SalaryPrediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>Respondent</th>\n",
       "      <th>MainBranch</th>\n",
       "      <th>Hobbyist</th>\n",
       "      <th>Age</th>\n",
       "      <th>Age1stCode</th>\n",
       "      <th>CompFreq</th>\n",
       "      <th>CompTotal</th>\n",
       "      <th>ConvertedComp</th>\n",
       "      <th>Country</th>\n",
       "      <th>CurrencyDesc</th>\n",
       "      <th>...</th>\n",
       "      <th>SurveyEase</th>\n",
       "      <th>SurveyLength</th>\n",
       "      <th>Trans</th>\n",
       "      <th>UndergradMajor</th>\n",
       "      <th>WebframeDesireNextYear</th>\n",
       "      <th>WebframeWorkedWith</th>\n",
       "      <th>WelcomeChange</th>\n",
       "      <th>WorkWeekHrs</th>\n",
       "      <th>YearsCode</th>\n",
       "      <th>YearsCodePro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>I am a developer by profession</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Germany</td>\n",
       "      <td>European Euro</td>\n",
       "      <td>...</td>\n",
       "      <td>Neither easy nor difficult</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>No</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>ASP.NET Core</td>\n",
       "      <td>ASP.NET;ASP.NET Core</td>\n",
       "      <td>Just as welcome now as I felt last year</td>\n",
       "      <td>50.0</td>\n",
       "      <td>36</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>I am a developer by profession</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Pound sterling</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Somewhat more welcome now than last year</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>I code primarily as a hobby</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Neither easy nor difficult</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Somewhat more welcome now than last year</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>I am a developer by profession</td>\n",
       "      <td>Yes</td>\n",
       "      <td>25.0</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Albanian lek</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Somewhat less welcome now than last year</td>\n",
       "      <td>40.0</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>I used to be a developer by profession, but no...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>31.0</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Easy</td>\n",
       "      <td>Too short</td>\n",
       "      <td>No</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>Django;Ruby on Rails</td>\n",
       "      <td>Ruby on Rails</td>\n",
       "      <td>Just as welcome now as I felt last year</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Respondent                                         MainBranch Hobbyist  \\\n",
       "0           1                     I am a developer by profession      Yes   \n",
       "1           2                     I am a developer by profession       No   \n",
       "2           3                        I code primarily as a hobby      Yes   \n",
       "3           4                     I am a developer by profession      Yes   \n",
       "4           5  I used to be a developer by profession, but no...      Yes   \n",
       "\n",
       "    Age Age1stCode CompFreq  CompTotal  ConvertedComp             Country  \\\n",
       "0   NaN         13  Monthly        NaN            NaN             Germany   \n",
       "1   NaN         19      NaN        NaN            NaN      United Kingdom   \n",
       "2   NaN         15      NaN        NaN            NaN  Russian Federation   \n",
       "3  25.0         18      NaN        NaN            NaN             Albania   \n",
       "4  31.0         16      NaN        NaN            NaN       United States   \n",
       "\n",
       "     CurrencyDesc  ...                  SurveyEase           SurveyLength  \\\n",
       "0   European Euro  ...  Neither easy nor difficult  Appropriate in length   \n",
       "1  Pound sterling  ...                         NaN                    NaN   \n",
       "2             NaN  ...  Neither easy nor difficult  Appropriate in length   \n",
       "3    Albanian lek  ...                         NaN                    NaN   \n",
       "4             NaN  ...                        Easy              Too short   \n",
       "\n",
       "  Trans                                     UndergradMajor  \\\n",
       "0    No  Computer science, computer engineering, or sof...   \n",
       "1   NaN  Computer science, computer engineering, or sof...   \n",
       "2   NaN                                                NaN   \n",
       "3    No  Computer science, computer engineering, or sof...   \n",
       "4    No  Computer science, computer engineering, or sof...   \n",
       "\n",
       "  WebframeDesireNextYear    WebframeWorkedWith  \\\n",
       "0           ASP.NET Core  ASP.NET;ASP.NET Core   \n",
       "1                    NaN                   NaN   \n",
       "2                    NaN                   NaN   \n",
       "3                    NaN                   NaN   \n",
       "4   Django;Ruby on Rails         Ruby on Rails   \n",
       "\n",
       "                              WelcomeChange WorkWeekHrs YearsCode YearsCodePro  \n",
       "0   Just as welcome now as I felt last year        50.0        36           27  \n",
       "1  Somewhat more welcome now than last year         NaN         7            4  \n",
       "2  Somewhat more welcome now than last year         NaN         4          NaN  \n",
       "3  Somewhat less welcome now than last year        40.0         7            4  \n",
       "4   Just as welcome now as I felt last year         NaN        15            8  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('survey_results_public.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>EdLevel</th>\n",
