{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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spoatecorregionsagprovinciasuphaareadepartamento
0Monte ribereno, talares orientales y bahia de ...PampaARBA - Gerencia de Servicios CatastralesBUENOS AIRES977451.943977452Magdalena
1Sudoeste de Buenos AiresEspinalARBA - Gerencia de Servicios CatastralesBUENOS AIRES2357878.3152357877Patagones
2Delta del rio ParanaDelta e islas del rio ParanaARBA - Gerencia de Servicios CatastralesBUENOS AIRES202074.295202074Campana
3Hornocal - Valle GrandePuna/Selva de las YungasDirec. Grl. de InmueblesJUJUY287358.817287359Valle Gran
4Susques - Jama - Catua - OlarozPuna/Altos AndesDirec. Grl. de InmueblesJUJUY268648.734268649Susques
5Piedemonte Andino del Area Metropolitana de Me...Altos Andes/Monte de Llanuras y Mesetas/Monte ...IDE MendozaMENDOZA31905.73531906Capital
6Piedemonte del Valle de UcoAltos Andes/Monte de Llanuras y Mesetas/Estepa...IDE MendozaMENDOZA48565.49548565Tupungato
7Oasis Norte - Cinturon VerdeMonte de Llanuras y MesetasIDE MendozaMENDOZA71719.63471720GuaymallÃ
8Laguna Llancanelo - Cuenca del Rio MalargueAltos Andes/Estepa PatagonicaIDE MendozaMENDOZA54999.00054999Malargï
\n", "
" ], "text/plain": [ " spoat \\\n", "0 Monte ribereno, talares orientales y bahia de ... \n", "1 Sudoeste de Buenos Aires \n", "2 Delta del rio Parana \n", "3 Hornocal - Valle Grande \n", "4 Susques - Jama - Catua - Olaroz \n", "5 Piedemonte Andino del Area Metropolitana de Me... \n", "6 Piedemonte del Valle de Uco \n", "7 Oasis Norte - Cinturon Verde \n", "8 Laguna Llancanelo - Cuenca del Rio Malargue \n", "\n", " ecorregion \\\n", "0 Pampa \n", "1 Espinal \n", "2 Delta e islas del rio Parana \n", "3 Puna/Selva de las Yungas \n", "4 Puna/Altos Andes \n", "5 Altos Andes/Monte de Llanuras y Mesetas/Monte ... \n", "6 Altos Andes/Monte de Llanuras y Mesetas/Estepa... \n", "7 Monte de Llanuras y Mesetas \n", "8 Altos Andes/Estepa Patagonica \n", "\n", " sag provincia supha \\\n", "0 ARBA - Gerencia de Servicios Catastrales BUENOS AIRES 977451.943 \n", "1 ARBA - Gerencia de Servicios Catastrales BUENOS AIRES 2357878.315 \n", "2 ARBA - Gerencia de Servicios Catastrales BUENOS AIRES 202074.295 \n", "3 Direc. Grl. de Inmuebles JUJUY 287358.817 \n", "4 Direc. Grl. de Inmuebles JUJUY 268648.734 \n", "5 IDE Mendoza MENDOZA 31905.735 \n", "6 IDE Mendoza MENDOZA 48565.495 \n", "7 IDE Mendoza MENDOZA 71719.634 \n", "8 IDE Mendoza MENDOZA 54999.000 \n", "\n", " area departamento \n", "0 977452 Magdalena \n", "1 2357877 Patagones \n", "2 202074 Campana \n", "3 287359 Valle Gran \n", "4 268649 Susques \n", "5 31906 Capital \n", "6 48565 Tupungato \n", "7 71720 Guaymallà \n", "8 54999 Malargï " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import sqlite3\n", "\n", "conn = sqlite3.connect('sitios2.sqlite')\n", "df = pd.read_sql_query(\"SELECT spoat, ecorregion, sag, provincia, supha, area, departamento FROM sitiospilotojson\", conn)\n", "conn.close()\n", "\n", "df" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "dictionary update sequence element #0 has length 4; 2 is required", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[2], line 8\u001b[0m\n\u001b[0;32m 4\u001b[0m file_to_read \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mD:/hugging/Agent/streamlit_agent/runs/alanis.pickle\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mrb\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m 6\u001b[0m loaded_dictionary \u001b[39m=\u001b[39m pickle\u001b[39m.