exercise-6-renaming-and-combining.ipynb

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   "source": [
    "**This notebook is an exercise in the [Pandas](https://www.kaggle.com/learn/pandas) course.  You can reference the tutorial at [this link](https://www.kaggle.com/residentmario/renaming-and-combining).**\n",
    "\n",
    "---\n"
   ]
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    "tags": []
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   "source": [
    "# Introduction\n",
    "\n",
    "Run the following cell to load your data and some utility functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6ac64a12",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup complete.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "reviews = pd.read_csv(\"../input/wine-reviews/winemag-data-130k-v2.csv\", index_col=0)\n",
    "\n",
    "from learntools.core import binder; binder.bind(globals())\n",
    "from learntools.pandas.renaming_and_combining import *\n",
    "print(\"Setup complete.\")"
   ]
  },
  {
   "cell_type": "markdown",
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    "tags": []
   },
   "source": [
    "# Exercises\n",
    "\n",
    "View the first several lines of your data by running the cell below:"
   ]
  },
  {
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       "<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>description</th>\n",
       "      <th>designation</th>\n",
       "      <th>points</th>\n",
       "      <th>price</th>\n",
       "      <th>province</th>\n",
       "      <th>region_1</th>\n",
       "      <th>region_2</th>\n",
       "      <th>taster_name</th>\n",
       "      <th>taster_twitter_handle</th>\n",
       "      <th>title</th>\n",
       "      <th>variety</th>\n",
       "      <th>winery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Aromas include tropical fruit, broom, brimston...</td>\n",
       "      <td>Vulkà Bianco</td>\n",
       "      <td>87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sicily &amp; Sardinia</td>\n",
       "      <td>Etna</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kerin O’Keefe</td>\n",
       "      <td>@kerinokeefe</td>\n",
       "      <td>Nicosia 2013 Vulkà Bianco  (Etna)</td>\n",
       "      <td>White Blend</td>\n",
       "      <td>Nicosia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>This is ripe and fruity, a wine that is smooth...</td>\n",
       "      <td>Avidagos</td>\n",
       "      <td>87</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Douro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Roger Voss</td>\n",
       "      <td>@vossroger</td>\n",
       "      <td>Quinta dos Avidagos 2011 Avidagos Red (Douro)</td>\n",
       "      <td>Portuguese Red</td>\n",
       "      <td>Quinta dos Avidagos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US</td>\n",
       "      <td>Tart and snappy, the flavors of lime flesh and...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Paul Gregutt</td>\n",
       "      <td>@paulgwine</td>\n",
       "      <td>Rainstorm 2013 Pinot Gris (Willamette Valley)</td>\n",
       "      <td>Pinot Gris</td>\n",
       "      <td>Rainstorm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US</td>\n",
       "      <td>Pineapple rind, lemon pith and orange blossom ...</td>\n",
       "      <td>Reserve Late Harvest</td>\n",
       "      <td>87</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>Lake Michigan Shore</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Alexander Peartree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>St. Julian 2013 Reserve Late Harvest Riesling ...</td>\n",
       "      <td>Riesling</td>\n",
       "      <td>St. Julian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US</td>\n",
       "      <td>Much like the regular bottling from 2012, this...</td>\n",
       "      <td>Vintner's Reserve Wild Child Block</td>\n",
       "      <td>87</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Paul Gregutt</td>\n",
       "      <td>@paulgwine</td>\n",
       "      <td>Sweet Cheeks 2012 Vintner's Reserve Wild Child...</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Sweet Cheeks</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "    country                                        description  \\\n",
       "0     Italy  Aromas include tropical fruit, broom, brimston...   \n",
       "1  Portugal  This is ripe and fruity, a wine that is smooth...   \n",
       "2        US  Tart and snappy, the flavors of lime flesh and...   \n",
       "3        US  Pineapple rind, lemon pith and orange blossom ...   \n",
       "4        US  Much like the regular bottling from 2012, this...   \n",
       "\n",
       "                          designation  points  price           province  \\\n",
