07_pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"media/coco-banner.jpg\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Pandas\n",
    "\n",
    "[_Pandas_](https://pandas.pydata.org/) is a Python package that provides fast, flexible, and expressive data structures designed to make working with \"relational\" or \"labeled\" data both easy and intuitive.  \n",
    "\n",
    "We will learn about:\n",
    "* How to create and store a DataFrame.\n",
    "* How to open a file.\n",
    "* Some ways to work with a DataFrame.\n",
    "* Plots\n",
    "\n",
    "---\n",
    "\n",
    "[_Pandas_](https://pandas.pydata.org/)  es un paquete de Python que proporciona estructuras de datos rápidas y flexibles diseñadas para que el trabajo con datos \"relacionales\" o \"etiquetados\" sea fácil e intuitivo.\n",
    "\n",
    "Vamos a aprender sobre:\n",
    "* Cómo crear y almacenar un DataFrame.\n",
    "* Cómo abrir un archivo.\n",
    "* Alguna formas para trabajar con un DataFrame.\n",
    "* Graficar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Pandas | Importar Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also import other packages that we're going use in this notebook | También importamos otros paquete que usaremos en este notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to create data? | ¿Cómo crear datos?\n",
    "\n",
    "### DataFrame\n",
    "\n",
    "A _DataFrame_ is a two-dimensional data structure, that is, the data is tabular aligned in rows and columns. \n",
    "One way to create a DataFrame is through a dictionary.\n",
    " \n",
    "---\n",
    "\n",
    "Un _DataFrame_ es una estructura de datos bidimensional, es decir, los datos tienen un formato tabular en filas y columnas.\n",
    "Una forma de crear un DataFrame es a través de un diccionario."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Rocío       Clara   Agustina\n",
      "0  Peruana  Venezolana  Argentina\n",
      "1      USA          UK     Canada\n",
      "2       40          25         36\n"
     ]
    }
   ],
   "source": [
    "dictionary_data = {\n",
    "    \"Rocío\": [\"Peruana\", \"USA\", 40],\n",
    "    \"Clara\": [\"Venezolana\", \"UK\", 25],\n",
    "    \"Agustina\": [\"Argentina\", \"Canada\", 36]\n",
    "}    \n",
    "\n",
    "data_frame = pd.DataFrame(dictionary_data)\n",
    "\n",
    "print(data_frame)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the indices go in ascending order starting from 0, but this can be changed using `index` when building the dataframe.\n",
    "\n",
    "---\n",
    "\n",
    "Como vemos, los indices van en orden ascendente a partir de 0, pero esto se puede cambiar usando `index` al construir el dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rocío</th>\n",
       "      <th>Clara</th>\n",
       "      <th>Agustina</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Paìs de origen</th>\n",
       "      <td>Peruana</td>\n",
       "      <td>Venezolana</td>\n",
       "      <td>Argentina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lugar de residencia</th>\n",
       "      <td>USA</td>\n",
       "      <td>UK</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Edad</th>\n",
       "      <td>40</td>\n",
       "      <td>25</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Rocío       Clara   Agustina\n",
       "Paìs de origen       Peruana  Venezolana  Argentina\n",
       "Lugar de residencia      USA          UK     Canada\n",
       "Edad                      40          25         36"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame = pd.DataFrame(\n",
    "    dictionary_data, \n",
    "    index=[\"Paìs de origen\", \"Lugar de residencia\", \"Edad\"]\n",
    ")\n",
    "\n",
    "data_frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To save the DataFrame as `csv`: | Para guardar un DadaFrame como `csv`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_frame.to_csv(\"miembros.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See the Pandas documentation to find other methods to save a DataFrame.\n",
    "\n",
    "---\n",
    "\n",
    "Investigue la documentación de Pandas para encontrar otros métodos para guardar un DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Series\n",
    "\n",
    "If the DataFrame is a table, a `Series` is a \"column\" and can be created from a list.\n",
    "\n",
    "----\n",
    "\n",
    "Si el DataFrame es una tabla, una `Serie` es una \"columna\" y se puede crear a partir de una lista."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1     40\n",
       "2    400\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = pd.Series([0, 40, 400])\n",
    "\n",
    "col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `Series` is essentially a simple column of a DataFrame, so indexes can be assigned, however the Series does not have a column name but it has a general name` Name`\n",
    "\n",
    "---\n",
    "\n",
    "Una `Serie` es en esencia una columna simple de un DataFrame, así que se pueden asignar índices, sin embargo la Serie no tiene un nombre de columna sino un nombre general `Name`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015       0\n",
       "2016      40\n",
       "2021     400\n",
       "Name: Numero de miembros, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = pd.Series(\n",
    "    [0, 40, 400], \n",
    "    index=['2015 ', '2016', '2021'], \n",
    "    name='Numero de miembros'\n",
    ")\n",
    "\n",
    "col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Open a File | Abrir un archivo\n",
    "\n",
    "We are going to work with a dataset from the [Rock Property Catalog (RPC)](https://dataunderground.org/gl/dataset/rock-property-catalog), available as _.csv_ file \"comma-separated values\" \n",
    "\n",
    "---\n",
    "\n",
    "Vamos a trabajar con un dataset proveniente de un [Catálogo de Propiedades de Rocas (RPC)](https://dataunderground.org/gl/dataset/rock-property-catalog), el cual se encuentra en un archivo _.csv_ \"comma-separated values\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Path to the file | Ruta al archivo\n",
    "\n",
    "data_folder = Path(\".\") / \"data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Open the file | Abrimos el archivo\n",
    "\n",
    "data = pd.read_csv(data_folder / \"RPC_4_lithologies_Messy.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To have a look at the first 5 rows of a DataFrame, we can use the `head` method.\n",
    "\n",
    "---\n",
    "\n",
    "Para echar un vistazo a las primeras 5 filas de DataFrame, podemos usar el método `head`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPC</th>\n",
       "      <th>Source</th>\n",
       "      <th>Description</th>\n",
       "      <th>Lithology</th>\n",
       "      <th>Vs [km/s]</th>\n",
       "      <th>VpVs ratio</th>\n",
       "      <th>Rho [kg/m³]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>104818</td>\n",
       "      <td>RPC</td>\n",
       "      <td>KIAMICHI FORMA. 4</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>1.5957</td>\n",
       "      <td>1.9086</td>\n",
       "      <td>2109.1206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>104893</td>\n",
       "      <td>RPC</td>\n",
       "      <td>KIAMICHI REMOLD 3</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>1.5171</td>\n",
       "      <td>1.9779</td>\n",
       "      <td>2090.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>104652</td>\n",
       "      <td>RPC</td>\n",
       "      <td>NAVAJO ARCO 2</td>\n",
       "      <td>SANDST.</td>\n",
       "      <td>2.0415</td>\n",
       "      <td>1.6476</td>\n",
       "      <td>2131.9900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>104752</td>\n",
       "      <td>RPC</td>\n",
       "      <td>NAVAJO ARCO REMOL 1</td>\n",
       "      <td>SANDST.</td>\n",
       "      <td>1.6062</td>\n",
       "      <td>1.9894</td>\n",
       "      <td>2184.9392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>104598</td>\n",
       "      <td>RPC</td>\n",
       "      <td>FERRON 82-3 1677.5</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>2.4486</td>\n",
       "      <td>1.7306</td>\n",
       "      <td>2472.2308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      RPC Source          Description  Lithology  Vs [km/s]  VpVs ratio  \\\n",
       "0  104818    RPC    KIAMICHI FORMA. 4  sandstone     1.5957      1.9086   \n",
       "1  104893    RPC    KIAMICHI REMOLD 3  sandstone     1.5171      1.9779   \n",
       "2  104652    RPC        NAVAJO ARCO 2    SANDST.     2.0415      1.6476   \n",
       "3  104752    RPC  NAVAJO ARCO REMOL 1    SANDST.     1.6062      1.9894   \n",
       "4  104598    RPC   FERRON 82-3 1677.5  sandstone     2.4486      1.7306   \n",
       "\n",
       "   Rho [kg/m³]  \n",
       "0    2109.1206  \n",
       "1    2090.0000  \n",
       "2    2131.9900  \n",
       "3    2184.9392  \n",
       "4    2472.2308  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPC</th>\n",
       "      <th>Source</th>\n",
       "      <th>Description</th>\n",
       "      <th>Lithology</th>\n",
       "      <th>Vs [km/s]</th>\n",
       "      <th>VpVs ratio</th>\n",
       "      <th>Rho [kg/m³]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>104818</td>\n",
       "      <td>RPC</td>\n",
       "      <td>KIAMICHI FORMA. 4</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>1.5957</td>\n",
       "      <td>1.9086</td>\n",
       "      <td>2109.1206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>104893</td>\n",
       "      <td>RPC</td>\n",
       "      <td>KIAMICHI REMOLD 3</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>1.5171</td>\n",
       "      <td>1.9779</td>\n",
       "      <td>2090.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>104652</td>\n",
       "      <td>RPC</td>\n",
       "      <td>NAVAJO ARCO 2</td>\n",
       "      <td>SANDST.</td>\n",
       "      <td>2.0415</td>\n",
       "      <td>1.6476</td>\n",
       "      <td>2131.9900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>104752</td>\n",
       "      <td>RPC</td>\n",