\u001b[39mload(file_to_read)\n\u001b[1;32m----> 8\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mdict\u001b[39;49m(loaded_dictionary))\n\u001b[0;32m 9\u001b[0m \u001b[39m# person1 = {\"name\": \"Marcus King\", \"Age\": \"22\", \"Profession\" : \"Author\"}\u001b[39;00m\n\u001b[0;32m 10\u001b[0m \n\u001b[0;32m 11\u001b[0m \u001b[39m# pickle.dump(person1, open(\"person1.p\", \"wb\"))\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[39m# # obj = pd.read_pickle(open(\"D:/hugging/Agent/streamlit_agent/runs/alanis.pickle\",\"rb\"))\u001b[39;00m\n\u001b[0;32m 16\u001b[0m \u001b[39m# print(obj)\u001b[39;00m\n", "\u001b[1;31mValueError\u001b[0m: dictionary update sequence element #0 has length 4; 2 is required" ] } ], "source": [ "import pickle\n", "import pandas as pd\n", "\n", "file_to_read = open(\"D:/hugging/Agent/streamlit_agent/runs/alanis.pickle\", \"rb\")\n", "\n", "loaded_dictionary = pickle.load(file_to_read)\n", "\n", "print(dict(loaded_dictionary))\n", "# person1 = {\"name\": \"Marcus King\", \"Age\": \"22\", \"Profession\" : \"Author\"}\n", "\n", "# pickle.dump(person1, open(\"person1.p\", \"wb\"))\n", "# file = open(\"D:/hugging/Agent/streamlit_agent/runs/alanis.pickle\",\"rb\")\n", "# obj = pickle.load(file, fix_imports = True, encoding = 'ASCII', errors = 'strict')\n", "# # obj = pickle.load(open(\"D:/hugging/Agent/streamlit_agent/runs/alanis.pickle\",\"rb\"))\n", "# # obj = pd.read_pickle(open(\"D:/hugging/Agent/streamlit_agent/runs/alanis.pickle\",\"rb\"))\n", "# print(obj)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import shelve\n", "filename = \"D:/hugging/Agent/streamlit_agent/runs/alanis\"\n", "d = shelve.open(filename) # open -- file may get suffix added by low-level\n", " # library\n", " \n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " []\n" ] } ], "source": [ "print(d,list(d.values()))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "d[key] = data # store data at key (overwrites old data if\n", " # using an existing key)\n", "data = d[key] # retrieve a COPY of data at key (raise KeyError if no\n", " # such key)\n", "del d[key] # delete data stored at key (raises KeyError\n", " # if no such key)\n", "flag = key in d # true if the key exists\n", "klist = list(d.keys()) # a list of all existing keys (slow!)\n", "\n", "# as d was opened WITHOUT writeback=True, beware:\n", "d['xx'] = [0, 1, 2] # this works as expected, but...\n", "d['xx'].append(3) # *this doesn't!* -- d['xx'] is STILL [0, 1, 2]!\n", "\n", "# having opened d without writeback=True, you need to code carefully:\n", "temp = d['xx'] # extracts the copy\n", "temp.append(5) # mutates the copy\n", "d['xx'] = temp # stores the copy right back, to persist it\n", "\n", "# or, d=shelve.open(filename,writeback=True) would let you just code\n", "# d['xx'].append(5) and have it work as expected, BUT it would also\n", "# consume more memory and make the d.close() operation slower.\n", "\n", "d.close() # close it" ] } ], "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": "3.11.4" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }