Maria _de_Fatima.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0b747e02-452a-4ae6-bb2a-c47e43a26e90",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# importando a biblioteca Pandas\n",
    "\n",
    "import pandas as pd\n",
    "# importando os pacotes que iremos utilizar\n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e6163a3a-ec68-4db5-8d59-7cdf6bac4336",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "\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>seller_id</th>\n",
       "      <th>seller_zip_code_prefix</th>\n",
       "      <th>seller_city</th>\n",
       "      <th>seller_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3442f8959a84dea7ee197c632cb2df15</td>\n",
       "      <td>13023</td>\n",
       "      <td>campinas</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d1b65fc7debc3361ea86b5f14c68d2e2</td>\n",
       "      <td>13844</td>\n",
       "      <td>mogi guacu</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ce3ad9de960102d0677a81f5d0bb7b2d</td>\n",
       "      <td>20031</td>\n",
       "      <td>rio de janeiro</td>\n",
       "      <td>RJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c0f3eea2e14555b6faeea3dd58c1b1c3</td>\n",
       "      <td>4195</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51a04a8a6bdcb23deccc82b0b80742cf</td>\n",
       "      <td>12914</td>\n",
       "      <td>braganca paulista</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3090</th>\n",
       "      <td>98dddbc4601dd4443ca174359b237166</td>\n",
       "      <td>87111</td>\n",
       "      <td>sarandi</td>\n",
       "      <td>PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3091</th>\n",
       "      <td>f8201cab383e484733266d1906e2fdfa</td>\n",
       "      <td>88137</td>\n",
       "      <td>palhoca</td>\n",
       "      <td>SC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3092</th>\n",
       "      <td>74871d19219c7d518d0090283e03c137</td>\n",
       "      <td>4650</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3093</th>\n",
       "      <td>e603cf3fec55f8697c9059638d6c8eb5</td>\n",
       "      <td>96080</td>\n",
       "      <td>pelotas</td>\n",
       "      <td>RS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3094</th>\n",
       "      <td>9e25199f6ef7e7c347120ff175652c3b</td>\n",
       "      <td>12051</td>\n",
       "      <td>taubate</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3095 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             seller_id  seller_zip_code_prefix  \\\n",
       "0     3442f8959a84dea7ee197c632cb2df15                   13023   \n",
       "1     d1b65fc7debc3361ea86b5f14c68d2e2                   13844   \n",
       "2     ce3ad9de960102d0677a81f5d0bb7b2d                   20031   \n",
       "3     c0f3eea2e14555b6faeea3dd58c1b1c3                    4195   \n",
       "4     51a04a8a6bdcb23deccc82b0b80742cf                   12914   \n",
       "...                                ...                     ...   \n",
       "3090  98dddbc4601dd4443ca174359b237166                   87111   \n",
       "3091  f8201cab383e484733266d1906e2fdfa                   88137   \n",
       "3092  74871d19219c7d518d0090283e03c137                    4650   \n",
       "3093  e603cf3fec55f8697c9059638d6c8eb5                   96080   \n",
       "3094  9e25199f6ef7e7c347120ff175652c3b                   12051   \n",
       "\n",
       "            seller_city seller_state  \n",
       "0              campinas           SP  \n",
       "1            mogi guacu           SP  \n",
       "2        rio de janeiro           RJ  \n",
       "3             sao paulo           SP  \n",
       "4     braganca paulista           SP  \n",
       "...                 ...          ...  \n",
       "3090            sarandi           PR  \n",
       "3091            palhoca           SC  \n",
       "3092          sao paulo           SP  \n",
       "3093            pelotas           RS  \n",
       "3094            taubate           SP  \n",
       "\n",
       "[3095 rows x 4 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vendedores = pd.read_csv('olist_sellers_dataset.csv')\n",
    "df_vendedores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f515b439-8839-4ddd-8aa4-077d76283583",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3095 entries, 0 to 3094\n",
      "Data columns (total 4 columns):\n",
      " #   Column                  Non-Null Count  Dtype \n",
      "---  ------                  --------------  ----- \n",
      " 0   seller_id               3095 non-null   object\n",
      " 1   seller_zip_code_prefix  3095 non-null   int64 \n",
      " 2   seller_city             3095 non-null   object\n",
      " 3   seller_state            3095 non-null   object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 96.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_vendedores.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "eeb3bdf0-f2e2-4e0e-bc3d-2d2b1789aece",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>seller_id</th>\n",
       "      <th>seller_state</th>\n",
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       "      <td>RJ</td>\n",
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       "      <th>3</th>\n",
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       "      <td>SP</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <th>3090</th>\n",
       "      <td>98dddbc4601dd4443ca174359b237166</td>\n",
       "      <td>PR</td>\n",
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       "    <tr>\n",
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       "      <td>RS</td>\n",
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       "    <tr>\n",
       "      <th>3094</th>\n",
       "      <td>9e25199f6ef7e7c347120ff175652c3b</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3095 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             seller_id seller_state\n",