       "0                        Vulkà Bianco      87    NaN  Sicily & Sardinia   \n",
       "1                            Avidagos      87   15.0              Douro   \n",
       "2                                 NaN      87   14.0             Oregon   \n",
       "3                Reserve Late Harvest      87   13.0           Michigan   \n",
       "4  Vintner's Reserve Wild Child Block      87   65.0             Oregon   \n",
       "\n",
       "              region_1           region_2         taster_name  \\\n",
       "0                 Etna                NaN       Kerin O’Keefe   \n",
       "1                  NaN                NaN          Roger Voss   \n",
       "2    Willamette Valley  Willamette Valley        Paul Gregutt   \n",
       "3  Lake Michigan Shore                NaN  Alexander Peartree   \n",
       "4    Willamette Valley  Willamette Valley        Paul Gregutt   \n",
       "\n",
       "  taster_twitter_handle                                              title  \\\n",
       "0          @kerinokeefe                  Nicosia 2013 Vulkà Bianco  (Etna)   \n",
       "1            @vossroger      Quinta dos Avidagos 2011 Avidagos Red (Douro)   \n",
       "2           @paulgwine       Rainstorm 2013 Pinot Gris (Willamette Valley)   \n",
       "3                   NaN  St. Julian 2013 Reserve Late Harvest Riesling ...   \n",
       "4           @paulgwine   Sweet Cheeks 2012 Vintner's Reserve Wild Child...   \n",
       "\n",
       "          variety               winery  \n",
       "0     White Blend              Nicosia  \n",
       "1  Portuguese Red  Quinta dos Avidagos  \n",
       "2      Pinot Gris            Rainstorm  \n",
       "3        Riesling           St. Julian  \n",
       "4      Pinot Noir         Sweet Cheeks  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews.head()"
   ]
  },
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   "source": [
    "## 1.\n",
    "`region_1` and `region_2` are pretty uninformative names for locale columns in the dataset. Create a copy of `reviews` with these columns renamed to `region` and `locale`, respectively."
   ]
  },
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   "outputs": [
    {
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       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.25, \"interactionType\": 1, \"questionType\": 1, \"questionId\": \"1_RenameCols\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"
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       "<span style=\"color:#33cc33\">Correct</span>"
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       "Correct"
      ]
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   ],
   "source": [
    "# Your code here\n",
    "renamed = reviews.rename({'region_1': 'region', 'region_2': 'locale'}, axis='columns')\n",
    "\n",
    "# Check your answer\n",
    "q1.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aad38a11",
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   "outputs": [],
   "source": [
    "#q1.hint()\n",
    "#q1.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "272dead6",
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   "source": [
    "## 2.\n",
    "Set the index name in the dataset to `wines`."
   ]
  },
  {
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       "Correct"
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       "\n",
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       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>wines</th>\n",
       "      <th>country</th>\n",
       "      <th>description</th>\n",
       "      <th>designation</th>\n",
       "      <th>points</th>\n",
       "      <th>price</th>\n",
       "      <th>province</th>\n",
       "      <th>region_1</th>\n",
       "      <th>region_2</th>\n",
       "      <th>taster_name</th>\n",
       "      <th>taster_twitter_handle</th>\n",
       "      <th>title</th>\n",
       "      <th>variety</th>\n",
       "      <th>winery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Aromas include tropical fruit, broom, brimston...</td>\n",
       "      <td>Vulkà Bianco</td>\n",
       "      <td>87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sicily &amp; Sardinia</td>\n",
       "      <td>Etna</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kerin O’Keefe</td>\n",
       "      <td>@kerinokeefe</td>\n",
       "      <td>Nicosia 2013 Vulkà Bianco  (Etna)</td>\n",
       "      <td>White Blend</td>\n",
       "      <td>Nicosia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>This is ripe and fruity, a wine that is smooth...</td>\n",
       "      <td>Avidagos</td>\n",
       "      <td>87</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Douro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Roger Voss</td>\n",
