Pandas_Cookbook_104.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selecting the smallest of the largest\n",
    "# 101. oldal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title_year</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009.0</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>7.9</td>\n",
       "      <td>237000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007.0</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>7.1</td>\n",
       "      <td>300000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>6.8</td>\n",
       "      <td>245000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012.0</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>8.5</td>\n",
       "      <td>250000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Star Wars: Episode VII - The Force Awakens</td>\n",
       "      <td>7.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   title_year                                 movie_title  imdb_score  \\\n",
       "0      2009.0                                      Avatar         7.9   \n",
       "1      2007.0    Pirates of the Caribbean: At World's End         7.1   \n",
       "2      2015.0                                     Spectre         6.8   \n",
       "3      2012.0                       The Dark Knight Rises         8.5   \n",
       "4         NaN  Star Wars: Episode VII - The Force Awakens         7.1   \n",
       "\n",
       "        budget  \n",
       "0  237000000.0  \n",
       "1  300000000.0  \n",
       "2  245000000.0  \n",
       "3  250000000.0  \n",
       "4          NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "movie = pd.read_csv('https://raw.githubusercontent.com/DatasRev/source-files/master/csv/movie.csv')\n",
    "movie2 = movie[['title_year','movie_title', 'imdb_score', 'budget']]\n",
    "movie2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title_year</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2725</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Towering Inferno</td>\n",
       "      <td>9.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>1994.0</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>9.3</td>\n",
       "      <td>25000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3402</th>\n",
       "      <td>1972.0</td>\n",
       "      <td>The Godfather</td>\n",
       "      <td>9.2</td>\n",
       "      <td>6000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2779</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Dekalog</td>\n",
       "      <td>9.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4312</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>Kickboxer: Vengeance</td>\n",
       "      <td>9.1</td>\n",
       "      <td>17000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      title_year               movie_title  imdb_score      budget\n",
       "2725         NaN          Towering Inferno         9.5         NaN\n",
       "1920      1994.0  The Shawshank Redemption         9.3  25000000.0\n",
       "3402      1972.0             The Godfather         9.2   6000000.0\n",
       "2779         NaN                   Dekalog         9.1         NaN\n",
       "4312      2016.0      Kickboxer: Vengeance         9.1  17000000.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the nlargest method to select the top 100 movies by imdb_score:\n",
    "movie2.nlargest(100, 'imdb_score').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title_year</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4804</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>Butterfly Girl</td>\n",
       "      <td>8.7</td>\n",
       "      <td>180000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4801</th>\n",
       "      <td>1997.0</td>\n",
       "      <td>Children of Heaven</td>\n",
       "      <td>8.5</td>\n",
       "      <td>180000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4706</th>\n",
       "      <td>1957.0</td>\n",
       "      <td>12 Angry Men</td>\n",
       "      <td>8.9</td>\n",
       "      <td>350000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4550</th>\n",
       "      <td>2011.0</td>\n",
       "      <td>A Separation</td>\n",
       "      <td>8.4</td>\n",
       "      <td>500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4636</th>\n",
       "      <td>2012.0</td>\n",
       "      <td>The Other Dream Team</td>\n",
       "      <td>8.4</td>\n",
       "      <td>500000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      title_year           movie_title  imdb_score    budget\n",
       "4804      2014.0        Butterfly Girl         8.7  180000.0\n",
       "4801      1997.0    Children of Heaven         8.5  180000.0\n",
       "4706      1957.0          12 Angry Men         8.9  350000.0\n",
       "4550      2011.0          A Separation         8.4  500000.0\n",
       "4636      2012.0  The Other Dream Team         8.4  500000.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Chain the nsmallest method to return the five lowest budget films among those with a top 100 score:\n",
    "movie2.nlargest(100, 'imdb_score').nsmallest(5, 'budget')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title_year</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3884</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>The Veil</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2375</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>My Big Fat Greek Wedding 2</td>\n",