       "      <td>NAVAJO ARCO REMOL 1</td>\n",
       "      <td>SANDST.</td>\n",
       "      <td>1.6062</td>\n",
       "      <td>1.9894</td>\n",
       "      <td>2184.9392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>104598</td>\n",
       "      <td>RPC</td>\n",
       "      <td>FERRON 82-3 1677.5</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>2.4486</td>\n",
       "      <td>1.7306</td>\n",
       "      <td>2472.2308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>104601</td>\n",
       "      <td>RPC</td>\n",
       "      <td>FERRON 82-3 1677.5</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>2.4387</td>\n",
       "      <td>1.7711</td>\n",
       "      <td>2458.9597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>104617</td>\n",
       "      <td>RPC</td>\n",
       "      <td>FERRON 82-3 1677.5</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>2.7267</td>\n",
       "      <td>1.6624</td>\n",
       "      <td>2397.1364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>104700</td>\n",
       "      <td>RPC</td>\n",
       "      <td>NAVAJO ARCO 3</td>\n",
       "      <td>SANDST.</td>\n",
       "      <td>2.0850</td>\n",
       "      <td>1.6971</td>\n",
       "      <td>2047.3621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>104674</td>\n",
       "      <td>RPC</td>\n",
       "      <td>NAVAJO ARCO 2</td>\n",
       "      <td>SANDST.</td>\n",
       "      <td>2.0154</td>\n",
       "      <td>1.7489</td>\n",
       "      <td>2210.5288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>104678</td>\n",
       "      <td>RPC</td>\n",
       "      <td>NAVAJO ARCO 2</td>\n",
       "      <td>SANDST.</td>\n",
       "      <td>2.0808</td>\n",
       "      <td>1.7134</td>\n",
       "      <td>2141.6202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>104657</td>\n",
       "      <td>RPC</td>\n",
       "      <td>NAVAJO ARCO 2</td>\n",
       "      <td>SANDST.</td>\n",
       "      <td>2.1177</td>\n",
       "      <td>1.6257</td>\n",
       "      <td>2194.5844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>104845</td>\n",
       "      <td>RPC</td>\n",
       "      <td>KIAMICHI FORMA. 1</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>1.9608</td>\n",
       "      <td>1.7096</td>\n",
       "      <td>2080.5501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>104666</td>\n",
       "      <td>RPC</td>\n",
       "      <td>NAVAJO ARCO 2</td>\n",
       "      <td>SANDST.</td>\n",
       "      <td>1.8192</td>\n",
       "      <td>1.8361</td>\n",
       "      <td>2158.1718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>104649</td>\n",
       "      <td>RPC</td>\n",
       "      <td>NAVAJO ARCO 2</td>\n",
       "      <td>SANDST.</td>\n",
       "      <td>2.0928</td>\n",
       "      <td>1.5859</td>\n",
       "      <td>2191.4937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>104711</td>\n",
       "      <td>RPC</td>\n",
       "      <td>NAVAJO ARCO REMOL 3</td>\n",
       "      <td>SANDST.</td>\n",
       "      <td>2.8870</td>\n",
       "      <td>1.5213</td>\n",
       "      <td>2178.4004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>104777</td>\n",
       "      <td>RPC</td>\n",
       "      <td>NAVAJO ARCO REMOL 3</td>\n",
       "      <td>SANDST.</td>\n",
       "      <td>1.7124</td>\n",
       "      <td>1.9739</td>\n",
       "      <td>2007.8981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>104827</td>\n",
       "      <td>RPC</td>\n",
       "      <td>KIAMICHI FORMA. 1</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>1.7580</td>\n",
       "      <td>1.7549</td>\n",
       "      <td>2115.6986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>104683</td>\n",
       "      <td>RPC</td>\n",
       "      <td>NAVAJO ARCO 2</td>\n",
       "      <td>SANDST.</td>\n",
       "      <td>2.1078</td>\n",
       "      <td>1.7161</td>\n",
       "      <td>2187.0308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>104712</td>\n",
       "      <td>RPC</td>\n",
       "      <td>NAVAJO ARCO REMOL 3</td>\n",
       "      <td>SANDST.</td>\n",
       "      <td>3.4550</td>\n",
       "      <td>1.5647</td>\n",
       "      <td>2115.2519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>104701</td>\n",
       "      <td>RPC</td>\n",
       "      <td>NAVAJO ARCO 3</td>\n",
       "      <td>SANDST.</td>\n",
       "      <td>2.1573</td>\n",
       "      <td>1.6783</td>\n",
       "      <td>2089.8802</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       RPC Source          Description  Lithology  Vs [km/s]  VpVs ratio  \\\n",
       "0   104818    RPC    KIAMICHI FORMA. 4  sandstone     1.5957      1.9086   \n",
       "1   104893    RPC    KIAMICHI REMOLD 3  sandstone     1.5171      1.9779   \n",
       "2   104652    RPC        NAVAJO ARCO 2    SANDST.     2.0415      1.6476   \n",
       "3   104752    RPC  NAVAJO ARCO REMOL 1    SANDST.     1.6062      1.9894   \n",
       "4   104598    RPC   FERRON 82-3 1677.5  sandstone     2.4486      1.7306   \n",
       "5   104601    RPC   FERRON 82-3 1677.5  sandstone     2.4387      1.7711   \n",
       "6   104617    RPC   FERRON 82-3 1677.5  sandstone     2.7267      1.6624   \n",
       "7   104700    RPC        NAVAJO ARCO 3    SANDST.     2.0850      1.6971   \n",
       "8   104674    RPC        NAVAJO ARCO 2    SANDST.     2.0154      1.7489   \n",
       "9   104678    RPC        NAVAJO ARCO 2    SANDST.     2.0808      1.7134   \n",
       "10  104657    RPC        NAVAJO ARCO 2    SANDST.     2.1177      1.6257   \n",
       "11  104845    RPC    KIAMICHI FORMA. 1  sandstone     1.9608      1.7096   \n",
       "12  104666    RPC        NAVAJO ARCO 2    SANDST.     1.8192      1.8361   \n",
       "13  104649    RPC        NAVAJO ARCO 2    SANDST.     2.0928      1.5859   \n",
       "14  104711    RPC  NAVAJO ARCO REMOL 3    SANDST.     2.8870      1.5213   \n",
       "15  104777    RPC  NAVAJO ARCO REMOL 3    SANDST.     1.7124      1.9739   \n",
       "16  104827    RPC    KIAMICHI FORMA. 1  sandstone     1.7580      1.7549   \n",
       "17  104683    RPC        NAVAJO ARCO 2    SANDST.     2.1078      1.7161   \n",
       "18  104712    RPC  NAVAJO ARCO REMOL 3    SANDST.     3.4550      1.5647   \n",
       "19  104701    RPC        NAVAJO ARCO 3    SANDST.     2.1573      1.6783   \n",
       "\n",
       "    Rho [kg/m³]  \n",
       "0     2109.1206  \n",
       "1     2090.0000  \n",
       "2     2131.9900  \n",
       "3     2184.9392  \n",
       "4     2472.2308  \n",
       "5     2458.9597  \n",
       "6     2397.1364  \n",
       "7     2047.3621  \n",
       "8     2210.5288  \n",
       "9     2141.6202  \n",
       "10    2194.5844  \n",
       "11    2080.5501  \n",
       "12    2158.1718  \n",
       "13    2191.4937  \n",
       "14    2178.4004  \n",
       "15    2007.8981  \n",
       "16    2115.6986  \n",
       "17    2187.0308  \n",
       "18    2115.2519  \n",
       "19    2089.8802  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Formative assessment 1\n",
    "\n",
    "What do you think the method `DataFrame.tail()` does? | ¿Qué creen que hace el método `DataFrame.tail()`?\n",
    "\n",
    "1. Show the last 5 lines\n",
    "2. Show the last 5 columns\n",
    "3. It is not a method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPC</th>\n",
       "      <th>Source</th>\n",
       "      <th>Description</th>\n",
       "      <th>Lithology</th>\n",
       "      <th>Vs [km/s]</th>\n",
       "      <th>VpVs ratio</th>\n",
       "      <th>Rho [kg/m³]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>102178</td>\n",
       "      <td>RPC</td>\n",
       "      <td>R.PRICE 10909.5 #31</td>\n",
       "      <td>dolomite</td>\n",
       "      <td>2.7592</td>\n",
       "      <td>1.6766</td>\n",
       "      <td>2414.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>102225</td>\n",
       "      <td>RPC</td>\n",
       "      <td>R.PRICE 10916.5 #38</td>\n",
       "      <td>dolomite</td>\n",
       "      <td>3.1168</td>\n",
       "      <td>1.7474</td>\n",
       "      <td>2475.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805</th>\n",
       "      <td>102088</td>\n",
       "      <td>RPC</td>\n",
       "      <td>R.PRICE 10883.5 #7</td>\n",
       "      <td>dolomite</td>\n",
       "      <td>2.9987</td>\n",
       "      <td>1.8162</td>\n",
       "      <td>2521.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>806</th>\n",
       "      <td>102213</td>\n",
       "      <td>RPC</td>\n",
       "      <td>R.PRICE 10913.5 #35</td>\n",
       "      <td>dolomite</td>\n",
       "      <td>2.9331</td>\n",
       "      <td>1.8233</td>\n",
       "      <td>2470.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>807</th>\n",
       "      <td>102086</td>\n",
       "      <td>RPC</td>\n",
       "      <td>R.PRICE 10883.5 #7</td>\n",
       "      <td>dolomite</td>\n",
       "      <td>2.8970</td>\n",
       "      <td>1.8460</td>\n",
       "      <td>2549.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        RPC Source          Description Lithology  Vs [km/s]  VpVs ratio  \\\n",
       "803  102178    RPC  R.PRICE 10909.5 #31  dolomite     2.7592      1.6766   \n",
       "804  102225    RPC  R.PRICE 10916.5 #38  dolomite     3.1168      1.7474   \n",
       "805  102088    RPC   R.PRICE 10883.5 #7  dolomite     2.9987      1.8162   \n",
       "806  102213    RPC  R.PRICE 10913.5 #35  dolomite     2.9331      1.8233   \n",
       "807  102086    RPC   R.PRICE 10883.5 #7  dolomite     2.8970      1.8460   \n",
       "\n",
       "     Rho [kg/m³]  \n",
       "803       2414.0  \n",
       "804       2475.7  \n",
       "805       2521.1  \n",
       "806       2470.8  \n",
       "807       2549.2  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution | Solución\n",
    "\n",
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the `shape` attribute to know how big the DataFrame is.\n",
    "\n",
    "---\n",
    "\n",