       "      <td>@vossroger</td>\n",
       "      <td>Quinta dos Avidagos 2011 Avidagos Red (Douro)</td>\n",
       "      <td>Portuguese Red</td>\n",
       "      <td>Quinta dos Avidagos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US</td>\n",
       "      <td>Tart and snappy, the flavors of lime flesh and...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Paul Gregutt</td>\n",
       "      <td>@paulgwine</td>\n",
       "      <td>Rainstorm 2013 Pinot Gris (Willamette Valley)</td>\n",
       "      <td>Pinot Gris</td>\n",
       "      <td>Rainstorm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US</td>\n",
       "      <td>Pineapple rind, lemon pith and orange blossom ...</td>\n",
       "      <td>Reserve Late Harvest</td>\n",
       "      <td>87</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>Lake Michigan Shore</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Alexander Peartree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>St. Julian 2013 Reserve Late Harvest Riesling ...</td>\n",
       "      <td>Riesling</td>\n",
       "      <td>St. Julian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US</td>\n",
       "      <td>Much like the regular bottling from 2012, this...</td>\n",
       "      <td>Vintner's Reserve Wild Child Block</td>\n",
       "      <td>87</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Paul Gregutt</td>\n",
       "      <td>@paulgwine</td>\n",
       "      <td>Sweet Cheeks 2012 Vintner's Reserve Wild Child...</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Sweet Cheeks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129966</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Notes of honeysuckle and cantaloupe sweeten th...</td>\n",
       "      <td>Brauneberger Juffer-Sonnenuhr Spätlese</td>\n",
       "      <td>90</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Mosel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Anna Lee C. Iijima</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dr. H. Thanisch (Erben Müller-Burggraef) 2013 ...</td>\n",
       "      <td>Riesling</td>\n",
       "      <td>Dr. H. Thanisch (Erben Müller-Burggraef)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129967</th>\n",
       "      <td>US</td>\n",
       "      <td>Citation is given as much as a decade of bottl...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Oregon Other</td>\n",
       "      <td>Paul Gregutt</td>\n",
       "      <td>@paulgwine</td>\n",
       "      <td>Citation 2004 Pinot Noir (Oregon)</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Citation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129968</th>\n",
       "      <td>France</td>\n",
       "      <td>Well-drained gravel soil gives this wine its c...</td>\n",
       "      <td>Kritt</td>\n",
       "      <td>90</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Alsace</td>\n",
       "      <td>Alsace</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Roger Voss</td>\n",
       "      <td>@vossroger</td>\n",
       "      <td>Domaine Gresser 2013 Kritt Gewurztraminer (Als...</td>\n",
       "      <td>Gewürztraminer</td>\n",
       "      <td>Domaine Gresser</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129969</th>\n",
       "      <td>France</td>\n",
       "      <td>A dry style of Pinot Gris, this is crisp with ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Alsace</td>\n",
       "      <td>Alsace</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Roger Voss</td>\n",
       "      <td>@vossroger</td>\n",
       "      <td>Domaine Marcel Deiss 2012 Pinot Gris (Alsace)</td>\n",
       "      <td>Pinot Gris</td>\n",
       "      <td>Domaine Marcel Deiss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129970</th>\n",
       "      <td>France</td>\n",
       "      <td>Big, rich and off-dry, this is powered by inte...</td>\n",
       "      <td>Lieu-dit Harth Cuvée Caroline</td>\n",
       "      <td>90</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Alsace</td>\n",
       "      <td>Alsace</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Roger Voss</td>\n",
       "      <td>@vossroger</td>\n",
       "      <td>Domaine Schoffit 2012 Lieu-dit Harth Cuvée Car...</td>\n",
       "      <td>Gewürztraminer</td>\n",
       "      <td>Domaine Schoffit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>129971 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "wines    country                                        description  \\\n",
       "0          Italy  Aromas include tropical fruit, broom, brimston...   \n",
       "1       Portugal  This is ripe and fruity, a wine that is smooth...   \n",
       "2             US  Tart and snappy, the flavors of lime flesh and...   \n",