       "      <td>6.1</td>\n",
       "      <td>18000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2794</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>Miracles from Heaven</td>\n",
       "      <td>6.8</td>\n",
       "      <td>13000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>Independence Day: Resurgence</td>\n",
       "      <td>5.5</td>\n",
       "      <td>165000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>Kung Fu Panda 3</td>\n",
       "      <td>7.2</td>\n",
       "      <td>145000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      title_year                   movie_title  imdb_score       budget\n",
       "3884      2016.0                      The Veil         4.7    4000000.0\n",
       "2375      2016.0    My Big Fat Greek Wedding 2         6.1   18000000.0\n",
       "2794      2016.0          Miracles from Heaven         6.8   13000000.0\n",
       "92        2016.0  Independence Day: Resurgence         5.5  165000000.0\n",
       "153       2016.0               Kung Fu Panda 3         7.2  145000000.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting the largest of each group by sorting\n",
    "movie2.sort_values('title_year', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title_year</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4312</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>Kickboxer: Vengeance</td>\n",
       "      <td>9.1</td>\n",
       "      <td>17000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4277</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>A Beginner's Guide to Snuff</td>\n",
       "      <td>8.7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3798</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>Airlift</td>\n",
       "      <td>8.5</td>\n",
       "      <td>4400000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>Captain America: Civil War</td>\n",
       "      <td>8.2</td>\n",
       "      <td>250000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>Godzilla Resurgence</td>\n",
       "      <td>8.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      title_year                  movie_title  imdb_score       budget\n",
       "4312      2016.0         Kickboxer: Vengeance         9.1   17000000.0\n",
       "4277      2016.0  A Beginner's Guide to Snuff         8.7          NaN\n",
       "3798      2016.0                      Airlift         8.5    4400000.0\n",
       "27        2016.0   Captain America: Civil War         8.2  250000000.0\n",
       "98        2016.0          Godzilla Resurgence         8.2          NaN"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's look at how to sort both year and score:\n",
    "movie3 = movie2.sort_values(['title_year','imdb_score'], ascending=False)\n",
    "movie3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title_year</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4312</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>Kickboxer: Vengeance</td>\n",
       "      <td>9.1</td>\n",
       "      <td>17000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3745</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>Running Forever</td>\n",
       "      <td>8.6</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4369</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>Queen of the Mountains</td>\n",
       "      <td>8.7</td>\n",
       "      <td>1400000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3935</th>\n",
       "      <td>2013.0</td>\n",
       "      <td>Batman: The Dark Knight Returns, Part 2</td>\n",
       "      <td>8.4</td>\n",
       "      <td>3500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012.0</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>8.5</td>\n",
       "      <td>250000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      title_year                              movie_title  imdb_score  \\\n",
       "4312      2016.0                     Kickboxer: Vengeance         9.1   \n",
       "3745      2015.0                          Running Forever         8.6   \n",
       "4369      2014.0                   Queen of the Mountains         8.7   \n",
       "3935      2013.0  Batman: The Dark Knight Returns, Part 2         8.4   \n",
       "3         2012.0                    The Dark Knight Rises         8.5   \n",
       "\n",
       "           budget  \n",
       "4312   17000000.0  \n",
       "3745    5000000.0  \n",
       "4369    1400000.0  \n",
       "3935    3500000.0  \n",
       "3     250000000.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, we use the drop_duplicates method to keep only the first row of every year:\n",
    "movie_top_year = movie3.drop_duplicates(subset='title_year')\n",
    "movie_top_year.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default behavior of the drop_duplicates method is to keep the first occurrence of\n",
    "each unique row, which would not drop any rows as each row is unique. However, the\n",
    "subset parameter alters it to only consider the column (or list of columns) given to it. In\n",
    "this example, only one row for each year will be returned. As we sorted by year and score in\n",
    "the last step, the highest scoring movie for each year is what we get."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible to sort one column in ascending order while simultaneously sorting another\n",
