├── README.md
├── 1_IntruduccionPandas.ipynb
├── 2_TiposDeDatos.ipynb
├── 5_InfoDataSet.ipynb
├── 4_ImportarDatos.ipynb
└── 6_TratamientoDatos.ipynb


/README.md:
--------------------------------------------------------------------------------
 1 | # <h2> Repositorio de GitHub dedicado al aprendizaje de Pandas y su importancia en el análisis de datos! 🐼💼<h2>
 2 | # <h3>📝 Descripción<h3>
 3 | En este repositorio, he recopilado una serie de conceptos clave, ejercicios prácticos y recursos útiles para que aquellos que están comenzando en el mundo del análisis de datos puedan sumergirse en el poder de Pandas. Como alguien que ha estado en la búsqueda de su primer empleo en este campo, entiendo lo crucial que es dominar esta herramienta y estar preparado para los desafíos del mundo real.
 4 | 
 5 | # <h3>🎯 Contenido del Repositorio:<h3>
 6 | 
 7 | # <h4> Jupyter Notebook: <h4>
 8 | Este notebook contiene un análisis dedicado al aprendizaje de Pandas y su importancia en el análisis de datos para aquellos que buscan su primer empleo en este campo! 🐼
 9 | 
10 | # <h4> Conjunto de Datos en csv : <h4>
11 | Se incluye el conjunto de datos utilizado en el análisis. 
12 | 
13 | https://drive.google.com/file/d/1HTCYiLHnIY72-O5qde0O5Tp5vVIuq2bl/view?usp=sharing
14 | 
15 | https://drive.google.com/file/d/1v-uBSxvs0cy0iBRTSl5p2dhWNiheLKC_/view?usp=sharing
16 | 
17 | # <h4> Cómo Utilizar Este Repositorio: <h4>
18 | Clona este repositorio en tu máquina local.
19 | Abre el Jupyter Notebook en tu entorno de trabajo de Python.
20 | Sigue las instrucciones y ejecuta las celdas de código paso a paso para comprender los conceptos y técnicas presentadas.
21 | Experimenta con el código y los datos por tu cuenta para obtener una comprensión más profunda.
22 | # <h4> Si te gusta alguno de los repositorios, podes hacer clic para darme tu apoyo en el ⭐️ botón y correr la voz 🦄⁣ <h4>
23 | ⁣
24 | 👩‍💻 Muchas gracias y Saludos!⁣
25 | =======
26 | 
27 | .
28 | .
29 | .
30 | .
31 | .
32 | El Cuaderno de estudio pertenece a Data con Steven
33 | 


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/1_IntruduccionPandas.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Curso Pandas"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "markdown",
 12 |    "metadata": {},
 13 |    "source": [
 14 |     "### Que es Pandas\n",
 15 |     "\n",
 16 |     "La biblioteca de software de código abierto Pandas está diseñada específicamente para la manipulación y el análisis de datos en el lenguaje Python. Es potente, flexible y fácil de usar.\n",
 17 |     "Vas a poder:\n",
 18 |     "\n",
 19 |     "**Manipular datos:** Te permite cargar datos desde diferentes fuentes, como archivos CSV, bases de datos incluso páginas web. Cargados los datos podes filtrar, ordenar, agregar o eliminar información.\n",
 20 |     "\n",
 21 |     "**Limpieza de datos:** A menudo los datos que obtenemos pueden tener valores faltantes, errores tipográficos o información duplicada. Pandas ofrece herramientas para limpiar datos de manera sencilla.\n",
 22 |     "\n",
 23 |     "**Análisis rápido:** Por ejemplo, calcular estadísticas como promedios, la mediana o la desviación estandar de tus datos.\n",
 24 |     "\n",
 25 |     "**Visualización de datos:** Además de manipular datos, Pandas puede trabajr muy bien con bibliotecas de visualización de datos, crear gráficos y visualizaciones informativas.\n",
 26 |     "\n",
 27 |     "\n"
 28 |    ]
 29 |   },
 30 |   {
 31 |    "cell_type": "markdown",
 32 |    "metadata": {},
 33 |    "source": [
 34 |     "### Estructura de Datos\n",
 35 |     "\n",
 36 |     "Pandas tiene dos estructuras de datos principales:\n",
 37 |     "\n",
 38 |     "**DataFrame:** Similar a una hoja de cálculo en Excel o una tabla en una base de datos, es una estructura bidimensional que organiza los datos en filas y columnas. Cada columna puede contener diferentes tipos de datos (números, texto, fechas, etc)\n",
 39 |     "\n",
 40 |     "**Series:** Es una estructura unidimensional similar a una lista o un vector, pero con etiquetas en lugar de índices numéricos. Puedes pensar en una serie como una sola columna de un DataFrame.\n"
 41 |    ]
 42 |   },
 43 |   {
 44 |    "cell_type": "markdown",
 45 |    "metadata": {},
 46 |    "source": [
 47 |     "### Instalar Pandas"
 48 |    ]
 49 |   },
 50 |   {
 51 |    "cell_type": "markdown",
 52 |    "metadata": {},
 53 |    "source": [
 54 |     "En la consola de conda: \n",
 55 |     "\n",
 56 |     "    conda install pandas"
 57 |    ]
 58 |   },
 59 |   {
 60 |    "cell_type": "markdown",
 61 |    "metadata": {},
 62 |    "source": [
 63 |     "### Importar Pandas"
 64 |    ]
 65 |   },
 66 |   {
 67 |    "cell_type": "markdown",
 68 |    "metadata": {},
 69 |    "source": [
 70 |     "    import pandas as df"
 71 |    ]
 72 |   },
 73 |   {
 74 |    "cell_type": "markdown",
 75 |    "metadata": {},
 76 |    "source": [
 77 |     "### Cargar Datos"
 78 |    ]
 79 |   },
 80 |   {
 81 |    "cell_type": "code",
 82 |    "execution_count": 10,
 83 |    "metadata": {},
 84 |    "outputs": [
 85 |     {
 86 |      "data": {
 87 |       "text/html": [
 88 |        "<div>\n",
 89 |        "<style scoped>\n",
 90 |        "    .dataframe tbody tr th:only-of-type {\n",
 91 |        "        vertical-align: middle;\n",
 92 |        "    }\n",
 93 |        "\n",
 94 |        "    .dataframe tbody tr th {\n",
 95 |        "        vertical-align: top;\n",
 96 |        "    }\n",
 97 |        "\n",
 98 |        "    .dataframe thead th {\n",
 99 |        "        text-align: right;\n",
100 |        "    }\n",
101 |        "</style>\n",
102 |        "<table border=\"1\" class=\"dataframe\">\n",
103 |        "  <thead>\n",
104 |        "    <tr style=\"text-align: right;\">\n",
105 |        "      <th></th>\n",
106 |        "      <th>Nombre Cliente</th>\n",
107 |        "      <th>Tipo Producto</th>\n",
108 |        "      <th>Precio (€/kg)</th>\n",
109 |        "      <th>Cantidad Vendida (kg)</th>\n",
110 |        "      <th>Fecha Venta</th>\n",
111 |        "      <th>Provincia Cliente</th>\n",
112 |        "    </tr>\n",
113 |        "  </thead>\n",
114 |        "  <tbody>\n",
115 |        "    <tr>\n",
116 |        "      <th>0</th>\n",
117 |        "      <td>Mas y Mas</td>\n",
118 |        "      <td>Almendras</td>\n",
119 |        "      <td>4.54</td>\n",
120 |        "      <td>1212</td>\n",
121 |        "      <td>2023-03-29</td>\n",
122 |        "      <td>Cádiz</td>\n",
123 |        "    </tr>\n",
124 |        "    <tr>\n",
125 |        "      <th>1</th>\n",
126 |        "      <td>Mercadona</td>\n",
127 |        "      <td>Nueces</td>\n",
128 |        "      <td>5.84</td>\n",
129 |        "      <td>591</td>\n",
130 |        "      <td>2023-12-05</td>\n",
131 |        "      <td>Sevilla</td>\n",
132 |        "    </tr>\n",
133 |        "    <tr>\n",
134 |        "      <th>2</th>\n",
135 |        "      <td>Alcampo</td>\n",
136 |        "      <td>Cacahuetes</td>\n",
137 |        "      <td>2.69</td>\n",
138 |        "      <td>2831</td>\n",
139 |        "      <td>2023-07-26</td>\n",
140 |        "      <td>Madrid</td>\n",
141 |        "    </tr>\n",
142 |        "    <tr>\n",
143 |        "      <th>3</th>\n",
144 |        "      <td>Mas y Mas</td>\n",
145 |        "      <td>Pistachos</td>\n",
146 |        "      <td>6.70</td>\n",
147 |        "      <td>516</td>\n",
148 |        "      <td>2023-10-07</td>\n",
149 |        "      <td>Alicante</td>\n",
150 |        "    </tr>\n",
151 |        "    <tr>\n",
152 |        "      <th>4</th>\n",
153 |        "      <td>Lidl</td>\n",
154 |        "      <td>Nueces</td>\n",
155 |        "      <td>6.14</td>\n",
156 |        "      <td>1283</td>\n",
157 |        "      <td>2023-05-12</td>\n",
158 |        "      <td>Barcelona</td>\n",
159 |        "    </tr>\n",
160 |        "    <tr>\n",
161 |        "      <th>...</th>\n",
162 |        "      <td>...</td>\n",
163 |        "      <td>...</td>\n",
164 |        "      <td>...</td>\n",
165 |        "      <td>...</td>\n",
166 |        "      <td>...</td>\n",
167 |        "      <td>...</td>\n",
168 |        "    </tr>\n",
169 |        "    <tr>\n",
170 |        "      <th>295</th>\n",
171 |        "      <td>Caprabo</td>\n",
172 |        "      <td>Nueces</td>\n",
173 |        "      <td>9.75</td>\n",
174 |        "      <td>2367</td>\n",
175 |        "      <td>2023-09-15</td>\n",
176 |        "      <td>Sevilla</td>\n",
177 |        "    </tr>\n",
178 |        "    <tr>\n",
179 |        "      <th>296</th>\n",
180 |        "      <td>Alcampo</td>\n",
181 |        "      <td>Pistachos</td>\n",
182 |        "      <td>3.82</td>\n",
183 |        "      <td>1951</td>\n",
184 |        "      <td>2023-09-19</td>\n",
185 |        "      <td>Madrid</td>\n",
186 |        "    </tr>\n",
187 |        "    <tr>\n",
188 |        "      <th>297</th>\n",
189 |        "      <td>Mas y Mas</td>\n",
190 |        "      <td>Pistachos</td>\n",
191 |        "      <td>5.22</td>\n",
192 |        "      <td>2811</td>\n",
193 |        "      <td>2023-05-04</td>\n",
194 |        "      <td>Valencia</td>\n",
195 |        "    </tr>\n",
196 |        "    <tr>\n",
197 |        "      <th>298</th>\n",
198 |        "      <td>Mas y Mas</td>\n",
199 |        "      <td>Cacahuetes</td>\n",
200 |        "      <td>8.07</td>\n",
201 |        "      <td>473</td>\n",
202 |        "      <td>2023-01-18</td>\n",
203 |        "      <td>Madrid</td>\n",
204 |        "    </tr>\n",
205 |        "    <tr>\n",
206 |        "      <th>299</th>\n",
207 |        "      <td>Aldi</td>\n",
208 |        "      <td>Nueces</td>\n",
209 |        "      <td>5.80</td>\n",
210 |        "      <td>1081</td>\n",
211 |        "      <td>2023-03-23</td>\n",
212 |        "      <td>Sevilla</td>\n",
213 |        "    </tr>\n",
214 |        "  </tbody>\n",
215 |        "</table>\n",
216 |        "<p>300 rows × 6 columns</p>\n",
217 |        "</div>"
218 |       ],
219 |       "text/plain": [
220 |        "    Nombre Cliente Tipo Producto  Precio (€/kg)  Cantidad Vendida (kg)  \\\n",
221 |        "0        Mas y Mas     Almendras           4.54                   1212   \n",
222 |        "1        Mercadona        Nueces           5.84                    591   \n",
223 |        "2          Alcampo    Cacahuetes           2.69                   2831   \n",
224 |        "3        Mas y Mas     Pistachos           6.70                    516   \n",
225 |        "4             Lidl        Nueces           6.14                   1283   \n",
226 |        "..             ...           ...            ...                    ...   \n",
227 |        "295        Caprabo        Nueces           9.75                   2367   \n",
228 |        "296        Alcampo     Pistachos           3.82                   1951   \n",
229 |        "297      Mas y Mas     Pistachos           5.22                   2811   \n",
230 |        "298      Mas y Mas    Cacahuetes           8.07                    473   \n",
231 |        "299           Aldi        Nueces           5.80                   1081   \n",
232 |        "\n",
233 |        "    Fecha Venta Provincia Cliente  \n",
234 |        "0    2023-03-29             Cádiz  \n",
235 |        "1    2023-12-05           Sevilla  \n",
236 |        "2    2023-07-26            Madrid  \n",
237 |        "3    2023-10-07          Alicante  \n",
238 |        "4    2023-05-12         Barcelona  \n",
239 |        "..          ...               ...  \n",
240 |        "295  2023-09-15           Sevilla  \n",
241 |        "296  2023-09-19            Madrid  \n",
242 |        "297  2023-05-04          Valencia  \n",
243 |        "298  2023-01-18            Madrid  \n",
244 |        "299  2023-03-23           Sevilla  \n",
245 |        "\n",
246 |        "[300 rows x 6 columns]"
247 |       ]
248 |      },
249 |      "execution_count": 10,
250 |      "metadata": {},
251 |      "output_type": "execute_result"
252 |     }
253 |    ],
254 |    "source": [
255 |     "import pandas as pd\n",
256 |     "df = pd.read_csv('/Users/jimenacambronero/Desktop/Proyectos para Portfolio/PythonPracticas/Datos/registros_ventas_frutos_secos.csv', delimiter=',')\n",
257 |     "df"
258 |    ]
259 |   },
260 |   {
261 |    "cell_type": "markdown",
262 |    "metadata": {},
263 |    "source": [
264 |     "# Visualizar Datos"
265 |    ]
266 |   },
267 |   {
268 |    "cell_type": "code",
269 |    "execution_count": 6,
270 |    "metadata": {},
271 |    "outputs": [
272 |     {
273 |      "data": {
274 |       "text/html": [
275 |        "<div>\n",
276 |        "<style scoped>\n",
277 |        "    .dataframe tbody tr th:only-of-type {\n",
278 |        "        vertical-align: middle;\n",
279 |        "    }\n",
280 |        "\n",
281 |        "    .dataframe tbody tr th {\n",
282 |        "        vertical-align: top;\n",
283 |        "    }\n",
284 |        "\n",
285 |        "    .dataframe thead th {\n",
286 |        "        text-align: right;\n",
287 |        "    }\n",
288 |        "</style>\n",
289 |        "<table border=\"1\" class=\"dataframe\">\n",
290 |        "  <thead>\n",
291 |        "    <tr style=\"text-align: right;\">\n",
292 |        "      <th></th>\n",
293 |        "      <th>Nombre Cliente</th>\n",
294 |        "      <th>Tipo Producto</th>\n",
295 |        "      <th>Precio (€/kg)</th>\n",
296 |        "      <th>Cantidad Vendida (kg)</th>\n",
297 |        "      <th>Fecha Venta</th>\n",
298 |        "      <th>Provincia Cliente</th>\n",
299 |        "    </tr>\n",
300 |        "  </thead>\n",
301 |        "  <tbody>\n",
302 |        "    <tr>\n",
303 |        "      <th>0</th>\n",
304 |        "      <td>Mas y Mas</td>\n",
305 |        "      <td>Almendras</td>\n",
306 |        "      <td>4.54</td>\n",
307 |        "      <td>1212</td>\n",
308 |        "      <td>2023-03-29</td>\n",
309 |        "      <td>Cádiz</td>\n",
310 |        "    </tr>\n",
311 |        "    <tr>\n",
312 |        "      <th>1</th>\n",
313 |        "      <td>Mercadona</td>\n",
314 |        "      <td>Nueces</td>\n",
315 |        "      <td>5.84</td>\n",
316 |        "      <td>591</td>\n",
317 |        "      <td>2023-12-05</td>\n",
318 |        "      <td>Sevilla</td>\n",
319 |        "    </tr>\n",
320 |        "    <tr>\n",
321 |        "      <th>2</th>\n",
322 |        "      <td>Alcampo</td>\n",
323 |        "      <td>Cacahuetes</td>\n",
324 |        "      <td>2.69</td>\n",
325 |        "      <td>2831</td>\n",
326 |        "      <td>2023-07-26</td>\n",
327 |        "      <td>Madrid</td>\n",
328 |        "    </tr>\n",
329 |        "    <tr>\n",
330 |        "      <th>3</th>\n",
331 |        "      <td>Mas y Mas</td>\n",
332 |        "      <td>Pistachos</td>\n",
333 |        "      <td>6.70</td>\n",
334 |        "      <td>516</td>\n",
335 |        "      <td>2023-10-07</td>\n",
336 |        "      <td>Alicante</td>\n",
337 |        "    </tr>\n",
338 |        "    <tr>\n",
339 |        "      <th>4</th>\n",
340 |        "      <td>Lidl</td>\n",
341 |        "      <td>Nueces</td>\n",
342 |        "      <td>6.14</td>\n",
343 |        "      <td>1283</td>\n",
344 |        "      <td>2023-05-12</td>\n",
345 |        "      <td>Barcelona</td>\n",
346 |        "    </tr>\n",
347 |        "  </tbody>\n",
348 |        "</table>\n",
349 |        "</div>"
350 |       ],
351 |       "text/plain": [
352 |        "  Nombre Cliente Tipo Producto  Precio (€/kg)  Cantidad Vendida (kg)  \\\n",
353 |        "0      Mas y Mas     Almendras           4.54                   1212   \n",
354 |        "1      Mercadona        Nueces           5.84                    591   \n",
355 |        "2        Alcampo    Cacahuetes           2.69                   2831   \n",
356 |        "3      Mas y Mas     Pistachos           6.70                    516   \n",
357 |        "4           Lidl        Nueces           6.14                   1283   \n",
358 |        "\n",
359 |        "  Fecha Venta Provincia Cliente  \n",
360 |        "0  2023-03-29             Cádiz  \n",
361 |        "1  2023-12-05           Sevilla  \n",
362 |        "2  2023-07-26            Madrid  \n",
363 |        "3  2023-10-07          Alicante  \n",
364 |        "4  2023-05-12         Barcelona  "
365 |       ]
366 |      },
367 |      "execution_count": 6,
368 |      "metadata": {},
369 |      "output_type": "execute_result"
370 |     }
371 |    ],
372 |    "source": [
373 |     "# Como ver los 5 primeros registros\n",
374 |     "df.head()"
375 |    ]
376 |   },
377 |   {
378 |    "cell_type": "code",
379 |    "execution_count": 7,
380 |    "metadata": {},
381 |    "outputs": [
382 |     {
383 |      "data": {
384 |       "text/html": [
385 |        "<div>\n",
386 |        "<style scoped>\n",
387 |        "    .dataframe tbody tr th:only-of-type {\n",
388 |        "        vertical-align: middle;\n",
389 |        "    }\n",
390 |        "\n",
391 |        "    .dataframe tbody tr th {\n",
392 |        "        vertical-align: top;\n",
393 |        "    }\n",
394 |        "\n",
395 |        "    .dataframe thead th {\n",
396 |        "        text-align: right;\n",
397 |        "    }\n",
398 |        "</style>\n",
399 |        "<table border=\"1\" class=\"dataframe\">\n",
400 |        "  <thead>\n",
401 |        "    <tr style=\"text-align: right;\">\n",
402 |        "      <th></th>\n",
403 |        "      <th>Nombre Cliente</th>\n",
404 |        "      <th>Tipo Producto</th>\n",
405 |        "      <th>Precio (€/kg)</th>\n",
406 |        "      <th>Cantidad Vendida (kg)</th>\n",
407 |        "      <th>Fecha Venta</th>\n",
408 |        "      <th>Provincia Cliente</th>\n",
409 |        "    </tr>\n",
410 |        "  </thead>\n",
411 |        "  <tbody>\n",
412 |        "    <tr>\n",
413 |        "      <th>0</th>\n",
414 |        "      <td>Mas y Mas</td>\n",
415 |        "      <td>Almendras</td>\n",
416 |        "      <td>4.54</td>\n",
417 |        "      <td>1212</td>\n",
418 |        "      <td>2023-03-29</td>\n",
419 |        "      <td>Cádiz</td>\n",
420 |        "    </tr>\n",
421 |        "    <tr>\n",
422 |        "      <th>1</th>\n",
423 |        "      <td>Mercadona</td>\n",
424 |        "      <td>Nueces</td>\n",
425 |        "      <td>5.84</td>\n",
426 |        "      <td>591</td>\n",
427 |        "      <td>2023-12-05</td>\n",
428 |        "      <td>Sevilla</td>\n",
429 |        "    </tr>\n",
430 |        "    <tr>\n",
431 |        "      <th>2</th>\n",
432 |        "      <td>Alcampo</td>\n",
433 |        "      <td>Cacahuetes</td>\n",
434 |        "      <td>2.69</td>\n",
435 |        "      <td>2831</td>\n",
436 |        "      <td>2023-07-26</td>\n",
437 |        "      <td>Madrid</td>\n",
438 |        "    </tr>\n",
439 |        "    <tr>\n",
440 |        "      <th>3</th>\n",
441 |        "      <td>Mas y Mas</td>\n",
442 |        "      <td>Pistachos</td>\n",
443 |        "      <td>6.70</td>\n",
444 |        "      <td>516</td>\n",
445 |        "      <td>2023-10-07</td>\n",
446 |        "      <td>Alicante</td>\n",
447 |        "    </tr>\n",
448 |        "    <tr>\n",
449 |        "      <th>4</th>\n",
450 |        "      <td>Lidl</td>\n",
451 |        "      <td>Nueces</td>\n",
452 |        "      <td>6.14</td>\n",
453 |        "      <td>1283</td>\n",
454 |        "      <td>2023-05-12</td>\n",
455 |        "      <td>Barcelona</td>\n",
456 |        "    </tr>\n",
457 |        "    <tr>\n",
458 |        "      <th>5</th>\n",
459 |        "      <td>Carrefour</td>\n",
460 |        "      <td>Almendras</td>\n",
461 |        "      <td>1.92</td>\n",
462 |        "      <td>2741</td>\n",
463 |        "      <td>2023-03-24</td>\n",
464 |        "      <td>Cádiz</td>\n",
465 |        "    </tr>\n",
466 |        "    <tr>\n",
467 |        "      <th>6</th>\n",
468 |        "      <td>Lidl</td>\n",
469 |        "      <td>Pistachos</td>\n",
470 |        "      <td>3.88</td>\n",
471 |        "      <td>2898</td>\n",
472 |        "      <td>2023-03-08</td>\n",
473 |        "      <td>Sevilla</td>\n",
474 |        "    </tr>\n",
475 |        "    <tr>\n",
476 |        "      <th>7</th>\n",
477 |        "      <td>Alcampo</td>\n",
478 |        "      <td>Almendras</td>\n",
479 |        "      <td>3.76</td>\n",
480 |        "      <td>2464</td>\n",
481 |        "      <td>2023-03-01</td>\n",
482 |        "      <td>Madrid</td>\n",
483 |        "    </tr>\n",
484 |        "    <tr>\n",
485 |        "      <th>8</th>\n",
486 |        "      <td>Dia</td>\n",
487 |        "      <td>Almendras</td>\n",
488 |        "      <td>6.46</td>\n",
489 |        "      <td>641</td>\n",
490 |        "      <td>2023-09-26</td>\n",
491 |        "      <td>Alicante</td>\n",
492 |        "    </tr>\n",
493 |        "    <tr>\n",
494 |        "      <th>9</th>\n",
495 |        "      <td>Consum</td>\n",
496 |        "      <td>Pistachos</td>\n",
497 |        "      <td>8.70</td>\n",
498 |        "      <td>646</td>\n",
499 |        "      <td>2023-11-23</td>\n",
500 |        "      <td>Cádiz</td>\n",
501 |        "    </tr>\n",
502 |        "  </tbody>\n",
503 |        "</table>\n",
504 |        "</div>"
505 |       ],
506 |       "text/plain": [
507 |        "  Nombre Cliente Tipo Producto  Precio (€/kg)  Cantidad Vendida (kg)  \\\n",
508 |        "0      Mas y Mas     Almendras           4.54                   1212   \n",
509 |        "1      Mercadona        Nueces           5.84                    591   \n",
510 |        "2        Alcampo    Cacahuetes           2.69                   2831   \n",
511 |        "3      Mas y Mas     Pistachos           6.70                    516   \n",
512 |        "4           Lidl        Nueces           6.14                   1283   \n",
513 |        "5      Carrefour     Almendras           1.92                   2741   \n",
514 |        "6           Lidl     Pistachos           3.88                   2898   \n",
515 |        "7        Alcampo     Almendras           3.76                   2464   \n",
516 |        "8            Dia     Almendras           6.46                    641   \n",
517 |        "9         Consum     Pistachos           8.70                    646   \n",
518 |        "\n",
519 |        "  Fecha Venta Provincia Cliente  \n",
520 |        "0  2023-03-29             Cádiz  \n",
521 |        "1  2023-12-05           Sevilla  \n",
522 |        "2  2023-07-26            Madrid  \n",
523 |        "3  2023-10-07          Alicante  \n",
524 |        "4  2023-05-12         Barcelona  \n",
525 |        "5  2023-03-24             Cádiz  \n",
526 |        "6  2023-03-08           Sevilla  \n",
527 |        "7  2023-03-01            Madrid  \n",
528 |        "8  2023-09-26          Alicante  \n",
529 |        "9  2023-11-23             Cádiz  "
530 |       ]
531 |      },
532 |      "execution_count": 7,
533 |      "metadata": {},
534 |      "output_type": "execute_result"
535 |     }
536 |    ],
537 |    "source": [
538 |     "# Como ver los primeros 10 registros\n",
539 |     "df[:10]"
540 |    ]
541 |   },
542 |   {
543 |    "cell_type": "code",
544 |    "execution_count": null,
545 |    "metadata": {},
546 |    "outputs": [],
547 |    "source": []
548 |   },
549 |   {
550 |    "cell_type": "code",
551 |    "execution_count": 8,
552 |    "metadata": {},
553 |    "outputs": [
554 |     {
555 |      "data": {
556 |       "text/html": [
557 |        "<div>\n",
558 |        "<style scoped>\n",
559 |        "    .dataframe tbody tr th:only-of-type {\n",
560 |        "        vertical-align: middle;\n",
561 |        "    }\n",
562 |        "\n",
563 |        "    .dataframe tbody tr th {\n",
564 |        "        vertical-align: top;\n",
565 |        "    }\n",
566 |        "\n",
567 |        "    .dataframe thead th {\n",
568 |        "        text-align: right;\n",
569 |        "    }\n",
570 |        "</style>\n",
571 |        "<table border=\"1\" class=\"dataframe\">\n",
572 |        "  <thead>\n",
573 |        "    <tr style=\"text-align: right;\">\n",
574 |        "      <th></th>\n",
575 |        "      <th>Nombre Cliente</th>\n",
576 |        "      <th>Tipo Producto</th>\n",
577 |        "    </tr>\n",
578 |        "  </thead>\n",
579 |        "  <tbody>\n",
580 |        "    <tr>\n",
581 |        "      <th>0</th>\n",
582 |        "      <td>Mas y Mas</td>\n",
583 |        "      <td>Almendras</td>\n",
584 |        "    </tr>\n",
585 |        "    <tr>\n",
586 |        "      <th>1</th>\n",
587 |        "      <td>Mercadona</td>\n",
588 |        "      <td>Nueces</td>\n",
589 |        "    </tr>\n",
590 |        "    <tr>\n",
591 |        "      <th>2</th>\n",
592 |        "      <td>Alcampo</td>\n",
593 |        "      <td>Cacahuetes</td>\n",
594 |        "    </tr>\n",
595 |        "    <tr>\n",
596 |        "      <th>3</th>\n",
597 |        "      <td>Mas y Mas</td>\n",
598 |        "      <td>Pistachos</td>\n",
599 |        "    </tr>\n",
600 |        "    <tr>\n",
601 |        "      <th>4</th>\n",
602 |        "      <td>Lidl</td>\n",
603 |        "      <td>Nueces</td>\n",
604 |        "    </tr>\n",
605 |        "    <tr>\n",
606 |        "      <th>...</th>\n",
607 |        "      <td>...</td>\n",
608 |        "      <td>...</td>\n",
609 |        "    </tr>\n",
610 |        "    <tr>\n",
611 |        "      <th>295</th>\n",
612 |        "      <td>Caprabo</td>\n",
613 |        "      <td>Nueces</td>\n",
614 |        "    </tr>\n",
615 |        "    <tr>\n",
616 |        "      <th>296</th>\n",
617 |        "      <td>Alcampo</td>\n",
618 |        "      <td>Pistachos</td>\n",
619 |        "    </tr>\n",
620 |        "    <tr>\n",
621 |        "      <th>297</th>\n",
622 |        "      <td>Mas y Mas</td>\n",
623 |        "      <td>Pistachos</td>\n",
624 |        "    </tr>\n",
625 |        "    <tr>\n",
626 |        "      <th>298</th>\n",
627 |        "      <td>Mas y Mas</td>\n",
628 |        "      <td>Cacahuetes</td>\n",
629 |        "    </tr>\n",
630 |        "    <tr>\n",
631 |        "      <th>299</th>\n",
632 |        "      <td>Aldi</td>\n",
633 |        "      <td>Nueces</td>\n",
634 |        "    </tr>\n",
635 |        "  </tbody>\n",
636 |        "</table>\n",
637 |        "<p>300 rows × 2 columns</p>\n",
638 |        "</div>"
639 |       ],
640 |       "text/plain": [
641 |        "    Nombre Cliente Tipo Producto\n",
642 |        "0        Mas y Mas     Almendras\n",
643 |        "1        Mercadona        Nueces\n",
644 |        "2          Alcampo    Cacahuetes\n",
645 |        "3        Mas y Mas     Pistachos\n",
646 |        "4             Lidl        Nueces\n",
647 |        "..             ...           ...\n",
648 |        "295        Caprabo        Nueces\n",
649 |        "296        Alcampo     Pistachos\n",
650 |        "297      Mas y Mas     Pistachos\n",
651 |        "298      Mas y Mas    Cacahuetes\n",
652 |        "299           Aldi        Nueces\n",
653 |        "\n",
654 |        "[300 rows x 2 columns]"
655 |       ]
656 |      },
657 |      "execution_count": 8,
658 |      "metadata": {},
659 |      "output_type": "execute_result"
660 |     }
661 |    ],
662 |    "source": [
663 |     "# Como visualizo 2 Columnas\n",
664 |     "df[['Nombre Cliente', 'Tipo Producto']] # doble corchete, sino dará error"
665 |    ]
666 |   }
667 |  ],
668 |  "metadata": {
669 |   "kernelspec": {
670 |    "display_name": "Python 3",
671 |    "language": "python",
672 |    "name": "python3"
673 |   },
674 |   "language_info": {
675 |    "codemirror_mode": {
676 |     "name": "ipython",
677 |     "version": 3
678 |    },
679 |    "file_extension": ".py",
680 |    "mimetype": "text/x-python",
681 |    "name": "python",
682 |    "nbconvert_exporter": "python",
683 |    "pygments_lexer": "ipython3",
684 |    "version": "3.9.13"
685 |   }
686 |  },
687 |  "nbformat": 4,
688 |  "nbformat_minor": 2
689 | }
690 | 


--------------------------------------------------------------------------------
/2_TiposDeDatos.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "# Tipos de Datos"
   8 |    ]
   9 |   },
  10 |   {
  11 |    "cell_type": "markdown",
  12 |    "metadata": {},
  13 |    "source": [
  14 |     "Tipos de Datos en Pandas, tomando en cuenta la capacidad de soportar vaalores nulos.\n",
  15 |     "\n",
  16 |     "**float:** Tipo de dato flotante en Numpy y puede contener valores decimales. Soporta datos nulos (NaN)\n",
  17 |     "\n",
  18 |     "**int:** Es el tipo de dato número entero de NumPy y no soporta nulos.\n",
  19 |     "\n",
  20 |     "**object** Es el tipo de dato de objeto de Numpy y se utiliza para representar texto o cualquier objeto de Python. Es versátil pero no es la elección óptima cuando se trata de datos numéricos o categóricos.\n",
  21 |     "\n",
  22 |     "**category:** Es untipo de dato introducido por Pandas específicamente diseñado para variables categóricas. Puede ayudar a ahorrar memoria y acelerar ciertas operaciones cuando trabajas con un conjunto de categorías limitado.\n",
  23 |     "\n",
  24 |     "**bool:** Es el tipo de dato booleano de Numpy y no soporta datos nulos.\n",
  25 |     "\n",
  26 |     "**boolean:** Es untipo de dato booleano introducido por pandas que puede manejar datos nulos.\n",
  27 |     "\n",
  28 |     "**datetime64:** Es el tipo de dato de fecha y hora de Numpy y no soporta datos nulos."