       "3             US  Pineapple rind, lemon pith and orange blossom ...   \n",
       "4             US  Much like the regular bottling from 2012, this...   \n",
       "...          ...                                                ...   \n",
       "129966   Germany  Notes of honeysuckle and cantaloupe sweeten th...   \n",
       "129967        US  Citation is given as much as a decade of bottl...   \n",
       "129968    France  Well-drained gravel soil gives this wine its c...   \n",
       "129969    France  A dry style of Pinot Gris, this is crisp with ...   \n",
       "129970    France  Big, rich and off-dry, this is powered by inte...   \n",
       "\n",
       "wines                              designation  points  price  \\\n",
       "0                                 Vulkà Bianco      87    NaN   \n",
       "1                                     Avidagos      87   15.0   \n",
       "2                                          NaN      87   14.0   \n",
       "3                         Reserve Late Harvest      87   13.0   \n",
       "4           Vintner's Reserve Wild Child Block      87   65.0   \n",
       "...                                        ...     ...    ...   \n",
       "129966  Brauneberger Juffer-Sonnenuhr Spätlese      90   28.0   \n",
       "129967                                     NaN      90   75.0   \n",
       "129968                                   Kritt      90   30.0   \n",
       "129969                                     NaN      90   32.0   \n",
       "129970           Lieu-dit Harth Cuvée Caroline      90   21.0   \n",
       "\n",
       "wines            province             region_1           region_2  \\\n",
       "0       Sicily & Sardinia                 Etna                NaN   \n",
       "1                   Douro                  NaN                NaN   \n",
       "2                  Oregon    Willamette Valley  Willamette Valley   \n",
       "3                Michigan  Lake Michigan Shore                NaN   \n",
       "4                  Oregon    Willamette Valley  Willamette Valley   \n",
       "...                   ...                  ...                ...   \n",
       "129966              Mosel                  NaN                NaN   \n",
       "129967             Oregon               Oregon       Oregon Other   \n",
       "129968             Alsace               Alsace                NaN   \n",
       "129969             Alsace               Alsace                NaN   \n",
       "129970             Alsace               Alsace                NaN   \n",
       "\n",
       "wines          taster_name taster_twitter_handle  \\\n",
       "0            Kerin O’Keefe          @kerinokeefe   \n",
       "1               Roger Voss            @vossroger   \n",
       "2             Paul Gregutt           @paulgwine    \n",
       "3       Alexander Peartree                   NaN   \n",
       "4             Paul Gregutt           @paulgwine    \n",
       "...                    ...                   ...   \n",
       "129966  Anna Lee C. Iijima                   NaN   \n",
       "129967        Paul Gregutt           @paulgwine    \n",
       "129968          Roger Voss            @vossroger   \n",
       "129969          Roger Voss            @vossroger   \n",
       "129970          Roger Voss            @vossroger   \n",
       "\n",
       "wines                                               title         variety  \\\n",
       "0                       Nicosia 2013 Vulkà Bianco  (Etna)     White Blend   \n",
       "1           Quinta dos Avidagos 2011 Avidagos Red (Douro)  Portuguese Red   \n",
       "2           Rainstorm 2013 Pinot Gris (Willamette Valley)      Pinot Gris   \n",
       "3       St. Julian 2013 Reserve Late Harvest Riesling ...        Riesling   \n",
       "4       Sweet Cheeks 2012 Vintner's Reserve Wild Child...      Pinot Noir   \n",
       "...                                                   ...             ...   \n",
       "129966  Dr. H. Thanisch (Erben Müller-Burggraef) 2013 ...        Riesling   \n",
       "129967                  Citation 2004 Pinot Noir (Oregon)      Pinot Noir   \n",
       "129968  Domaine Gresser 2013 Kritt Gewurztraminer (Als...  Gewürztraminer   \n",
       "129969      Domaine Marcel Deiss 2012 Pinot Gris (Alsace)      Pinot Gris   \n",
       "129970  Domaine Schoffit 2012 Lieu-dit Harth Cuvée Car...  Gewürztraminer   \n",
       "\n",
       "wines                                     winery  \n",
       "0                                        Nicosia  \n",
       "1                            Quinta dos Avidagos  \n",
       "2                                      Rainstorm  \n",
       "3                                     St. Julian  \n",
