notas-curso.ipynb

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   "source": "Apuntes de:\n\n![](images/ads_720.png)",
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   "source": "# ¿Para qué sirve la estadística descriptiva?\n\n---\n\nLa estadística descriptiva sirve para 2 cosas:\n- Anális exploratorio de la información.\n- Preprocesamiento de la información antes de tener un modelo de machine learning.",
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   "source": "## Estadística descriptiva vs. inferencial\n\n**Estadística descriptiva:** resumir un historial de datos.\n\n**Estadística inferencial:** predecir con datos.\n\n\n### ¿Por qué aprender estadística?\n\n- Resumir grandes cantidades de información para tomar mejores decisiones.\n- Responder preguntas con relevancia social.\n- Reconocer patrones en los datos.\n\n## Flujo de trabajo en data science\n\nFlujo del manejo de datos\n![](images/Untitled.png)\n\n![](images/Untitled1.png)\n\nLa estadística descriptiva está presente en los 2 primeros bloques, mientras que la inferencial está en los últimos.\n\n### Flujo\n\n- Estadísticos para ingesta y procesamiento:\n    1. ¿Cuáles son los tipos de datos con los que estás trabajando? (identifícalos)\n    2. Define el flujo de procesamiento de los datos para que sean útiles\n- Estadísticos para analítica exploración:\n\n    3. Análisis exploratorio de los datos (correlaciones y reducciones)",
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  {
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   "source": "# Estadística descriptiva para analítica\n---\n\n## Tipos de datos en estadística inferencial\nPuedes ver los tipos de datos con `dtypes`\n- **Datos categóricos:** ordinales (object) y nominales (bool)\n- **Datos numéricos:** discretos (int64) y continuos (float64)\n\nDataset con esos tipos de datos:\nhttps://www.kaggle.com/lepchenkov/usedcarscatalog\n",
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    "execution_start": 1624243251111,
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   "source": "import pandas as pd\n\ndf = pd.read_csv('cars.csv')\ndf",
   "execution_count": 1,
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        {
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        {
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         "engine_fuel": "gasoline",
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         "body_type": "minivan",
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        },
        {
         "manufacturer_name": "Chrysler",
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         "body_type": "minivan",
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         "_deepnote_index_column": 38530
        }
       ]
      },
      "text/plain": "      manufacturer_name  model_name transmission   color  odometer_value  \\\n0                Subaru     Outback    automatic  silver          190000   \n1                Subaru     Outback    automatic    blue          290000   \n2                Subaru    Forester    automatic     red          402000   \n3                Subaru     Impreza   mechanical    blue           10000   \n4                Subaru      Legacy    automatic   black          280000   \n...                 ...         ...          ...     ...             ...   \n38526          Chrysler         300    automatic  silver          290000   \n38527          Chrysler  PT Cruiser   mechanical    blue          321000   \n38528          Chrysler         300    automatic    blue          777957   \n38529          Chrysler  PT Cruiser   mechanical   black           20000   \n38530          Chrysler     Voyager    automatic  silver          297729   \n\n       year_produced engine_fuel  engine_has_gas engine_type  engine_capacity  \\\n0               2010    gasoline           False    gasoline              2.5   \n1               2002    gasoline           False    gasoline              3.0   \n2               2001    gasoline           False    gasoline              2.5   \n3               1999    gasoline           False    gasoline              3.0   \n4               2001    gasoline           False    gasoline              2.5   \n...              ...         ...             ...         ...              ...   \n38526           2000    gasoline           False    gasoline              3.5   \n38527           2004      diesel           False      diesel              2.2   \n38528           2000    gasoline           False    gasoline              3.5   \n38529           2001    gasoline           False    gasoline              2.0   \n38530           2000    gasoline           False    gasoline              2.4   \n\n       ... feature_1  feature_2 feature_3 feature_4  feature_5  feature_6  \\\n0      ...      True       True      True     False       True      False   \n1      ...      True      False     False      True       True      False   \n2      ...      True      False     False     False      False      False   \n3      ...     False      False     False     False      False      False   \n4      ...      True      False      True      True      False      False   \n...    ...       ...        ...       ...       ...        ...        ...   \n38526  ...      True      False     False      True       True      False   \n38527  ...      True      False     False      True       True      False   \n38528  ...      True      False     False      True       True      False   \n38529  ...      True      False     False     False      False      False   \n38530  ...     False      False     False     False      False      False   \n\n      feature_7  feature_8  feature_9  duration_listed  \n0          True       True       True               16  \n1         False      False       True               83  \n2         False       True       True              151  \n3         False      False      False               86  \n4         False      False       True                7  \n...         ...        ...        ...              ...  \n38526     False       True       True              301  \n38527     False       True       True              317  \n38528     False       True       True              369  \n38529     False      False       True              490  \n38530     False      False       True              632  \n\n[38531 rows x 30 columns]",
      "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>manufacturer_name</th>\n      <th>model_name</th>\n      <th>transmission</th>\n      <th>color</th>\n      <th>odometer_value</th>\n      <th>year_produced</th>\n      <th>engine_fuel</th>\n      <th>engine_has_gas</th>\n      <th>engine_type</th>\n      <th>engine_capacity</th>\n      <th>...</th>\n      <th>feature_1</th>\n      <th>feature_2</th>\n      <th>feature_3</th>\n      <th>feature_4</th>\n      <th>feature_5</th>\n      <th>feature_6</th>\n      <th>feature_7</th>\n      <th>feature_8</th>\n      <th>feature_9</th>\n      <th>duration_listed</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Subaru</td>\n      <td>Outback</td>\n      <td>automatic</td>\n      <td>silver</td>\n      <td>190000</td>\n      <td>2010</td>\n      <td>gasoline</td>\n      <td>False</td>\n      <td>gasoline</td>\n      <td>2.5</td>\n      <td>...</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>16</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Subaru</td>\n      <td>Outback</td>\n      <td>automatic</td>\n      <td>blue</td>\n      <td>290000</td>\n      <td>2002</td>\n      <td>gasoline</td>\n      <td>False</td>\n      <td>gasoline</td>\n      <td>3.0</td>\n      <td>...</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>83</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Subaru</td>\n      <td>Forester</td>\n      <td>automatic</td>\n      <td>red</td>\n      <td>402000</td>\n      <td>2001</td>\n      <td>gasoline</td>\n      <td>False</td>\n      <td>gasoline</td>\n      <td>2.5</td>\n      <td>...</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>True</td>\n      <td>151</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Subaru</td>\n      <td>Impreza</td>\n      <td>mechanical</td>\n      <td>blue</td>\n      <td>10000</td>\n      <td>1999</td>\n      <td>gasoline</td>\n      <td>False</td>\n      <td>gasoline</td>\n      <td>3.0</td>\n      <td>...</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>86</td>\n    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     "data": {
      "text/plain": "manufacturer_name     object\nmodel_name            object\ntransmission          object\ncolor                 object\nodometer_value         int64\nyear_produced          int64\nengine_fuel           object\nengine_has_gas          bool\nengine_type           object\nengine_capacity      float64\nbody_type             object\nhas_warranty            bool\nstate                 object\ndrivetrain            object\nprice_usd            float64\nis_exchangeable         bool\nlocation_region       object\nnumber_of_photos       int64\nup_counter             int64\nfeature_0               bool\nfeature_1               bool\nfeature_2               bool\nfeature_3               bool\nfeature_4               bool\nfeature_5               bool\nfeature_6               bool\nfeature_7               bool\nfeature_8               bool\nfeature_9               bool\nduration_listed        int64\ndtype: object"
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  {
   "cell_type": "markdown",
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         "duration_listed": 2232,
         "_deepnote_index_column": "max"
        }
       ],
       "rows_bottom": null
      },
      "text/plain": "       odometer_value  year_produced  engine_capacity     price_usd  \\\ncount    38531.000000   38531.000000     38521.000000  38531.000000   \nmean    248864.638447    2002.943734         2.055161   6639.971021   \nstd     136072.376530       8.065731         0.671178   6428.152018   \nmin          0.000000    1942.000000         0.200000      1.000000   \n25%     158000.000000    1998.000000         1.600000   2100.000000   \n50%     250000.000000    2003.000000         2.000000   4800.000000   \n75%     325000.000000    2009.000000         2.300000   8990.000000   \nmax    1000000.000000    2019.000000         8.000000  50000.000000   \n\n       number_of_photos    up_counter  duration_listed  \ncount      38531.000000  38531.000000     38531.000000  \nmean           9.649062     16.306091        80.577249  \nstd            6.093217     43.286933       112.826569  \nmin            1.000000      1.000000         0.000000  \n25%            5.000000      2.000000        23.000000  \n50%            8.000000      5.000000        59.000000  \n75%           12.000000     16.000000        91.000000  \nmax           86.000000   1861.000000      2232.000000  ",
      "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>odometer_value</th>\n      <th>year_produced</th>\n      <th>engine_capacity</th>\n      <th>price_usd</th>\n      <th>number_of_photos</th>\n      <th>up_counter</th>\n      <th>duration_listed</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>38531.000000</td>\n      <td>38531.000000</td>\n      <td>38521.000000</td>\n      <td>38531.000000</td>\n      <td>38531.000000</td>\n      <td>38531.000000</td>\n      <td>38531.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>248864.638447</td>\n      <td>2002.943734</td>\n      <td>2.055161</td>\n      <td>6639.971021</td>\n      <td>9.649062</td>\n      <td>16.306091</td>\n      <td>80.577249</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>136072.376530</td>\n      <td>8.065731</td>\n      <td>0.671178</td>\n      <td>6428.152018</td>\n      <td>6.093217</td>\n      <td>43.286933</td>\n      <td>112.826569</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>1942.000000</td>\n      <td>0.200000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>158000.000000</td>\n      <td>1998.000000</td>\n      <td>1.600000</td>\n      <td>2100.000000</td>\n      <td>5.000000</td>\n      <td>2.000000</td>\n      <td>23.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>250000.000000</td>\n      <td>2003.000000</td>\n      <td>2.000000</td>\n      <td>4800.000000</td>\n      <td>8.000000</td>\n      <td>5.000000</td>\n      <td>59.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>325000.000000</td>\n      <td>2009.000000</td>\n      <td>2.300000</td>\n      <td>8990.000000</td>\n      <td>12.000000</td>\n      <td>16.000000</td>\n      <td>91.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1000000.000000</td>\n      <td>2019.000000</td>\n      <td>8.000000</td>\n      <td>50000.000000</td>\n      <td>86.000000</td>\n      <td>1861.000000</td>\n      <td>2232.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "## Medidas de tendencia central\n\n- Media → promedio (puede ser afectado por algo que se sale bastante del promedio)\n- Mediana → dato central\n- Moda → dato que más se repite (es de ayuda un diagrama de frecuencias y no aplica para datos numéricos continuos)\n\n![](images/Untitled2.png)\nTodos los estadísticos descriptivos se pueden representar en este diagrama (distribuciones).\n\n### Media o promedio → mean(df)\n\n![](images/Untitled3.png)\n\n### Mediana → median(df)\n\nCuando hay valores atípicos fuertes que puedan sesgar la media, es mejor emplear la mediana.\n\n![](images/Untitled4.png)\n\nSiempre y cuando los valores del DataFrame estén ordenados",