  29 |    ]
  30 |   },
  31 |   {
  32 |    "cell_type": "markdown",
  33 |    "metadata": {},
  34 |    "source": [
  35 |     "### Crear Datos"
  36 |    ]
  37 |   },
  38 |   {
  39 |    "cell_type": "markdown",
  40 |    "metadata": {},
  41 |    "source": [
  42 |     "Crear una Serie"
  43 |    ]
  44 |   },
  45 |   {
  46 |    "cell_type": "code",
  47 |    "execution_count": 1,
  48 |    "metadata": {},
  49 |    "outputs": [
  50 |     {
  51 |      "data": {
  52 |       "text/plain": [
  53 |        "0    Pedro\n",
  54 |        "1    Maria\n",
  55 |        "2     Juan\n",
  56 |        "dtype: object"
  57 |       ]
  58 |      },
  59 |      "execution_count": 1,
  60 |      "metadata": {},
  61 |      "output_type": "execute_result"
  62 |     }
  63 |    ],
  64 |    "source": [
  65 |     "import pandas as pd\n",
  66 |     "\n",
  67 |     "serie=pd.Series(['Pedro', 'Maria','Juan'])\n",
  68 |     "serie"
  69 |    ]
  70 |   },
  71 |   {
  72 |    "cell_type": "markdown",
  73 |    "metadata": {},
  74 |    "source": [
  75 |     "Indexa la serie:0,1,2\n",
  76 |     "\n",
  77 |     "Dice los valores: Pedro...\n",
  78 |     "\n",
  79 |     "Define el tipo de dato: Object\n"
  80 |    ]
  81 |   },
  82 |   {
  83 |    "cell_type": "code",
  84 |    "execution_count": 2,
  85 |    "metadata": {},
  86 |    "outputs": [
  87 |     {
  88 |      "data": {
  89 |       "text/plain": [
  90 |        "RangeIndex(start=0, stop=3, step=1)"
  91 |       ]
  92 |      },
  93 |      "execution_count": 2,
  94 |      "metadata": {},
  95 |      "output_type": "execute_result"
  96 |     }
  97 |    ],
  98 |    "source": [
  99 |     "serie.index"
 100 |    ]
 101 |   },
 102 |   {
 103 |    "cell_type": "markdown",
 104 |    "metadata": {},
 105 |    "source": [
 106 |     "RangeIndex: que es un rango.\n",
 107 |     "\n",
 108 |     "Empieza en 0.\n",
 109 |     "\n",
 110 |     "Termina en 3, por lo tanto indice 2 (Recuerda que empieza en 0)\n",
 111 |     "\n",
 112 |     "Y va de 1 en 1 (step)"
 113 |    ]
 114 |   },
 115 |   {
 116 |    "cell_type": "code",
 117 |    "execution_count": 3,
 118 |    "metadata": {},
 119 |    "outputs": [
 120 |     {
 121 |      "data": {
 122 |       "text/plain": [
 123 |        "array(['Pedro', 'Maria', 'Juan'], dtype=object)"
 124 |       ]
 125 |      },
 126 |      "execution_count": 3,
 127 |      "metadata": {},
 128 |      "output_type": "execute_result"
 129 |     }
 130 |    ],
 131 |    "source": [
 132 |     "serie.values"
 133 |    ]
 134 |   },
 135 |   {
 136 |    "cell_type": "markdown",
 137 |    "metadata": {},
 138 |    "source": [
 139 |     "Lo devuelve como un objeto de Numpy\n",
 140 |     "\n",
 141 |     "Devuelve el tipo de objeto"
 142 |    ]
 143 |   },
 144 |   {
 145 |    "cell_type": "markdown",
 146 |    "metadata": {},
 147 |    "source": [
 148 |     "### Definir uno mismo el Indice"
 149 |    ]
 150 |   },
 151 |   {
 152 |    "cell_type": "markdown",
 153 |    "metadata": {},
 154 |    "source": [
 155 |     "Desde una **Lista Propia**"
 156 |    ]
 157 |   },
 158 |   {
 159 |    "cell_type": "code",
 160 |    "execution_count": 4,
 161 |    "metadata": {},
 162 |    "outputs": [
 163 |     {
 164 |      "name": "stdout",
 165 |      "output_type": "stream",
 166 |      "text": [
 167 |       "Esto es el indice:  [20, 22, 24, 26, 28, 30]\n",
 168 |       "Estos son los valores:  ['Pedro', 'María', 'Juan', 'Fran', 'Victor', 'Jose']\n"
 169 |      ]
 170 |     }
 171 |    ],
 172 |    "source": [
 173 |     "indice = list(range(20,31,2))\n",
 174 |     "print('Esto es el indice: ', indice)\n",
 175 |     "\n",
 176 |     "valores = ['Pedro', 'María', 'Juan', 'Fran', 'Victor', 'Jose']\n",
 177 |     "print('Estos son los valores: ',valores)\n",
 178 |     "\n",
 179 |     "#Cómo los uno?\n",
 180 |     "indice_propio= pd.Series(valores, index=indice)"
 181 |    ]
 182 |   },
 183 |   {
 184 |    "cell_type": "code",
 185 |    "execution_count": 5,
 186 |    "metadata": {},
 187 |    "outputs": [
 188 |     {
 189 |      "data": {
 190 |       "text/plain": [
 191 |        "20     Pedro\n",
 192 |        "22     María\n",
 193 |        "24      Juan\n",
 194 |        "26      Fran\n",
 195 |        "28    Victor\n",
 196 |        "30      Jose\n",
 197 |        "dtype: object"
 198 |       ]
 199 |      },
 200 |      "execution_count": 5,
 201 |      "metadata": {},
 202 |      "output_type": "execute_result"
 203 |     }
 204 |    ],
 205 |    "source": [
 206 |     "indice_propio"
 207 |    ]
 208 |   },
 209 |   {
 210 |    "cell_type": "markdown",
 211 |    "metadata": {},
 212 |    "source": [
 213 |     "Desde un **Diccionario**\n",
 214 |     "\n",
 215 |     "Crea el diccionario de las listas anteriores"
 216 |    ]
 217 |   },
 218 |   {
 219 |    "cell_type": "code",
 220 |    "execution_count": 6,
 221 |    "metadata": {},
 222 |    "outputs": [
 223 |     {
 224 |      "data": {
 225 |       "text/plain": [
 226 |        "{20: 'Pedro', 22: 'María', 24: 'Juan', 26: 'Fran', 28: 'Victor', 30: 'Jose'}"
 227 |       ]
 228 |      },
 229 |      "execution_count": 6,
 230 |      "metadata": {},
 231 |      "output_type": "execute_result"
 232 |     }
 233 |    ],
 234 |    "source": [
 235 |     "# Con el método ZIP.\n",
 236 |     "diccionario = dict(zip(indice, valores))\n",
 237 |     "diccionario"
 238 |    ]
 239 |   },
 240 |   {
 241 |    "cell_type": "code",
 242 |    "execution_count": 7,
 243 |    "metadata": {},
 244 |    "outputs": [
 245 |     {
 246 |      "name": "stdout",
 247 |      "output_type": "stream",
 248 |      "text": [
 249 |       "{20: 'Pedro', 22: 'María', 24: 'Juan', 26: 'Fran', 28: 'Victor', 30: 'Jose'}\n"
 250 |      ]
 251 |     }
 252 |    ],
 253 |    "source": [
 254 |     "# Otro ejemplo de como crearlo con un list comprehension, como recordatorio.\n",
 255 |     "diccionario_comprehension = {indice[i]: valores[i] for i in range(len(indice))}\n",
 256 |     "print(diccionario_comprehension)"
 257 |    ]
 258 |   },
 259 |   {
 260 |    "cell_type": "code",
 261 |    "execution_count": 8,
 262 |    "metadata": {},
 263 |    "outputs": [
 264 |     {
 265 |      "data": {
 266 |       "text/plain": [
 267 |        "20     Pedro\n",
 268 |        "22     María\n",
 269 |        "24      Juan\n",
 270 |        "26      Fran\n",
 271 |        "28    Victor\n",
 272 |        "30      Jose\n",
 273 |        "dtype: object"
 274 |       ]
 275 |      },
 276 |      "execution_count": 8,
 277 |      "metadata": {},
 278 |      "output_type": "execute_result"
 279 |     }
 280 |    ],
 281 |    "source": [
 282 |     "serie_dict = pd.Series(diccionario)\n",
 283 |     "serie_dict"
 284 |    ]
 285 |   },
 286 |   {
 287 |    "cell_type": "markdown",
 288 |    "metadata": {},
 289 |    "source": [
 290 |     "Desde un **Array de NumPy**"
 291 |    ]
 292 |   },
 293 |   {
 294 |    "cell_type": "code",
 295 |    "execution_count": 9,
 296 |    "metadata": {},
 297 |    "outputs": [
 298 |     {
 299 |      "data": {
 300 |       "text/plain": [
 301 |        "0     Pedro\n",
 302 |        "1     María\n",
 303 |        "2      Juan\n",
 304 |        "3      Fran\n",
 305 |        "4    Victor\n",
 306 |        "5      Jose\n",
 307 |        "dtype: object"
 308 |       ]
 309 |      },
 310 |      "execution_count": 9,
 311 |      "metadata": {},
 312 |      "output_type": "execute_result"
 313 |     }
 314 |    ],
 315 |    "source": [
 316 |     "import numpy as np\n",
 317 |     "np_array = np.array(valores)\n",
 318 |     "array_de_numpy = pd.Series(np_array)\n",
 319 |     "array_de_numpy"
 320 |    ]
 321 |   },
 322 |   {
 323 |    "cell_type": "markdown",
 324 |    "metadata": {},
 325 |    "source": [
 326 |     "ahora al reves de **Pandas** a **Numpy**"
 327 |    ]
 328 |   },
 329 |   {
 330 |    "cell_type": "code",
 331 |    "execution_count": 10,
 332 |    "metadata": {},
 333 |    "outputs": [
 334 |     {
 335 |      "name": "stdout",
 336 |      "output_type": "stream",
 337 |      "text": [
 338 |       "['Pedro' 'María' 'Juan' 'Fran' 'Victor' 'Jose']\n",
 339 |       "<class 'numpy.ndarray'>\n",
 340 |       "<class 'pandas.core.series.Series'>\n"
 341 |      ]
 342 |     }
 343 |    ],
 344 |    "source": [
 345 |     "a_numpy = array_de_numpy.to_numpy()\n",
 346 |     "print(a_numpy)\n",
 347 |     "print(type(a_numpy))\n",
 348 |     "print(type(array_de_numpy))"
 349 |    ]
 350 |   },
 351 |   {
 352 |    "cell_type": "markdown",
 353 |    "metadata": {},
 354 |    "source": [
 355 |     "### Crear un DataFrame de una Serie"
 356 |    ]
 357 |   },
 358 |   {
 359 |    "cell_type": "code",
 360 |    "execution_count": 11,
 361 |    "metadata": {},
 362 |    "outputs": [
 363 |     {
 364 |      "data": {
 365 |       "text/html": [
 366 |        "<div>\n",
 367 |        "<style scoped>\n",
 368 |        "    .dataframe tbody tr th:only-of-type {\n",
 369 |        "        vertical-align: middle;\n",
 370 |        "    }\n",
 371 |        "\n",
 372 |        "    .dataframe tbody tr th {\n",
 373 |        "        vertical-align: top;\n",
 374 |        "    }\n",
 375 |        "\n",
 376 |        "    .dataframe thead th {\n",
 377 |        "        text-align: right;\n",
 378 |        "    }\n",
 379 |        "</style>\n",
 380 |        "<table border=\"1\" class=\"dataframe\">\n",
 381 |        "  <thead>\n",
 382 |        "    <tr style=\"text-align: right;\">\n",
 383 |        "      <th></th>\n",
 384 |        "      <th>0</th>\n",
 385 |        "    </tr>\n",
 386 |        "  </thead>\n",
 387 |        "  <tbody>\n",
 388 |        "    <tr>\n",
 389 |        "      <th>0</th>\n",
 390 |        "      <td>Pedro</td>\n",
 391 |        "    </tr>\n",
 392 |        "    <tr>\n",
 393 |        "      <th>1</th>\n",
 394 |        "      <td>María</td>\n",
 395 |        "    </tr>\n",
 396 |        "    <tr>\n",
 397 |        "      <th>2</th>\n",
 398 |        "      <td>Juan</td>\n",
 399 |        "    </tr>\n",
 400 |        "    <tr>\n",
 401 |        "      <th>3</th>\n",
 402 |        "      <td>Fran</td>\n",
 403 |        "    </tr>\n",
 404 |        "    <tr>\n",
 405 |        "      <th>4</th>\n",
 406 |        "      <td>Victor</td>\n",
 407 |        "    </tr>\n",
 408 |        "    <tr>\n",
 409 |        "      <th>5</th>\n",
 410 |        "      <td>Jose</td>\n",
 411 |        "    </tr>\n",
 412 |        "  </tbody>\n",
 413 |        "</table>\n",
 414 |        "</div>"
 415 |       ],
 416 |       "text/plain": [
 417 |        "        0\n",
 418 |        "0   Pedro\n",
 419 |        "1   María\n",
 420 |        "2    Juan\n",
 421 |        "3    Fran\n",
 422 |        "4  Victor\n",
 423 |        "5    Jose"
 424 |       ]
 425 |      },
 426 |      "execution_count": 11,
 427 |      "metadata": {},
 428 |      "output_type": "execute_result"
 429 |     }
 430 |    ],
 431 |    "source": [
 432 |     "array_de_numpy.to_frame()"
 433 |    ]
 434 |   },
 435 |   {
 436 |    "cell_type": "markdown",
 437 |    "metadata": {},
 438 |    "source": [
 439 |     "### Atributos de una Serie"
 440 |    ]
 441 |   },
 442 |   {
 443 |    "cell_type": "code",
 444 |    "execution_count": 12,
 445 |    "metadata": {},
 446 |    "outputs": [
 447 |     {
 448 |      "data": {
 449 |       "text/plain": [
 450 |        "20     Pedro\n",
 451 |        "22     María\n",
 452 |        "24      Juan\n",
 453 |        "26      Fran\n",
 454 |        "28    Victor\n",
 455 |        "30      Jose\n",
 456 |        "dtype: object"
 457 |       ]
 458 |      },
 459 |      "execution_count": 12,
 460 |      "metadata": {},
 461 |      "output_type": "execute_result"
 462 |     }
 463 |    ],
 464 |    "source": [
 465 |     "indice_propio"
 466 |    ]
 467 |   },
 468 |   {
 469 |    "cell_type": "code",
 470 |    "execution_count": 13,
 471 |    "metadata": {},
 472 |    "outputs": [
 473 |     {
 474 |      "data": {
 475 |       "text/plain": [
 476 |        "dtype('O')"
 477 |       ]
 478 |      },
 479 |      "execution_count": 13,
 480 |      "metadata": {},
 481 |      "output_type": "execute_result"
 482 |     }
 483 |    ],
 484 |    "source": [
 485 |     "# Devuelve el tipo de datos de los elementos en la Serie\n",
 486 |     "indice_propio.dtype"
 487 |    ]
 488 |   },
 489 |   {
 490 |    "cell_type": "code",
 491 |    "execution_count": 14,
 492 |    "metadata": {},
 493 |    "outputs": [
 494 |     {
 495 |      "data": {
 496 |       "text/plain": [
 497 |        "6"
 498 |       ]
 499 |      },
 500 |      "execution_count": 14,
 501 |      "metadata": {},
 502 |      "output_type": "execute_result"
 503 |     }
 504 |    ],
 505 |    "source": [
 506 |     "# Devuelve el número total de elementos de la Serie.\n",
 507 |     "indice_propio.size"
 508 |    ]
 509 |   },
 510 |   {
 511 |    "cell_type": "code",
 512 |    "execution_count": 15,
 513 |    "metadata": {},
 514 |    "outputs": [
 515 |     {
 516 |      "data": {
 517 |       "text/plain": [
 518 |        "(6,)"
 519 |       ]
 520 |      },
 521 |      "execution_count": 15,
 522 |      "metadata": {},
 523 |      "output_type": "execute_result"
 524 |     }
 525 |    ],
 526 |    "source": [
 527 |     "# Proporciona la forma (número de filas y columnas) de la Serie como una tupla.\n",
 528 |     "indice_propio.shape"
 529 |    ]
 530 |   },
 531 |   {
 532 |    "cell_type": "code",
 533 |    "execution_count": 16,
 534 |    "metadata": {},
 535 |    "outputs": [
 536 |     {
 537 |      "name": "stdout",
 538 |      "output_type": "stream",
 539 |      "text": [
 540 |       "RangeIndex(start=0, stop=3, step=1)\n",
 541 |       "0    Pedro\n",
 542 |       "1    Maria\n",
 543 |       "2     Juan\n",
 544 |       "dtype: object\n"
 545 |      ]
 546 |     }
 547 |    ],
 548 |    "source": [
 549 |     "# Muestra la información sobre el índice de la Serie, incluyendo sus etiquetas y tipo de índice.\n",
 550 |     "print(serie.index)\n",
 551 |     "print(serie)"
 552 |    ]
 553 |   },
 554 |   {
 555 |    "cell_type": "markdown",
 556 |    "metadata": {},
 557 |    "source": [
 558 |     "# Crear un DataFrame"
 559 |    ]
 560 |   },
 561 |   {
 562 |    "cell_type": "markdown",
 563 |    "metadata": {},
 564 |    "source": [
 565 |     "desde un **Diccionario**"
 566 |    ]
 567 |   },
 568 |   {
 569 |    "cell_type": "code",
 570 |    "execution_count": 18,
 571 |    "metadata": {},
 572 |    "outputs": [
 573 |     {
 574 |      "name": "stdout",
 575 |      "output_type": "stream",
 576 |      "text": [
 577 |       "Este es el diccionario: \n",
 578 |       "\n",
 579 |       " nombre    [Pedro, Maria, Juan]\n",
 580 |       "edad              [30, 40, 50]\n",
 581 |       "dtype: object\n",
 582 |       "\n",
 583 |       "\n",
 584 |       "Este es el diccionario en DataFrame:\n",
 585 |       "\n",
 586 |       "    nombre  edad\n",
 587 |       "10  Pedro    30\n",
 588 |       "20  Maria    40\n",
 589 |       "30   Juan    50\n"
 590 |      ]
 591 |     }
 592 |    ],
 593 |    "source": [
 594 |     "dicc1= {'nombre': ['Pedro', 'Maria', 'Juan'], 'edad': [30, 40, 50]}\n",
 595 |     "print('Este es el diccionario: \\n\\n', pd.Series(dicc1))\n",
 596 |     "\n",
 597 |     "df_dicc= pd.DataFrame(dicc1,['10','20','30']) #No hace falta ponerle indice si no quiero\n",
 598 |     "print('\\n\\nEste es el diccionario en DataFrame:\\n\\n', df_dicc)"
 599 |    ]
 600 |   },
 601 |   {
 602 |    "cell_type": "code",
 603 |    "execution_count": 19,
 604 |    "metadata": {},
 605 |    "outputs": [
 606 |     {
 607 |      "data": {
 608 |       "text/html": [
 609 |        "<div>\n",
 610 |        "<style scoped>\n",
 611 |        "    .dataframe tbody tr th:only-of-type {\n",
 612 |        "        vertical-align: middle;\n",
 613 |        "    }\n",
 614 |        "\n",
 615 |        "    .dataframe tbody tr th {\n",
 616 |        "        vertical-align: top;\n",
 617 |        "    }\n",
 618 |        "\n",
 619 |        "    .dataframe thead th {\n",
 620 |        "        text-align: right;\n",
 621 |        "    }\n",
 622 |        "</style>\n",
 623 |        "<table border=\"1\" class=\"dataframe\">\n",
 624 |        "  <thead>\n",
 625 |        "    <tr style=\"text-align: right;\">\n",
 626 |        "      <th></th>\n",
 627 |        "      <th>nombre</th>\n",
 628 |        "      <th>edad</th>\n",
 629 |        "    </tr>\n",
 630 |        "  </thead>\n",
 631 |        "  <tbody>\n",
 632 |        "    <tr>\n",
 633 |        "      <th>10</th>\n",
 634 |        "      <td>Pedro</td>\n",
 635 |        "      <td>30</td>\n",
 636 |        "    </tr>\n",
 637 |        "    <tr>\n",
 638 |        "      <th>20</th>\n",
 639 |        "      <td>Maria</td>\n",
 640 |        "      <td>40</td>\n",
 641 |        "    </tr>\n",
 642 |        "    <tr>\n",
 643 |        "      <th>30</th>\n",
 644 |        "      <td>Juan</td>\n",
 645 |        "      <td>50</td>\n",
 646 |        "    </tr>\n",
 647 |        "  </tbody>\n",
 648 |        "</table>\n",
 649 |        "</div>"
 650 |       ],
 651 |       "text/plain": [
 652 |        "   nombre  edad\n",
 653 |        "10  Pedro    30\n",
 654 |        "20  Maria    40\n",
 655 |        "30   Juan    50"
 656 |       ]
 657 |      },
 658 |      "execution_count": 19,
 659 |      "metadata": {},
 660 |      "output_type": "execute_result"
 661 |     }
 662 |    ],
 663 |    "source": [
 664 |     "df_dicc"
 665 |    ]
 666 |   },
 667 |   {
 668 |    "cell_type": "markdown",
 669 |    "metadata": {},
 670 |    "source": [
 671 |     "Desde **Listas** de **Listas**"
 672 |    ]
 673 |   },
 674 |   {
 675 |    "cell_type": "code",
 676 |    "execution_count": 20,
 677 |    "metadata": {},
 678 |    "outputs": [
 679 |     {
 680 |      "data": {
 681 |       "text/html": [
 682 |        "<div>\n",
 683 |        "<style scoped>\n",
 684 |        "    .dataframe tbody tr th:only-of-type {\n",
 685 |        "        vertical-align: middle;\n",
 686 |        "    }\n",
 687 |        "\n",
 688 |        "    .dataframe tbody tr th {\n",
 689 |        "        vertical-align: top;\n",
 690 |        "    }\n",
 691 |        "\n",
 692 |        "    .dataframe thead th {\n",
 693 |        "        text-align: right;\n",
 694 |        "    }\n",
 695 |        "</style>\n",
 696 |        "<table border=\"1\" class=\"dataframe\">\n",
 697 |        "  <thead>\n",
 698 |        "    <tr style=\"text-align: right;\">\n",
 699 |        "      <th></th>\n",
 700 |        "      <th>nombre</th>\n",
 701 |        "      <th>edad</th>\n",
 702 |        "    </tr>\n",
 703 |        "  </thead>\n",
 704 |        "  <tbody>\n",
 705 |        "    <tr>\n",
 706 |        "      <th>10</th>\n",