    "Usamos el atributo `shape` para saber que tan grande es el DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(808, 7)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`DataFrame.describe()` gets the summary statistics of only the columns that have numerical data.\n",
    "All other columns are ignored, unless you use the argument `include=\"all\"`.\n",
    "\n",
    "---\n",
    "\n",
    "`DataFrame.describe()` muestra una estadística de las columnas que tienen datos numéricos.\n",
    "Todas las demás columnas se ignoran, a menos que use el argumento `include=\"all\"`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPC</th>\n",
       "      <th>Vs [km/s]</th>\n",
       "      <th>VpVs ratio</th>\n",
       "      <th>Rho [kg/m³]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>808.000000</td>\n",
       "      <td>808.000000</td>\n",
       "      <td>808.000000</td>\n",
       "      <td>760.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>102277.626238</td>\n",
       "      <td>2.116353</td>\n",
       "      <td>1.887390</td>\n",
       "      <td>2319.261095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1537.484250</td>\n",
       "      <td>0.747915</td>\n",
       "      <td>0.377769</td>\n",
       "      <td>255.732807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>100031.000000</td>\n",
       "      <td>0.441300</td>\n",
       "      <td>1.337600</td>\n",
       "      <td>1721.753500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>101112.750000</td>\n",
       "      <td>1.527225</td>\n",
       "      <td>1.666075</td>\n",
       "      <td>2115.586925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>102083.500000</td>\n",
       "      <td>2.202600</td>\n",
       "      <td>1.779850</td>\n",
       "      <td>2382.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>103284.750000</td>\n",
       "      <td>2.733000</td>\n",
       "      <td>1.943675</td>\n",
       "      <td>2529.143800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>105090.000000</td>\n",
       "      <td>3.665000</td>\n",
       "      <td>4.707800</td>\n",
       "      <td>2823.287700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 RPC   Vs [km/s]  VpVs ratio  Rho [kg/m³]\n",
       "count     808.000000  808.000000  808.000000   760.000000\n",
       "mean   102277.626238    2.116353    1.887390  2319.261095\n",
       "std      1537.484250    0.747915    0.377769   255.732807\n",
       "min    100031.000000    0.441300    1.337600  1721.753500\n",
       "25%    101112.750000    1.527225    1.666075  2115.586925\n",
       "50%    102083.500000    2.202600    1.779850  2382.300000\n",
       "75%    103284.750000    2.733000    1.943675  2529.143800\n",
       "max    105090.000000    3.665000    4.707800  2823.287700"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`DataFrame.dtypes` shows the type of variable per column. It is recommended to review additional [documentation](https://pandas.pydata.org/docs/user_guide/text.html) and [references](https://stackoverflow.com/questions/21018654/strings-in-a-dataframe-but-dtype-is-object#:~:text=But%20for%20strings%2C%20the%20length,this%20kind%20ndarray%20is%20object.)  to understand why the \"Source\", \"Description\" and \"Lithology\" columns are presented as objects and not as strings.\n",
    "\n",
    "---\n",
    "\n",
    "`DataFrame.dtypes` muestra qué tipo de dato contiene cada columna. Se recomienda revisar [documentacion](https://pandas.pydata.org/docs/user_guide/text.html) y [referencias](https://stackoverflow.com/questions/21018654/strings-in-a-dataframe-but-dtype-is-object#:~:text=But%20for%20strings%2C%20the%20length,this%20kind%20ndarray%20is%20object.) adicionales para entender el por qué las columnas \"Source\", \"Description\" y \"Lithology\" se presentan como objetos y no como strings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RPC              int64\n",
       "Source          object\n",
       "Description     object\n",
       "Lithology       object\n",
       "Vs [km/s]      float64\n",
       "VpVs ratio     float64\n",
       "Rho [kg/m³]    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFrame columns\n",
    "\n",
    "If we want to access the _Description_ property of the DataFrame, this notation can be used:\n",
    "\n",
    "---\n",
    "\n",
    "Si quiero acceder a la propiedad _Description_ del DataFrame se puede usar esta notación:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        KIAMICHI FORMA. 4\n",
       "1        KIAMICHI REMOLD 3\n",
       "2            NAVAJO ARCO 2\n",
       "3      NAVAJO ARCO REMOL 1\n",
       "4       FERRON 82-3 1677.5\n",
       "              ...         \n",
       "803    R.PRICE 10909.5 #31\n",
       "804    R.PRICE 10916.5 #38\n",
       "805     R.PRICE 10883.5 #7\n",
       "806    R.PRICE 10913.5 #35\n",
       "807     R.PRICE 10883.5 #7\n",
       "Name: Description, Length: 808, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Description\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1.5957\n",
       "1      1.5171\n",
       "2      2.0415\n",
       "3      1.6062\n",
       "4      2.4486\n",
       "        ...  \n",
       "803    2.7592\n",
       "804    3.1168\n",
       "805    2.9987\n",
       "806    2.9331\n",
       "807    2.8970\n",
       "Name: Vs [km/s], Length: 808, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Vs [km/s]\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting values | Seleccionando valores\n",
    "\n",
    "To access a value at the position `[i, j]` of a DataFrame, we have two options, using the index or the label.\n",
    "Remember that a DataFrame provides an index as a way to identify the rows of the table; a row, then, has a position inside the table as well as a label, which uniquely identifies its entry in the DataFrame.\n",
    "\n",
    "---\n",
    "\n",
    "Para acceder a un valor en la posición `[i, j]` de un DataFrame, tenemos dos opciones, usando el *indice o la etiqueta*.\n",
    "Recuerde que un DataFrame proporciona un índice como una forma de identificar las filas de la tabla; una fila, entonces, tiene una posición dentro de la tabla, así como una etiqueta, que identifica de forma única su entrada en el DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPC</th>\n",
       "      <th>Source</th>\n",
       "      <th>Description</th>\n",
       "      <th>Lithology</th>\n",
       "      <th>Vs [km/s]</th>\n",
       "      <th>VpVs ratio</th>\n",
       "      <th>Rho [kg/m³]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>104818</td>\n",
       "      <td>RPC</td>\n",
       "      <td>KIAMICHI FORMA. 4</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>1.5957</td>\n",
       "      <td>1.9086</td>\n",
       "      <td>2109.1206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>104893</td>\n",
       "      <td>RPC</td>\n",
       "      <td>KIAMICHI REMOLD 3</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>1.5171</td>\n",
       "      <td>1.9779</td>\n",
       "      <td>2090.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>104652</td>\n",
       "      <td>RPC</td>\n",
       "      <td>NAVAJO ARCO 2</td>\n",
       "      <td>SANDST.</td>\n",
       "      <td>2.0415</td>\n",
       "      <td>1.6476</td>\n",
       "      <td>2131.9900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>104752</td>\n",
       "      <td>RPC</td>\n",
       "      <td>NAVAJO ARCO REMOL 1</td>\n",
       "      <td>SANDST.</td>\n",
       "      <td>1.6062</td>\n",
       "      <td>1.9894</td>\n",
       "      <td>2184.9392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>104598</td>\n",
       "      <td>RPC</td>\n",
       "      <td>FERRON 82-3 1677.5</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>2.4486</td>\n",
       "      <td>1.7306</td>\n",
       "      <td>2472.2308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      RPC Source          Description  Lithology  Vs [km/s]  VpVs ratio  \\\n",
       "0  104818    RPC    KIAMICHI FORMA. 4  sandstone     1.5957      1.9086   \n",
       "1  104893    RPC    KIAMICHI REMOLD 3  sandstone     1.5171      1.9779   \n",
       "2  104652    RPC        NAVAJO ARCO 2    SANDST.     2.0415      1.6476   \n",
       "3  104752    RPC  NAVAJO ARCO REMOL 1    SANDST.     1.6062      1.9894   \n",
       "4  104598    RPC   FERRON 82-3 1677.5  sandstone     2.4486      1.7306   \n",
       "\n",
       "   Rho [kg/m³]  \n",
       "0    2109.1206  \n",
       "1    2090.0000  \n",
       "2    2131.9900  \n",
       "3    2184.9392  \n",
       "4    2472.2308  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using the position | Usando la posición\n",
    "\n",
    "Use `DataFrame.iloc[..., ...]` to select values by their position.\n",
    "\n",
    "`[i, j]` The first one is the row position and the second one is the column position.\n",
    "\n",
    "---\n",
    "\n",
    "Utilice `DataFrame.iloc [..., ...]` para seleccionar valores por su posición.\n",
    "\n",
    "`[i, j]` El primero es la posición de la fila y el segundo es la posición de la columna."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "104818"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First element of the row number zero and the column RPC \n",
    "# Primer elemento de la fila cero y la columna RPC\n",
    "\n",
    "data.iloc[0, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2109.1206\n",
       "1      2090.0000\n",
       "2      2131.9900\n",
       "3      2184.9392\n",
       "4      2472.2308\n",
       "         ...    \n",
       "803    2414.0000\n",
       "804    2475.7000\n",
       "805    2521.1000\n",
       "806    2470.8000\n",
       "807    2549.2000\n",
       "Name: Rho [kg/m³], Length: 808, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All the rows of the las column called Rho\n",
    "# Toda las filas de la última columna llamada Rho\n",
    "\n",
    "data.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use what we saw in the first notebook about indexes. | Podemos usar lo que vimos en el primer notebook sobre los índices."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Formative assessment 2\n",
