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Puroh #1

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4f89a10
agregada mi solucion al reto 1
puroh Dec 14, 2020
73ed1e5
solucion del mini reto 1
puroh Feb 2, 2021
759ccce
Desarrollada la priemra parte de scrapping utilizando pandas directam…
puroh Feb 4, 2021
05fed33
cambios menores
puroh Feb 5, 2021
c0ac6f0
localzado el head de la tabla con beautifullsoup
puroh Feb 5, 2021
26264a8
Agregado la aprte de web scraping con BS4, como resutado se obtiene u…
puroh Feb 8, 2021
2167788
Se pone el index 'Especie' en el dataframe
puroh Feb 8, 2021
44c61e7
Se cambio los tipos de datos de las columnas 'Hora' y 'Vencimiento'
puroh Feb 8, 2021
76b6023
Moficicado el dataframe segun el tipo de datos, excepto por 'Variació…
puroh Feb 10, 2021
0f632ff
Se parsean todos los datos como se ha requerido en el reto, se añaden…
puroh Feb 13, 2021
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394 changes: 394 additions & 0 deletions 001 Orden Inverso por Grupos/soluciones 001/purohr_001.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Mini reto 001"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Ordenar las subseries de forma descendente\n",
"\n",
"La finalidad es ordenar de forma descendente las subseries 1..n que hay en la serie.\n",
"\n",
"Output solicitado:\n",
"\n",
"[6,5,4,3,2,1,4,3,2,1,5,4,3,2,1,2,1,9,8,7,6,5,4,3,2,1]"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1\n",
"1 2\n",
"2 3\n",
"3 4\n",
"4 5\n",
"5 6\n",
"6 1\n",
"7 2\n",
"8 3\n",
"9 4\n",
"10 1\n",
"11 2\n",
"12 3\n",
"13 4\n",
"14 5\n",
"15 1\n",
"16 2\n",
"17 1\n",
"18 2\n",
"19 3\n",
"20 4\n",
"21 5\n",
"22 6\n",
"23 7\n",
"24 8\n",
"25 9\n",
"dtype: int64"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"serie = pd.Series([1,2,3,4,5,6,1,2,3,4,1,2,3,4,5,1,2,1,2,3,4,5,6,7,8,9])\n",
"serie"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Comprobando que se incluyan todos los indices, asi se organizara la información. El comando verifica en que posiciones estan los 1, que es el valor que se repite en todas las series"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1\n",
"6 1\n",
"10 1\n",
"15 1\n",
"17 1\n",
"dtype: int64"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a=serie[serie < serie.iloc[1]]\n",
"a"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A la serie siguiente se le agrega el ultmimo indice, necesario para que mas adelante se sepa donde termina la jultima mini serie."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1\n",
"6 1\n",
"10 1\n",
"15 1\n",
"17 1\n",
"25 9\n",
"dtype: int64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ultimo=pd.Series(serie[len(serie)-1],index=[len(serie)-1])\n",
"a=a.append(ultimo)\n",
"a"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Se comprueba la linea que sirve para modificar el orden de los datos en la serie"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5 6\n",
"4 5\n",
"3 4\n",
"2 3\n",
"1 2\n",
"0 1\n",
"dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"serie.iloc[a.index[0]: a.index[1]].sort_values(ascending=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Se utiliza para verificar que los indices existan y para comprobar que si esten en el lugar correcto"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Int64Index([0, 6, 10, 15, 17, 25], dtype='int64')"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.index"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Comprobando que se organicen de manera correcta los datos numericos "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5 6\n",
"4 5\n",
"3 4\n",
"2 3\n",
"1 2\n",
"0 1\n",
"dtype: int64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"final=serie.iloc[list(a.iteritems())[0][0] : list(a.iteritems())[1][0]].sort_values(ascending=False)\n",
"final"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Comprobando que se impriman los indices y su valor correspondiente"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[(0, 1), (6, 1), (10, 1), (15, 1), (17, 1), (25, 9)]\n"
]
}
],
"source": [
"print(list(a.iteritems()))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Seleccionando los elementos de la lista"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(6, 1)\n"
]
}
],
"source": [
"print(list(a.iteritems())[1])\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ahora se utiliza un for para que recorra cada elemento de la lista, se organiza y se imprime en el orden pedido en el minireto"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5 6\n",
"4 5\n",
"3 4\n",
"2 3\n",
"1 2\n",
"0 1\n",
"dtype: int64\n",
"9 4\n",
"8 3\n",
"7 2\n",
"6 1\n",
"dtype: int64\n",
"14 5\n",
"13 4\n",
"12 3\n",
"11 2\n",
"10 1\n",
"dtype: int64\n",
"16 2\n",
"15 1\n",
"dtype: int64\n",
"24 8\n",
"23 7\n",
"22 6\n",
"21 5\n",
"20 4\n",
"19 3\n",
"18 2\n",
"17 1\n",
"dtype: int64\n"
]
}
],
"source": [
"for index_row,iteracion in zip(a.iteritems(),range(1,len(a+1))):\n",
" #print(index_row[0])\n",
" print(serie.iloc[index_row[0]:a.index[iteracion]].sort_values(ascending=False))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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