Obtencion_est_descriptivas_MapasCalor_boxplots.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Obtención de estadisticas descriptivas, mapas de calor y boxplots\n",
    "\n",
    "## Fernando de Jesús Mendivil Terminel - A0023220"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para ejecutar esto, solo se necesita darle al botón \"Run\", sin dar click en ninguna linea."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Librerías usadas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Guardar el archivo (con los datos a usar) en una variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape:  (308, 5)\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Carat</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Clarity</th>\n",
       "      <th>Lab</th>\n",
       "      <th>Price ($US)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.30</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>911.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.30</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1057.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.30</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1057.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.30</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>882.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1148.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.31</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1088.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.31</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>998.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.31</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>998.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.31</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>788.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.31</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>788.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Carat  Colour  Clarity  Lab  Price ($US)\n",
       "0   0.30       1        5    1        911.4\n",
       "1   0.30       2        4    1       1057.0\n",
       "2   0.30       4        2    1       1057.0\n",
       "3   0.30       4        4    1        882.0\n",
       "4   0.31       1        4    1       1148.7\n",
       "5   0.31       2        4    1       1088.5\n",
       "6   0.31       3        4    1        998.9\n",
       "7   0.31       4        3    1        998.9\n",
       "8   0.31       5        5    1        788.2\n",
       "9   0.31       6        4    1        788.2"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Leer los datos a analizar\n",
    "diamantes = pd.read_csv(\"Diamonds are forever.csv\", na_values=':')\n",
    "print('shape: ', diamantes.shape)\n",
    "diamantes.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Carat</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Clarity</th>\n",
       "      <th>Lab</th>\n",
       "      <th>Price ($US)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>1.01</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6995.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>1.01</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6505.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>1.01</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6603.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>1.01</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>6407.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>1.01</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6211.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>1.01</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5722.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>1.02</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>7557.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>1.06</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6923.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>1.02</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6271.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>1.09</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6374.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Carat  Colour  Clarity  Lab  Price ($US)\n",
       "298   1.01       4        3    3       6995.1\n",
       "299   1.01       4        5    3       6505.1\n",
       "300   1.01       5        3    3       6603.1\n",
       "301   1.01       5        4    3       6407.1\n",
       "302   1.01       6        2    3       6211.1\n",
       "303   1.01       6        4    3       5722.5\n",
       "304   1.02       3        3    3       7557.2\n",
       "305   1.06       5        3    3       6923.0\n",
       "306   1.02       5        5    3       6271.3\n",
       "307   1.09       6        3    3       6374.9"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamantes.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1 En el archivo \"Diamonds are forever.csv\", hay 308 filas y 5 columnas; en total, son 1540 datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Carat          308\n",
       "Colour         308\n",
       "Clarity        308\n",
       "Lab            308\n",
       "Price ($US)    308\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamantes.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2 Aquí se puede observar que no hay datos nulos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Carat          0\n",
       "Colour         0\n",
       "Clarity        0\n",
       "Lab            0\n",
       "Price ($US)    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamantes.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3 Valores máximos y mínimos para cada columna/variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Carat              1.1\n",
       "Colour             6.0\n",
       "Clarity            5.0\n",
       "Lab                3.0\n",
       "Price ($US)    11205.6\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamantes.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Carat            0.18\n",
       "Colour           1.00\n",
       "Clarity          1.00\n",
       "Lab              1.00\n",
