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imersao_dados_4_aula01.ipynb
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"cell_type": "markdown",
"source": [
"---\n",
"# Aula 1\n",
"[Aula 01 - Seu primeiro Colab com Python e Pandas | Imersão Dados 4ª edição](https://www.youtube.com/watch?v=780mwgm9hJc)\n",
"\n",
"---"
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"source": [
"import pandas as pd\n",
"import re\n",
"\n",
"#from pandas_profiling import ProfileReport\n",
"pd.set_option('display.precision', 2)\n",
"pd.set_option('display.float_format', lambda x: '%.2f' % x)"
]
},
{
"cell_type": "code",
"source": [
"# Load real state data from source CSV:\n",
"url = \"https://gist.githubusercontent.com/tgcsantos/3bdb29eba6ce391e90df2b72205ba891/raw/22fa920e80c9fa209a9fccc8b52d74cc95d1599b/dados_imoveis.csv\"\n",
"\n",
"imoveis = pd.read_csv(url)"
],
"metadata": {
"id": "5fR-U04f3p9l"
},
"execution_count": 2,
"outputs": []
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{
"cell_type": "code",
"source": [
"imoveis.head()"
],
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" Rua Bairro Cidade Metragem \\\n",
"0 Avenida Itacira, 255 Planalto Paulista São Paulo 1000 \n",
"1 Rua Aurelia Perez Alvarez, 42 Jardim dos Estados São Paulo 496 \n",
"2 Rua Alba Valdez Jardim Reimberg São Paulo 125 \n",
"3 NaN Jardim Morumbi São Paulo 310 \n",
"4 Rua Tobias Barreto, 195 Mooca São Paulo 100 \n",
"\n",
" Quartos Banheiros Vagas Valor \n",
"0 4 8 6 R$ 7.000.000 \n",
"1 4 4 4 R$ 3.700.000 \n",
"2 4 3 2 R$ 380.000 \n",
"3 3 2 4 R$ 685.000 \n",
"4 3 2 2 R$ 540.000 "
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" <td>Rua Aurelia Perez Alvarez, 42</td>\n",
" <td>Jardim dos Estados</td>\n",
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" <td>496</td>\n",
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" <th>4</th>\n",
" <td>Rua Tobias Barreto, 195</td>\n",
" <td>Mooca</td>\n",
" <td>São Paulo</td>\n",
" <td>100</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
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"text/plain": [
" Rua Bairro Cidade Metragem \\\n",
"7138 Rua Alessandro Araldi, 109 Jardim dos Pinheiros São Paulo 179 \n",
"9350 NaN Vila Santa Catarina São Paulo 189 \n",
"4351 Rua Puréus Jardim Guedala São Paulo 526 \n",
"4595 NaN Cidade Jardim São Paulo 567 \n",
"3405 Rua Barão do Bananal Vila Pompéia São Paulo 142 \n",
"196 Rua Constantino de Sousa Campo Belo São Paulo 266 \n",
"2402 Rua Catuicara Jardim Novo Mundo São Paulo 220 \n",
"1009 Rua Aquiramum Alto de Pinheiros São Paulo 560 \n",
"1168 Rua Anunze Boaçava São Paulo 425 \n",
"7068 Avenida Barão do Rego Barros Vila Congonhas São Paulo 450 \n",
"\n",
" Quartos Banheiros Vagas Valor \n",
"7138 3 4 3 R$ 820.000 \n",
"9350 3 1 2 R$ 840.000 \n",
"4351 4 5 3 R$ 12.000\\n /Mês \n",
"4595 4 6 6 R$ 3.300.000 \n",
"3405 3 3 4 R$ 1.400.000 \n",
"196 6 6 6 R$ 1.750.000 \n",
"2402 4 5 3 R$ 2.700.000 \n",
"1009 4 4 4 R$ 4.500.000 \n",
"1168 3 5 4 R$ 3.800.000 \n",
"7068 4 10 8 R$ 1.340.999 "
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" <td>Rua Alessandro Araldi, 109</td>\n",
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" <td>Rua Barão do Bananal</td>\n",
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" <td>3</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>R$ 1.400.000</td>\n",
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" <td>Rua Constantino de Sousa</td>\n",
" <td>Campo Belo</td>\n",
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" <td>266</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>R$ 1.750.000</td>\n",
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" <th>2402</th>\n",
" <td>Rua Catuicara</td>\n",
" <td>Jardim Novo Mundo</td>\n",
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" <td>220</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>R$ 2.700.000</td>\n",
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" <td>560</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>R$ 4.500.000</td>\n",
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" <th>1168</th>\n",
" <td>Rua Anunze</td>\n",
" <td>Boaçava</td>\n",
" <td>São Paulo</td>\n",
" <td>425</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>R$ 3.800.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7068</th>\n",
" <td>Avenida Barão do Rego Barros</td>\n",
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{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 10008 entries, 0 to 10007\n",
"Data columns (total 8 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Rua 6574 non-null object\n",
" 1 Bairro 9996 non-null object\n",
" 2 Cidade 10008 non-null object\n",
" 3 Metragem 10008 non-null int64 \n",
" 4 Quartos 10008 non-null int64 \n",
" 5 Banheiros 10008 non-null int64 \n",
" 6 Vagas 10008 non-null int64 \n",
" 7 Valor 10008 non-null object\n",
"dtypes: int64(4), object(4)\n",
"memory usage: 625.6+ KB\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"imoveis[\"Bairro\"][6522]"
],
"metadata": {
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"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "P9rkcINCSiai",
"outputId": "3e40a0dd-b313-4606-c50f-4e6e47edc4de"
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"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'Vila Paulo Silas'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"source": [
"imoveis.Metragem.describe()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "FYtNWwbkTopx",
"outputId": "ef95039b-1b5d-4d5a-e824-9db8e758ff3a"
},
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"count 10008.00\n",
"mean 442.30\n",
"std 7515.38\n",
"min 10.00\n",
"25% 173.00\n",
"50% 290.00\n",
"75% 460.25\n",
"max 750000.00\n",
"Name: Metragem, dtype: float64"
]
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"source": [
"area_media = round(imoveis[\"Metragem\"].mean(), 2)\n",
"area_media"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "CZtnuG1wVDqg",
"outputId": "e787f10c-d536-4aab-d52d-f64f90720cad"
},
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"442.3"
]
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"source": [
"sum(imoveis[\"Bairro\"] == \"Vila Mariana\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "W-LPCx0qVzvS",
"outputId": "9eee28e7-d663-45dd-8ff0-afa51f552a51"
},
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"184"
]
},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"source": [
"tem_imoveis_vila = (imoveis[\"Bairro\"] == \"Vila Mariana\")\n",
"tem_imoveis_vila"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "VPAYgHC3WQmy",
"outputId": "9da5a75e-db13-424b-d6b3-8ded7d4e5709"
},
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 False\n",
"1 False\n",
"2 False\n",
"3 False\n",
"4 False\n",
" ... \n",
"10003 False\n",
"10004 False\n",
"10005 False\n",
"10006 False\n",
"10007 False\n",
"Name: Bairro, Length: 10008, dtype: bool"
]
},
"metadata": {},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"source": [
"imoveis_vila_mariana = imoveis[tem_imoveis_vila]\n",
"imoveis_vila_mariana"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "2DnvKduJW39N",
"outputId": "cfd982fe-c6a5-4b50-d226-ee8e1f779bb6"
},
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Rua Bairro Cidade Metragem \\\n",
"100 Rua Pero Correia Vila Mariana São Paulo 250 \n",
"120 Praça Doutor Carvalho Franco Vila Mariana São Paulo 250 \n",
"155 Rua Professor João Marinho Vila Mariana São Paulo 170 \n",
"160 NaN Vila Mariana São Paulo 228 \n",
"334 Rua Pero Correia, 892 Vila Mariana São Paulo 350 \n",
"... ... ... ... ... \n",
"9645 Rua Sousa Ramos Vila Mariana São Paulo 285 \n",
"9701 Rua Benito Juarez Vila Mariana São Paulo 171 \n",
"9737 Rua Benito Juarez Vila Mariana São Paulo 171 \n",
"9807 NaN Vila Mariana São Paulo 275 \n",
"9852 Rua França Pinto Vila Mariana São Paulo 280 \n",
"\n",
" Quartos Banheiros Vagas Valor \n",
"100 3 3 1 R$ 1.350.000 \n",
"120 3 5 2 R$ 2.450.000 \n",
"155 3 4 2 R$ 1.490.000 \n",
"160 4 5 3 R$ 2.200.000 \n",
"334 6 4 10 R$ 1.700.000 \n",
"... ... ... ... ... \n",
"9645 3 5 5 R$ 1.650.000 \n",
"9701 3 2 4 R$ 1.650.000 \n",
"9737 3 2 4 R$ 1.650.000 \n",
"9807 3 1 6 R$ 1.500.000 \n",
"9852 3 3 4 R$ 1.600.000 \n",
"\n",
"[184 rows x 8 columns]"
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" <th>9645</th>\n",
" <td>Rua Sousa Ramos</td>\n",
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" <td>285</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
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" <th>9701</th>\n",
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" <td>Vila Mariana</td>\n",
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" <td>171</td>\n",
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" <td>Vila Mariana</td>\n",
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" <td>275</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
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" <th>9852</th>\n",
" <td>Rua França Pinto</td>\n",
" <td>Vila Mariana</td>\n",
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" <td>280</td>\n",
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},
"metadata": {},
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]
},
{
"cell_type": "code",
"source": [
"area_media_vila_m = round(imoveis_vila_mariana[\"Metragem\"].mean(), 2)\n",
"print(f'Vila Mariana: {area_media_vila_m}')\n",
"print(f'Geral: {area_media}')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "KAZjmOobXJk8",
"outputId": "f3a2523b-a48b-4bee-aef6-7ccd341a909d"
},
"execution_count": 12,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Vila Mariana: 233.62\n",
"Geral: 442.3\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"imoveis[\"Bairro\"].value_counts()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "pAiOk2ZbXYK8",