    "column in descending order. To accomplish this, pass in a list of booleans to the ascending\n",
    "parameter that corresponds to how you would like each column sorted. The following sorts\n",
    "title_year and content_rating in descending order and budget in ascending order. It\n",
    "then finds the lowest budget film for each year with the highest IMDB Score:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_title</th>\n",
       "      <th>title_year</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4312</th>\n",
       "      <td>Kickboxer: Vengeance</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>9.1</td>\n",
       "      <td>17000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3745</th>\n",
       "      <td>Running Forever</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4804</th>\n",
       "      <td>Butterfly Girl</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>180000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3935</th>\n",
       "      <td>Batman: The Dark Knight Returns, Part 2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>3500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>Django Unchained</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>100000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3853</th>\n",
       "      <td>Samsara</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>4000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Inception</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>160000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>Inglourious Basterds</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>75000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>185000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646</th>\n",
       "      <td>U2 3D</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  movie_title  title_year  imdb_score  \\\n",
       "4312                     Kickboxer: Vengeance      2016.0         9.1   \n",
       "3745                          Running Forever      2015.0         8.6   \n",
       "4804                           Butterfly Girl      2014.0         8.7   \n",
       "3935  Batman: The Dark Knight Returns, Part 2      2013.0         8.4   \n",
       "293                          Django Unchained      2012.0         8.5   \n",
       "3853                                  Samsara      2011.0         8.5   \n",
       "97                                  Inception      2010.0         8.8   \n",
       "582                      Inglourious Basterds      2009.0         8.3   \n",
       "66                            The Dark Knight      2008.0         9.0   \n",
       "2646                                    U2 3D      2007.0         8.4   \n",
       "\n",
       "           budget  \n",
       "4312   17000000.0  \n",
       "3745    5000000.0  \n",
       "4804     180000.0  \n",
       "3935    3500000.0  \n",
       "293   100000000.0  \n",
       "3853    4000000.0  \n",
       "97    160000000.0  \n",
       "582    75000000.0  \n",
       "66    185000000.0  \n",
       "2646          NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "movie4 = movie[['movie_title', 'title_year', 'imdb_score', 'budget']]\n",
    "movie4_sorted = movie4.sort_values(['title_year','imdb_score', 'budget'],\n",
    "ascending=[False, False, True])\n",
    "movie4_sorted.drop_duplicates(subset=['title_year']).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, drop_duplicates keeps the very first appearance, but this behavior may be\n",
    "modified by passing the keep parameter last to select the last row of each group or False to\n",
    "drop all duplicates entirely."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Replicating nlargest with sort_values\n",
    "we will replicate the Selecting the smallest from the largest recipe with the\n",
    "sort_values method and explore the differences between the two."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>movie_title</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4804</th>\n",
       "      <td>Butterfly Girl</td>\n",
       "      <td>8.7</td>\n",
       "      <td>180000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4801</th>\n",
       "      <td>Children of Heaven</td>\n",
       "      <td>8.5</td>\n",
       "      <td>180000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4706</th>\n",
       "      <td>12 Angry Men</td>\n",
       "      <td>8.9</td>\n",
       "      <td>350000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4550</th>\n",
       "      <td>A Separation</td>\n",
       "      <td>8.4</td>\n",
       "      <td>500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4636</th>\n",
       "      <td>The Other Dream Team</td>\n",
       "      <td>8.4</td>\n",
       "      <td>500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2215</th>\n",
       "      <td>Psycho</td>\n",
       "      <td>8.5</td>\n",
       "      <td>806947.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4425</th>\n",
       "      <td>Casablanca</td>\n",
       "      <td>8.6</td>\n",
       "      <td>950000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4397</th>\n",
       "      <td>The Good, the Bad and the Ugly</td>\n",
       "      <td>8.9</td>\n",
       "      <td>1200000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4395</th>\n",
       "      <td>Reservoir Dogs</td>\n",