       "4                                   Sweet Cheeks  \n",
       "...                                          ...  \n",
       "129966  Dr. H. Thanisch (Erben Müller-Burggraef)  \n",
       "129967                                  Citation  \n",
       "129968                           Domaine Gresser  \n",
       "129969                      Domaine Marcel Deiss  \n",
       "129970                          Domaine Schoffit  \n",
       "\n",
       "[129971 rows x 13 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reindexed = reviews.rename_axis('wines', axis='columns')\n",
    "\n",
    "# Check your answer\n",
    "q2.check()\n",
    "\n",
    "reindexed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4a3f9a6f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T07:39:12.880245Z",
     "iopub.status.busy": "2024-03-12T07:39:12.879799Z",
     "iopub.status.idle": "2024-03-12T07:39:12.884377Z",
     "shell.execute_reply": "2024-03-12T07:39:12.883374Z"
    },
    "papermill": {
     "duration": 0.018414,
     "end_time": "2024-03-12T07:39:12.886821",
     "exception": false,
     "start_time": "2024-03-12T07:39:12.868407",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#q2.hint()\n",
    "#q2.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ed73f92",
   "metadata": {
    "papermill": {
     "duration": 0.009766,
     "end_time": "2024-03-12T07:39:12.907906",
     "exception": false,
     "start_time": "2024-03-12T07:39:12.898140",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 3.\n",
    "The [Things on Reddit](https://www.kaggle.com/residentmario/things-on-reddit/data) dataset includes product links from a selection of top-ranked forums (\"subreddits\") on reddit.com. Run the cell below to load a dataframe of products mentioned on the */r/gaming* subreddit and another dataframe for products mentioned on the *r//movies* subreddit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "95876603",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T07:39:12.931152Z",
     "iopub.status.busy": "2024-03-12T07:39:12.930551Z",
     "iopub.status.idle": "2024-03-12T07:39:12.949301Z",
     "shell.execute_reply": "2024-03-12T07:39:12.948067Z"
    },
    "papermill": {
     "duration": 0.034085,
     "end_time": "2024-03-12T07:39:12.952018",
     "exception": false,
     "start_time": "2024-03-12T07:39:12.917933",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gaming_products = pd.read_csv(\"../input/things-on-reddit/top-things/top-things/reddits/g/gaming.csv\")\n",
    "gaming_products['subreddit'] = \"r/gaming\"\n",
    "movie_products = pd.read_csv(\"../input/things-on-reddit/top-things/top-things/reddits/m/movies.csv\")\n",
    "movie_products['subreddit'] = \"r/movies\"\n",
    "type(movie_products['subreddit'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03f7ba9d",
   "metadata": {
    "papermill": {
     "duration": 0.009915,
     "end_time": "2024-03-12T07:39:12.972440",
     "exception": false,
     "start_time": "2024-03-12T07:39:12.962525",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Create a `DataFrame` of products mentioned on *either* subreddit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b9139c19",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T07:39:12.996195Z",
     "iopub.status.busy": "2024-03-12T07:39:12.994893Z",
     "iopub.status.idle": "2024-03-12T07:39:13.008581Z",
     "shell.execute_reply": "2024-03-12T07:39:13.007707Z"
    },
    "papermill": {
     "duration": 0.028388,
     "end_time": "2024-03-12T07:39:13.011158",
     "exception": false,
     "start_time": "2024-03-12T07:39:12.982770",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.25, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"3_ConcatReddit\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#33cc33\">Correct</span>"
      ],
      "text/plain": [
       "Correct"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined_products = pd.concat([gaming_products, movie_products])\n",
    "\n",
    "# Check your answer\n",
    "q3.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "43abeda8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T07:39:13.035064Z",
     "iopub.status.busy": "2024-03-12T07:39:13.033948Z",
     "iopub.status.idle": "2024-03-12T07:39:13.038730Z",
     "shell.execute_reply": "2024-03-12T07:39:13.037569Z"
    },
    "papermill": {
     "duration": 0.019352,
     "end_time": "2024-03-12T07:39:13.041295",
     "exception": false,
     "start_time": "2024-03-12T07:39:13.021943",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#q3.hint()\n",
    "#q3.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dfe9c68",
   "metadata": {
    "papermill": {
     "duration": 0.009857,
     "end_time": "2024-03-12T07:39:13.061474",
     "exception": false,