"outputId": "fbe3339e-6a2e-4bb4-bedc-cbfc5412767c"
},
"execution_count": 13,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Alto de Pinheiros 409\n",
"Jardim Guedala 403\n",
"Jardim Paulista 309\n",
"Brooklin Paulista 247\n",
"Jardim Europa 240\n",
" ... \n",
"Jardim das Imbuias 1\n",
"Vila Represa 1\n",
"Vila Nancy 1\n",
"Sítio Represa 1\n",
"Vila Invernada 1\n",
"Name: Bairro, Length: 701, dtype: int64"
]
},
"metadata": {},
"execution_count": 13
}
]
},
{
"cell_type": "code",
"source": [
"n_imoveis_bairro = imoveis[\"Bairro\"].value_counts()\n",
"n_imoveis_bairro.head(10).plot.bar()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
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"id": "2uGH9Ug2Yy4h",
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"execution_count": 14,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f662c7f87d0>"
]
},
"metadata": {},
"execution_count": 14
},
{
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
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IiB5Kco+I6KEk94iIHkpyj4jooST3iIgeSnKPiOihJPeIiB5Kco+I6KEk94iIHkpyj4jooST3iIgeSnKPiOihJPeIiB4aW3KX9BRJl0taKenIcZUTERF3NJbkLmkj4B+BfYBdgIMk7TKOsiIi4o7GVXPfA1hp+wrbvwM+Cew/prIiImIG2V78Pyo9B3iK7ZfU4xcCj7b9iqHHHA4cXg8fCFy+wGK3Bn62wL+xUJMQA0xGHJMQA0xGHJMQA0xGHJMQA0xGHIsRw462l466Y+MF/uH1Zvs44LjF+nuSVthevlh/b1pjmJQ4JiGGSYljEmKYlDgmIYZJiWPcMYyrWeYaYIeh4+3ruYiIaGBcyf17wM6S7itpU+B5wMljKisiImYYS7OM7VskvQL4d2Aj4MO2LxlHWUMWrYlnASYhBpiMOCYhBpiMOCYhBpiMOCYhBpiMOMYaw1g6VCMioluZoRoR0UNJ7hERPZTkHhHRQ52Nc18oSXcBfmP7NkkPAB4EfNn27xvGsDPwNsoSC5sPztu+X6sYhmK514wY/rNx+RPzWnRN0tOAvwZ2pFxjAmx7yw5ieSrwENb8P3lL6zgmQZevRV2S5RLbD2pRHkx3zf1MYHNJ2wFfBV4IfLRxDB8BjgFuAfYCTgT+uWUAkvaT9APgR8A3gCuBL7eMoZqE12JPSd+T9GtJv5N0q6RftYyhei9wCHBP21va3qKjxH4s8FzgzygfMAdQPnBax3GGpNNn/jSOodPXwvatwOWS7tOqTGxP5Q9wbv39Z8Dr6u3zG8dwTv190cxzDWO4ALgncF493gs4voP/j0l4LVYA9wfOowzBPRR4WwevxRnAktbljojjwhm/7wp8s4M4Hjn08zjg3cA7N7TXglIhvRE4jTLv52Tg5HGVN7XNMoAkPQY4GDisntuocQw3S1oC/KCO67+G8qZp6fe2r5e0RNIS22dIem/jGGAyXgtsr5S0kUtN6SOSzgPe0DiM1wGnSPoGcPNQbO9uHMdv6u+bJN0buB7YtnEM2D5nxqlvSzq7cRiT8Fr8n5aFTXNyfzXlov2c7Usk3Y9SY2rpVcCdgVdS2lj3pnwdb+kGSXel1Ao+Luk64L8bxwB3fC32Al7UOIab6ozo8yW9E7iWbpoe3wr8mtK2u2kH5Q98UdJWwP8FzgUMfKh1EJLuMXS4hFKDv1vjMDp/LWx/Q9I2wKPqqbNtXzeu8qZ+ElNNbNj+ddexdGHQsUy5aA6mXDT/bPvnjeM4wPan5zo35hh2BH5KSah/TnktPmB7ZasYahwX2961ZZlzkbQZsLntX3ZQ9o8oyVSUPpkfAW+x/a2GMWxm++bBbcoH728H5xrFcCDlw+XrlNfifwB/aftfx1LetCZ3SQ+ldNrdg/JCrQJe5PEvc4CkL1DerCPZ3m/cMQzF8g7br5/rXIM4zrX9iLnONYhjU8rIKQOXu+wn0FT91vA1219tXfaIWB4LLGPoW7rtEzsLqCOT8P6UdAHwpEFtXdJSyvvk4eMob5qbZf4JeI3tMwAkPQH4IPDYBmX/XYMy5utJwMxEvs+Ic2MhaR9gX2A7Se8bumtLSi2tmTrU7Vjgh5QP/PtKepnt1qOH/gT4C0k3A7+no6GQkj4G7AScD9xaT5tSKWoZx+bAnwKPr+V/EzjW9m8blP0HwHbAnSTtTvm/gPL+vPO4y59hyYxmmOsZY7PhNCf3uwwSO4Dtr9cmirGz/Y0W5cxG0p9QLpidJF04dNcWwLcbhvJflFEq+wHDHWc3UppGWnoXsNegGUbSTsCXaDw01PYWLcubxXJgF3f/9fxEyvvh/fX4+cDHKMMRx+3JwB9Tlh1/F6uT+43AGxuUP+wrkv4d+EQ9fi5wyrgKm+Zmmc9ROkY+Vk+9AHik7Wc2jKGziTuS7gbcvZY/vAH5ja3b22s8m7hOIJN0d2AH2xfO8bTFjuF7th81dCxKp9WjZnnaYpb/INvflzTyq77tc1vEMRTPp4FX2r62Zbkj4rjU9i5znRtzDM+2/ZlW5c0WB2U4KJShmJ8bV1nTXHN/MXA08FlWf9V7ceMYPgIcBbyHMjrkUBqNzqgdY7+U9L+Bn9i+uTZNPUzSibZvaBHHkFMl7Ud5T50DXCfpO7Zb1t5XSDoFOInynjgA+J6kZwHY/uyYy38t8FJKDXEmU0ZTtbQ1cGkddjg8JLNZn1B1rqQ9bX8XQNKjKd/2Wtpe0paUGvsHgUcAR7buF6kfME0+ZKay5l6n8n7N9l4dx3GO7UdKusj2Q4fPNYzhfMrX72WUr3ifBx5ie99WMdQ4zrO9u6SXUGrtR0m60PbDGsbwkVnutu3WH/6dkvRHo863alaUdBHlQ20Tyj7JgyUx7gN8v3HN/QLbD5f0ZODlwP8GPta4Q/VZwDuAe1Gah8baFzOVNXfbt0q6TdLduhjaNWQSJu7c5rI5yrOA99t+f52409rGkrYFDgTe1EH52D60i3IHBt8Q1qbBN4eZ5X2jDg/d2fbXJN2ZthP9ntawrLkM2tr3BU6sc2M02xPG4J3A021f1qKwqUzu1a+BiySdytCkHduvbBjDJExi+r2kgygThp5ez23SOAaAt1B23vqW7e/VSWU/aBmApO0pnXa3t2kCr7J9daMQBq//vSijtgbrp+wFfIfShNiMpJcCh1OGC+9EGTVyLPDEFuXbvmoolrtT9lUezjlX3eFJ43OOpK8C9wXeIGkL4LaG5QP8tFVihyltlgGQNDKJ2j6hdSxdkrQL5Wvm/7P9CUn3BQ60/Y6OQ2uuftD/C2t2sh9s+0mN4/gqcMigI7N+o/mo7Sc3juN8YA/gLNu713O3NyE2jOOvKSNWfsjq+SG23awPon7D3g24wvYNku4JbNey01/S3wN/APwba/aBjOVDf2qTe5cmaRJT1yS9zvY7Jb2fEa9Jy29Sks63vdtc5xrEcZntBw8dL6Es9/rgWZ42jjjOsv3oof6QjSkL7jXrB6lxXA48tKMJZRMzgmktfUJj6wuaumYZSSfZPnCos2YNjd64g0lMz6J8Eg+Wtj2IMv197CbkdQAYfM1sPfphlOslvYDV44gPokwUae20EeOZv9ZBHN+Q9EbKBJ4nUeZFfKGDOC4GtgLGto7KLCZmBFPrPqGpq7lL2tb2tbWj6A6G2/kaxLLC9vK5zo2p7Il5HSZFfS3eDzyGcuF+hzLOu+nGJTWWZwJ/WA/PHOd45lliWEJZMfV/UToU/x34UOtJTZKWU0ZxXUy3QzI71bpPaOqS+7AZIwHuBGxs+8aG5V8GPNX2FfX4vsAprb9+d2lSmqjq8NgTbR/corw54mi6486kk3QJZbmQixjqxGwxJHOSRjC17hOaumaZgREjAban4UiA6s+Br0u6glIz2hF4WYuCJd3I6KTaeh2TiVhnpw6P3VHSpl207c6I43JJ9+niGwOsMb58pNZt7sBNtt8398PG4umz3GfajmBaanu43f2jkl49rsKmtuY+QSMBNqOsQghlYkazJURjTZJOBB5M2eFmeHhs000yJJ0J7A6cPSOOVt9iZt0+rnWTnaR3U5pjTmbNZpmmyzF0TdJplFntw31Ch9oeS4V0amvuwM22fzeYh1BHArRuS7wz8BpgR9svlbSzpAfa/mLDGEbuydi61qjVa3bPjKPlBtk/rD9LKAuodaXpjjszTWB/y+71955D55p2Zkr6q1Hn3Xaz8BdT2tzfw+o+obF1sk5zcp+EkQAfoayj8ph6fA3waaBZcqesejiwOWWSxuWUXd5bGu5E3pyyrss91vLYsbB9dMvy1qbV9P65SNqTkkweTNnAZCPgvxs22QHQ9TIh1fDuZJtTZs82m1AEt3/ottvrYYqbZTofCTAYGTMYR1zPXeAxLb4/z5geAfyp7Zd0FcNQLK3X2TmD0d8emi7YNSlJVdIK4HmUCsdyyizmB9huuqesytZyfwvc2/Y+deLdY2wf3zKOGTFtBvy77Sc0LPMBwDHANrZ3lfQwYD/bfzOO8qa25m77Nsrqbh/sMIzf1VE6htvXD++0zd32uSqr7jU1Y5LIEkoyaf3++ouh25sDz6bxhiHVPzAiqXYQx6RsGP5RyrfcwZpD/wF8CugsuVOWDdm+cZkfBP6SMnII2xdK+hcgyX2YpMcBb6aMUNmY1aNEWrbxHgV8BdhB0scp41f/uGH5SHrN0OESylKm/9Uyhmp4ksgtwJWURcSasX3OjFPfVlnutrkJSaqTsmH41rZPkvQGAJeF7m6d60mLacYIoo2ApZT1kFq6s+2zZ6xXNrbKx9Qmd8qn/p9T2rybvlEGbJ8q6VxKR5EoExJ+1jiM4Y7DWyht8M03JZiEdlVJw238S4BHUjbJbm1SkuoLKYnsFZRrZQfKt5nW/ruu5TL4hrsn0Ho11+EVKm+hLOLV+lvdz+q3+8Hr8BzKe2MsprnN/SzbzZsfZsTwh6PO2z6zdSyTQGUP04ew5q5UzWpHQyN2RLmAfwS8xfa3WsVQ49iRMtV+E0pSvRvwAdft/zY0tcnu/cCulFmqS4HntFy0ayiWe7Hm+7PZqDKVlVKPo6wY+gvK+/PgcY1umrrkPtS2eyClVvJZOho7W2dnDmxOGXd/TuPV7pYCr+OOSbV1J+KxlHbMvYAPAc+hbHF3WMs4YiInMQ2GKj+Q8sF7ueuWjA3L34/SdHhvygfvjsBltluPKkNlr+cltm+U9Grb7x1LOVOY3M+Y5W63TmrDJO0AvNd2s6++KsvLforSmfhyynryq2y/vlUMNY4LbT9s6PddgS/b/h8Nyn6d7XfW2wfY/vTQfX9ru8lGyHMk1ZspY/DfZvuCMccxmMR0RP09PN3dto+847PGS9Ku3HGv4RMbln8BZVz911xWyNwLeEHXlQ9J/2l75FyVBf/taUvuk0ylp+QSt90+bLDV3+1b2mnGRtGN4jjb9h6SvktZLfN6ymtx/wZln+u6Xdrw7VHHY45jtpmhG1OaJd48GDbbIJ7zZpbV8vUYKvMo4AmU5H4KsA9lU5fnNIxhMGz5AmB327d1PWy5xvVj2zuM429PbYdqHaf6bMreobf/Oxq38Q6vYT7YDKD1lOrB19tra5v3f9F48lD1BUlbAf+X8hqYdsNUtZbbo47HZh5tpz/UWtYVHxNJepztb9eDx9JNx+5zgIcD59k+tI57/+c5nrPYbqjfJs8EPi7pOtac2NSVsdWupza5U5YQ/SVltExXY8uH1zC/BfjE4EJq6G8k3Y2ybvX7gS0pnXjNSNqNMttvW9ufkfRFYHO329/Wa7k96rhTto9qWNxhwIfr+0OUTrwuNgn/Ta0p3yJpS0qb91hqq7PYD/gt5do4mNLJ3aQiqNkX+bvT2Mqd1mYZSRfb3rXrOLoiaXNKG/v9KUupHt/B0K7Bmh0voHzIPprSptx0YlkdM/3frL5YbhrcRfmQ6WJP2YlRkzsNP2xnlv8B4I2UiV2vpex/fL4bbF5RJ/QdR1k59iLgMNuXjrvcSTDNyf044P22L+qg7P2B7W3/Yz0+izK8C+D1wx16Y4zhU5QmmW9S2jCvsv2qcZc7Io5LgEfZvqmOZf5K6/b+SVRnLt/H9uUdx9Hp8NQR8SwDtmw1DLIuwfAGSnPMfsBL3Hgv26500f62WB5P2dH8ckkXSrpIUqtxs6+jLF86sBnwKEqn0csbxbCL7RfY/idKm+bYR6Wsxc22bwKwfT3T/Z5aFJKeDpxPmb2MpN0knTz7s8YSx7GULf7+jPIt5gDKEMDWcTxz6NvDlcB/SnpGo+KX2D7V9s210rV0zmf0xDS3ue/TYdmb2v7x0PG3amK7vo5hbeH2ccJ1OnejYu/gfkOJS8BOw4nMG9hWatWbKXMevg5g+3yVXbpae+zQ8NSjJb0L+HIHcRzloW0Gbd9QR9D8W4Oyt9KauzGtceyGOzG1NnXJXdKWtn8FNNtOb4S7Dx/YfsXQYauawcMl/areFmXp41/Rfiem/WccT8TOTB37ve1fzvjA7aL98zf1902S7k0ZnrptB3GM+jbXKvd8gzV3Yxo+br0TE1ByGGuO8Pv5OMqZuuRO2YPwaZQOvJkMtFg47CxJL53ZcSjpZZTdd8bO9kYtypmLJ2Tt8mGtLp5ZXCLp+cBGknYGXknZmKG1L44YnvqhDuJYobI34E8AAAyISURBVLIb0z/W4yMYff0uuhadtvNV88PRlFE7gw/7seWsqe1Q7VJdn+LfKEMwB+PaH0lpe3+G7Z92FduGbG0XT+OVQgc7dL2JNfca+Gvbv20Zx4yYNqPt8NThsu9C2Z3qf9ZTpwJ/Y3sSxpk3I+kHlHXsmywuOHXJfcTQphfbbrqjylAse7N6x6NLbJ/eRRxRtL54JtWMNuY76HM78yST9BXgWYMBCGMvbwqT+wY7tClm1/riGVH+F5h9wa5WG2TfRhmtc/7g1JphuOlEpklZ3K5rknanbFpyFmsudvjKcZQ3jW3uS2yfWm9/WnUDgA1d1+3MKluI/SWrN08ZxNHyAn4D8J0672DsF88Ig87kZwF/wOop9gcBLZvqnkWZMPQwykzuT7jb5YY/Tlnc7mkMLW7XMgBJmwB/AgyW6f4GcGzj1Sn/CTid0uJw27gLm8aa+xWsuZ3a3w0fb2hfOSeonfkC4FhmbJ7iO+6ONM4Yzga+xYyLx/YJrWKocaywvXyucw3iuAtlNNNzgXsCb+qiA3wSFreT9CHK+vqD98ILgVvdcK/hUQu5jdM01tw32KFNa/EXwK4T0M58i+1jOo5hE9uvmfthY3cXSfezfQVAHePeav7DsN9S1l/6FeUb1eazP3xsJmFxu0fNWAHy9FohaenLkg4HvsCa3ywzFBI27KFNa/FDVq+l0qUvSPpT4HM0eOOuRdOLZxZ/Dny9fssUJbG+rFXhtaP/eZSJVF8D/t72itmfNVadL24H3CppJ9s/hNt3RWq9PedB9fdwU3KGQk6iSRid0bqTZpY4fjTidNPmoUmIYSiWzYAH1cPv2262cmntUL2Q0kRlZnTytnpvTMridjWWJ1Kuk+EP3Bf3eYTb1NXcJ8wk1JqbdtKsje0uptdPXAxDdqZsK7c5ZTZxy52HJuXb7QmsubjdLkDzxe2qb7H6/wSg2YJukva2ffrahqiOq58wNfcFmIRac+tOmhHld/LGnbQYZsTT+c5Dk0DSRbYfWm9vTNlTt+kuUEOx3GEHqlHnxlT20baPkvSREXePbWjq1Nbc6yzA11KWVX1pneb9QNtfbBjGJNSau25n/iPKa/D0Efe16uCehBiGTcLOQ5Og88XtJP0BsB1l7aXdWT3mf0vKhu5j57pBS+v+wqmtudf1zM8BXmR715rsv2N7t4YxdFprrjFMTDtzFFq9n+w5wF6URe4us/2gOZ7aK1q9iQqsuZFKs8XtJB0C/DGwnDV3TrsR+Gijb5azjuCy/e5xlDu1NXdgJ9vPlXQQgMtmEa2rBl3XmjtvZ+7qjTtpMcywoi7Y9UFKBeTXwP9rHEPnJmFxuzrH4QRJz7b9mY7C2KL+fiBl34fBkthPZ4wLDU5zcv+dym43BpC0E+33Um06tGnYBLUzbzH3Q8auk4tnbWz/ab15bF0SodnOQ8PqaJXDuOO0/y72Ue3E8Af/qEpAiw9+20fX8s8EHmH7xnr8ZuBL4yp3mpP7UZSdbnaQ9HHgcZSvX810XGueiHbmoTfuPWZ+Y1GjDSq6unhmkrTWzjlJj7B97truH5OPAd8HnkzZDPpgykbmG5JJqHwMbAP8buj4d/XcWExtmzuAyp6de1La8L7bbCnNyak1TwxJ3wb2cdlIBUkPBj7thpuYS7oceNhgTHkda36h7QfO/sxFK/+MenNzShvvBZT35sOAFbYf0yKOoXjOs737YNp/XV/lm7b3bBlHFJLeBBxImegH8AzgU7bfNo7ypq7mPqJ2dG39fR9J92lUO+q81jyB7cx/S5ml+lRK88iJlJpiSycCZ0savng+2qpw23sBSPos5RvERfV4V8rWe60NRqvcUGP4CXCvDuLoXNdNVLU/8ETKNoeD/Y4PtX3e2Mqctpr7pNWOulLHUsNa2pltv6CDmJ5BWdp1C+DZtv+jYdkCtqdsczi4eM4c58UzSyyX2H7IXOcaxPES4DOUa+MjwF2Bv7J9bMs4JoGkT1OaqJ7PUBOV7WaTqobH/Tcpb9qS+0CtHR01s3bUYqLIJNWaazvzU4fambcAvmT7D2d/5qKV/37WnN7+RMrM3Suh+YSuphfPLHF8gjIEcDC2/WDgrrYPWvuzYpwmoYlK0gnAP9j+Xovypq5ZZsgDB4kdwPbFtZ23hUkandG0k2aEmQtSNVvid4RzJT2q1cUzi0Mpa4cPaoVnAs1WzJykyscEmYQmqkcDB0u6ivLhPxjv/7BxFDbNNffOa0dd15prmU07aeaIZVPgAfXwcrfdCAFJ36csUtXk4plUQ012Iw1GF21IJqGJStKOo87bvmos5U1xct+cNXdWORM4xg03IZ6A0RmT1M78BMpCUVdSkuoOwCG2z2wYQ9OLZ0T5J9k+UNJFjNhub0P7kInRJN2LNTt1/3Ms5Uxrcp8Ek1BrnqB25nOA59u+vB4/gLK92yM7iKXJxTOi3G1tXzsBHzKvs/3OEf0hgziaLgc9CWrF69nAMtbcWOctDWPYD3gXcG/gOsqyw5eNq6N9mtvcO9XF0Ka1mJR25k0GiR3A9n/UTqtm1nbxUIa/tXAXSY+z/e0ZcT2O0sbbyqX1d5cbdEyaz1N2pTqH9jPZB/6aMi/na7Vzdy9gbKPaktzXk21LOqXWmlvPPBzWtJNmFitU9qkc7gNpnVyaXjwjvJc1l6IY+FW9b9S8iHHYR9Iv3Hjv2Am3ve2ndBzD721fL2mJpCW2z5D03nEVNvXJXdJdAWz/uoPiJ6HW/OQOyx72J8ARwOAr/zeBDzSOoenFM8I2wyO4BmxfJGlZwzj+A/g7SdsCJ1Gax5r3w0yY70h66Kj/n4ZuqPnqm8DHJV3H6lUzF93UtrlLeiilWeQelNrqKkoH3sUNY5iY0RldtTPPiGFTyvBQ081oma9R+j3eDtyT0jTzKNuPbVT+D2zvvJb7Vtq+f4s4hsrckbKX6vMoy+1+gpLom00umxSSLqVcqz+iNMs0v1ZVliX/bS37BZQ15T8+c02mRStvipP7d4A32T6jHj8B+NtWF3Its9OOsxpD006aWeJ4At2Plml68Ywo/xPA6bY/OOP8S4An2X5uizhGUdmo4sOU0V2dL8XbWpfXqqQbuWPH9mB58t9SJv29yfZpi1ruFCf3C2w/fK5zjWLprNYs6QJgb2a0M9s+rFUMNY7ORst0dfGMiGMbysip37F6MtdyYFPgmbZbdqoOtrbbh1JzfyLwdcr/yedbxjFJJuEb7jBJGwG7Uiohi7rI3jS3uV8h6f9QljWFUlO7omUAEzA6A7pvZx7obLSM7bUu6zp88dTf44zjp8Bj6wfsoKwv2T59nOXOJOlJlL0G9qXMmP4kcLjtsbXvTroJuVbvwPatwAV12Oqimuaa+92Bo4HHU2pt36SsLXNDwxg6rzV33c48FMdHgFtZc7TMRp6QjSEkvcz2P3UdRwuSTgf+BfiM7V90Hc8kmIRrtbVpTu4H2P70XOfGHMMK28vrG2d327e1bhrqup15KI7NKKNlHl9PfRP4wGD2bkSXJuFabW2ak/u5th8x17kxx9BZrXlS2plrLBsBl3gD2wA6psfQtfo2YGs6+obb0tQld0n7UNoSDwQ+NXTXlsAutvdoGMtE1JpHxDW2TppZyvw88Gddd1BFjCLpLqy+Vg8G7ka5Pq7vNLAxmsbk/nBgd0p7+18N3XUjcEaLNsZJqjXPpmU7s8oKmbtTOvBu77izvV+L8iNiTVOX3AckbWz7lq7jmKmLWvMkkPRHo87b/kbrWCIGhipig8rXIOENJjFt2UlgDUxdcl/bcqpM2NrdG9LojJkkbQ1c72l7c0X0yDQm95EzzQZazg4NkLQnpUP555SFuz5G6bBaArzI9lc6DC82cCr7PrycsvTAhcCHJ/Eb/zhMXXJfG0mPBw6yfUTXsWxIJK0A3kjpoDoO2Mf2dyU9iDIbcvdOA4wNmqRPUbbY+yZltu5VbrgpdpemeYbqYL2M5wMHUBYE+my3EW2QNrb9VQBJb7H9XQDb3y9L3kd0ahfXzWwkHU/7PY47M3XJva5ZclD9+RllOKRs79VpYBuu24Zu/2bGff34WhjT7PaVSW3fsiFVOKauWUbSbZSvWIfZXlnPXWH7ft1GtmGSdCurlzu+E3DT4C5gc9tNd2OKGDb0/oQ136O9Hy0zdTV34FmUVe7OkPQVyqJIG87H8YTZEJePjemxIb8/p67mPlBnnO1PaZ7Zm7Jxx+cG7b8RERuyqU3uw+oKkQcAz7X9xK7jiYjoWi+Se0RErGlJ1wFERMTiS3KPiOihJPeIiB5Kco+I6KH/D97G9XNvYs8fAAAAAElFTkSuQmCC\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"source": [
"# Desafios Aula 1\n",
"\n",
"1. Realizar a média da metragem para cada um dos bairros (Paulo).\n",
"2. Duas formas de selecionar os dados por bairro (consultar os métodos na documentação do pandas) (Thiago).\n",
"3. Explorar alguns gráficos na documentação e aplicar nas demais colunas do DF, tentar colocar alguma conclusão (Thiago).\n",
"4. Mais informações estatísticas, como: média, mediana, mín, máx (Vivi).\n",
"5. Descobrir quais são os bairros que não tem nome de Rua. (Vivi)."
],
"metadata": {
"id": "ECNTtjSsaTu-"
}
},
{
"cell_type": "code",
"source": [
"# 1. Realizar a média da metragem para cada um dos bairros:\n",
"media_por_bairro = imoveis[[\"Bairro\", \"Metragem\"]].groupby(\"Bairro\").mean().sort_values(by = 'Metragem', ascending = False)\n",
"media_por_bairro"
],
"metadata": {
"id": "zKpdD2UxZXNv",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 455
},
"outputId": "643338df-555b-42c6-9b89-48b64b897e08"
},
"execution_count": 15,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Metragem\n",
"Bairro \n",
"Sítio Represa 37000.00\n",
"Vila Castelo 2850.00\n",
"Jardim Guedala 2486.64\n",
"Jardim das Camélias 1700.00\n",
"Chácara Flora 1260.73\n",
"... ...\n",
"Cidade Antônio Estevão de Carvalho 57.50\n",
"Jardim Nova Germania 57.00\n",
"Jardim Benfica 48.00\n",
"Vila Leonor 46.00\n",
"Vila Guaca 10.00\n",
"\n",
"[701 rows x 1 columns]"
],
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Metragem</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Bairro</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Sítio Represa</th>\n",
" <td>37000.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Vila Castelo</th>\n",
" <td>2850.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jardim Guedala</th>\n",
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" <tr>\n",
" <th>Jardim das Camélias</th>\n",
" <td>1700.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Chácara Flora</th>\n",
" <td>1260.73</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cidade Antônio Estevão de Carvalho</th>\n",
" <td>57.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jardim Nova Germania</th>\n",
" <td>57.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jardim Benfica</th>\n",
" <td>48.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Vila Leonor</th>\n",
" <td>46.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Vila Guaca</th>\n",
" <td>10.00</td>\n",
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},
"metadata": {},
"execution_count": 15
}
]
},
{
"cell_type": "code",
"source": [
"# 2. Duas formas de selecionar os dados por bairro (consultar os métodos na documentação do pandas):\n",
"print(imoveis[\"Bairro\"].count())\n",
"print(imoveis.get('Bairro').count())\n",
"print(imoveis.Bairro.count())"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gMvmWE-GDclp",
"outputId": "ae5631ca-7e4c-47a7-8236-ce7d5c9e1f4d"
},
"execution_count": 16,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"9996\n",
"9996\n",
"9996\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# 3. Explorar alguns gráficos na documentação e aplicar nas demais colunas do DF:\n",
"imoveis.hist('Quartos')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 316
},
"id": "N0KxC0pbE6yf",
"outputId": "eb40e2fc-feab-4a3f-d49d-01fd94a9a604"
},
"execution_count": 17,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f662c6f94d0>]],\n",
" dtype=object)"
]
},
"metadata": {},
"execution_count": 17
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [
"media_por_bairro['Metragem'].head(10).plot()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 297
},
"outputId": "85beed8b-ca41-4c99-a58b-6171b831a673",
"id": "AUYCBva7S__l"
},
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f662c23bf90>"
]
},
"metadata": {},
"execution_count": 18
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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MbDIazjWI90ramE5BzUmxBcBTuWG2pVip+DHA7ojo7BOvKC65YWaT0VATxFXA8cAyYDvwTyPWon5IWiVpvaT1bW1tYzHLsrjkhplNRkNKEBHxbER0RUQ38EWyU0gATwOLcoMuTLFS8eeB2ZLq+sRLzffqiFgeEcubmpqG0vRR45IbZjbZDClBSGrOvXw7ULjDaQ1wvqQGSccCS4B7gHuBJemOpSlkF7LXREQAdwDvSOOvBG4ZSpvGm0tumNlkUzfQAJK+BpwBzJW0DbgMOEPSMiCAJ4E/BYiITZJuAh4BOoFLIqIrTee9wG1ALbA6IjalWXwIuFHSJ4EHgGtGbOnGUOv83pIbL0/XJMzMKtmACSIiLigSLvklHhGfAj5VJL4WWFskvpXeU1QVq/dOpj1OEGY2KfiX1CPEJTfMbLJxghghLrlhZpONE8QIamme5TuZzGzScIIYQUubG11yw8wmDSeIEdT73xA+zWRmlc8JYgS1NrvkhplNHk4QI8glN8xsMnGCGGEtzY3++1EzmxScIEZYa/MsHnvWJTfMrPI5QYywQsmNJ57bP95NMTMbFieIEVYoueEL1WZW6ZwgRlih5IYvVJtZpXOCGGGFkhs+gjCzSucEMQpammf5TiYzq3hOEKNgaXMjz+5pZ+f+Q+PdFDOzIXOCGAU9JTd8msnMKpgTxCgolNzwf0OYWSVzghgFx8xsoMklN8yswg2YICStlrRD0sO52NGS1kl6LD3PSXFJulLSFkkbJZ2UG2dlGv4xSStz8ZMlPZTGuVKSRnohx0OrS26YWYUr5wjiWmBFn9ilwO0RsQS4Pb0GOAdYkh6rgKsgSyjAZcCpZP8/fVkhqaRh/iQ3Xt95VaTW+S65YWaVbcAEERE/BHb2CZ8LXJe6rwPOy8Wvj8xdwGxJzcDZwLqI2BkRu4B1wIrUrzEi7oqIAK7PTauitTa75IaZVbahXoOYFxHbU/czwLzUvQB4KjfcthTrL76tSLzitfi/Icyswg37InXa848RaMuAJK2StF7S+ra2trGY5ZAd3zST+lr5QrWZVayhJohn0+kh0vOOFH8aWJQbbmGK9RdfWCReVERcHRHLI2J5U1PTEJs+NrKSG7N8BGFmFWuoCWINULgTaSVwSy5+Ybqb6TTghXQq6jbgLElz0sXps4DbUr89kk5Ldy9dmJtWxWt1yQ0zq2Dl3Ob6NeBnwMslbZN0MfAZ4C2SHgPenF4DrAW2AluALwJ/ARARO4FPAPemx8dTjDTMl9I4jwPfGZlFG38uuWFmlaxuoAEi4oISvd5UZNgALikxndXA6iLx9cCJA7WjEuVLbrzuhLnj3Bozs8HxL6lHkUtumFklc4IYRYWSG5uf8Z1MZlZ5nCBGWWtzo+9kMrOK5AQxygolNzpdcsPMKowTxCgrlNzY6pIbZlZhnCBGmUtumFmlcoIYZS65YWaVyglilBVKbvgX1WZWaZwgxkBrs2symVnlcYIYA63zXXLDzCqPE8QYaG3uLblhZlYpnCDGQM+dTP5FtZlVECeIMTA3ldzwdQgzqyROEGOkZb4vVJtZZXGCGCNLmxtdcsPMKooTxBhpaZ7lkhtmVlGcIMZI4U4mn2Yys0rhBDFGXHLDzCrNsBKEpCclPSRpg6T1KXa0pHWSHkvPc1Jckq6UtEXSRkkn5aazMg3/mKSVw1ukicklN8ys0ozEEcQbI2JZRCxPry8Fbo+IJcDt6TXAOcCS9FgFXAVZQgEuA04FTgEuKySVycYlN8yskozGKaZzgetS93XAebn49ZG5C5gtqRk4G1gXETsjYhewDlgxCu0ady65YWaVZLgJIoDvSbpP0qoUmxcR21P3M8C81L0AeCo37rYUKxWfdFxyw8wqyXATxOkRcRLZ6aNLJP12vmdEBFkSGRGSVklaL2l9W1vbSE12zLjkhplVkmEliIh4Oj3vAG4mu4bwbDp1RHrekQZ/GliUG31hipWKF5vf1RGxPCKWNzU1Dafp48IlN8yskgw5QUiaIWlWoRs4C3gYWAMU7kRaCdySutcAF6a7mU4DXkinom4DzpI0J12cPivFJqWW+b6TycwqQ90wxp0H3CypMJ2vRsR3Jd0L3CTpYuCXwLvS8GuBtwJbgAPAewAiYqekTwD3puE+HhE7h9GuCW1pcyNf/umTdHZ1U1frn6GY2cQ15AQREVuBVxeJPw+8qUg8gEtKTGs1sHqobakkLc2zONTZzRPP7WfJvFnj3Rwzs5K8CzvGCncyPeLrEGY2wTlBjLHj5mYlNzb7TiYzm+CcIMbYlLqs5IbvZDKzic4JYhy0zp/FZhftM7MJzgliHLQ2N/LMnoPscskNM5vAnCDGQe8vqn2aycwmruH8DsKGqHAn0yU33E/TrAZmTa1n1tS63HMdjbnuWQ2H92+cWs/MqXXU1micl8TMJjMniHEwd2YDH/3dVjY/s5e9BzvYe7CT5/cd4snn9rP3YCd7D3ZyqIz/rp4xpfawpHJEd0PfeP0Rycc/1jOzUpwgxskfv+G4fvsf7OhKyaKjJ2kUuvcUie1t72DXgUP8aucB9h7sYM/BTg51lpdkmmdPY+GcaSyaMz17Pjp7XjhnOnOm15N+LW9mVcYJYoKaWl/L1PpammY1DHka7Z1d7OtJJJ09iSOfdHa/eIhf736Rbbte5P5f7mLPwc7DpjFjSi0L50xn0dFZwigkjsLro6bVD3dRzWyCcoKYxBrqammYWcsxM8tPMi+82MG2XQfYtitLGk/tLHQf4GePP8/+Q12HDd84te6whLGoJ4FkyWRGgzcxs0rlT68d5qhp9Rw17She8dKjjugXEew+0NGTMJ5KieSpnQfY2raf//pFGwc7Dj+tNWd6fU+yKJzCWnh0byKZWl87VotmZoPkBGFlk8ScGVOYM2MKr1xYPIE8v/9Q7qjjxZ4ksnn7Xr7/6I4jrovMndnAoqOn8dLZ0zh6+hRmT6/nqGn1zJ4+hdnT6pk9vT7Fsn71vqhuNmacIGzESGLuzAbmzmzgNb8x54j+3d1B2772nlNYhUTy1K4DPPrrPex+sYPdBw7R3c9/EM5sqEsJJD2mTeGo6fW5ZFJILFNS/3qOml5PQ52PVMwGywnCxkxNjZjXOJV5jVM5+TeLD9PdHext7+SFAx3sfvEQuw90sPvFDl44kHXvSvEXUnzzC3t44cUOdh/ooLOfzDKtvrZPAjn8yKQQm9lQT00N1ErU1oiaGvV29zxzeDx1lzOe7wizSuIEYRNKTY3SdZB6foPpZY8XEexr72T3gY6ehFFIMC+kI5NdBzrS60Ns2bGv54ilo2vE/jZ9QDXisKSRJZZ8IqGnX31tDbU1oq5G1NWK2pqarDv3uj6NW1cr6lL/7HWuu8/r+vy0apXiNbnu3nnXjEBCG+4UpCzBz5xax4yGOmY1ZM/Tp9Q64Y4yJwibFCSlHwLWH/YH5wOJCF7s6EpHJ4fY395FV3fQHUFXd9AVQXd3pBg98Z7+Pd30GfbIcYpPl6Lz6kqxzq6gs7ubru6goyvFurtp7+ims7srxbt7pt/R3U1XV9DZnR6pX0d3bxsmixrBjClZsjg8edQys6GemQ21Pf1mNmSPfIKZkX5IOqOhjun1tdS4MsERnCCsqkli+pQ6pk+p46Wzp413c0ZdRJY48omls0Qi6ijj1/wDz2/4be6O4MChLva3d7L/UPb7nf3tnewrPA5m8X3tXew72EHb3vbefu2dZSVF9SSb2p5kMnNqHTOm9CaY6VPqqK0BkZ0uREJkR3wSPacQpd5hsrjSPFKM7EhZ9A5fo8LrFDtsujosLsE5JzaPSakdJwizKiJlp5jqa6mKW4wjgvbO7p5EUkga+QSzv6dfF/vaO9jf3tXT7/l9B7KEdKiTA+1ddEd2FBiMTPIbqs2fmEdtzei/fxMmQUhaAfxfoBb4UkR8ZpybZGYVTlJPVYK5g/jBaLkistOIkZJGdwQRWfIIevt1B1AkFvQO35t4joz1Tjd7PWWMbveeEAlCUi3weeAtwDbgXklrIuKR8W2ZmVlpkqgVDP9S/MQ0UX51dAqwJSK2RsQh4Ebg3HFuk5lZVZsoCWIB8FTu9bYUO4ykVZLWS1rf1tY2Zo0zM6tGEyVBlCUiro6I5RGxvKmpabybY2Y2qU2UBPE0HHb7+sIUMzOzcTJREsS9wBJJx0qaApwPrBnnNpmZVbUJcRdTRHRKei9wG9ltrqsjYtM4N8vMrKpNiAQBEBFrgbXj3Q4zM8soxvPngMMgqQ345RBHnws8N4LNqXReH728Lg7n9dFrsqyL34yIsu7yqdgEMRyS1kfE8vFux0Th9dHL6+JwXh+9qnFdTJSL1GZmNsE4QZiZWVHVmiCuHu8GTDBeH728Lg7n9dGr6tZFVV6DMDOzgVXrEYSZmQ1gwiUISRdJeul4t8Mqi6RXSPr98W6H2WQyLglC0t9I2iRpo6QNkk5N8RXAqRHxa0kf6TPOTwc5j6407Ycl/Yek2SO4CEMmad8wxr1c0l+l7o9LevMwpjVP0lclbZV0n6SfSXr7UKfXZ9pnSLp1sMNImi/pRkmPpzatlfSygaYnqQ74J+D+kWh/uSRdK+mJtJ1tkPS+FH9S0twhTG9Q26yk8yQtzb0e7jZxp6RfSVIu9u2hbrN9luffJU3vZ9i3Sbo0dS+W9HCJ4b6UX+ZcfFDfD0Mx2PUgKST9W+51naS2gT4bIy3tdP9LiX79rrcxTxCSfgv4PeCkiHgV8GZ6S33PB96Xug9LEBHxukHO6sWIWBYRJwI7gUuG3upM+iIaE8qUfH8i4m8j4vtDnTbwbeCHEXFcRJxMVv9q4dBaO3ypTTcDd0bE8alNHwbmlTH6EuCyiBh2gceB1nsRH0zb2bKIuHKY8xvsNnse0PNlWWqbSH/IVa7dwOvTeLOB5kGM2/czkl+eQ8CflRovItaU8y+SEfHHxf5IrNj3w1h+XkvYD5woqfBn529hkEVIR3sZBvpeHY8jiGbguYhoB4iI5yLi16nfRcCrJX0GmJb2Pm6A3uydPlD/kPZKHpL0h2XM82ek/5eQdLyk76Y91B9JaknxayX9q7L/m/iFpN9L8YskrZH0A+B2STMkrZZ0j6QHJJ2bhntFim1QdmS0JMW/nea1SdKqFJsp6XZJ96dlKExjsaSfS7oeeBhYpOxo6xeSfgy8vLBAqb3vSN1PSvp0mvd6SSdJuk3ZnnixD+WZwKGI+NdCICJ+GRGfyy1zzx6HpFslnZG6z1J2tHF/2iucmeIrJG2WdD/w33LjnpKGf0DSTyX1LEN+mLS8rwQuLAwTEQ9GxI/SYDMlfSPN44aUUJD0t8D1wDWSrs7FT5D0fUkPprYeP8j1flVal5skfazkljUASf87basPS3p/f/Mj2+YL8+t3m5X0OuBtwD+k9/34ItvEZ9P78U5JF6RlfljSZ/tp8o1kOwuQvY/fyi1L0c+esiO8H0laAzyiPkcAyo56jwJOSONtl3RA0n5JZ0v6lqRnJN2bRvlH4KXK9rYPpe1neprW4+o9+/Cx3Dz2FWtLihX7DNam9VVYlv9V5L07Ns37IUmfHGg9lLAW+N3UfQHwtdx0jk5t2yjpLkmvSvHLJX1F0k+Ar6j/z+O+1JZNaXs/RdmR4FZJb8u1Y1GKPybpsr7rraTs/0/H7gHMBDYAvwC+APxOrt+dwPLUva/PePvS8x8A68iK+s0DfgU0F5lPYfha4N+BFen17cCS1H0q8IPUfS3wXbKkuYTsT4umkiWtbcDRabi/A/576p6dlmMG8Dng3Sk+BZiWugvjTSP7MthHVgOrMcXnAlvI/rNwMdANnJb6nQw8BEwHGtNwf5Vr7ztS95PAn6fuK4CNwCygCXi2yLp5H3BFP+/RRcC/5F7fCpyR2vpDYEaKfwj427SenkrrTcBNwK1pmEagLnW/Gfhm6j6jzzDvT23vGSY3/zOAF8iOcGrIvjxP77N+BXwV+P30+m7g7al7alqHZa33PtOtJdsuX1VkPV0LPEG2PW8AXpl7P+bm3r8ZZNv9JuA1peZHtm0U5vc9yttm39GnPflt4q9T90vJPidNaR38ADivyPLcmaa/MbXje6mt/X720vuzHzg2DbeYtK2n138NbAb+HPgx8NkU/1pa5mbgj0H2VDEAAAhiSURBVFP3McA3gABOJztC2gP8FXAW8GtgOdl2cCvw230+74e1pcRn8Jj03qzLDTO7yPpYA1yYui8ZaD0U+w4CXpWWZyrZNnIGvdv958iOfCHbaduQui8H7qP3O+QiinweU3cA56Tum9N7Vg+8Oje9i4DtabkL66Do92zfx5gfgkXEPkknA28A3gh8XdKlEXFtmZM4HfhaRHQBz0r6L+C1HFkefJqkDWR7YY8C65Tt7b4O+Hf1nmbN/5P5TRHRDTwmaSvQkuLrImJn6j4LeFvaK4Lsjf8Nsi+tv5G0EPhWRDyW+r9Pvef2F5Ft2AL+TtJvk31RLKD3VMovI+Ku1P0G4OaIOACQ9opKKfR7CJgZEXuBvZLaJc2OiN2lRpT0ebL1eigiXtvPPE4j+8D+JK2/KWm5W4AnCsus7LzrqjTOUcB1yo6ogmzj7eso4D1k6+HNJYa5JyK2pelvIPsS+jHwhrRnXkf2RbhR0p3Agoi4GSAiDqbx6ilvvQO8K+1t1pF9gS0l++Ls64MR8Y1iK4tsnd4cEfvT/L9F9p6uKTY/skSyj2yb/DnlbbP9+Xp6fi3Zqbu21I4bgN8mO83YVxfZej2f7Avqydx8S3329pC9P0/0mVbhM9hEtpN1DfA/gDdKeohsB+tgRGyX1Em2E1D4X5jnI+LHqb11ad7zyRLpDcCLZEl3CdlOS17ftvT9DC4hW7/HSfoc8J9kX6x9vZ4sGQB8BSgceZX7HUREbJS0mOzooW8x0tML04+IH0g6RlJj6rcmIl4s0qa+DpHt2EL22W+PiI60fhfnhlsXEc9Dz3Z4OrB+oImPyzm6tGLvBO5MC7KSbO9nJL0YEcvSoeltZHsA1wK7I2JZqaaVeL0/FxPwBxHx8z7DPirpbrLDybWS/pTsS+jNwG9FxIH0xXUq8G6yD83J6c18kizR9J3XYLSn5+5cd+F13/d5E70bPhFxibKLqoUNppPDTz8W2iayDe2C/MQklVqfAJ8A7oiIt6cPyp0lhvkR2d7WhSWGyS9TF1AnqQH4f8CyiHgmnXKYWmTcgrLWu6RjyfZYXxsRuyRdO8B0h6LY/PZHxExJXyH7n/Zyttmy5jFIN5LtjV4+xHkVtp/CZ/CjZEeRh9KOwscj4p9TYv8/faZT2Fbzn0Wl1yIr0PnuiOjvyy2/bs/gyM/g1PS+vho4m+zayLvIkldfI/FDsTVkp83OINuLL0ex9VmQ3xY7Ih0KkPvsR0S3Dr9+Ueq7rV/jcZH65WkjKVhG8aqsHWmPr68fAX+YziE2ke0J3VNqfmnv+33AB4ADwBOS3pnaorSRFLxTUo2k44HjyPYy+roN+J9Sz7nu16Tn44CtkV2ovIXsy+4oYFfaMFvI9sBJ8R3pS+qNwG+WaP4PgfMkTZM0Cxip2zh/AEyV9Oe5WP4OkyeBZWldLCL7sgK4C3i9pBMAlF2PeRnZ6YPFab1BtrdUcBS9F+YuKtGeo4A7yPaMP1cISnqVpDf0sxzTyLbhF9KedmFvbC+wTdJ5aToNaUeh3PXeSPYBfUHSPOCcftrQnx+RvX/TJc0A3p5ipeZHmt9ZZHvJA22ze8lOJQ7kHuB3JM1VdsH6AuC/Bmj3p8mdL8/Fy/nsPQu8JLW3geymlIJaYEfqPrufNsxVdkMLZEnjx2SfvWay9x1JCyS9pJ9pQInPYNohqomIbwIfBU4qMu5P6L0e8+5cfFDfQcBq4GMR8VCf+I8K002J7LmI2FNk/Ccp/nkcjLcou+Yxjezmhp+UM9J4XKSeSXbK4RFJG8kO3S8vMtzVZKcLbugTv5nsUP9Bsi+6v46IZ/qbYUQ8kMa5gOwNuVjSg2R70ufmBv0V2Rv9HeDPCqcm+vgE2SmQjZI2pdeQ7YE8nA6pTyS7cPpdsj3dR4HPkJ0XP0T24V+ejp4uJPuCLdbu+8lOEzyY2nRvseEGK+1xnEf2pfGEpHuA68iuKUC28TxBdpHvStLto+kUxUXA19J79zOgJa2nVcB/KrsouiM3u78HPi3pAUofsf492RfSdOB4YEFat58GSr636bTZNWTnVG/j8PXzR2SnFjYCPyU7PVHuen8QeCD1/yplfpiKTOd+siOAe8je+y+lbbHU/Kbn5vcrBt5mbwQ+qOwGgOP7Tjc3/e3ApWRJ+EHgvoi4pZ/hIyL+MSL6lrYu67MXER3Ax9PyrOPw9fwk8BlJ95GdUirl18Al6bMj4KqI+B5Z8vlyeg+/wcAJsu9nsHBabwHZGYwNwL+R3THX11+mNjyUhi8Y1HdQRGyL4ne4XQ6cnLbRz5CdSSmm6OdxkO4Bvpna/c0BjsB6uNRGkk4j3NrP+eSRmMergS9GxFD2AMzMxtSE+yX1ZKXsdtOvkR3OmplNeD6CMDOzonwEYWZmRTlBmJlZUU4QZmZWlBOEWQnqrUZaqOc0YMFIjUFVUbOx4ovUZiVI2hcRhWKEZwMfiYjfGcJ06iKis9Rrs4nKRxBm5WkEdkHparypX9GqokVeT5X05TT+A+mX3WYTynjXSzebyArF5qaSlXg4M8UPklWK3ZNKNtwlaU0ceTh+EnBiRDyRSinkX3+A7EfLr0wlIL4n6WUlfr1vNi58BGFWWuEPb1qAFcD1qQZXoRrvRuD7HF4VNq9vVdH869PJSjwQEZvJ6pG9bJSWw2xIfARhVoaI+Fk6WmgC3krpqrB5faupDrW6qtm48BGEWRnSaaBa4HnKrwrbn3wlz5eR/adIserBZuPGRxBmpRWuQUB2WmllRHSlCsP/kap8rqdEVdgBfAG4Kk2jE7go0t/wmk0Uvs3VzMyK8ikmMzMrygnCzMyKcoIwM7OinCDMzKwoJwgzMyvKCcLMzIpygjAzs6KcIMzMrKj/D/rgY6t/HoOVAAAAAElFTkSuQmCC\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [
"# 4. Mais informações estatísticas, como: média, mediana, mín, máx:\n",
"imoveis.describe()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 300
},
"id": "hX0VWgzsQVbF",
"outputId": "5b12f633-5dcb-43de-f8a2-45ad78df9254"
},
"execution_count": 19,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Metragem Quartos Banheiros Vagas\n",
"count 10008.00 10008.00 10008.00 10008.00\n",
"mean 442.30 3.48 3.88 4.10\n",
"std 7515.38 1.06 1.99 2.82\n",
"min 10.00 1.00 1.00 1.00\n",
"25% 173.00 3.00 2.00 2.00\n",
"50% 290.00 3.00 4.00 4.00\n",
"75% 460.25 4.00 5.00 5.00\n",
"max 750000.00 16.00 40.00 50.00"
],
"text/html": [
"\n",
" <div id=\"df-456f4737-e019-40dc-8915-3fd1a5cf551c\">\n",
" <div class=\"colab-df-container\">\n",
" <div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Metragem</th>\n",
" <th>Quartos</th>\n",
" <th>Banheiros</th>\n",
" <th>Vagas</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>10008.00</td>\n",
" <td>10008.00</td>\n",
" <td>10008.00</td>\n",
" <td>10008.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>442.30</td>\n",
" <td>3.48</td>\n",
" <td>3.88</td>\n",
" <td>4.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>7515.38</td>\n",
" <td>1.06</td>\n",
" <td>1.99</td>\n",
" <td>2.82</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>10.00</td>\n",
" <td>1.00</td>\n",
" <td>1.00</td>\n",
" <td>1.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>173.00</td>\n",
" <td>3.00</td>\n",
" <td>2.00</td>\n",
" <td>2.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>290.00</td>\n",
" <td>3.00</td>\n",
" <td>4.00</td>\n",
" <td>4.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>460.25</td>\n",
" <td>4.00</td>\n",
" <td>5.00</td>\n",
" <td>5.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>750000.00</td>\n",
" <td>16.00</td>\n",
" <td>40.00</td>\n",
" <td>50.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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" const dataTable =\n",
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"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
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" await google.colab.output.renderOutput(dataTable, element);\n",
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" element.appendChild(docLink);\n",
" }\n",
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" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 19
}
]
},
{
"cell_type": "code",
"source": [
"# 5. Descobrir quais são os bairros que não tem nome de Rua:\n",
"bairros_rua_NA = set(imoveis[imoveis[\"Rua\"].isna()][\"Bairro\"])\n",
"print(len(bairros_rua_NA), \"\\n\")\n",
"print(bairros_rua_NA)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "rblRaypbQWgt",
"outputId": "060457b8-120d-4818-9858-70b6afcac10a"
},
"execution_count": 20,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"394 \n",
"\n",
"{'Paineiras do Morumbi', 'Saúde', 'Parque da Vila Prudente', 'Vila Gomes Cardim', 'Vila Brasilina', 'Burgo Paulista', 'Retiro Morumbi', 'Aricanduva', 'Parque dos Príncipes', 'Liberdade', 'Belenzinho', 'Jardim Sílvia (Zona Oeste)', 'Brooklin Paulista', 'Chácara Monte Alegre', 'Vila Santa Clara', 'Vila Antonina', 'Jardim das Bandeiras', 'Parque Imperial', 'Cupecê', 'Jardim Monte Kemel', 'Vila Nova Caledonia', 'Morumbi', 'Jardim Camargo Novo', 'Vila Ester (Zona Norte)', 'Chácara Califórnia', 'Chácara Japonesa', 'Parque Monteiro Soares', 'Sacomã', 'Vila Regente Feijó', 'Vila Pompéia', 'Tatuapé', 'Vila Moinho Velho', 'Praia Paulistinha', \"Jardim D'Abril\", 'Caxingui', 'Vila ESão Pauloerança', 'Vila das Mercês', 'Vila Progredior', 'Vila Matilde', 'Vila Nova Pauliceia', 'Jardim Satélite', 'Jardim Planalto', 'Vila Vasconcelos', 'Chácara Inglesa', 'Cerqueira César', 'Alto de Pinheiros', 'Vila Medeiros', 'Pacaembu', 'Jardim América', 'Jardim Vila Mariana', 'Jardim Franca', 'Vila Ida', 'Vila Santo Estéfano', 'Jardim Petropolis', 'Jardim Teresa', 'Vila Lúcia', 'Super Quadra Morumbi', 'Vila Paulista', 'Jardim Itacolomi', 'Vila Nova Conceição', 'Vila Clementino', 'Vila Ayrosa', 'Vila Prudente', 'Vila São Paulo', 'Jardim Floresta', 'Vila Mazzei', 'Vila Olímpia', 'Jardim Luzitânia', 'Vila Nair', 'Vila Inah', 'Vila Aparecida', 'Jardim Cidade Pirituba', 'Jardim Morro Verde', 'Itaim Bibi', 'Jardim Londrina', 'Vila Jacuí', 'Jardim Virginia Bianca', 'Indianópolis', 'Pinheiros', 'Vila Sonia', 'Parque Santa Rita', 'Jardim Vergueiro (Sacomã)', 'Aclimação', 'Parque Jabaquara', 'Jardim Peri', 'Jardim Brasília (Zona Leste)', 'Jardim Maria Duarte', 'Jardim Pirituba', 'Vila Ré', 'Vila Cruzeiro', 'Vila Carmosina', 'Parque Colonial', 'Vila Agueda', 'Jardim Novo Mundo', 'Vila Maria Alta', 'Vila Macedópolis', 'Campo Belo', 'Vila Mariana', 'Vila Nova Caledônia', 'Vila Gomes', 'Tremembé', 'Real Parque', 'Chácara Meyer', 'Campininha', 'Parque Edu Chaves', 'Vila Talarico', 'Jardim Everest', 'Jardim da Glória', 'Jardim Cidália', 'Água Rasa', 'Jardim