 707 |        "      <td>Pedro</td>\n",
 708 |        "      <td>30</td>\n",
 709 |        "    </tr>\n",
 710 |        "    <tr>\n",
 711 |        "      <th>20</th>\n",
 712 |        "      <td>Maria</td>\n",
 713 |        "      <td>40</td>\n",
 714 |        "    </tr>\n",
 715 |        "    <tr>\n",
 716 |        "      <th>30</th>\n",
 717 |        "      <td>Juan</td>\n",
 718 |        "      <td>50</td>\n",
 719 |        "    </tr>\n",
 720 |        "  </tbody>\n",
 721 |        "</table>\n",
 722 |        "</div>"
 723 |       ],
 724 |       "text/plain": [
 725 |        "   nombre  edad\n",
 726 |        "10  Pedro    30\n",
 727 |        "20  Maria    40\n",
 728 |        "30   Juan    50"
 729 |       ]
 730 |      },
 731 |      "execution_count": 20,
 732 |      "metadata": {},
 733 |      "output_type": "execute_result"
 734 |     }
 735 |    ],
 736 |    "source": [
 737 |     "datos_df = [['Pedro', 30],['Maria',40],['Juan',50]]\n",
 738 |     "columnas_df = ['nombre', 'edad']\n",
 739 |     "indice_df = [10,20,30]\n",
 740 |     "\n",
 741 |     "lists_df= pd.DataFrame(data=datos_df, columns=columnas_df, index=indice_df)\n",
 742 |     "lists_df"
 743 |    ]
 744 |   },
 745 |   {
 746 |    "cell_type": "markdown",
 747 |    "metadata": {},
 748 |    "source": [
 749 |     "De una **Array 2D**"
 750 |    ]
 751 |   },
 752 |   {
 753 |    "cell_type": "code",
 754 |    "execution_count": 21,
 755 |    "metadata": {},
 756 |    "outputs": [
 757 |     {
 758 |      "data": {
 759 |       "text/html": [
 760 |        "<div>\n",
 761 |        "<style scoped>\n",
 762 |        "    .dataframe tbody tr th:only-of-type {\n",
 763 |        "        vertical-align: middle;\n",
 764 |        "    }\n",
 765 |        "\n",
 766 |        "    .dataframe tbody tr th {\n",
 767 |        "        vertical-align: top;\n",
 768 |        "    }\n",
 769 |        "\n",
 770 |        "    .dataframe thead th {\n",
 771 |        "        text-align: right;\n",
 772 |        "    }\n",
 773 |        "</style>\n",
 774 |        "<table border=\"1\" class=\"dataframe\">\n",
 775 |        "  <thead>\n",
 776 |        "    <tr style=\"text-align: right;\">\n",
 777 |        "      <th></th>\n",
 778 |        "      <th>c1</th>\n",
 779 |        "      <th>c2</th>\n",
 780 |        "      <th>c3</th>\n",
 781 |        "    </tr>\n",
 782 |        "  </thead>\n",
 783 |        "  <tbody>\n",
 784 |        "    <tr>\n",
 785 |        "      <th>f1</th>\n",
 786 |        "      <td>Pedro</td>\n",
 787 |        "      <td>Maria</td>\n",
 788 |        "      <td>Juam</td>\n",
 789 |        "    </tr>\n",
 790 |        "    <tr>\n",
 791 |        "      <th>f2</th>\n",
 792 |        "      <td>30</td>\n",
 793 |        "      <td>40</td>\n",
 794 |        "      <td>50</td>\n",
 795 |        "    </tr>\n",
 796 |        "  </tbody>\n",
 797 |        "</table>\n",
 798 |        "</div>"
 799 |       ],
 800 |       "text/plain": [
 801 |        "       c1     c2    c3\n",
 802 |        "f1  Pedro  Maria  Juam\n",
 803 |        "f2     30     40    50"
 804 |       ]
 805 |      },
 806 |      "execution_count": 21,
 807 |      "metadata": {},
 808 |      "output_type": "execute_result"
 809 |     }
 810 |    ],
 811 |    "source": [
 812 |     "array_2d= np.array([['Pedro', 'Maria', 'Juam'], [30,40,50]])\n",
 813 |     "array_data= pd.DataFrame(array_2d, index= ['f1','f2'], columns=['c1','c2','c3'])\n",
 814 |     "array_data"
 815 |    ]
 816 |   },
 817 |   {
 818 |    "cell_type": "code",
 819 |    "execution_count": 22,
 820 |    "metadata": {},
 821 |    "outputs": [
 822 |     {
 823 |      "data": {
 824 |       "text/plain": [
 825 |        "array([['Pedro', 'Maria', 'Juam'],\n",
 826 |        "       ['30', '40', '50']], dtype='<U21')"
 827 |       ]
 828 |      },
 829 |      "execution_count": 22,
 830 |      "metadata": {},
 831 |      "output_type": "execute_result"
 832 |     }
 833 |    ],
 834 |    "source": [
 835 |     "array_2d"
 836 |    ]
 837 |   },
 838 |   {
 839 |    "cell_type": "code",
 840 |    "execution_count": 23,
 841 |    "metadata": {},
 842 |    "outputs": [
 843 |     {
 844 |      "data": {
 845 |       "text/plain": [
 846 |        "array([['Pedro', 'Maria', 'Juam'],\n",
 847 |        "       ['30', '40', '50']], dtype=object)"
 848 |       ]
 849 |      },
 850 |      "execution_count": 23,
 851 |      "metadata": {},
 852 |      "output_type": "execute_result"
 853 |     }
 854 |    ],
 855 |    "source": [
 856 |     "array_data.to_numpy()"
 857 |    ]
 858 |   },
 859 |   {
 860 |    "cell_type": "markdown",
 861 |    "metadata": {},
 862 |    "source": [
 863 |     "De **DataFrame** a **Series**"
 864 |    ]
 865 |   },
 866 |   {
 867 |    "cell_type": "code",
 868 |    "execution_count": 24,
 869 |    "metadata": {},
 870 |    "outputs": [
 871 |     {
 872 |      "data": {
 873 |       "text/html": [
 874 |        "<div>\n",
 875 |        "<style scoped>\n",
 876 |        "    .dataframe tbody tr th:only-of-type {\n",
 877 |        "        vertical-align: middle;\n",
 878 |        "    }\n",
 879 |        "\n",
 880 |        "    .dataframe tbody tr th {\n",
 881 |        "        vertical-align: top;\n",
 882 |        "    }\n",
 883 |        "\n",
 884 |        "    .dataframe thead th {\n",
 885 |        "        text-align: right;\n",
 886 |        "    }\n",
 887 |        "</style>\n",
 888 |        "<table border=\"1\" class=\"dataframe\">\n",
 889 |        "  <thead>\n",
 890 |        "    <tr style=\"text-align: right;\">\n",
 891 |        "      <th></th>\n",
 892 |        "      <th>c1</th>\n",
 893 |        "      <th>c2</th>\n",
 894 |        "      <th>c3</th>\n",
 895 |        "    </tr>\n",
 896 |        "  </thead>\n",
 897 |        "  <tbody>\n",
 898 |        "    <tr>\n",
 899 |        "      <th>f1</th>\n",
 900 |        "      <td>Pedro</td>\n",
 901 |        "      <td>Maria</td>\n",
 902 |        "      <td>Juam</td>\n",
 903 |        "    </tr>\n",
 904 |        "    <tr>\n",
 905 |        "      <th>f2</th>\n",
 906 |        "      <td>30</td>\n",
 907 |        "      <td>40</td>\n",
 908 |        "      <td>50</td>\n",
 909 |        "    </tr>\n",
 910 |        "  </tbody>\n",
 911 |        "</table>\n",
 912 |        "</div>"
 913 |       ],
 914 |       "text/plain": [
 915 |        "       c1     c2    c3\n",
 916 |        "f1  Pedro  Maria  Juam\n",
 917 |        "f2     30     40    50"
 918 |       ]
 919 |      },
 920 |      "execution_count": 24,
 921 |      "metadata": {},
 922 |      "output_type": "execute_result"
 923 |     }
 924 |    ],
 925 |    "source": [
 926 |     "array_data"
 927 |    ]
 928 |   },
 929 |   {
 930 |    "cell_type": "code",
 931 |    "execution_count": 25,
 932 |    "metadata": {},
 933 |    "outputs": [
 934 |     {
 935 |      "data": {
 936 |       "text/plain": [
 937 |        "f1    Pedro\n",
 938 |        "f2       30\n",
 939 |        "Name: c1, dtype: object"
 940 |       ]
 941 |      },
 942 |      "execution_count": 25,
 943 |      "metadata": {},
 944 |      "output_type": "execute_result"
 945 |     }
 946 |    ],
 947 |    "source": [
 948 |     "# Devuelve una Series\n",
 949 |     "array_data.c1"
 950 |    ]
 951 |   },
 952 |   {
 953 |    "cell_type": "code",
 954 |    "execution_count": 26,
 955 |    "metadata": {},
 956 |    "outputs": [
 957 |     {
 958 |      "data": {
 959 |       "text/plain": [
 960 |        "pandas.core.series.Series"
 961 |       ]
 962 |      },
 963 |      "execution_count": 26,
 964 |      "metadata": {},
 965 |      "output_type": "execute_result"
 966 |     }
 967 |    ],
 968 |    "source": [
 969 |     "type(array_data.c1)"
 970 |    ]
 971 |   },
 972 |   {
 973 |    "cell_type": "code",
 974 |    "execution_count": 27,
 975 |    "metadata": {},
 976 |    "outputs": [
 977 |     {
 978 |      "data": {
 979 |       "text/plain": [
 980 |        "f1    Pedro\n",
 981 |        "f2       30\n",
 982 |        "Name: c1, dtype: object"
 983 |       ]
 984 |      },
 985 |      "execution_count": 27,
 986 |      "metadata": {},
 987 |      "output_type": "execute_result"
 988 |     }
 989 |    ],
 990 |    "source": [
 991 |     "# Devuelve una Series\n",
 992 |     "array_data['c1']"
 993 |    ]
 994 |   },
 995 |   {
 996 |    "cell_type": "code",
 997 |    "execution_count": 28,
 998 |    "metadata": {},
 999 |    "outputs": [
1000 |     {
1001 |      "data": {
1002 |       "text/plain": [
1003 |        "pandas.core.series.Series"
1004 |       ]
1005 |      },
1006 |      "execution_count": 28,
1007 |      "metadata": {},
1008 |      "output_type": "execute_result"
1009 |     }
1010 |    ],
1011 |    "source": [
1012 |     "type(array_data['c1'])"
1013 |    ]
1014 |   },
1015 |   {
1016 |    "cell_type": "code",
1017 |    "execution_count": 29,
1018 |    "metadata": {},
1019 |    "outputs": [
1020 |     {
1021 |      "data": {
1022 |       "text/plain": [
1023 |        "pandas.core.frame.DataFrame"
1024 |       ]
1025 |      },
1026 |      "execution_count": 29,
1027 |      "metadata": {},
1028 |      "output_type": "execute_result"
1029 |     }
1030 |    ],
1031 |    "source": [
1032 |     "# Si pongo dos corchetes me devuelve un DataFrame\n",
1033 |     "type(array_data[['c1']])"
1034 |    ]
1035 |   },
1036 |   {
1037 |    "cell_type": "markdown",
1038 |    "metadata": {},
1039 |    "source": []
1040 |   }
1041 |  ],
1042 |  "metadata": {
1043 |   "kernelspec": {
1044 |    "display_name": "Python 3",
1045 |    "language": "python",
1046 |    "name": "python3"
1047 |   },
1048 |   "language_info": {
1049 |    "codemirror_mode": {
1050 |     "name": "ipython",
1051 |     "version": 3
1052 |    },
1053 |    "file_extension": ".py",
1054 |    "mimetype": "text/x-python",
1055 |    "name": "python",
1056 |    "nbconvert_exporter": "python",
1057 |    "pygments_lexer": "ipython3",
1058 |    "version": "3.9.13"
1059 |   }
1060 |  },
1061 |  "nbformat": 4,
1062 |  "nbformat_minor": 2
1063 | }
1064 | 


--------------------------------------------------------------------------------
/5_InfoDataSet.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "## Previsualizar un Archivo"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "markdown",
 12 |    "metadata": {},
 13 |    "source": [
 14 |     "A veces, cuando necesitas trabajar con un archivo de datos, no tienes toda la información sobre cómo está estructurado. No sabes si las columnas tienen nombres, cuál es el separador entre los datos, o incluso si los datos comienzan en la primera línea.\n",
 15 |     "\n",
 16 |     "En este caso, puedes utilizar Python de manera básica para echar un vistazo al archivo y obtener algunas pistas. Aquí está el proceso:\n",
 17 |     "\n",
 18 |     "1. Abres el archivo utilizando `open`.\n",
 19 |     "2. Lees un número de líneas del archivo utilizando `readlines`. Esto te proporciona una lista de líneas del archivo, y puedes tomar un subconjunto de ellas utilizando \"slice\" para obtener solo las que necesitas.\n",
 20 |     "3. Es importante cerrar el archivo después de usarlo. Es una buena práctica no dejar los archivos abiertos en tu programa.\n",
 21 |     "\n",
 22 |     "Esta vista previa de las primeras líneas del archivo te ayudará a comprender cómo está estructurado. Por ejemplo, puedes descubrir si los datos no comienzan en la primera fila, si no hay nombres de columnas, o si el separador entre los datos es un tabulador u otro carácter. Esta información te ayudará a parametrizar correctamente las herramientas de importación de datos que uses más adelante."
 23 |    ]
 24 |   },
 25 |   {
 26 |    "cell_type": "code",
 27 |    "execution_count": 1,
 28 |    "metadata": {},
 29 |    "outputs": [
 30 |     {
 31 |      "name": "stdout",
 32 |      "output_type": "stream",
 33 |      "text": [
 34 |       "['\\ufeffNombre Cliente;Tipo Producto;Precio (€/kg);Cantidad Vendida (kg);Fecha Venta;Provincia Cliente;id\\n', 'Mas y Mas;Almendras;4.54;1212;29/3/23;Cádiz;389932\\n', 'Mercadona;Nueces;5.84;591;5/12/23;Sevilla;389933\\n', 'Alcampo;Cacahuetes;2.69;2831;26/7/23;Madrid;389934\\n', 'Mas y Mas;Pistachos;6.7;516;7/10/23;Alicante;389935\\n', 'Lidl;Nueces;6.14;1283;12/5/23;Barcelona;389936\\n', 'Carrefour;Almendras;1.92;2741;24/3/23;Cádiz;389937\\n', 'Lidl;Pistachos;3.88;2898;8/3/23;Sevilla;389938\\n', 'Alcampo;Almendras;3.76;2464;1/3/23;Madrid;389939\\n', 'Dia;Almendras;6.46;641;26/9/23;Alicante;389940\\n']\n"
 35 |      ]
 36 |     }
 37 |    ],
 38 |    "source": [
 39 |     "previsualizacion = open('/Users/jimenacambronero/Desktop/Proyectos para Portfolio/PythonPracticas/Datos/Frutos_Secos.csv','r')\n",
 40 |     "print(previsualizacion.readlines()[0:10])\n",
 41 |     "previsualizacion.close() # importante cerrar para q no quede abierto en memoria"
 42 |    ]
 43 |   },
 44 |   {
 45 |    "cell_type": "markdown",
 46 |    "metadata": {},
 47 |    "source": [
 48 |     "## Calidad de Datos"
 49 |    ]
 50 |   },
 51 |   {
 52 |    "cell_type": "markdown",
 53 |    "metadata": {},
 54 |    "source": [
 55 |     "La fase de calidad de datos es crucial en un proyecto de Data Science porque afecta directamente la confiabilidad y precisión de los resultados que obtendrás al analizar y modelar datos:\n",
 56 |     "\n",
 57 |     "1. Toma de decisiones precisa: Los datos de baja calidad pueden llevar a conclusiones incorrectas. Si tus datos no son confiables, las decisiones basadas en ellos pueden ser erróneas y costosas.\n",
 58 |     "\n",
 59 |     "2. Modelado efectivo: Los modelos de Data Science dependen de datos de calidad para funcionar correctamente. Datos defectuosos pueden llevar a modelos poco precisos, lo que reduce la efectividad de tus análisis y predicciones.\n",
 60 |     "\n",
 61 |     "3. Eficiencia y ahorro de tiempo: Mejorar la calidad de los datos desde el principio ahorra tiempo en la limpieza y corrección de datos más adelante en el proyecto. Esto permite una ejecución más eficiente de tus tareas de análisis.\n"
 62 |    ]
 63 |   },
 64 |   {
 65 |    "cell_type": "code",
 66 |    "execution_count": 4,
 67 |    "metadata": {},
 68 |    "outputs": [
 69 |     {
 70 |      "data": {
 71 |       "text/html": [
 72 |        "<div>\n",
 73 |        "<style scoped>\n",
 74 |        "    .dataframe tbody tr th:only-of-type {\n",
 75 |        "        vertical-align: middle;\n",
 76 |        "    }\n",
 77 |        "\n",
 78 |        "    .dataframe tbody tr th {\n",
 79 |        "        vertical-align: top;\n",
 80 |        "    }\n",
 81 |        "\n",
 82 |        "    .dataframe thead th {\n",
 83 |        "        text-align: right;\n",
 84 |        "    }\n",
 85 |        "</style>\n",
 86 |        "<table border=\"1\" class=\"dataframe\">\n",
 87 |        "  <thead>\n",
 88 |        "    <tr style=\"text-align: right;\">\n",
 89 |        "      <th></th>\n",
 90 |        "      <th>Nombre Cliente</th>\n",
 91 |        "      <th>Tipo Producto</th>\n",
 92 |        "      <th>Precio (€/kg)</th>\n",
 93 |        "      <th>Cantidad Vendida (kg)</th>\n",
 94 |        "      <th>Fecha Venta</th>\n",
 95 |        "      <th>Provincia Cliente</th>\n",
 96 |        "    </tr>\n",
 97 |        "    <tr>\n",
 98 |        "      <th>id</th>\n",
 99 |        "      <th></th>\n",
100 |        "      <th></th>\n",
101 |        "      <th></th>\n",
102 |        "      <th></th>\n",
103 |        "      <th></th>\n",
104 |        "      <th></th>\n",
105 |        "    </tr>\n",
106 |        "  </thead>\n",
107 |        "  <tbody>\n",
108 |        "    <tr>\n",
109 |        "      <th>389932</th>\n",
110 |        "      <td>Mas y Mas</td>\n",
111 |        "      <td>Almendras</td>\n",
112 |        "      <td>4.54</td>\n",
113 |        "      <td>1212</td>\n",
114 |        "      <td>29/3/23</td>\n",
115 |        "      <td>Cádiz</td>\n",
116 |        "    </tr>\n",
117 |        "    <tr>\n",
118 |        "      <th>389933</th>\n",
119 |        "      <td>Mercadona</td>\n",
120 |        "      <td>Nueces</td>\n",
121 |        "      <td>5.84</td>\n",
122 |        "      <td>591</td>\n",
123 |        "      <td>5/12/23</td>\n",
124 |        "      <td>Sevilla</td>\n",
125 |        "    </tr>\n",
126 |        "  </tbody>\n",
127 |        "</table>\n",
128 |        "</div>"
129 |       ],
130 |       "text/plain": [
131 |        "       Nombre Cliente Tipo Producto  Precio (€/kg)  Cantidad Vendida (kg)  \\\n",
132 |        "id                                                                          \n",
133 |        "389932      Mas y Mas     Almendras           4.54                   1212   \n",
134 |        "389933      Mercadona        Nueces           5.84                    591   \n",
135 |        "\n",
136 |        "       Fecha Venta Provincia Cliente  \n",
137 |        "id                                    \n",
138 |        "389932     29/3/23             Cádiz  \n",
139 |        "389933     5/12/23           Sevilla  "
140 |       ]
141 |      },
142 |      "execution_count": 4,
143 |      "metadata": {},
144 |      "output_type": "execute_result"
145 |     }
146 |    ],
147 |    "source": [
148 |     "import pandas as pd\n",
149 |     "\n",
150 |     "df = pd.read_csv('/Users/jimenacambronero/Desktop/Proyectos para Portfolio/PythonPracticas/Datos/Frutos_Secos.csv',sep= ';', index_col='id')\n",
151 |     "df.head(2)"
152 |    ]
153 |   },
154 |   {
155 |    "cell_type": "markdown",
156 |    "metadata": {},
157 |    "source": [
158 |     "## Muestreo"
159 |    ]
160 |   },
161 |   {
162 |    "cell_type": "markdown",
163 |    "metadata": {},
164 |    "source": [
165 |     "La función `sample()` en pandas es como una herramienta para elegir una parte de tus datos de manera aleatoria. Imagina que tienes una caja llena de datos, y con `sample()`, puedes sacar algunos datos de esa caja según tus necesidades.\n",
166 |     "\n",
167 |     "Los parámetros más importantes son:\n",
168 |     "\n",
169 |     "1. **n**: Esto es cuántos datos quieres tomar de la caja en total. Si dices `n=10`, sacarás 10 datos al azar.\n",
170 |     "\n",
171 |     "2. **frac**: En lugar de decir cuántos datos, puedes decir qué parte del total quieres tomar. Si dices `frac=0.2`, tomarás el 20% de los datos.\n",
172 |     "\n",
173 |     "3. **replace**: Esto es como si pusieras de vuelta los datos que sacas. Si está en `False`, una vez que tomas un dato, no lo puedes tomar de nuevo. Si está en `True`, podrías tomar el mismo dato más de una vez.\n",
174 |     "\n",
175 |     "4. **random_state**: Es como una semilla mágica que te asegura que, si usas el mismo número, siempre obtendrás los mismos datos al azar. Esto es útil para hacer tu trabajo reproducible, es decir, que otros puedan obtener los mismos resultados que tú."