   "metadata": {
    "tags": [],
    "cell_id": "00002-84b22034-6a1a-4a40-b045-598d1ed1cc51",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "markdown",
   "source": "### Aplicación y notas en Python (Deepnote)",
   "metadata": {
    "tags": [],
    "cell_id": "00001-8be26d95-2546-4aa2-97bf-9dc2491d35a2",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00009-c3fb46ac-fd9c-4330-b3f3-22df6f51af1b",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "7083d37c",
    "execution_start": 1624243251626,
    "execution_millis": 180,
    "deepnote_cell_type": "code"
   },
   "source": "import pandas as pd\n\ndf = pd.read_csv('cars.csv')",
   "execution_count": 4,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00010-0c056276-bed8-4c86-ba1b-a8032bfc6dae",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "a929b675",
    "execution_start": 1624243251812,
    "execution_millis": 21,
    "deepnote_cell_type": "code"
   },
   "source": "#Media\ndf['price_usd'].mean()",
   "execution_count": 5,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 5,
     "data": {
      "text/plain": "6639.971021255613"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00011-ea31f504-4508-4daf-bbf9-aec01d2d6360",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "ab65e1d",
    "execution_start": 1624243251825,
    "execution_millis": 188866013,
    "deepnote_cell_type": "code"
   },
   "source": "#Mediana\n",
   "execution_count": 6,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00012-344cb3a1-df1c-4d33-878e-baecca6b6f8d",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "deb01ed7",
    "execution_start": 1624243251877,
    "execution_millis": 1280,
    "deepnote_cell_type": "code"
   },
   "source": "#Grafico en pandas de un histograma de frecuencia\ndf['price_usd'].plot.hist(bins=20) #bins da los intervalos de valores en los que se trazará el diagrama",
   "execution_count": 7,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 7,
     "data": {
      "text/plain": "<AxesSubplot:ylabel='Frequency'>"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 402,
       "height": 248
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "Debido a que hay un fuerte sesgo de los autos con precios más elevados, la **mediana** funciona mejor que la **media**.\n\nResutla más interersante analizar los precios por marcas:\n\n- **Pro tip**: usar [seaborn](https://seaborn.pydata.org/tutorial/distributions.html)",
   "metadata": {
    "tags": [],
    "cell_id": "00013-8501ac1a-7332-4f75-b7a5-29f7923ee088",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00014-d9c53e2b-bf59-4fb9-ab4c-eb15f434c96f",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "11ff1084",
    "execution_start": 1624243253165,
    "execution_millis": 22473,
    "deepnote_cell_type": "code"
   },
   "source": "import seaborn as sns\n\n#distribution plot para hacer un histograma con las marcas de carros\nsns.displot(df, x='price_usd', hue='manufacturer_name') #hue crea un histograma por cada una de las categorías de manufactorer_name",
   "execution_count": 8,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 8,
     "data": {
      "text/plain": "<seaborn.axisgrid.FacetGrid at 0x7f4921bc8090>"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 471.75x360 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 470,
       "height": 858
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00016-0b3db94f-1e53-4c19-90e5-7eb82a13db03",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "e77a25d",
    "execution_start": 1624243275677,
    "execution_millis": 1319,
    "deepnote_cell_type": "code"
   },
   "source": "#Histograma, de barras apiladadf con el tipo de combustible que necesitan\nsns.displot(df, x='price_usd', hue='engine_type', multiple='stack')",
   "execution_count": 9,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 9,
     "data": {
      "text/plain": "<seaborn.axisgrid.FacetGrid at 0x7f49058b76d0>"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 438x360 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 436,
       "height": 352
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "No se ven los autos eléctricos, así que hay que contar cuántos hay",
   "metadata": {
    "tags": [],
    "cell_id": "00015-6199d67b-9669-4918-8500-b329135cba25",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00017-c90a9288-738a-4d4b-92cf-391c5f5c2354",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "4c516938",
    "execution_start": 1624243277005,
    "execution_millis": 77,
    "deepnote_cell_type": "code"
   },
   "source": "df.groupby('engine_type').count()",
   "execution_count": 10,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 10,
     "data": {
      "application/vnd.deepnote.dataframe.v2+json": {
       "row_count": 3,
       "column_count": 29,
       "columns": [
        {
         "name": "manufacturer_name",
         "dtype": "int64",
         "stats": {
          "unique_count": 3,
          "nan_count": 0,
          "min": "10",
          "max": "25647",
          "histogram": [
           {
            "bin_start": 10,
            "bin_end": 2573.7,
            "count": 1
           },
           {
            "bin_start": 2573.7,
            "bin_end": 5137.4,
            "count": 0
           },
           {
            "bin_start": 5137.4,
            "bin_end": 7701.099999999999,
            "count": 0
           },
           {
            "bin_start": 7701.099999999999,
            "bin_end": 10264.8,
            "count": 0
           },
           {
            "bin_start": 10264.8,
            "bin_end": 12828.5,
            "count": 0
           },
           {
            "bin_start": 12828.5,
            "bin_end": 15392.199999999999,
            "count": 1
           },
           {
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      },
      "text/plain": "             manufacturer_name  model_name  transmission  color  \\\nengine_type                                                       \ndiesel                   12874       12874         12874  12874   \nelectric                    10          10            10     10   \ngasoline                 25647       25647         25647  25647   \n\n             odometer_value  year_produced  engine_fuel  engine_has_gas  \\\nengine_type                                                               \ndiesel                12874          12874        12874           12874   \nelectric                 10             10           10              10   \ngasoline              25647          25647        25647           25647   \n\n             engine_capacity  body_type  ...  feature_1  feature_2  feature_3  \\\nengine_type                              ...                                    \ndiesel                 12874      12874  ...      12874      12874      12874   \nelectric                   0         10  ...         10         10         10   \ngasoline               25647      25647  ...      25647      25647      25647   \n\n             feature_4  feature_5  feature_6  feature_7  feature_8  feature_9  \\\nengine_type                                                                     \ndiesel           12874      12874      12874      12874      12874      12874   \nelectric            10         10         10         10         10         10   \ngasoline         25647      25647      25647      25647      25647      25647   \n\n             duration_listed  \nengine_type                   \ndiesel                 12874  \nelectric                  10  \ngasoline               25647  \n\n[3 rows x 29 columns]",
      "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>manufacturer_name</th>\n      <th>model_name</th>\n      <th>transmission</th>\n      <th>color</th>\n      <th>odometer_value</th>\n      <th>year_produced</th>\n      <th>engine_fuel</th>\n      <th>engine_has_gas</th>\n      <th>engine_capacity</th>\n      <th>body_type</th>\n      <th>...</th>\n      <th>feature_1</th>\n      <th>feature_2</th>\n      <th>feature_3</th>\n      <th>feature_4</th>\n      <th>feature_5</th>\n      <th>feature_6</th>\n      <th>feature_7</th>\n      <th>feature_8</th>\n      <th>feature_9</th>\n      <th>duration_listed</th>\n    </tr>\n    <tr>\n      <th>engine_type</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></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>diesel</th>\n      <td>12874</td>\n      <td>12874</td>\n      <td>12874</td>\n      <td>12874</td>\n      <td>12874</td>\n      <td>12874</td>\n      <td>12874</td>\n      <td>12874</td>\n      <td>12874</td>\n      <td>12874</td>\n      <td>...</td>\n      <td>12874</td>\n      <td>12874</td>\n      <td>12874</td>\n      <td>12874</td>\n      <td>12874</td>\n      <td>12874</td>\n      <td>12874</td>\n      <td>12874</td>\n      <td>12874</td>\n      <td>12874</td>\n    </tr>\n    <tr>\n      <th>electric</th>\n      <td>10</td>\n      <td>10</td>\n      <td>10</td>\n      <td>10</td>\n      <td>10</td>\n      <td>10</td>\n      <td>10</td>\n      <td>10</td>\n      <td>0</td>\n      <td>10</td>\n      <td>...</td>\n      <td>10</td>\n      <td>10</td>\n      <td>10</td>\n      <td>10</td>\n      <td>10</td>\n      <td>10</td>\n      <td>10</td>\n      <td>10</td>\n      <td>10</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>gasoline</th>\n      <td>25647</td>\n      <td>25647</td>\n      <td>25647</td>\n      <td>25647</td>\n      <td>25647</td>\n      <td>25647</td>\n      <td>25647</td>\n      <td>25647</td>\n      <td>25647</td>\n      <td>25647</td>\n      <td>...</td>\n      <td>25647</td>\n      <td>25647</td>\n      <td>25647</td>\n      <td>25647</td>\n      <td>25647</td>\n      <td>25647</td>\n      <td>25647</td>\n      <td>25647</td>\n      <td>25647</td>\n      <td>25647</td>\n    </tr>\n  </tbody>\n</table>\n<p>3 rows × 29 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "Para hacer una inspección de una marca en específico se pueden aplicar filtros",
   "metadata": {
    "tags": [],
    "cell_id": "00018-aa7ae17f-ea4c-401d-a8aa-9b41ac110d52",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "b623e53d",
    "execution_start": 1623180866174,
    "execution_millis": 0,
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00019-9787795a-a791-4827-aac5-5fbf25623124",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "8307f99c",
    "execution_start": 1624243277084,
    "execution_millis": 463,
    "deepnote_cell_type": "code"
   },
   "source": "Q7_df = df[(df['manufacturer_name'] == 'Audi') & (df['model_name'] == 'Q7')]\nsns.histplot(Q7_df, x='price_usd', hue='year_produced')",
   "execution_count": 11,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 11,
     "data": {
      "text/plain": "<AxesSubplot:xlabel='price_usd', ylabel='Count'>"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 382,
       "height": 263
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "## Medidas de dispersión\n\n- Rango: abarca desde el mínimo hasta el máximo en el conjunto de datos (todos los datos)\n- Rango intercuartil (IQR): 4 subdivisiones homogéneas de los datos\n- Desviación estándar\n\n![Picture title](images/image-20210608-145124.png)\n\nEl diagrama de caja es la visualización para representar simplificadamente la dispersión de los datos en referencia a la mediana.",
   "metadata": {
    "tags": [],
    "cell_id": "00020-797dd1a3-86ca-456f-bc0f-72f4b6bcb3e4",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "markdown",
   "source": "### Desviación estándar\n\nEs la medida de dispersión de datos más utilizada. Matemáticamente es la raíz de la media del error cuadrático. Cuando se está trabajando con muestras en vez de la población, se hace una corrección haciendo que se divida para `n-1`.\n\n![Picture title](images/image-20210608-150624.png)\n\nSi los datos siguen una distribución normal, si se considera todos los datos que están en un rango de `promedio ± 3*desviación estándar` se estaría abarcando el 99.72% de los datos de la distribución. Los puntos que se salen de eso no corresponden con el patrón y se conocen como outliers y a veces se los descarga. En otras palabras, si los datos están más allá de `3*std`, se descartan.\n\nSi siguen una distribución asimétrica, no aplica lo anterior, si no que se crean funciones para determinar qué datos son relevantes. Pero generalmente solo se usa para desviación estándar para distribuciones simétricas.",
   "metadata": {
    "tags": [],
    "cell_id": "00021-cc29d001-89c0-47de-88d3-6c4d2048ae81",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "markdown",
   "source": "### Medidas de dispersión en Python",
   "metadata": {
    "tags": [],
    "cell_id": "00022-4824a88e-4afa-41bf-9f11-cf6c247dea9d",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00023-c1f18f28-a397-42aa-86f0-ffc461ce578a",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "cd8983d5",
    "execution_start": 1624243277549,
    "execution_millis": 155,
    "deepnote_cell_type": "code"
   },
   "source": "import pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\ndf = pd.read_csv('cars.csv')",
   "execution_count": 12,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "Calcular la desviación estándar",
   "metadata": {
    "tags": [],
    "cell_id": "00024-2b720f42-72c5-408c-b051-e44ce038dd00",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00025-473f1ee1-c744-4f0b-9eae-52c1e64865ae",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "ceb22c35",