Vila Formosa', 'Interlagos', 'Parque Residencial Oratorio', 'Jardim Sarah', 'Jardim Leonor Mendes de Barros', 'Parque Maria Domitila', 'Vila Bancária Munhoz', 'Vila Andrade', 'Parque Novo Mundo', 'Parque Alves de Lima', 'Vila Santos', 'Vila Ema', 'Bosque da Saúde', 'Jardim Santo Elias', 'Jardim Vila Carrão', 'Vila Araguaia', 'Brooklin Novo', 'Alto da Lapa', 'Vila Bela', 'Vila Albertina', 'Vila Canero', 'Cidade Vargas', 'Vila Prado', 'Jardim Cordeiro', 'Vila Elvira', 'Vila Império', 'Parque São Rafael', 'Cidade Ademar', 'Jardim Anália Franco', 'Vila Morse', 'Brás', 'Vila Inglesa', 'Jardim Triana', 'Jardim Rizzo', 'Vila Califórnia', 'Siciliano', 'Água Branca', 'Jardim Vitória Régia', 'Chácara Santo Antônio (Zona Sul)', 'Santa Cecília', 'Cidade Patriarca', 'Vila Curuçá', \"Jardim Panorama D'Oeste\", 'Jardim Ester Yolanda', 'Parque Casa de Pedra', 'Água Fria', 'Lauzane Paulista', 'Jardim Boa Vista (Zona Oeste)', 'Vila Califórnia(Zona Sul)', 'Vila São José (Ipiranga)', 'Parque Santo Antônio (Aricanduva)', 'Vila Isa', 'Vila Mascote', 'Sumaré', 'Jardim Bonfiglioli', 'Butantã', 'Vila Boaçava', 'Jardim Independência', 'Vila Pedroso', 'Jardim Brasil (Zona Sul)', 'Paraíso do Morumbi', 'Consolação', 'Vila Tramontano', 'Jardim Ernestina', 'Jardim Miriam', 'Vila Cardoso Franco', 'Jardim Hípico', 'Jardim Celeste', 'Parque Continental', 'Vila Gustavo', 'Jardim Petrópolis', 'Vila Monumento', 'Vila Sao Paulo', 'Jardim Eliana', 'Jardim Prudência', 'Vila Cláudia', 'Lapa', 'Jardim Sul São Paulo', 'Instituto de Previdência', 'Vila Anhangüera', 'Vila Guedes', 'Jardim Célia (Zona Sul)', 'Jardim Amália', 'Vila Caraguatá', 'Jardim Santo Amaro', 'Barra Funda', 'Vila Santa Maria', 'Mooca', 'Vila Morumbi', 'Vila Bertioga', 'Jardim Cidalia', 'Vila Perus', 'Vila Ponte Rasa', 'Jardim Morumbi', 'Parada XV de Novembro', 'Vila Cordeiro', 'Vila Nivi', 'Vila Romana', 'Jardim Jussara', 'Parque Savoy City', 'Vila Paiva', 'Conjunto Residencial Vista Verde', 'Chácara Mafalda', 'Jardim Guedala', 'Planalto Paulista', 'Vila Mafra', 'Jardim Marajoara', 'Vila Salete', 'Chácara Tatuapé', 'Vila das Belezas', 'Vila Darli', 'Granja Julieta', 'Jardim Dom Bosco', 'Conjunto Residencial Morada do Sol', 'Jardim das Acácias', 'Santana', 'Vila Gea', 'Brasilândia', 'Jardim Consórcio', 'Vila Monte Alegre', 'Jardim Colombo', 'Vila Carbone', 'Jardim Heliomar', 'Jardim Palmares (Zona Sul)', 'Vila Analia', 'Vila Carrão', 'Vila Alpina', 'Tucuruvi', 'Jardim Guanca', 'Cidade Centenário', 'Vila Constancia', 'Vila Arriete', 'Vila Leopoldina', 'Jaraguá', 'Vila América', 'Sumarezinho', 'Vila Firmiano Pinto', 'Vila Gumercindo', 'Jardim Patente', 'Jardim Japão', 'Jardim Panorama', 'Chora Menino', 'Jardim Paulista', 'Parque Boturussu', 'Jardim do Colégio (Zona Norte)', 'Sítio do Mandaqui', 'Jardim Textil', 'Jardim Rincão', 'Fazenda Morumbi', 'Parque Nações Unidas', 'Vila Congonhas', 'Conjunto Residencial Butantã', 'Itaquera', 'Vila São Silvestre', 'Ipiranga', 'Cidade Jardim', 'Jabaquara', 'Lar São Paulo', 'Vila Sílvia', 'Conjunto Habitacional Castro Alves', 'Vila Oratório', 'Jardim Castelo', 'Vila Ivg', 'Jardim Jabaquara', 'Vila Dom Pedro I', 'Vila Antônio', 'Jardim Coimbra', 'Nova Piraju', 'Vila Barbosa', 'Santo Amaro', 'Parque São Domingos', 'Parque Guarani', 'Jardim Ana Maria', 'Jardim Maria Estela', 'Vila Primavera', 'Vila Madalena', 'Moema', 'Vila Paulicéia', 'Vila Nova Curuçá', 'Vila Lageado', 'Penha de França', 'Vila Anglo Brasileira', 'Vila Sônia', 'Perdizes', 'Alto da Boa Vista', 'Cidade Domitila', 'Jardim das Camélias', 'Jardim Umuarama', 'Jardim Bélgica', 'Brooklin', 'Alto da Mooca', 'Jardim Santa Cruz (Campo Grande)', 'Jardim Trussardi', 'Vila Conde do Pinhal', 'Parque da Mooca', 'Rio Pequeno', 'Jardim Ampliação', 'Jardim Ponte Rasa', 'Jardim Danfer', 'Jaguaré', 'Cidade São Mateus', 'Jardim Santa Helena', 'Vila Virginia', 'Vila Antonieta', 'Vila Polopoli', 'Jardim Analia Franco', 'Vila Santana', 'Vila Paulo Silas', 'Cidade Antônio Estevão de Carvalho', 'Vila São Francisco', 'Paraíso', 'Vila Marieta', 'Higienópolis', 'Cambuci', 'Vila Buenos Aires', 'Cidade Dutra', 'Vila Tiradentes', 'Parque São Lucas', 'Jardim Marília', 'Lapa de Baixo', 'Jardim Santa Maria', 'Jardim Sabará', 'Vila Mira', 'Parque Peruche', 'Vila Londrina', 'Jardim Guarau', 'Vila Palmeiras', 'Capão Redondo', 'Boaçava', 'Vila Comercial', 'Jardim Leonor', 'Jardim Ester', 'Jardim Monte Alegre', 'Vila Formosa', 'Vila Vera', 'Parque da Lapa', 'Jardim Taboão', 'Vila Simone', 'Jardim São Carlos (Zona Sul)', 'Vila Santa Catarina', 'Jardim das Vertentes', 'Vila Guarani (Z Sul)', 'Engenheiro Goulart', 'Mirandópolis', 'Quinta da Paineira', 'Vila da Saúde', 'Vila Adalgisa', 'Vila Ipojuca', 'Vila Brasílio Machado', 'Jardim Vivan', 'Vila Sabrina', 'Vila Suzana', 'Bela Aliança', 'Chácara Flora', 'Vila Gertrudes', 'Jardim Paulistano', 'Vila Rui Barbosa', 'Itaberaba', 'Jardim Europa', 'Jardim Tremembe', 'Cidade Monções', 'Jardim Aeroporto', 'Jardim Campo Grande', 'Parque Mandaqui', 'Vila do Encontro', 'Vila Romano', 'Jardim Viana', 'Vila Moraes', 'Jardim Nice', 'Vila Campo Grande', 'Vila Marari', 'Cidade Mãe do Céu', 'Jardim Patente Novo', 'Paraisópolis', 'Vila Bela Aliança', 'Vila Alexandria', 'Jardim da Saúde', 'Água Funda', 'City América', 'Jardim Gonzaga', 'Chácara Belenzinho', 'Ferreira', 'Vila Romero', 'Jardim dos Estados'}\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"---\n",
"# Aula 2\n",
"[Aula 02 - Tratamento de dados e primeiros gráficos | Imersão Dados 4ª edição](https://www.youtube.com/watch?v=diFXICKmgi0)\n",
"\n",
"---"
],
"metadata": {
"id": "mvF1mTMkgyDd"
}
},
{
"cell_type": "code",
"source": [
"imoveis['Valor'][0:10]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-N9hr4WqDwdb",
"outputId": "d8fbba83-3ebf-4e7c-c315-333758a507ee"
},
"execution_count": 21,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 R$ 7.000.000\n",
"1 R$ 3.700.000\n",
"2 R$ 380.000\n",
"3 R$ 685.000\n",
"4 R$ 540.000\n",
"5 R$ 1.980.000\n",
"6 R$ 850.000\n",
"7 R$ 450.000\n",
"8 R$ 199.900\n",
"9 R$ 585.000\n",
"Name: Valor, dtype: object"
]
},
"metadata": {},
"execution_count": 21
}
]
},
{
"cell_type": "code",
"source": [
"# Remover tudo que não for dígito, ponto, ou vírgula:\n",
"imoveis['Valor_inteiro'] = [re.sub(r'[^\\d.,]', '', x) for x in imoveis['Valor']]\n",
"print('Contém \".\":', sum(imoveis['Valor_inteiro'].str.find('.') != -1))\n",
"print('Contém \",\":', sum(imoveis['Valor_inteiro'].str.find(',') != -1))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "iMnaELFwEB5V",
"outputId": "ea1c2c16-76bd-4397-c570-5aeafaa09b3b"
},
"execution_count": 22,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Contém \".\": 10008\n",
"Contém \",\": 0\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Só contém separadores de milhar e 'R$', aproveitar só os números:\n",
"imoveis['Valor_inteiro'] = [re.sub(r'\\D', '', x) for x in imoveis['Valor']]\n",
"imoveis['Valor_inteiro'] = imoveis['Valor_inteiro'].astype('int64')\n",
"imoveis['Valor_inteiro'][0:10]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "F6H86ry9GlMT",
"outputId": "ac4f35bd-aca5-4b40-86f2-9170e4d37364"
},
"execution_count": 23,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 7000000\n",
"1 3700000\n",
"2 380000\n",
"3 685000\n",
"4 540000\n",
"5 1980000\n",
"6 850000\n",
"7 450000\n",
"8 199900\n",
"9 585000\n",
"Name: Valor_inteiro, dtype: int64"
]
},
"metadata": {},
"execution_count": 23
}
]
},
{
"cell_type": "code",
"source": [
"round(imoveis['Valor_inteiro'].mean(), 2)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ZreoI05WHiI6",
"outputId": "086c8762-98a3-4482-f8d9-cdecd5e63a9d"
},
"execution_count": 24,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"2794169.56"
]
},
"metadata": {},
"execution_count": 24
}
]
},
{
"cell_type": "code",
"source": [
"imoveis[['Moeda', 'Valor_num', 'Tipo_valor']] = imoveis['Valor'].str.split(expand = True)\n",
"imoveis[['Valor', 'Valor_inteiro', 'Moeda', 'Valor_num', 'Tipo_valor']].head(10)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 363
},
"id": "txNnJ_LxH7pI",
"outputId": "f5efbf49-4242-448a-b689-67321c1d12e6"
},
"execution_count": 25,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Valor Valor_inteiro Moeda Valor_num Tipo_valor\n",
"0 R$ 7.000.000 7000000 R$ 7.000.000 None\n",
"1 R$ 3.700.000 3700000 R$ 3.700.000 None\n",
"2 R$ 380.000 380000 R$ 380.000 None\n",
"3 R$ 685.000 685000 R$ 685.000 None\n",
"4 R$ 540.000 540000 R$ 540.000 None\n",
"5 R$ 1.980.000 1980000 R$ 1.980.000 None\n",
"6 R$ 850.000 850000 R$ 850.000 None\n",
"7 R$ 450.000 450000 R$ 450.000 None\n",
"8 R$ 199.900 199900 R$ 199.900 None\n",
"9 R$ 585.000 585000 R$ 585.000 None"
],
"text/html": [
"\n",
" <div id=\"df-45c4136a-2a64-40b0-9a56-20c278a58246\">\n",
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" <div>\n",
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" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Valor</th>\n",
" <th>Valor_inteiro</th>\n",
" <th>Moeda</th>\n",
" <th>Valor_num</th>\n",
" <th>Tipo_valor</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>R$ 7.000.000</td>\n",
" <td>7000000</td>\n",
" <td>R$</td>\n",
" <td>7.000.000</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>R$ 3.700.000</td>\n",
" <td>3700000</td>\n",
" <td>R$</td>\n",
" <td>3.700.000</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>R$ 380.000</td>\n",
" <td>380000</td>\n",
" <td>R$</td>\n",
" <td>380.000</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>R$ 685.000</td>\n",
" <td>685000</td>\n",
" <td>R$</td>\n",
" <td>685.000</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>R$ 540.000</td>\n",
" <td>540000</td>\n",
" <td>R$</td>\n",
" <td>540.000</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>R$ 1.980.000</td>\n",
" <td>1980000</td>\n",
" <td>R$</td>\n",