176 |    ]
177 |   },
178 |   {
179 |    "cell_type": "code",
180 |    "execution_count": 6,
181 |    "metadata": {},
182 |    "outputs": [
183 |     {
184 |      "data": {
185 |       "text/plain": [
186 |        "(300, 6)"
187 |       ]
188 |      },
189 |      "execution_count": 6,
190 |      "metadata": {},
191 |      "output_type": "execute_result"
192 |     }
193 |    ],
194 |    "source": [
195 |     "# Nos devolvera la cnatidad de registros y la cantidad de columnas\n",
196 |     "df.shape"
197 |    ]
198 |   },
199 |   {
200 |    "cell_type": "code",
201 |    "execution_count": 8,
202 |    "metadata": {},
203 |    "outputs": [
204 |     {
205 |      "data": {
206 |       "text/plain": [
207 |        "(100, 6)"
208 |       ]
209 |      },
210 |      "execution_count": 8,
211 |      "metadata": {},
212 |      "output_type": "execute_result"
213 |     }
214 |    ],
215 |    "source": [
216 |     "muestra = df.sample(n=100)\n",
217 |     "muestra.shape"
218 |    ]
219 |   },
220 |   {
221 |    "cell_type": "markdown",
222 |    "metadata": {},
223 |    "source": [
224 |     "## Vision General de nuestro DataSet"
225 |    ]
226 |   },
227 |   {
228 |    "cell_type": "markdown",
229 |    "metadata": {},
230 |    "source": [
231 |     "### Información general del Dataset"
232 |    ]
233 |   },
234 |   {
235 |    "cell_type": "code",
236 |    "execution_count": 10,
237 |    "metadata": {},
238 |    "outputs": [
239 |     {
240 |      "name": "stdout",
241 |      "output_type": "stream",
242 |      "text": [
243 |       "<class 'pandas.core.frame.DataFrame'>\n",
244 |       "Int64Index: 300 entries, 389932 to 390231\n",
245 |       "Data columns (total 6 columns):\n",
246 |       " #   Column                 Non-Null Count  Dtype  \n",
247 |       "---  ------                 --------------  -----  \n",
248 |       " 0   Nombre Cliente         300 non-null    object \n",
249 |       " 1   Tipo Producto          300 non-null    object \n",
250 |       " 2   Precio (€/kg)          300 non-null    float64\n",
251 |       " 3   Cantidad Vendida (kg)  300 non-null    int64  \n",
252 |       " 4   Fecha Venta            300 non-null    object \n",
253 |       " 5   Provincia Cliente      300 non-null    object \n",
254 |       "dtypes: float64(1), int64(1), object(4)\n",
255 |       "memory usage: 16.4+ KB\n"
256 |      ]
257 |     }
258 |    ],
259 |    "source": [
260 |     "df.info()"
261 |    ]
262 |   },
263 |   {
264 |    "cell_type": "code",
265 |    "execution_count": 9,
266 |    "metadata": {},
267 |    "outputs": [
268 |     {
269 |      "name": "stdout",
270 |      "output_type": "stream",
271 |      "text": [
272 |       "<class 'pandas.core.frame.DataFrame'>\n",
273 |       "Int64Index: 300 entries, 389932 to 390231\n",
274 |       "Data columns (total 6 columns):\n",
275 |       " #   Column                 Non-Null Count  Dtype  \n",
276 |       "---  ------                 --------------  -----  \n",
277 |       " 0   Nombre Cliente         300 non-null    object \n",
278 |       " 1   Tipo Producto          300 non-null    object \n",
279 |       " 2   Precio (€/kg)          300 non-null    float64\n",
280 |       " 3   Cantidad Vendida (kg)  300 non-null    int64  \n",
281 |       " 4   Fecha Venta            300 non-null    object \n",
282 |       " 5   Provincia Cliente      300 non-null    object \n",
283 |       "dtypes: float64(1), int64(1), object(4)\n",
284 |       "memory usage: 83.6 KB\n"
285 |      ]
286 |     }
287 |    ],
288 |    "source": [
289 |     "# Memory_ usage = deep te da la info exacta de la memoria\n",
290 |     "# Si vemos que ocupa mucho, podemos trabajar con muestras o cambiar el tipo de datos, object ocupa más que category\n",
291 |     "df.info(memory_usage='deep')"
292 |    ]
293 |   },
294 |   {
295 |    "cell_type": "markdown",
296 |    "metadata": {},
297 |    "source": [
298 |     "### Indice"
299 |    ]
300 |   },
301 |   {
302 |    "cell_type": "code",
303 |    "execution_count": 11,
304 |    "metadata": {},
305 |    "outputs": [
306 |     {
307 |      "data": {
308 |       "text/plain": [
309 |        "Int64Index([389932, 389933, 389934, 389935, 389936, 389937, 389938, 389939,\n",
310 |        "            389940, 389941,\n",
311 |        "            ...\n",
312 |        "            390222, 390223, 390224, 390225, 390226, 390227, 390228, 390229,\n",
313 |        "            390230, 390231],\n",
314 |        "           dtype='int64', name='id', length=300)"
315 |       ]
316 |      },
317 |      "execution_count": 11,
318 |      "metadata": {},
319 |      "output_type": "execute_result"
320 |     }
321 |    ],
322 |    "source": [
323 |     "df.index"
324 |    ]
325 |   },
326 |   {
327 |    "cell_type": "markdown",
328 |    "metadata": {},
329 |    "source": [
330 |     "### Columnas"
331 |    ]
332 |   },
333 |   {
334 |    "cell_type": "code",
335 |    "execution_count": 12,
336 |    "metadata": {},
337 |    "outputs": [
338 |     {
339 |      "data": {
340 |       "text/plain": [
341 |        "Index(['Nombre Cliente', 'Tipo Producto', 'Precio (€/kg)',\n",
342 |        "       'Cantidad Vendida (kg)', 'Fecha Venta', 'Provincia Cliente'],\n",
343 |        "      dtype='object')"
344 |       ]
345 |      },
346 |      "execution_count": 12,
347 |      "metadata": {},
348 |      "output_type": "execute_result"
349 |     }
350 |    ],
351 |    "source": [
352 |     "# Ver el valor de las columnas\n",
353 |     "df.columns"
354 |    ]
355 |   },
356 |   {
357 |    "cell_type": "markdown",
358 |    "metadata": {},
359 |    "source": [
360 |     "### Tipo de Variables"
361 |    ]
362 |   },
363 |   {
364 |    "cell_type": "code",
365 |    "execution_count": 13,
366 |    "metadata": {},
367 |    "outputs": [
368 |     {
369 |      "data": {
370 |       "text/plain": [
371 |        "Nombre Cliente            object\n",
372 |        "Tipo Producto             object\n",
373 |        "Precio (€/kg)            float64\n",
374 |        "Cantidad Vendida (kg)      int64\n",
375 |        "Fecha Venta               object\n",
376 |        "Provincia Cliente         object\n",
377 |        "dtype: object"
378 |       ]
379 |      },
380 |      "execution_count": 13,
381 |      "metadata": {},
382 |      "output_type": "execute_result"
383 |     }
384 |    ],
385 |    "source": [
386 |     "# Para conocer los tipos de dato sque tenemos\n",
387 |     "df.dtypes"
388 |    ]
389 |   },
390 |   {
391 |    "cell_type": "code",
392 |    "execution_count": 15,
393 |    "metadata": {},
394 |    "outputs": [
395 |     {
396 |      "data": {
397 |       "text/plain": [
398 |        "dtype('float64')"
399 |       ]
400 |      },
401 |      "execution_count": 15,
402 |      "metadata": {},
403 |      "output_type": "execute_result"
404 |     }
405 |    ],
406 |    "source": [
407 |     "# Saber el valor de una de las columnas. Podes indexar\n",
408 |     "df['Precio (€/kg)'].dtypes"
409 |    ]
410 |   },
411 |   {
412 |    "cell_type": "markdown",
413 |    "metadata": {},
414 |    "source": [
415 |     "### Estadísticos"
416 |    ]
417 |   },
418 |   {
419 |    "cell_type": "code",
420 |    "execution_count": 16,
421 |    "metadata": {},
422 |    "outputs": [
423 |     {
424 |      "data": {
425 |       "text/html": [
426 |        "<div>\n",
427 |        "<style scoped>\n",
428 |        "    .dataframe tbody tr th:only-of-type {\n",
429 |        "        vertical-align: middle;\n",
430 |        "    }\n",
431 |        "\n",
432 |        "    .dataframe tbody tr th {\n",
433 |        "        vertical-align: top;\n",
434 |        "    }\n",
435 |        "\n",
436 |        "    .dataframe thead th {\n",
437 |        "        text-align: right;\n",
438 |        "    }\n",
439 |        "</style>\n",
440 |        "<table border=\"1\" class=\"dataframe\">\n",
441 |        "  <thead>\n",
442 |        "    <tr style=\"text-align: right;\">\n",
443 |        "      <th></th>\n",
444 |        "      <th>Precio (€/kg)</th>\n",
445 |        "      <th>Cantidad Vendida (kg)</th>\n",
446 |        "    </tr>\n",
447 |        "  </thead>\n",
448 |        "  <tbody>\n",
449 |        "    <tr>\n",
450 |        "      <th>count</th>\n",
451 |        "      <td>300.000000</td>\n",
452 |        "      <td>300.000000</td>\n",
453 |        "    </tr>\n",
454 |        "    <tr>\n",
455 |        "      <th>mean</th>\n",
456 |        "      <td>5.598333</td>\n",
457 |        "      <td>1616.950000</td>\n",
458 |        "    </tr>\n",
459 |        "    <tr>\n",
460 |        "      <th>std</th>\n",
461 |        "      <td>2.673505</td>\n",
462 |        "      <td>842.463231</td>\n",
463 |        "    </tr>\n",
464 |        "    <tr>\n",
465 |        "      <th>min</th>\n",
466 |        "      <td>0.970000</td>\n",
467 |        "      <td>100.000000</td>\n",
468 |        "    </tr>\n",
469 |        "    <tr>\n",
470 |        "      <th>25%</th>\n",
471 |        "      <td>3.525000</td>\n",
472 |        "      <td>976.250000</td>\n",
473 |        "    </tr>\n",
474 |        "    <tr>\n",
475 |        "      <th>50%</th>\n",
476 |        "      <td>5.475000</td>\n",
477 |        "      <td>1668.000000</td>\n",
478 |        "    </tr>\n",
479 |        "    <tr>\n",
480 |        "      <th>75%</th>\n",
481 |        "      <td>7.622500</td>\n",
482 |        "      <td>2345.000000</td>\n",
483 |        "    </tr>\n",
484 |        "    <tr>\n",
485 |        "      <th>max</th>\n",
486 |        "      <td>11.570000</td>\n",
487 |        "      <td>2986.000000</td>\n",
488 |        "    </tr>\n",
489 |        "  </tbody>\n",
490 |        "</table>\n",
491 |        "</div>"
492 |       ],
493 |       "text/plain": [
494 |        "       Precio (€/kg)  Cantidad Vendida (kg)\n",
495 |        "count     300.000000             300.000000\n",
496 |        "mean        5.598333            1616.950000\n",
497 |        "std         2.673505             842.463231\n",
498 |        "min         0.970000             100.000000\n",
499 |        "25%         3.525000             976.250000\n",
500 |        "50%         5.475000            1668.000000\n",
501 |        "75%         7.622500            2345.000000\n",
502 |        "max        11.570000            2986.000000"
503 |       ]
504 |      },
505 |      "execution_count": 16,
506 |      "metadata": {},
507 |      "output_type": "execute_result"
508 |     }
509 |    ],
510 |    "source": [
511 |     "# Devuelve solo los numéricos\n",
512 |     "df.describe()"
513 |    ]
514 |   },
515 |   {
516 |    "cell_type": "code",
517 |    "execution_count": 17,
518 |    "metadata": {},
519 |    "outputs": [
520 |     {
521 |      "data": {
522 |       "text/html": [
523 |        "<div>\n",
524 |        "<style scoped>\n",
525 |        "    .dataframe tbody tr th:only-of-type {\n",
526 |        "        vertical-align: middle;\n",
527 |        "    }\n",
528 |        "\n",
529 |        "    .dataframe tbody tr th {\n",
530 |        "        vertical-align: top;\n",
531 |        "    }\n",
532 |        "\n",
533 |        "    .dataframe thead th {\n",
534 |        "        text-align: right;\n",
535 |        "    }\n",
536 |        "</style>\n",
537 |        "<table border=\"1\" class=\"dataframe\">\n",
538 |        "  <thead>\n",
539 |        "    <tr style=\"text-align: right;\">\n",
540 |        "      <th></th>\n",
541 |        "      <th>Nombre Cliente</th>\n",
542 |        "      <th>Tipo Producto</th>\n",
543 |        "      <th>Precio (€/kg)</th>\n",
544 |        "      <th>Cantidad Vendida (kg)</th>\n",
545 |        "      <th>Fecha Venta</th>\n",
546 |        "      <th>Provincia Cliente</th>\n",
547 |        "    </tr>\n",
548 |        "  </thead>\n",
549 |        "  <tbody>\n",
550 |        "    <tr>\n",
551 |        "      <th>count</th>\n",
552 |        "      <td>300</td>\n",
553 |        "      <td>300</td>\n",
554 |        "      <td>300.000000</td>\n",
555 |        "      <td>300.000000</td>\n",
556 |        "      <td>300</td>\n",
557 |        "      <td>300</td>\n",
558 |        "    </tr>\n",
559 |        "    <tr>\n",
560 |        "      <th>unique</th>\n",
561 |        "      <td>11</td>\n",
562 |        "      <td>4</td>\n",
563 |        "      <td>NaN</td>\n",
564 |        "      <td>NaN</td>\n",
565 |        "      <td>204</td>\n",
566 |        "      <td>6</td>\n",
567 |        "    </tr>\n",
568 |        "    <tr>\n",
569 |        "      <th>top</th>\n",
570 |        "      <td>Mercadona</td>\n",
571 |        "      <td>Cacahuetes</td>\n",
572 |        "      <td>NaN</td>\n",
573 |        "      <td>NaN</td>\n",
574 |        "      <td>6/12/23</td>\n",
575 |        "      <td>Barcelona</td>\n",
576 |        "    </tr>\n",
577 |        "    <tr>\n",
578 |        "      <th>freq</th>\n",
579 |        "      <td>33</td>\n",
580 |        "      <td>100</td>\n",
581 |        "      <td>NaN</td>\n",
582 |        "      <td>NaN</td>\n",
583 |        "      <td>5</td>\n",
584 |        "      <td>57</td>\n",
585 |        "    </tr>\n",
586 |        "    <tr>\n",
587 |        "      <th>mean</th>\n",
588 |        "      <td>NaN</td>\n",
589 |        "      <td>NaN</td>\n",
590 |        "      <td>5.598333</td>\n",
591 |        "      <td>1616.950000</td>\n",
592 |        "      <td>NaN</td>\n",
593 |        "      <td>NaN</td>\n",
594 |        "    </tr>\n",
595 |        "    <tr>\n",
596 |        "      <th>std</th>\n",
597 |        "      <td>NaN</td>\n",
598 |        "      <td>NaN</td>\n",
599 |        "      <td>2.673505</td>\n",
600 |        "      <td>842.463231</td>\n",
601 |        "      <td>NaN</td>\n",
602 |        "      <td>NaN</td>\n",
603 |        "    </tr>\n",
604 |        "    <tr>\n",
605 |        "      <th>min</th>\n",
606 |        "      <td>NaN</td>\n",
607 |        "      <td>NaN</td>\n",
608 |        "      <td>0.970000</td>\n",
609 |        "      <td>100.000000</td>\n",
610 |        "      <td>NaN</td>\n",
611 |        "      <td>NaN</td>\n",
612 |        "    </tr>\n",
613 |        "    <tr>\n",
614 |        "      <th>25%</th>\n",
615 |        "      <td>NaN</td>\n",
616 |        "      <td>NaN</td>\n",
617 |        "      <td>3.525000</td>\n",
618 |        "      <td>976.250000</td>\n",
619 |        "      <td>NaN</td>\n",
620 |        "      <td>NaN</td>\n",
621 |        "    </tr>\n",
622 |        "    <tr>\n",
623 |        "      <th>50%</th>\n",
624 |        "      <td>NaN</td>\n",
625 |        "      <td>NaN</td>\n",
626 |        "      <td>5.475000</td>\n",
627 |        "      <td>1668.000000</td>\n",
628 |        "      <td>NaN</td>\n",
629 |        "      <td>NaN</td>\n",
630 |        "    </tr>\n",
631 |        "    <tr>\n",
632 |        "      <th>75%</th>\n",
633 |        "      <td>NaN</td>\n",
634 |        "      <td>NaN</td>\n",
635 |        "      <td>7.622500</td>\n",
636 |        "      <td>2345.000000</td>\n",
637 |        "      <td>NaN</td>\n",
638 |        "      <td>NaN</td>\n",
639 |        "    </tr>\n",
640 |        "    <tr>\n",
641 |        "      <th>max</th>\n",
642 |        "      <td>NaN</td>\n",
643 |        "      <td>NaN</td>\n",
644 |        "      <td>11.570000</td>\n",
645 |        "      <td>2986.000000</td>\n",
646 |        "      <td>NaN</td>\n",
647 |        "      <td>NaN</td>\n",
648 |        "    </tr>\n",
649 |        "  </tbody>\n",
650 |        "</table>\n",
651 |        "</div>"
652 |       ],
653 |       "text/plain": [
654 |        "       Nombre Cliente Tipo Producto  Precio (€/kg)  Cantidad Vendida (kg)  \\\n",
655 |        "count             300           300     300.000000             300.000000   \n",
656 |        "unique             11             4            NaN                    NaN   \n",
657 |        "top         Mercadona    Cacahuetes            NaN                    NaN   \n",
658 |        "freq               33           100            NaN                    NaN   \n",
659 |        "mean              NaN           NaN       5.598333            1616.950000   \n",
660 |        "std               NaN           NaN       2.673505             842.463231   \n",
661 |        "min               NaN           NaN       0.970000             100.000000   \n",
662 |        "25%               NaN           NaN       3.525000             976.250000   \n",
663 |        "50%               NaN           NaN       5.475000            1668.000000   \n",
664 |        "75%               NaN           NaN       7.622500            2345.000000   \n",
665 |        "max               NaN           NaN      11.570000            2986.000000   \n",
666 |        "\n",
667 |        "       Fecha Venta Provincia Cliente  \n",
668 |        "count          300               300  \n",
669 |        "unique         204                 6  \n",
670 |        "top        6/12/23         Barcelona  \n",
671 |        "freq             5                57  \n",
672 |        "mean           NaN               NaN  \n",
673 |        "std            NaN               NaN  \n",
674 |        "min            NaN               NaN  \n",
675 |        "25%            NaN               NaN  \n",
676 |        "50%            NaN               NaN  \n",
677 |        "75%            NaN               NaN  \n",
678 |        "max            NaN               NaN  "
679 |       ]
680 |      },
681 |      "execution_count": 17,
682 |      "metadata": {},
683 |      "output_type": "execute_result"
684 |     }
685 |    ],
686 |    "source": [
687 |     "# Devuelve los que yo decida incluir, cacahuate es el registro que más aparece pero no significa que se ael más vendido\n",
688 |     "df.describe(include='all')"
689 |    ]
690 |   },
691 |   {
692 |    "cell_type": "code",
693 |    "execution_count": 20,
694 |    "metadata": {},
695 |    "outputs": [
696 |     {
697 |      "data": {
698 |       "text/html": [
699 |        "<div>\n",
700 |        "<style scoped>\n",
701 |        "    .dataframe tbody tr th:only-of-type {\n",
702 |        "        vertical-align: middle;\n",
703 |        "    }\n",
704 |        "\n",
705 |        "    .dataframe tbody tr th {\n",
706 |        "        vertical-align: top;\n",
707 |        "    }\n",
708 |        "\n",
709 |        "    .dataframe thead th {\n",
710 |        "        text-align: right;\n",
711 |        "    }\n",
712 |        "</style>\n",
713 |        "<table border=\"1\" class=\"dataframe\">\n",
714 |        "  <thead>\n",
715 |        "    <tr style=\"text-align: right;\">\n",
716 |        "      <th></th>\n",
717 |        "      <th>Nombre Cliente</th>\n",
718 |        "      <th>Tipo Producto</th>\n",
719 |        "      <th>Fecha Venta</th>\n",
720 |        "      <th>Provincia Cliente</th>\n",
721 |        "    </tr>\n",
722 |        "  </thead>\n",
723 |        "  <tbody>\n",
724 |        "    <tr>\n",
725 |        "      <th>count</th>\n",
726 |        "      <td>300</td>\n",
727 |        "      <td>300</td>\n",
728 |        "      <td>300</td>\n",
729 |        "      <td>300</td>\n",
730 |        "    </tr>\n",
731 |        "    <tr>\n",
732 |        "      <th>unique</th>\n",
733 |        "      <td>11</td>\n",
734 |        "      <td>4</td>\n",
735 |        "      <td>204</td>\n",
736 |        "      <td>6</td>\n",
737 |        "    </tr>\n",
738 |        "    <tr>\n",
739 |        "      <th>top</th>\n",
740 |        "      <td>Mercadona</td>\n",
741 |        "      <td>Cacahuetes</td>\n",
742 |        "      <td>6/12/23</td>\n",
743 |        "      <td>Barcelona</td>\n",
744 |        "    </tr>\n",
745 |        "    <tr>\n",
746 |        "      <th>freq</th>\n",
747 |        "      <td>33</td>\n",
748 |        "      <td>100</td>\n",
749 |        "      <td>5</td>\n",
750 |        "      <td>57</td>\n",
751 |        "    </tr>\n",
752 |        "  </tbody>\n",
753 |        "</table>\n",
754 |        "</div>"
755 |       ],
756 |       "text/plain": [
757 |        "       Nombre Cliente Tipo Producto Fecha Venta Provincia Cliente\n",
758 |        "count             300           300         300               300\n",
759 |        "unique             11             4         204                 6\n",
760 |        "top         Mercadona    Cacahuetes     6/12/23         Barcelona\n",
761 |        "freq               33           100           5                57"
762 |       ]
763 |      },
764 |      "execution_count": 20,
765 |      "metadata": {},
766 |      "output_type": "execute_result"
767 |     }
768 |    ],
769 |    "source": [
770 |     "# Solo los objetos (vocal o = objeto)\n",
771 |     "df.describe(include ='O')"
772 |    ]
773 |   },
774 |   {
775 |    "cell_type": "code",
776 |    "execution_count": 22,
777 |    "metadata": {},
778 |    "outputs": [
779 |     {
780 |      "data": {
781 |       "text/html": [
782 |        "<div>\n",
783 |        "<style scoped>\n",
784 |        "    .dataframe tbody tr th:only-of-type {\n",
785 |        "        vertical-align: middle;\n",
786 |        "    }\n",
787 |        "\n",
788 |        "    .dataframe tbody tr th {\n",
789 |        "        vertical-align: top;\n",
790 |        "    }\n",
791 |        "\n",
792 |        "    .dataframe thead th {\n",
793 |        "        text-align: right;\n",
794 |        "    }\n",
795 |        "</style>\n",
796 |        "<table border=\"1\" class=\"dataframe\">\n",
797 |        "  <thead>\n",
798 |        "    <tr style=\"text-align: right;\">\n",
799 |        "      <th></th>\n",
800 |        "      <th>count</th>\n",
801 |        "      <th>unique</th>\n",
802 |        "      <th>top</th>\n",
803 |        "      <th>freq</th>\n",
804 |        "    </tr>\n",
805 |        "  </thead>\n",
806 |        "  <tbody>\n",
807 |        "    <tr>\n",
808 |        "      <th>Nombre Cliente</th>\n",
809 |        "      <td>300</td>\n",
810 |        "      <td>11</td>\n",
811 |        "      <td>Mercadona</td>\n",
812 |        "      <td>33</td>\n",
813 |        "    </tr>\n",
814 |        "    <tr>\n",
815 |        "      <th>Tipo Producto</th>\n",
816 |        "      <td>300</td>\n",
817 |        "      <td>4</td>\n",
818 |        "      <td>Cacahuetes</td>\n",
819 |        "      <td>100</td>\n",
820 |        "    </tr>\n",
821 |        "    <tr>\n",
822 |        "      <th>Fecha Venta</th>\n",
823 |        "      <td>300</td>\n",
824 |        "      <td>204</td>\n",
825 |        "      <td>6/12/23</td>\n",
826 |        "      <td>5</td>\n",
827 |        "    </tr>\n",
828 |        "    <tr>\n",
829 |        "      <th>Provincia Cliente</th>\n",
830 |        "      <td>300</td>\n",
831 |        "      <td>6</td>\n",
832 |        "      <td>Barcelona</td>\n",
833 |        "      <td>57</td>\n",
834 |        "    </tr>\n",
835 |        "  </tbody>\n",
836 |        "</table>\n",
837 |        "</div>"
838 |       ],
839 |       "text/plain": [
840 |        "                  count unique         top freq\n",
841 |        "Nombre Cliente      300     11   Mercadona   33\n",
842 |        "Tipo Producto       300      4  Cacahuetes  100\n",
843 |        "Fecha Venta         300    204     6/12/23    5\n",
844 |        "Provincia Cliente   300      6   Barcelona   57"
845 |       ]
846 |      },
847 |      "execution_count": 22,
848 |      "metadata": {},
849 |      "output_type": "execute_result"
850 |     }
851 |    ],
852 |    "source": [
853 |     "# Ver el traspuesto con .T \n",
854 |     "df.describe(include='O').T"
855 |    ]
856 |   },
857 |   {
858 |    "cell_type": "code",
859 |    "execution_count": 23,
860 |    "metadata": {},
861 |    "outputs": [
862 |     {
863 |      "data": {
864 |       "text/html": [
865 |        "<div>\n",
866 |        "<style scoped>\n",
867 |        "    .dataframe tbody tr th:only-of-type {\n",
868 |        "        vertical-align: middle;\n",
869 |        "    }\n",
870 |        "\n",
871 |        "    .dataframe tbody tr th {\n",
872 |        "        vertical-align: top;\n",
873 |        "    }\n",
874 |        "\n",
875 |        "    .dataframe thead th {\n",
876 |        "        text-align: right;\n",
877 |        "    }\n",
878 |        "</style>\n",
879 |        "<table border=\"1\" class=\"dataframe\">\n",
880 |        "  <thead>\n",
881 |        "    <tr style=\"text-align: right;\">\n",
882 |        "      <th></th>\n",
883 |        "      <th>count</th>\n",
884 |        "      <th>mean</th>\n",
885 |        "      <th>std</th>\n",
886 |        "      <th>min</th>\n",
887 |        "      <th>25%</th>\n",
888 |        "      <th>50%</th>\n",
889 |        "      <th>75%</th>\n",
890 |        "      <th>max</th>\n",
891 |        "    </tr>\n",
892 |        "  </thead>\n",
893 |        "  <tbody>\n",
894 |        "    <tr>\n",
895 |        "      <th>Precio (€/kg)</th>\n",
896 |        "      <td>300.0</td>\n",
897 |        "      <td>5.598333</td>\n",
898 |        "      <td>2.673505</td>\n",
899 |        "      <td>0.97</td>\n",
900 |        "      <td>3.525</td>\n",
901 |        "      <td>5.475</td>\n",
902 |        "      <td>7.6225</td>\n",
903 |        "      <td>11.57</td>\n",
904 |        "    </tr>\n",
905 |        "    <tr>\n",
906 |        "      <th>Cantidad Vendida (kg)</th>\n",
907 |        "      <td>300.0</td>\n",
908 |        "      <td>1616.950000</td>\n",
909 |        "      <td>842.463231</td>\n",
910 |        "      <td>100.00</td>\n",
911 |        "      <td>976.250</td>\n",
912 |        "      <td>1668.000</td>\n",
913 |        "      <td>2345.0000</td>\n",
914 |        "      <td>2986.00</td>\n",
915 |        "    </tr>\n",
916 |        "  </tbody>\n",
917 |        "</table>\n",
918 |        "</div>"
919 |       ],
920 |       "text/plain": [
921 |        "                       count         mean         std     min      25%  \\\n",
922 |        "Precio (€/kg)          300.0     5.598333    2.673505    0.97    3.525   \n",
923 |        "Cantidad Vendida (kg)  300.0  1616.950000  842.463231  100.00  976.250   \n",
924 |        "\n",
925 |        "                            50%        75%      max  \n",
926 |        "Precio (€/kg)             5.475     7.6225    11.57  \n",
927 |        "Cantidad Vendida (kg)  1668.000  2345.0000  2986.00  "
928 |       ]
929 |      },
930 |      "execution_count": 23,
931 |      "metadata": {},
932 |      "output_type": "execute_result"
933 |     }
934 |    ],
935 |    "source": [
936 |     "df.describe().T"
937 |    ]
938 |   }
939 |  ],
940 |  "metadata": {
941 |   "kernelspec": {
942 |    "display_name": "Python 3",
943 |    "language": "python",
944 |    "name": "python3"
945 |   },
946 |   "language_info": {
947 |    "codemirror_mode": {
948 |     "name": "ipython",
949 |     "version": 3
950 |    },
951 |    "file_extension": ".py",
952 |    "mimetype": "text/x-python",
953 |    "name": "python",
954 |    "nbconvert_exporter": "python",
955 |    "pygments_lexer": "ipython3",
956 |    "version": "3.9.13"
957 |   }
958 |  },
959 |  "nbformat": 4,
960 |  "nbformat_minor": 2
961 | }
962 | 


--------------------------------------------------------------------------------
/4_ImportarDatos.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "# Importar Datos"
   8 |    ]
   9 |   },
  10 |   {
  11 |    "cell_type": "markdown",
  12 |    "metadata": {},
  13 |    "source": [
  14 |     "3 formas de Importar Datos\n",
  15 |     "\n",
  16 |     "**CSV**\n",
  17 |     "\n",
  18 |     "**EXCEL**\n",
  19 |     "\n",
  20 |     "**PORTAPAPELES**\n",
  21 |     "\n",
  22 |     "A partir de ahí vendran otros archivos como pickle, pero si entendes la dinámica de estos 3 el resto será más sencillo."