       "      <th>YearsCodePro</th>\n",
       "      <th>Employment</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Master’s degree (M.A., M.S., M.Eng., MBA, etc.)</td>\n",
       "      <td>27</td>\n",
       "      <td>Independent contractor, freelancer, or self-em...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
       "      <td>4</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Master’s degree (M.A., M.S., M.Eng., MBA, etc.)</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United States</td>\n",
       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
       "      <td>8</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Country                                          EdLevel  \\\n",
       "0             Germany  Master’s degree (M.A., M.S., M.Eng., MBA, etc.)   \n",
       "1      United Kingdom     Bachelor’s degree (B.A., B.S., B.Eng., etc.)   \n",
       "2  Russian Federation                                              NaN   \n",
       "3             Albania  Master’s degree (M.A., M.S., M.Eng., MBA, etc.)   \n",
       "4       United States     Bachelor’s degree (B.A., B.S., B.Eng., etc.)   \n",
       "\n",
       "  YearsCodePro                                         Employment  Salary  \n",
       "0           27  Independent contractor, freelancer, or self-em...     NaN  \n",
       "1            4                                 Employed full-time     NaN  \n",
       "2          NaN                                                NaN     NaN  \n",
       "3            4                                                NaN     NaN  \n",
       "4            8                                 Employed full-time     NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[[\"Country\",\"EdLevel\",\"YearsCodePro\",\"Employment\",\"ConvertedComp\"]]\n",
    "df = df.rename({\"ConvertedComp\":\"Salary\"},axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Country</th>\n",
       "      <th>EdLevel</th>\n",
       "      <th>YearsCodePro</th>\n",
       "      <th>Employment</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>United States</td>\n",
       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
       "      <td>13</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>116000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Master’s degree (M.A., M.S., M.Eng., MBA, etc.)</td>\n",
       "      <td>4</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>32315.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
       "      <td>2</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>40070.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Some college/university study without earning ...</td>\n",
       "      <td>7</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>14268.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Secondary school (e.g. American high school, G...</td>\n",
       "      <td>20</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>38916.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country                                            EdLevel  \\\n",
       "7    United States       Bachelor’s degree (B.A., B.S., B.Eng., etc.)   \n",
       "9   United Kingdom    Master’s degree (M.A., M.S., M.Eng., MBA, etc.)   \n",
       "10  United Kingdom       Bachelor’s degree (B.A., B.S., B.Eng., etc.)   \n",
       "11           Spain  Some college/university study without earning ...   \n",
       "12     Netherlands  Secondary school (e.g. American high school, G...   \n",
       "\n",
       "   YearsCodePro          Employment    Salary  \n",
       "7            13  Employed full-time  116000.0  \n",
       "9             4  Employed full-time   32315.0  \n",
       "10            2  Employed full-time   40070.0  \n",
       "11            7  Employed full-time   14268.0  \n",
       "12           20  Employed full-time   38916.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df[\"Salary\"].notnull()]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 34756 entries, 7 to 64154\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Country       34756 non-null  object \n",
      " 1   EdLevel       34188 non-null  object \n",
      " 2   YearsCodePro  34621 non-null  object \n",
      " 3   Employment    34717 non-null  object \n",
      " 4   Salary        34756 non-null  float64\n",
      "dtypes: float64(1), object(4)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country         0\n",
       "EdLevel         0\n",
       "YearsCodePro    0\n",
       "Employment      0\n",
       "Salary          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df[\"Employment\"] == \"Employed full-time\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(\"Employment\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 30019 entries, 7 to 64154\n",
      "Data columns (total 4 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Country       30019 non-null  object \n",
      " 1   EdLevel       30019 non-null  object \n",
      " 2   YearsCodePro  30019 non-null  object \n",
      " 3   Salary        30019 non-null  float64\n",
      "dtypes: float64(1), object(3)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United States     7569\n",
       "India             2425\n",
       "United Kingdom    2287\n",
       "Germany           1903\n",
       "Canada            1178\n",
       "                  ... \n",
       "Benin                1\n",
       "Fiji                 1\n",
       "San Marino           1\n",
       "Guinea               1\n",
       "Andorra              1\n",
       "Name: Country, Length: 154, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Country\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def country_adjust(categories,threshold):\n",