" <td>1.980.000</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>R$ 850.000</td>\n",
" <td>850000</td>\n",
" <td>R$</td>\n",
" <td>850.000</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>R$ 450.000</td>\n",
" <td>450000</td>\n",
" <td>R$</td>\n",
" <td>450.000</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>R$ 199.900</td>\n",
" <td>199900</td>\n",
" <td>R$</td>\n",
" <td>199.900</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>R$ 585.000</td>\n",
" <td>585000</td>\n",
" <td>R$</td>\n",
" <td>585.000</td>\n",
" <td>None</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-45c4136a-2a64-40b0-9a56-20c278a58246')\"\n",
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" }\n",
"\n",
" .colab-df-convert:hover {\n",
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" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-45c4136a-2a64-40b0-9a56-20c278a58246 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-45c4136a-2a64-40b0-9a56-20c278a58246');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 25
}
]
},
{
"cell_type": "code",
"source": [
"imoveis_tipo_val_nulo = imoveis['Tipo_valor'].isnull()\n",
"imoveis_tipo_val_nulo.head(10)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5z4fuYsyJvOr",
"outputId": "6195b452-6f2d-4a55-d45e-7e54a266428b"
},
"execution_count": 26,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 True\n",
"1 True\n",
"2 True\n",
"3 True\n",
"4 True\n",
"5 True\n",
"6 True\n",
"7 True\n",
"8 True\n",
"9 True\n",
"Name: Tipo_valor, dtype: bool"
]
},
"metadata": {},
"execution_count": 26
}
]
},
{
"cell_type": "code",
"source": [
"imoveis_venda = imoveis[imoveis_tipo_val_nulo]\n",
"imoveis_venda['Tipo_valor'].unique() ## Confirmar que só sobraram Tipo_valor nulos (relativos a anúncios de venda)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "CZ38F9BRL23o",
"outputId": "8b0db1c0-93b1-4e3f-8eed-72b5c9088f93"
},
"execution_count": 27,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([None], dtype=object)"
]
},
"metadata": {},
"execution_count": 27
}
]
},
{
"cell_type": "code",
"source": [
"# De 10.008 para 9.156 imóveis\n",
"print('Total de imóveis:', len(imoveis))\n",
"print('Total à venda:', len(imoveis_venda))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "p82lVU0fOvz1",
"outputId": "88054a54-8b09-424d-f4ab-f4470a3c58fc"
},
"execution_count": 28,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Total de imóveis: 10008\n",
"Total à venda: 9156\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"imoveis_venda.describe()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 300
},
"id": "4UENVOEuf5Yh",
"outputId": "21b552fc-2937-4cf4-b432-52bc60bddad3"
},
"execution_count": 29,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Metragem Quartos Banheiros Vagas Valor_inteiro\n",
"count 9156.00 9156.00 9156.00 9156.00 9156.00\n",
"mean 434.42 3.45 3.80 3.96 3052202.96\n",
"std 7856.42 1.05 1.97 2.60 3944208.19\n",
"min 10.00 1.00 1.00 1.00 13000.00\n",
"25% 168.00 3.00 2.00 2.00 890000.00\n",
"50% 276.00 3.00 4.00 4.00 1800000.00\n",
"75% 450.00 4.00 5.00 5.00 3500000.00\n",
"max 750000.00 16.00 40.00 50.00 65000000.00"
],
"text/html": [
"\n",
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" }\n",
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" }\n",
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" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Metragem</th>\n",
" <th>Quartos</th>\n",
" <th>Banheiros</th>\n",
" <th>Vagas</th>\n",
" <th>Valor_inteiro</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>9156.00</td>\n",
" <td>9156.00</td>\n",
" <td>9156.00</td>\n",
" <td>9156.00</td>\n",
" <td>9156.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>434.42</td>\n",
" <td>3.45</td>\n",
" <td>3.80</td>\n",
" <td>3.96</td>\n",
" <td>3052202.96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>7856.42</td>\n",
" <td>1.05</td>\n",
" <td>1.97</td>\n",
" <td>2.60</td>\n",
" <td>3944208.19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>10.00</td>\n",
" <td>1.00</td>\n",
" <td>1.00</td>\n",
" <td>1.00</td>\n",
" <td>13000.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>168.00</td>\n",
" <td>3.00</td>\n",
" <td>2.00</td>\n",
" <td>2.00</td>\n",
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" <td>4.00</td>\n",
" <td>4.00</td>\n",
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" <tr>\n",
" <th>75%</th>\n",
" <td>450.00</td>\n",
" <td>4.00</td>\n",
" <td>5.00</td>\n",
" <td>5.00</td>\n",
" <td>3500000.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>750000.00</td>\n",
" <td>16.00</td>\n",
" <td>40.00</td>\n",
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" <td>65000000.00</td>\n",
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"\n",
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"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
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" element.appendChild(docLink);\n",
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]
},
{
"cell_type": "code",
"source": [
"imoveis_venda.describe(include = 'all')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 394
},
"id": "VJzJGwuIgZlD",
"outputId": "0773f433-068d-43fb-8b23-ffe124e6e255"
},
"execution_count": 30,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Rua Bairro Cidade Metragem Quartos \\\n",
"count 6107 9146 9156 9156.00 9156.00 \n",
"unique 3006 697 1 NaN NaN \n",
"top Rua Alberto Faria Alto de Pinheiros São Paulo NaN NaN \n",
"freq 24 369 9156 NaN NaN \n",
"mean NaN NaN NaN 434.42 3.45 \n",
"std NaN NaN NaN 7856.42 1.05 \n",
"min NaN NaN NaN 10.00 1.00 \n",
"25% NaN NaN NaN 168.00 3.00 \n",
"50% NaN NaN NaN 276.00 3.00 \n",
"75% NaN NaN NaN 450.00 4.00 \n",
"max NaN NaN NaN 750000.00 16.00 \n",
"\n",
" Banheiros Vagas Valor Valor_inteiro Moeda Valor_num \\\n",
"count 9156.00 9156.00 9156 9156.00 9156 9156 \n",
"unique NaN NaN 939 NaN 1 939 \n",
"top NaN NaN R$ 2.500.000 NaN R$ 2.500.000 \n",
"freq NaN NaN 182 NaN 9156 182 \n",
"mean 3.80 3.96 NaN 3052202.96 NaN NaN \n",
"std 1.97 2.60 NaN 3944208.19 NaN NaN \n",
"min 1.00 1.00 NaN 13000.00 NaN NaN \n",
"25% 2.00 2.00 NaN 890000.00 NaN NaN \n",
"50% 4.00 4.00 NaN 1800000.00 NaN NaN \n",
"75% 5.00 5.00 NaN 3500000.00 NaN NaN \n",
"max 40.00 50.00 NaN 65000000.00 NaN NaN \n",
"\n",
" Tipo_valor \n",
"count 0 \n",
"unique 0 \n",
"top NaN \n",
"freq NaN \n",
"mean NaN \n",
"std NaN \n",
"min NaN \n",
"25% NaN \n",
"50% NaN \n",
"75% NaN \n",
"max NaN "
],
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" <th></th>\n",
" <th>Rua</th>\n",
" <th>Bairro</th>\n",
" <th>Cidade</th>\n",
" <th>Metragem</th>\n",
" <th>Quartos</th>\n",
" <th>Banheiros</th>\n",
" <th>Vagas</th>\n",
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" <th>Tipo_valor</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>6107</td>\n",
" <td>9146</td>\n",
" <td>9156</td>\n",
" <td>9156.00</td>\n",
" <td>9156.00</td>\n",
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" <tr>\n",
" <th>std</th>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>7856.42</td>\n",
" <td>1.05</td>\n",
" <td>1.97</td>\n",
" <td>2.60</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>10.00</td>\n",
" <td>1.00</td>\n",
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" <tr>\n",
" <th>25%</th>\n",
" <td>NaN</td>\n",
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" <td>3.00</td>\n",
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" <tr>\n",
" <th>max</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>750000.00</td>\n",
" <td>16.00</td>\n",
" <td>40.00</td>\n",
" <td>50.00</td>\n",
" <td>NaN</td>\n",
" <td>65000000.00</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d34d1e22-8d04-452d-a7c9-5f6925ad142e')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
" \n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
" <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
" </svg>\n",
" </button>\n",
" \n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-d34d1e22-8d04-452d-a7c9-5f6925ad142e button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-d34d1e22-8d04-452d-a7c9-5f6925ad142e');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 30
}
]
},
{
"cell_type": "code",
"source": [
"imoveis_venda['Valor_inteiro'].hist(bins = 50, figsize=(20, 5))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 350
},
"id": "OLSdKuwRgNSD",
"outputId": "6159e248-9a52-4801-f20d-e43b5c56b53f"
},
"execution_count": 31,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f662c09c390>"
]
},
"metadata": {},
"execution_count": 31
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1440x360 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [
"import matplotlib.pyplot as plt\n",
"import seaborn as sb"
],
"metadata": {
"id": "BFmDQB0snjXw"
},
"execution_count": 32,
"outputs": []
},
{
"cell_type": "code",
"source": [
"sb.set()\n",
"plt.figure(figsize=(16, 9))\n",
"hist = sb.histplot(data = imoveis_venda, x = 'Valor_inteiro', kde = True)\n",
"hist.set_title('Histograma de Valores de imóveis à venda')\n",
"hist.set_xlabel('Dezenas de milhões de reais (R$)')\n",
"hist.set_ylabel('Quantidade de imóveis')\n",
"plt.xlim((0, 15_000_000))\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 573
},
"id": "gjhq_MHTam0H",
"outputId": "33baa04e-285a-47f2-ceda-8d549e807098"
},
"execution_count": 33,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1152x648 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"# Desafios Aula 2\n",
"\n",
"1. Criar uma escala de valores em milhões.\n",
"2. Deixar o gráfico do histograma de valores legível (alterar labels, cores, título, escala).\n",
"3. Preço do metro quadrado por bairro e plotar em um gráfico ideal.\n",
"4. Explorar as bibliotecas de visualizações e colocar as suas conclusão.\n",
"5. Pesquisar um visualização para analisar os quartis, mediana e outliers."