     "start_time": "2024-03-12T07:39:13.051617",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 4.\n",
    "The [Powerlifting Database](https://www.kaggle.com/open-powerlifting/powerlifting-database) dataset on Kaggle includes one CSV table for powerlifting meets and a separate one for powerlifting competitors. Run the cell below to load these datasets into dataframes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5457aea9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-12T07:39:13.084335Z",
     "iopub.status.busy": "2024-03-12T07:39:13.083547Z",
     "iopub.status.idle": "2024-03-12T07:39:14.188526Z",
     "shell.execute_reply": "2024-03-12T07:39:14.187418Z"
    },
    "papermill": {
     "duration": 1.119711,
     "end_time": "2024-03-12T07:39:14.191428",
     "exception": false,
     "start_time": "2024-03-12T07:39:13.071717",
     "status": "completed"
    },
    "tags": []
   },
   "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>MeetID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Equipment</th>\n",
       "      <th>Age</th>\n",
       "      <th>Division</th>\n",
       "      <th>BodyweightKg</th>\n",
       "      <th>WeightClassKg</th>\n",
       "      <th>Squat4Kg</th>\n",
       "      <th>BestSquatKg</th>\n",
       "      <th>Bench4Kg</th>\n",
       "      <th>BestBenchKg</th>\n",
       "      <th>Deadlift4Kg</th>\n",
       "      <th>BestDeadliftKg</th>\n",
       "      <th>TotalKg</th>\n",
       "      <th>Place</th>\n",
       "      <th>Wilks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Angie Belk Terry</td>\n",
       "      <td>F</td>\n",
       "      <td>Wraps</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Mst 45-49</td>\n",
       "      <td>59.60</td>\n",
       "      <td>60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.31</td>\n",
       "      <td>138.35</td>\n",
       "      <td>1</td>\n",
       "      <td>155.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Dawn Bogart</td>\n",
       "      <td>F</td>\n",
       "      <td>Single-ply</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Mst 40-44</td>\n",
       "      <td>58.51</td>\n",
       "      <td>60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>142.88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>163.29</td>\n",
       "      <td>401.42</td>\n",
       "      <td>1</td>\n",
       "      <td>456.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386412</th>\n",
       "      <td>8481</td>\n",
       "      <td>Jeff Bumanglag</td>\n",
       "      <td>M</td>\n",
       "      <td>Multi-ply</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Elite</td>\n",
       "      <td>126.73</td>\n",
       "      <td>140</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>320.00</td>\n",
       "      <td>320.00</td>\n",
       "      <td>3</td>\n",
       "      <td>181.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386413</th>\n",
       "      <td>8481</td>\n",
       "      <td>Shane Hammock</td>\n",
       "      <td>M</td>\n",
       "      <td>Multi-ply</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Elite</td>\n",
       "      <td>129.46</td>\n",
       "      <td>140</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>362.50</td>\n",
       "      <td>362.50</td>\n",
       "      <td>2</td>\n",
       "      <td>205.18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>386414 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        MeetID              Name Sex   Equipment   Age   Division  \\\n",
       "0            0  Angie Belk Terry   F       Wraps  47.0  Mst 45-49   \n",
       "1            0       Dawn Bogart   F  Single-ply  42.0  Mst 40-44   \n",
       "...        ...               ...  ..         ...   ...        ...   \n",
       "386412    8481    Jeff Bumanglag   M   Multi-ply   NaN      Elite   \n",
       "386413    8481     Shane Hammock   M   Multi-ply   NaN      Elite   \n",
       "\n",
       "        BodyweightKg WeightClassKg  Squat4Kg  BestSquatKg  Bench4Kg  \\\n",
       "0              59.60            60       NaN        47.63       NaN   \n",
       "1              58.51            60       NaN       142.88       NaN   \n",
       "...              ...           ...       ...          ...       ...   \n",
       "386412        126.73           140       NaN          NaN       NaN   \n",
       "386413        129.46           140       NaN          NaN       NaN   \n",
       "\n",
       "        BestBenchKg  Deadlift4Kg  BestDeadliftKg  TotalKg Place   Wilks  \n",
       "0             20.41          NaN           70.31   138.35     1  155.05  \n",
       "1             95.25          NaN          163.29   401.42     1  456.38  \n",
       "...             ...          ...             ...      ...   ...     ...  \n",