    "    categorial_map={}\n",
    "    for i in range(len(categories)):\n",
    "        if(categories.values[i] >= threshold):\n",
    "            categorial_map[categories.index[i]]=categories.index[i]\n",
    "        else:\n",
    "            categorial_map[categories.index[i]]=\"Other\"\n",
    "    return categorial_map\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_map = country_adjust(df[\"Country\"].value_counts(),400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Other                 8549\n",
       "United States         7569\n",
       "India                 2425\n",
       "United Kingdom        2287\n",
       "Germany               1903\n",
       "Canada                1178\n",
       "Brazil                 991\n",
       "France                 972\n",
       "Spain                  670\n",
       "Australia              659\n",
       "Netherlands            654\n",
       "Poland                 566\n",
       "Italy                  560\n",
       "Russian Federation     522\n",
       "Sweden                 514\n",
       "Name: Country, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Country\"] = df[\"Country\"].map(country_map)\n",
    "df[\"Country\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig , ax = plt.subplots(1,1, figsize=(12,7))\n",
    "df.boxplot('Salary' , 'Country' , ax=ax)\n",
    "plt.suptitle(\"Salary USD vs Country\")\n",
    "plt.title(\"\")\n",
    "plt.ylabel(\"Salary\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df[\"Salary\"] <=150000]\n",
    "df = df[df[\"Salary\"] >= 10000]\n",
    "df = df[df[\"Country\"] != \"Other\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig , ax = plt.subplots(1,1, figsize=(12,7))\n",
    "df.boxplot('Salary' , 'Country' , ax=ax)\n",
    "plt.suptitle(\"Salary USD vs Country\")\n",
    "plt.title(\"\")\n",
    "plt.ylabel(\"Salary\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['13', '4', '2', '7', '20', '1', '3', '10', '12', '29', '6', '28',\n",
       "       '8', '15', '25', '9', '11', 'Less than 1 year', '5', '21', '16',\n",
       "       '14', '18', '32', '22', '38', '23', '26', '27', '17', '24', '30',\n",
       "       '19', '34', '33', '36', '40', '39', '31', '37', '45', '35', '41',\n",
       "       '42', 'More than 50 years', '43', '44', '50'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"YearsCodePro\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_experience(x):\n",
    "    if(x==\"Less than 1 year\"):\n",
    "        return 0.5\n",
    "    elif(x==\"More than 50 years\"):\n",
    "        return 50\n",
    "    else:\n",
    "        return float(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"YearsCodePro\"] = df[\"YearsCodePro\"].apply(clean_experience)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([13. ,  4. ,  2. ,  7. , 20. ,  1. ,  3. , 10. , 12. , 29. ,  6. ,\n",
       "       28. ,  8. , 15. , 25. ,  9. , 11. ,  0.5,  5. , 21. , 16. , 14. ,\n",
       "       18. , 32. , 22. , 38. , 23. , 26. , 27. , 17. , 24. , 30. , 19. ,\n",
       "       34. , 33. , 36. , 40. , 39. , 31. , 37. , 45. , 35. , 41. , 42. ,\n",
       "       50. , 43. , 44. ])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"YearsCodePro\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Bachelor’s degree (B.A., B.S., B.Eng., etc.)',\n",
       "       'Master’s degree (M.A., M.S., M.Eng., MBA, etc.)',\n",
       "       'Some college/university study without earning a degree',\n",
       "       'Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)',\n",
       "       'Associate degree (A.A., A.S., etc.)',\n",
       "       'Professional degree (JD, MD, etc.)',\n",
       "       'Other doctoral degree (Ph.D., Ed.D., etc.)',\n",
       "       'Primary/elementary school',\n",
       "       'I never completed any formal education'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"EdLevel\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_education(x):\n",
    "    if(x==\"Bachelor’s degree (B.A., B.S., B.Eng., etc.)\"):\n",
    "        return \"Bachelor’s degree\"\n",
    "    elif(x==\"Master’s degree (M.A., M.S., M.Eng., MBA, etc.)\"):\n",
    "        return \"Master’s degree\"\n",
    "    elif(x==\"Professional degree (JD, MD, etc.)\" or x==\"Other doctoral degree (Ph.D., Ed.D., etc.)\"):\n",
    "        return \"Post Graduation\"\n",
    "    else:\n",
    "        return \"Less than a Bachelor\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"EdLevel\"] = df[\"EdLevel\"].apply(clean_education)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bachelor’s degree       8372\n",
       "Master’s degree         4444\n",
       "Less than a Bachelor    3306\n",
       "Post Graduation          877\n",
       "Name: EdLevel, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"EdLevel\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 1, 3])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Label Encoding for Education and country\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "encoder_education = LabelEncoder()\n",
    "df[\"EdLevel\"] = encoder_education.fit_transform(df[\"EdLevel\"])\n",
    "df[\"EdLevel\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([13, 12, 10,  7,  4,  2,  6,  1,  3,  5, 11,  8,  0,  9])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder_country = LabelEncoder()\n",
    "df[\"Country\"] = encoder_country.fit_transform(df[\"Country\"])\n",
    "df[\"Country\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Training the model\n",
    "\n",
    "X = df.iloc[:,:-1]\n",
    "Y = df.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X , X_test , Y , Y_test = train_test_split(X,Y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "linear_model = LinearRegression()\n",