       "      <td>8.4</td>\n",
       "      <td>1200000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4369</th>\n",
       "      <td>Queen of the Mountains</td>\n",
       "      <td>8.7</td>\n",
       "      <td>1400000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         movie_title  imdb_score     budget\n",
       "4804                  Butterfly Girl         8.7   180000.0\n",
       "4801              Children of Heaven         8.5   180000.0\n",
       "4706                    12 Angry Men         8.9   350000.0\n",
       "4550                    A Separation         8.4   500000.0\n",
       "4636            The Other Dream Team         8.4   500000.0\n",
       "2215                          Psycho         8.5   806947.0\n",
       "4425                      Casablanca         8.6   950000.0\n",
       "4397  The Good, the Bad and the Ugly         8.9  1200000.0\n",
       "4395                  Reservoir Dogs         8.4  1200000.0\n",
       "4369          Queen of the Mountains         8.7  1400000.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie = pd.read_csv('C:\\Anaconda\\data\\movie.csv')\n",
    "movie2 = movie[['movie_title', 'imdb_score', 'budget']]\n",
    "movie_smallest_largest = movie2.nlargest(100, 'imdb_score') \\\n",
    ".nsmallest(10, 'budget')\n",
    "movie_smallest_largest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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",
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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>movie_title</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4815</th>\n",
       "      <td>A Charlie Brown Christmas</td>\n",
       "      <td>8.4</td>\n",
       "      <td>150000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4801</th>\n",
       "      <td>Children of Heaven</td>\n",
       "      <td>8.5</td>\n",
       "      <td>180000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4804</th>\n",
       "      <td>Butterfly Girl</td>\n",
       "      <td>8.7</td>\n",
       "      <td>180000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4706</th>\n",
       "      <td>12 Angry Men</td>\n",
       "      <td>8.9</td>\n",
       "      <td>350000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4636</th>\n",
       "      <td>The Other Dream Team</td>\n",
       "      <td>8.4</td>\n",
       "      <td>500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2215</th>\n",
       "      <td>Psycho</td>\n",
       "      <td>8.5</td>\n",
       "      <td>806947.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4425</th>\n",
       "      <td>Casablanca</td>\n",
       "      <td>8.6</td>\n",
       "      <td>950000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4395</th>\n",
       "      <td>Reservoir Dogs</td>\n",
       "      <td>8.4</td>\n",
       "      <td>1200000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4397</th>\n",
       "      <td>The Good, the Bad and the Ugly</td>\n",
       "      <td>8.9</td>\n",
       "      <td>1200000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848</th>\n",
       "      <td>Stargate SG-1</td>\n",
       "      <td>8.4</td>\n",
       "      <td>1400000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         movie_title  imdb_score     budget\n",
       "4815       A Charlie Brown Christmas         8.4   150000.0\n",
       "4801              Children of Heaven         8.5   180000.0\n",
       "4804                  Butterfly Girl         8.7   180000.0\n",
       "4706                    12 Angry Men         8.9   350000.0\n",
       "4636            The Other Dream Team         8.4   500000.0\n",
       "2215                          Psycho         8.5   806947.0\n",
       "4425                      Casablanca         8.6   950000.0\n",
       "4395                  Reservoir Dogs         8.4  1200000.0\n",
       "4397  The Good, the Bad and the Ugly         8.9  1200000.0\n",
       "848                    Stargate SG-1         8.4  1400000.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can use sort_values with head again to grab the lowest five by budget:\n",
    "movie2.sort_values('imdb_score', ascending=False).head(100) \\\n",
    ".sort_values('budget').head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take a look at the output from the first DataFrame from step 1 and compare it with the\n",
    "output from step 3. Are they the same? No! What happened?\n",
    "The issue arises because more than 100 movies exist with a rating of at least 8.4. Each of the\n",
    "methods, nlargest and sort_values, breaks ties differently, which results in a slightly\n",
    "different 100-row DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculating a trailing stop order price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the purposes of this recipe, we will only be examining stop orders used to sell currently\n",
    "owned stocks. In a typical stop order, the price does not change throughout the lifetime of\n",
    "the order. For instance, if you purchased a stock for 100 per share, you might want to set a\n",
    "stop order at 90 per share to limit your downside to 10%.\n",
    "A more advanced strategy would be to continually modify the sale price of the stop order to\n",
    "track the value of the stock if it increases in value.\n",
    "This is called a trailing stop order.\n",