       "386412          NaN          NaN          320.00   320.00     3  181.85  \n",
       "386413          NaN          NaN          362.50   362.50     2  205.18  \n",
       "\n",
       "[386414 rows x 17 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "powerlifting_meets = pd.read_csv(\"../input/powerlifting-database/meets.csv\")\n",
    "powerlifting_competitors = pd.read_csv(\"../input/powerlifting-database/openpowerlifting.csv\")\n",
    "pd.set_option('display.max_rows', 5)\n",
    "#powerlifting_meets\n",
    "powerlifting_competitors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0e4b633d",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2024-03-12T07:39:14.232763Z"
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     "exception": false,
     "start_time": "2024-03-12T07:39:14.202569",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MeetID</th>\n",
       "      <th>MeetPath</th>\n",
       "      <th>Federation</th>\n",
       "      <th>Date</th>\n",
       "      <th>MeetCountry</th>\n",
       "      <th>MeetState</th>\n",
       "      <th>MeetTown</th>\n",
       "      <th>MeetName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>365strong/1601</td>\n",
       "      <td>365Strong</td>\n",
       "      <td>2016-10-29</td>\n",
       "      <td>USA</td>\n",
       "      <td>NC</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>2016 Junior &amp; Senior National Powerlifting Cha...</td>\n",
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       "      <td>USA</td>\n",
       "      <td>MO</td>\n",
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       "      <td>Thanksgiving Powerlifting Classic</td>\n",
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       "      <th>8480</th>\n",
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       "      <td>2016-03-05</td>\n",
       "      <td>USA</td>\n",
       "      <td>OH</td>\n",
       "      <td>Columbus</td>\n",
       "      <td>2016 XPC PRO Finals</td>\n",
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       "    <tr>\n",
       "      <th>8481</th>\n",
       "      <td>8481</td>\n",
       "      <td>xpc/2017-finals</td>\n",
       "      <td>XPC</td>\n",
       "      <td>2017-03-03</td>\n",
       "      <td>USA</td>\n",
       "      <td>OH</td>\n",
       "      <td>Columbus</td>\n",
       "      <td>2017 XPC Finals</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8482 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      MeetID             MeetPath Federation        Date MeetCountry  \\\n",
       "0          0       365strong/1601  365Strong  2016-10-29         USA   \n",
       "1          1       365strong/1602  365Strong  2016-11-19         USA   \n",
       "...      ...                  ...        ...         ...         ...   \n",
       "8480    8480  xpc/2016-pro-finals        XPC  2016-03-05         USA   \n",
       "8481    8481      xpc/2017-finals        XPC  2017-03-03         USA   \n",
       "\n",
       "     MeetState   MeetTown                                           MeetName  \n",
       "0           NC  Charlotte  2016 Junior & Senior National Powerlifting Cha...  \n",
       "1           MO      Ozark                  Thanksgiving Powerlifting Classic  \n",
       "...        ...        ...                                                ...  \n",
       "8480        OH   Columbus                                2016 XPC PRO Finals  \n",
       "8481        OH   Columbus                                    2017 XPC Finals  \n",
       "\n",
       "[8482 rows x 8 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "powerlifting_meets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "264a3473",
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     "start_time": "2024-03-12T07:39:14.248089",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Both tables include references to a `MeetID`, a unique key for each meet (competition) included in the database. Using this, generate a dataset combining the two tables into one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9281143d",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.25, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"4_JoinLifting\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"
      ],
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       "<IPython.core.display.Javascript object>"
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    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#33cc33\">Correct</span>"
      ],
      "text/plain": [
       "Correct"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "powerlifting_combined = powerlifting_meets.set_index('MeetID').join(powerlifting_competitors.set_index('MeetID'))\n",
    "\n",