    "linear_model.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_linear = linear_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29399.25748518112\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "error = np.sqrt(metrics.mean_squared_error(Y_test,y_pred_linear))\n",
    "print(error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=DecisionTreeRegressor(random_state=0),\n",
       "             param_grid={'max_depth': [None, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20,\n",
       "                                       22, 24, 26, 28, 30]})"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's try Decision Tree Model to reduce error\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "max_depth = [None,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30]\n",
    "parameters = {\"max_depth\":max_depth}\n",
    "\n",
    "regressor = DecisionTreeRegressor(random_state=0)\n",
    "\n",
    "gs = GridSearchCV(regressor,parameters)\n",
    "gs.fit(X,Y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeRegressor(max_depth=8, random_state=0)\n",
      "$21545.342280538673\n"
     ]
    }
   ],
   "source": [
    "print(gs.best_estimator_)\n",
    "\n",
    "y_pred_dtree = gs.predict(X_test)\n",
    "\n",
    "error_dtree = np.sqrt(metrics.mean_squared_error(Y_test,y_pred_dtree))\n",
    "print(\"${}\".format(error_dtree))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDRegressor()"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Trying Gradient Descent optimisation\n",
    "\n",
    "from sklearn.linear_model import SGDRegressor\n",
    "\n",
    "gradient_model = SGDRegressor()\n",
    "gradient_model.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_gradient = gradient_model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31105.6716912698\n"
     ]
    }
   ],
   "source": [
    "error = np.sqrt(metrics.mean_squared_error(Y,y_pred_gradient))\n",
    "print(error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#So I will use decision tree with random_state=0 and max_depth=8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Country</th>\n",
       "      <th>EdLevel</th>\n",
       "      <th>YearsCodePro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29531</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32545</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2845</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56875</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21489</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34413</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33193</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48170</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50293</th>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60586</th>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13599 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  EdLevel  YearsCodePro\n",
       "29531        9        0           1.0\n",
       "32545       13        0          10.0\n",
       "2845        13        0          37.0\n",
       "56875       13        0          15.0\n",
       "21489        6        1          20.0\n",
       "...        ...      ...           ...\n",
       "34413        6        2           4.0\n",
       "33193        6        1          20.0\n",
       "48170        2        0          14.0\n",
       "50293       13        2           8.0\n",
       "60586       12        2          30.0\n",
       "\n",
       "[13599 rows x 3 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now I will setup my model for user input and predict the salary\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['United States', 'Master’s degree', '15']], dtype='<U15')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.array([[\"United States\",\"Master’s degree\",15]])\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[13.,  2., 15.]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:,0] = encoder_country.transform(X[:,0])\n",
    "X[:,1] = encoder_education.transform(X[:,1])\n",
    "X = X.astype(float)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python310\\lib\\site-packages\\sklearn\\base.py:445: UserWarning: X does not have valid feature names, but DecisionTreeRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([118682.99453552])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_input = gs.predict(X)\n",
    "y_pred_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Saving the Models using pickle library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\"model_dt\":gs,\"model_linear\":linear_model,\"model_gradient\":gradient_model,\"le_country\":encoder_country,\"le_education\":encoder_education}\n",
    "with open(\"saved_steps.pkl\",\"wb\") as file:\n",
    "    pickle.dump(data,file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"saved_steps.pkl\",\"rb\") as file:\n",
    "    data = pickle.load(file)\n",
    "\n",
    "model_loaded = data[\"model_dt\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python310\\lib\\site-packages\\sklearn\\base.py:445: UserWarning: X does not have valid feature names, but DecisionTreeRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "y_pred_loaded = model_loaded.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[118682.99453552]\n"
     ]
    }
   ],
   "source": [
    "print(y_pred_loaded)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "2be5faf79681da6f2a61fdfdd5405d65d042280f7fba6178067603e3a2925119"
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  "kernelspec": {
   "display_name": "Python 3.10.0 64-bit",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "pygments_lexer": "ipython3",
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 },