    "execution_start": 1624243277707,
    "execution_millis": 13,
    "deepnote_cell_type": "code"
   },
   "source": "df['price_usd'].std()",
   "execution_count": 13,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 13,
     "data": {
      "text/plain": "6428.1520182029035"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00026-7e216ad2-1c74-47ab-8eb8-84861f7c2819",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "dcfa63b7",
    "execution_start": 1624243277727,
    "execution_millis": 14,
    "deepnote_cell_type": "code"
   },
   "source": "#Rango = valor max - valor min\nrango = df['price_usd'].max() - df['price_usd'].min()\nrango",
   "execution_count": 14,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 14,
     "data": {
      "text/plain": "49999.0"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00027-b56779f3-6e14-45aa-98cd-e89b2e4cbaba",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "cb32d79d",
    "execution_start": 1624243277742,
    "execution_millis": 64,
    "deepnote_cell_type": "code"
   },
   "source": "#Quartiles\nmedian = df['price_usd'].median()\nQ1 = df['price_usd'].quantile(q=0.25) #toma el primer 25% de todos los datos\nQ3 = df['price_usd'].quantile(q=0.75)\nmin_val = df['price_usd'].quantile(q=0)\nmax_val = df['price_usd'].quantile(q=1)\nprint(min_val, Q1, median, Q3, max_val)",
   "execution_count": 15,
   "outputs": [
    {
     "name": "stdout",
     "text": "1.0 2100.0 4800.0 8990.0 50000.0\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00028-f7bf0234-7ec8-4657-b96f-6b4e98572ec7",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "7e09e3cb",
    "execution_start": 1624243277798,
    "execution_millis": 0,
    "deepnote_cell_type": "code"
   },
   "source": "iqr = Q3 - Q1\niqr",
   "execution_count": 16,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 16,
     "data": {
      "text/plain": "6890.0"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "### Límites para detección de outliers (con datos simétricamente distribuídos)",
   "metadata": {
    "tags": [],
    "cell_id": "00029-942db10f-81be-4cd9-9137-14a5321b58ec",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00030-965a89f1-8db4-4b49-b998-bdebcd17c586",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "2b48cb0d",
    "execution_start": 1624243277799,
    "execution_millis": 7,
    "deepnote_cell_type": "code"
   },
   "source": "minlimit = Q1 - 1.5*iqr\nmaxlimit = Q3 + 1.3*iqr\nprint(minlimit, maxlimit)",
   "execution_count": 17,
   "outputs": [
    {
     "name": "stdout",
     "text": "-8235.0 17947.0\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "El valor es negativo porque se está aplicando una ecuación de una distribución simética a una no simétrica.",
   "metadata": {
    "tags": [],
    "cell_id": "00031-83aa7ca9-76f4-4678-897e-94b28e039490",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00032-9bf25907-f66c-4635-882f-c86aac4a9a5a",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "17d9c872",
    "execution_start": 1624243277800,
    "execution_millis": 381,
    "deepnote_cell_type": "code"
   },
   "source": "sns.histplot(df['price_usd'])",
   "execution_count": 18,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 18,
     "data": {
      "text/plain": "<AxesSubplot:xlabel='price_usd', ylabel='Count'>"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 395,
       "height": 263
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00033-cfc43285-f891-4583-8356-34685747832a",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "658357b0",
    "execution_start": 1624243278178,
    "execution_millis": 108,
    "deepnote_cell_type": "code"
   },
   "source": "sns.boxplot(df['price_usd'])",
   "execution_count": 19,
   "outputs": [
    {
     "name": "stderr",
     "text": "/shared-libs/python3.7/py/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n",
     "output_type": "stream"
    },
    {
     "output_type": "execute_result",
     "execution_count": 19,
     "data": {
      "text/plain": "<AxesSubplot:xlabel='price_usd'>"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 353,
       "height": 263
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00034-cbcb0151-3ea5-46db-84ea-d9e3e44a0299",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "cb31edcc",
    "execution_start": 1624243278329,
    "execution_millis": 181,
    "deepnote_cell_type": "code"
   },
   "source": "sns.boxplot(x='engine_fuel', y='price_usd', data=df)",
   "execution_count": 20,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 20,
     "data": {
      "text/plain": "<AxesSubplot:xlabel='engine_fuel', ylabel='price_usd'>"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 402,
       "height": 263
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "## Exploración visual de los datos\n\nHay que saber qué gráfico es el correcto para enseñar los datos en cuestión.\n\n---\nEsta página tiene todos los gráficos y explica en qué consiste cada uno de ellos y deja ejemplos de uso reales. También te permite clasificarlos en función de los inputs y formas.\n\n**Enlace: https://datavizproject.com/**\n\n---\n",
   "metadata": {
    "tags": [],
    "cell_id": "00035-ff251421-8bd8-4752-9a0e-6e73aec503d3",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "markdown",
   "source": "### Diagramas de dispersión en el análisis de datos\n\nPara esto se va a trabajar con un nuevo dataset clásico. Se trata de iris y lo que trae es datos de atributos especiales de unas flores llamadas iris, vienen en 3 especies distintas. El dataset viene por defecto en seaborn.",
   "metadata": {
    "tags": [],
    "cell_id": "00038-63a0bd79-a9d0-4dcb-9b18-bf189c76ff9b",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00039-7d23c12c-0019-4352-9b60-7fd67ea8c457",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "9461a882",
    "execution_start": 1624243278507,
    "execution_millis": 389,
    "deepnote_cell_type": "code"
   },
   "source": "import pandas as pd\nimport seaborn as sns\n\niris = sns.load_dataset('iris')\niris.head()",
   "execution_count": 21,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 21,
     "data": {
      "application/vnd.deepnote.dataframe.v2+json": {
       "row_count": 5,
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         "stats": {
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          "histogram": [
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        },
        {
         "name": "sepal_width",
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          ]
         }
        },
        {
         "name": "petal_length",
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        },
        {
         "name": "petal_width",
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            "bin_end": -0.19999999999999998,
            "count": 0
           },
           {
            "bin_start": -0.19999999999999998,
            "bin_end": -0.09999999999999998,
            "count": 0
           },
           {
            "bin_start": -0.09999999999999998,
            "bin_end": 5.551115123125783e-17,
            "count": 0
           },
           {
            "bin_start": 5.551115123125783e-17,
            "bin_end": 0.10000000000000003,
            "count": 0
           },
           {
            "bin_start": 0.10000000000000003,
            "bin_end": 0.2,
            "count": 0
           },
           {
            "bin_start": 0.2,
            "bin_end": 0.3000000000000001,
            "count": 5
           },
           {
            "bin_start": 0.3000000000000001,
            "bin_end": 0.4000000000000001,
            "count": 0
           },
           {
            "bin_start": 0.4000000000000001,
            "bin_end": 0.5,
            "count": 0
           },
           {
            "bin_start": 0.5,
            "bin_end": 0.6000000000000001,
            "count": 0
           },
           {
            "bin_start": 0.6000000000000001,
            "bin_end": 0.7,
            "count": 0
           }
          ]
         }
        },
        {
         "name": "species",
         "dtype": "object",
         "stats": {
          "unique_count": 1,
          "nan_count": 0,
          "categories": [
           {
            "name": "setosa",
            "count": 5
           }
          ]
         }
        },
        {
         "name": "_deepnote_index_column",
         "dtype": "int64"
        }
       ],
       "rows_top": [
        {
         "sepal_length": 5.1,
         "sepal_width": 3.5,
         "petal_length": 1.4,
         "petal_width": 0.2,
         "species": "setosa",
         "_deepnote_index_column": 0
        },
        {
         "sepal_length": 4.9,
         "sepal_width": 3,
         "petal_length": 1.4,
         "petal_width": 0.2,
         "species": "setosa",
         "_deepnote_index_column": 1
        },
        {
         "sepal_length": 4.7,
         "sepal_width": 3.2,
         "petal_length": 1.3,
         "petal_width": 0.2,
         "species": "setosa",
         "_deepnote_index_column": 2
        },
        {
         "sepal_length": 4.6,
         "sepal_width": 3.1,
         "petal_length": 1.5,
         "petal_width": 0.2,
         "species": "setosa",
         "_deepnote_index_column": 3
        },
        {
         "sepal_length": 5,
         "sepal_width": 3.6,
         "petal_length": 1.4,
         "petal_width": 0.2,
         "species": "setosa",
         "_deepnote_index_column": 4
        }
       ],
       "rows_bottom": null
      },
      "text/plain": "   sepal_length  sepal_width  petal_length  petal_width species\n0           5.1          3.5           1.4          0.2  setosa\n1           4.9          3.0           1.4          0.2  setosa\n2           4.7          3.2           1.3          0.2  setosa\n3           4.6          3.1           1.5          0.2  setosa\n4           5.0          3.6           1.4          0.2  setosa",
      "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>sepal_length</th>\n      <th>sepal_width</th>\n      <th>petal_length</th>\n      <th>petal_width</th>\n      <th>species</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>5.1</td>\n      <td>3.5</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.9</td>\n      <td>3.0</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.7</td>\n      <td>3.2</td>\n      <td>1.3</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.6</td>\n      <td>3.1</td>\n      <td>1.5</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>3.6</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "Documentación de scatterplot (diagrama de dispersión): http://seaborn.pydata.org/generated/seaborn.scatterplot.html",
   "metadata": {
    "tags": [],
    "cell_id": "00040-416b9a39-3845-4afe-8654-b270ce33584a",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00040-f830f725-bdb2-4513-b629-6db566eda5a1",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "cea6d77e",
    "execution_start": 1624243278919,
    "execution_millis": 250,
    "deepnote_cell_type": "code"
   },
   "source": "sns.scatterplot(data=iris, x='sepal_length', y='petal_length', hue='species')",
   "execution_count": 22,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 22,
     "data": {
      "text/plain": "<AxesSubplot:xlabel='sepal_length', ylabel='petal_length'>"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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4+eabiY+P5/Tp08yePZv4+HgSEhJ4+eWXAZg9ezbLli0DYOvWrYwZM4ZBgwYxYsQISkpKqKysZM6cOSQkJDBkyBB++eUXi37l5+dz9dVXk5iYyKhRo9izZ4+pf7NmzeKCCy5g1qxZZ/37K2fO4GzgL0P+QqSnFtgFgpsH3MzAgDMLXB1pV/Yu7v/1fo4WHuV0yWkWbFzAhvQNdj1Hemk6d626i1WnVpFTkcPnhz/nid+foLi6GAAnnRM39ruRvr6NQymn9ZrGkOAhdu3H+aZDUzdSykwhxGkhRJyU8hAwCUjusBPuWQrf3QMNT9eLTmuvARL/cMaHnTFjBvfeey9z584FYOnSpTz88MNs2LCBLVu2IKVk2rRprFu3jujoaI4cOcLixYsZNWoU27dvJy0tjX379gGYShI3qK6uZsaMGSxZsoThw4dTXFyMm5sbr7zyCkII9u7dy8GDB5kyZQqHDx822/fJJ59kyJAhfP3116xZs4abb76ZXbt2AZCcnMz69etxc+vYuxmlfXH+cXxw6Qdmo246sq7JmVp1cpVF2ycHP+HiHhfbrWxuSlEKeZV5Zm27cnaRWpJqyqHH+sby9pS3OVVyCr2Tnp7ePfHQe9jl/OerczHqZh7wcf2Im+PAnA470+qnG4N8g5oKrf0sAv2QIUPIzs4mPT2dnJwc/Pz82Lt3Lz///DNDhmh3GqWlpRw5coTo6Gh69OjBqFFaqdHY2FiOHz/OvHnzuPzyy5kyZYrZsQ8dOkRYWBjDh2tLh3l7ewOwfv165s2bB0C/fv3o0aOHRaBfv349X3zxBQATJ04kLy+P4mLtzmjatGkqyHchQe5BZzVNv2GhjcMFhwlwCyAhMIFwT9uWqyypLmFf7j6OFR4j3DOc+MB4gt2DTdsD3SwrP4a4h6AT9vvg39IfOCfhhKuTq+m1URrJKMvgcMFhDM4G3J3difGJsVsfzkcdHuillLuAVmsw2FWRZU34NtttcP3117Ns2TIyMzOZMWMGJ0+e5NFHH+WOO+4we19KSgoeHo13H35+fuzevZsVK1bwxhtvsHTpUhYtWnTW/WlP0z4o3d/PKT/z2IbHTK8TAhN4efzLhHiEWLV/nbGOzw9/zsvbXza1jY8azzNjnjE9dJ0QPYEPkj8wjQ931jkzs/9MnHX2CxO9fHsxPmo8a0+vNbXNiZ9DtFfjQIwdWTu47efbqJXa86UAQwDvTn2XXr697NaP841jlUDwidTSNS21n6UZM2Zw2223kZuby6+//srevXt5/PHHuemmm/D09CQtLQ293rLGRW5uLi4uLlx33XXExcUxc+ZMs+1xcXFkZGSwdetWhg8fTklJCW5ubowdO5aPP/6YiRMncvjwYU6dOkVcXJxZDfyG9zz++OOsXbuWwMBA0ycCxXFklmXywrYXzNr25u7lUMEhqwP96ZLTLNy50Kxt7em1HCk8YlqIup9/PxZfspidOTupqathcPBguw9J9HH14bGRjzEtdhonik8Q5xfHoKBBpvowlbWVvLnnTVOQB8irzGNLxhYV6M+CYwX6SU+Y5+gB9G5a+1kaOHAgJSUlREREEBYWRlhYGAcOHGD0aG0ih6enJx999BFOTk5m+6WlpTFnzhzT6JvnnnvObLuLiwtLlixh3rx5VFRU4ObmxqpVq7j77ru56667SEhIwNnZmffffx9XV1ezfRcsWMCf/vQnEhMTcXd3VzXsHVRVXVWLMz3La8qtPkZlXSUBhgD+MuQvGKURV2dXPj3wqcUx+vr3pa9/2zVlUktSySnPIdAtkCjvKKv70CDEI4TJHpNb3FZjrCGrzHKt5OZ5fcU2DlemmD1LtZx8Uap2Jz/pibPKzzsiVaa4e6mqreKpjU/x3fHvTG0uOhc+veJTqwt9lVSXsCVjC09vepr8ynxcnVy5d+i9jA0fSw9f6wt9rUtdxyPrHqGkpgQPvQf/uPAfjI8ab9c8/icHPuG5LeY3RIumLGJ42HC7ncPRnHdlikn8gwrsikNxdXblrsF34ePqw/Ljy+nh3YN7h91LH98+7e9cL688j+e2PEd+ZT6gfUp4YdsL9JvSjx5YF+hPF5/moXUPmYp2ldWU8fC6h1l65VK7Piyd0mMK1XXVLE5ejJfei/lD5xMfGG+345+PHC/QK0oHKKspw0k4ndWwyLyKPFx1rni6era4vbymnLKashZH50R5RfFA0gPMiZ+Du7M7ni4tH6M1WRVZZmUYQBvdklaaRpKVYyWyK7ItKjNW1lWSXZ5t10Af6B7I7PjZXBF7BXqdHh9D156M1B2oQK8obSisKuTX07/yQfIH+Lj4cFvibSSFJNm0uMTJopOsPLWS5ceXE+QWxC0DbmFM+Bh0usZ0x+9pv/NB8gdklGVwWcxlTO45mVifWLPjOOmczIZD2iLQLRB/g7/pjr5BqEeo1ccIMARgcDJQWVdpatPr9C0Oy7SH7rbQd1em6tErShvWnlrLYxse43DBYbZmbeWOlXewL3efTcf49ti3vLLjFY4VHmNTxibu+eUetmU3PovanrWde365hw3pGzhedJzXdr3GF4e/oNZov/IVoR6h/GXwX3Bz1uZW6ISOOQPnEOpmfaCP9o7m6QueNtWmcdY5s2D0Anp4228xD6VjqDt6RWlFWXUZi5PNRzJJJJsyNjEkxLop+SlFKXx++HOzthpjDYfyDzEidAQARwqOUFVXZfaezw9/zrV9rrXbkMK0kjT+t/t/zOw/EyEEzsKZVadWMTx0uNUPY3VCx5QeU+jr15es8iyC3YPp4d3DruPslY6h/gspSiucdE54uXhZtNsyHV/vpMdd7262EhFgVlJAr9NzQ9wNBLgFUGusxeBsYO2ptWYBtKq2igP5B0gpTsHH1YeB/gMJ9rA+jeOsc6awspC3977daj9A+8N0MP8gtcZa4vzjLNZqddI50cu3lxrT3s2o1M0ZSk9PZ/r06Tbvd9lll1nUu2nuiSeeYNUqy7ojyrllcDZwR+IdCISpzVPvycgwy2qUrYnwjOD2xNvN2gIMAQz0byxqFucfx47sHSzctZA397zJqztf5Q/9/mCWEvnl9C/M+nEWj294nHvW3MOj6x8lpzzH6n5EeUdxy8BbzNoGBw2mt29v0+ujBUeZ/dNsHlz3II+uf5SbfrjJ5jSV0jWpO/ozFB4ebqo+2VRtbS3Ozq1f1h9++KHdYz/99NNn1TfFfoaHDue9S97j9/Tf8dR7MiZ8jM0rFU2MmojPeB82ZW4iyC2IkWEjiQ9qHC6YXZ7N4YLGOkZGaeT1Xa8zNmIsfgY/ssuzeX7L82bH3JK5hUP5h6yun6PX6bl5wM0kBCWwI2sHfXz7MDx0OAFuAab3/Jr6q9nEpIraCj458AnPXPAMTjqnlg6rdBMOF+i/P/49r+x4hcyyTEI9Qpk/dD6Xx15+Vsd85JFHiIqKMlWvXLBgAZ6enrz//vvs27eP999/ny+//JLS0lLq6ur48ccfmT17Nvv27SMuLo709HQWLlxIUlISPXv2ZNu2bZSWlnLppZdy4YUX8vvvvxMREcE333yDm5sbs2fP5oorrmD69Ols3bqV+fPnU1ZWhqurK6tXryYvL49Zs2ZRVqYNdXvttdcYM2bMWV87xZKLkwvDQoYxLGRYq++RUpJakkpxdTFhHmH4u/mbbfc1+DKpxyQm9ZjU4v4tzXrNKs+iorYCP/yorK20GC0DUFJjuV9b/N38mRQ9iUnRLffjdIll+ZATxSeok3U40Rjos8qyyKnIIcAtgDCPMJv6oHQOh0rdfH/8exb8voCMsgwkkoyyDBb8voDvj39/VsedMWMGS5c21rVfunQpI0eaf3zfsWMHy5Yt49dff+V///sffn5+JCcn88wzz7B9+/YWj3vkyBHmzp3L/v378fX1NVWibNBQwviVV15h9+7drFq1Cjc3N4KDg1m5ciU7duxgyZIl3HPPPWf1+ylnrrqumu+Ofcf076Zzw/c3cPOPN5OcZ1sl7ljfWIuZpVf3vtp0tx7sHszF0eaL1DgLZ2K87VvRcULUBIu26X2mm+XxN6VvYsbyGdz4/Y3csPwGNqRtoCvNrlda5lCB/pUdr5iN8QVtQscrO145q+M2LVO8e/du/Pz8iIoyr/ExefJk/P21O7n169dzww03ABAfH09iYmKLx42JiWHw4MEADBs2jJSUFLPtLZUwdnZ2pqamhttuu42EhASuv/56kpM7rsS/0rYjBUf4+4a/U16r1Yw5WXKSJzc8adPKTP38+/HqhFeJ9orGRefCH/r+gTnxc0zDGA3OBuYPm8+02Gk465yJ9Ynlfxf/r92aNLYaGjKUBaMX4G/wx1PvyT1D7mF85HjT9tSSVP76619N6Z38ynzuW3sfp0pO2bUfiv05VOoms6zlBYRba7dF8zLFzZ1JWeCmRcqcnJyoqKho492NXn75ZUJCQti9ezdGoxGDoestYnG+SC21LIF9sOAguZW5Vs/o1Ov0jIsax8DAgZTVlBHmGWa2jipAD+8eLBizgLlD5uLu7N4h67l6uXhxXd/rGBc5jjpZR4h7CEI0PojOKs8yrQTVoKK2gsyyTDWWvotzqEAf6hFKRllGi+1nq3mZ4qqqqlbfe8EFF7B06VImTJhAcnIye/fuPaNztlbCuKioiMjISHQ6HYsXL6auru5Mfy3lLLW2WIe3i23lojdnbObzQ5+TXpbOpT0v5aLIi4j2iTZ7j95Jb/NiI2eitQe8fq5+uOhcqDZWm9qchTP+Bv8W3690HQ6Vupk/dD4GJ/O7W4OTgflD55/1sZuXKW7L3XffTU5ODgMGDOCxxx5j4MCB+PjYXq+jaQnjQYMGMXnyZCorK7n77rtZvHgxgwYN4uDBg2qRkU7U168vswfONr120bnw9JinbVpNalf2LuatmceKkyvYm7uXf237F18f+7rVheU7Sw/vHjw+6nHT8wSB4NGRj9LTp2fndkxpl8OVKe6IUTe2qquro6amBoPBwLFjx7j44os5dOgQLi72WXfzbKkyxfZVVlPG0YKjFFYVEuUVRYxPjFnKoz1LDi3h2U3PmrV56j35+LKPifWNbWWvzlFdV83xouNklWUR4h5CrG+s3daTVc7ceVem+PLYy895YG+uvLycCRMmUFNTg5SS//3vf10myCv256H3YFDwoDPe30lYjlHX6/RmI3FqjDUczj9MSnEKvq6+9PPrR4B7gMV+bamsreRQ/iFOl54m0C2Qfv798HX1tekYLk4u9PPvRz//fjbtp3Quhwv0XYGXlxfNP5koSmv6+fcjwBBgNlnp1vhbzVIiv6X+xn1r78MotXTO+KjxLBi9wGzCU1uklPxw4gee/P1JU9v0PtO5P+n+Fss8KI6lWwR6KaVNH4WV1nWlVJ2iiQ+M5+UJL7Pu9DrSytIYFzHOtI4rQE55Ds9uetYU5EFb7/VQ3CHGRFg3US61NNVidu2yI8u4steVDA0ZapffQ+m6unygNxgM5OXlERAQoIL9WZJSkpeX51DDMY8VHiO3IpdAQyC9/CwLbdXU1ZBamopAEOEVYTFsEYDiDKjIB88Q8OiYGujlNeWkl6VjcDIQ4Rlh8W95SPAQhgS3XBGzvLacnArLujaFVYVWn7+spoyKWsvhu82HS1ojryKPvMo8/A3+HVaLXrGvLh/oIyMjSU1NJSfH+gJOSusMBgORkZGd3Q27WJe6juc2P0dqaSphHmE8OuJRJkQ3zu7MLMvknT3vsOzIMgSCmf1nckv8LY3BSUo4tga+uRtKMiGwL1z9OkRat+KStVKKUnh+y/NsSN+Au7M79yfdzxWxV1hdBTPYLZixEWP5Le03U5tO6Ij2jm5jL3Oh7qH09etrVlPH1cmVaC/rjwGwI2sHf1//d9M1/78L/k+t5doNdPlRN4rSkuS8ZO5YeYfZXa2X3os3J79JQlACAEsOLuHZzeajWf5x4T+4steV2oucQ/DmOKhtMpvaJxL+vAa8QuzSz5q6Gp7a+BTfHPvGrH3R1EVm6Zm2lNWU8eOJH/npxE9sztxMiHsIN/W/iVFho+gfYP3oqUP5h3h+y/Nsy9pGtFc0T4x+ghGhI6z+pJxems6M5TMsrvmSK5cQ5RXV+o5Kh2tv1I1DjaNXzh9pJWkWqYuSmhLTTNU6Yx3fn7CscbTm1JrGFwWntCDvGQLRo8DgC0Wp2ped5FXmsfLkSvQ6PYOCBplmkB4vPG71MXIrcnlq41MA3J54O+Mix/HWnrc4XmT9MUArh/zapNf4/prv+fDSDxkZNtKmdGhGWUaL1zy9NN2mfijnXoenboQQKUAJUAfUtvVXR1Ea1Bpr2Z2zmy+OfEFFTQXT+05naPBQ3PTaUnh+Bj+cdc5my+3phI4AgzYKxUnnxOCgwezM3ml23PjAxvLAeASQMulRftZVsaUkhUn9JzC+rJQwN1+7/R6eek+u7n01fgY/dmTtIMoriuv6XEe4h/UzXD31noR5hLE5czObMzeb2ht+V6sVpeJx+Gc8kr+GHmNg4LUQZH29HF9X3xavuZ/Bz7Z+KOfcubqjnyClHKyCvGKtfbn7uHXFrXx37DtWnVrFnavuZFtWY1qvv39/5g6aa7bPHQl3mI3vvrLXlYS4N6Zgor2izSo05niH8te8zbx6dBmbs7bxj8Mf85JTKRVe9iu96+niSZRXFAt3LWRjxkaWH1/OG7vfsKlWTYBbAE+OftJsxakrY6+0rS5+TSWsfR6+vw9O/Aprn4NPb4Bi6+/Go72jeWj4Q2Zt9w+73+5VNBX76/IPY5Xz06qTq6iT5jV8Pkj+gNFho3F2csbDxYOre19N/4D+ZJRmEOoRSpxfHN6ujTVm+vj1YfElizlaeBSd0NHbr7dZ/fTjpac5XHTU7BwrTq3mtkGnbV5cpDW55bks2rfIrK28tpzjRcdNzxKsMTp8NEuvWMrJ4pP4uvrSx68PPq42lNUoOAG7PjJvyz8G2QfA27pPF3qdnqt7XU1CYAIZZRmEuofS2683eqcWRjIpXcq5CPQS+FkIIYE3pZRvNd0ohLgduB0gOtq2EQBK91VdV83RwqOklqQS4BZAX7++ZhN3Wsod65p9AA10DyTQve3hfRFeEUR4RbS4rekSgW05WXySY4XHcHFyoa9vX5vWahVCtPi72DpUWCd09PHrY7GG61mzsR9uUhJfWUV8aRnoKqHOqG4Xu4Fz8Z/oQillmhAiGFgphDgopVzXsLE+8L8F2qibc9AfpQtYdXIVj/z2CBLtP/nNA27mrkF34eniCcCk6El8mPyh2V39rIGzcHay3z/ZWJ9YiyGHl8VcZjZsMTkvmdt+vs003nyA/wBeHP+i1aNMAtwC+NPAOTy/9Z+mNg+9B7Ge53iUin8sDJ4FOz9obAvsC0E2lDKoq4GdH8KPTdI3k5+GEXeC3rX1/ZRO1+GBXkqZVv89WwjxFTACWNf2XoojSy1J5ZlNz5iCPGhpmSk9pzAoSKsZkxCYwHtT3+Pro19TXlvOtX2ubXVC0ZkKdA/kpYteYvWp1WzN3MqE6AmMixiHm7P2wLemroZ3975rNqkoOT+Z7ZnbrQ70pWU5nCxKYd6QeezI2kGAWwC9fHuRX5IOIfb9fdrk7ArjH4HIYZD8rfYwtv80q9M2AOQdhRV/M29btQB6TYLQ+BZ3UbqGDg30QggPQCelLKn/eQqgVr4+z5VUl1BaU2rRnl/RuC6qk86JISFDGHK2wdBYB0WnAQE+UaAzT//09OnJNb2vYVL0JAIMAXi5NqaPKmorOJh/0OKQJ4pPWLTlVuRSWl1KkHuQ2USo0qoivj2xnKq6Kvr59+NkyUm+PfYtjw259+x+rzPhEwHDZmtfrZESCk+DrNOuV9NPUBUF0GTEjfZ+ozarWOnSOnrUTQiwXgixG9gCfC+l/KmDz6l0cSHuIRYjNfQ6PVHedk5nlGTCmmdh4Qj430j47SUoM59hvTVzK7N+nMWVX1/Jnavu5EDeAdM2b1dvLou5zOKww4IbFwqvM9axLnUdNyy/gSu/vpJ719zL0YLGB7wBnhFMDr+QWmMt+3L3kVqijdGP7Yo13MvzYMMr8PooWDgcVj0JRU1G5fhEQfOKmQYf8FHP1rq6Dg30UsrjUspB9V8DpZT/15HnU7oHfzd/nh/3PH39tDHcQW5B/GfCf4j1sXPt9SM/w/p/Q20V1FTAL8/CicasYUpRCnNXzzWtebond4+2JmpFYxXJq3pfxeWxlyMQGJwM3Dv0XrOSxEcLj3LPmnvIKs8CYFPmJp7d9Cyl1donFr2LG7cOuIWxodpi8h56D54Y+lcGBnXBQmInf9eCe3WZlo/f+BocajLpzDcKbvhEy/cD+Pasf92zM3qr2EA9L1c6xYCAASyasoicyhy8XbwJdrd+JItVjEbY9alle/K3EH8doD0raF7o63TJaTLKMkzlfyO9Inlq9FPckXgHzsKZCK8IszrxJ4tPWgwD3Z69nazyLNOD5Z7BCbw47gUyik9qRc0Cu2gt90MtfNje9TEMmQX6+kJ40aPgTz9Dea52d+9p5/9uSoewKdALIcYAPZvuJ6X8oNUdFKUNPgYfqxfQtplOB2GD4dTv5u0hjQ8Nm465b+Dq5Iqn3rOxoaIQ15TfiEn+Btx8od+VEHuRaXNLY9n9Df7mBcsqi3E/tYVeyV+Bb7T2ELQzHl6WZMLxtXBkJUQMg75TIaBJxc/gFv4AhSZC8xWkPIO0r5bU1UDqVtj3JeicIf4aiEgCneXiKsq5Y3XqRgjxIfAicCEwvP5LzXRVuq4hfzTPKXuFQ/8rTC97+fbi5gE3m+3y4PAHzatCHvkZlsyEvZ/Dlrfh0xlworGKZF+/vmZ5fIHg7yP/br4g/YHv4NM/wO5P4dd/wuIrINvyIW+Hqq2CdS/BV3fAvmWw4lFYejMUZza+p89U8O3R+NrgC8NvtXiA3abTm+H9y2Dr27D5dXjvMkhVhQo7my139EnAANmVyl0qSltCE+DWlZCdDAjtbr5JPtlD78EdiXcwIWoC2eXZRHhGEOcf15iaKc+HTf8zP2ZNhZbnjxkLaDV3Hhn+CFf3vpqCygJ6ePcwPXsAoDQHfmn2aKqiADJ2tXwH3VEKTsC2d8zbsvZBzgHwrv+jFNQXbvkOsvaDsQaCB0Jgb+vPISVsfkv73sBYC3uWQvTIs/8dlDNmS6DfB4QCGR3UF0Wxv4Be5umJZrxdvUkKbeWDqawDoYMx92g5auEEBSlaEGzCz82P0W6jWzmG0eL9gDbs0xZSQu5hyD8Obn4QPAAMzVJPZblaSYPaCm0ylF/PJuczan1prx9+PbSvM1VX1UJbpWWbck61G+iFEN+hlTHwApKFEFsA039NKeW0juueonQijyC46GH4Zm7jsMyIodrDSWt5hcDYv8KPDze26d0gzMbFxE+sg0/+0Fg7P+lWmPg4uNdXjixKhW/mwfH6Mszu/jDzKwgfrL32i4H46VrapoFPtH0/VQgBw2+Dw80e6ibeYL9zKGfEmjv6Fzu8F4rSFdXWaLn5pmPv03ZA4UngAuuPEz8dDH6wbZE2NHH4rbY9jC3Nge/mmy+Qsu1dGHg1xIzTXp/e0hjkQUs7rXsRpr+rzYp1cYNJT0LEEO1BaY8LYPAftYVW7KnHGLhpGWx6A5z0MOpOiBxh33MoNms30EspfwUQQvxTSvlw021CiH8Cv3ZQ385LxVXFlFSX4Gfww13v3tndaV1tFZRmgd4DPGysi96gulxLNxi8tREtLSnO0FIo3hE2F+CyWnGGljf3DDH/XWrKIG275ftzWniQWp4PVaXacMOGoYgNPAJh0AyIv1YbidLa71GaU78QSig4N6kIWVms5dibK8lq/DnvmOX2tG3avg0jZPyiYfRfYMTtliNp7MXFHfpMhtgJ2u+pRtt0CbZMmJrcQtul9uqIAruyd3Hbytu49MtLuW/tfRzOP9z+Tp0h96iWzvjvYHj3Yji62vacc/YBWDYH/jtIG4VystkwyMoi2LoIXh+tzWxd94J5YLOXo2u0lMgbY+DzW+BU48IeGHxgwFWW+0SNavxZSjj+Kyy6RPtdvroDclr57+akbznI11bDoR/hnYnaNf3+fshvEtg9gyCqhWcA/k1mF4e3kAoacJWWwrHoRwcFebNzOKsg34W0G+iFEHcJIfYCcUKIPU2+TgB7Or6L54dTxae4e9XdJOclI5H8nv47D//2MAWVBZ3dNXPVFbD6KS2lYazTHg5+8gfISrb+GBWF8NVdWi5XGiFzL3w8HXKPNL7n5EZtkYyKAm2m5i//Z5n7PVsZu2HZbMjcowXslN/g279AwWltuxAw9GZt2CFod+MX/hWimqQisg/U9/2Q9rskfw0/PaLd3Vsrcy98diMUntJGqez8ANa/rKWOQPvEc/kLEDRAe+3iAdNeg5CBjceIGA7jH9X+mADEjNfy5SrYKliXo/8E+BF4DnikSXuJlFJVM7KTU8WnKKkpMWs7WniUtNK0rrVUW0k6HPzOvM1YqwW6MCsX0ig8DRnmS/xRXaZVRwysr7d+6AfL/ba/D4kzLFMjZyr3iPbJwaztMOQfBb/6ujv+sVqeu+CkFkT9YxuDKUDeEairNj/GsdVQnAZBVi5eknPIfEgiaGPuxz2olR0Abajo7OXacV09tYerTT8duPvB2Acg4XqoqwWfcGhSoE05v1mToy8CioQQc5tvE0LopZQtjB1TbNV00Y0Gep0eD2ePFt7difTu4B5oURyM5kvjZe6HY6u0HHyfKRCZpI02Ae2OVO8ONeXNjtFklql/C3VvguLMg+zZamk5PycXywDp6tX6w9OWZva6+Wm/X4OqMkjbqs1I9Y6AXhMguH/bx/COMD8GaM8P2noeknOo9WsO2jOAUxvh5Abt00DMOPMhmIrDsiVHvwPIAQ4DR+p/ThFC7BBCDGtzT6VdvXx7Mb3PdLO2+UPnm8/S7Aq8w+CyF8zbYidod5wNspK12ZErn4Df/6vl4I/90rjdPwYmP2N+jMQZENQk+PWdal5HxcVTe4hoz1REaIJ2B9zU2L9q0/6tFTwQ+l5i3nbJ84134gCHf4QPrtKKhK14FBZfaZ6mCh+kpV4aCB1c+k/bHnK3d81ra+D3V2HpLNj8Bnw7D774M5RmW38OpdsS1k50FUK8DSyTUq6ofz0FuA54D3hFSnnWU9+SkpLktm3n73TpgsoCDuQfILssm0ivSPoH9DevmdJV1FRpee28w+AWoAWqpgtqb31Xe6DYVGgizP4BDPV3y1Vl2jHyj2n7hiZa1k/JO6a9p64WQgdqk4TsrSAF0ndrKSm/nloNGFsLdZVkaTNdy3IhoDeEJTbeSZflwtsTtPx7U9e+DYl/aHxdlAaZu6GiSFv1KTTBvBZ8e9q75jmH4PUxlvXkb/4GYsdbfx6lSxJCbJdStlqSxpaZsaOklLc1vJBS/iyEeFFKeYcQQq0jZgd+Bj/GhI/p7G60T+8KUcO1r5Y0T8kAVJWAbBJkXD2gx2jtqzXtzGq1C7+eZ5++cPXShmbqnLW78KbpEmOt9vyhudpmM0gN3uARAs5u2kgZW5dMbO+aG2stgzxoRcgUh2dL6iZDCPGwEKJH/ddDQJYQwgloYW61ct6KHm2ZYhlzj5a7djRVJdpiHW9dBB9dC2+OM6t5j1eo9rs35eSi3fU3KMuDlU9qwys/vFo7lq2FwNq75n49od8V5ts9Q6x/YKx0a7akbgKBJ9GqVwJsAJ4CioBoKeXR1va11vmeunEYxjrtod/6l7VJVSPv0nLuHoGd3TP7O7UJFk01b/PtAbeuAq/6FFBJlraAx9Z3tG0XzIeokY2jZo6ugY+uMT9G5AiY+YVlPZvWWHPN80/A7s8g+SuIHAkj71BrvTqI9lI3Vgf6c0EFegdTW6WlBlw9W9lerS1g4erV+lDAglNaUbCzSeGUZgOi9RrqZ2P/V/D5bMv2uzdb1pGpKtXu5p2bTVja+ZE2Aa0pnRPcs0urX2+L9q45QGWJll6yNT2kdFl2y9ELIfoCD2C58MjEs+mg4sCcXbWvluQc1tZwPfSDlsa4+CltOGCDkmxtvP6G/2jBa8TtkDDdtnx6eb4WiNe9oAXO8Y9Cv2ngZuVdsjVaCsRB/Vt+oNta8G3pGDHjLddntUZb17yBQY2vP9/YkqP/HNgJPAY82ORLUWxTWQzf3gN7PoOqYkhZDx9eY16v5eQGbRRJ4SktFbHmGTi8wrbzHPtFO0ZJRn11x7lwaoN9f5fgAXDFfxqDq3c4XPVay6UHWhM2GCY+oT3MBQjoA5Of1uYbKIod2PLZrVZK+XqH9UQ5fxSehNMbzduqirUaOg0pmsM/at9dPLRx5VUlsGcJDL6p7bREA6NRm0nb3J7PIc6OJZr0blqZhJ4XaKUdfKO1B7C2MHjDBfdAv8uhulTL43dEmkk5b9kS6L8TQtwNfIV5PXpVBkGxjd5NuwNuPsTQtckdrH+sVm+9qljLOXsGQ94JcGqSlijN0kanZOyFkAFaDZqGIKvTaROzUtaZn+NsFtVojc5JW+jjbDjpz+2KU8p5xZZAf0v996bpGgm0MFddUdrgHQ1j5sO6fzW29ZoEPk1mk/YYA5/9UbuTBy2tccMnjeV7q8rgl+dg+3uN+yTOgMteasxBD7tFK77WMMbc4KPVcFeU84zVgV5KGdP+uxTFCkUntVrpEx/XArmLh1ZvvfBk4x13yobGIA/1VR0/1v4gODlrhceaBnnQUjsj79Bmt4L2/daVWgE14QThQ8xrzCjKecKWUTfuwP1oY+ZvF0L0AeKklMut2NcJ2AakSSmvaO/9igMozYXs/driIkF9zYdH1lbBsTXal5O+cXbmgCZjyZsXTQMozWxc97S2UnvwOeiP0FDEcfcSbfHupkLj2x4rXler/dEoywGvCAho4QNqabZWqsHJVXtQqkatKN2MLamb94DtQMMc/TS0kTjtBnpgPnAAsOO4NqXLyj0Ga//RuD6pfyxc81ZjyQS/HhA7UVv6riHIu/s3ligG6D9NWy6vqRG3N45B9+sFo+fBmqe14O5s0IZP+ve2vp+11bB3KSy/Tys17OIJ1y+GPhc3vif7oDZOPueA9jrheq0gm3dYi4dUlK7IluGVvaSU/wJqAKSU5TTeS7VKCBEJXA68c0Y9VLqf1C3mi1DnH4dNC7VFS0CbHHX5izDybq0cb/9p2kLWTVdMihoBMz7WCnMF9IarFmppmwZVhfDLs4138LWV2h+Xqmb15duSexi+u6exnnx1KXx1m1YvH7TZplvfbgzyoOX8T2+y/hyK0gXYckdfLYRwQ3sAixCiF01G37ThP8BDQIufd4UQtwO3A0RHd7GSvI6sLE8b+WLNUEVbZR+wbDu1SRsl499Tex3QCyY/peXU3f0tp/q7uEP/KyDmIm3N2OZrypZmWRYLq62Ckkzr67cUp1sugVier6VqfKO08f5HV1vul74TBl5j2a4oXZQtd/RPAj8BUUKIj4HVaAG8VUKIK4BsKWULKyxrpJRvSSmTpJRJQUFq7HCHK06H9f+Bt8fDR9dpBbhsXe+1PS098IwepRXRapB3DFY+Du9fCl/fDem7Wj6WwavlhcM9Qy0nFDm7mpdLbo93uGUhMHf/xlmtBm/oPclyv7DB1p9DUboAqwO9lHIlcC0wG/gUSJJSrm1ntwuAaUKIFOAzYKIQ4qMz6qly9qSEHR/Cqie1GaenN2kzUjPsvPRv5AiIb7KgR0AvGDUXXOrL91aVwPcPaAtgFNcvTfjRNVpteGu5+sCExxpXYdK7wYS/g6sNj4EC+8KVrzYulu3qBde83bhoiM5Jey4Q3GRt1sQZWqVIRelG2k3dCCGGNmvKqP8eLYSIllLuaG1fKeWjwKP1xxkPPCClnHlmXVXOWkkmbG42udlYqy1OHTHEfucJ7AWXPg9DZ2k59MC+5qNZCk5qD2KbKs/X6t9YW8um4BhsfBVG3dVYBXLL29qQSm8rZ6Y6u8CgG7QaO2XZLY+6CYrTFufIP6qNugnso9ZiVboda3L0L7WxTQKqqFl34eSi1SevKDBvd3Fv+f1no7JQy3VXl2rT+Y11jWkSZ1fzYZUNmi7Y0R5ng/Zp4Ldm/zxtOQZofQqKazuv7xmkShIo3Zo1i4NPsOZAQojJ9emd1o6zFlhrdc8U+/MI0IplLWnyoconSptIZE+5R+CDq6E4VXutc9JG1cRepL32i4GxD2qjZBrEjrdtMpN/b0j6E2xb1NiWeIM2zl1RFDP2LEj9T6DVQK90Eb0na+uInt6iLUoRPdr+y/Wd2tQY5EG7m1/zrJZWcfXUZraOuF37A5O+S0v1RI20bWESVw8Y/wj0vhgy92t1YqJGWr9Qh6KcR+wZ6NsdU690AXqDVmmx5wUdd47yPC2o974YkNrKRqe3aMMfG4ZzuvtB3yna15nyDNEqPva73C7dVhRHZc9A33WWqlI6V/RoyDmoFS2TEkLiYepzWupIUZRzzpZx9IpinfJc2P2pFuQBsvbBkRWWD18VRTkn7BnoU+x4LKU7y0q2bDvys7YwR3OVxVphMUVROow14+ivbWu7lPLL+u9tvk85jwS1sAhHjzHmD0rzT8CuTyH5K+0h6sg7264yqSjKGbMmR39lG9sk8KWd+qI4isgRMOBqSP5ae+0dARfe17iuanUZ/PwYHKwvfJp7WLvj//OqlhfKVhTlrFgzjn7OueiI4kC8w+DKV2D0XG1mbEAv8Ils3F6Q0hjkG5RmQc4hFegVpQPYNOpGCHE5MBAwNLRJKZ+2d6eUNmTu0xb0cHKFsEHmpX27EjdfrdRwS3TO2pexWW7eSW/+Ou+oth6ssRZCBmrrwiqKYjNbVph6A3AHJqDVlp8ObOmgfiktObUJPrhKq70O4NsDZn5hvmBHd+AXC6P/Ahv+09gWOQKCmwTy7APwwTStjAJolSpvWQ4RzUsvKYrSHlvu6MdIKROFEHuklE8JIV4CfuyojinN1FbBb/9uDPKgrbGa8lv3C/TOei3QRwyDk79rd+sx4xrLAwMcXtEY5EHL6295S1uApHlpYUVR2mRLoG9YjLNcCBEO5AFqPbVzpbZSW7e0uaJUy7buwDMIBkzTvlqSf9yyLeeQNhZfBXpFsYkt4+iXCyF8gReAHWjj5j/tgD4pLTH4wNBbLNt7jj33fTkX4i61bBs2WyvhoCiKTWwJ9P+SUhZKKb8AegD9gGc7pltKixKmw9i/aotteAbD1W9A5PDO7lXH6DEGrnhZW/HJxRMmPg59L+nsXilKtySktK5EjRBih5RyaHttZyMpKUlu27bNXofrvuqqG1c9as5o1IYi6pzPjxrpxRnamrHeEY0LjCiKYkYIsV1KmdTadmtmxoYCEYCbEGIIjVUqvdFG4Sj2kn0Q9nwGJ36F/tNgwDWNi2kDVBTD8V9gy5vgHgij7tRmlTpyztpbPQZSlLNlzcPYqWjrxEYC/27SXgz8rQP6dH4qzoDPbtKWrANI2wGnt8K1bzYuXXf0Z/ji1sZ9Dn0Pf1qhLYWnKIrSCmtmxi4GFgshrqvPzysdIfdQY5BvcOh7rSZMWKK2oPb6/5hvN9bC8bUq0CuK0iZbHsZuEEK8K4T4EUAIMUAIcWt7OylWEi2kX4RobBe6lvP2zWeTKoqiNGNLoH8PWAGE178+DNxr7w6dt4LiIKLZnfngmRAQq/3s4gEXPWi+Xe+mrbWqKIrSBlsmTAVKKZcKIR4FkFLWCiHqOqhf5x/PYLjuHTiyClI3Qa9JWhDXuzW+J3Y8zPoG9n+pDTvsf5VW70ZRFKUNtgT6MiFEAPVLBgohRgFFHdKr85V/DIy8Tftqid4Neo3XvhRFUaxkS6C/H/gWiBVCbACC0AqbKYqiKF2YLYE+GfgKKAdKgK/R8vSKoihKF2bLw9gP0Moe/AN4FegLfNgRnVIURVHsx5Y7+ngpZdOVH34RQrSwCnQjIYQBWAe41p9rmZTySdu7qVgt+wDs+xLStkP8ddD7YvAK6exeKYrSiWwJ9DuEEKOklJsAhBAjgfYK01QBE6WUpUIIPbBeCPFjwzEUOys4BR9dB8Vp2utjq2HMPTDpSXCyaTExRVEciC2pm2HA70KIFCFECrARGC6E2CuE2NPSDlJTWv9SX/9lXRU1xXbZ+xuDfIPNr2sLlCiKct6y5TbvjGrECiGcgO1Ab2ChlHJzs+23A7cDREerhaE7hvrbqijnM6sDvZTyjG4LpZR1wOD6RUu+EkLESyn3Ndn+FvAWaGWKz+QcSr3gAeAVDiXpjW0j7wLfnp3WJUVROt85S9xKKQuFEL+gfTLY1977lTPg1wNmfQl7v4D0Jg9jVX5eUc5rHRoBhBBBQE19kHcDJgP/7MhznveC+8Okxzq7F4qidCEdfasXhlbi2Antwe9SKeXyDj6noiiK0kSHBnop5R5gSEeeQ1EURWmbLcMrFUVRlG5IBXpFURQHpwK9oiiKg1OBXlEUxcGpQK8oiuLgVKBXFEVxcCrQK4qiODgV6BVFURycCvSKoigOTgV6RVEUB6cCvaIoioNTgV5RFMXBqUCvKIri4FSgVxRFcXAq0CuKojg4FegVRVEcnAr0iqIoDk4FekVRFAenAr2iKIqDU4FeURTFwalAryiK4uBUoFcURXFwKtAriqI4OBXoFUVRHFyHBnohRJQQ4hchRLIQYr8QYn5Hnk9RFEWx5NzBx68F/iql3CGE8AK2CyFWSimTO/i8iqIoSr0OvaOXUmZIKXfU/1wCHAAiOvKciqIoirlzlqMXQvQEhgCbz9U5u6LiihpKKmo69ByVNXXklVYhpezQ8yiK0j10dOoGACGEJ/AFcK+UsrjZttuB2wGio6PPRXc6RXFFDasPZrPwl6M46wT3TOrDRX2D8HC173+CXacLeHX1UQ5mlnDV4HBuGB5FdICHXc+hKEr3Ijr6rk8IoQeWAyuklP9u671JSUly27ZtHdqfzrJifyZ3fLjdrG3xnOFcFBdst3Mczyll2msbKK2qNbVdPTicf16XiKveyW7nURSlaxFCbJdSJrW2vaNH3QjgXeBAe0HekRmNkg83nrRo/2pnml3Pczir1CzIA3yzO53Uwgq7nkdRlO6lo3P0FwCzgIlCiF31X5d18Dm7HJ1OEOzlatEe5GWw63kMesv/nG56J/ROarqEopzPOnrUzXoppZBSJkopB9d//dCR5+yqZo7qgUuTgOumd+LKQWF2PUf/MG8SI3zM2v46pS9Rfm52PY+iKN1Lh+fobeHIOXopJXvTithyIh+dTjAyxp+B4T7t72ij0/nlbE3J51R+OUOifRkS5Ye3m97u51EUpetoL0d/TkbdKCCEIDHSl8RI3w49T4SvGzVRvvQJ9iTY29BhQT4lt5SUvHJ83PQMDPfBxdn+Hw4rqus4kVtKnVHSM9ADL4P6g6UoZ0IFegdSWVPHlztSeeq7ZKpqjYT5GHhj5lAGRfnZ9TybjufxwOe7SS2owMVJx/yLe/PHEdH4eVg+hzhTmUUVvPTzYT7fngrA2D6BPHt1PD3UUFFFsZl6SudADmWW8Lev9lFVawQgo6iSh77YS0FZtd3OkVVUwbPLk0kt0EbyVNcZeWHFYfakFtntHAAbj+WZgjzAb0dy+XZ3ul3PoSjnCxXo7UhKSWllDUZj6889iitqKK48u8CbW1pFUYXlMVILygGIj/Bm6sAQgrxcOZRZQk5p1Vmdr6nskir2pRdbtJ/KL7fbOQA2HMuzaPtpXyaVNXV2PY+inA9U6sZOjueUsmTraVYfzOaivkHcOCKK3sFepu15pVWsPZzDhxtP4iQEsy/oydi+Afi6WZ/uSMktYd2RPJZsPY2XwZk5F8RwQS9/PA0uAIT5GPj75f3ZlpLPidwyLksIw8vVGT93++W2/dz1xAZ6cDy3zKw9zMe+Q0WHRvuyrMkdPcCFfQJx7YBnAYri6FSgt4P8smruW7KL3fXpi6PZpaw/ksPHfx5FYP34+Q1Hc/nr0t2mfbafKuDNWcOYOjDU6vOsPZzLgm8bC39uOZHPOzcnMbF/CABeBj2vrTlKUX0tncNZpUwfFmHXB7KR/h48fuUA5n2y0zQ5a9aoaOIj7DuCaGyfQEbG+LP5RD4AvYI8uH5YJNocPEVRbKECvR2k5JaZgnyDQ1mlHM8tJdDLldpaI0u3nbbY77vd6VYH+szCCj7dbH4Mo9QejDYE+hO5ZaYg3+DLHWncPb43sUGetvxKbZoQF8ynt40kJa8cfw89A0K98fO034NYgCh/D/43cyjHskuprZP0rh9FpCiK7VSgt1JKbhnJ6cXUGI30D/Omb0hjWqa1macN7c7OOjxbKF7WvKBZan4Ze9KKyS6uJNLPjcFRfqZPBM5OAjcXy3o1bi6Nx2ipHy7OOpx0jXfB1bVGtp/M50h2KR4uzsRHeBMX6m22T0ZhBfvTiymqqKFPiCcDwrxxbnLs0ooaCitqyC+rxigl+RXVdg/0ACUVteSWVFNjNBLo5aoCvaKcIRXorXA4q4SZ724mu1h7qOnh4sSnt48yjYmPDfTgqsHhfLOrcVTIlAEh9GpyF33DiGhWHsimrv5BrYuTjisSG2fGZhdX8tLKI2b1b+6/uA93ju+Ni7OOQC8Dt14Yw7xPd5q2e7o6MyLG3/Q6xNuFXoEeHGuSP//zhbFE+bmbXv9yMJu5n+ygtr4fcSGe/GfGYPrXT95KL6zgL5/sYMepQgB0At69ZTgT+jUWX/txfyYPfbGHhrl2I2L8ef7aBLt+amjvmiuKYj0V6K2w5kC2KeAAlFXX8cHvKfxz+iCcdAIPgzOPXNqPif2C2XGygEFRvoyKDTDLjY/pFch7s5P49XAOTjodF/UJYnSvxiB9IKPYosjZa78cY1zfIAZH+1FVW0duSSVvzBzK+iO5eBn0jIz1p7CssV/Hc8qZMjAUo5RkFlfSK8iTI9klJGcUEx/hQ2ZxBS+tPGQK8qClmPakFZkC/b60IlOQBy099NTy/QyK8sHfw5XjOaX866dDNJ1QveVEPvvTi+wa6Nu75oqiWO+8CPS1dUZO5pdTXWskyt8NT1fbHk6eyCvlqsHhXNw/GIFg5+kCtqYUUFtnxEmnpVPCfNy4anAEVw1ueQEtF2cdg6N88HDVoxPQJ9gTna4xHVJUUYO/hwtXDw7H3dWZ/NJqvt6VRkn9A8+qGiMfbDpFjwB3Lu4fTHWt5PkfDzKpXzCXD9KOUVxZw+u/HsPDxQl/Txe+252OUcKsUT0BKKusI6Ow0qJvxRWNFS+LKy0XRUkvqKSiug48oLy6jtwyy+GaxZXmVTPtcc2bO5xdanbNFUWxjsMH+sLyaj7YmMJra45RXWdkXJ9AnrpqIDGB1t99Xjskkq92pnHvkt3UGSUX9w/mkUvibKrxfjCzmFdXH+WHfRkI4Johkdw1PtY0BLN3sAd3XdSL/609SkF5DZF+bjx2eX9iArS0i7ebnoemxvHhppM89vV+9E6C6cMiuaBPoOkcMYEeuDjpKKuuoyxfm9A0KtafHv5aUbNof3euGhLOR5tOmfWtT0jjtegd5IkQmN2xXzs0guD6SptRfm6M7xvEL4dyTNuddYJeQY0zVu1xzS/uH8KSrebDK28cHqXq6ivKGXD4Qck7Txfy75VHqK7TZouuO5LLB7+fNOXKrXE0p5TPtp427bPqQDa/H8u3qR+rD2Tx/d4MpNTSIV/sSOW3I7mm7VW1Rl5YcYiCcu2OOrWggldWH6G6Vjun0Whka0o+v9dPJKqpk3y65bRZesPdxYlXbhhMbKAHOgET+wUzf1IfU9A2Iukf6s20QeHonQRhPgYeviQOtybBc0C4D2/NSiLSzw1nnWDG8EjuGt8Lff34dR93F+6b3JepA0PQCegR4M6rfxzC0OjGNJQ9rvmIGH+euXogvu563F2cuG9yHyYNCLHlkiuKUs/h7+j31Q97vDIxjCAvVz7fnsoP+zL4y8TeBDQbKVJTZ2xx5MrGFmZprj2czR0XxVoU2mrpGDV1Rn45mENz64/kMueCGABO5lWYAmODrOIqUgvLiQ32JLu4itUHsy2Osf1kPlcP0dJFyekl/HvlIZ6+Kh5vNz370oq48e3NfHHXGEJ93cgvreYfPxygZ6AHf74wlsKKal5fe4yHLunHyNgAQEsxTR4QwtBoXypq6gjxMpiCfIPESF9e+sMg0gsq8HB1JqLJw15ovOYDw73xNujZcaqg1WveGh83F2aN6snk/tozhzAfgxpDryhnyOEDfWKEF2/MHMrXO9PYl17MQ1Pj0AlhNtwxtaCcFfsyWb4ngxEx/lw3LNJs+OSAcG+W78kwO258uA8eTYY7puSWsXxPOqsOZDM+Lohpg8JNDyf1TjoSIn3YdrLA7BgDIxqHNQZ7WwZADxcn/OsLhWlVIr05mWdeaqBPk34Ge7uSU1rNXR/vMLWFehvwr58Z62VwZkC4D1tT8tnfpIxBaAvDFtsKyFnFlaw+kMWy7WkMDPfihhHRZiWX+4V68vgV/dl0PJ+iihrmTuhNda2xxSGm7Qm184xbRTkfOXzqxsnJiXs+3cVP+7PYciKfx7/ZT43RaMr1VlTX8eKKQzzz/QF2ni7kzXXH+dP7W0lvsvzeuD5B9GsSUMN8DPwhKcr0MLWwvJqHv9jDiz8fZtfpQv6z6gj3LdlFXpMaM9MGhRPZZAGQXkEeTGoyZHFguBd/mdCrsd86wWNXDDDNOHVzdeb6YZEEerqY3pMY6UP/MK8mx/DmhuFRptcuTjqeuKI/MfV/cDwNeh69rB9eTQLuJfEhJERaP6u1zih5//cT/O2rfew4VcCHm05x0zubOdFkSKevhyv//PEQK5O1a/7vlYcJ9zWo/LqidBKHv6PfearQIiWy+PeTXDIwlFAfN07ll/FNs6qIqQUVHMkqIdxXC8zxET68PnMoBzJLqK0z0i/Um76hjQH2RG6Zaap+g92pRRzPLTPdGQ+J9mPR7OEcyixGJwT9Qr3pFdz4cNLHzZVbL4xhVGwgWcUVRPm7k9CkrEBZVS3/XX2E64ZG4qrX4SQEp/LL2Xw8n+E9tbRLqI8bD0zuyyXxoeSVVhET6Gmx4tTQaD++nXchJ3JK8XB1pm+IF34eLlgrraCcd39LMWsrLK/hYEYxMYHaA9m9qUUW1/zt305weUIYPu7Wn0tRFPtw+EDf0phrZ52gId2rEwIBNH9MqGu2X0yQp+nOuLnm7zWdu1lOuW+Il1lKqDk/D1cu7NNyykQIkAjeXHfcrP3RS/uZvQ70NjC+nRmkMYEepqBsK50Q2jVtVkSy6TVo6ZrrnYTdc+xlVbUcyCgmrbCCcB83+od542lw+H/SimIzh0/dJPXww71Z6YDbx8US4l0/5DDAnZmjepht7x/qRVwbAbm52EAPpjQbETK2dwCxQfZbJMPdxZlbL4wxazPodQyNtu+iIu2J8HNj3qTeZm1hPgb6hzU+bxje09/imt97cV+7FlerqTXy0eaTTH9jI/M/28X1b25k0YYTVKkyxopi4bxYM3bjsVxW7M8it7SKS+NDGR0bgH+Th41ZxZVsOJrLmoPZDIn2Y2K/YJvveNMKyll3JJcNR3MZFRvARX2DiPJ3b39HK1XV1vGvHw8S5G1g9+lCPA3O9AryJNTblauHRNrtPNbIL6ti07F8ftqfQVyoN5MHhFh8UtmbWshP+zLJKqniysRwknr6WdT2ORuHMou57L/rzYZsCgHfzxvLgHDvNvZUFMfT3pqxDhHoSypryCiqxNPV2ZRXdzRFFdVc9doGTuaX0yvIk8qaOlILKrhrfC8evqRf+wdwMFtP5HP9mxst2j+5bSRjegW2sIeiOK72An23T90czirhT+9vY8rL67j8v7+xfE861bWO9/Hdx82F64ZFIqVW775hKb+RTYqanU8i/dwIajYE1Nddb1bATVEUTbcO9GVVtTz13X62pmgjXgrKa5j36U4OZpZ0cs86xrVDIrhpZDROOm0ewIJpAxga7dvZ3eoUYb5uvHXzMNPw0r4hnrxzc5Jd02WK4ii69RCF7OJKNhw1n7UqpTbc0RHL2Ub4ubPgyoHcNjYWvZOwmJF6vhkS7cent40iv6waP3cXm4aJKsr5pEMDvRBiEXAFkC2ljLf38T0NzoT7GEgvMq/IGGjnRTDqjJKdpwpYtj2V8upark+KYnhPfwydMAFI76yj5xkOjXREvu4u+Kqx+YrSpo5O3bwPXNJRBw/yMvB/1ybg3GTc9lWDwukXZv3QSGvsPl3IDW9t4rOtp/l2dwaz3t3C5hOW9W8URVG6og69o5dSrhNC9OzIc4zrE8R38y7kRG4Zfu56+oV64edh3zv6n/Znmi3WAfDObycYExtoUfBLURSlq+n0HL0Q4nbgdoDo6Gib93fSCfqHeZtN2LG3loagSgmoYoqKonQDnX47KqV8S0qZJKVMCgoK6uzutOiS+FCLaf23XhjT6qLgiqIoXUmn39F3B4Miffn0tlF8tuUUZVW13DSqByNizm3pAUVRlDOlAr0VnJ10jIjxZ8R5OjlJUZTurUNzD0KIT4GNQJwQIlUIcWtHnk9RFEWx1NGjbm7syOMriqIo7VNPExVFURycCvSKoigOTgV6RVEUB6cCvaIoioPrUguPCCFygJOd3I1AILeT+2AN1U/76i79hO7TV9VP+2utrz2klK3OOO1Sgb4rEEJsa2ullq5C9dO+uks/ofv0VfXT/s60ryp1oyiK4uBUoFcURXFwKtBbequzO2Al1U/76i79hO7TV9VP+zujvqocvaIoioNTd/SKoigOTgV6RVEUB3feBnohhJMQYqcQYnkL22YLIXKEELvqv/7cGX2s70uKEGJvfT+2tbBdCCH+K4Q4KoTYI4QY2kX7OV4IUdTkmj7RSf30FUIsE0IcFEIcEEKMbra9q1zP9vrZVa5nXJM+7BJCFAsh7m32nk6/plb2s6tc0/uEEPuFEPuEEJ8KIQzNtrsKIZbUX8/NVi3XKqU8L7+A+4FPgOUtbJsNvNbZfazvSwoQ2Mb2y4Af0RY2HAVs7qL9HN/Ste6Efi4G/lz/swvg20WvZ3v97BLXs1mfnIBMtMk7Xe6aWtHPTr+mQARwAnCrf70UmN3sPXcDb9T/fAOwpL3jnpd39EKISOBy4J3O7osdXAV8IDWbAF8hRFhnd6orEkL4AOOAdwGklNVSysJmb+v062llP7uiScAxKWXz2e2dfk2baa2fXYUz4CaEcAbcgfRm269CuxEAWAZMEkK0uYL1eRnogf8ADwHGNt5zXf3HzGVCiKhz060WSeBnIcT2+oXUm4sATjd5nVrfdq6110+A0UKI3UKIH4UQA89l5+rFADnAe/Vpu3eEEB7N3tMVrqc1/YTOv57N3QB82kJ7V7imTbXWT+jkayqlTANeBE4BGUCRlPLnZm8zXU8pZS1QBAS0ddzzLtALIa4AsqWU29t423dATyllIrCSxr+eneFCKeVQ4FJgrhBiXCf2pS3t9XMH2kflQcCrwNfnuH+g3SkNBV6XUg4ByoBHOqEf7bGmn13hepoIIVyAacDnndmP9rTTz06/pkIIP7Q79hggHPAQQsw82+Oed4EeuACYJoRIAT4DJgohPmr6BillnpSyqv7lO8Cwc9tFs76k1X/PBr4CRjR7SxrQ9BNHZH3bOdVeP6WUxVLK0vqffwD0QojAc9zNVCBVSrm5/vUytIDaVFe4nu32s4tcz6YuBXZIKbNa2NYVrmmDVvvZRa7pxcAJKWWOlLIG+BIY0+w9putZn97xAfLaOuh5F+illI9KKSOllD3RPsKtkVKa/cVslj+cBhw4h11s2g8PIYRXw8/AFGBfs7d9C9xcP7JhFNpHvYyu1k8hRGhDHlEIMQLt316b/zjtTUqZCZwWQsTVN00Ckpu9rdOvpzX97ArXs5kbaT0d0unXtIlW+9lFrukpYJQQwr2+L5OwjD/fArfU/zwdLYa1OfO1Q9eM7U6EEE8D26SU3wL3CCGmAbVAPtoonM4QAnxV/2/PGfhESvmTEOJOACnlG8APaKMajgLlwJwu2s/pwF1CiFqgArihvX+cHWQe8HH9R/jjwJwueD2t6WdXuZ4Nf9wnA3c0aety19SKfnb6NZVSbhZCLENLI9UCO4G3msWnd4EPhRBH0eLTDe0dV5VAUBRFcXDnXepGURTlfKMCvaIoioNTgV5RFMXBqUCvKIri4FSgVxRFcXAq0CuKojg4FegVpV59mVqLstVNts8WQrzWAeedLYQIb/I6pZNnuSoORgV6Rel8s9HqmihKh1CBXulW6sstfF9fYXCfEGKGEGKYEOLX+sqZKxpKWAgh1gohXhHaIhL76qe1I4QYIYTYWF8Z8vcmpQZs6UeQEOILIcTW+q8L6tsXCCEW1Z/7uBDinib7PC6EOCSEWC+0BSUeEEJMB5LQZsHuEkK41b99nhBih9AWc+l31hdOOa+pQK90N5cA6VLKQVLKeOAntEqD06WUw4BFwP81eb+7lHIw2mINi+rbDgJj6ytDPgH84wz68QrwspRyOHAd5msb9AOmohV2e1IIoRdCNLxvEFphrSQAKeUyYBtwk5RysJSyov4YufXVQF8HHjiD/imKiap1o3Q3e4GXhBD/BJYDBUA8sLK+1o4TWh3vBp8CSCnXCSG8hRC+gBewWAjRB62Ovv4M+nExMEA0rvfgLYTwrP/5+/rqp1VCiGy0WkAXAN9IKSuBSiHEd+0c/8v679uBa8+gf4piogK90q1IKQ8Lbc3Ry4BngTXAfinl6NZ2aeH1M8AvUsprhLbe5toz6IoOGFUfuE3qA39Vk6Y6zuz/s4ZjnOn+imKiUjdKt1I/OqVcSvkR8AIwEggS9Ytn16dJmq4MNKO+/UK08rhFaPW7G+qhzz7DrvyMVmGyoV+D23n/BuBKIYSh/s7/iibbStA+ZShKh1B3Ckp3kwC8IIQwAjXAXWjlXP8rtLVWndGWitxf//5KIcROtPTMn+rb/oWWunkM+P4M+3EPsFAIsaf+nOuAO1t7s5RyqxDiW2APkIWWgiqq3/w+8IYQogJo7ZOJopwxVaZYcVhCiLXAA1LKbZ3dFwAhhKeUslQI4Y72h+F2KeWOzu6X4vjUHb2inDtvCSEGAAZgsQryyrmi7ugVpRkhxBxgfrPmDVLKuZ3RH0U5WyrQK4qiODg16kZRFMXBqUCvKIri4FSgVxRFcXAq0CuKoji4/wdiLDrZAFXdHAAAAABJRU5ErkJggg==\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 378,
       "height": 263
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "Jointplot muestra un scatterplot y la distribución de los datos.\n\nDocumentación de jointplot: http://seaborn.pydata.org/generated/seaborn.jointplot.html",
   "metadata": {
    "tags": [],
    "cell_id": "00042-03fbfb06-4446-4364-9823-43a5ee0db2a2",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "a7b66770",
    "execution_start": 1623208483864,
    "execution_millis": 38,
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00043-88af0e9e-3eec-4383-8e6f-64a1df3c7d47",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "6104f320",
    "execution_start": 1624243279221,
    "execution_millis": 514,
    "deepnote_cell_type": "code"
   },
   "source": "sns.jointplot(data=iris, x='sepal_length', y='petal_length', hue='species')",
   "execution_count": 23,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 23,
     "data": {
      "text/plain": "<seaborn.axisgrid.JointGrid at 0x7f4901f58e90>"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 432x432 with 3 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 415,
       "height": 424
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "Documentación de boxplot: http://seaborn.pydata.org/generated/seaborn.boxplot.html",
   "metadata": {
    "tags": [],
    "cell_id": "00044-a3616999-1359-44bb-945a-9f6861df59ee",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00045-fa974d32-9df0-4874-abca-a6210380826e",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "2f3e583c",
    "execution_start": 1624243279736,
    "execution_millis": 159,
    "deepnote_cell_type": "code"
   },
   "source": "sns.boxplot(data=iris, x='species', y='sepal_length')",
   "execution_count": 24,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 24,
     "data": {
      "text/plain": "<AxesSubplot:xlabel='species', ylabel='sepal_length'>"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 387,
       "height": 262
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "Documentación de barplot: http://seaborn.pydata.org/generated/seaborn.barplot.html",
   "metadata": {
    "tags": [],
    "cell_id": "00046-d87d496e-f4cc-4aa5-b4df-191ffb3a5ab3",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00047-8594d4aa-7c81-4c02-bcf2-632d909b6c52",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "bc8c9f47",
    "execution_start": 1624243279916,
    "execution_millis": 184,
    "deepnote_cell_type": "code"
   },
   "source": "sns.barplot(data=iris, x='species', y='sepal_length')",
   "execution_count": 25,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 25,
     "data": {
      "text/plain": "<AxesSubplot:xlabel='species', ylabel='sepal_length'>"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 377,
       "height": 263
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "# Estadística en la ingesta de datos\n---\n\n## Pipelines de procesamiento para variables numéricas\n\n### Escalamiento lineal\n\nEs importante normalizar los datos (hacer escalamiento lineal), antes de pasarlos por un modelo de machine learning. Esto porque los modelos son eficientes si están en el mismo rango `[-1, 1]`. Si no están en ese rango, tienen que ser transformados (escalados).\n\nHay diferentes tipos de escalamiento lineal (max-min, Clipping, Z-score, Winsorizing, etc.). Se los usa normalmente cuando la data es simétrica o está uniformemente distribuida.",
   "metadata": {
    "tags": [],
    "cell_id": "00048-415542ce-533a-43ba-9f7f-4672a3b74be1",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "markdown",