],
"metadata": {
"id": "gfsyXX-7fakA"
}
},
{
"cell_type": "code",
"source": [
"# 1. Criar histograma com valores em milhões.\n",
"(imoveis_venda['Valor_inteiro'] / 1_000_000).hist(bins = 100, figsize=(26, 8))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 501
},
"id": "RbHAHaUYPcq-",
"outputId": "a4c6801e-2318-44fd-dee4-351f21566d95"
},
"execution_count": 34,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f661d4ff4d0>"
]
},
"metadata": {},
"execution_count": 34
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1872x576 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"imoveis_venda.dtypes"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "vJi1SMRSCl5h",
"outputId": "2edb4976-ea46-4811-9d7d-8105f4cc5a10"
},
"execution_count": 35,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Rua object\n",
"Bairro object\n",
"Cidade object\n",
"Metragem int64\n",
"Quartos int64\n",
"Banheiros int64\n",
"Vagas int64\n",
"Valor object\n",
"Valor_inteiro int64\n",
"Moeda object\n",
"Valor_num object\n",
"Tipo_valor object\n",
"dtype: object"
]
},
"metadata": {},
"execution_count": 35
}
]
},
{
"cell_type": "code",
"source": [
"# 3. Preço do metro quadrado por bairro e plotar em um gráfico ideal.\n",
"imoveis_venda[\"Custo_por_metro2\"] = imoveis_venda[\"Valor_inteiro\"] / imoveis_venda[\"Metragem\"].astype(float)\n",
"m2_por_bairro_media = imoveis_venda[[\"Bairro\", \"Custo_por_metro2\"]].groupby('Bairro').mean()\n",
"m2_por_bairro_mediana = imoveis_venda[[\"Bairro\", \"Custo_por_metro2\"]].groupby('Bairro').median()\n",
"m2_por_bairro_mediana.sort_values(by='Custo_por_metro2', ascending=False).plot(rot=90, figsize=(26, 8))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 720
},
"id": "AAvtENAeB-vy",
"outputId": "4e2b5c8f-3eca-4e1b-ef59-4f6278bea87e"
},
"execution_count": 40,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" \n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f661ac36950>"
]
},
"metadata": {},
"execution_count": 40
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1872x576 with 1 Axes>"
],
"image/png": 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+HnvsMc2YMUOlpaVatWqVdu7c6XQsIKH4joN0M2vWLI0cOVJbtmzRggULtGLFCp1yyilOxwIS5v7771dZWZl69eol0zQlsSMKSEcU5VMEA12RLkpLSzVgwAAtXrz4iMf4IoNU17Fjx7hjr9errKwsh9IAydGrVy/5/X4ZhqFx48apqKhIM2bMcDoWkDCx33G8Xq+2bNmis846i+84SFnV1dUaN26cli1bpv79+6tfv34aP368pkyZ4nQ0ICHWr1+vDRs28D0eSHMU5du42tpa7d+/P3olwfPPPx/d8jdkyBB16tTJyXhAq5szZ44ktnYjPfXv3z/avibC5XLp3HPP1b333qvevXs7lAxIDJcr9FW1a9eu2rhxo7p3766DBw86nApIrMO/42zdulVLlixxKA2QeG63W5KUnZ2tXbt2qXPnztq/f7/DqYDEOemkk6KfewDpi6J8G/fggw/qlFNO0dVXXy0ptM37nHPOUVNTkz766CPNmjXL4YRAYrz11ls666yzlJOTo5UrV2rz5s2aNGmSevTo4XQ0IGFmzJihjIwMjR07VrZtq7S0VNXV1erRo4dmz57NYhVSztVXX62DBw9q2rRpuummm1RTU6Pbb7/d6VhAUp122mn68MMPnY4BJMzAgQN14MABFRcXq6ioSB6PR5dcconTsYCEue2223T99ddr8ODB8ng80fMTJkxwMBWAZDNs27adDoFjd9lll2nVqlXRVdbCwkKVlZXJtm1deeWVWrFihcMJgcQoKCjQmjVrtHXrVk2bNk2jR4/Wa6+9pmXLljkdDUiYoqIilZSUtHiuoKBAzzzzjEPJAACtJbanfDAY1ObNm/XSSy8d8e9/IBXt2rVLtbW1OuOMM5yOAiTMjBkz9Omnn+rMM8+Mmw81d+5cB1MBSDaulG/jTNOM2/Z07bXXSgr11o6dXA+kGpfLJcMw9PLLL6u4uFhXXXWVXnjhBadjAQnV0NCgHTt2RHeE7NixQ/X19ZLEwFeklJUrV8q2bV1xxRVx55966ilZlqUxY8Y4lAxIvNie8i6XS6eccor++7//28FEQGK8/fbbqq6u1k9+8pPouW7duunFF19UbW2tvve97zmYDkicDz/8UOvWrTuiLSWA9EJRvo2rqamRbdvRf5kXFhZKCl1VQ89VpDK/369//OMf+utf/6p7771XkhQIBBxOBSTW9OnTNXbsWJ1zzjmybVsfffSR7r77btXV1WnEiBFOxwNazcqVK/Xoo48ecX7kyJG6+uqrKcojpdGKDOniT3/6U/R7fKyzzjpL//mf/6nHHnss+aGAJDj11FNVX1+vnJwcp6MAcBBF+Tbu/PPP1yOPPKLJkyfHnV+yZInOP/98h1IBiTdt2jTdeeeduuCCC3T66adr+/bt6tmzp9OxgIS65JJLNGDAAL3//vuSpH79+kUHel9//fVORgNald/vV25u7hHnc3Nz5ff7HUgEJB47RJBu6urqWpwHdfLJJzPoFSktNzdXRUVFGjJkSFxP+VtvvdXBVACSjaJ8G3fzzTdrwoQJ2rhxo/r16ydJ2rx5s6qqqrR8+XKH0wGJM3z4cA0fPjx63KtXL/3pT39yMBGQHJ07d9aFF14Y3RnS0NCgrKwsh1MBrevQoUNHfayuri6JSYDkYYcI0s1X7exubGxMYhIguXr37q3evXs7HQOAwyjKt3GdOnVSWVmZnnnmGX300UeSpMsvv1yjRo2iSIOU9+qrr2rLli1x8xOmTJniYCIgsV588UXNmTNHe/fulaRo+7ItW7Y4nAxoXQMGDNAjjzyiSZMmxZ1/9NFH6TGMlMUOEaSbk08+Wa+99pp++MMfxp3ftGmTunXr5lAqILECgYC++OILzZkzx+koABxGUT4FZGZmaty4cU7HAJJqwYIF2rx5s7Zu3aqLL75YGzZs0KBBg5yOBSTU/Pnz9eCDD+q8886TaZpOxwES5pZbbtEvfvELbdiwIboT8P3339f+/fv1f//3fw6nAxKDHSJIN9OnT9f111+vcePG6dxzz5UU+nf9ypUrtXDhQofTAYlhWZb++c9/Oh0DwHGAv9EDaJPKy8u1ZMkSderUSffcc49KSkoYboyU1759e33ve9+jII+U17lzZ5WVlWnMmDHyer3yer0qKipSWVmZunTp4nQ8ICEiO0QOxw4RpKpzzz1XS5cu1a5du/S73/1Ov/vd77Rr1y49+uij0SI9kIouuOAC3XPPPXr//fe1devW6A+A9GLYtm07HQIAvqkxY8Zo9erVuuyyy7Rq1Sq53W4VFBTomWeecToakDCLFi1SXl6eRo4cqYyMjOh52pUBQNu3b98+/eIXv1CHDh1a3CHCghQApIZhw4Ydcc4wDG3YsMGBNACcQlEeQJt09dVXa9GiRZo3b55qamrUpUsXvfPOO1q5cqXT0YCE6du3b/S+YRj0lAeAFNPY2Bg3K+qss85iVhQAAEAKoiifQmpqarR9+/a4oZfnn3++g4mAxNm3b5/atWunQCCgpUuXqqamRldddRVDoQAAAAAAx7VNmzZp27Zt+sUvfqGqqiodOnRIvXr1cjoWgCSiKJ8i1q5dq3nz5unQoUPKz89XZWWl+vbtq9LSUqejAa0uEAho9uzZTKwHAAAAALQpDz/8sMrLy7V37169+OKL+uKLLzRjxgytWLHC6WgAksjldAC0joULF6qkpEQTJ05UWVmZ/va3v2ndunVOxwISgon1SFcVFRWaPXu2Kioq5PV6o+dpX4NUxk5AAACQSp599lmtXr1a48aNkySdeOKJqq2tdTgVgGSjKJ8iXC6XOnXqpEAgIEkaPHiwFixY4HAqIHEiE+sLCwuVnZ0dPX/aaac5mApIrLvuukvTp0/X3LlztXjxYi1fvlw5OTlOxwIShp2AAJD6uOgA6SYzM1NutzvunGEYDqUB4BSK8inC4/HItm317NlTjz/+uLp37676+nqnYwEJ89xzz0mSXnrppeg5JtYj1Xm9Xg0aNEi2bSu9FYcpAAAXHElEQVQ/P18zZszQmDFjNHnyZKejAQnBTkCkK3aIIJ1w0QHSzYknnqi3335bhmEoGAxq4cKFOv30052OBSDJKMqniGnTpqm2tlY333yz7rrrLtXU1Gj27NlOxwISZuPGjU5HAJLOsixJUvv27VVRUaGuXbuqurra4VRA4rATEOmIHSJIN1x0gHTzm9/8RjNnztQnn3yifv36aeDAgXy/AdIQRfkUMWjQIElSXl6eHnvsMWfDAEm0Z88evfvuuzr11FN15plnOh0HSKiRI0equrpakydPVnFxsYLBoKZOnep0LCBh2AmIdMQOEaQbLjpAuunSpYseffRRNTQ0KBgMsjMESFMU5du45cuXf+XjEyZMSFISIDnWrl2rO+64Q+3atdMdd9yhu+++W927d9fnn3+uadOm6corr3Q6IpAw1113nSTpoosu0ptvvqmmpibl5uY6nApIHHYCIh2xQwTphosOkC5qa2u1f/9+nXLKKZJCrVgjbcqGDBmiTp06ORkPQJJRlG/jPvjgA6cjAEm1cOFCrVq1SocOHdJ1112n1atXq0+fPvryyy81ceJEivJIeZWVlaqsrIwWayRp6NChDiYCEoedgEhH7BBBuuGiA6SLBx98UKeccoquvvpqSdL8+fN1zjnnqKmpSR999JFmzZrlcEIAyURRvo2bO3eu0xGApDJNU3369JEkdevWLXq/a9eu0a2vQKp64IEHtHLlSvXp00emaUoKDTimKI9Uw05ApDN2iCBdbN269SsfP+2005KUBEiOt956SzNnzowet2vXTn/4wx9k2zYXlwFpiKJ8G/fOO+9owIABKi8vb/FxCjVINYZhRO9nZGTEPRYpUgKp6oUXXtD69eu5egwpj52ASGfsEEG6+KpBroZhaMOGDUlMAySeaZpyu93R42uvvVZS6PMeaWMDIH1QlG/jysrKNGDAAC1evPiIx7h6Eqlo+/btGjt27BH3bdvWZ5995mAyIPG6dOlCQR5pgZ2ASEfsEEG62bhxo9MRgKSqqamRbdvRC80KCwslScFgUAcPHnQyGgAHUJRv4y677DJJ0uOPP+5wEiA5Hn74YacjAEkX2Q113nnn6cYbb9SIESPidoqwAItUw05ApCN2iCDdeL1eeTweNTQ0tPh4VlZWkhMBiXX++efrkUceOWKXyJIlS3T++ec7lAqAUwzbtm2nQ+DYDR8+XJZlacyYMSosLFR+fr7TkQAAreyqq6466mOGYWjZsmVJTAMk3m9+8xvde++9LX72+cwDQGq4/PLLVVpaqr59+8owDMWWJgzD0JYtWxxMB7S+qqoqTZgwQR06dFC/fv0kSZs3b1ZVVZWWL1+uzp07O5wQQDJRlE8Br7/+ukpLS7V+/XoNGDBAY8aM0cUXXyyXi40QAACg7Xn77bc1cOBAp2MAScUOEaSbxsZGZWZmOh0DSKrGxkY988wz+uijjyRJZ511lkaNGsXOECANUZRPIXV1dVq7dq1KSkr0+eefq6CgQLfffrvTsQAAraiyslKVlZUKBALRcxRqkGrYCYh0xA4RpJuBAwdqxIgRGjt2rM477zyn4wAAkFQU5VOMbdsqLy/XH//4R23btk3vvfee05EAAK3kgQce0MqVK9WnTx+ZpimJQg1SFzsBkW7YIYJ0s2PHDpWVlam0tFSZmZkqKipSYWEhLTwAAGmBonyK+PTTT7V69WqtWbNG+fn5KioqUkFBgdq1a+d0NCBhtm7dqjfeeEOSdMEFF6hPnz4OJwIS6yc/+YlKS0uVm5vrdBQgadgJiHTBDhGks02bNqmkpEQbN27U97//fY0dO1YXX3yx07EAAEgYivJt3JNPPqmSkhJVVlaqoKBARUVF6tu3r9OxgIQrKyvTAw88EG3b8fLLL+vmm2/W6NGjHU4GJM6VV16pJ554wukYQNKxExDpgh0iSHc7d+7U9OnT9eGHHzLoFQCQ0ijKt3GTJk2Kfll3u91OxwGSZvTo0VqyZIm6dOkiSdq7d68mTpyoNWvWOJwMSJz7779fX3zxhUaMGKGMjIzoeXrKI1WxExDpih0iSCe2beuVV15RSUmJXn31VQ0ePFhjxozRRRdd5HQ0IGGYEwWAojyANmn06NFHFOBbOgekEob/IV2wExBghwhS3/bt21VaWqqysjJ17NhRRUVFGj16tDp06OB0NCCh7r//fpWVlalXr17MiQLSGPsgAbRJp5xyiv7whz9o/PjxkqSVK1eqR48eDqcCEuvxxx8/4lxjY6MDSYDEWr9+va677jp2AiItHW2HCJBqfv7zn2vUqFH63//9X5199tlOxwGSZv369dqwYYOysrKcjgLAQVwpD6BNqqqq0pw5c/Taa6/JMAz98Ic/1B133KFOnTo5HQ1IinfffVerVq3Siy++qLfeesvpOACAb4kdIkg3Xq9XHo/H6RhA0l1zzTVasmQJ80KANEdRHgCANqKqqkqlpaVavXq1du7cqV//+tcaNWqUunXr5nQ0AMC3xKwoAEgPW7Zs0QMPPKDBgwfHLUxNmDDBwVQAko2iPIA2pby8/CsfZzgOUtH69eu1evVq/f3vf9cll1yiyy67TLfccos2btzodDQAAAAA38CMGTP06aef6swzz5RlWdHzc+fOdTAVgGRjrwyANmXx4sVHfcwwDIrySElTpkzRoEGD9Pzzz6tjx46SQp93AAAAAG3Lhx9+qHXr1vF9HkhzFOUBtCktDboEUt2iRYtUUlKiSy+9VMOGDVNhYaHTkQAAAFpNZWWlKisrFQgEoue42Aap6tRTT1V9fb1ycnKcjgLAQbSvAdAmtdTGJjc3V2eccYby8vIcSAQk3oEDB7RmzRqVlJRo69atmjRpkgoKCtS7d2+nowEAAByT+++/X2VlZerVq5dM05QU2hG4bNkyh5MBiXHjjTfqww8/1JAhQ+J6yt96660OpgKQbBTlAbRJ48eP1+bNm3XmmWdKkj7++GOdeeaZ+vLLLzVnzhz9+Mc/djghkFgffvihVq9ereeee05vvPGG03EAAACOyU9/+lM9/fTTysrKcjoKkBR/+tOfWjw/ZcqUJCcB4CSK8gDapFtuuUXXXHONzjnnHEmhAuXSpUt1ww036MYbb9TTTz/tcEIgObxeb9wVNgAAAG3JNddcoyVLlsjlorsuACB98F89AG1SRUVFtCAvSWeffbY+/vhj9enTR6w1Ip1QkAcAAG3Zbbfdpuuvv16DBw+O+14zYcIEB1MBre/555/XpZdequXLl7f4OJ95IL1QlAfQJmVlZenZZ5