    "\n",
    "How can we see the values of the _Lithology_ column that are between rows 100 and 120?\n",
    "\n",
    "---\n",
    "\n",
    "¿Cómo podemos ver los valores de de la columna _Lithology_ que están entre las filas 100 y 120?\n",
    "\n",
    "1. `data.iloc[100:120]`\n",
    "2. `data.iloc[100:121, 4]`\n",
    "3. `data.iloc[100:120, 4]`\n",
    "4. `data.iloc[100:121, \"Lithology\"]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100    1.7415\n",
       "101    2.7330\n",
       "102    2.1981\n",
       "103    1.5213\n",
       "104    2.7852\n",
       "105    2.1486\n",
       "106    2.7207\n",
       "107    1.7800\n",
       "108    3.3790\n",
       "109    2.7267\n",
       "110    2.4387\n",
       "111    1.5423\n",
       "112    1.9668\n",
       "113    2.5416\n",
       "114    2.1114\n",
       "115    2.4009\n",
       "116    2.3892\n",
       "117    2.6853\n",
       "118    2.1225\n",
       "119    2.8332\n",
       "120    2.7948\n",
       "Name: Vs [km/s], dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution | Solución\n",
    "\n",
    "data.iloc[100:121, 4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using the label | Usando la etiqueta\n",
    "\n",
    "Use `DataFrame.loc[..., ...]` to select values by their label.\n",
    "\n",
    "`[index, label]` The first one is the row index and the second one is the column label.\n",
    "\n",
    "---\n",
    "\n",
    "Use `DataFrame.loc[..., ...]` para seleccionar valores por su etiqueta.\n",
    "\n",
    "`[índice, etiqueta]` El primero es el índice de la fila y el segundo es la etiqueta de la columna."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "104752"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[3, \"RPC\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3     104752\n",
       "4     104598\n",
       "5     104601\n",
       "6     104617\n",
       "7     104700\n",
       "8     104674\n",
       "9     104678\n",
       "10    104657\n",
       "Name: RPC, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[3:10, \"RPC\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Formative assessment 3\n",
    "\n",
    "How can we see the values of the _Lithology_ and _Rho_ columns that are between rows 100 and 120?\n",
    "\n",
    "---\n",
    "\n",
    "¿Cómo podemos ver los valores de de las columna _Lithology_ y _Rho [kg/m³]_ que están entre las filas 100 y 120?\n",
    "\n",
    "1. `data.loc[100:120]`\n",
    "2. `data.loc[100:121, [4, 7]]`\n",
    "3. `data.iloc[100:121, [\"Lithology\", \"Rho [kg/m³]\"]]`\n",
    "4. `data.loc[100:120, [\"Lithology\", \"Rho [kg/m³]\"]]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lithology</th>\n",
       "      <th>Rho [kg/m³]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>SANDST.</td>\n",
       "      <td>2015.5767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>sandstone</td>\n",
       "      <td>2351.9722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>SANDST.</td>\n",
       "      <td>2197.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>SANDST.</td>\n",
       "      <td>2060.5531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>sandstone</td>\n",
       "      <td>2430.7511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>SANDST.</td>\n",
       "      <td>2208.8304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>sandstone</td>\n",
       "      <td>2385.2903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>sandstone</td>\n",
       "      <td>2425.7601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>SANDST.</td>\n",
       "      <td>2096.1722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>sandstone</td>\n",
       "      <td>2457.0679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>sandstone</td>\n",
       "      <td>2400.6189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>sandstone</td>\n",
       "      <td>2099.4915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>sandstone</td>\n",
       "      <td>2066.5739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>sandstone</td>\n",
       "      <td>2403.1474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>SANDST.</td>\n",
       "      <td>2133.2576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>sandstone</td>\n",
       "      <td>2417.3564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>sandstone</td>\n",
       "      <td>2467.3356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>sandstone</td>\n",
       "      <td>2466.6559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>SANDST.</td>\n",
       "      <td>2056.4048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>sandstone</td>\n",
       "      <td>2356.3583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>sandstone</td>\n",
       "      <td>2419.0819</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Lithology  Rho [kg/m³]\n",
       "100    SANDST.    2015.5767\n",
       "101  sandstone    2351.9722\n",
       "102    SANDST.    2197.0000\n",
       "103    SANDST.    2060.5531\n",
       "104  sandstone    2430.7511\n",
       "105    SANDST.    2208.8304\n",
       "106  sandstone    2385.2903\n",
       "107  sandstone    2425.7601\n",
       "108    SANDST.    2096.1722\n",
       "109  sandstone    2457.0679\n",
       "110  sandstone    2400.6189\n",
       "111  sandstone    2099.4915\n",
       "112  sandstone    2066.5739\n",
       "113  sandstone    2403.1474\n",
       "114    SANDST.    2133.2576\n",
       "115  sandstone    2417.3564\n",
       "116  sandstone    2467.3356\n",
       "117  sandstone    2466.6559\n",
       "118    SANDST.    2056.4048\n",
       "119  sandstone    2356.3583\n",
       "120  sandstone    2419.0819"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution | Solución\n",
    "data.loc[100:120, [\"Lithology\", \"Rho [kg/m³]\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conditional selection | Selección condicional\n",
    "\n",
    "We use the conditional syntax to find the data that satisfies a given condition.\n",
    "\n",
    "---\n",
    "\n",
    "Usamos la sintaxis condicional para encontrar los datos que satisfacen una dada condición."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, we can check if there are densities greater than 2000 $kg/m^3$ | Por ejemplo podemos chequear si hay densidades mayores a 2000 $kg/m^3$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      True\n",
       "1      True\n",
       "2      True\n",
       "3      True\n",
       "4      True\n",
       "       ... \n",
       "803    True\n",
       "804    True\n",
       "805    True\n",
       "806    True\n",
       "807    True\n",
       "Name: Rho [kg/m³], Length: 808, dtype: bool"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Rho [kg/m³]\"] > 2000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want all the data where the lithology is sandstone. What would the condition be? | Queremos todos los datos donde la litología es arenisca. ¿Cómo sería la condición?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPC</th>\n",
       "      <th>Source</th>\n",
       "      <th>Description</th>\n",
       "      <th>Lithology</th>\n",
       "      <th>Vs [km/s]</th>\n",
       "      <th>VpVs ratio</th>\n",
       "      <th>Rho [kg/m³]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>104818</td>\n",
       "      <td>RPC</td>\n",
       "      <td>KIAMICHI FORMA. 4</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>1.5957</td>\n",
       "      <td>1.9086</td>\n",
       "      <td>2109.1206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>104893</td>\n",
       "      <td>RPC</td>\n",
       "      <td>KIAMICHI REMOLD 3</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>1.5171</td>\n",
       "      <td>1.9779</td>\n",
       "      <td>2090.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>104598</td>\n",
       "      <td>RPC</td>\n",
       "      <td>FERRON 82-3 1677.5</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>2.4486</td>\n",
       "      <td>1.7306</td>\n",
       "      <td>2472.2308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>104601</td>\n",
       "      <td>RPC</td>\n",
       "      <td>FERRON 82-3 1677.5</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>2.4387</td>\n",
       "      <td>1.7711</td>\n",
       "      <td>2458.9597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>104617</td>\n",
       "      <td>RPC</td>\n",
       "      <td>FERRON 82-3 1677.5</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>2.7267</td>\n",
       "      <td>1.6624</td>\n",
       "      <td>2397.1364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>104578</td>\n",
       "      <td>RPC</td>\n",
       "      <td>FERRON 82-3 1677.5</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>2.4009</td>\n",
       "      <td>1.6154</td>\n",
       "      <td>2468.3633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>104849</td>\n",
       "      <td>RPC</td>\n",
       "      <td>KIAMICHI REMOLD 2</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>2.4060</td>\n",
       "      <td>1.6056</td>\n",
       "      <td>2109.8846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>104589</td>\n",
       "      <td>RPC</td>\n",
       "      <td>FERRON 82-3 1677.5</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>2.7450</td>\n",
       "      <td>1.5734</td>\n",
       "      <td>2425.2311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>104640</td>\n",
       "      <td>RPC</td>\n",
       "      <td>FERRON 82-3 1677.5</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>2.8365</td>\n",
       "      <td>1.6011</td>\n",
       "      <td>2364.6583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>104599</td>\n",
       "      <td>RPC</td>\n",
       "      <td>FERRON 82-3 1677.5</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>2.4315</td>\n",
       "      <td>1.7368</td>\n",
       "      <td>2467.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        RPC Source         Description  Lithology  Vs [km/s]  VpVs ratio  \\\n",
       "0    104818    RPC   KIAMICHI FORMA. 4  sandstone     1.5957      1.9086   \n",
       "1    104893    RPC   KIAMICHI REMOLD 3  sandstone     1.5171      1.9779   \n",
       "4    104598    RPC  FERRON 82-3 1677.5  sandstone     2.4486      1.7306   \n",
       "5    104601    RPC  FERRON 82-3 1677.5  sandstone     2.4387      1.7711   \n",
       "6    104617    RPC  FERRON 82-3 1677.5  sandstone     2.7267      1.6624   \n",
       "..      ...    ...                 ...        ...        ...         ...   \n",
       "191  104578    RPC  FERRON 82-3 1677.5  sandstone     2.4009      1.6154   \n",
       "192  104849    RPC   KIAMICHI REMOLD 2  sandstone     2.4060      1.6056   \n",
       "193  104589    RPC  FERRON 82-3 1677.5  sandstone     2.7450      1.5734   \n",
       "195  104640    RPC  FERRON 82-3 1677.5  sandstone     2.8365      1.6011   \n",
       "199  104599    RPC  FERRON 82-3 1677.5  sandstone     2.4315      1.7368   \n",
       "\n",
       "     Rho [kg/m³]  \n",
       "0      2109.1206  \n",
       "1      2090.0000  \n",
       "4      2472.2308  \n",
       "5      2458.9597  \n",
       "6      2397.1364  \n",
       "..           ...  \n",
       "191    2468.3633  \n",
       "192    2109.8846  \n",
       "193    2425.2311  \n",
       "195    2364.6583  \n",
       "199    2467.0000  \n",
       "\n",
       "[110 rows x 7 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "condicion = data[\"Lithology\"] == \"sandstone\"\n",
    "\n",
    "data[condicion]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning our data using Pandas | Limpiando nuestra data usando Pandas\n",
    "\n",
    "The DataFrame loaded before is a smaller dataset of lab data (Vp, Vs, Rho) for 808 samples from the _Rock Property Catalog_, which has some problems like:\n",
    "* repeated rows\n",
    "* missing data\n",
    "* different nomenclature\n",
    "* redundant information, etc.\n",
    "\n",
    "So, before doing any plot or analysis of the DataFrame data, we need to examine the data and fix these problems.\n",
    "\n",
    "---\n",
    "\n",
    "El DataFrame cargado antes es un conjunto mas pequeño de datos de laboratorio (Vp, Vs, Rho) para 808 muestras de un _Catalogo de Propiedades de las Rocas_. \n",
    "Éste tiene algunos problemas como:\n",
    "* filas repetidas\n",
    "* datos faltantes\n",
    "* nomenclatura diferente\n",
    "* información redundante, etc. \n",
    "\n",
    "Entonces, antes de realizar cualquier gráfica o análisis de los datos del DataFrame, necesitamos examinar los datos y arreglar los problemas mencionados."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reload the DataFrame again to avoid errors | Recargamos otra vez el DataFrame para no tener errores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 808 entries, 0 to 807\n",
      "Data columns (total 7 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   RPC          808 non-null    int64  \n",
      " 1   Source       808 non-null    object \n",
      " 2   Description  808 non-null    object \n",
      " 3   Lithology    808 non-null    object \n",
      " 4   Vs [km/s]    808 non-null    float64\n",
      " 5   VpVs ratio   808 non-null    float64\n",
      " 6   Rho [kg/m³]  760 non-null    float64\n",
      "dtypes: float64(3), int64(1), object(3)\n",
      "memory usage: 44.3+ KB\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(data_folder/\"RPC_4_lithologies_Messy.csv\")\n",
    "\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding _null_ values in our DataFrame | Encontrando valores _null_ en nuestro DataFrame\n",
    "\n",
    "We use the `isnull` method, which returns a _boolean object_ of the same size as the DataFrame that indicates if the values are _NaN_.\n",
    "`any` returns _True_ if any element of the iterable is true.\n",
    "If the iterable is empty, return _False_. \n",
    "\n",
    "---\n",
    "\n",
    "Usamos el método `isnull` que devuelve un _objeto booleano_ del mismo tamaño del DataFrame que indica si los valores son _NaN_.\n",
    "`any` devuelve _True_ si algún elemento del iterable es verdadero.\n",
    "Si el iterable está vacío, devuelve _False_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPC</th>\n",
       "      <th>Source</th>\n",
       "      <th>Description</th>\n",
       "      <th>Lithology</th>\n",
       "      <th>Vs [km/s]</th>\n",
       "      <th>VpVs ratio</th>\n",
       "      <th>Rho [kg/m³]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>806</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>807</th>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>808 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       RPC  Source  Description  Lithology  Vs [km/s]  VpVs ratio  Rho [kg/m³]\n",
       "0    False   False        False      False      False       False        False\n",
       "1    False   False        False      False      False       False        False\n",
       "2    False   False        False      False      False       False        False\n",
       "3    False   False        False      False      False       False        False\n",
       "4    False   False        False      False      False       False        False\n",
       "..     ...     ...          ...        ...        ...         ...          ...\n",
       "803  False   False        False      False      False       False        False\n",
       "804  False   False        False      False      False       False        False\n",
       "805  False   False        False      False      False       False        False\n",
       "806  False   False        False      False      False       False        False\n",
       "807  False   False        False      False      False       False        False\n",
       "\n",
       "[808 rows x 7 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().values.any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we make a fast plot to check this. | Ahora, hacemos una gráfica rápida para chequear lo mencionado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names = [\"RPC\", \"Vs [km/s]\", \"VpVs ratio\", \"Rho [kg/m³]\"]\n",
    "\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2, sharex=True, figsize=(15, 5))\n",
    "\n",
    "#RPC\n",
    "data[names[0]].plot(ax=axes[0, 0])\n",
    "axes[0,0].set_title(names[0])\n",
    "\n",
    "#Vs\n",
    "data[names[1]].plot(ax=axes[0, 1])\n",
    "axes[0,1].set_title(names[1])\n",
    "\n",
    "#VpVs ratio\n",
    "data[names[2]].plot(ax=axes[1, 0])\n",
    "axes[1,0].set_title(names[2])\n",
    "\n",
    "#Rho\n",
    "data[names[3]].plot(ax=axes[1, 1])\n",
    "axes[1,1].set_title(names[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The density column has NaN values, so we will use the `dropna` method to remove the rows which only have NaN values.\n",
    "\n",
    "---\n",
    "\n",
    "La columna de densidad tiene valores de NaN, por lo que usaremos el método `dropna` para eliminar la fila que solo tiene valores de NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(760, 7)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.dropna()\n",
    "\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've removed 48 rows, so the DataFrame has 760 rows. | Hemos eliminado 48 filas, por lo que el DataFrame tiene 760 filas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove duplicates rows | Remover filas duplicadas\n",
    "\n",
    "Now, we will find and remove duplicate rows using `duplicated()`. \n",
    "\n",
    "---\n",
    "\n",
    "Ahora vamos a encontrar y eliminar la filas repetidas usando `duplicated()`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select duplicate rows except first occurrence based on all columns | Selecciona filas duplicadas excepto la primera aparición basada en todas las columnas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPC</th>\n",
       "      <th>Source</th>\n",
       "      <th>Description</th>\n",
       "      <th>Lithology</th>\n",
       "      <th>Vs [km/s]</th>\n",
       "      <th>VpVs ratio</th>\n",
       "      <th>Rho [kg/m³]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>104646</td>\n",
       "      <td>RPC</td>\n",
       "      <td>NAVAJO ARCO 2</td>\n",
       "      <td>SANDST.</td>\n",
       "      <td>1.8489</td>\n",
       "      <td>1.6602</td>\n",
       "      <td>2210.1729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>104703</td>\n",
       "      <td>RPC</td>\n",
       "      <td>NAVAJO ARCO 3</td>\n",
       "      <td>SANDST.</td>\n",
       "      <td>2.1426</td>\n",
       "      <td>1.7459</td>\n",
       "      <td>2096.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>100066</td>\n",
       "      <td>RPC</td>\n",
       "      <td>Hard shale</td>\n",
       "      <td>shale</td>\n",
       "      <td>2.4267</td>\n",
       "      <td>1.6081</td>\n",
       "      <td>2642.6244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>101009</td>\n",
       "      <td>RPC</td>\n",
       "      <td>Gray Shale</td>\n",
       "      <td>shale</td>\n",
       "      <td>2.2070</td>\n",
       "      <td>1.3899</td>\n",
       "      <td>2664.8891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>102283</td>\n",
       "      <td>RPC</td>\n",
       "      <td>R.PRICE 10928.5 #50</td>\n",
       "      <td>dolomite</td>\n",
       "      <td>2.6050</td>\n",
       "      <td>1.7128</td>\n",
       "      <td>2300.8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>102289</td>\n",
       "      <td>RPC</td>\n",
       "      <td>R.PRICE 10928.5 #50</td>\n",
       "      <td>dolomite</td>\n",
       "      <td>2.3655</td>\n",
       "      <td>1.6921</td>\n",
       "      <td>2331.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>102169</td>\n",
       "      <td>RPC</td>\n",
       "      <td>R.PRICE 10908.5 #30</td>\n",
       "      <td>dolomite</td>\n",
       "      <td>2.6575</td>\n",
       "      <td>1.8272</td>\n",
       "      <td>2415.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>102214</td>\n",
       "      <td>RPC</td>\n",
       "      <td>R.PRICE 10913.5 #35</td>\n",
       "      <td>dolomite</td>\n",
       "      <td>2.9757</td>\n",