  23 |    ]
  24 |   },
  25 |   {
  26 |    "cell_type": "markdown",
  27 |    "metadata": {},
  28 |    "source": [
  29 |     "## Como importar un CSV"
  30 |    ]
  31 |   },
  32 |   {
  33 |    "cell_type": "markdown",
  34 |    "metadata": {},
  35 |    "source": [
  36 |     "## Importar un csv"
  37 |    ]
  38 |   },
  39 |   {
  40 |    "cell_type": "code",
  41 |    "execution_count": 4,
  42 |    "metadata": {},
  43 |    "outputs": [
  44 |     {
  45 |      "data": {
  46 |       "text/html": [
  47 |        "<div>\n",
  48 |        "<style scoped>\n",
  49 |        "    .dataframe tbody tr th:only-of-type {\n",
  50 |        "        vertical-align: middle;\n",
  51 |        "    }\n",
  52 |        "\n",
  53 |        "    .dataframe tbody tr th {\n",
  54 |        "        vertical-align: top;\n",
  55 |        "    }\n",
  56 |        "\n",
  57 |        "    .dataframe thead th {\n",
  58 |        "        text-align: right;\n",
  59 |        "    }\n",
  60 |        "</style>\n",
  61 |        "<table border=\"1\" class=\"dataframe\">\n",
  62 |        "  <thead>\n",
  63 |        "    <tr style=\"text-align: right;\">\n",
  64 |        "      <th></th>\n",
  65 |        "      <th>Nombre Cliente</th>\n",
  66 |        "      <th>Tipo Producto</th>\n",
  67 |        "      <th>Precio (€/kg)</th>\n",
  68 |        "      <th>Cantidad Vendida (kg)</th>\n",
  69 |        "      <th>Fecha Venta</th>\n",
  70 |        "      <th>Provincia Cliente</th>\n",
  71 |        "    </tr>\n",
  72 |        "    <tr>\n",
  73 |        "      <th>id</th>\n",
  74 |        "      <th></th>\n",
  75 |        "      <th></th>\n",
  76 |        "      <th></th>\n",
  77 |        "      <th></th>\n",
  78 |        "      <th></th>\n",
  79 |        "      <th></th>\n",
  80 |        "    </tr>\n",
  81 |        "  </thead>\n",
  82 |        "  <tbody>\n",
  83 |        "    <tr>\n",
  84 |        "      <th>389932</th>\n",
  85 |        "      <td>Mas y Mas</td>\n",
  86 |        "      <td>Almendras</td>\n",
  87 |        "      <td>4.54</td>\n",
  88 |        "      <td>1212</td>\n",
  89 |        "      <td>29/3/23</td>\n",
  90 |        "      <td>Cádiz</td>\n",
  91 |        "    </tr>\n",
  92 |        "    <tr>\n",
  93 |        "      <th>389933</th>\n",
  94 |        "      <td>Mercadona</td>\n",
  95 |        "      <td>Nueces</td>\n",
  96 |        "      <td>5.84</td>\n",
  97 |        "      <td>591</td>\n",
  98 |        "      <td>5/12/23</td>\n",
  99 |        "      <td>Sevilla</td>\n",
 100 |        "    </tr>\n",
 101 |        "    <tr>\n",
 102 |        "      <th>389934</th>\n",
 103 |        "      <td>Alcampo</td>\n",
 104 |        "      <td>Cacahuetes</td>\n",
 105 |        "      <td>2.69</td>\n",
 106 |        "      <td>2831</td>\n",
 107 |        "      <td>26/7/23</td>\n",
 108 |        "      <td>Madrid</td>\n",
 109 |        "    </tr>\n",
 110 |        "    <tr>\n",
 111 |        "      <th>389935</th>\n",
 112 |        "      <td>Mas y Mas</td>\n",
 113 |        "      <td>Pistachos</td>\n",
 114 |        "      <td>6.70</td>\n",
 115 |        "      <td>516</td>\n",
 116 |        "      <td>7/10/23</td>\n",
 117 |        "      <td>Alicante</td>\n",
 118 |        "    </tr>\n",
 119 |        "    <tr>\n",
 120 |        "      <th>389936</th>\n",
 121 |        "      <td>Lidl</td>\n",
 122 |        "      <td>Nueces</td>\n",
 123 |        "      <td>6.14</td>\n",
 124 |        "      <td>1283</td>\n",
 125 |        "      <td>12/5/23</td>\n",
 126 |        "      <td>Barcelona</td>\n",
 127 |        "    </tr>\n",
 128 |        "    <tr>\n",
 129 |        "      <th>...</th>\n",
 130 |        "      <td>...</td>\n",
 131 |        "      <td>...</td>\n",
 132 |        "      <td>...</td>\n",
 133 |        "      <td>...</td>\n",
 134 |        "      <td>...</td>\n",
 135 |        "      <td>...</td>\n",
 136 |        "    </tr>\n",
 137 |        "    <tr>\n",
 138 |        "      <th>390227</th>\n",
 139 |        "      <td>Caprabo</td>\n",
 140 |        "      <td>Nueces</td>\n",
 141 |        "      <td>9.75</td>\n",
 142 |        "      <td>2367</td>\n",
 143 |        "      <td>15/9/23</td>\n",
 144 |        "      <td>Sevilla</td>\n",
 145 |        "    </tr>\n",
 146 |        "    <tr>\n",
 147 |        "      <th>390228</th>\n",
 148 |        "      <td>Alcampo</td>\n",
 149 |        "      <td>Pistachos</td>\n",
 150 |        "      <td>3.82</td>\n",
 151 |        "      <td>1951</td>\n",
 152 |        "      <td>19/9/23</td>\n",
 153 |        "      <td>Madrid</td>\n",
 154 |        "    </tr>\n",
 155 |        "    <tr>\n",
 156 |        "      <th>390229</th>\n",
 157 |        "      <td>Mas y Mas</td>\n",
 158 |        "      <td>Pistachos</td>\n",
 159 |        "      <td>5.22</td>\n",
 160 |        "      <td>2811</td>\n",
 161 |        "      <td>4/5/23</td>\n",
 162 |        "      <td>Valencia</td>\n",
 163 |        "    </tr>\n",
 164 |        "    <tr>\n",
 165 |        "      <th>390230</th>\n",
 166 |        "      <td>Mas y Mas</td>\n",
 167 |        "      <td>Cacahuetes</td>\n",
 168 |        "      <td>8.07</td>\n",
 169 |        "      <td>473</td>\n",
 170 |        "      <td>18/1/23</td>\n",
 171 |        "      <td>Madrid</td>\n",
 172 |        "    </tr>\n",
 173 |        "    <tr>\n",
 174 |        "      <th>390231</th>\n",
 175 |        "      <td>Aldi</td>\n",
 176 |        "      <td>Nueces</td>\n",
 177 |        "      <td>5.80</td>\n",
 178 |        "      <td>1081</td>\n",
 179 |        "      <td>23/3/23</td>\n",
 180 |        "      <td>Sevilla</td>\n",
 181 |        "    </tr>\n",
 182 |        "  </tbody>\n",
 183 |        "</table>\n",
 184 |        "<p>300 rows × 6 columns</p>\n",
 185 |        "</div>"
 186 |       ],
 187 |       "text/plain": [
 188 |        "       Nombre Cliente Tipo Producto  Precio (€/kg)  Cantidad Vendida (kg)  \\\n",
 189 |        "id                                                                          \n",
 190 |        "389932      Mas y Mas     Almendras           4.54                   1212   \n",
 191 |        "389933      Mercadona        Nueces           5.84                    591   \n",
 192 |        "389934        Alcampo    Cacahuetes           2.69                   2831   \n",
 193 |        "389935      Mas y Mas     Pistachos           6.70                    516   \n",
 194 |        "389936           Lidl        Nueces           6.14                   1283   \n",
 195 |        "...               ...           ...            ...                    ...   \n",
 196 |        "390227        Caprabo        Nueces           9.75                   2367   \n",
 197 |        "390228        Alcampo     Pistachos           3.82                   1951   \n",
 198 |        "390229      Mas y Mas     Pistachos           5.22                   2811   \n",
 199 |        "390230      Mas y Mas    Cacahuetes           8.07                    473   \n",
 200 |        "390231           Aldi        Nueces           5.80                   1081   \n",
 201 |        "\n",
 202 |        "       Fecha Venta Provincia Cliente  \n",
 203 |        "id                                    \n",
 204 |        "389932     29/3/23             Cádiz  \n",
 205 |        "389933     5/12/23           Sevilla  \n",
 206 |        "389934     26/7/23            Madrid  \n",
 207 |        "389935     7/10/23          Alicante  \n",
 208 |        "389936     12/5/23         Barcelona  \n",
 209 |        "...            ...               ...  \n",
 210 |        "390227     15/9/23           Sevilla  \n",
 211 |        "390228     19/9/23            Madrid  \n",
 212 |        "390229      4/5/23          Valencia  \n",
 213 |        "390230     18/1/23            Madrid  \n",
 214 |        "390231     23/3/23           Sevilla  \n",
 215 |        "\n",
 216 |        "[300 rows x 6 columns]"
 217 |       ]
 218 |      },
 219 |      "execution_count": 4,
 220 |      "metadata": {},
 221 |      "output_type": "execute_result"
 222 |     }
 223 |    ],
 224 |    "source": [
 225 |     "import pandas as pd \n",
 226 |     "df = pd.read_csv('/Users/jimenacambronero/Desktop/Proyectos para Portfolio/PythonPracticas/Datos/Frutos_Secos.csv', delimiter=';', index_col='id' )\n",
 227 |     "\n",
 228 |     "df"
 229 |    ]
 230 |   },
 231 |   {
 232 |    "cell_type": "code",
 233 |    "execution_count": 5,
 234 |    "metadata": {},
 235 |    "outputs": [
 236 |     {
 237 |      "name": "stdout",
 238 |      "output_type": "stream",
 239 |      "text": [
 240 |       "<class 'pandas.core.frame.DataFrame'>\n",
 241 |       "Int64Index: 300 entries, 389932 to 390231\n",
 242 |       "Data columns (total 6 columns):\n",
 243 |       " #   Column                 Non-Null Count  Dtype  \n",
 244 |       "---  ------                 --------------  -----  \n",
 245 |       " 0   Nombre Cliente         300 non-null    object \n",
 246 |       " 1   Tipo Producto          300 non-null    object \n",
 247 |       " 2   Precio (€/kg)          300 non-null    float64\n",
 248 |       " 3   Cantidad Vendida (kg)  300 non-null    int64  \n",
 249 |       " 4   Fecha Venta            300 non-null    object \n",
 250 |       " 5   Provincia Cliente      300 non-null    object \n",
 251 |       "dtypes: float64(1), int64(1), object(4)\n",
 252 |       "memory usage: 16.4+ KB\n"
 253 |      ]
 254 |     }
 255 |    ],
 256 |    "source": [
 257 |     "#Información general\n",
 258 |     "df.info()"
 259 |    ]
 260 |   },
 261 |   {
 262 |    "cell_type": "code",
 263 |    "execution_count": 8,
 264 |    "metadata": {},
 265 |    "outputs": [
 266 |     {
 267 |      "data": {
 268 |       "text/html": [
 269 |        "<div>\n",
 270 |        "<style scoped>\n",
 271 |        "    .dataframe tbody tr th:only-of-type {\n",
 272 |        "        vertical-align: middle;\n",
 273 |        "    }\n",
 274 |        "\n",
 275 |        "    .dataframe tbody tr th {\n",
 276 |        "        vertical-align: top;\n",
 277 |        "    }\n",
 278 |        "\n",
 279 |        "    .dataframe thead th {\n",
 280 |        "        text-align: right;\n",
 281 |        "    }\n",
 282 |        "</style>\n",
 283 |        "<table border=\"1\" class=\"dataframe\">\n",
 284 |        "  <thead>\n",
 285 |        "    <tr style=\"text-align: right;\">\n",
 286 |        "      <th></th>\n",
 287 |        "      <th>Nombre Cliente</th>\n",
 288 |        "      <th>Tipo Producto</th>\n",
 289 |        "      <th>Precio (€/kg)</th>\n",
 290 |        "      <th>Cantidad Vendida (kg)</th>\n",
 291 |        "      <th>Fecha Venta</th>\n",
 292 |        "      <th>Provincia Cliente</th>\n",
 293 |        "    </tr>\n",
 294 |        "    <tr>\n",
 295 |        "      <th>id</th>\n",
 296 |        "      <th></th>\n",
 297 |        "      <th></th>\n",
 298 |        "      <th></th>\n",
 299 |        "      <th></th>\n",
 300 |        "      <th></th>\n",
 301 |        "      <th></th>\n",
 302 |        "    </tr>\n",
 303 |        "  </thead>\n",
 304 |        "  <tbody>\n",
 305 |        "    <tr>\n",
 306 |        "      <th>389932</th>\n",
 307 |        "      <td>Mas y Mas</td>\n",
 308 |        "      <td>Almendras</td>\n",
 309 |        "      <td>4.54</td>\n",
 310 |        "      <td>1212</td>\n",
 311 |        "      <td>2023-03-29</td>\n",
 312 |        "      <td>Cádiz</td>\n",
 313 |        "    </tr>\n",
 314 |        "    <tr>\n",
 315 |        "      <th>389933</th>\n",
 316 |        "      <td>Mercadona</td>\n",
 317 |        "      <td>Nueces</td>\n",
 318 |        "      <td>5.84</td>\n",
 319 |        "      <td>591</td>\n",
 320 |        "      <td>2023-05-12</td>\n",
 321 |        "      <td>Sevilla</td>\n",
 322 |        "    </tr>\n",
 323 |        "    <tr>\n",
 324 |        "      <th>389934</th>\n",
 325 |        "      <td>Alcampo</td>\n",
 326 |        "      <td>Cacahuetes</td>\n",
 327 |        "      <td>2.69</td>\n",
 328 |        "      <td>2831</td>\n",
 329 |        "      <td>2023-07-26</td>\n",
 330 |        "      <td>Madrid</td>\n",
 331 |        "    </tr>\n",
 332 |        "    <tr>\n",
 333 |        "      <th>389935</th>\n",
 334 |        "      <td>Mas y Mas</td>\n",
 335 |        "      <td>Pistachos</td>\n",
 336 |        "      <td>6.70</td>\n",
 337 |        "      <td>516</td>\n",
 338 |        "      <td>2023-07-10</td>\n",
 339 |        "      <td>Alicante</td>\n",
 340 |        "    </tr>\n",
 341 |        "    <tr>\n",
 342 |        "      <th>389936</th>\n",
 343 |        "      <td>Lidl</td>\n",
 344 |        "      <td>Nueces</td>\n",
 345 |        "      <td>6.14</td>\n",
 346 |        "      <td>1283</td>\n",
 347 |        "      <td>2023-12-05</td>\n",
 348 |        "      <td>Barcelona</td>\n",
 349 |        "    </tr>\n",
 350 |        "  </tbody>\n",
 351 |        "</table>\n",
 352 |        "</div>"
 353 |       ],
 354 |       "text/plain": [
 355 |        "       Nombre Cliente Tipo Producto  Precio (€/kg)  Cantidad Vendida (kg)  \\\n",
 356 |        "id                                                                          \n",
 357 |        "389932      Mas y Mas     Almendras           4.54                   1212   \n",
 358 |        "389933      Mercadona        Nueces           5.84                    591   \n",
 359 |        "389934        Alcampo    Cacahuetes           2.69                   2831   \n",
 360 |        "389935      Mas y Mas     Pistachos           6.70                    516   \n",
 361 |        "389936           Lidl        Nueces           6.14                   1283   \n",
 362 |        "\n",
 363 |        "       Fecha Venta Provincia Cliente  \n",
 364 |        "id                                    \n",
 365 |        "389932  2023-03-29             Cádiz  \n",
 366 |        "389933  2023-05-12           Sevilla  \n",
 367 |        "389934  2023-07-26            Madrid  \n",
 368 |        "389935  2023-07-10          Alicante  \n",
 369 |        "389936  2023-12-05         Barcelona  "
 370 |       ]
 371 |      },
 372 |      "execution_count": 8,
 373 |      "metadata": {},
 374 |      "output_type": "execute_result"
 375 |     }
 376 |    ],
 377 |    "source": [
 378 |     "# Lee el archivo csv con la columna 'Fecha Venta' como fechas parseadas\n",
 379 |     "df = pd.read_csv ('/Users/jimenacambronero/Desktop/Proyectos para Portfolio/PythonPracticas/Datos/Frutos_Secos.csv', delimiter=';', index_col='id', parse_dates=['Fecha Venta'])\n",
 380 |     "\n",
 381 |     "# Muestra las primeras filas del dataFrame para verificar que la fecha se haya parseado correctamente\n",
 382 |     "df.head()"
 383 |    ]
 384 |   },
 385 |   {
 386 |    "cell_type": "code",
 387 |    "execution_count": 9,
 388 |    "metadata": {},
 389 |    "outputs": [
 390 |     {
 391 |      "name": "stdout",
 392 |      "output_type": "stream",
 393 |      "text": [
 394 |       "<class 'pandas.core.frame.DataFrame'>\n",
 395 |       "Int64Index: 300 entries, 389932 to 390231\n",
 396 |       "Data columns (total 6 columns):\n",
 397 |       " #   Column                 Non-Null Count  Dtype         \n",
 398 |       "---  ------                 --------------  -----         \n",
 399 |       " 0   Nombre Cliente         300 non-null    object        \n",
 400 |       " 1   Tipo Producto          300 non-null    object        \n",
 401 |       " 2   Precio (€/kg)          300 non-null    float64       \n",
 402 |       " 3   Cantidad Vendida (kg)  300 non-null    int64         \n",
 403 |       " 4   Fecha Venta            300 non-null    datetime64[ns]\n",
 404 |       " 5   Provincia Cliente      300 non-null    object        \n",
 405 |       "dtypes: datetime64[ns](1), float64(1), int64(1), object(3)\n",
 406 |       "memory usage: 16.4+ KB\n"
 407 |      ]
 408 |     }
 409 |    ],
 410 |    "source": [
 411 |     "#Información general. Vemos que la columna fecha de venta ahora es del tipo datetime64\n",
 412 |     "df.info()"
 413 |    ]
 414 |   },
 415 |   {
 416 |    "cell_type": "markdown",
 417 |    "metadata": {},
 418 |    "source": [
 419 |     "## Importar desde un Excel"
 420 |    ]
 421 |   },
 422 |   {
 423 |    "cell_type": "code",
 424 |    "execution_count": 11,
 425 |    "metadata": {},
 426 |    "outputs": [],
 427 |    "source": [
 428 |     "# Parseamos la fecha al momento de la carga\n",
 429 |     "df_excel = pd.read_excel('/Users/jimenacambronero/Desktop/Proyectos para Portfolio/PythonPracticas/Datos/Frutos_Secos.xlsx', index_col='id', parse_dates=['Fecha Venta'])"
 430 |    ]
 431 |   },
 432 |   {
 433 |    "cell_type": "code",
 434 |    "execution_count": 12,
 435 |    "metadata": {},
 436 |    "outputs": [
 437 |     {
 438 |      "name": "stdout",
 439 |      "output_type": "stream",
 440 |      "text": [
 441 |       "<class 'pandas.core.frame.DataFrame'>\n",
 442 |       "Int64Index: 300 entries, 389932 to 390231\n",
 443 |       "Data columns (total 6 columns):\n",
 444 |       " #   Column                 Non-Null Count  Dtype         \n",
 445 |       "---  ------                 --------------  -----         \n",
 446 |       " 0   Nombre Cliente         300 non-null    object        \n",
 447 |       " 1   Tipo Producto          300 non-null    object        \n",
 448 |       " 2   Precio (€/kg)          300 non-null    float64       \n",
 449 |       " 3   Cantidad Vendida (kg)  300 non-null    int64         \n",
 450 |       " 4   Fecha Venta            300 non-null    datetime64[ns]\n",
 451 |       " 5   Provincia Cliente      300 non-null    object        \n",
 452 |       "dtypes: datetime64[ns](1), float64(1), int64(1), object(3)\n",
 453 |       "memory usage: 16.4+ KB\n"
 454 |      ]
 455 |     }
 456 |    ],
 457 |    "source": [
 458 |     "df_excel.info()"
 459 |    ]
 460 |   },
 461 |   {
 462 |    "cell_type": "code",
 463 |    "execution_count": 13,
 464 |    "metadata": {},
 465 |    "outputs": [
 466 |     {
 467 |      "data": {
 468 |       "text/html": [
 469 |        "<div>\n",
 470 |        "<style scoped>\n",
 471 |        "    .dataframe tbody tr th:only-of-type {\n",
 472 |        "        vertical-align: middle;\n",
 473 |        "    }\n",
 474 |        "\n",
 475 |        "    .dataframe tbody tr th {\n",
 476 |        "        vertical-align: top;\n",
 477 |        "    }\n",
 478 |        "\n",
 479 |        "    .dataframe thead th {\n",
 480 |        "        text-align: right;\n",
 481 |        "    }\n",
 482 |        "</style>\n",
 483 |        "<table border=\"1\" class=\"dataframe\">\n",
 484 |        "  <thead>\n",
 485 |        "    <tr style=\"text-align: right;\">\n",
 486 |        "      <th></th>\n",
 487 |        "      <th>Nombre Cliente</th>\n",
 488 |        "      <th>Tipo Producto</th>\n",
 489 |        "      <th>Precio (€/kg)</th>\n",
 490 |        "      <th>Cantidad Vendida (kg)</th>\n",
 491 |        "      <th>Fecha Venta</th>\n",
 492 |        "      <th>Provincia Cliente</th>\n",
 493 |        "    </tr>\n",
 494 |        "    <tr>\n",
 495 |        "      <th>id</th>\n",
 496 |        "      <th></th>\n",
 497 |        "      <th></th>\n",
 498 |        "      <th></th>\n",
 499 |        "      <th></th>\n",
 500 |        "      <th></th>\n",
 501 |        "      <th></th>\n",
 502 |        "    </tr>\n",
 503 |        "  </thead>\n",
 504 |        "  <tbody>\n",
 505 |        "    <tr>\n",
 506 |        "      <th>389932</th>\n",
 507 |        "      <td>Mas y Mas</td>\n",
 508 |        "      <td>Almendras</td>\n",
 509 |        "      <td>4.54</td>\n",
 510 |        "      <td>1212</td>\n",
 511 |        "      <td>2023-03-29</td>\n",
 512 |        "      <td>Cádiz</td>\n",
 513 |        "    </tr>\n",
 514 |        "    <tr>\n",
 515 |        "      <th>389933</th>\n",
 516 |        "      <td>Mercadona</td>\n",
 517 |        "      <td>Nueces</td>\n",
 518 |        "      <td>5.84</td>\n",
 519 |        "      <td>591</td>\n",
 520 |        "      <td>2023-05-12</td>\n",
 521 |        "      <td>Sevilla</td>\n",
 522 |        "    </tr>\n",
 523 |        "    <tr>\n",
 524 |        "      <th>389934</th>\n",
 525 |        "      <td>Alcampo</td>\n",
 526 |        "      <td>Cacahuetes</td>\n",
 527 |        "      <td>2.69</td>\n",
 528 |        "      <td>2831</td>\n",
 529 |        "      <td>2023-07-26</td>\n",
 530 |        "      <td>Madrid</td>\n",
 531 |        "    </tr>\n",
 532 |        "    <tr>\n",
 533 |        "      <th>389935</th>\n",
 534 |        "      <td>Mas y Mas</td>\n",
 535 |        "      <td>Pistachos</td>\n",
 536 |        "      <td>6.70</td>\n",
 537 |        "      <td>516</td>\n",
 538 |        "      <td>2023-07-10</td>\n",
 539 |        "      <td>Alicante</td>\n",
 540 |        "    </tr>\n",
 541 |        "    <tr>\n",
 542 |        "      <th>389936</th>\n",
 543 |        "      <td>Lidl</td>\n",
 544 |        "      <td>Nueces</td>\n",
 545 |        "      <td>6.14</td>\n",
 546 |        "      <td>1283</td>\n",
 547 |        "      <td>2023-12-05</td>\n",
 548 |        "      <td>Barcelona</td>\n",
 549 |        "    </tr>\n",
 550 |        "  </tbody>\n",
 551 |        "</table>\n",
 552 |        "</div>"
 553 |       ],
 554 |       "text/plain": [
 555 |        "       Nombre Cliente Tipo Producto  Precio (€/kg)  Cantidad Vendida (kg)  \\\n",
 556 |        "id                                                                          \n",
 557 |        "389932      Mas y Mas     Almendras           4.54                   1212   \n",
 558 |        "389933      Mercadona        Nueces           5.84                    591   \n",
 559 |        "389934        Alcampo    Cacahuetes           2.69                   2831   \n",
 560 |        "389935      Mas y Mas     Pistachos           6.70                    516   \n",
 561 |        "389936           Lidl        Nueces           6.14                   1283   \n",
 562 |        "\n",
 563 |        "       Fecha Venta Provincia Cliente  \n",
 564 |        "id                                    \n",
 565 |        "389932  2023-03-29             Cádiz  \n",
 566 |        "389933  2023-05-12           Sevilla  \n",
 567 |        "389934  2023-07-26            Madrid  \n",
 568 |        "389935  2023-07-10          Alicante  \n",
 569 |        "389936  2023-12-05         Barcelona  "
 570 |       ]
 571 |      },
 572 |      "execution_count": 13,
 573 |      "metadata": {},
 574 |      "output_type": "execute_result"
 575 |     }
 576 |    ],
 577 |    "source": [
 578 |     "df_excel.head()"
 579 |    ]
 580 |   },
 581 |   {
 582 |    "cell_type": "markdown",
 583 |    "metadata": {},
 584 |    "source": [
 585 |     "## Importar desde el Portapapeles"
 586 |    ]
 587 |   },
 588 |   {
 589 |    "cell_type": "code",
 590 |    "execution_count": 14,
 591 |    "metadata": {},
 592 |    "outputs": [
 593 |     {
 594 |      "data": {
 595 |       "text/html": [
 596 |        "<div>\n",
 597 |        "<style scoped>\n",
 598 |        "    .dataframe tbody tr th:only-of-type {\n",
 599 |        "        vertical-align: middle;\n",
 600 |        "    }\n",
 601 |        "\n",
 602 |        "    .dataframe tbody tr th {\n",
 603 |        "        vertical-align: top;\n",
 604 |        "    }\n",
 605 |        "\n",
 606 |        "    .dataframe thead th {\n",
 607 |        "        text-align: right;\n",
 608 |        "    }\n",
 609 |        "</style>\n",
 610 |        "<table border=\"1\" class=\"dataframe\">\n",
 611 |        "  <thead>\n",
 612 |        "    <tr style=\"text-align: right;\">\n",
 613 |        "      <th></th>\n",
 614 |        "      <th>0</th>\n",
 615 |        "      <th>1</th>\n",
 616 |        "      <th>2</th>\n",
 617 |        "      <th>3</th>\n",
 618 |        "      <th>4</th>\n",
 619 |        "    </tr>\n",
 620 |        "  </thead>\n",
 621 |        "  <tbody>\n",
 622 |        "    <tr>\n",
 623 |        "      <th>0</th>\n",
 624 |        "      <td>('/Users/jimenacambronero/Desktop/Proyectos</td>\n",
 625 |        "      <td>para</td>\n",
 626 |        "      <td>Portfolio/PythonPracticas/Datos/Frutos_Secos.c...</td>\n",
 627 |        "      <td>delimiter=';',</td>\n",
 628 |        "      <td>index_col='id'</td>\n",
 629 |        "    </tr>\n",
 630 |        "  </tbody>\n",
 631 |        "</table>\n",
 632 |        "</div>"
 633 |       ],
 634 |       "text/plain": [
 635 |        "                                             0     1  \\\n",
 636 |        "0  ('/Users/jimenacambronero/Desktop/Proyectos  para   \n",
 637 |        "\n",
 638 |        "                                                   2               3  \\\n",
 639 |        "0  Portfolio/PythonPracticas/Datos/Frutos_Secos.c...  delimiter=';',   \n",
 640 |        "\n",
 641 |        "                4  \n",
 642 |        "0  index_col='id'  "
 643 |       ]
 644 |      },
 645 |      "execution_count": 14,
 646 |      "metadata": {},
 647 |      "output_type": "execute_result"
 648 |     }
 649 |    ],
 650 |    "source": [
 651 |     "df_portapapeles = pd.read_clipboard(header=None)\n",
 652 |     "df_portapapeles.head(2)"
 653 |    ]
 654 |   },
 655 |   {
 656 |    "cell_type": "markdown",
 657 |    "metadata": {},
 658 |    "source": [
 659 |     "# Bases de datos relacionales -SQL Alchemy-"
 660 |    ]
 661 |   },
 662 |   {
 663 |    "cell_type": "markdown",
 664 |    "metadata": {},
 665 |    "source": [
 666 |     "SQLAlchemy es una biblioteca de Python que proporciona una forma flexible y de alto rendimiento para trabajar con bases de datos relacionales. Se utiliza comúnmente como un Object Relational Mapper (ORM), que es una técnica que facilita la interacción con bases de datos utilizando objetos y consultas en lugar de escribir directamente en SQL."
 667 |    ]
 668 |   },
 669 |   {
 670 |    "cell_type": "code",
 671 |    "execution_count": 1,
 672 |    "metadata": {},
 673 |    "outputs": [],
 674 |    "source": [
 675 |     "import sqlalchemy as sa"
 676 |    ]
 677 |   },
 678 |   {
 679 |    "cell_type": "code",
 680 |    "execution_count": 26,
 681 |    "metadata": {},
 682 |    "outputs": [],
 683 |    "source": [
 684 |     "# Crear la conexion a la base de datos\n",
 685 |     "conexion= sa.create_engine('sqlite:////Users/jimenacambronero/Desktop/Proyectos para Portfolio/PythonPracticas/Datos/pruebassqlalchemy.db')"
 686 |    ]
 687 |   },
 688 |   {
 689 |    "cell_type": "code",
 690 |    "execution_count": 35,
 691 |    "metadata": {},
 692 |    "outputs": [],
 693 |    "source": [
 694 |     "# Hacer la query para cargar los datos\n",
 695 |     "df_sql= pd.read_sql('SELECT * FROM Registros_Ventas',conexion)"
 696 |    ]
 697 |   },
 698 |   {
 699 |    "cell_type": "code",
 700 |    "execution_count": 36,
 701 |    "metadata": {},
 702 |    "outputs": [
 703 |     {
 704 |      "data": {
 705 |       "text/html": [
 706 |        "<div>\n",
 707 |        "<style scoped>\n",
 708 |        "    .dataframe tbody tr th:only-of-type {\n",
 709 |        "        vertical-align: middle;\n",
 710 |        "    }\n",
 711 |        "\n",
 712 |        "    .dataframe tbody tr th {\n",
 713 |        "        vertical-align: top;\n",
 714 |        "    }\n",
 715 |        "\n",
 716 |        "    .dataframe thead th {\n",
 717 |        "        text-align: right;\n",
 718 |        "    }\n",
 719 |        "</style>\n",
 720 |        "<table border=\"1\" class=\"dataframe\">\n",
 721 |        "  <thead>\n",
 722 |        "    <tr style=\"text-align: right;\">\n",
 723 |        "      <th></th>\n",
 724 |        "      <th>Nombre Cliente</th>\n",
 725 |        "      <th>Tipo Producto</th>\n",
 726 |        "      <th>Precio (€/kg)</th>\n",
 727 |        "      <th>Cantidad Vendida (kg)</th>\n",
 728 |        "      <th>Fecha Venta</th>\n",
 729 |        "      <th>Provincia Cliente</th>\n",
 730 |        "    </tr>\n",
 731 |        "  </thead>\n",
 732 |        "  <tbody>\n",
 733 |        "    <tr>\n",
 734 |        "      <th>0</th>\n",
 735 |        "      <td>Mas y Mas</td>\n",
 736 |        "      <td>Almendras</td>\n",
 737 |        "      <td>4.54</td>\n",
 738 |        "      <td>1212</td>\n",
 739 |        "      <td>2023-03-29</td>\n",
 740 |        "      <td>Cádiz</td>\n",
 741 |        "    </tr>\n",
 742 |        "    <tr>\n",
 743 |        "      <th>1</th>\n",
 744 |        "      <td>Mercadona</td>\n",
 745 |        "      <td>Nueces</td>\n",
 746 |        "      <td>5.84</td>\n",
 747 |        "      <td>591</td>\n",
 748 |        "      <td>2023-12-05</td>\n",
 749 |        "      <td>Sevilla</td>\n",
 750 |        "    </tr>\n",
 751 |        "    <tr>\n",
 752 |        "      <th>2</th>\n",
 753 |        "      <td>Alcampo</td>\n",
 754 |        "      <td>Cacahuetes</td>\n",
 755 |        "      <td>2.69</td>\n",
 756 |        "      <td>2831</td>\n",
 757 |        "      <td>2023-07-26</td>\n",
 758 |        "      <td>Madrid</td>\n",
 759 |        "    </tr>\n",
 760 |        "    <tr>\n",
 761 |        "      <th>3</th>\n",
 762 |        "      <td>Mas y Mas</td>\n",
 763 |        "      <td>Pistachos</td>\n",
 764 |        "      <td>6.70</td>\n",
 765 |        "      <td>516</td>\n",
 766 |        "      <td>2023-10-07</td>\n",
 767 |        "      <td>Alicante</td>\n",
 768 |        "    </tr>\n",
 769 |        "    <tr>\n",
 770 |        "      <th>4</th>\n",
 771 |        "      <td>Lidl</td>\n",
 772 |        "      <td>Nueces</td>\n",
 773 |        "      <td>6.14</td>\n",
 774 |        "      <td>1283</td>\n",
 775 |        "      <td>2023-05-12</td>\n",
 776 |        "      <td>Barcelona</td>\n",
 777 |        "    </tr>\n",
 778 |        "  </tbody>\n",
 779 |        "</table>\n",
 780 |        "</div>"
 781 |       ],
 782 |       "text/plain": [
 783 |        "  Nombre Cliente Tipo Producto  Precio (€/kg)  Cantidad Vendida (kg)  \\\n",
 784 |        "0      Mas y Mas     Almendras           4.54                   1212   \n",
 785 |        "1      Mercadona        Nueces           5.84                    591   \n",
 786 |        "2        Alcampo    Cacahuetes           2.69                   2831   \n",
 787 |        "3      Mas y Mas     Pistachos           6.70                    516   \n",
 788 |        "4           Lidl        Nueces           6.14                   1283   \n",
 789 |        "\n",
 790 |        "  Fecha Venta Provincia Cliente  \n",
 791 |        "0  2023-03-29             Cádiz  \n",
 792 |        "1  2023-12-05           Sevilla  \n",
 793 |        "2  2023-07-26            Madrid  \n",
 794 |        "3  2023-10-07          Alicante  \n",
 795 |        "4  2023-05-12         Barcelona  "
 796 |       ]
 797 |      },
 798 |      "execution_count": 36,
 799 |      "metadata": {},
 800 |      "output_type": "execute_result"
 801 |     }
 802 |    ],
 803 |    "source": [
 804 |     "# Trabajar normalmente como un dataframe de Pandas\n",
 805 |     "df_sql.head()"
 806 |    ]
 807 |   },
 808 |   {
 809 |    "cell_type": "markdown",
 810 |    "metadata": {},
 811 |    "source": [
 812 |     "# Guardar Datos"
 813 |    ]
 814 |   },
 815 |   {
 816 |    "cell_type": "markdown",
 817 |    "metadata": {},
 818 |    "source": [
 819 |     "CSV\n",
 820 |     "\n",
 821 |     "    df.to_csv('Prueba.csv')\n",
 822 |     "\n",
 823 |     "EXCEL\n",
 824 |     "\n",
 825 |     "    df.to_excel('Prueba.xlsx')"
 826 |    ]
 827 |   },
 828 |   {
 829 |    "cell_type": "markdown",
 830 |    "metadata": {},
 831 |    "source": [
 832 |     "# Situaciones Frecuentes"
 833 |    ]
 834 |   },
 835 |   {
 836 |    "cell_type": "markdown",
 837 |    "metadata": {},
 838 |    "source": [
 839 |     "## Separadores No Estandar\n",
 840 |     "\n",
 841 |     "A menudo, cuando trabajas con archivos, la información está organizada en columnas y las columnas están separadas por caracteres especiales como comas, punto y coma o tabulaciones.\n",
 842 |     "\n",
 843 |     "Pero a veces, en lugar de comas o punto y coma, pueden usar cualquier otro carácter para separar las columnas. Entonces, lo primero que debes hacer es descubrir qué carácter están usando para separar las columnas, para que podamos leer el archivo correctamente. Esto se hace utilizando el parámetro \"sep\".\n",
 844 |     "\n",
 845 |     "La mayoría de las veces, esta información sobre el carácter separador se encuentra en la documentación del archivo o en las instrucciones proporcionadas."