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "The cell magics in IPython"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "IPython has a system of commands we call 'magics' that provide effectively a mini command language that is orthogonal to the syntax of Python and is extensible by the user with new commands.  Magics are meant to be typed interactively, so they use command-line conventions, such as using whitespace for separating arguments, dashes for options and other conventions typical of a command-line environment.\n",
      "\n",
      "Magics come in two kinds:\n",
      "\n",
      "* Line magics: these are commands prepended by one `%` character and whose arguments only extend to the end of the current line.\n",
      "* Cell magics: these use *two* percent characters as a marker (`%%`), and they receive as argument *both* the current line where they are declared and the whole body of the cell.  Note that cell magics can *only* be used as the first line in a cell, and as a general principle they can't be 'stacked' (i.e. you can only use one cell magic per cell).  A few of them, because of how they operate, can be stacked, but that is something you will discover on a case by case basis.\n",
      "\n",
      "The `%lsmagic` magic is used to list all available magics, and it will show both line and cell magics currently defined:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%lsmagic"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "json": [
        "{\"cell\": {\"prun\": \"ExecutionMagics\", \"file\": \"Other\", \"!\": \"OSMagics\", \"capture\": \"ExecutionMagics\", \"timeit\": \"ExecutionMagics\", \"script\": \"ScriptMagics\", \"ruby\": \"Other\", \"system\": \"OSMagics\", \"perl\": \"Other\", \"HTML\": \"Other\", \"bash\": \"Other\", \"python\": \"Other\", \"SVG\": \"Other\", \"javascript\": \"DisplayMagics\", \"writefile\": \"OSMagics\", \"pypy\": \"Other\", \"python3\": \"Other\", \"latex\": \"DisplayMagics\", \"sx\": \"OSMagics\", \"svg\": \"DisplayMagics\", \"html\": \"DisplayMagics\", \"sh\": \"Other\", \"time\": \"ExecutionMagics\", \"debug\": \"ExecutionMagics\"}, \"line\": {\"psource\": \"NamespaceMagics\", \"logstart\": \"LoggingMagics\", \"popd\": \"OSMagics\", \"loadpy\": \"CodeMagics\", \"install_ext\": \"ExtensionMagics\", \"colors\": \"BasicMagics\", \"who_ls\": \"NamespaceMagics\", \"install_profiles\": \"DeprecatedMagics\", \"pprint\": \"BasicMagics\", \"save\": \"CodeMagics\", \"tb\": \"ExecutionMagics\", \"pylab\": \"PylabMagics\", \"killbgscripts\": \"ScriptMagics\", \"quickref\": \"BasicMagics\", \"magic\": \"BasicMagics\", \"dhist\": \"OSMagics\", \"edit\": \"KernelMagics\", \"logstop\": \"LoggingMagics\", \"gui\": \"BasicMagics\", \"alias_magic\": \"BasicMagics\", \"debug\": \"ExecutionMagics\", \"page\": \"BasicMagics\", \"logstate\": \"LoggingMagics\", \"ed\": \"Other\", \"pushd\": \"OSMagics\", \"timeit\": \"ExecutionMagics\", \"rehashx\": \"OSMagics\", \"hist\": \"Other\", \"qtconsole\": \"KernelMagics\", \"dirs\": \"OSMagics\", \"run\": \"ExecutionMagics\", \"reset_selective\": \"NamespaceMagics\", \"pinfo2\": \"NamespaceMagics\", \"matplotlib\": \"PylabMagics\", \"automagic\": \"AutoMagics\", \"doctest_mode\": \"KernelMagics\", \"logoff\": \"LoggingMagics\", \"reload_ext\": \"ExtensionMagics\", \"pdb\": \"ExecutionMagics\", \"load\": \"CodeMagics\", \"lsmagic\": \"BasicMagics\", \"autosave\": \"KernelMagics\", \"cd\": \"OSMagics\", \"pastebin\": \"CodeMagics\", \"prun\": \"ExecutionMagics\", \"autocall\": \"AutoMagics\", \"bookmark\": \"OSMagics\", \"connect_info\": \"KernelMagics\", \"system\": \"OSMagics\", \"whos\": \"NamespaceMagics\", \"toc\": \"TimerMagics\", \"unload_ext\": \"ExtensionMagics\", \"store\": \"StoreMagics\", \"more\": \"KernelMagics\", \"gist\": \"Other\", \"pdef\": \"NamespaceMagics\", \"precision\": \"BasicMagics\", \"pinfo\": \"NamespaceMagics\", \"pwd\": \"OSMagics\", \"psearch\": \"NamespaceMagics\", \"reset\": \"NamespaceMagics\", \"recall\": \"HistoryMagics\", \"xdel\": \"NamespaceMagics\", \"xmode\": \"BasicMagics\", \"rerun\": \"HistoryMagics\", \"logon\": \"LoggingMagics\", \"history\": \"HistoryMagics\", \"pycat\": \"OSMagics\", \"unalias\": \"OSMagics\", \"install_default_config\": \"DeprecatedMagics\", \"env\": \"OSMagics\", \"load_ext\": \"ExtensionMagics\", \"config\": \"ConfigMagics\", \"tic\": \"TimerMagics\", \"profile\": \"BasicMagics\", \"pfile\": \"NamespaceMagics\", \"less\": \"KernelMagics\", \"who\": \"NamespaceMagics\", \"notebook\": \"BasicMagics\", \"man\": \"KernelMagics\", \"sx\": \"OSMagics\", \"macro\": \"ExecutionMagics\", \"clear\": \"KernelMagics\", \"alias\": \"OSMagics\", \"time\": \"ExecutionMagics\", \"sc\": \"OSMagics\", \"rep\": \"Other\", \"pdoc\": \"NamespaceMagics\"}}"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "Available line magics:\n",
        "%alias  %alias_magic  %autocall  %automagic  %autosave  %bookmark  %cd  %clear  %colors  %config  %connect_info  %debug  %dhist  %dirs  %doctest_mode  %ed  %edit  %env  %gist  %gui  %hist  %history  %install_default_config  %install_ext  %install_profiles  %killbgscripts  %less  %load  %load_ext  %loadpy  %logoff  %logon  %logstart  %logstate  %logstop  %lsmagic  %macro  %magic  %man  %matplotlib  %more  %notebook  %page  %pastebin  %pdb  %pdef  %pdoc  %pfile  %pinfo  %pinfo2  %popd  %pprint  %precision  %profile  %prun  %psearch  %psource  %pushd  %pwd  %pycat  %pylab  %qtconsole  %quickref  %recall  %rehashx  %reload_ext  %rep  %rerun  %reset  %reset_selective  %run  %save  %sc  %store  %sx  %system  %tb  %tic  %time  %timeit  %toc  %unalias  %unload_ext  %who  %who_ls  %whos  %xdel  %xmode\n",
        "\n",
        "Available cell magics:\n",
        "%%!  %%HTML  %%SVG  %%bash  %%capture  %%debug  %%file  %%html  %%javascript  %%latex  %%perl  %%prun  %%pypy  %%python  %%python3  %%ruby  %%script  %%sh  %%svg  %%sx  %%system  %%time  %%timeit  %%writefile\n",
        "\n",
        "Automagic is ON, % prefix IS NOT needed for line magics."
