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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>spoat</th>\n",
" <th>ecorregion</th>\n",
" <th>sag</th>\n",
" <th>provincia</th>\n",
" <th>supha</th>\n",
" <th>area</th>\n",
" <th>departamento</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Monte ribereno, talares orientales y bahia de ...</td>\n",
" <td>Pampa</td>\n",
" <td>ARBA - Gerencia de Servicios Catastrales</td>\n",
" <td>BUENOS AIRES</td>\n",
" <td>977451.943</td>\n",
" <td>977452</td>\n",
" <td>Magdalena</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Sudoeste de Buenos Aires</td>\n",
" <td>Espinal</td>\n",
" <td>ARBA - Gerencia de Servicios Catastrales</td>\n",
" <td>BUENOS AIRES</td>\n",
" <td>2357878.315</td>\n",
" <td>2357877</td>\n",
" <td>Patagones</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Delta del rio Parana</td>\n",
" <td>Delta e islas del rio Parana</td>\n",
" <td>ARBA - Gerencia de Servicios Catastrales</td>\n",
" <td>BUENOS AIRES</td>\n",
" <td>202074.295</td>\n",
" <td>202074</td>\n",
" <td>Campana</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Hornocal - Valle Grande</td>\n",
" <td>Puna/Selva de las Yungas</td>\n",
" <td>Direc. Grl. de Inmuebles</td>\n",
" <td>JUJUY</td>\n",
" <td>287358.817</td>\n",
" <td>287359</td>\n",
" <td>Valle Gran</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Susques - Jama - Catua - Olaroz</td>\n",
" <td>Puna/Altos Andes</td>\n",
" <td>Direc. Grl. de Inmuebles</td>\n",
" <td>JUJUY</td>\n",
" <td>268648.734</td>\n",
" <td>268649</td>\n",
" <td>Susques</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Piedemonte Andino del Area Metropolitana de Me...</td>\n",
" <td>Altos Andes/Monte de Llanuras y Mesetas/Monte ...</td>\n",
" <td>IDE Mendoza</td>\n",
" <td>MENDOZA</td>\n",
" <td>31905.735</td>\n",
" <td>31906</td>\n",
" <td>Capital</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Piedemonte del Valle de Uco</td>\n",
" <td>Altos Andes/Monte de Llanuras y Mesetas/Estepa...</td>\n",
" <td>IDE Mendoza</td>\n",
" <td>MENDOZA</td>\n",
" <td>48565.495</td>\n",
" <td>48565</td>\n",
" <td>Tupungato</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Oasis Norte - Cinturon Verde</td>\n",
" <td>Monte de Llanuras y Mesetas</td>\n",
" <td>IDE Mendoza</td>\n",
" <td>MENDOZA</td>\n",
" <td>71719.634</td>\n",
" <td>71720</td>\n",
" <td>GuaymallÃ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Laguna Llancanelo - Cuenca del Rio Malargue</td>\n",
" <td>Altos Andes/Estepa Patagonica</td>\n",
" <td>IDE Mendoza</td>\n",
" <td>MENDOZA</td>\n",
" <td>54999.000</td>\n",
" <td>54999</td>\n",
" <td>Malargï</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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": [
"<shelve.DbfilenameShelf object at 0x000001F51BA9F710> []\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"
]
}
],
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"name": "python",
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