 846 |    ]
 847 |   },
 848 |   {
 849 |    "cell_type": "code",
 850 |    "execution_count": 37,
 851 |    "metadata": {},
 852 |    "outputs": [
 853 |     {
 854 |      "data": {
 855 |       "text/html": [
 856 |        "<div>\n",
 857 |        "<style scoped>\n",
 858 |        "    .dataframe tbody tr th:only-of-type {\n",
 859 |        "        vertical-align: middle;\n",
 860 |        "    }\n",
 861 |        "\n",
 862 |        "    .dataframe tbody tr th {\n",
 863 |        "        vertical-align: top;\n",
 864 |        "    }\n",
 865 |        "\n",
 866 |        "    .dataframe thead th {\n",
 867 |        "        text-align: right;\n",
 868 |        "    }\n",
 869 |        "</style>\n",
 870 |        "<table border=\"1\" class=\"dataframe\">\n",
 871 |        "  <thead>\n",
 872 |        "    <tr style=\"text-align: right;\">\n",
 873 |        "      <th></th>\n",
 874 |        "      <th>Nombre</th>\n",
 875 |        "      <th>Edad</th>\n",
 876 |        "      <th>Ciudad</th>\n",
 877 |        "    </tr>\n",
 878 |        "  </thead>\n",
 879 |        "  <tbody>\n",
 880 |        "    <tr>\n",
 881 |        "      <th>0</th>\n",
 882 |        "      <td>Juan</td>\n",
 883 |        "      <td>25</td>\n",
 884 |        "      <td>Madrid</td>\n",
 885 |        "    </tr>\n",
 886 |        "    <tr>\n",
 887 |        "      <th>1</th>\n",
 888 |        "      <td>Ana</td>\n",
 889 |        "      <td>30</td>\n",
 890 |        "      <td>Barcelona</td>\n",
 891 |        "    </tr>\n",
 892 |        "    <tr>\n",
 893 |        "      <th>2</th>\n",
 894 |        "      <td>Carlos</td>\n",
 895 |        "      <td>22</td>\n",
 896 |        "      <td>Sevilla</td>\n",
 897 |        "    </tr>\n",
 898 |        "  </tbody>\n",
 899 |        "</table>\n",
 900 |        "</div>"
 901 |       ],
 902 |       "text/plain": [
 903 |        "   Nombre  Edad     Ciudad\n",
 904 |        "0    Juan    25     Madrid\n",
 905 |        "1     Ana    30  Barcelona\n",
 906 |        "2  Carlos    22    Sevilla"
 907 |       ]
 908 |      },
 909 |      "execution_count": 37,
 910 |      "metadata": {},
 911 |      "output_type": "execute_result"
 912 |     }
 913 |    ],
 914 |    "source": [
 915 |     "pd.read_csv( '/Users/jimenacambronero/Desktop/Proyectos para Portfolio/PythonPracticas/Datos/ejemplo_tabulador.csv', sep='\\t').head(5) "
 916 |    ]
 917 |   },
 918 |   {
 919 |    "cell_type": "markdown",
 920 |    "metadata": {},
 921 |    "source": [
 922 |     "## Cuando los datos NO comienzan en la Primera Fila"
 923 |    ]
 924 |   },
 925 |   {
 926 |    "cell_type": "code",
 927 |    "execution_count": 38,
 928 |    "metadata": {},
 929 |    "outputs": [
 930 |     {
 931 |      "data": {
 932 |       "text/html": [
 933 |        "<div>\n",
 934 |        "<style scoped>\n",
 935 |        "    .dataframe tbody tr th:only-of-type {\n",
 936 |        "        vertical-align: middle;\n",
 937 |        "    }\n",
 938 |        "\n",
 939 |        "    .dataframe tbody tr th {\n",
 940 |        "        vertical-align: top;\n",
 941 |        "    }\n",
 942 |        "\n",
 943 |        "    .dataframe thead th {\n",
 944 |        "        text-align: right;\n",
 945 |        "    }\n",
 946 |        "</style>\n",
 947 |        "<table border=\"1\" class=\"dataframe\">\n",
 948 |        "  <thead>\n",
 949 |        "    <tr style=\"text-align: right;\">\n",
 950 |        "      <th></th>\n",
 951 |        "      <th>Juan</th>\n",
 952 |        "      <th>25</th>\n",
 953 |        "      <th>Madrid</th>\n",
 954 |        "    </tr>\n",
 955 |        "  </thead>\n",
 956 |        "  <tbody>\n",
 957 |        "    <tr>\n",
 958 |        "      <th>0</th>\n",
 959 |        "      <td>Ana</td>\n",
 960 |        "      <td>30</td>\n",
 961 |        "      <td>Barcelona</td>\n",
 962 |        "    </tr>\n",
 963 |        "    <tr>\n",
 964 |        "      <th>1</th>\n",
 965 |        "      <td>Carlos</td>\n",
 966 |        "      <td>22</td>\n",
 967 |        "      <td>Sevilla</td>\n",
 968 |        "    </tr>\n",
 969 |        "  </tbody>\n",
 970 |        "</table>\n",
 971 |        "</div>"
 972 |       ],
 973 |       "text/plain": [
 974 |        "     Juan  25     Madrid\n",
 975 |        "0     Ana  30  Barcelona\n",
 976 |        "1  Carlos  22    Sevilla"
 977 |       ]
 978 |      },
 979 |      "execution_count": 38,
 980 |      "metadata": {},
 981 |      "output_type": "execute_result"
 982 |     }
 983 |    ],
 984 |    "source": [
 985 |     "# skiprows se salta la primera fila, cabecera, directamente carga como cabecera el primer registro\n",
 986 |     "pd.read_csv('/Users/jimenacambronero/Desktop/Proyectos para Portfolio/PythonPracticas/Datos/ejemplo_tabulador.csv', \n",
 987 |     "            sep='\\t',\n",
 988 |     "            skiprows=1).head(5)"
 989 |    ]
 990 |   },
 991 |   {
 992 |    "cell_type": "markdown",
 993 |    "metadata": {},
 994 |    "source": [
 995 |     "## Columnas Sin Nombre"
 996 |    ]
 997 |   },
 998 |   {
 999 |    "cell_type": "markdown",
1000 |    "metadata": {},
1001 |    "source": [
1002 |     "A veces puede suceder que no queremos cargar los nombres de las columnas sino que los queremos poner más tarde"
1003 |    ]
1004 |   },
1005 |   {
1006 |    "cell_type": "code",
1007 |    "execution_count": 39,
1008 |    "metadata": {},
1009 |    "outputs": [
1010 |     {
1011 |      "data": {
1012 |       "text/html": [
1013 |        "<div>\n",
1014 |        "<style scoped>\n",
1015 |        "    .dataframe tbody tr th:only-of-type {\n",
1016 |        "        vertical-align: middle;\n",
1017 |        "    }\n",
1018 |        "\n",
1019 |        "    .dataframe tbody tr th {\n",
1020 |        "        vertical-align: top;\n",
1021 |        "    }\n",
1022 |        "\n",
1023 |        "    .dataframe thead th {\n",
1024 |        "        text-align: right;\n",
1025 |        "    }\n",
1026 |        "</style>\n",
1027 |        "<table border=\"1\" class=\"dataframe\">\n",
1028 |        "  <thead>\n",
1029 |        "    <tr style=\"text-align: right;\">\n",
1030 |        "      <th></th>\n",
1031 |        "      <th>0</th>\n",
1032 |        "      <th>1</th>\n",
1033 |        "      <th>2</th>\n",
1034 |        "    </tr>\n",
1035 |        "  </thead>\n",
1036 |        "  <tbody>\n",
1037 |        "    <tr>\n",
1038 |        "      <th>0</th>\n",
1039 |        "      <td>Nombre</td>\n",
1040 |        "      <td>Edad</td>\n",
1041 |        "      <td>Ciudad</td>\n",
1042 |        "    </tr>\n",
1043 |        "    <tr>\n",
1044 |        "      <th>1</th>\n",
1045 |        "      <td>Juan</td>\n",
1046 |        "      <td>25</td>\n",
1047 |        "      <td>Madrid</td>\n",
1048 |        "    </tr>\n",
1049 |        "    <tr>\n",
1050 |        "      <th>2</th>\n",
1051 |        "      <td>Ana</td>\n",
1052 |        "      <td>30</td>\n",
1053 |        "      <td>Barcelona</td>\n",
1054 |        "    </tr>\n",
1055 |        "    <tr>\n",
1056 |        "      <th>3</th>\n",
1057 |        "      <td>Carlos</td>\n",
1058 |        "      <td>22</td>\n",
1059 |        "      <td>Sevilla</td>\n",
1060 |        "    </tr>\n",
1061 |        "  </tbody>\n",
1062 |        "</table>\n",
1063 |        "</div>"
1064 |       ],
1065 |       "text/plain": [
1066 |        "        0     1          2\n",
1067 |        "0  Nombre  Edad     Ciudad\n",
1068 |        "1    Juan    25     Madrid\n",
1069 |        "2     Ana    30  Barcelona\n",
1070 |        "3  Carlos    22    Sevilla"
1071 |       ]
1072 |      },
1073 |      "execution_count": 39,
1074 |      "metadata": {},
1075 |      "output_type": "execute_result"
1076 |     }
1077 |    ],
1078 |    "source": [
1079 |     "# header = none no cargara nada \n",
1080 |     "pd.read_csv('/Users/jimenacambronero/Desktop/Proyectos para Portfolio/PythonPracticas/Datos/ejemplo_tabulador.csv', \n",
1081 |     "            sep='\\t',\n",
1082 |     "            header=None).head(5)"
1083 |    ]
1084 |   },
1085 |   {
1086 |    "cell_type": "code",
1087 |    "execution_count": 40,
1088 |    "metadata": {},
1089 |    "outputs": [
1090 |     {
1091 |      "data": {
1092 |       "text/html": [
1093 |        "<div>\n",
1094 |        "<style scoped>\n",
1095 |        "    .dataframe tbody tr th:only-of-type {\n",
1096 |        "        vertical-align: middle;\n",
1097 |        "    }\n",
1098 |        "\n",
1099 |        "    .dataframe tbody tr th {\n",
1100 |        "        vertical-align: top;\n",
1101 |        "    }\n",
1102 |        "\n",
1103 |        "    .dataframe thead th {\n",
1104 |        "        text-align: right;\n",
1105 |        "    }\n",
1106 |        "</style>\n",
1107 |        "<table border=\"1\" class=\"dataframe\">\n",
1108 |        "  <thead>\n",
1109 |        "    <tr style=\"text-align: right;\">\n",
1110 |        "      <th></th>\n",
1111 |        "      <th>Estas</th>\n",
1112 |        "      <th>son mis</th>\n",
1113 |        "      <th>cabeceras</th>\n",
1114 |        "    </tr>\n",
1115 |        "  </thead>\n",
1116 |        "  <tbody>\n",
1117 |        "    <tr>\n",
1118 |        "      <th>0</th>\n",
1119 |        "      <td>Carlos</td>\n",
1120 |        "      <td>22</td>\n",
1121 |        "      <td>Sevilla</td>\n",
1122 |        "    </tr>\n",
1123 |        "  </tbody>\n",
1124 |        "</table>\n",
1125 |        "</div>"
1126 |       ],
1127 |       "text/plain": [
1128 |        "    Estas  son mis cabeceras\n",
1129 |        "0  Carlos       22   Sevilla"
1130 |       ]
1131 |      },
1132 |      "execution_count": 40,
1133 |      "metadata": {},
1134 |      "output_type": "execute_result"
1135 |     }
1136 |    ],
1137 |    "source": [
1138 |     "# Cómo poner nombres en la importación\n",
1139 |     "# Como ahora la cabecera corre fila tenemos que ponerle 2 en skiprows, si no no importara la fila 84\n",
1140 |     "\n",
1141 |     "cabecera = ['Estas', 'son mis', 'cabeceras']\n",
1142 |     "\n",
1143 |     "pd.read_csv('/Users/jimenacambronero/Desktop/Proyectos para Portfolio/PythonPracticas/Datos/ejemplo_tabulador.csv', \n",
1144 |     "            sep='\\t',\n",
1145 |     "            skiprows=2,\n",
1146 |     "            header=0,\n",
1147 |     "            names=cabecera).head(5)"
1148 |    ]
1149 |   },
1150 |   {
1151 |    "cell_type": "markdown",
1152 |    "metadata": {},
1153 |    "source": [
1154 |     "## Nulos no Habituales"
1155 |    ]
1156 |   },
1157 |   {
1158 |    "cell_type": "markdown",
1159 |    "metadata": {},
1160 |    "source": [
1161 |     "Si tenes una serie de números 'falsos' como en alguos casos el -999 los podes identificar como nulos."
1162 |    ]
1163 |   },
1164 |   {
1165 |    "cell_type": "code",
1166 |    "execution_count": 42,
1167 |    "metadata": {},
1168 |    "outputs": [
1169 |     {
1170 |      "data": {
1171 |       "text/html": [
1172 |        "<div>\n",
1173 |        "<style scoped>\n",
1174 |        "    .dataframe tbody tr th:only-of-type {\n",
1175 |        "        vertical-align: middle;\n",
1176 |        "    }\n",
1177 |        "\n",
1178 |        "    .dataframe tbody tr th {\n",
1179 |        "        vertical-align: top;\n",
1180 |        "    }\n",
1181 |        "\n",
1182 |        "    .dataframe thead th {\n",
1183 |        "        text-align: right;\n",
1184 |        "    }\n",
1185 |        "</style>\n",
1186 |        "<table border=\"1\" class=\"dataframe\">\n",
1187 |        "  <thead>\n",
1188 |        "    <tr style=\"text-align: right;\">\n",
1189 |        "      <th></th>\n",
1190 |        "      <th>ID</th>\n",
1191 |        "      <th>Nombre</th>\n",
1192 |        "      <th>Años</th>\n",
1193 |        "      <th>Puntos</th>\n",
1194 |        "    </tr>\n",
1195 |        "  </thead>\n",
1196 |        "  <tbody>\n",
1197 |        "    <tr>\n",
1198 |        "      <th>0</th>\n",
1199 |        "      <td>1</td>\n",
1200 |        "      <td>Alice</td>\n",
1201 |        "      <td>25.0</td>\n",
1202 |        "      <td>95.0</td>\n",
1203 |        "    </tr>\n",
1204 |        "    <tr>\n",
1205 |        "      <th>1</th>\n",
1206 |        "      <td>2</td>\n",
1207 |        "      <td>Bob</td>\n",
1208 |        "      <td>NaN</td>\n",
1209 |        "      <td>82.0</td>\n",
1210 |        "    </tr>\n",
1211 |        "    <tr>\n",
1212 |        "      <th>2</th>\n",
1213 |        "      <td>3</td>\n",
1214 |        "      <td>Charlie</td>\n",
1215 |        "      <td>30.0</td>\n",
1216 |        "      <td>NaN</td>\n",
1217 |        "    </tr>\n",
1218 |        "    <tr>\n",
1219 |        "      <th>3</th>\n",
1220 |        "      <td>4</td>\n",
1221 |        "      <td>David</td>\n",
1222 |        "      <td>22.0</td>\n",
1223 |        "      <td>90.0</td>\n",
1224 |        "    </tr>\n",
1225 |        "    <tr>\n",
1226 |        "      <th>4</th>\n",
1227 |        "      <td>5</td>\n",
1228 |        "      <td>Eva</td>\n",
1229 |        "      <td>35.0</td>\n",
1230 |        "      <td>88.0</td>\n",
1231 |        "    </tr>\n",
1232 |        "  </tbody>\n",
1233 |        "</table>\n",
1234 |        "</div>"
1235 |       ],
1236 |       "text/plain": [
1237 |        "   ID   Nombre  Años  Puntos\n",
1238 |        "0   1    Alice  25.0    95.0\n",
1239 |        "1   2      Bob   NaN    82.0\n",
1240 |        "2   3  Charlie  30.0     NaN\n",
1241 |        "3   4    David  22.0    90.0\n",
1242 |        "4   5      Eva  35.0    88.0"
1243 |       ]
1244 |      },
1245 |      "execution_count": 42,
1246 |      "metadata": {},
1247 |      "output_type": "execute_result"
1248 |     }
1249 |    ],
1250 |    "source": [
1251 |     "cabecera = ['ID', 'Nombre', 'Años', 'Puntos']\n",
1252 |     "\n",
1253 |     "# cargara Nan cdo encuentro un -999\n",
1254 |     "pd.read_csv('/Users/jimenacambronero/Desktop/Proyectos para Portfolio/PythonPracticas/Datos/ejemplo_valores_nulos.csv', \n",
1255 |     "            sep=',',\n",
1256 |     "            header=0,\n",
1257 |     "            names=cabecera,\n",
1258 |     "            na_values=-999).head(5)"
1259 |    ]
1260 |   }
1261 |  ],
1262 |  "metadata": {
1263 |   "kernelspec": {
1264 |    "display_name": "Python 3",
1265 |    "language": "python",
1266 |    "name": "python3"
1267 |   },
1268 |   "language_info": {
1269 |    "codemirror_mode": {
1270 |     "name": "ipython",
1271 |     "version": 3
1272 |    },
1273 |    "file_extension": ".py",
1274 |    "mimetype": "text/x-python",
1275 |    "name": "python",
1276 |    "nbconvert_exporter": "python",
1277 |    "pygments_lexer": "ipython3",
1278 |    "version": "3.9.13"
1279 |   }
1280 |  },
1281 |  "nbformat": 4,
1282 |  "nbformat_minor": 2
1283 | }
1284 | 


--------------------------------------------------------------------------------
/6_TratamientoDatos.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "# Tratamiento de Datos"
   8 |    ]
   9 |   },
  10 |   {
  11 |    "cell_type": "markdown",
  12 |    "metadata": {},
  13 |    "source": [
  14 |     "## Identificar Nulos"
  15 |    ]
  16 |   },
  17 |   {
  18 |    "cell_type": "markdown",
  19 |    "metadata": {},
  20 |    "source": [
  21 |     "### Conteo por Variable"
  22 |    ]
  23 |   },
  24 |   {
  25 |    "cell_type": "code",
  26 |    "execution_count": 4,
  27 |    "metadata": {},
  28 |    "outputs": [
  29 |     {
  30 |      "ename": "AttributeError",
  31 |      "evalue": "'DataFrame' object has no attribute 'isa'",
  32 |      "output_type": "error",
  33 |      "traceback": [
  34 |       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
  35 |       "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
  36 |       "\u001b[0;32m/var/folders/2d/p3zg32ds4p598pyxblpsp5ph0000gn/T/ipykernel_2676/1660468150.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# .isna = es nulo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_excel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/Users/jimenacambronero/Desktop/Proyectos para Portfolio/PythonPracticas/Datos/Frutos_Secos.xlsx'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_col\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparse_dates\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Fecha Venta'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misa\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
  37 |       "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5573\u001b[0m         ):\n\u001b[1;32m   5574\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5575\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5576\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5577\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
  38 |       "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'isa'"
  39 |      ]
  40 |     }
  41 |    ],
  42 |    "source": [
  43 |     "import pandas as pd\n",
  44 |     "\n",
  45 |     "# .isna = es nulo\n",
  46 |     "df = pd.read_excel('/Users/jimenacambronero/Desktop/Proyectos para Portfolio/PythonPracticas/Datos/Frutos_Secos.xlsx', index_col='id', parse_dates=['Fecha Venta'])\n",
  47 |     "df.isa()"
  48 |    ]
  49 |   },
  50 |   {
  51 |    "cell_type": "code",
  52 |    "execution_count": 5,
  53 |    "metadata": {},
  54 |    "outputs": [
  55 |     {
  56 |      "data": {
  57 |       "text/plain": [
  58 |        "Nombre Cliente           0\n",
  59 |        "Tipo Producto            0\n",
  60 |        "Precio (€/kg)            0\n",
  61 |        "Cantidad Vendida (kg)    0\n",
  62 |        "Fecha Venta              0\n",
  63 |        "Provincia Cliente        0\n",
  64 |        "dtype: int64"
  65 |       ]
  66 |      },
  67 |      "execution_count": 5,
  68 |      "metadata": {},
  69 |      "output_type": "execute_result"
  70 |     }
  71 |    ],
  72 |    "source": [
  73 |     "# conteo de nulos\n",
  74 |     "df.isna().sum()"
  75 |    ]
  76 |   },
  77 |   {
  78 |    "cell_type": "code",
  79 |    "execution_count": 6,
  80 |    "metadata": {},
  81 |    "outputs": [
  82 |     {
  83 |      "data": {
  84 |       "text/html": [
  85 |        "<div>\n",
  86 |        "<style scoped>\n",
  87 |        "    .dataframe tbody tr th:only-of-type {\n",
  88 |        "        vertical-align: middle;\n",
  89 |        "    }\n",
  90 |        "\n",
  91 |        "    .dataframe tbody tr th {\n",
  92 |        "        vertical-align: top;\n",
  93 |        "    }\n",
  94 |        "\n",
  95 |        "    .dataframe thead th {\n",
  96 |        "        text-align: right;\n",
  97 |        "    }\n",
  98 |        "</style>\n",
  99 |        "<table border=\"1\" class=\"dataframe\">\n",
 100 |        "  <thead>\n",
 101 |        "    <tr style=\"text-align: right;\">\n",
 102 |        "      <th></th>\n",
 103 |        "      <th>ID</th>\n",
 104 |        "      <th>Nombre</th>\n",
 105 |        "      <th>Edad</th>\n",
 106 |        "      <th>Puntuacion</th>\n",
 107 |        "    </tr>\n",
 108 |        "  </thead>\n",
 109 |        "  <tbody>\n",
 110 |        "    <tr>\n",
 111 |        "      <th>0</th>\n",
 112 |        "      <td>1</td>\n",
 113 |        "      <td>Nombre1</td>\n",
 114 |        "      <td>NaN</td>\n",
 115 |        "      <td>95.0</td>\n",
 116 |        "    </tr>\n",
 117 |        "    <tr>\n",
 118 |        "      <th>1</th>\n",
 119 |        "      <td>2</td>\n",
 120 |        "      <td>Nombre2</td>\n",
 121 |        "      <td>25.0</td>\n",
 122 |        "      <td>NaN</td>\n",
 123 |        "    </tr>\n",
 124 |        "    <tr>\n",
 125 |        "      <th>2</th>\n",
 126 |        "      <td>3</td>\n",
 127 |        "      <td>Nombre3</td>\n",
 128 |        "      <td>NaN</td>\n",
 129 |        "      <td>NaN</td>\n",
 130 |        "    </tr>\n",
 131 |        "    <tr>\n",
 132 |        "      <th>3</th>\n",
 133 |        "      <td>4</td>\n",
 134 |        "      <td>Nombre4</td>\n",
 135 |        "      <td>30.0</td>\n",
 136 |        "      <td>NaN</td>\n",
 137 |        "    </tr>\n",
 138 |        "    <tr>\n",
 139 |        "      <th>4</th>\n",
 140 |        "      <td>5</td>\n",
 141 |        "      <td>Nombre5</td>\n",
 142 |        "      <td>25.0</td>\n",
 143 |        "      <td>NaN</td>\n",
 144 |        "    </tr>\n",
 145 |        "    <tr>\n",
 146 |        "      <th>5</th>\n",
 147 |        "      <td>6</td>\n",
 148 |        "      <td>Nombre6</td>\n",
 149 |        "      <td>NaN</td>\n",
 150 |        "      <td>90.0</td>\n",
 151 |        "    </tr>\n",
 152 |        "    <tr>\n",
 153 |        "      <th>6</th>\n",
 154 |        "      <td>7</td>\n",
 155 |        "      <td>Nombre7</td>\n",
 156 |        "      <td>30.0</td>\n",
 157 |        "      <td>95.0</td>\n",
 158 |        "    </tr>\n",
 159 |        "    <tr>\n",
 160 |        "      <th>7</th>\n",
 161 |        "      <td>8</td>\n",
 162 |        "      <td>Nombre8</td>\n",
 163 |        "      <td>30.0</td>\n",
 164 |        "      <td>NaN</td>\n",
 165 |        "    </tr>\n",
 166 |        "    <tr>\n",
 167 |        "      <th>8</th>\n",
 168 |        "      <td>9</td>\n",
 169 |        "      <td>Nombre9</td>\n",
 170 |        "      <td>NaN</td>\n",
 171 |        "      <td>95.0</td>\n",
 172 |        "    </tr>\n",
 173 |        "    <tr>\n",
 174 |        "      <th>9</th>\n",
 175 |        "      <td>10</td>\n",
 176 |        "      <td>Nombre10</td>\n",
 177 |        "      <td>25.0</td>\n",
 178 |        "      <td>90.0</td>\n",
 179 |        "    </tr>\n",
 180 |        "    <tr>\n",
 181 |        "      <th>10</th>\n",
 182 |        "      <td>11</td>\n",
 183 |        "      <td>Nombre11</td>\n",
 184 |        "      <td>30.0</td>\n",
 185 |        "      <td>90.0</td>\n",
 186 |        "    </tr>\n",
 187 |        "    <tr>\n",
 188 |        "      <th>11</th>\n",
 189 |        "      <td>12</td>\n",
 190 |        "      <td>Nombre12</td>\n",
 191 |        "      <td>25.0</td>\n",
 192 |        "      <td>90.0</td>\n",
 193 |        "    </tr>\n",
 194 |        "    <tr>\n",
 195 |        "      <th>12</th>\n",
 196 |        "      <td>13</td>\n",
 197 |        "      <td>Nombre13</td>\n",
 198 |        "      <td>30.0</td>\n",
 199 |        "      <td>95.0</td>\n",
 200 |        "    </tr>\n",
 201 |        "    <tr>\n",
 202 |        "      <th>13</th>\n",
 203 |        "      <td>14</td>\n",
 204 |        "      <td>Nombre14</td>\n",
 205 |        "      <td>30.0</td>\n",
 206 |        "      <td>90.0</td>\n",
 207 |        "    </tr>\n",
 208 |        "    <tr>\n",
 209 |        "      <th>14</th>\n",
 210 |        "      <td>15</td>\n",
 211 |        "      <td>Nombre15</td>\n",
 212 |        "      <td>30.0</td>\n",
 213 |        "      <td>90.0</td>\n",
 214 |        "    </tr>\n",
 215 |        "    <tr>\n",
 216 |        "      <th>15</th>\n",
 217 |        "      <td>16</td>\n",
 218 |        "      <td>Nombre16</td>\n",
 219 |        "      <td>NaN</td>\n",
 220 |        "      <td>95.0</td>\n",
 221 |        "    </tr>\n",
 222 |        "    <tr>\n",
 223 |        "      <th>16</th>\n",
 224 |        "      <td>17</td>\n",
 225 |        "      <td>Nombre17</td>\n",
 226 |        "      <td>NaN</td>\n",
 227 |        "      <td>NaN</td>\n",
 228 |        "    </tr>\n",
 229 |        "    <tr>\n",
 230 |        "      <th>17</th>\n",
 231 |        "      <td>18</td>\n",
 232 |        "      <td>Nombre18</td>\n",
 233 |        "      <td>30.0</td>\n",
 234 |        "      <td>NaN</td>\n",
 235 |        "    </tr>\n",
 236 |        "    <tr>\n",
 237 |        "      <th>18</th>\n",
 238 |        "      <td>19</td>\n",
 239 |        "      <td>Nombre19</td>\n",
 240 |        "      <td>25.0</td>\n",
 241 |        "      <td>NaN</td>\n",
 242 |        "    </tr>\n",
 243 |        "    <tr>\n",
 244 |        "      <th>19</th>\n",
 245 |        "      <td>20</td>\n",
 246 |        "      <td>Nombre20</td>\n",
 247 |        "      <td>25.0</td>\n",
 248 |        "      <td>90.0</td>\n",
 249 |        "    </tr>\n",
 250 |        "    <tr>\n",
 251 |        "      <th>20</th>\n",
 252 |        "      <td>21</td>\n",
 253 |        "      <td>Nombre21</td>\n",
 254 |        "      <td>25.0</td>\n",
 255 |        "      <td>NaN</td>\n",
 256 |        "    </tr>\n",
 257 |        "    <tr>\n",
 258 |        "      <th>21</th>\n",
 259 |        "      <td>22</td>\n",
 260 |        "      <td>Nombre22</td>\n",
 261 |        "      <td>NaN</td>\n",
 262 |        "      <td>95.0</td>\n",
 263 |        "    </tr>\n",
 264 |        "    <tr>\n",
 265 |        "      <th>22</th>\n",
 266 |        "      <td>23</td>\n",
 267 |        "      <td>Nombre23</td>\n",
 268 |        "      <td>30.0</td>\n",
 269 |        "      <td>90.0</td>\n",
 270 |        "    </tr>\n",
 271 |        "    <tr>\n",
 272 |        "      <th>23</th>\n",
 273 |        "      <td>24</td>\n",
 274 |        "      <td>Nombre24</td>\n",
 275 |        "      <td>NaN</td>\n",
 276 |        "      <td>95.0</td>\n",
 277 |        "    </tr>\n",
 278 |        "    <tr>\n",
 279 |        "      <th>24</th>\n",
 280 |        "      <td>25</td>\n",
 281 |        "      <td>Nombre25</td>\n",
 282 |        "      <td>30.0</td>\n",
 283 |        "      <td>NaN</td>\n",
 284 |        "    </tr>\n",
 285 |        "    <tr>\n",
 286 |        "      <th>25</th>\n",
 287 |        "      <td>26</td>\n",
 288 |        "      <td>Nombre26</td>\n",
 289 |        "      <td>NaN</td>\n",
 290 |        "      <td>NaN</td>\n",
 291 |        "    </tr>\n",
 292 |        "    <tr>\n",
 293 |        "      <th>26</th>\n",
 294 |        "      <td>27</td>\n",
 295 |        "      <td>Nombre27</td>\n",
 296 |        "      <td>25.0</td>\n",
 297 |        "      <td>NaN</td>\n",
 298 |        "    </tr>\n",
 299 |        "    <tr>\n",
 300 |        "      <th>27</th>\n",
 301 |        "      <td>28</td>\n",
 302 |        "      <td>Nombre28</td>\n",
 303 |        "      <td>30.0</td>\n",
 304 |        "      <td>90.0</td>\n",
 305 |        "    </tr>\n",
 306 |        "    <tr>\n",
 307 |        "      <th>28</th>\n",
 308 |        "      <td>29</td>\n",
 309 |        "      <td>Nombre29</td>\n",
 310 |        "      <td>NaN</td>\n",
 311 |        "      <td>90.0</td>\n",
 312 |        "    </tr>\n",
 313 |        "    <tr>\n",
 314 |        "      <th>29</th>\n",
 315 |        "      <td>30</td>\n",
 316 |        "      <td>Nombre30</td>\n",
 317 |        "      <td>25.0</td>\n",
 318 |        "      <td>90.0</td>\n",
 319 |        "    </tr>\n",
 320 |        "    <tr>\n",
 321 |        "      <th>30</th>\n",
 322 |        "      <td>31</td>\n",
 323 |        "      <td>Nombre31</td>\n",
 324 |        "      <td>NaN</td>\n",
 325 |        "      <td>95.0</td>\n",
 326 |        "    </tr>\n",
 327 |        "    <tr>\n",
 328 |        "      <th>31</th>\n",
 329 |        "      <td>32</td>\n",
 330 |        "      <td>Nombre32</td>\n",
 331 |        "      <td>25.0</td>\n",
 332 |        "      <td>95.0</td>\n",
 333 |        "    </tr>\n",
 334 |        "    <tr>\n",
 335 |        "      <th>32</th>\n",
 336 |        "      <td>33</td>\n",
 337 |        "      <td>Nombre33</td>\n",
 338 |        "      <td>25.0</td>\n",
 339 |        "      <td>NaN</td>\n",
 340 |        "    </tr>\n",
 341 |        "    <tr>\n",
 342 |        "      <th>33</th>\n",
 343 |        "      <td>34</td>\n",
 344 |        "      <td>Nombre34</td>\n",
 345 |        "      <td>30.0</td>\n",
 346 |        "      <td>95.0</td>\n",
 347 |        "    </tr>\n",
 348 |        "    <tr>\n",
 349 |        "      <th>34</th>\n",
 350 |        "      <td>35</td>\n",
 351 |        "      <td>Nombre35</td>\n",
 352 |        "      <td>25.0</td>\n",
 353 |        "      <td>NaN</td>\n",
 354 |        "    </tr>\n",
 355 |        "    <tr>\n",
 356 |        "      <th>35</th>\n",
 357 |        "      <td>36</td>\n",
 358 |        "      <td>Nombre36</td>\n",
 359 |        "      <td>30.0</td>\n",
 360 |        "      <td>90.0</td>\n",
 361 |        "    </tr>\n",
 362 |        "    <tr>\n",
 363 |        "      <th>36</th>\n",
 364 |        "      <td>37</td>\n",
 365 |        "      <td>Nombre37</td>\n",
 366 |        "      <td>25.0</td>\n",
 367 |        "      <td>NaN</td>\n",
 368 |        "    </tr>\n",
 369 |        "    <tr>\n",
 370 |        "      <th>37</th>\n",
 371 |        "      <td>38</td>\n",
 372 |        "      <td>Nombre38</td>\n",
 373 |        "      <td>NaN</td>\n",
 374 |        "      <td>NaN</td>\n",
 375 |        "    </tr>\n",
 376 |        "    <tr>\n",
 377 |        "      <th>38</th>\n",
 378 |        "      <td>39</td>\n",
 379 |        "      <td>Nombre39</td>\n",
 380 |        "      <td>30.0</td>\n",
 381 |        "      <td>NaN</td>\n",
 382 |        "    </tr>\n",
 383 |        "    <tr>\n",
 384 |        "      <th>39</th>\n",
 385 |        "      <td>40</td>\n",
 386 |        "      <td>Nombre40</td>\n",
 387 |        "      <td>30.0</td>\n",
 388 |        "      <td>NaN</td>\n",
 389 |        "    </tr>\n",
 390 |        "    <tr>\n",
 391 |        "      <th>40</th>\n",
 392 |        "      <td>41</td>\n",
 393 |        "      <td>Nombre41</td>\n",
 394 |        "      <td>30.0</td>\n",
 395 |        "      <td>90.0</td>\n",
 396 |        "    </tr>\n",
 397 |        "    <tr>\n",
 398 |        "      <th>41</th>\n",
 399 |        "      <td>42</td>\n",
 400 |        "      <td>Nombre42</td>\n",
 401 |        "      <td>NaN</td>\n",
 402 |        "      <td>90.0</td>\n",
 403 |        "    </tr>\n",
 404 |        "    <tr>\n",
 405 |        "      <th>42</th>\n",
 406 |        "      <td>43</td>\n",
 407 |        "      <td>Nombre43</td>\n",
 408 |        "      <td>30.0</td>\n",
 409 |        "      <td>NaN</td>\n",
 410 |        "    </tr>\n",
 411 |        "    <tr>\n",
 412 |        "      <th>43</th>\n",
 413 |        "      <td>44</td>\n",
 414 |        "      <td>Nombre44</td>\n",
 415 |        "      <td>30.0</td>\n",
 416 |        "      <td>NaN</td>\n",
 417 |        "    </tr>\n",
 418 |        "    <tr>\n",
 419 |        "      <th>44</th>\n",
 420 |        "      <td>45</td>\n",
 421 |        "      <td>Nombre45</td>\n",
 422 |        "      <td>25.0</td>\n",
 423 |        "      <td>95.0</td>\n",
 424 |        "    </tr>\n",
 425 |        "    <tr>\n",
 426 |        "      <th>45</th>\n",
 427 |        "      <td>46</td>\n",
 428 |        "      <td>Nombre46</td>\n",
 429 |        "      <td>NaN</td>\n",
 430 |        "      <td>95.0</td>\n",
 431 |        "    </tr>\n",
 432 |        "    <tr>\n",
 433 |        "      <th>46</th>\n",
 434 |        "      <td>47</td>\n",
 435 |        "      <td>Nombre47</td>\n",
 436 |        "      <td>25.0</td>\n",
 437 |        "      <td>95.0</td>\n",
 438 |        "    </tr>\n",
 439 |        "    <tr>\n",
 440 |        "      <th>47</th>\n",
 441 |        "      <td>48</td>\n",
 442 |        "      <td>Nombre48</td>\n",
 443 |        "      <td>NaN</td>\n",
 444 |        "      <td>90.0</td>\n",
 445 |        "    </tr>\n",
 446 |        "    <tr>\n",
 447 |        "      <th>48</th>\n",
 448 |        "      <td>49</td>\n",
 449 |        "      <td>Nombre49</td>\n",
 450 |        "      <td>25.0</td>\n",
 451 |        "      <td>95.0</td>\n",
 452 |        "    </tr>\n",
 453 |        "    <tr>\n",
 454 |        "      <th>49</th>\n",
 455 |        "      <td>50</td>\n",
 456 |        "      <td>Nombre50</td>\n",
 457 |        "      <td>30.0</td>\n",
 458 |        "      <td>90.0</td>\n",
 459 |        "    </tr>\n",
 460 |        "  </tbody>\n",
 461 |        "</table>\n",
 462 |        "</div>"
 463 |       ],
 464 |       "text/plain": [
 465 |        "    ID    Nombre  Edad  Puntuacion\n",
 466 |        "0    1   Nombre1   NaN        95.0\n",
 467 |        "1    2   Nombre2  25.0         NaN\n",
 468 |        "2    3   Nombre3   NaN         NaN\n",
 469 |        "3    4   Nombre4  30.0         NaN\n",
 470 |        "4    5   Nombre5  25.0         NaN\n",
 471 |        "5    6   Nombre6   NaN        90.0\n",
 472 |        "6    7   Nombre7  30.0        95.0\n",
 473 |        "7    8   Nombre8  30.0         NaN\n",
 474 |        "8    9   Nombre9   NaN        95.0\n",
 475 |        "9   10  Nombre10  25.0        90.0\n",
 476 |        "10  11  Nombre11  30.0        90.0\n",
 477 |        "11  12  Nombre12  25.0        90.0\n",
 478 |        "12  13  Nombre13  30.0        95.0\n",
 479 |        "13  14  Nombre14  30.0        90.0\n",
 480 |        "14  15  Nombre15  30.0        90.0\n",
 481 |        "15  16  Nombre16   NaN        95.0\n",
 482 |        "16  17  Nombre17   NaN         NaN\n",
 483 |        "17  18  Nombre18  30.0         NaN\n",
 484 |        "18  19  Nombre19  25.0         NaN\n",
 485 |        "19  20  Nombre20  25.0        90.0\n",
 486 |        "20  21  Nombre21  25.0         NaN\n",
 487 |        "21  22  Nombre22   NaN        95.0\n",
 488 |        "22  23  Nombre23  30.0        90.0\n",
 489 |        "23  24  Nombre24   NaN        95.0\n",
 490 |        "24  25  Nombre25  30.0         NaN\n",
 491 |        "25  26  Nombre26   NaN         NaN\n",
 492 |        "26  27  Nombre27  25.0         NaN\n",
 493 |        "27  28  Nombre28  30.0        90.0\n",
 494 |        "28  29  Nombre29   NaN        90.0\n",
 495 |        "29  30  Nombre30  25.0        90.0\n",
 496 |        "30  31  Nombre31   NaN        95.0\n",
 497 |        "31  32  Nombre32  25.0        95.0\n",
 498 |        "32  33  Nombre33  25.0         NaN\n",
 499 |        "33  34  Nombre34  30.0        95.0\n",
 500 |        "34  35  Nombre35  25.0         NaN\n",
 501 |        "35  36  Nombre36  30.0        90.0\n",
 502 |        "36  37  Nombre37  25.0         NaN\n",
 503 |        "37  38  Nombre38   NaN         NaN\n",
 504 |        "38  39  Nombre39  30.0         NaN\n",
 505 |        "39  40  Nombre40  30.0         NaN\n",
 506 |        "40  41  Nombre41  30.0        90.0\n",
 507 |        "41  42  Nombre42   NaN        90.0\n",
 508 |        "42  43  Nombre43  30.0         NaN\n",
 509 |        "43  44  Nombre44  30.0         NaN\n",
 510 |        "44  45  Nombre45  25.0        95.0\n",
 511 |        "45  46  Nombre46   NaN        95.0\n",
 512 |        "46  47  Nombre47  25.0        95.0\n",
 513 |        "47  48  Nombre48   NaN        90.0\n",
 514 |        "48  49  Nombre49  25.0        95.0\n",
 515 |        "49  50  Nombre50  30.0        90.0"
 516 |       ]
 517 |      },
 518 |      "execution_count": 6,
 519 |      "metadata": {},
 520 |      "output_type": "execute_result"
 521 |     }
 522 |    ],
 523 |    "source": [
 524 |     "#cargamos otro archivo con registros nulos\n",
 525 |     "df_nulos= pd.read_csv('/Users/jimenacambronero/Desktop/Proyectos para Portfolio/PythonPracticas/Datos/registros_nulos.csv')\n",
 526 |     "df_nulos"
 527 |    ]
 528 |   },
 529 |   {
 530 |    "cell_type": "code",
 531 |    "execution_count": 7,
 532 |    "metadata": {},
 533 |    "outputs": [
 534 |     {
 535 |      "data": {
 536 |       "text/plain": [
 537 |        "ID             0\n",
 538 |        "Nombre         0\n",
 539 |        "Edad          15\n",
 540 |        "Puntuacion    20\n",
 541 |        "dtype: int64"
 542 |       ]
 543 |      },
 544 |      "execution_count": 7,
 545 |      "metadata": {},
 546 |      "output_type": "execute_result"
 547 |     }
 548 |    ],
 549 |    "source": [
 550 |     "df_nulos.isna().sum()"
 551 |    ]
 552 |   },
 553 |   {
 554 |    "cell_type": "code",
 555 |    "execution_count": 9,
 556 |    "metadata": {},
 557 |    "outputs": [
 558 |     {
 559 |      "data": {
 560 |       "text/plain": [
 561 |        "Puntuacion    20\n",
 562 |        "Edad          15\n",
 563 |        "ID             0\n",
 564 |        "Nombre         0\n",
 565 |        "dtype: int64"
 566 |       ]
 567 |      },
 568 |      "execution_count": 9,
 569 |      "metadata": {},
 570 |      "output_type": "execute_result"
 571 |     }
 572 |    ],
 573 |    "source": [
 574 |     "df_nulos.isna().sum().sort_values(ascending=False)"
 575 |    ]
 576 |   },
 577 |   {
 578 |    "cell_type": "markdown",
 579 |    "metadata": {},
 580 |    "source": [
 581 |     "### Porcentaje por Variable"
 582 |    ]
 583 |   },
 584 |   {
 585 |    "cell_type": "code",
 586 |    "execution_count": 12,
 587 |    "metadata": {},
 588 |    "outputs": [
 589 |     {
 590 |      "data": {
 591 |       "text/plain": [
 592 |        "Puntuacion    40.0\n",
 593 |        "Edad          30.0\n",
 594 |        "ID             0.0\n",
 595 |        "Nombre         0.0\n",
 596 |        "dtype: float64"
 597 |       ]
 598 |      },
 599 |      "execution_count": 12,
 600 |      "metadata": {},
 601 |      "output_type": "execute_result"
 602 |     }
 603 |    ],
 604 |    "source": [
 605 |     "df_nulos.isna().mean().sort_values(ascending= False)*100\n",
 606 |     "#obtendremos el porcentaje de nulos"
 607 |    ]
 608 |   },
 609 |   {
 610 |    "cell_type": "markdown",
 611 |    "metadata": {},
 612 |    "source": [
 613 |     "## Conteo de Duplicados"
 614 |    ]
 615 |   },
 616 |   {
 617 |    "cell_type": "code",
 618 |    "execution_count": 13,
 619 |    "metadata": {},
 620 |    "outputs": [],
 621 |    "source": [
 622 |     "df_duplicados= pd.read_excel('/Users/jimenacambronero/Desktop/Proyectos para Portfolio/PythonPracticas/Datos/Frutos_Secos_duplicados.xlsx')"
 623 |    ]
 624 |   },
 625 |   {
 626 |    "cell_type": "code",
 627 |    "execution_count": 14,
 628 |    "metadata": {},
 629 |    "outputs": [
 630 |     {
 631 |      "data": {
 632 |       "text/plain": [
 633 |        "23"
 634 |       ]
 635 |      },
 636 |      "execution_count": 14,
 637 |      "metadata": {},
 638 |      "output_type": "execute_result"
 639 |     }
 640 |    ],
 641 |    "source": [
 642 |     "#Saber cuantos duplicados hay\n",
 643 |     "df_duplicados.duplicated().sum()"
 644 |    ]
 645 |   },
 646 |   {
 647 |    "cell_type": "markdown",
 648 |    "metadata": {},
 649 |    "source": [
 650 |     "### Localizar Duplicados"
 651 |    ]
 652 |   },
 653 |   {
 654 |    "cell_type": "code",
 655 |    "execution_count": 15,
 656 |    "metadata": {},
 657 |    "outputs": [
 658 |     {
 659 |      "data": {
 660 |       "text/html": [
 661 |        "<div>\n",
 662 |        "<style scoped>\n",
 663 |        "    .dataframe tbody tr th:only-of-type {\n",
 664 |        "        vertical-align: middle;\n",
 665 |        "    }\n",
 666 |        "\n",
 667 |        "    .dataframe tbody tr th {\n",
 668 |        "        vertical-align: top;\n",
 669 |        "    }\n",
 670 |        "\n",
 671 |        "    .dataframe thead th {\n",
 672 |        "        text-align: right;\n",
 673 |        "    }\n",
 674 |        "</style>\n",
 675 |        "<table border=\"1\" class=\"dataframe\">\n",
 676 |        "  <thead>\n",
 677 |        "    <tr style=\"text-align: right;\">\n",
 678 |        "      <th></th>\n",
 679 |        "      <th>Nombre Cliente</th>\n",
 680 |        "      <th>Tipo Producto</th>\n",
 681 |        "      <th>Precio (€/kg)</th>\n",
 682 |        "      <th>Cantidad Vendida (kg)</th>\n",
 683 |        "      <th>Fecha Venta</th>\n",
 684 |        "      <th>Provincia Cliente</th>\n",
 685 |        "      <th>id</th>\n",
 686 |        "    </tr>\n",
 687 |        "  </thead>\n",
 688 |        "  <tbody>\n",
 689 |        "    <tr>\n",
 690 |        "      <th>300</th>\n",
 691 |        "      <td>Caprabo</td>\n",
 692 |        "      <td>Cacahuetes</td>\n",
 693 |        "      <td>6.24</td>\n",
 694 |        "      <td>1626</td>\n",
 695 |        "      <td>5/2/23</td>\n",
 696 |        "      <td>Valencia</td>\n",
 697 |        "      <td>389955</td>\n",
 698 |        "    </tr>\n",
 699 |        "    <tr>\n",
 700 |        "      <th>301</th>\n",
 701 |        "      <td>Caprabo</td>\n",
 702 |        "      <td>Cacahuetes</td>\n",
 703 |        "      <td>6.24</td>\n",
 704 |        "      <td>1626</td>\n",
 705 |        "      <td>5/2/23</td>\n",
 706 |        "      <td>Valencia</td>\n",
 707 |        "      <td>389955</td>\n",
 708 |        "    </tr>\n",
 709 |        "    <tr>\n",
 710 |        "      <th>302</th>\n",
 711 |        "      <td>Caprabo</td>\n",
 712 |        "      <td>Cacahuetes</td>\n",
 713 |        "      <td>6.24</td>\n",
 714 |        "      <td>1626</td>\n",
 715 |        "      <td>5/2/23</td>\n",
 716 |        "      <td>Valencia</td>\n",
 717 |        "      <td>389955</td>\n",
 718 |        "    </tr>\n",
 719 |        "    <tr>\n",
 720 |        "      <th>303</th>\n",
 721 |        "      <td>Caprabo</td>\n",
 722 |        "      <td>Cacahuetes</td>\n",
 723 |        "      <td>6.24</td>\n",
 724 |        "      <td>1626</td>\n",
 725 |        "      <td>5/2/23</td>\n",
 726 |        "      <td>Valencia</td>\n",
 727 |        "      <td>389955</td>\n",
 728 |        "    </tr>\n",
 729 |        "    <tr>\n",
 730 |        "      <th>304</th>\n",
 731 |        "      <td>Caprabo</td>\n",
 732 |        "      <td>Cacahuetes</td>\n",
 733 |        "      <td>6.24</td>\n",
 734 |        "      <td>1626</td>\n",
 735 |        "      <td>5/2/23</td>\n",
 736 |        "      <td>Valencia</td>\n",
 737 |        "      <td>389955</td>\n",
 738 |        "    </tr>\n",
 739 |        "    <tr>\n",
 740 |        "      <th>305</th>\n",
 741 |        "      <td>Caprabo</td>\n",
 742 |        "      <td>Cacahuetes</td>\n",
 743 |        "      <td>6.24</td>\n",
 744 |        "      <td>1626</td>\n",
 745 |        "      <td>5/2/23</td>\n",
 746 |        "      <td>Valencia</td>\n",
 747 |        "      <td>389955</td>\n",
 748 |        "    </tr>\n",
 749 |        "    <tr>\n",
 750 |        "      <th>306</th>\n",
 751 |        "      <td>Caprabo</td>\n",
 752 |        "      <td>Cacahuetes</td>\n",
 753 |        "      <td>6.24</td>\n",
 754 |        "      <td>1626</td>\n",
 755 |        "      <td>5/2/23</td>\n",
 756 |        "      <td>Valencia</td>\n",
 757 |        "      <td>389955</td>\n",
 758 |        "    </tr>\n",
 759 |        "    <tr>\n",
 760 |        "      <th>307</th>\n",
 761 |        "      <td>Caprabo</td>\n",
 762 |        "      <td>Cacahuetes</td>\n",
 763 |        "      <td>6.24</td>\n",
 764 |        "      <td>1626</td>\n",
 765 |        "      <td>5/2/23</td>\n",
 766 |        "      <td>Valencia</td>\n",
 767 |        "      <td>389955</td>\n",
 768 |        "    </tr>\n",
 769 |        "    <tr>\n",
 770 |        "      <th>308</th>\n",
 771 |        "      <td>Caprabo</td>\n",
 772 |        "      <td>Cacahuetes</td>\n",
 773 |        "      <td>6.24</td>\n",
 774 |        "      <td>1626</td>\n",
 775 |        "      <td>5/2/23</td>\n",
 776 |        "      <td>Valencia</td>\n",
 777 |        "      <td>389955</td>\n",
 778 |        "    </tr>\n",
 779 |        "    <tr>\n",
 780 |        "      <th>309</th>\n",
 781 |        "      <td>Caprabo</td>\n",
 782 |        "      <td>Cacahuetes</td>\n",
 783 |        "      <td>6.24</td>\n",
 784 |        "      <td>1626</td>\n",
 785 |        "      <td>5/2/23</td>\n",
 786 |        "      <td>Valencia</td>\n",
 787 |        "      <td>389955</td>\n",
 788 |        "    </tr>\n",
 789 |        "    <tr>\n",
 790 |        "      <th>310</th>\n",
 791 |        "      <td>Caprabo</td>\n",
 792 |        "      <td>Cacahuetes</td>\n",
 793 |        "      <td>6.24</td>\n",
 794 |        "      <td>1626</td>\n",
 795 |        "      <td>5/2/23</td>\n",
 796 |        "      <td>Valencia</td>\n",
 797 |        "      <td>389955</td>\n",
 798 |        "    </tr>\n",
 799 |        "    <tr>\n",
 800 |        "      <th>311</th>\n",
 801 |        "      <td>Caprabo</td>\n",
 802 |        "      <td>Cacahuetes</td>\n",
 803 |        "      <td>6.24</td>\n",
 804 |        "      <td>1626</td>\n",
 805 |        "      <td>5/2/23</td>\n",
 806 |        "      <td>Valencia</td>\n",
 807 |        "      <td>389955</td>\n",
 808 |        "    </tr>\n",
 809 |        "    <tr>\n",
 810 |        "      <th>312</th>\n",
 811 |        "      <td>Caprabo</td>\n",
 812 |        "      <td>Cacahuetes</td>\n",
 813 |        "      <td>6.24</td>\n",
 814 |        "      <td>1626</td>\n",
 815 |        "      <td>5/2/23</td>\n",
 816 |        "      <td>Valencia</td>\n",
 817 |        "      <td>389955</td>\n",
 818 |        "    </tr>\n",
 819 |        "    <tr>\n",
 820 |        "      <th>313</th>\n",
 821 |        "      <td>Caprabo</td>\n",
 822 |        "      <td>Cacahuetes</td>\n",
 823 |        "      <td>6.24</td>\n",
 824 |        "      <td>1626</td>\n",
 825 |        "      <td>5/2/23</td>\n",
 826 |        "      <td>Valencia</td>\n",
 827 |        "      <td>389955</td>\n",
 828 |        "    </tr>\n",
 829 |        "    <tr>\n",
 830 |        "      <th>314</th>\n",
 831 |        "      <td>Caprabo</td>\n",
 832 |        "      <td>Cacahuetes</td>\n",
 833 |        "      <td>6.24</td>\n",
 834 |        "      <td>1626</td>\n",
 835 |        "      <td>5/2/23</td>\n",
 836 |        "      <td>Valencia</td>\n",
 837 |        "      <td>389955</td>\n",
 838 |        "    </tr>\n",
 839 |        "    <tr>\n",
 840 |        "      <th>315</th>\n",
 841 |        "      <td>Caprabo</td>\n",
 842 |        "      <td>Cacahuetes</td>\n",
 843 |        "      <td>6.24</td>\n",
 844 |        "      <td>1626</td>\n",
 845 |        "      <td>5/2/23</td>\n",
 846 |        "      <td>Valencia</td>\n",
 847 |        "      <td>389955</td>\n",
 848 |        "    </tr>\n",
 849 |        "    <tr>\n",
 850 |        "      <th>316</th>\n",
 851 |        "      <td>Caprabo</td>\n",
 852 |        "      <td>Cacahuetes</td>\n",
 853 |        "      <td>6.24</td>\n",
 854 |        "      <td>1626</td>\n",
 855 |        "      <td>5/2/23</td>\n",
 856 |        "      <td>Valencia</td>\n",
 857 |        "      <td>389955</td>\n",
 858 |        "    </tr>\n",
 859 |        "    <tr>\n",
 860 |        "      <th>317</th>\n",
 861 |        "      <td>Caprabo</td>\n",
 862 |        "      <td>Cacahuetes</td>\n",
 863 |        "      <td>6.24</td>\n",
 864 |        "      <td>1626</td>\n",
 865 |        "      <td>5/2/23</td>\n",
 866 |        "      <td>Valencia</td>\n",
 867 |        "      <td>389955</td>\n",
 868 |        "    </tr>\n",
 869 |        "    <tr>\n",
 870 |        "      <th>318</th>\n",
 871 |        "      <td>Caprabo</td>\n",
 872 |        "      <td>Cacahuetes</td>\n",
 873 |        "      <td>6.24</td>\n",
 874 |        "      <td>1626</td>\n",
 875 |        "      <td>5/2/23</td>\n",
 876 |        "      <td>Valencia</td>\n",
 877 |        "      <td>389955</td>\n",
 878 |        "    </tr>\n",
 879 |        "    <tr>\n",
 880 |        "      <th>319</th>\n",
 881 |        "      <td>Caprabo</td>\n",
 882 |        "      <td>Cacahuetes</td>\n",
 883 |        "      <td>6.24</td>\n",
 884 |        "      <td>1626</td>\n",
 885 |        "      <td>5/2/23</td>\n",
 886 |        "      <td>Valencia</td>\n",
 887 |        "      <td>389955</td>\n",
 888 |        "    </tr>\n",
 889 |        "    <tr>\n",
 890 |        "      <th>320</th>\n",
 891 |        "      <td>Caprabo</td>\n",
 892 |        "      <td>Cacahuetes</td>\n",
 893 |        "      <td>6.24</td>\n",
 894 |        "      <td>1626</td>\n",
 895 |        "      <td>5/2/23</td>\n",