       "      <td>1.8412</td>\n",
       "      <td>2473.4000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        RPC Source          Description Lithology  Vs [km/s]  VpVs ratio  \\\n",
       "77   104646    RPC        NAVAJO ARCO 2   SANDST.     1.8489      1.6602   \n",
       "139  104703    RPC        NAVAJO ARCO 3   SANDST.     2.1426      1.7459   \n",
       "378  100066    RPC           Hard shale     shale     2.4267      1.6081   \n",
       "384  101009    RPC           Gray Shale     shale     2.2070      1.3899   \n",
       "669  102283    RPC  R.PRICE 10928.5 #50  dolomite     2.6050      1.7128   \n",
       "677  102289    RPC  R.PRICE 10928.5 #50  dolomite     2.3655      1.6921   \n",
       "679  102169    RPC  R.PRICE 10908.5 #30  dolomite     2.6575      1.8272   \n",
       "720  102214    RPC  R.PRICE 10913.5 #35  dolomite     2.9757      1.8412   \n",
       "\n",
       "     Rho [kg/m³]  \n",
       "77     2210.1729  \n",
       "139    2096.0000  \n",
       "378    2642.6244  \n",
       "384    2664.8891  \n",
       "669    2300.8000  \n",
       "677    2331.2000  \n",
       "679    2415.0000  \n",
       "720    2473.4000  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove the duplicated rows | Eliminamos las filas duplicadas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(752, 7)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop_duplicates()\n",
    "\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've removed 8 rows, so the DataFrame has 752 rows. | Hemos eliminado 8 filas, por lo que el DataFrame tiene 752 filas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check that it does not have repeating rows | Comprueba que no tenga filas repetidas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RPC            False\n",
       "Source         False\n",
       "Description    False\n",
       "Lithology      False\n",
       "Vs [km/s]      False\n",
       "VpVs ratio     False\n",
       "Rho [kg/m³]    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.duplicated()].any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fixing values | Corrigiendo valores\n",
    "\n",
    "We need to fix some lithology names.\n",
    "We will change _SANDST._ to _sandstone_.\n",
    "To do that, we use the `replace` method.\n",
    "\n",
    "---\n",
    "\n",
    "Necesitamos arreglar algunos nombres de litología.\n",
    "Cambiaremos _SANDST._ por _sandstone_.\n",
    "Para ello usamos el metodo `replace`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPC</th>\n",
       "      <th>Source</th>\n",
       "      <th>Description</th>\n",
       "      <th>Lithology</th>\n",
       "      <th>Vs [km/s]</th>\n",
       "      <th>VpVs ratio</th>\n",
       "      <th>Rho [kg/m³]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>104818</td>\n",
       "      <td>RPC</td>\n",
       "      <td>KIAMICHI FORMA. 4</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>1.5957</td>\n",
       "      <td>1.9086</td>\n",
       "      <td>2109.1206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>104893</td>\n",
       "      <td>RPC</td>\n",
       "      <td>KIAMICHI REMOLD 3</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>1.5171</td>\n",
       "      <td>1.9779</td>\n",
       "      <td>2090.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>104652</td>\n",
       "      <td>RPC</td>\n",
       "      <td>NAVAJO ARCO 2</td>\n",
       "      <td>SANDST.</td>\n",
       "      <td>2.0415</td>\n",
       "      <td>1.6476</td>\n",
       "      <td>2131.9900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>104752</td>\n",
       "      <td>RPC</td>\n",
       "      <td>NAVAJO ARCO REMOL 1</td>\n",
       "      <td>SANDST.</td>\n",
       "      <td>1.6062</td>\n",
       "      <td>1.9894</td>\n",
       "      <td>2184.9392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>104598</td>\n",
       "      <td>RPC</td>\n",
       "      <td>FERRON 82-3 1677.5</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>2.4486</td>\n",
       "      <td>1.7306</td>\n",
       "      <td>2472.2308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      RPC Source          Description  Lithology  Vs [km/s]  VpVs ratio  \\\n",
       "0  104818    RPC    KIAMICHI FORMA. 4  sandstone     1.5957      1.9086   \n",
       "1  104893    RPC    KIAMICHI REMOLD 3  sandstone     1.5171      1.9779   \n",
       "2  104652    RPC        NAVAJO ARCO 2    SANDST.     2.0415      1.6476   \n",
       "3  104752    RPC  NAVAJO ARCO REMOL 1    SANDST.     1.6062      1.9894   \n",
       "4  104598    RPC   FERRON 82-3 1677.5  sandstone     2.4486      1.7306   \n",
       "\n",
       "   Rho [kg/m³]  \n",
       "0    2109.1206  \n",
       "1    2090.0000  \n",
       "2    2131.9900  \n",
       "3    2184.9392  \n",
       "4    2472.2308  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPC</th>\n",
       "      <th>Source</th>\n",
       "      <th>Description</th>\n",
       "      <th>Lithology</th>\n",
       "      <th>Vs [km/s]</th>\n",
       "      <th>VpVs ratio</th>\n",
       "      <th>Rho [kg/m³]</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>104818</td>\n",
       "      <td>RPC</td>\n",
       "      <td>KIAMICHI FORMA. 4</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>1.5957</td>\n",
       "      <td>1.9086</td>\n",
       "      <td>2109.1206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>104893</td>\n",
       "      <td>RPC</td>\n",
       "      <td>KIAMICHI REMOLD 3</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>1.5171</td>\n",
       "      <td>1.9779</td>\n",
       "      <td>2090.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>104652</td>\n",
       "      <td>RPC</td>\n",
       "      <td>NAVAJO ARCO 2</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>2.0415</td>\n",
       "      <td>1.6476</td>\n",
       "      <td>2131.9900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>104752</td>\n",
       "      <td>RPC</td>\n",
       "      <td>NAVAJO ARCO REMOL 1</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>1.6062</td>\n",
       "      <td>1.9894</td>\n",
       "      <td>2184.9392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>104598</td>\n",
       "      <td>RPC</td>\n",
       "      <td>FERRON 82-3 1677.5</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>2.4486</td>\n",
       "      <td>1.7306</td>\n",
       "      <td>2472.2308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      RPC Source          Description  Lithology  Vs [km/s]  VpVs ratio  \\\n",
       "0  104818    RPC    KIAMICHI FORMA. 4  sandstone     1.5957      1.9086   \n",
       "1  104893    RPC    KIAMICHI REMOLD 3  sandstone     1.5171      1.9779   \n",
       "2  104652    RPC        NAVAJO ARCO 2  sandstone     2.0415      1.6476   \n",
       "3  104752    RPC  NAVAJO ARCO REMOL 1  sandstone     1.6062      1.9894   \n",
       "4  104598    RPC   FERRON 82-3 1677.5  sandstone     2.4486      1.7306   \n",
       "\n",
       "   Rho [kg/m³]  \n",
       "0    2109.1206  \n",
       "1    2090.0000  \n",
       "2    2131.9900  \n",
       "3    2184.9392  \n",
       "4    2472.2308  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Lithology\"] = data[\"Lithology\"].replace(\"SANDST.\", \"sandstone\")\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding a column | Agregando una columna\n",
    "\n",
    "The DataFrame doesn't have the value of _Vp_.\n",
    "So, we are going to calculate and add it to the DataFrame as a new column.\n",
    "\n",
    "---\n",
    "\n",
    "El DataFrame no tiene el valor de _Vp_.\n",
    "Entonces, nosotros vamos a calcularlo y agregarlo en el DataFrame como una nueva columna.\n",
    "\n",
    "\n",
    "$$ V_p V_s ratio = \\frac{V_p}{V_s} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPC</th>\n",
       "      <th>Source</th>\n",
       "      <th>Description</th>\n",
       "      <th>Lithology</th>\n",
       "      <th>Vs [km/s]</th>\n",
       "      <th>VpVs ratio</th>\n",
       "      <th>Rho [kg/m³]</th>\n",
       "      <th>Vp [km/s]</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>104818</td>\n",
       "      <td>RPC</td>\n",
       "      <td>KIAMICHI FORMA. 4</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>1.5957</td>\n",
       "      <td>1.9086</td>\n",
       "      <td>2109.1206</td>\n",
       "      <td>3.045553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>104893</td>\n",
       "      <td>RPC</td>\n",
       "      <td>KIAMICHI REMOLD 3</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>1.5171</td>\n",
       "      <td>1.9779</td>\n",
       "      <td>2090.0000</td>\n",
       "      <td>3.000672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>104652</td>\n",
       "      <td>RPC</td>\n",
       "      <td>NAVAJO ARCO 2</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>2.0415</td>\n",
       "      <td>1.6476</td>\n",
       "      <td>2131.9900</td>\n",
       "      <td>3.363575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>104752</td>\n",
       "      <td>RPC</td>\n",
       "      <td>NAVAJO ARCO REMOL 1</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>1.6062</td>\n",
       "      <td>1.9894</td>\n",
       "      <td>2184.9392</td>\n",
       "      <td>3.195374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>104598</td>\n",
       "      <td>RPC</td>\n",
       "      <td>FERRON 82-3 1677.5</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>2.4486</td>\n",
       "      <td>1.7306</td>\n",
       "      <td>2472.2308</td>\n",
       "      <td>4.237547</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      RPC Source          Description  Lithology  Vs [km/s]  VpVs ratio  \\\n",
       "0  104818    RPC    KIAMICHI FORMA. 4  sandstone     1.5957      1.9086   \n",
       "1  104893    RPC    KIAMICHI REMOLD 3  sandstone     1.5171      1.9779   \n",
       "2  104652    RPC        NAVAJO ARCO 2  sandstone     2.0415      1.6476   \n",
       "3  104752    RPC  NAVAJO ARCO REMOL 1  sandstone     1.6062      1.9894   \n",