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Since in the introductory section we already covered the most frequently used line magics, we will focus here on the cell magics, which offer a great amount of power.\n",
      "\n",
      "Let's load matplotlib and numpy so we can use numerics/plotting at will later on."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "<!--====-->\n",
      "Some simple cell magics"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Timing the execution of code; the 'timeit' magic exists both in line and cell form:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit np.linalg.eigvals(np.random.rand(100,100))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "100 loops, best of 3: 6.05 ms per loop\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%timeit a = np.random.rand(100, 100)\n",
      "np.linalg.eigvals(a)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "100 loops, best of 3: 6.07 ms per loop\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The `%%capture` magic can be used to capture the stdout/err of any block of python code, either to discard it (if it's noise to you) or to store it in a variable for later use:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%capture capt\n",
      "from __future__ import print_function\n",
      "import sys\n",
      "print('Hello stdout')\n",
      "print('and stderr', file=sys.stderr)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "capt.stdout, capt.stderr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "('Hello stdout\\n', 'and stderr\\n')"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "capt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Hello stdout\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "and stderr\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The `%%writefile` magic is a very useful tool that writes the cell contents as a named file:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%writefile foo.py\n",
      "print('Hello world')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Writing foo.py\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%run foo"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Hello world\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "<!--====-->\n",
      "Magics for running code under other interpreters"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "IPython has a `%%script` cell magic, which lets you run a cell in\n",
      "a subprocess of any interpreter on your system, such as: bash, ruby, perl, zsh, R, etc.\n",
      "\n",
      "It can even be a script of your own, which expects input on stdin."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To use it, simply pass a path or shell command to the program you want to run on the `%%script` line,\n",
      "and the rest of the cell will be run by that script, and stdout/err from the subprocess are captured and displayed."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%script python\n",
      "import sys\n",
      "print 'hello from Python %s' % sys.version"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "hello from Python 2.7.2 (default, Oct 11 2012, 20:14:37) \n",
        "[GCC 4.2.1 Compatible Apple Clang 4.0 (tags/Apple/clang-418.0.60)]\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%script python3\n",
      "import sys\n",
      "print('hello from Python: %s' % sys.version)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "hello from Python: 3.3.1 (v3.3.1:d9893d13c628, Apr  6 2013, 11:07:11) \n",
        "[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)]\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "IPython also creates aliases for a few common interpreters, such as bash, ruby, perl, etc.\n",
      "\n",
      "These are all equivalent to `%%script <name>`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%ruby\n",
      "puts \"Hello from Ruby #{RUBY_VERSION}\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Hello from Ruby 1.9.3\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%bash\n",
      "echo \"hello from $BASH\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "hello from /usr/local/bin/bash\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Exercise: write your own script that numbers input lines"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Write a file, called `lnum.py`, such that the following cell works as shown (hint: don't forget about the executable bit!): "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%script ./lnum.py\n",
      "my first line\n",
      "my second\n",
      "more"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0: my first line\n",
        "1: my second\n",
        "2: more\n",
        "---- END ----\n"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Capturing output"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can also capture stdout/err from these subprocesses into Python variables, instead of letting them go directly to stdout/err"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%bash\n",
      "echo \"hi, stdout\"\n",
      "echo \"hello, stderr\" >&2\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "hi, stdout\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "hello, stderr\n"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%bash --out output --err error\n",
      "echo \"hi, stdout\"\n",
      "echo \"hello, stderr\" >&2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(error)\n",
      "print(output)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "hello, stderr\n",
        "\n",
        "hi, stdout\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Background Scripts"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "These scripts can be run in the background, by adding the `--bg` flag.\n",
      "\n",
      "When you do this, output is discarded unless you use the `--out/err`\n",
      "flags to store output as above."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%ruby --bg --out ruby_lines\n",
      "for n in 1...10\n",
      "    sleep 1\n",
      "    puts \"line #{n}\"\n",
      "    STDOUT.flush\n",
      "end"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Starting job # 0 in a separate thread.\n"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "When you do store output of a background thread, these are the stdout/err *pipes*,\n",
      "rather than the text of the output."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ruby_lines"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "<open file '<fdopen>', mode 'rb' at 0x112cd55d0>"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(ruby_lines.read())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "line 1\n",
        "line 2\n",
        "line 3\n",
        "line 4\n",
        "line 5\n",
        "line 6\n",
        "line 7\n",
        "line 8\n",
        "line 9\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Cython Magic Functions Extension"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Loading the extension"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "IPtyhon has a `cythonmagic` extension that contains a number of magic functions for working with Cython code. This extension can be loaded using the `%load_ext` magic as follows:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext cythonmagic"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The `%%cython_pyximport` magic allows you to enter arbitrary Cython code into a cell. That Cython code is written as a `.pyx` file in the current working directory and then imported using `pyximport`.  You have the specify the name of the module that the Code will appear in. All symbols from the module are imported automatically by the magic function."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%cython_pyximport foo\n",
      "def f(x):\n",
      "    return 4.0*x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "40.0"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "The %cython magic"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Probably the most important magic is the `%cython` magic.  This is similar to the `%%cython_pyximport` magic, but doesn't require you to specify a module name. Instead, the `%%cython` magic manages everything using temporary files in the `~/.cython/magic` directory.  All of the symbols in the Cython module are imported automatically by the magic.\n",
      "\n",
      "Here is a simple example of a Black-Scholes options pricing algorithm written in Cython:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%cython\n",
      "cimport cython\n",
      "from libc.math cimport exp, sqrt, pow, log, erf\n",
      "\n",
      "@cython.cdivision(True)\n",
      "cdef double std_norm_cdf(double x) nogil:\n",
      "    return 0.5*(1+erf(x/sqrt(2.0)))\n",
      "\n",
      "@cython.cdivision(True)\n",
      "def black_scholes(double s, double k, double t, double v,\n",
      "                 double rf, double div, double cp):\n",
      "    \"\"\"Price an option using the Black-Scholes model.\n",
      "    \n",
      "    s : initial stock price\n",
      "    k : strike price\n",
      "    t : expiration time\n",
      "    v : volatility\n",
      "    rf : risk-free rate\n",
      "    div : dividend\n",
      "    cp : +1/-1 for call/put\n",
      "    \"\"\"\n",
      "    cdef double d1, d2, optprice\n",
      "    with nogil:\n",
      "        d1 = (log(s/k)+(rf-div+0.5*pow(v,2))*t)/(v*sqrt(t))\n",
      "        d2 = d1 - v*sqrt(t)\n",
      "        optprice = cp*s*exp(-div*t)*std_norm_cdf(cp*d1) - \\\n",
      "            cp*k*exp(-rf*t)*std_norm_cdf(cp*d2)\n",
      "    return optprice"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "black_scholes(100.0, 100.0, 1.0, 0.3, 0.03, 0.0, -1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "10.327861752731728"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit black_scholes(100.0, 100.0, 1.0, 0.3, 0.03, 0.0, -1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1000000 loops, best of 3: 366 ns per loop\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Cython allows you to specify additional libraries to be linked with your extension, you can do so with the `-l` flag (also spelled `--lib`).  Note that this flag can be passed more than once to specify multiple libraries, such as `-lm -llib2 --lib lib3`.  Here's a simple example of how to access the system math library:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%cython -lm\n",
      "from libc.math cimport sin\n",
      "print 'sin(1)=', sin(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "sin(1)= 0.841470984808\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can similarly use the `-I/--include` flag to add include directories to the search path, and `-c/--compile-args` to add extra flags that are passed to Cython via the `extra_compile_args` of the distutils `Extension` class.  Please see [the Cython docs on C library usage](http://docs.cython.org/src/tutorial/clibraries.html) for more details on the use of these flags."
