total = []\n",
    "    for row in reader:\n",
    "        result = parse_row_chocolate(row, \"chocolate\")\n",
    "        if result:\n",
    "            total.append((result))\n",
    "    return total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 5.66 s\n"
     ]
    },
    {
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       " ...]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "extra_test(reader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Id', 'ProductId', 'UserId', 'ProfileName', 'HelpfulnessNumerator', 'HelpfulnessDenominator', 'Score', 'Time', 'Summary', 'Text']\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.close()"
   ]
  }
 ],
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