    "Concretely, if the same 100 stock increases to 120, then a trailing stop order 10 below the\n",
    "current market value would move the sale price to 108.\n",
    "The trailing stop order never moves down and is always tied to the maximum value since\n",
    "the time of purchase. \n",
    "If the stock fell from 120 to 110, the stop order would still remain at\n",
    "108. It would only increase if the price moved above 120."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>Returns</th>\n",
       "      <th>StockName</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-12-03</th>\n",
       "      <td>113.00</td>\n",
       "      <td>113.4200</td>\n",
       "      <td>110.7300</td>\n",
       "      <td>112.09</td>\n",
       "      <td>34732772</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-04</th>\n",
       "      <td>111.94</td>\n",
       "      <td>112.6373</td>\n",
       "      <td>108.2115</td>\n",
       "      <td>108.52</td>\n",
       "      <td>45196984</td>\n",
       "      <td>-0.031849</td>\n",
       "      <td>Microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-06</th>\n",
       "      <td>105.82</td>\n",
       "      <td>109.2400</td>\n",
       "      <td>105.0000</td>\n",
       "      <td>109.19</td>\n",
       "      <td>49107431</td>\n",
       "      <td>0.006174</td>\n",
       "      <td>Microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-07</th>\n",
       "      <td>108.38</td>\n",
       "      <td>109.4500</td>\n",
       "      <td>104.3000</td>\n",
       "      <td>104.82</td>\n",
       "      <td>45044937</td>\n",
       "      <td>-0.040022</td>\n",
       "      <td>Microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-10</th>\n",
       "      <td>104.80</td>\n",
       "      <td>107.9800</td>\n",
       "      <td>103.8900</td>\n",
       "      <td>107.59</td>\n",
       "      <td>40801525</td>\n",
       "      <td>0.026426</td>\n",
       "      <td>Microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-11</th>\n",
       "      <td>109.80</td>\n",
       "      <td>110.9500</td>\n",
       "      <td>107.4400</td>\n",
       "      <td>108.59</td>\n",
       "      <td>42381947</td>\n",
       "      <td>0.009295</td>\n",
       "      <td>Microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-12</th>\n",
       "      <td>110.89</td>\n",
       "      <td>111.2700</td>\n",
       "      <td>109.0400</td>\n",
       "      <td>109.08</td>\n",
       "      <td>36183020</td>\n",
       "      <td>0.004512</td>\n",
       "      <td>Microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-13</th>\n",
       "      <td>109.58</td>\n",
       "      <td>110.8700</td>\n",
       "      <td>108.6300</td>\n",
       "      <td>109.45</td>\n",
       "      <td>31333362</td>\n",
       "      <td>0.003392</td>\n",
       "      <td>Microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-14</th>\n",
       "      <td>108.25</td>\n",
       "      <td>109.2600</td>\n",
       "      <td>105.5000</td>\n",
       "      <td>106.03</td>\n",
       "      <td>47043136</td>\n",
       "      <td>-0.031247</td>\n",
       "      <td>Microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-17</th>\n",
       "      <td>105.41</td>\n",
       "      <td>105.8000</td>\n",
       "      <td>101.7100</td>\n",
       "      <td>102.89</td>\n",
       "      <td>56957314</td>\n",
       "      <td>-0.029614</td>\n",
       "      <td>Microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-18</th>\n",
       "      <td>103.75</td>\n",
       "      <td>104.5100</td>\n",
       "      <td>102.5200</td>\n",
       "      <td>103.97</td>\n",
       "      <td>49319196</td>\n",
       "      <td>0.010497</td>\n",
       "      <td>Microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-19</th>\n",
       "      <td>103.65</td>\n",
       "      <td>106.8800</td>\n",
       "      <td>101.3500</td>\n",
       "      <td>103.69</td>\n",
       "      <td>68198186</td>\n",
       "      <td>-0.002693</td>\n",
       "      <td>Microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-20</th>\n",
       "      <td>103.05</td>\n",
       "      <td>104.3100</td>\n",
       "      <td>98.7800</td>\n",
       "      <td>101.51</td>\n",
       "      <td>70334184</td>\n",
       "      <td>-0.021024</td>\n",
       "      <td>Microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-21</th>\n",
       "      <td>101.63</td>\n",
       "      <td>103.0000</td>\n",
       "      <td>97.4600</td>\n",
       "      <td>98.23</td>\n",
       "      <td>111242070</td>\n",
       "      <td>-0.032312</td>\n",
       "      <td>Microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-24</th>\n",
       "      <td>97.68</td>\n",
       "      <td>97.9700</td>\n",
       "      <td>93.9800</td>\n",
       "      <td>94.13</td>\n",
       "      <td>43935192</td>\n",
       "      <td>-0.041739</td>\n",
       "      <td>Microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-26</th>\n",
       "      <td>95.14</td>\n",
       "      <td>100.6900</td>\n",
       "      <td>93.9600</td>\n",
       "      <td>100.56</td>\n",
       "      <td>51634793</td>\n",
       "      <td>0.068310</td>\n",
       "      <td>Microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-27</th>\n",
       "      <td>99.30</td>\n",
       "      <td>101.1900</td>\n",
       "      <td>96.4000</td>\n",