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Rmagic Functions Extension"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "IPython has an `rmagic` extension that contains a some magic functions for working with R via rpy2. This extension can be loaded using the `%load_ext` magic as follows:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "%load_ext rmagic"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A typical use case one imagines is having some numpy arrays, wanting to compute some statistics of interest on these\n",
      " arrays and return the result back to python. Let's suppose we just want to fit a simple linear model to a scatterplot."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X = np.array([0,1,2,3,4])\n",
      "Y = np.array([3,5,4,6,7])\n",
      "plt.scatter(X, Y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "<matplotlib.collections.PathCollection at 0x112ab1f90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x112a87c50>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can accomplish this by first pushing variables to R, fitting a model and returning the results. The line magic %Rpush copies its arguments to variables of the same name in rpy2. The %R line magic evaluates the string in rpy2 and returns the results. In this case, the coefficients of a linear model."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%Rpush X Y\n",
      "%R lm(Y~X)$coef"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "array([ 3.2,  0.9])"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "It is also possible to return more than one value with %R."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%R resid(lm(Y~X)); coef(lm(X~Y))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "array([-2.5,  0.9])"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "One can also easily capture the results of %R into python objects. Like R, the return value of this multiline expression (multiline in the sense that it is separated by ';') is the final value, which is \n",
      "the *coef(lm(X~Y))*."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To pull other variables from R, there are two more line magics, %Rpull and %Rget. Both are useful after some R code has been executed and there are variables\n",
      "in the rpy2 namespace that one would like to retrieve. The main difference is that one\n",
      " returns the value (%Rget), while the other pulls it to self.shell.user_ns (%Rpull). Imagine we've stored the results\n",
      "of some calculation in the variable \"a\" in rpy2's namespace. By using the %R magic, we can obtain these results and\n",
      "store them in b. We can also pull them directly to user_ns with %Rpull. They are both views on the same data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = %R a=resid(lm(Y~X))\n",
      "%Rpull a\n",
      "print(a)\n",
      "assert id(b.data) == id(a.data)\n",
      "%R -o a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[-0.2  0.9 -1.   0.1  0.2]\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Plotting and capturing output"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "R's console (i.e. its stdout() connection) is captured by ipython, as are any plots which are published as PNG files like the notebook with arguments --pylab inline. As a call to %R may produce a return value (see above) we must ask what happens to a magic like the one below. The R code specifies that something is published to the notebook. If anything is published to the notebook, that call to %R returns None."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from __future__ import print_function\n",
      "v1 = %R plot(X,Y); print(summary(lm(Y~X))); vv=mean(X)*mean(Y)\n",
      "print('v1 is:', v1)\n",
      "v2 = %R mean(X)*mean(Y)\n",
      "print('v2 is:', v2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\n",
        "Call:\n",
        "lm(formula = Y ~ X)\n",
        "\n",
        "Residuals:\n",
        "   1    2    3    4    5 \n",
        "-0.2  0.9 -1.0  0.1  0.2 \n",
        "\n",
        "Coefficients:\n",
        "            Estimate Std. Error t value Pr(>|t|)  \n",
        "(Intercept)   3.2000     0.6164   5.191   0.0139 *\n",
        "X             0.9000     0.2517   3.576   0.0374 *\n",
        "---\n",
        "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
        "\n",
        "Residual standard error: 0.7958 on 3 degrees of freedom\n",
        "Multiple R-squared:   0.81,\tAdjusted R-squared:  0.7467 \n",
        "F-statistic: 12.79 on 1 and 3 DF,  p-value: 0.03739\n",
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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V8cILL8Tzzz8fb7/9dowePbpZCsWzzuKx9b6K2957771m7cedCBAgsKECzzzzTLzyyitx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\nAAAAAElFTkSuQmCC\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "v1 is: [ 10.]\n",
        "v2 is: [ 10.]\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Cell level magic"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Often, we will want to do more than a simple linear regression model. There may be several lines of R code that we want to \n",
      "use before returning to python. This is the cell-level magic.\n",
      "\n",
      "\n",
      "For the cell level magic, inputs can be passed via the -i or --inputs argument in the line. These variables are copied \n",
      "from the shell namespace to R's namespace using rpy2.robjects.r.assign. It would be nice not to have to copy these into R: rnumpy ( http://bitbucket.org/njs/rnumpy/wiki/API ) has done some work to limit or at least make transparent the number of copies of an array. This seems like a natural thing to try to build on. Arrays can be output from R via the -o or --outputs argument in the line. All other arguments are sent to R's png function, which is the graphics device used to create the plots.\n",