    "# Check your answer\n",
    "q4.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cfd63d92",
   "metadata": {
    "execution": {
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   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MeetPath</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>95.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>163.29</td>\n",
       "      <td>401.42</td>\n",
       "      <td>1</td>\n",
       "      <td>456.38</td>\n",
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       "      <th>...</th>\n",
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       "      <td>M</td>\n",
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       "               MeetPath Federation        Date MeetCountry MeetState  \\\n",
       "MeetID                                                                 \n",
       "0        365strong/1601  365Strong  2016-10-29         USA        NC   \n",
       "0        365strong/1601  365Strong  2016-10-29         USA        NC   \n",
       "...                 ...        ...         ...         ...       ...   \n",
       "8481    xpc/2017-finals        XPC  2017-03-03         USA        OH   \n",
       "8481    xpc/2017-finals        XPC  2017-03-03         USA        OH   \n",
       "\n",
       "         MeetTown                                           MeetName  \\\n",
       "MeetID                                                                 \n",
       "0       Charlotte  2016 Junior & Senior National Powerlifting Cha...   \n",
       "0       Charlotte  2016 Junior & Senior National Powerlifting Cha...   \n",
       "...           ...                                                ...   \n",
       "8481     Columbus                                    2017 XPC Finals   \n",
       "8481     Columbus                                    2017 XPC Finals   \n",
       "\n",
       "                    Name Sex   Equipment  ...  WeightClassKg Squat4Kg  \\\n",
       "MeetID                                    ...                           \n",
       "0       Angie Belk Terry   F       Wraps  ...             60      NaN   \n",
       "0            Dawn Bogart   F  Single-ply  ...             60      NaN   \n",
       "...                  ...  ..         ...  ...            ...      ...   \n",
       "8481      Jeff Bumanglag   M   Multi-ply  ...            140      NaN   \n",
       "8481       Shane Hammock   M   Multi-ply  ...            140      NaN   \n",
       "\n",
       "        BestSquatKg Bench4Kg  BestBenchKg  Deadlift4Kg  BestDeadliftKg  \\\n",
       "MeetID                                                                   \n",
       "0             47.63      NaN        20.41          NaN           70.31   \n",
       "0            142.88      NaN        95.25          NaN          163.29   \n",
       "...             ...      ...          ...          ...             ...   \n",
       "8481            NaN      NaN          NaN          NaN          320.00   \n",
       "8481            NaN      NaN          NaN          NaN          362.50   \n",
       "\n",
       "        TotalKg  Place   Wilks  \n",
       "MeetID                          \n",
       "0        138.35      1  155.05  \n",
       "0        401.42      1  456.38  \n",
       "...         ...    ...     ...  \n",
       "8481     320.00      3  181.85  \n",
       "8481     362.50      2  205.18  \n",
       "\n",
       "[386414 rows x 23 columns]"
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   ],
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   "source": [
    "#q4.hint()\n",
    "#q4.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57e1b4fa",
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   },
   "source": [
    "# Congratulations!\n",
    "\n",
    "You've finished the Pandas micro-course.  Many data scientists feel efficiency with Pandas is the most useful and practical skill they have, because it allows you to progress quickly in any project you have.\n",
    "\n",
    "If you'd like to apply your new skills to examining geospatial data, you're encouraged to check out our **[Geospatial Analysis](https://www.kaggle.com/learn/geospatial-analysis)** micro-course.\n",
    "\n",
    "You can also take advantage of your Pandas skills by entering a **[Kaggle Competition](https://www.kaggle.com/competitions)** or by answering a question you find interesting using **[Kaggle Datasets](https://www.kaggle.com/datasets)**."
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "---\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "*Have questions or comments? Visit the [course discussion forum](https://www.kaggle.com/learn/pandas/discussion) to chat with other learners.*"
   ]
  }
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