       "      <td>101.18</td>\n",
       "      <td>49498509</td>\n",
       "      <td>0.006165</td>\n",
       "      <td>Microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-28</th>\n",
       "      <td>102.09</td>\n",
       "      <td>102.4100</td>\n",
       "      <td>99.5200</td>\n",
       "      <td>100.39</td>\n",
       "      <td>38169312</td>\n",
       "      <td>-0.007808</td>\n",
       "      <td>Microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>101.29</td>\n",
       "      <td>102.4000</td>\n",
       "      <td>100.4400</td>\n",
       "      <td>101.57</td>\n",
       "      <td>33173765</td>\n",
       "      <td>0.011754</td>\n",
       "      <td>Microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>99.55</td>\n",
       "      <td>101.7500</td>\n",
       "      <td>98.9400</td>\n",
       "      <td>101.12</td>\n",
       "      <td>35329345</td>\n",
       "      <td>-0.004430</td>\n",
       "      <td>Microsoft</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              open      high       low   close     volume   Returns  StockName\n",
       "date                                                                          \n",
       "2018-12-03  113.00  113.4200  110.7300  112.09   34732772       NaN  Microsoft\n",
       "2018-12-04  111.94  112.6373  108.2115  108.52   45196984 -0.031849  Microsoft\n",
       "2018-12-06  105.82  109.2400  105.0000  109.19   49107431  0.006174  Microsoft\n",
       "2018-12-07  108.38  109.4500  104.3000  104.82   45044937 -0.040022  Microsoft\n",
       "2018-12-10  104.80  107.9800  103.8900  107.59   40801525  0.026426  Microsoft\n",
       "2018-12-11  109.80  110.9500  107.4400  108.59   42381947  0.009295  Microsoft\n",
       "2018-12-12  110.89  111.2700  109.0400  109.08   36183020  0.004512  Microsoft\n",
       "2018-12-13  109.58  110.8700  108.6300  109.45   31333362  0.003392  Microsoft\n",
       "2018-12-14  108.25  109.2600  105.5000  106.03   47043136 -0.031247  Microsoft\n",
       "2018-12-17  105.41  105.8000  101.7100  102.89   56957314 -0.029614  Microsoft\n",
       "2018-12-18  103.75  104.5100  102.5200  103.97   49319196  0.010497  Microsoft\n",
       "2018-12-19  103.65  106.8800  101.3500  103.69   68198186 -0.002693  Microsoft\n",
       "2018-12-20  103.05  104.3100   98.7800  101.51   70334184 -0.021024  Microsoft\n",
       "2018-12-21  101.63  103.0000   97.4600   98.23  111242070 -0.032312  Microsoft\n",
       "2018-12-24   97.68   97.9700   93.9800   94.13   43935192 -0.041739  Microsoft\n",
       "2018-12-26   95.14  100.6900   93.9600  100.56   51634793  0.068310  Microsoft\n",
       "2018-12-27   99.30  101.1900   96.4000  101.18   49498509  0.006165  Microsoft\n",
       "2018-12-28  102.09  102.4100   99.5200  100.39   38169312 -0.007808  Microsoft\n",
       "2018-12-31  101.29  102.4000  100.4400  101.57   33173765  0.011754  Microsoft\n",
       "2019-01-02   99.55  101.7500   98.9400  101.12   35329345 -0.004430  Microsoft"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from iexfinance.stocks import get_historical_data\n",
    "from datetime import datetime\n",
    "import pandas as pd\n",
    "start = datetime(2018, 12, 1)\n",
    "end = datetime(2019, 1, 24)\n",
    "dataset = get_historical_data(\"MSFT\", start, end, output_format='pandas')\n",
    "dataset['Returns']=dataset['close'].pct_change()\n",
    "dataset['StockName']='Microsoft'\n",
    "dataset.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2018-12-03    112.09\n",
       "2018-12-04    112.09\n",
       "2018-12-06    112.09\n",
       "2018-12-07    112.09\n",
       "2018-12-10    112.09\n",
       "2018-12-11    112.09\n",
       "2018-12-12    112.09\n",
       "2018-12-13    112.09\n",
       "2018-12-14    112.09\n",
       "2018-12-17    112.09\n",
       "2018-12-18    112.09\n",
       "2018-12-19    112.09\n",
       "2018-12-20    112.09\n",
       "2018-12-21    112.09\n",
       "2018-12-24    112.09\n",
       "2018-12-26    112.09\n",
       "2018-12-27    112.09\n",
       "2018-12-28    112.09\n",
       "2018-12-31    112.09\n",
       "2019-01-02    112.09\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Use the cummax method to track the highest closing price until the current date:\n",
    "MSFT_close=dataset['close']\n",
    "MSFT_cummax = MSFT_close.cummax()\n",
    "MSFT_cummax.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2018-12-03    100.881\n",
       "2018-12-04    100.881\n",
       "2018-12-06    100.881\n",
       "2018-12-07    100.881\n",
       "2018-12-10    100.881\n",
       "2018-12-11    100.881\n",
       "2018-12-12    100.881\n",
       "2018-12-13    100.881\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To limit the downside to 10%, we multiply tsla_cummax by 0.9.\n",
    "# This creates the trailing stop order:\n",
    "MSFT_trailing_stop = MSFT_cummax * .9\n",
    "MSFT_trailing_stop.head(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cummax method works by retaining the maximum value encountered up to and\n",
    "including the current value. Multiplying this series by 0.9, or whatever cushion you would\n",
    "like to use, creates the trailing stop order. In this particular example, Microsoft decreased in\n",
    "value but its trailing stop has not decreased."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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