      "\n",
      "We can redo the above calculations in one ipython cell. We might also want to add some output such as a summary\n",
      " from R or perhaps the standard plotting diagnostics of the lm."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -i X,Y -o XYcoef\n",
      "XYlm = lm(Y~X)\n",
      "XYcoef = coef(XYlm)\n",
      "print(summary(XYlm))\n",
      "par(mfrow=c(2,2))\n",
      "plot(XYlm)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\n",
        "Call:\n",
        "lm(formula = Y ~ X)\n",
        "\n",
        "Residuals:\n",
        "   1    2    3    4    5 \n",
        "-0.2  0.9 -1.0  0.1  0.2 \n",
        "\n",
        "Coefficients:\n",
        "            Estimate Std. Error t value Pr(>|t|)  \n",
        "(Intercept)   3.2000     0.6164   5.191   0.0139 *\n",
        "X             0.9000     0.2517   3.576   0.0374 *\n",
        "---\n",
        "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
        "\n",
        "Residual standard error: 0.7958 on 3 degrees of freedom\n",
        "Multiple R-squared:   0.81,\tAdjusted R-squared:  0.7467 \n",
        "F-statistic: 12.79 on 1 and 3 DF,  p-value: 0.03739\n",
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Ox4sAIiHNmDGDjR23b99OLVu2tFUHN97KyUVBIAQI3Lhxg3744QdXzvfcc49r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gsvtJqW/4JAZjCOIreQenMVGQ3qhYSI4ri50PUahQuROWcmGfw7TheCpTOiYCFDGKLYIRQlCAy4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\neOEG4kg14GU8goAgI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      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "octavemagic: Octave inside IPython"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The `octavemagic` extension provides the ability to interact with Octave.  It depends on the `oct2py` and `h5py` packages,\n",
      "which may be installed using `easy_install`.  It has been closely modeled after the R extension, so many of its names and usage patterns are the same.\n",
      "\n",
      "To enable the extension, load it as follows:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext octavemagic"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 109
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Overview"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Loading the extension enables three magic functions: `%octave`, `%octave_push`, and `%octave_pull`.\n",
      "\n",
      "The first is for executing one or more lines of Octave, while the latter allow moving variables between the Octave and Python workspace.\n",
      "Here you see an example of how to execute a single line of Octave, and how to transfer the generated value back to Python:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = %octave [1 2; 3 4];\n",
      "x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 110,
       "text": [
        "array([[ 1.,  2.],\n",
        "       [ 3.,  4.]])"
       ]
      }
     ],
     "prompt_number": 110
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = [1, 2, 3]\n",
      "\n",
      "%octave_push a\n",
      "%octave a = a * 2;\n",
      "%octave_pull a\n",
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 111,
       "text": [
        "array([[2, 4, 6]])"
       ]
      }
     ],
     "prompt_number": 111
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "When using the cell magic, `%%octave` (note the double `%`), multiple lines of Octave can be executed together.  Unlike\n",
      "with the single cell magic, no value is returned, so we use the `-i` and `-o` flags to specify input and output variables."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%octave -i x -o y\n",
      "y = x + 3;"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 116
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 117,
       "text": [
        "array([[ 4.,  5.],\n",
        "       [ 6.,  7.]])"
       ]
      }
     ],
     "prompt_number": 117
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Plotting"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot output is automatically captured and displayed, and using the `-f` flag you may choose its format (currently, `png` and `svg` are supported)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%octave -f svg\n",
      "\n",
      "p = [12 -2.5 -8 -0.1 8];\n",
      "x = 0:0.01:1;\n",
      "\n",
      "polyout(p, 'x')\n",
      "plot(x, polyval(p, x));"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "text": [
        "12*x^4 - 2.5*x^3 - 8*x^2 - 0.1*x^1 + 8"
       ]
      },
      {
       "output_type": "display_data",
       "svg": [
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    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The plot size is adjusted using the `-s` flag:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%octave -s 500,500\n",
      "\n",
      "# butterworth filter, order 2, cutoff pi/2 radians\n",
      "b = [0.292893218813452  0.585786437626905  0.292893218813452];\n",
      "a = [1  0  0.171572875253810];\n",
      "freqz(b, a, 32);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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QELB///4jR47U1dUZGxu/9957a9askWZRsIaGBmrCZ6ln7G8mJFM05Pvuu5CY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\nY2RHRMr/A5YdmP24XbLsAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 119
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%octave -s 600,200 -f png\n",
      "\n",
      "subplot(121);\n",
      "[x, y] = meshgrid(0:0.1:3);\n",
      "r = sin(x - 0.5).^2 + cos(y - 0.5).^2;\n",
      "surf(x, y, r);\n",
      "\n",
      "subplot(122);\n",
      "sombrero()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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ValbmrH2bWUVv0aOH1pVHujZ12U/e6PsMNZi1t8mswImVNaw+p7SKiYqKWrRo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      }
     ],
     "prompt_number": 120
    }
   ],
   "metadata": {}
  }
 ]
}