       "Price ($US)    446.60\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamantes.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Las variables del estudio de diamantes son las siguientes:\n",
    "|Variable|Tipo|Descripción|\n",
    "|--------|----|-----------|\n",
    "|Carat|Cuantitativa|Peso del diamante en carats|\n",
    "|Colour|Cualitativa|Valor numérico de la calidad del color; va de 1 (sin color) a 6 (casi sin color)|\n",
    "|Clarity|Cualitativa|Valor numérico de la claridad; va de 1 (perfecto) a 5 (inclusiones muy pequeñas)|\n",
    "|Lab|Cualitativa|Laboratorio que evaluó el diamante, en orden de 1 a 3: GEM=Gemological Institute of America, INT=International Gemmological Institute, DHC=Diamond High Council|\n",
    "|Price ($US)|Cuantitativa|Valor del diamante|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Carat             0.630909\n",
       "Colour            3.750000\n",
       "Clarity           3.152597\n",
       "Lab               1.766234\n",
       "Price ($US)    3513.638636\n",
       "dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamantes.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Carat             0.62\n",
       "Colour            4.00\n",
       "Clarity           3.00\n",
       "Lab               2.00\n",
       "Price ($US)    2950.50\n",
       "dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamantes.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Carat             0.277183\n",
       "Colour            1.402360\n",
       "Clarity           1.293718\n",
       "Lab               0.833283\n",
       "Price ($US)    2382.181001\n",
       "dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamantes.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Gráficos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1 Mapa de calor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matriz_correlacion = diamantes.corr().round(2)\n",
    "sns.heatmap(data=matriz_correlacion, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2 Boxplots\n",
    "Decidí hacer tres porque el rango del peso en carats y del precio en dólares estadounidenses eran muy diferentes, entonces hice uno para cada uno (Las variables Colour, Clarity y Lab tienen rangos similares, por lo que las deje en una misma figura)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,7))\n",
    "\n",
    "plt.boxplot(diamantes['Carat'], notch=True)\n",
    "plt.title(\"Boxplot de carats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "datos = [diamantes['Colour'],diamantes['Clarity'],diamantes['Lab']]\n",
    "\n",
    "fig = plt.figure(figsize=(10,7))\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "\n",
    "box = ax.boxplot(datos, notch=True)\n",
    "plt.title(\"Boxplots de color, claridad y laboratorios\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,7))\n",
    "\n",
    "plt.boxplot(diamantes['Price ($US)'], notch=True)\n",
    "plt.title(\"Boxplot del precio en dólares\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.3 Histogramas\n",
    "De datos cualitativos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colores = diamantes['Colour']\n",
    "histogram = Counter(min(color // 1 * 1, 6) for color in colores)\n",
    "\n",
    "plt.bar([x for x in histogram.keys()],  # Desplazar por 5 las barras a la derecha\n",
    "        histogram.values(),                 # Darle la altura correcta a cada barra\n",
    "        1,                                 # Darle un ancho de 10 a cada barra\n",
    "        edgecolor=(0, 0, 0))                # Líneas negras para cada barra\n",
    "\n",
    "plt.axis([0, 7, 0, 90])                  # Eje x desde -5 a 105,\n",
    "                                           # Eje y desde 0 a 5\n",
    "\n",
    "plt.xticks([i for i in range(7)])    # Etiquetas de X de 0, 10, ..., 100\n",
    "plt.xlabel(\"Color\")                        # Etiqueta del eje x\n",
    "plt.ylabel(\"# de diamantes\")             # Etiqueta del eje y\n",
    "plt.title(\"Distribución de color de diamantes\")# Título del histograma\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "claridades = diamantes['Clarity']\n",
    "histogram = Counter(min(claridad // 1 * 1, 5) for claridad in claridades)\n",
    "\n",
    "plt.bar([x for x in histogram.keys()],  # Desplazar por 5 las barras a la derecha\n",
    "        histogram.values(),                 # Darle la altura correcta a cada barra\n",
    "        1,                                 # Darle un ancho de 10 a cada barra\n",
    "        edgecolor=(0, 0, 0))                # Líneas negras para cada barra\n",
    "\n",
    "plt.axis([0, 6, 0, 90])                  # Eje x desde -5 a 105,\n",
    "                                           # Eje y desde 0 a 5\n",
    "\n",
    "plt.xticks([i for i in range(6)])    # Etiquetas de X de 0, 10, ..., 100\n",
    "plt.xlabel(\"Claridad\")                        # Etiqueta del eje x\n",
    "plt.ylabel(\"# de diamantes\")             # Etiqueta del eje y\n",
    "plt.title(\"Distribución de color de diamantes\")# Título del histograma\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labs = diamantes['Lab']\n",
    "histogram = Counter(min(lab // 1 * 1, 5) for lab in labs)\n",
    "\n",
    "plt.bar([x for x in histogram.keys()],  # Desplazar por 5 las barras a la derecha\n",
    "        histogram.values(),                 # Darle la altura correcta a cada barra\n",
    "        1,                                 # Darle un ancho de 10 a cada barra\n",
    "        edgecolor=(0, 0, 0))                # Líneas negras para cada barra\n",
    "\n",
    "plt.axis([0, 4, 0, 160])                  # Eje x desde -5 a 105,\n",
    "                                           # Eje y desde 0 a 5\n",
    "\n",
    "plt.xticks([i for i in range(4)])    # Etiquetas de X de 0, 10, ..., 100\n",
    "plt.xlabel(\"Laboratorio\")                        # Etiqueta del eje x\n",
    "plt.ylabel(\"# de diamantes\")             # Etiqueta del eje y\n",
    "plt.title(\"Distribución de color de diamantes\")# Título del histograma\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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