       "4  104598    RPC   FERRON 82-3 1677.5  sandstone     2.4486      1.7306   \n",
       "\n",
       "   Rho [kg/m³]  Vp [km/s]  \n",
       "0    2109.1206   3.045553  \n",
       "1    2090.0000   3.000672  \n",
       "2    2131.9900   3.363575  \n",
       "3    2184.9392   3.195374  \n",
       "4    2472.2308   4.237547  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Vp [km/s]\"] = data[\"VpVs ratio\"] * data[\"Vs [km/s]\"]\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing columns | Eliminar columnas\n",
    "\n",
    "We are going to remove from the DataFrame the columns that are not relevant: _Index_,  _Description_, and _VpVs ratio_. \n",
    "For that, use the `drop` method.\n",
    "\n",
    "---\n",
    "\n",
    "Vamos a eliminar del DataFrame las columnas que no sean relevantes: _Index_, _Description_,  y _VpVs ratio_.\n",
    "Para ello uso el método `drop`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Vs [km/s]</th>\n",
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       "      <th>2</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>104752</td>\n",
       "      <td>RPC</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>1.6062</td>\n",
       "      <td>2184.9392</td>\n",
       "      <td>3.195374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>104598</td>\n",
       "      <td>RPC</td>\n",
       "      <td>sandstone</td>\n",
       "      <td>2.4486</td>\n",
       "      <td>2472.2308</td>\n",
       "      <td>4.237547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>102178</td>\n",
       "      <td>RPC</td>\n",
       "      <td>dolomite</td>\n",
       "      <td>2.7592</td>\n",
       "      <td>2414.0000</td>\n",
       "      <td>4.626075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>102225</td>\n",
       "      <td>RPC</td>\n",
       "      <td>dolomite</td>\n",
       "      <td>3.1168</td>\n",
       "      <td>2475.7000</td>\n",
       "      <td>5.446296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805</th>\n",
       "      <td>102088</td>\n",
       "      <td>RPC</td>\n",
       "      <td>dolomite</td>\n",
       "      <td>2.9987</td>\n",
       "      <td>2521.1000</td>\n",
       "      <td>5.446239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>806</th>\n",
       "      <td>102213</td>\n",
       "      <td>RPC</td>\n",
       "      <td>dolomite</td>\n",
       "      <td>2.9331</td>\n",
       "      <td>2470.8000</td>\n",
       "      <td>5.347921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>807</th>\n",
       "      <td>102086</td>\n",
       "      <td>RPC</td>\n",
       "      <td>dolomite</td>\n",
       "      <td>2.8970</td>\n",
       "      <td>2549.2000</td>\n",
       "      <td>5.347862</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>752 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        RPC Source  Lithology  Vs [km/s]  Rho [kg/m³]  Vp [km/s]\n",
       "0    104818    RPC  sandstone     1.5957    2109.1206   3.045553\n",
       "1    104893    RPC  sandstone     1.5171    2090.0000   3.000672\n",
       "2    104652    RPC  sandstone     2.0415    2131.9900   3.363575\n",
       "3    104752    RPC  sandstone     1.6062    2184.9392   3.195374\n",
       "4    104598    RPC  sandstone     2.4486    2472.2308   4.237547\n",
       "..      ...    ...        ...        ...          ...        ...\n",
       "803  102178    RPC   dolomite     2.7592    2414.0000   4.626075\n",
       "804  102225    RPC   dolomite     3.1168    2475.7000   5.446296\n",
       "805  102088    RPC   dolomite     2.9987    2521.1000   5.446239\n",
       "806  102213    RPC   dolomite     2.9331    2470.8000   5.347921\n",
       "807  102086    RPC   dolomite     2.8970    2549.2000   5.347862\n",
       "\n",
       "[752 rows x 6 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop([\"Description\", \"VpVs ratio\"], axis=1, inplace=True)  \n",
    "# axis=1 is because we want to drop columns, see the documentation for more details \n",
    "# inplace=True means that we don't generate a copy of the dataFrame, we do it in the same DataFrame, see the documentation for more details.\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting our DataFrame | Graficando nuestro DataFrame\n",
    "\n",
    "We are going to plot _Vp & Vs_ and the _density_ according to the different types of lithology of our DataFrame.\n",
    "\n",
    "Let's see how many types of lithology there are. \n",
    "`value_counts` returns a Series containing counts of unique rows in the DataFrame.\n",
    "\n",
    "---\n",
    "\n",
    "Vamos a graficar _Vp & Vs_ y la _densidad_ según los diferentes tipos de litología de nuestro DataFrame.\n",
    "\n",
    "Veamos cuántos tipos de litología hay.\n",
    "`value_counts()` devuelve una serie que contiene recuentos de cuántas veces aparece un valor en el DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sandstone    200\n",
       "shale        200\n",
       "dolomite     200\n",
       "limestone    152\n",
       "Name: Lithology, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Lithology\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's group our data according to the four types of lithology using `groupby()`. | Agrupemos nuestros datos según los 4 tipos de litología usando `groupby()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f454c3f4690>\n"
     ]
    }
   ],
   "source": [
    "litologias = data.groupby('Lithology')\n",
    "\n",
    "print(litologias)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Con este loop, agrupamos con base a cada diferente litología. En este caso la variable \"name\" representa el nombre del grupo y \"group\" representa el dataframe que resulta después de haber ejecutado la agrupación. \n",
    "\n",
    "---\n",
    "\n",
    "This loop groups split the data into groups based on lithology. In this case, “name” represents the group name and “group” represents the actual grouped data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " dolomite \n",
      " \n",
      "         RPC Source Lithology  Vs [km/s]  Rho [kg/m³]  Vp [km/s]\n",
      "604  102242    RPC  dolomite     2.5558       2318.2   4.265119\n",
      "605  102096    RPC  dolomite     3.4449       2705.9   5.708544\n",
      "606  102196    RPC  dolomite     2.8412       2435.4   4.855611\n",
      "607  102071    RPC  dolomite     3.0348       2573.3   4.986783\n",
      "608  102089    RPC  dolomite     3.0709       2560.0   5.544817\n",
      "..      ...    ...       ...        ...          ...        ...\n",
      "803  102178    RPC  dolomite     2.7592       2414.0   4.626075\n",
      "804  102225    RPC  dolomite     3.1168       2475.7   5.446296\n",
      "805  102088    RPC  dolomite     2.9987       2521.1   5.446239\n",
      "806  102213    RPC  dolomite     2.9331       2470.8   5.347921\n",
      "807  102086    RPC  dolomite     2.8970       2549.2   5.347862\n",
      "\n",
      "[200 rows x 6 columns] \n",
      " \n",
      "\n",
      "\n",
      " limestone \n",
      " \n",
      "         RPC Source  Lithology  Vs [km/s]  Rho [kg/m³]  Vp [km/s]\n",
      "404  100145    RPC  limestone     1.8190    2442.3054   3.306032\n",
      "405  100146    RPC  limestone     1.8190    2462.3277   3.306032\n",
      "406  101422    RPC  limestone     0.4413    2112.0846   2.055928\n",
      "407  101423    RPC  limestone     0.4413    2121.2965   2.077552\n",
      "408  101486    RPC  limestone     0.7320    1830.9624   2.023760\n",
      "..      ...    ...        ...        ...          ...        ...\n",
      "551  101783    RPC  limestone     1.3671    2101.2318   3.024572\n",
      "552  101784    RPC  limestone     1.3993    2135.4726   3.121419\n",
      "553  101820    RPC  limestone     1.2054    2090.4700   2.690935\n",
      "554  101823    RPC  limestone     1.5715    2160.5141   2.873959\n",
      "555  101824    RPC  limestone     1.7438    2160.7635   3.143025\n",
      "\n",
      "[152 rows x 6 columns] \n",
      " \n",
      "\n",
      "\n",
      " sandstone \n",
      " \n",
      "         RPC Source  Lithology  Vs [km/s]  Rho [kg/m³]  Vp [km/s]\n",
      "0    104818    RPC  sandstone     1.5957    2109.1206   3.045553\n",
      "1    104893    RPC  sandstone     1.5171    2090.0000   3.000672\n",
      "2    104652    RPC  sandstone     2.0415    2131.9900   3.363575\n",
      "3    104752    RPC  sandstone     1.6062    2184.9392   3.195374\n",
      "4    104598    RPC  sandstone     2.4486    2472.2308   4.237547\n",
      "..      ...    ...        ...        ...          ...        ...\n",
      "197  104710    RPC  sandstone     2.6480    2116.2235   3.768104\n",
      "198  104682    RPC  sandstone     2.0709    2185.7369   3.560084\n",
      "199  104599    RPC  sandstone     2.4315    2467.0000   4.223029\n",
      "200  104693    RPC  sandstone     2.1555    2121.2656   3.610247\n",
      "201  104751    RPC  sandstone     1.4754    2200.6200   3.030324\n",
      "\n",
      "[200 rows x 6 columns] \n",
      " \n",
      "\n",
      "\n",
      " shale \n",
      " \n",
      "         RPC Source Lithology  Vs [km/s]  Rho [kg/m³]  Vp [km/s]\n",
      "202  101023    RPC     shale     2.9573    2716.6386   4.953773\n",
      "203  100978    RPC     shale     2.3180    2695.5085   3.444548\n",
      "204  100993    RPC     shale     2.2185    2561.0159   3.507670\n",
      "205  101048    RPC     shale     1.6333    2564.4067   2.988776\n",
      "206  100417    RPC     shale     2.2210    2419.4478   3.641996\n",
      "..      ...    ...       ...        ...          ...        ...\n",
      "399  101113    RPC     shale     3.0540    2781.6913   5.266928\n",
      "400  100424    RPC     shale     2.3290    2421.7737   3.535888\n",
      "401  100992    RPC     shale     2.8455    2667.2169   5.092307\n",
      "402  100044    RPC     shale     1.6677    2570.0527   3.324727\n",
      "403  100975    RPC     shale     2.7873    2521.5015   5.077067\n",
      "\n",
      "[200 rows x 6 columns] \n",
      " \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for name, group in litologias:\n",