   "source": "### Tipos de escalamiento lineal\n\n- **Min-max**: hace una transformación para que los datos entren en el rango `[-1, 1]` por medio de una fórmula. Es uno de los más usados. **Funciona mejor para datos uniformemente distribuidos**.\n- **Clipping**: fuerza a que los datos que se salen del rango, se transformen en datos dentro del mismo. Este método no es muy recomendado porque descarta valores outliers que puede que estén funcionando bien.\n- **Winsorizing**: una variación del clipping que usa los quartils como extremos.\n- **Z-Score**: es uno de los más comunes porque está basado en el promedio y desviación estándar. **Funciona mejor para datos distribuidos \"normalmente\" (forma de campana de Gauss)**.\n\n![Picture title](images/image-20210609-082604.png)\n\n![Picture title](images/image-20210609-083656.png)",
   "metadata": {
    "tags": [],
    "cell_id": "00049-49077991-1af8-4dc8-9e18-a74ca17d00fa",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "markdown",
   "source": "### Transformaciones lineales en Python\n\nSe utilizará este dataset de scikit-learn: https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html",
   "metadata": {
    "tags": [],
    "cell_id": "00050-38013604-8bfc-40b9-a334-7f1dfd06a241",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00051-0991d859-9788-40fb-94d1-ac0e52a236fc",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "c43f3536",
    "execution_start": 1624243280097,
    "execution_millis": 963,
    "deepnote_cell_type": "code"
   },
   "source": "import timeit #para medir el tiempo de ejecución de los modelos\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn import datasets, linear_model #datasets para descargar un modelo y linear_model para hacer una regresión lineal\n\nX, y = datasets.load_diabetes(return_X_y=True) #carga el dataset\nraw = X[:, None, 2] #transformación en las dimensiones para que se ajuste al formato de entrada del preprocesamiento",
   "execution_count": 26,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00052-e44d2ccd-2bc0-4b6c-9f48-d28ee8d15002",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "dba0c3",
    "execution_start": 1624243281069,
    "execution_millis": 242,
    "deepnote_cell_type": "code"
   },
   "source": "#reglas de escalamiento lineal, aplicamos max-min\nmax_raw = max(raw)\n#raw = datos crudos\nmin_raw = min(raw)\nscaled = (2*raw - max_raw - min_raw)/(max_raw - min_raw)\n\n# es importante tener una noción de los datos originales antes y después de escalarlos:\nfig, axs = plt.subplots(2, 1, sharex=True)\naxs[0].hist(raw)\naxs[1].hist(scaled)",
   "execution_count": 27,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 27,
     "data": {
      "text/plain": "(array([32., 66., 98., 90., 64., 50., 23., 12.,  5.,  2.]),\n array([-1. , -0.8, -0.6, -0.4, -0.2,  0. ,  0.2,  0.4,  0.6,  0.8,  1. ]),\n <BarContainer object of 10 artists>)"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 375,
       "height": 250
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00053-6ff7ddef-cd9f-4201-b440-8167991b0770",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "1562e49",
    "execution_start": 1624243281298,
    "execution_millis": 3,
    "deepnote_cell_type": "code"
   },
   "source": "# modelos para entrenamiento\ndef train_raw():\n    linear_model.LinearRegression().fit(raw, y)\n\ndef train_scaled():\n    linear_model.LinearRegression().fit(scaled, y)",
   "execution_count": 28,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00054-abd3131b-588f-4d91-9e5b-8449c60d47e6",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "d2ef4ec8",
    "execution_start": 1624243281305,
    "execution_millis": 76,
    "deepnote_cell_type": "code"
   },
   "source": "raw_time = timeit.timeit(train_raw, number=100) #repite la ejecución del código 100 veces y sobre eso calcula el tiempo\nscaled_time = timeit.timeit(train_scaled, number=100)\nprint(f'train raw: {raw_time}')\nprint(f'train scaled: {scaled_time}')",
   "execution_count": 29,
   "outputs": [
    {
     "name": "stdout",
     "text": "train raw: 0.037671185011276975\ntrain scaled: 0.03234461801184807\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "**Se puede ver como al normalizar los datos, el algoritmo se vuelve más eficiente.**\n\n---\n\nScikit Learn tiene una parte de prepocesamiento, en su documentación encontrarás cómo estandarizar datos numéricos y catergóricos.\n\nUtilidades de Scikit Learn: https://scikit-learn.org/stable/modules/preprocessing.html\n\n---",
   "metadata": {
    "tags": [],
    "cell_id": "00055-f74c04f0-c390-487e-a0b8-0a2e0c7f53cc",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "markdown",
   "source": "### Transformación no lineal\n\nCuando la data no es simétrica o uniforme, sino que está muy sesgada, se le aplica una transformación para que tengan una distribución simétrica y se pueda aplicar los escalamientos lineales.\n\nHay diferentes tipos de de funciones no lineales: logaritmos, sigmoides, polinomiales, etc. Estas funciones se les puede aplicar a los datos para transformarlos y hacerlos homogéneos.\n\n#### Tanh(x)\n\nLa tanh siempre está en un rango de -1 a 1 en Y, por lo que, cuando los valores de X son muy altos, estarán muy cercanos al |1|. También se podría calibrar los datos para ajustar la curva, dividiéndolos por un parámetro a.\n\n![Picture title](images/image-20210609-085317.png)\n\n#### Raíz cuadrada\n\nOtras funciones polinomiales, por ejemplo la raíz cuadrada (x½), también pueden hacer que una distribución se normalice.\n\n![Picture title](images/image-20210609-085600.png)\n\nHay un abanico gigante de maneras para hacer estas transformaciones con funciones no lineales. Los anteriores fueron solo 2 ejemplos. Las más famosas son la tanh y la sigmoide porque ambas amplian los valores importantes y acercan al 0, mientras que a los outliers los minimizan y acercan a -1 y 1.",
   "metadata": {
    "tags": [],
    "cell_id": "00051-71e76231-61b7-4f15-b479-0c3cf5dbf4a8",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "markdown",
   "source": "### Transformaciones no lineales en Python",
   "metadata": {
    "tags": [],
    "cell_id": "00051-e27244e9-5807-4378-a586-d8007e620913",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00058-8367f1a8-2f9d-4936-93cf-28da7c1a8772",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "f2d578bb",
    "execution_start": 1624243281388,
    "execution_millis": 130,
    "deepnote_cell_type": "code"
   },
   "source": "df = pd.read_csv('cars.csv')",
   "execution_count": 30,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00059-6e7d22e6-b640-44bd-a8f2-ce6b64ae9dd0",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "da1786ef",
    "execution_start": 1624243281536,
    "execution_millis": 171,
    "deepnote_cell_type": "code"
   },
   "source": "# Acá se puede apreciar como la distribución está fuertemente sesgada\ndf.price_usd.hist()",
   "execution_count": 31,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 31,
     "data": {
      "text/plain": "<AxesSubplot:>"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 388,
       "height": 248
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00060-cd693bb1-223a-48c4-9402-7d0422752a01",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "ad314610",
    "execution_start": 1624243281704,
    "execution_millis": 186,
    "deepnote_cell_type": "code"
   },
   "source": "# Transformación con tanh(x)\n\n# Esta línea toma la columna y le aplica a toda una función matemática\np = 10000\ndf.price_usd.apply(lambda x: np.tanh(x/p)).hist()",
   "execution_count": 32,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 32,
     "data": {
      "text/plain": "<AxesSubplot:>"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 381,
       "height": 248
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "En esta documentación encontrarás diversas maneras de hacer esas transformaciones no lineales. De esa manera podrás aplicar las funciones que trae Scikit Learn para hacer las transformaciones.\n\nMapear datos a una distribución Gaussiana: https://scikit-learn.org/stable/auto_examples/preprocessing/plot_map_data_to_normal.html",
   "metadata": {
    "tags": [],
    "cell_id": "00061-04ab527f-bd6d-49c0-a896-3fba7ba34d05",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "84d54642",
    "execution_start": 1623250092458,
    "execution_millis": 1,
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "markdown",
   "source": "\n## Pipelines de procesamiento para variables categóricas\n\nCuando se tiene variables categóricas se hace un mapeo numérico. Para eso hay 2 métodos, de manera que sean fácilmente interpretables en modelos de machine learning:\n\n- **Dummy**: es la representación más compacta que se puede tener de los datos. Es mejor usarla cuando los inputs son variables linealmente independientes (no tienen un grado de correlación significativo). Es decir, las cuando se sabe que las categorías son independientes entre sí.\n- **One-hot**: es más extenso. Permite incluir categorías que no estaban en el dataset inicialmente. De forma que si se filtra una categoría que no estaba incluida, igual se pueda representar numéricamente y no de error en el modelo (este modelo es más cool y es el que se usa).\n\nHay errores en la notación de Pandas y los tratan como que ambos modelos son lo mismo, pero en la realidad el Dummy no se usa. Aún así, en Pandas el método es `.get_dummies()`.\n\nEjemplo de aplicación de ambos:\n\n![Picture title](images/image-20210609-104522.png)",
   "metadata": {
    "tags": [],
    "cell_id": "00062-71d09976-21eb-44a1-bf26-d87c7c6c764a",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "markdown",
   "source": "### Procesamiento de datos categóricos en Python",
   "metadata": {
    "tags": [],
    "cell_id": "00063-a2f0720f-af00-4e1b-a382-58f0890cdf5b",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00063-6d2f5144-51fa-46a3-be17-56270cf01db1",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "7083d37c",
    "execution_start": 1624243281891,
    "execution_millis": 148,
    "deepnote_cell_type": "code"
   },
   "source": "import pandas as pd\n\ndf = pd.read_csv('cars.csv')",
   "execution_count": 33,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "Documentación de Pandas dummies: https://pandas.pydata.org/docs/reference/api/pandas.get_dummies.html",
   "metadata": {
    "tags": [],
    "cell_id": "00065-176b4c40-74d3-4b92-a78c-a56d766a0a11",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00066-a5fd2b6a-b38c-4b9b-957f-5fd93a1afda6",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "48707498",
    "execution_start": 1624243282043,
    "execution_millis": 57,
    "deepnote_cell_type": "code"
   },
   "source": "pd.get_dummies(df['engine_type'])",
   "execution_count": 34,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 34,
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         "_deepnote_index_column": 38395
        },
        {
         "diesel": 0,
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         "gasoline": 1,
         "_deepnote_index_column": 38396
        },
        {
         "diesel": 0,
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         "gasoline": 1,
         "_deepnote_index_column": 38397
        },
        {
         "diesel": 1,
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         "_deepnote_index_column": 38398
        },
        {
         "diesel": 0,
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         "_deepnote_index_column": 38399
        },
        {
         "diesel": 0,
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         "gasoline": 1,
         "_deepnote_index_column": 38400
        },
        {
         "diesel": 0,
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         "_deepnote_index_column": 38401
        },
        {
         "diesel": 0,
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         "gasoline": 1,
         "_deepnote_index_column": 38402
        },
        {
         "diesel": 0,
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         "_deepnote_index_column": 38403
        },
        {
         "diesel": 0,
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         "gasoline": 1,
         "_deepnote_index_column": 38404
        },
        {
         "diesel": 0,
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         "gasoline": 1,
         "_deepnote_index_column": 38405
        },
        {
         "diesel": 0,
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         "gasoline": 1,
         "_deepnote_index_column": 38406
        },
        {
         "diesel": 0,
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         "_deepnote_index_column": 38407
        },
        {
         "diesel": 0,
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         "gasoline": 1,
         "_deepnote_index_column": 38408
        },
        {
         "diesel": 0,
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         "gasoline": 1,
         "_deepnote_index_column": 38409
        },
        {
         "diesel": 0,
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         "gasoline": 1,
         "_deepnote_index_column": 38410
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38411
        },
        {
         "diesel": 0,
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         "gasoline": 1,
         "_deepnote_index_column": 38412
        },
        {
         "diesel": 0,
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         "gasoline": 1,
         "_deepnote_index_column": 38413
        },
        {
         "diesel": 0,
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         "gasoline": 1,
         "_deepnote_index_column": 38414
        },
        {
         "diesel": 1,
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         "gasoline": 0,
         "_deepnote_index_column": 38415
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38416
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38417
        },
        {
         "diesel": 1,
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         "gasoline": 0,
         "_deepnote_index_column": 38418
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38419
        },
        {
         "diesel": 0,
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         "gasoline": 1,
         "_deepnote_index_column": 38420
        },
        {
         "diesel": 1,
         "electric": 0,
         "gasoline": 0,
         "_deepnote_index_column": 38421
        },
        {
         "diesel": 1,
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         "gasoline": 0,
         "_deepnote_index_column": 38422
        },
        {
         "diesel": 0,
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         "_deepnote_index_column": 38423
        },
        {
         "diesel": 1,
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        },
        {
         "diesel": 0,
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        },
        {
         "diesel": 0,
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         "_deepnote_index_column": 38426
        },
        {
         "diesel": 1,
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         "_deepnote_index_column": 38427
        },
        {
         "diesel": 0,
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        },
        {
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        },
        {
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        },
        {
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        },
        {
         "diesel": 1,
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         "_deepnote_index_column": 38432
        },
        {
         "diesel": 0,
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         "_deepnote_index_column": 38433
        },
        {
         "diesel": 0,
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         "_deepnote_index_column": 38434
        },
        {
         "diesel": 0,
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         "_deepnote_index_column": 38435
        },
        {
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        },
        {
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        },
        {
         "diesel": 1,
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         "_deepnote_index_column": 38438
        },
        {
         "diesel": 0,
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         "_deepnote_index_column": 38439
        },
        {
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         "_deepnote_index_column": 38440
        },
        {
         "diesel": 1,
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         "_deepnote_index_column": 38441
        },
        {
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         "_deepnote_index_column": 38442
        },
        {
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         "_deepnote_index_column": 38443
        },
        {
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         "_deepnote_index_column": 38444
        },
        {
         "diesel": 0,
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         "_deepnote_index_column": 38445
        },
        {
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        },
        {
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        {
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        },
        {
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         "_deepnote_index_column": 38449
        },
        {
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         "gasoline": 1,
         "_deepnote_index_column": 38450
        },
        {
         "diesel": 1,
         "electric": 0,
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         "_deepnote_index_column": 38451
        },
        {
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        },
        {
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         "_deepnote_index_column": 38453
        },
        {
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         "_deepnote_index_column": 38454
        },
        {
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         "_deepnote_index_column": 38455
        },
        {
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         "_deepnote_index_column": 38456
        },
        {
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        },
        {
         "diesel": 0,
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         "_deepnote_index_column": 38458
        },
        {
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         "_deepnote_index_column": 38459
        },
        {
         "diesel": 0,
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         "_deepnote_index_column": 38460
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38461
        },
        {
         "diesel": 0,
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         "_deepnote_index_column": 38462
        },
        {
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        },
        {
         "diesel": 0,
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         "_deepnote_index_column": 38464
        },
        {
         "diesel": 0,
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         "gasoline": 1,
         "_deepnote_index_column": 38465
        },
        {
         "diesel": 0,
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         "gasoline": 1,
         "_deepnote_index_column": 38466
        },
        {
         "diesel": 0,
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         "gasoline": 1,
         "_deepnote_index_column": 38467
        },
        {
         "diesel": 0,
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         "gasoline": 1,
         "_deepnote_index_column": 38468
        },
        {
         "diesel": 0,
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         "gasoline": 1,
         "_deepnote_index_column": 38469
        },
        {
         "diesel": 0,
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        },
        {
         "diesel": 1,
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        },
        {
         "diesel": 0,
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         "_deepnote_index_column": 38472
        },
        {
         "diesel": 1,
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         "gasoline": 0,
         "_deepnote_index_column": 38473
        },
        {
         "diesel": 0,
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         "_deepnote_index_column": 38474
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38475
        },
        {
         "diesel": 1,
         "electric": 0,
         "gasoline": 0,
         "_deepnote_index_column": 38476
        },
        {
         "diesel": 0,
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         "gasoline": 1,
         "_deepnote_index_column": 38477
        },
        {
         "diesel": 0,
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         "gasoline": 1,
         "_deepnote_index_column": 38478
        },
        {
         "diesel": 0,
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         "_deepnote_index_column": 38479
        },
        {
         "diesel": 0,
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         "gasoline": 1,
         "_deepnote_index_column": 38480
        },
        {
         "diesel": 0,
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         "_deepnote_index_column": 38481
        },
        {
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         "_deepnote_index_column": 38482
        },
        {
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         "_deepnote_index_column": 38483
        },
        {
         "diesel": 1,
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         "gasoline": 0,
         "_deepnote_index_column": 38484
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38485
        },
        {
         "diesel": 0,
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         "gasoline": 1,
         "_deepnote_index_column": 38486
        },
        {
         "diesel": 0,
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         "gasoline": 1,
         "_deepnote_index_column": 38487
        },
        {
         "diesel": 1,
         "electric": 0,
         "gasoline": 0,
         "_deepnote_index_column": 38488
        },
        {
         "diesel": 0,
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         "gasoline": 1,
         "_deepnote_index_column": 38489
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38490
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38491
        },
        {
         "diesel": 1,
         "electric": 0,
         "gasoline": 0,
         "_deepnote_index_column": 38492
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38493
        },
        {
         "diesel": 1,
         "electric": 0,
         "gasoline": 0,
         "_deepnote_index_column": 38494
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38495
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38496
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38497
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38498
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38499
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38500
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38501
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38502
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38503
        },
        {
         "diesel": 1,
         "electric": 0,
         "gasoline": 0,
         "_deepnote_index_column": 38504
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38505
        },
        {
         "diesel": 1,
         "electric": 0,
         "gasoline": 0,
         "_deepnote_index_column": 38506
        },
        {
         "diesel": 1,
         "electric": 0,
         "gasoline": 0,
         "_deepnote_index_column": 38507
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38508
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38509
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38510
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38511
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38512
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38513
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38514
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38515
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38516
        },
        {
         "diesel": 1,
         "electric": 0,
         "gasoline": 0,
         "_deepnote_index_column": 38517
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38518
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38519
        },
        {
         "diesel": 1,
         "electric": 0,
         "gasoline": 0,
         "_deepnote_index_column": 38520
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38521
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38522
        },
        {
         "diesel": 1,
         "electric": 0,
         "gasoline": 0,
         "_deepnote_index_column": 38523
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38524
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38525
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38526
        },
        {
         "diesel": 1,
         "electric": 0,
         "gasoline": 0,
         "_deepnote_index_column": 38527
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38528
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38529
        },
        {
         "diesel": 0,
         "electric": 0,
         "gasoline": 1,
         "_deepnote_index_column": 38530
        }
       ]
      },
      "text/plain": "       diesel  electric  gasoline\n0           0         0         1\n1           0         0         1\n2           0         0         1\n3           0         0         1\n4           0         0         1\n...       ...       ...       ...\n38526       0         0         1\n38527       1         0         0\n38528       0         0         1\n38529       0         0         1\n38530       0         0         1\n\n[38531 rows x 3 columns]",
      "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>diesel</th>\n      <th>electric</th>\n      <th>gasoline</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>38526</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>38527</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>38528</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>38529</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>38530</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>38531 rows × 3 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "Documentación de One-hot con Scikit: https://scikit-learn.org/stable/modules/preprocessing.html#encoding-categorical-features",