/VqFGjJEnPPvusMjMzJYkp9gAAAEAb8fDDD2vv3r3asmWLLMtyOg6QMJ988okuvfRSffDBB05HAXAcoH0NgDZp27ZtuuWWW/TJJ5/IMAyddtppmjdvnrp37653331XgwcPdjoiAAAAgH/jpz/9qdatW8eFNQCAtEJRHkCbVltbK0nKzc11OAmQHDU1Ndq+fbuampqi584//3wHEwEAABy7yZMn6/e//71ycnKcjgIk1NHa1kTQvgZIL7SvAdBmVVZWqrKyUoFAIHpu6NChDiYCEmvt2rWaN2+eDh06pPz8fFVWVqpv374qLS11OhoAAMAxyc3NVVFRkYYMGRLXU/7WW291MBXQ+iJta6qrq/Xmm29q0KBBkqRNmzbpBz/4AUV5IM1QlAfQJj3wwANauXKl+vTpI9M0JYV6yVOURypbuHChSkpKNHHiRJWVlelvf/ub1q1b53QsAACAY9a7d2/17t3b6RhAws2dO1dSaHfI008/rR49ekiSduzYofvuu8/JaAAcQFEeQJv0wgsvaP369bStQVpxuVzq1KlTdHfI4MGDtWDBAodTAQAAHLspU6Y4HQFIql27dkUL8pLUo0cP7dy508FEAJxAUR5Am9SlSxcK8kg7Ho9Htm2rZ8+eevzxx9W9e3fV19c7HQsAAOAbe/7553XppZcetc82rTyQqjp37qyHHnpI48aNkyStXr1anTt3djgVgGSjKA+gTTrvvPN04403asSIEcrIyIiep30NUtm0adNUW1urm2++WXfddZdqamo0e/Zsp2MBAAB8Y5988okuvfTSaJ9tIF3MmzdP9913nwoKCiRJF1xwgebNm+dwKgDJZti2bTsdAgC+qauuuuqIc4ZhaNmyZQ6kAQAAAADgqwUCAT300EOaOnWq01EAOIyiPICU0djYqMzMTKdjAK3uaNu6I9jeDQAA2hq+3yBdjR07VqtWrXI6BgCH0b4GQJv37rvvatWqVXrxxRf11ltvOR0HaHVs6wYAAKkm8v2murpab775pgYNGiRJ2rRpk37wgx9QlEfK+tGPfqQlS5aosLBQ2dnZ0fNZWVkOpgKQbFwpD6BNqqqqUmlpqVavXq2dO3fq17/+tUaNGqVu3bo5HQ0AAADA1zR58mT95je/UY8ePSRJO3bs0H333aeFCxc6nAxIjL59+0bvG4Yh27ZlGIa2bNniYCoAycaV8gDalPXr12v16tX6+9//rksuuURz5szRLbfcosmTJzsdDUiYd955RwMGDFB5eXmLjzPgGAAAtFW7du2KFuQlqUePHtq5c6eDiYDEqqiocDoCgOMARXkAbcqUKVM0aNAgPf/88+rYsaOk0NUFQCorKyvTgAEDtHjx4iMeMwyDojwAAGizOnfurIceekjjxo2TJK1evVqdO3d2OBUAAIlF+xoAbUp5eblKSkr0+uuva9iwYSosLNSsWbO0YcMGp6MBCfP2229r4MCBTscAAABodV9++aXuu+8+vfHGG5KkCy64QLNmzVLXrl0dTgYkRkVFhWbPnq2Kigp5vd7oedrXAOmFojyANunAgQNas2aNSkpKtHXrVk2aNEkFBQXq3bu309GAVjd8+HBZlqUxY8aosLBQ+fn5TkcCAAD41gKBgB566CFNnTrV6ShA0vz85z/XtGnTNHfuXC1evFjLly9XTk4OLVmBNGM6HQAAjkWHDh109dVXq6ysTE8++aQOHjyo4uJip2MBCbF+/Xrdfffd2rZtmy699FJNnjxZ69atk9/vdzoaAADAMbMsSy+//LLTMYCk8nq9GjRokGzbVn5+vmbMmKF169Y5HQtAklGUB9DmnX322brzzjv1yiuvOB0FSJgLLrhA8+bN08svv6yf/OQneuyxx3TRRRdp7ty5TkcDAAA4Zj/60Y+0ZMkSVVVVqaGhIfoDpCrLsiRJ7du3V0VFhaqrq1VdXe1wKgDJRvsaAADaGNu2VV5erj/+8Y/atm2b3nvvPacjAQAAHJO+fftG7xuGIdu2ZRgG/bWRspYuXarCwkJt3rxZ06ZNUzAY1NSpUzVx4kSnowFIIoryAAC0EZ9++qlWr16tNWvWKD8/X0VFRSooKFC7du2cjgYAAADgK2zduvWIc4FAQJKUnZ2trl27yuPxJDsWAIdQlAcA4Dj35JNPqqSkRJWVlSooKFBRUVHcVWUAAAAAjm/Dhg074pxhGJIkv9+v+vp6zZw5U2PHjk12NAAOoCgPoM2qqanR9u3b1dTUFD13/vnnO5gISIxJkyZpzJgxuvjii+V2u52OAwAA0GoqKio0e/ZsVVRUyOv1Rs/TvgbpZs+ePbr22mu1du1ap6MASAKX0wEA4FisXbtW8+bN06FDh5Sfn6/Kykr17dtXpaWlTkcDWt0jjzzidAQAAICEuOuuuzR9+nTNnTtXixcv1vLly5WTk+N0LCDp8vPzVVxc7HQMAEliOh0AAI7FwoULVVJSop49e2rdunVavHixvvvd7zodCwAAAMA34PV6NWjQINm2rfz8fM2YMUPr1q1zOhbgiKuuusrpCACShKI8gDbJ5XKpU6dO0cE4gwcP1ubNmx1OBQAAAOCbsCxLktS+fXtVVFSourpa1dXVDqcCACCxaF8DoE3yeDyybVs9e/bU448/ru7du6u+vt7pWAAAAAC+gZEjR6q6ulqTJ09WcXGxgsGgpk6d6nQsAAASikGvANqkTZs26ZxzzlFVVZXuuusu1dTU6KabbtIPf/hDp6MBAAAA+De2bt16xLnILtjs7Gx17dpVHo8n2bEAAEgKivIAAAAAACCphg0bdsQ5wzAkSX6/X/X19Zo5c6bGjh2b7GgAACQcRXkAbcry5cu/8vEJEyYkKQkAAACARNmzZ4+uvfZarV271ukoAAC0OnrKA2hTPvjgA6cjAAAAAEiw/Px8FRcXOx0DAICE4Ep5AAAAAAAAAACShCvlAbQp77zzjgYMGKDy8vIWHx86dGiSEwEAAAAAAABfH0V5AG1KWVmZBgwYoMWLFx/xmGEYFOUBAAAAAABwXKN9DYA25e2339bAgQOdjgEAAAAAAAAcE4ryANqU4cOHy7IsjRkzRoWFhcrPz3c6EgAAAAAAAPC1UZQH0Oa8/vrrKi0t1fr16zVgwACNGTNGF198sVwuOnIBAAAAAADg+EZRHkCbVVdXp7Vr16qkpESff/65CgoKdPvttzsdCwAAAAAAADgqivIA2jTbtlVeXq4//vGP2rZtm9577z2nIwEAAAAAAABHRa8HAG3Sp59+qtWrV2vNmjXKz89XUVGRCgoKnI4FAAAAAAAAfCWulAfQpjz55JMqKSlRZWWlCgoKVFRUpL59+zodCwAAAAAAAPhauFIeQJuyfv16XXfddbr44ovldrudjgMAAAAAAAB8I1wpDwAAAAAAAABAkphOBwAAAAAAAAAAIF1QlAcAAAAAAAAAIEkoygMAAADQrl271L9/fwUCAaejAAAAACmNnvIAAABAChk2bJj27dsny7LkcrnUv39/3X333TrppJOcjgYAAABAXCkPAAAApJyFCxfq3Xff1auvvqpOnTrp3nvv/VbvZ9u2gsFg3Dm/3/+t3hMAAABIVxTlAQAAgBSVkZGhESNGaNu2bZKkl156SYX/f3v371L1Hsdx/GXnQoZDx6UGWyIOJwkSG47g4mAdihoKF5ezOIQgEugfIDR1oHSQptDNxUGQfkw2NAWBi0tQY8dAJzlQB5TwTvdwL5TDvXUuHR6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},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"m2_por_bairro_media.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 238
},
"id": "h-wG3pXD1lM-",
"outputId": "af4b7014-55a2-43e3-b4b8-ec4932a5429c"
},
"execution_count": 41,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Custo_por_metro2\n",
"Bairro \n",
"Aclimação 7656.17\n",
"Alto da Boa Vista 5849.57\n",
"Alto da Lapa 7606.73\n",
"Alto da Mooca 5691.74\n",
"Alto de Pinheiros 11269.98"
],
"text/html": [
"\n",
" <div id=\"df-db08bc4f-97d6-4f4c-b10b-2cf45c539e43\">\n",
" <div class=\"colab-df-container\">\n",
" <div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
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" </tr>\n",
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" <th>Bairro</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
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" <td>7656.17</td>\n",
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" <td>5849.57</td>\n",
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" <td>11269.98</td>\n",
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"\n",
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" + ' to learn more about interactive tables.';\n",
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" }\n",
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]
},
"metadata": {},
"execution_count": 41
}
]
},
{
"cell_type": "code",
"source": [
"m2_por_bairro_mediana.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 238
},
"id": "1ZWEj4gK1B_I",
"outputId": "8837a237-0678-47ce-e18e-115e97f75632"
},
"execution_count": 42,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Custo_por_metro2\n",
"Bairro \n",
"Aclimação 7272.73\n",
"Alto da Boa Vista 5831.93\n",
"Alto da Lapa 7402.60\n",
"Alto da Mooca 5801.28\n",
"Alto de Pinheiros 10703.12"
],
"text/html": [
"\n",
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" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
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" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-4bc66487-479f-45aa-8f1e-2bd062e7be9f');\n",
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" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
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},
"metadata": {},
"execution_count": 42
}
]
},
{
"cell_type": "code",
"source": [
"# 5. Pesquisar um visualização para analisar os quartis, mediana e outliers.\n",
"imoveis_venda.boxplot(column=['Valor_inteiro'], vert=False, figsize=(26, 6))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 396
},
"id": "PeJjM51vUpE9",
"outputId": "e9a904b6-a337-4c3f-9b20-e67abf05d590"
},
"execution_count": 43,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f661aadab10>"
]
},
"metadata": {},
"execution_count": 43
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1872x432 with 1 Axes>"
],
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},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"m2_por_bairro_mediana.hist(bins = 100, figsize = (26, 6))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 428
},
"id": "18mXBcmr1syW",
"outputId": "c87693dc-cdf2-41ca-e8e2-c4707a2c0db2"
},
"execution_count": 48,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f661a84e910>]],\n",
" dtype=object)"
]
},
"metadata": {},
"execution_count": 48
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1872x432 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"m2_por_bairro_media.hist(bins = 100, figsize = (26, 6))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 428
},
"id": "F2O4hWGh1va1",
"outputId": "4ca9658f-5845-481c-b65b-eeaf26483976"
},
"execution_count": 49,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f661a8bbbd0>]],\n",
" dtype=object)"
]
},
"metadata": {},
"execution_count": 49
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1872x432 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"---\n",
"# Aula 3\n",
"[Aula 03 - Gráficos, Time Series e Análise Exploratória | Imersão Dados 4ª edição](https://www.youtube.com/watch?v=SxzlBJK3i7M)\n",
"\n",
"---"
],
"metadata": {
"id": "vD6xfh0S6Rhs"
}
},
{
"cell_type": "markdown",
"source": [
"# Desafios Aula 3\n",
"\n",
"1. Tentar vincular dados do IBGE com os dados de imóveis.\n",
"2. Tratar os outliers e comparar com os resultados.\n",
"3. Agrupar por mais de uma categoria e realizar as análises.\n",
"4. Organize o colab para deixar com cara de projeto."
],
"metadata": {
"id": "3ZVjhgdZ5-Ki"
}
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "HwwboCEU58Ys"
},
"execution_count": null,
"outputs": []
}
]
}
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