    "    print('\\n',name,'\\n','\\n',group,'\\n','\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot _Vs & Vp_ | Grafico _Vs & Vp_**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,5))\n",
    "\n",
    "for name, group in litologias:\n",
    "    plt.plot(\n",
    "        group[\"Vp [km/s]\"], \n",
    "        group[\"Vs [km/s]\"], \n",
    "        marker='o', \n",
    "        linestyle='',\n",
    "        ms=6, \n",
    "        label=name, \n",
    "        alpha=0.5       # transparency / trasparencia\n",
    "    )\n",
    "\n",
    "#ax.legend(loc=1)\n",
    "plt.legend()\n",
    "plt.xlabel('$V_\\mathrm{P}\\ [km/s]$', size=12)\n",
    "plt.ylabel('$V_\\mathrm{S}\\ [km/s]$', size=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot the _density_ | Grafico la _densidad_**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,5))\n",
    "\n",
    "for name, group in litologias:\n",
    "    plt.hist(\n",
    "        group[\"Rho [kg/m³]\"], \n",
    "        histtype='bar', \n",
    "        stacked=True, \n",
    "        rwidth=0.8, \n",
    "        alpha=0.8, \n",
    "        label=name\n",
    "    )\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel('$\\\\rho\\ [g/m^3]$', size=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Homework | Tarea\n",
    "\n",
    "Before doing your homework, _create_ a notebook called `homework_pandas_python.ipynb` into your _local repository_ called `coco_coding_homework`.\n",
    "There you will _solve the activities_ proposed below.\n",
    "Then, _push_ the notebook with the solution into your _remote repository_ in your GitHub account.\n",
    "In this way, one of the trainers will be able to see your answers and give you feedback.\n",
    "\n",
    "Antes de hacer la tarea, _cree un notebook_ llamado `homework_pandas_python.ipynb` en su _repositorio local_ llamado `coco_coding_homework`.\n",
    "Allí es donde _resolverá las actividades_ propuestas a continuación.\n",
    "Luego haga un _push_ del notebook con las respuestas al _repositorio remoto_ en su cuenta de GitHub.\n",
    "De esta forma, alguno de los trainers podrá ver sus respuestas y darle una devolución de las actividades resueltas.\n",
    "\n",
    "### Main Task | Tarea principal\n",
    "\n",
    "In this opportunity, we share with you a DataFrame with the bathymetry and gravity disturbance profile of the island of Oahu in Hawaii.\n",
    "\n",
    "**Steps:**\n",
    "1. Load the file.\n",
    "2. Clean the DataFrame:\n",
    "    * Remove NaN values, if any\n",
    "    * Remove duplicate rows, if any\n",
    "    * Fix other problems like out of range values \n",
    "3. Plot the _gravity disturbance & distance_ and the _bathymetry & distance_ using `subplot`.\n",
    "   Make the plot as if you were going to use it for a scientific article. \n",
    "\n",
    "---\n",
    "\n",
    "En esta oportunidad, compartimos contigo un DataFrame con el perfil de batimetría y perturbación de la gravedad de la isla de Oahu en Hawaii.\n",
    "\n",
    "**Pasos:**\n",
    "1. Cargue el archivo.\n",
    "2. Limpie el DataFrame verificando si el DataFrame tiene:\n",
    "    * valores NaN y elimínelos.\n",
    "    * filas duplicadas y eliminarlas.\n",
    "    * cualquier otro problema como valores fuera de rango y elimínelos.\n",
    "3. Grafique la _gravity disturbance & distance_ y _bathimetry & distance_ usando `subplot`.\n",
    "   Haga las gráficas como si fuera a ponerla en un artículo.\n",
    "\n",
    "\n",
    "### Bonus track\n",
    "\n",
    "We can see from the bathymetry profile that there is a long-wavelength component (almost a gaussian shape) right around the islands.\n",
    "This is due to the _thermal swell_ of the hotspot and not part of the flexure response of the oceanic lithosphere.\n",
    "The swell is isostatically compensated, as seen in the gravity disturbance.\n",
    "We'll fit a gaussian curve to the profile data and remove it from our observations to obtain a residual.\n",
    "\n",
    "**Steps:**\n",
    "1. Define the gaussian function:\n",
    "   $$ h_{gauss} (x) = h_{0} + G \\exp \\left(-\\dfrac{(x - x_0)^2}{\\sigma^2}\\right) $$\n",
    "    \n",
    "2.  Estimate the parameters of this function ($h_0$, $G$, $x_0$, $\\sigma$). You can start with ($h_0$, $G$, $x_0$, $\\sigma$) = (hawaii.distance_km, h0=-5.5, G=1.5, x0=1300, sigma=450).\n",
    "3. Calculate the thermal swell.\n",
    "4. Calculate a residual topography and store it in our original `DataFrame`.\n",
    "   $$ residual = topography_{km} - swell$$\n",
    "5. Make the plot using `subplot` as if you were going to put it in an article. \n",
    "\n",
    "---\n",
    "\n",
    "Podemos ver en el perfil de batimetría que hay un componente de longitud de onda larga (casi una forma gaussiana) alrededor de las islas.\n",
    "Esto se debe al _oleaje térmico_ del hotspot y no es parte de la respuesta de flexión de la litosfera oceánica.\n",
    "El oleaje se compensa isostáticamente, como se ve en la perturbación de la gravedad.\n",
    "Ajustaremos una curva gaussiana a los datos del perfil y la eliminaremos de nuestras observaciones para obtener un residuo.\n",
    "\n",
    "**Pasos:**\n",
    "1. Defina la función gaussiana:\n",
    "   $$h_{gauss} (x) = h_{0} + G \\exp \\left(-\\dfrac{(x - x_0)^2}{\\sigma^2}\\right)$$\n",
    "    \n",
    "2. Estime los parámetros de esta función (($h_0$, $G$, $x_0$, $\\sigma$). Puede comenzar con $h_0$, $G$, $x_0$, $\\sigma$) = (hawaii.distance_km, h0=-5.5, G=1.5, x0=1300, sigma=450).\n",
    "3. Calcule el oleaje térmico.\n",
    "4. Calcule una topografía residual y guárdela en el `DataFrame` original.\n",
    "   $$ residual = topografia_ {km} - swell $$\n",
    "5. Grafique los resultados usando `subplot` como si fueras a ponerla en un artículo."
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    "## Extra material to keep learning | Material extra para seguir aprendiendo\n",
    "\n",
    "**Pandas** is very powerful and has many more functionalities. Check out their documentation and tutorials to learn much more.\n",
    "\n",
    "---\n",
    "\n",
    "**Pandas** es muy potente y tiene muchas mas funcionalidades. Revise su documentación y tutoriales para aprender mucho mas. \n",
    "\n",
    "* [Pandas documentation](https://pandas.pydata.org/docs/getting_started/index.html)\n",
    "* [Intro to Python by GeoLatinas](https://github.com/GeoLatinas/Intro-to-python)\n",
    "* [Pandas: Python Data Analysis Library](https://johnfoster.pge.utexas.edu/numerical-methods-book/ScientificPython_Pandas.html)\n",
    "\n",
    "\n",
    "### Other interesting libraries | Otras librerías interesantes\n",
    "\n",
    "If you got here and you want to continue learning Python libraries that can help you with your studies or research in earth science, we leave you some libraries that could be useful for you:\n",
    "\n",
    "---\n",
    "\n",
    "Si llegaste hasta acá y quieres seguir aprendiendo paquetes que puedan ayudarte a tus estudios o investigaciones en ciencia de la tierra, te dejamos algunas librerías que podrían serte útiles:\n",
    "\n",
    "* [Xarray](https://xarray.pydata.org/en/stable/) introduces labels in the form of dimensions, coordinates and attributes on top of raw NumPy-like arrays, which allows for a more intuitive, more concise, and less error-prone developer experience. \n",
    "  It is inspired by and borrows heavily from pandas.\n",
    "  It is particularly tailored to working with netCDF files, which were the source of xarray’s data model, and integrates tightly with dask for parallel computing.\n",
    "  \n",
    "* [PyGMT](https://www.pygmt.org/dev/) is a library for processing geospatial and geophysical data and making publication quality maps and figures. \n",
    "  It provides a Pythonic interface for the Generic Mapping Tools (GMT), a command-line program widely used in the Earth Sciences.\n",
    "  \n",
    "* [pyproj](https://pyproj4.github.io/pyproj/stable/) is a python interface to [PROJ](https://proj.org/) (cartographic projections and coordinate transformations library).\n",
    "\n",
    "* [Seaborn](https://seaborn.pydata.org/) is a data visualization library based on matplotlib. \n",
    "  It provides a high-level interface for drawing attractive and informative statistical graphics.\n",
    "  \n",
    "### Pandas in action | Pandas en acción\n",
    "\n",
    "* Si a ti te interesa explorar otros tipos de datasets con temáticas diferentes a Ciencias de la Tierra y seguir aprendiendo a usar Pandas, te recomendamos visitar este [repositorio](https://github.com/MGomezN/enpecyt_2017) que tiene un notebook en donde se analiza la percepción que tiene la población mexicana sobre la ciencia y la tecnología. Te proponemos que explores el notebook, apliques tus conocimientos de pandas y compares tus resultados con los del siguiente [artículo](https://letraslibres.com/ciencia-y-tecnologia/la-ciencia-esa-aterradora-desconocida/)\n",
    "* El portal \"Nuestro Mundo en Datos\" tiene la misión de hacer accesible \"datos e investigación que contribuyen a combatir los grandes problemas del mundo\". Te recomendamos en particular el dataset de [democracia](https://ourworldindata.org/democracy) que podrás descargar en .csv.\n",
    "\n",
    "---\n",
    "\n",
    "* If you are interested in exploring other types of datasets with themes other than Earth Sciences, we recommend this [repository](https://github.com/MGomezN/enpecyt_2017).The notebook analyzes the Mexican population's perception of science and technology in 2017. We encourage you to explore the notebook, apply your knowledge of Pandas and compare your results with those of the following [article](https://letraslibres.com/ciencia-y-tecnologia/la-ciencia-esa-aterradora-desconocida/).\n",
    "\n",
    "* The project \"Our World in Data\" has the mission of making accessible “research and data to make progress against the world’s largest problems”.  We particularly recommend the [democracy](https://ourworldindata.org/democracy) dataset that you can download as .csv.\n",
    "\n",
    "\n",
    "  "
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