 896 |        "      <td>Valencia</td>\n",
 897 |        "      <td>389955</td>\n",
 898 |        "    </tr>\n",
 899 |        "    <tr>\n",
 900 |        "      <th>321</th>\n",
 901 |        "      <td>Caprabo</td>\n",
 902 |        "      <td>Cacahuetes</td>\n",
 903 |        "      <td>6.24</td>\n",
 904 |        "      <td>1626</td>\n",
 905 |        "      <td>5/2/23</td>\n",
 906 |        "      <td>Valencia</td>\n",
 907 |        "      <td>389955</td>\n",
 908 |        "    </tr>\n",
 909 |        "    <tr>\n",
 910 |        "      <th>322</th>\n",
 911 |        "      <td>Caprabo</td>\n",
 912 |        "      <td>Cacahuetes</td>\n",
 913 |        "      <td>6.24</td>\n",
 914 |        "      <td>1626</td>\n",
 915 |        "      <td>5/2/23</td>\n",
 916 |        "      <td>Valencia</td>\n",
 917 |        "      <td>389955</td>\n",
 918 |        "    </tr>\n",
 919 |        "  </tbody>\n",
 920 |        "</table>\n",
 921 |        "</div>"
 922 |       ],
 923 |       "text/plain": [
 924 |        "    Nombre Cliente Tipo Producto  Precio (€/kg)  Cantidad Vendida (kg)  \\\n",
 925 |        "300        Caprabo    Cacahuetes           6.24                   1626   \n",
 926 |        "301        Caprabo    Cacahuetes           6.24                   1626   \n",
 927 |        "302        Caprabo    Cacahuetes           6.24                   1626   \n",
 928 |        "303        Caprabo    Cacahuetes           6.24                   1626   \n",
 929 |        "304        Caprabo    Cacahuetes           6.24                   1626   \n",
 930 |        "305        Caprabo    Cacahuetes           6.24                   1626   \n",
 931 |        "306        Caprabo    Cacahuetes           6.24                   1626   \n",
 932 |        "307        Caprabo    Cacahuetes           6.24                   1626   \n",
 933 |        "308        Caprabo    Cacahuetes           6.24                   1626   \n",
 934 |        "309        Caprabo    Cacahuetes           6.24                   1626   \n",
 935 |        "310        Caprabo    Cacahuetes           6.24                   1626   \n",
 936 |        "311        Caprabo    Cacahuetes           6.24                   1626   \n",
 937 |        "312        Caprabo    Cacahuetes           6.24                   1626   \n",
 938 |        "313        Caprabo    Cacahuetes           6.24                   1626   \n",
 939 |        "314        Caprabo    Cacahuetes           6.24                   1626   \n",
 940 |        "315        Caprabo    Cacahuetes           6.24                   1626   \n",
 941 |        "316        Caprabo    Cacahuetes           6.24                   1626   \n",
 942 |        "317        Caprabo    Cacahuetes           6.24                   1626   \n",
 943 |        "318        Caprabo    Cacahuetes           6.24                   1626   \n",
 944 |        "319        Caprabo    Cacahuetes           6.24                   1626   \n",
 945 |        "320        Caprabo    Cacahuetes           6.24                   1626   \n",
 946 |        "321        Caprabo    Cacahuetes           6.24                   1626   \n",
 947 |        "322        Caprabo    Cacahuetes           6.24                   1626   \n",
 948 |        "\n",
 949 |        "    Fecha Venta Provincia Cliente      id  \n",
 950 |        "300      5/2/23          Valencia  389955  \n",
 951 |        "301      5/2/23          Valencia  389955  \n",
 952 |        "302      5/2/23          Valencia  389955  \n",
 953 |        "303      5/2/23          Valencia  389955  \n",
 954 |        "304      5/2/23          Valencia  389955  \n",
 955 |        "305      5/2/23          Valencia  389955  \n",
 956 |        "306      5/2/23          Valencia  389955  \n",
 957 |        "307      5/2/23          Valencia  389955  \n",
 958 |        "308      5/2/23          Valencia  389955  \n",
 959 |        "309      5/2/23          Valencia  389955  \n",
 960 |        "310      5/2/23          Valencia  389955  \n",
 961 |        "311      5/2/23          Valencia  389955  \n",
 962 |        "312      5/2/23          Valencia  389955  \n",
 963 |        "313      5/2/23          Valencia  389955  \n",
 964 |        "314      5/2/23          Valencia  389955  \n",
 965 |        "315      5/2/23          Valencia  389955  \n",
 966 |        "316      5/2/23          Valencia  389955  \n",
 967 |        "317      5/2/23          Valencia  389955  \n",
 968 |        "318      5/2/23          Valencia  389955  \n",
 969 |        "319      5/2/23          Valencia  389955  \n",
 970 |        "320      5/2/23          Valencia  389955  \n",
 971 |        "321      5/2/23          Valencia  389955  \n",
 972 |        "322      5/2/23          Valencia  389955  "
 973 |       ]
 974 |      },
 975 |      "execution_count": 15,
 976 |      "metadata": {},
 977 |      "output_type": "execute_result"
 978 |     }
 979 |    ],
 980 |    "source": [
 981 |     "df_duplicados[df_duplicados.duplicated()]"
 982 |    ]
 983 |   },
 984 |   {
 985 |    "cell_type": "code",
 986 |    "execution_count": 16,
 987 |    "metadata": {},
 988 |    "outputs": [
 989 |     {
 990 |      "data": {
 991 |       "text/html": [
 992 |        "<div>\n",
 993 |        "<style scoped>\n",
 994 |        "    .dataframe tbody tr th:only-of-type {\n",
 995 |        "        vertical-align: middle;\n",
 996 |        "    }\n",
 997 |        "\n",
 998 |        "    .dataframe tbody tr th {\n",
 999 |        "        vertical-align: top;\n",
1000 |        "    }\n",
1001 |        "\n",
1002 |        "    .dataframe thead th {\n",
1003 |        "        text-align: right;\n",
1004 |        "    }\n",
1005 |        "</style>\n",
1006 |        "<table border=\"1\" class=\"dataframe\">\n",
1007 |        "  <thead>\n",
1008 |        "    <tr style=\"text-align: right;\">\n",
1009 |        "      <th></th>\n",
1010 |        "      <th>Nombre Cliente</th>\n",
1011 |        "      <th>Tipo Producto</th>\n",
1012 |        "      <th>Precio (€/kg)</th>\n",
1013 |        "      <th>Cantidad Vendida (kg)</th>\n",
1014 |        "      <th>Fecha Venta</th>\n",
1015 |        "      <th>Provincia Cliente</th>\n",
1016 |        "      <th>id</th>\n",
1017 |        "    </tr>\n",
1018 |        "  </thead>\n",
1019 |        "  <tbody>\n",
1020 |        "    <tr>\n",
1021 |        "      <th>23</th>\n",
1022 |        "      <td>Caprabo</td>\n",
1023 |        "      <td>Cacahuetes</td>\n",
1024 |        "      <td>6.24</td>\n",
1025 |        "      <td>1626</td>\n",
1026 |        "      <td>5/2/23</td>\n",
1027 |        "      <td>Valencia</td>\n",
1028 |        "      <td>389955</td>\n",
1029 |        "    </tr>\n",
1030 |        "    <tr>\n",
1031 |        "      <th>300</th>\n",
1032 |        "      <td>Caprabo</td>\n",
1033 |        "      <td>Cacahuetes</td>\n",
1034 |        "      <td>6.24</td>\n",
1035 |        "      <td>1626</td>\n",
1036 |        "      <td>5/2/23</td>\n",
1037 |        "      <td>Valencia</td>\n",
1038 |        "      <td>389955</td>\n",
1039 |        "    </tr>\n",
1040 |        "    <tr>\n",
1041 |        "      <th>301</th>\n",
1042 |        "      <td>Caprabo</td>\n",
1043 |        "      <td>Cacahuetes</td>\n",
1044 |        "      <td>6.24</td>\n",
1045 |        "      <td>1626</td>\n",
1046 |        "      <td>5/2/23</td>\n",
1047 |        "      <td>Valencia</td>\n",
1048 |        "      <td>389955</td>\n",
1049 |        "    </tr>\n",
1050 |        "    <tr>\n",
1051 |        "      <th>302</th>\n",
1052 |        "      <td>Caprabo</td>\n",
1053 |        "      <td>Cacahuetes</td>\n",
1054 |        "      <td>6.24</td>\n",
1055 |        "      <td>1626</td>\n",
1056 |        "      <td>5/2/23</td>\n",
1057 |        "      <td>Valencia</td>\n",
1058 |        "      <td>389955</td>\n",
1059 |        "    </tr>\n",
1060 |        "    <tr>\n",
1061 |        "      <th>303</th>\n",
1062 |        "      <td>Caprabo</td>\n",
1063 |        "      <td>Cacahuetes</td>\n",
1064 |        "      <td>6.24</td>\n",
1065 |        "      <td>1626</td>\n",
1066 |        "      <td>5/2/23</td>\n",
1067 |        "      <td>Valencia</td>\n",
1068 |        "      <td>389955</td>\n",
1069 |        "    </tr>\n",
1070 |        "    <tr>\n",
1071 |        "      <th>304</th>\n",
1072 |        "      <td>Caprabo</td>\n",
1073 |        "      <td>Cacahuetes</td>\n",
1074 |        "      <td>6.24</td>\n",
1075 |        "      <td>1626</td>\n",
1076 |        "      <td>5/2/23</td>\n",
1077 |        "      <td>Valencia</td>\n",
1078 |        "      <td>389955</td>\n",
1079 |        "    </tr>\n",
1080 |        "    <tr>\n",
1081 |        "      <th>305</th>\n",
1082 |        "      <td>Caprabo</td>\n",
1083 |        "      <td>Cacahuetes</td>\n",
1084 |        "      <td>6.24</td>\n",
1085 |        "      <td>1626</td>\n",
1086 |        "      <td>5/2/23</td>\n",
1087 |        "      <td>Valencia</td>\n",
1088 |        "      <td>389955</td>\n",
1089 |        "    </tr>\n",
1090 |        "    <tr>\n",
1091 |        "      <th>306</th>\n",
1092 |        "      <td>Caprabo</td>\n",
1093 |        "      <td>Cacahuetes</td>\n",
1094 |        "      <td>6.24</td>\n",
1095 |        "      <td>1626</td>\n",
1096 |        "      <td>5/2/23</td>\n",
1097 |        "      <td>Valencia</td>\n",
1098 |        "      <td>389955</td>\n",
1099 |        "    </tr>\n",
1100 |        "    <tr>\n",
1101 |        "      <th>307</th>\n",
1102 |        "      <td>Caprabo</td>\n",
1103 |        "      <td>Cacahuetes</td>\n",
1104 |        "      <td>6.24</td>\n",
1105 |        "      <td>1626</td>\n",
1106 |        "      <td>5/2/23</td>\n",
1107 |        "      <td>Valencia</td>\n",
1108 |        "      <td>389955</td>\n",
1109 |        "    </tr>\n",
1110 |        "    <tr>\n",
1111 |        "      <th>308</th>\n",
1112 |        "      <td>Caprabo</td>\n",
1113 |        "      <td>Cacahuetes</td>\n",
1114 |        "      <td>6.24</td>\n",
1115 |        "      <td>1626</td>\n",
1116 |        "      <td>5/2/23</td>\n",
1117 |        "      <td>Valencia</td>\n",
1118 |        "      <td>389955</td>\n",
1119 |        "    </tr>\n",
1120 |        "    <tr>\n",
1121 |        "      <th>309</th>\n",
1122 |        "      <td>Caprabo</td>\n",
1123 |        "      <td>Cacahuetes</td>\n",
1124 |        "      <td>6.24</td>\n",
1125 |        "      <td>1626</td>\n",
1126 |        "      <td>5/2/23</td>\n",
1127 |        "      <td>Valencia</td>\n",
1128 |        "      <td>389955</td>\n",
1129 |        "    </tr>\n",
1130 |        "    <tr>\n",
1131 |        "      <th>310</th>\n",
1132 |        "      <td>Caprabo</td>\n",
1133 |        "      <td>Cacahuetes</td>\n",
1134 |        "      <td>6.24</td>\n",
1135 |        "      <td>1626</td>\n",
1136 |        "      <td>5/2/23</td>\n",
1137 |        "      <td>Valencia</td>\n",
1138 |        "      <td>389955</td>\n",
1139 |        "    </tr>\n",
1140 |        "    <tr>\n",
1141 |        "      <th>311</th>\n",
1142 |        "      <td>Caprabo</td>\n",
1143 |        "      <td>Cacahuetes</td>\n",
1144 |        "      <td>6.24</td>\n",
1145 |        "      <td>1626</td>\n",
1146 |        "      <td>5/2/23</td>\n",
1147 |        "      <td>Valencia</td>\n",
1148 |        "      <td>389955</td>\n",
1149 |        "    </tr>\n",
1150 |        "    <tr>\n",
1151 |        "      <th>312</th>\n",
1152 |        "      <td>Caprabo</td>\n",
1153 |        "      <td>Cacahuetes</td>\n",
1154 |        "      <td>6.24</td>\n",
1155 |        "      <td>1626</td>\n",
1156 |        "      <td>5/2/23</td>\n",
1157 |        "      <td>Valencia</td>\n",
1158 |        "      <td>389955</td>\n",
1159 |        "    </tr>\n",
1160 |        "    <tr>\n",
1161 |        "      <th>313</th>\n",
1162 |        "      <td>Caprabo</td>\n",
1163 |        "      <td>Cacahuetes</td>\n",
1164 |        "      <td>6.24</td>\n",
1165 |        "      <td>1626</td>\n",
1166 |        "      <td>5/2/23</td>\n",
1167 |        "      <td>Valencia</td>\n",
1168 |        "      <td>389955</td>\n",
1169 |        "    </tr>\n",
1170 |        "    <tr>\n",
1171 |        "      <th>314</th>\n",
1172 |        "      <td>Caprabo</td>\n",
1173 |        "      <td>Cacahuetes</td>\n",
1174 |        "      <td>6.24</td>\n",
1175 |        "      <td>1626</td>\n",
1176 |        "      <td>5/2/23</td>\n",
1177 |        "      <td>Valencia</td>\n",
1178 |        "      <td>389955</td>\n",
1179 |        "    </tr>\n",
1180 |        "    <tr>\n",
1181 |        "      <th>315</th>\n",
1182 |        "      <td>Caprabo</td>\n",
1183 |        "      <td>Cacahuetes</td>\n",
1184 |        "      <td>6.24</td>\n",
1185 |        "      <td>1626</td>\n",
1186 |        "      <td>5/2/23</td>\n",
1187 |        "      <td>Valencia</td>\n",
1188 |        "      <td>389955</td>\n",
1189 |        "    </tr>\n",
1190 |        "    <tr>\n",
1191 |        "      <th>316</th>\n",
1192 |        "      <td>Caprabo</td>\n",
1193 |        "      <td>Cacahuetes</td>\n",
1194 |        "      <td>6.24</td>\n",
1195 |        "      <td>1626</td>\n",
1196 |        "      <td>5/2/23</td>\n",
1197 |        "      <td>Valencia</td>\n",
1198 |        "      <td>389955</td>\n",
1199 |        "    </tr>\n",
1200 |        "    <tr>\n",
1201 |        "      <th>317</th>\n",
1202 |        "      <td>Caprabo</td>\n",
1203 |        "      <td>Cacahuetes</td>\n",
1204 |        "      <td>6.24</td>\n",
1205 |        "      <td>1626</td>\n",
1206 |        "      <td>5/2/23</td>\n",
1207 |        "      <td>Valencia</td>\n",
1208 |        "      <td>389955</td>\n",
1209 |        "    </tr>\n",
1210 |        "    <tr>\n",
1211 |        "      <th>318</th>\n",
1212 |        "      <td>Caprabo</td>\n",
1213 |        "      <td>Cacahuetes</td>\n",
1214 |        "      <td>6.24</td>\n",
1215 |        "      <td>1626</td>\n",
1216 |        "      <td>5/2/23</td>\n",
1217 |        "      <td>Valencia</td>\n",
1218 |        "      <td>389955</td>\n",
1219 |        "    </tr>\n",
1220 |        "    <tr>\n",
1221 |        "      <th>319</th>\n",
1222 |        "      <td>Caprabo</td>\n",
1223 |        "      <td>Cacahuetes</td>\n",
1224 |        "      <td>6.24</td>\n",
1225 |        "      <td>1626</td>\n",
1226 |        "      <td>5/2/23</td>\n",
1227 |        "      <td>Valencia</td>\n",
1228 |        "      <td>389955</td>\n",
1229 |        "    </tr>\n",
1230 |        "    <tr>\n",
1231 |        "      <th>320</th>\n",
1232 |        "      <td>Caprabo</td>\n",
1233 |        "      <td>Cacahuetes</td>\n",
1234 |        "      <td>6.24</td>\n",
1235 |        "      <td>1626</td>\n",
1236 |        "      <td>5/2/23</td>\n",
1237 |        "      <td>Valencia</td>\n",
1238 |        "      <td>389955</td>\n",
1239 |        "    </tr>\n",
1240 |        "    <tr>\n",
1241 |        "      <th>321</th>\n",
1242 |        "      <td>Caprabo</td>\n",
1243 |        "      <td>Cacahuetes</td>\n",
1244 |        "      <td>6.24</td>\n",
1245 |        "      <td>1626</td>\n",
1246 |        "      <td>5/2/23</td>\n",
1247 |        "      <td>Valencia</td>\n",
1248 |        "      <td>389955</td>\n",
1249 |        "    </tr>\n",
1250 |        "    <tr>\n",
1251 |        "      <th>322</th>\n",
1252 |        "      <td>Caprabo</td>\n",
1253 |        "      <td>Cacahuetes</td>\n",
1254 |        "      <td>6.24</td>\n",
1255 |        "      <td>1626</td>\n",
1256 |        "      <td>5/2/23</td>\n",
1257 |        "      <td>Valencia</td>\n",
1258 |        "      <td>389955</td>\n",
1259 |        "    </tr>\n",
1260 |        "  </tbody>\n",
1261 |        "</table>\n",
1262 |        "</div>"
1263 |       ],
1264 |       "text/plain": [
1265 |        "    Nombre Cliente Tipo Producto  Precio (€/kg)  Cantidad Vendida (kg)  \\\n",
1266 |        "23         Caprabo    Cacahuetes           6.24                   1626   \n",
1267 |        "300        Caprabo    Cacahuetes           6.24                   1626   \n",
1268 |        "301        Caprabo    Cacahuetes           6.24                   1626   \n",
1269 |        "302        Caprabo    Cacahuetes           6.24                   1626   \n",
1270 |        "303        Caprabo    Cacahuetes           6.24                   1626   \n",
1271 |        "304        Caprabo    Cacahuetes           6.24                   1626   \n",
1272 |        "305        Caprabo    Cacahuetes           6.24                   1626   \n",
1273 |        "306        Caprabo    Cacahuetes           6.24                   1626   \n",
1274 |        "307        Caprabo    Cacahuetes           6.24                   1626   \n",
1275 |        "308        Caprabo    Cacahuetes           6.24                   1626   \n",
1276 |        "309        Caprabo    Cacahuetes           6.24                   1626   \n",
1277 |        "310        Caprabo    Cacahuetes           6.24                   1626   \n",
1278 |        "311        Caprabo    Cacahuetes           6.24                   1626   \n",
1279 |        "312        Caprabo    Cacahuetes           6.24                   1626   \n",
1280 |        "313        Caprabo    Cacahuetes           6.24                   1626   \n",
1281 |        "314        Caprabo    Cacahuetes           6.24                   1626   \n",
1282 |        "315        Caprabo    Cacahuetes           6.24                   1626   \n",
1283 |        "316        Caprabo    Cacahuetes           6.24                   1626   \n",
1284 |        "317        Caprabo    Cacahuetes           6.24                   1626   \n",
1285 |        "318        Caprabo    Cacahuetes           6.24                   1626   \n",
1286 |        "319        Caprabo    Cacahuetes           6.24                   1626   \n",
1287 |        "320        Caprabo    Cacahuetes           6.24                   1626   \n",
1288 |        "321        Caprabo    Cacahuetes           6.24                   1626   \n",
1289 |        "322        Caprabo    Cacahuetes           6.24                   1626   \n",
1290 |        "\n",
1291 |        "    Fecha Venta Provincia Cliente      id  \n",
1292 |        "23       5/2/23          Valencia  389955  \n",
1293 |        "300      5/2/23          Valencia  389955  \n",
1294 |        "301      5/2/23          Valencia  389955  \n",
1295 |        "302      5/2/23          Valencia  389955  \n",
1296 |        "303      5/2/23          Valencia  389955  \n",
1297 |        "304      5/2/23          Valencia  389955  \n",
1298 |        "305      5/2/23          Valencia  389955  \n",
1299 |        "306      5/2/23          Valencia  389955  \n",
1300 |        "307      5/2/23          Valencia  389955  \n",
1301 |        "308      5/2/23          Valencia  389955  \n",
1302 |        "309      5/2/23          Valencia  389955  \n",
1303 |        "310      5/2/23          Valencia  389955  \n",
1304 |        "311      5/2/23          Valencia  389955  \n",
1305 |        "312      5/2/23          Valencia  389955  \n",
1306 |        "313      5/2/23          Valencia  389955  \n",
1307 |        "314      5/2/23          Valencia  389955  \n",
1308 |        "315      5/2/23          Valencia  389955  \n",
1309 |        "316      5/2/23          Valencia  389955  \n",
1310 |        "317      5/2/23          Valencia  389955  \n",
1311 |        "318      5/2/23          Valencia  389955  \n",
1312 |        "319      5/2/23          Valencia  389955  \n",
1313 |        "320      5/2/23          Valencia  389955  \n",
1314 |        "321      5/2/23          Valencia  389955  \n",
1315 |        "322      5/2/23          Valencia  389955  "
1316 |       ]
1317 |      },
1318 |      "execution_count": 16,
1319 |      "metadata": {},
1320 |      "output_type": "execute_result"
1321 |     }
1322 |    ],
1323 |    "source": [
1324 |     "# Ver duplicados de otra manera\n",
1325 |     "df_duplicados[df_duplicados.duplicated(keep=False)]"
1326 |    ]
1327 |   },
1328 |   {
1329 |    "cell_type": "markdown",
1330 |    "metadata": {},
1331 |    "source": [
1332 |     "## Análisis de Valores Únicos\n",
1333 |     "Si una variable solo tiene un valor no te aporta nada en tu análisis"
1334 |    ]
1335 |   },
1336 |   {
1337 |    "cell_type": "markdown",
1338 |    "metadata": {},
1339 |    "source": [
1340 |     "### Número e Valores Únicos"
1341 |    ]
1342 |   },
1343 |   {
1344 |    "cell_type": "code",
1345 |    "execution_count": 17,
1346 |    "metadata": {},
1347 |    "outputs": [],
1348 |    "source": [
1349 |     "df = pd.read_excel('/Users/jimenacambronero/Desktop/Proyectos para Portfolio/PythonPracticas/Datos/Frutos_Secos_unicos.xlsx')"
1350 |    ]
1351 |   },
1352 |   {
1353 |    "cell_type": "code",
1354 |    "execution_count": 19,
1355 |    "metadata": {},
1356 |    "outputs": [
1357 |     {
1358 |      "data": {
1359 |       "text/plain": [
1360 |        "Nombre Cliente            11\n",
1361 |        "Tipo Producto              4\n",
1362 |        "Precio (€/kg)            258\n",
1363 |        "Cantidad Vendida (kg)    284\n",
1364 |        "Fecha Venta              204\n",
1365 |        "Provincia Cliente          6\n",
1366 |        "id                       300\n",
1367 |        "Completado                 1\n",
1368 |        "dtype: int64"
1369 |       ]
1370 |      },
1371 |      "execution_count": 19,
1372 |      "metadata": {},
1373 |      "output_type": "execute_result"
1374 |     }
1375 |    ],
1376 |    "source": [
1377 |     "df.nunique()"
1378 |    ]
1379 |   },
1380 |   {
1381 |    "cell_type": "code",
1382 |    "execution_count": 20,
1383 |    "metadata": {},
1384 |    "outputs": [
1385 |     {
1386 |      "data": {
1387 |       "text/plain": [
1388 |        "0      Si\n",
1389 |        "1      Si\n",
1390 |        "2      Si\n",
1391 |        "3      Si\n",
1392 |        "4      Si\n",
1393 |        "       ..\n",
1394 |        "295    Si\n",
1395 |        "296    Si\n",
1396 |        "297    Si\n",
1397 |        "298    Si\n",
1398 |        "299    Si\n",
1399 |        "Name: Completado, Length: 300, dtype: object"
1400 |       ]
1401 |      },
1402 |      "execution_count": 20,
1403 |      "metadata": {},
1404 |      "output_type": "execute_result"
1405 |     }
1406 |    ],
1407 |    "source": [
1408 |     " # puede eliminarse porque es una constante"
1409 |    ]
1410 |   },
1411 |   {
1412 |    "cell_type": "markdown",
1413 |    "metadata": {},
1414 |    "source": [
1415 |     "### Valores Únicos Diferentes"
1416 |    ]
1417 |   },
1418 |   {
1419 |    "cell_type": "code",
1420 |    "execution_count": 21,
1421 |    "metadata": {},
1422 |    "outputs": [
1423 |     {
1424 |      "data": {
1425 |       "text/plain": [
1426 |        "array(['Cádiz', 'Sevilla', 'Madrid', 'Alicante', 'Barcelona', 'Valencia'],\n",
1427 |        "      dtype=object)"
1428 |       ]
1429 |      },
1430 |      "execution_count": 21,
1431 |      "metadata": {},
1432 |      "output_type": "execute_result"
1433 |     }
1434 |    ],
1435 |    "source": [
1436 |     "df['Provincia Cliente'].unique()"
1437 |    ]
1438 |   }
1439 |  ],
1440 |  "metadata": {
1441 |   "kernelspec": {
1442 |    "display_name": "Python 3",
1443 |    "language": "python",
1444 |    "name": "python3"
1445 |   },
1446 |   "language_info": {
1447 |    "codemirror_mode": {
1448 |     "name": "ipython",
1449 |     "version": 3
1450 |    },
1451 |    "file_extension": ".py",
1452 |    "mimetype": "text/x-python",
1453 |    "name": "python",
1454 |    "nbconvert_exporter": "python",
1455 |    "pygments_lexer": "ipython3",
1456 |    "version": "3.9.13"
1457 |   }
1458 |  },
1459 |  "nbformat": 4,
1460 |  "nbformat_minor": 2
1461 | }
1462 | 


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