   "metadata": {
    "tags": [],
    "cell_id": "00067-e39da3de-d82b-4118-94e9-ceed3d609d27",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00068-341e9ca1-53cd-4169-800d-5e70202995b0",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "42ea23c9",
    "execution_start": 1624243282094,
    "execution_millis": 188859028,
    "deepnote_cell_type": "code"
   },
   "source": "import sklearn.preprocessing as preprocessing\n\nencoder = preprocessing.OneHotEncoder(handle_unknown='ignore')",
   "execution_count": 35,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00069-54d4fc3b-9a18-4fe4-9f7b-207245f75e6f",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "5fab404e",
    "execution_start": 1624243282121,
    "execution_millis": 37,
    "deepnote_cell_type": "code"
   },
   "source": "encoder.fit(df[['engine_type']].values)",
   "execution_count": 36,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 36,
     "data": {
      "text/plain": "OneHotEncoder(handle_unknown='ignore')"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00070-5e3ad27d-563a-4464-9be7-769e82aed48e",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "bfd6a5e8",
    "execution_start": 1624243282122,
    "execution_millis": 36,
    "deepnote_cell_type": "code"
   },
   "source": "encoder.transform([['gasoline'],['diesel'], ['aceite']]).toarray()",
   "execution_count": 37,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 37,
     "data": {
      "text/plain": "array([[0., 0., 1.],\n       [1., 0., 0.],\n       [0., 0., 0.]])"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "Las variables numéricas discretas (números enteros) también pueden ser codificadas como categóricas",
   "metadata": {
    "tags": [],
    "cell_id": "00071-04f6b7ce-3100-48f6-ac50-7410f5d81f02",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00072-27ef122c-06c6-4267-9ff8-284e33482a74",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "ee182087",
    "execution_start": 1624243282123,
    "execution_millis": 35,
    "deepnote_cell_type": "code"
   },
   "source": "encoder.fit(df[['year_produced']].values)",
   "execution_count": 38,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 38,
     "data": {
      "text/plain": "OneHotEncoder(handle_unknown='ignore')"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00073-3b769e5a-3ff9-46c8-bb34-ed1d245e409a",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "41b80921",
    "execution_start": 1624243282126,
    "execution_millis": 33,
    "deepnote_cell_type": "code"
   },
   "source": "encoder.transform([[2016], [2009], [190]]).toarray()",
   "execution_count": 39,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 39,
     "data": {
      "text/plain": "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n        0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "En este caso la dimensionalidad del dataset se ve demasiado afectada, así que hay que buscar hacer una reducción de los datos.",
   "metadata": {
    "tags": [],
    "cell_id": "00074-cb949695-39e7-4e74-b324-b9ae9ae35b78",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "markdown",
   "source": "## Correlaciones: covarianza y coeficiente de correlación\n\nSi 2 variables están correlacionadas, estarían aportando la misma información, por lo que no sería útil tener las 2 variables en el modelo si su correlación es muy alta.\n\nLa forma de encontrar las correlaciones es usando al covarianza:\n\n![Picture title](images/image-20210609-111204.png)\n\nPero como las escalas de X y Y pueden ser distintas, entonces se usa el coeficiente de correlación (ρ):\n\n![Picture title](images/image-20210609-111237.png)\n\nMientas más alto sea el coeficiente de correlación (más cercano a 1), más alta es la correlación y viceversa (más cercano a 0), y si el valor es cercano a -1, entonces hay una correlación inversa:\n\n![Picture title](images/image-20210609-111459.png)",
   "metadata": {
    "tags": [],
    "cell_id": "00075-830333ad-1364-48ef-a120-58470a369607",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "markdown",
   "source": "### Matriz de covarianza\n\nCuando hay muchas variables (lo que pasa normalmente), se debe calcular todas las posibles covarianzas de las parejas de datos del dataset. El resultado de este cálculo, representado en una matriz, es la matriz de covarianza.\n\n![Picture title](images/image-20210609-113436.png)\n\nSiempre se la usa en los análisis exploratorios de datos.",
   "metadata": {
    "tags": [],
    "cell_id": "00076-67847264-1cd9-4044-97a1-c1384aa17151",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "markdown",
   "source": "### Matriz de covarianza en Python",
   "metadata": {
    "tags": [],
    "cell_id": "00077-f0fbd9ff-80db-46f6-8d67-3c22e6d46acf",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00078-82c9e8c2-d773-45c0-a5fc-e6b341b33631",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "822c6af2",
    "execution_start": 1624243282140,
    "execution_millis": 4,
    "deepnote_cell_type": "code"
   },
   "source": "import numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.preprocessing import StandardScaler \n\niris = sns.load_dataset('iris')",
   "execution_count": 40,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00079-b154d250-3e77-498d-a725-03ac1ff54ae6",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "7a405016",
    "execution_start": 1624243282151,
    "execution_millis": 4054,
    "deepnote_cell_type": "code"
   },
   "source": "sns.pairplot(iris, hue='species') #este gráfico no sirve si hay demasiadas variables",
   "execution_count": 41,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 41,
     "data": {
      "text/plain": "<seaborn.axisgrid.PairGrid at 0x7f48fbcbed50>"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 804.75x720 with 20 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 801,
       "height": 709
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00081-8e987680-15ed-45c7-b707-e95b89964092",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "edb6b6a8",
    "execution_start": 1624243286202,
    "execution_millis": 1,
    "deepnote_cell_type": "code"
   },
   "source": "iris.columns",
   "execution_count": 42,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 42,
     "data": {
      "text/plain": "Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width',\n       'species'],\n      dtype='object')"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00080-058a0d7a-3b01-405e-bebf-db9eede8cb01",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "b6eeb99b",
    "execution_start": 1624243286207,
    "execution_millis": 23,
    "deepnote_cell_type": "code"
   },
   "source": "scaler = StandardScaler()\nscaled = scaler.fit_transform(\n    iris[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']]\n)\nscaled.T",
   "execution_count": 43,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 43,
     "data": {
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1.44883158e+00,\n         1.71209594e+00,  1.44883158e+00,  9.22302838e-01,\n         1.05393502e+00,  1.44883158e+00,  7.90670654e-01]])"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00082-98da3167-9ee0-4a0c-8a43-1086285c0bba",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "d011a353",
    "execution_start": 1624243286226,
    "execution_millis": 16,
    "deepnote_cell_type": "code"
   },
   "source": "covariance_matrix = np.cov(scaled.T)\ncovariance_matrix",
   "execution_count": 44,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 44,
     "data": {
      "text/plain": "array([[ 1.00671141, -0.11835884,  0.87760447,  0.82343066],\n       [-0.11835884,  1.00671141, -0.43131554, -0.36858315],\n       [ 0.87760447, -0.43131554,  1.00671141,  0.96932762],\n       [ 0.82343066, -0.36858315,  0.96932762,  1.00671141]])"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00083-de89d259-1114-4cf4-abc9-33ad8e2554c6",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "b1455b82",
    "execution_start": 1624243286239,
    "execution_millis": 304,
    "deepnote_cell_type": "code"
   },
   "source": "# Mapa de calor de la matriz de covarianza\nplt.figure(figsize=(10,10))\nsns.set(font_scale=1.5)\nhm = sns.heatmap(covariance_matrix,\n                 cbar=True,\n                 annot=True,\n                 square=True,\n                 fmt='.2f',\n                 annot_kws={'size': 12},\n                 yticklabels=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'],\n                 xticklabels=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'])",
   "execution_count": 45,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 720x720 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 593,
       "height": 562
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "Este proceso se conoce como reducción de datos y consiste en eliminar datos altamente correlacionados , se basa en la matriz de covarianza y se le llama análisis de componentes principales.",
   "metadata": {
    "tags": [],
    "cell_id": "00084-5d0d73d2-ca1d-4e20-9d13-855db74b61ec",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "585c534a",
    "execution_start": 1623257696396,
    "execution_millis": 205,
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "markdown",
   "source": "# Bonus: comandos de Pandas y Seaborn usados en el curso\n---\n\n|Código|▶ Función|\n|---|---|\n|**Pandas**||\n|`pd.read_csv('ruta')`|-> Lee el DataFrame y lo puedes almacenar en una variable {df}|\n|`df.dtypes`|-> Los tipos de todas las columnas|\n|`df.describe()`|-> Conjunto completo de estadísticos descriptivos (fundamentales)|\n|`df.groupby('categoria').count()`|-> Agrupa los datos por la variable categórica y los cuenta|\n|`df['categoria'].mean()`|-> Saca la media de todos los datos de esa categoría|\n|`df['category'].plot.hist(bins=20)`|-> Histograma de frecuencua con intervalos de valores (bins)|\n|---------------------------------|---------------------------------|\n|**Seaborn**||\n|`sns.scatterplot(data=dataset, x='x_column', y='y_column')`|-> Grafica un scatterplot (diagrama de dispersión). Si añades `hue='categoría'`, aparece en colores clasificados.|\n|`sns.jointplot(data=dataset, x='x_column', y='y_column')`|-> Grafica un scatterplot y sus distribuciones. Si añades `hue='categoría'`, aparece en colores clasificados.|\n|`sns.boxplot(data=iris, x='species', y='sepal_length')`|-> Grafica los cuartiles.|\n|`sns.barplot(data=iris, x='species', y='sepal_length')`|-> Grafica un diagrama de barras.|",
   "metadata": {
    "tags": [],
    "cell_id": "00098-50f92046-1e39-4721-b3ca-0d91cb9ab5a2",
    "deepnote_cell_type": "markdown"
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   "cell_type": "markdown",
   "source": "<a style='text-decoration:none;line-height:16px;display:flex;color:#5B5B62;padding:10px;justify-content:end;' href='https://deepnote.com?utm_source=created-in-deepnote-cell&projectId=cfa882b6-c07f-43fe-9901-1c2e471ce120' target=\"_blank\">\n<img alt='Created in deepnote.com' style='display:inline;max-height:16px;margin:0px;margin-right:7.5px;' src='data:image/svg+xml;base64,PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz4KPHN2ZyB3aWR0aD0iODBweCIgaGVpZ2h0PSI4MHB4IiB2aWV3Qm94PSIwIDAgODAgODAiIHZlcnNpb249IjEuMSIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIiB4bWxuczp4bGluaz0iaHR0cDovL3d3dy53My5vcmcvMTk5OS94bGluayI+CiAgICA8IS0tIEdlbmVyYXRvcjogU2tldGNoIDU0LjEgKDc2NDkwKSAtIGh0dHBzOi8vc2tldGNoYXBwLmNvbSAtLT4KICAgIDx0aXRsZT5Hcm91cCAzPC90aXRsZT4KICAgIDxkZXNjPkNyZWF0ZWQgd2l0aCBTa2V0Y2guPC9kZXNjPgogICAgPGcgaWQ9IkxhbmRpbmciIHN0cm9rZT0ibm9uZSIgc3Ryb2tlLXdpZHRoPSIxIiBmaWxsPSJub25lIiBmaWxsLXJ1bGU9ImV2ZW5vZGQiPgogICAgICAgIDxnIGlkPSJBcnRib2FyZCIgdHJhbnNmb3JtPSJ0cmFuc2xhdGUoLTEyMzUuMDAwMDAwLCAtNzkuMDAwMDAwKSI+CiAgICAgICAgICAgIDxnIGlkPSJHcm91cC0zIiB0cmFuc2Zvcm09InRyYW5zbGF0ZSgxMjM1LjAwMDAwMCwgNzkuMDAwMDAwKSI+CiAgICAgICAgICAgICAgICA8cG9seWdvbiBpZD0iUGF0aC0yMCIgZmlsbD0iIzAyNjVCNCIgcG9pbnRzPSIyLjM3NjIzNzYyIDgwIDM4LjA0NzY2NjcgODAgNTcuODIxNzgyMiA3My44MDU3NTkyIDU3LjgyMTc4MjIgMzIuNzU5MjczOSAzOS4xNDAyMjc4IDMxLjY4MzE2ODMiPjwvcG9seWdvbj4KICAgICAgICAgICAgICAgIDxwYXRoIGQ9Ik0zNS4wMDc3MTgsODAgQzQyLjkwNjIwMDcsNzYuNDU0OTM1OCA0Ny41NjQ5MTY3LDcxLjU0MjI2NzEgNDguOTgzODY2LDY1LjI2MTk5MzkgQzUxLjExMjI4OTksNTUuODQxNTg0MiA0MS42NzcxNzk1LDQ5LjIxMjIyODQgMjUuNjIzOTg0Niw0OS4yMTIyMjg0IEMyNS40ODQ5Mjg5LDQ5LjEyNjg0NDggMjkuODI2MTI5Niw0My4yODM4MjQ4IDM4LjY0NzU4NjksMzEuNjgzMTY4MyBMNzIuODcxMjg3MSwzMi41NTQ0MjUgTDY1LjI4MDk3Myw2Ny42NzYzNDIxIEw1MS4xMTIyODk5LDc3LjM3NjE0NCBMMzUuMDA3NzE4LDgwIFoiIGlkPSJQYXRoLTIyIiBmaWxsPSIjMDAyODY4Ij48L3BhdGg+CiAgICAgICAgICAgICAgICA8cGF0aCBkPSJNMCwzNy43MzA0NDA1IEwyNy4xMTQ1MzcsMC4yNTcxMTE0MzYgQzYyLjM3MTUxMjMsLTEuOTkwNzE3MDEgODAsMTAuNTAwMzkyNyA4MCwzNy43MzA0NDA1IEM4MCw2NC45NjA0ODgyIDY0Ljc3NjUwMzgsNzkuMDUwMzQxNCAzNC4zMjk1MTEzLDgwIEM0Ny4wNTUzNDg5LDc3LjU2NzA4MDggNTMuNDE4MjY3Nyw3MC4zMTM2MTAzIDUzLjQxODI2NzcsNTguMjM5NTg4NSBDNTMuNDE4MjY3Nyw0MC4xMjg1NTU3IDM2LjMwMzk1NDQsMzcuNzMwNDQwNSAyNS4yMjc0MTcsMzcuNzMwNDQwNSBDMTcuODQzMDU4NiwzNy43MzA0NDA1IDkuNDMzOTE5NjYsMzcuNzMwNDQwNSAwLDM3LjczMDQ0MDUgWiIgaWQ9IlBhdGgtMTkiIGZpbGw9IiMzNzkzRUYiPjwvcGF0aD4KICAgICAgICAgICAgPC9nPgogICAgICAgIDwvZz4KICAgIDwvZz4KPC9zdmc+' > </img>\nCreated in <span style='font-weight:600;margin-left:4px;'>Deepnote</span></a>",
   "metadata": {
    "tags": [],
    "created_in_deepnote_cell": true,
    "deepnote_cell_type": "markdown"
   }
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 ],
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