       "0     3442f8959a84dea7ee197c632cb2df15           SP\n",
       "1     d1b65fc7debc3361ea86b5f14c68d2e2           SP\n",
       "2     ce3ad9de960102d0677a81f5d0bb7b2d           RJ\n",
       "3     c0f3eea2e14555b6faeea3dd58c1b1c3           SP\n",
       "4     51a04a8a6bdcb23deccc82b0b80742cf           SP\n",
       "...                                ...          ...\n",
       "3090  98dddbc4601dd4443ca174359b237166           PR\n",
       "3091  f8201cab383e484733266d1906e2fdfa           SC\n",
       "3092  74871d19219c7d518d0090283e03c137           SP\n",
       "3093  e603cf3fec55f8697c9059638d6c8eb5           RS\n",
       "3094  9e25199f6ef7e7c347120ff175652c3b           SP\n",
       "\n",
       "[3095 rows x 2 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  filtrando somente as colunas que vou utilizar\n",
    "\n",
    "columns_drop = ['seller_zip_code_prefix', 'seller_city']\n",
    "df_vendedores= df_vendedores.drop(columns_drop, axis=1)\n",
    "df_vendedores\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "aec556f3-8f56-430e-86f1-4f98a626ddd9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seller_state\n",
       "SP    1849\n",
       "PR     349\n",
       "MG     244\n",
       "SC     190\n",
       "RJ     171\n",
       "RS     129\n",
       "GO      40\n",
       "DF      30\n",
       "ES      23\n",
       "BA      19\n",
       "CE      13\n",
       "PE       9\n",
       "PB       6\n",
       "RN       5\n",
       "MS       5\n",
       "MT       4\n",
       "RO       2\n",
       "SE       2\n",
       "PI       1\n",
       "AC       1\n",
       "MA       1\n",
       "AM       1\n",
       "PA       1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Em que estado se encontra maior números de vendedores?\n",
    "\n",
    "df_vendedores = df_vendedores['seller_state'].value_counts()\n",
    "df_vendedores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "64b799fc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# o Estado em que mais se encontram vendedores consequentemente é SÃO PAULO.\n",
    "data = {\n",
    "    'SP': 1849,\n",
    "    'PR': 349,\n",
    "    'MG': 244,\n",
    "    'SC': 190,\n",
    "    'RJ': 171,\n",
    "    'RS': 129,\n",
    "    'GO': 40,\n",
    "    'DF': 30,\n",
    "    'ES': 23,\n",
    "    'BA': 19,\n",
    "    'CE': 13,\n",
    "    'PE': 9,\n",
    "    'PB': 6,\n",
    "    'RN': 5,\n",
    "    'MS': 5,\n",
    "    'MT': 4,\n",
    "    'RO': 2,\n",
    "    'SE': 2,\n",
    "    'PI': 1,\n",
    "    'AC': 1,\n",
    "    'MA': 1,\n",
    "    'AM': 1,\n",
    "    'PA': 1\n",
    "}\n",
    "\n",
    "# Criando o gráfico de barras\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.bar(data.keys(), data.values(), color='green')\n",
    "plt.title('Número de Vendedores por Estado')\n",
    "plt.xlabel('Estado')\n",
    "plt.ylabel('Número de Vendedores')\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4acd3c27-7f6a-4a3b-b295-7d0cbba5ca2e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>customer_id</th>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th>customer_zip_code_prefix</th>\n",
       "      <th>customer_city</th>\n",
       "      <th>customer_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06b8999e2fba1a1fbc88172c00ba8bc7</td>\n",
       "      <td>861eff4711a542e4b93843c6dd7febb0</td>\n",
       "      <td>14409</td>\n",
       "      <td>franca</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18955e83d337fd6b2def6b18a428ac77</td>\n",
       "      <td>290c77bc529b7ac935b93aa66c333dc3</td>\n",
       "      <td>9790</td>\n",
       "      <td>sao bernardo do campo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4e7b3e00288586ebd08712fdd0374a03</td>\n",
       "      <td>060e732b5b29e8181a18229c7b0b2b5e</td>\n",
       "      <td>1151</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b2b6027bc5c5109e529d4dc6358b12c3</td>\n",
       "      <td>259dac757896d24d7702b9acbbff3f3c</td>\n",
       "      <td>8775</td>\n",
       "      <td>mogi das cruzes</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4f2d8ab171c80ec8364f7c12e35b23ad</td>\n",
       "      <td>345ecd01c38d18a9036ed96c73b8d066</td>\n",
       "      <td>13056</td>\n",
       "      <td>campinas</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99436</th>\n",
       "      <td>17ddf5dd5d51696bb3d7c6291687be6f</td>\n",
       "      <td>1a29b476fee25c95fbafc67c5ac95cf8</td>\n",
       "      <td>3937</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99437</th>\n",
       "      <td>e7b71a9017aa05c9a7fd292d714858e8</td>\n",
       "      <td>d52a67c98be1cf6a5c84435bd38d095d</td>\n",
       "      <td>6764</td>\n",
       "      <td>taboao da serra</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99438</th>\n",
       "      <td>5e28dfe12db7fb50a4b2f691faecea5e</td>\n",
       "      <td>e9f50caf99f032f0bf3c55141f019d99</td>\n",
       "      <td>60115</td>\n",
       "      <td>fortaleza</td>\n",
       "      <td>CE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99439</th>\n",
       "      <td>56b18e2166679b8a959d72dd06da27f9</td>\n",
       "      <td>73c2643a0a458b49f58cea58833b192e</td>\n",
       "      <td>92120</td>\n",
       "      <td>canoas</td>\n",
       "      <td>RS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99440</th>\n",
       "      <td>274fa6071e5e17fe303b9748641082c8</td>\n",
       "      <td>84732c5050c01db9b23e19ba39899398</td>\n",
       "      <td>6703</td>\n",
       "      <td>cotia</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99441 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            customer_id                customer_unique_id  \\\n",
       "0      06b8999e2fba1a1fbc88172c00ba8bc7  861eff4711a542e4b93843c6dd7febb0   \n",
       "1      18955e83d337fd6b2def6b18a428ac77  290c77bc529b7ac935b93aa66c333dc3   \n",
       "2      4e7b3e00288586ebd08712fdd0374a03  060e732b5b29e8181a18229c7b0b2b5e   \n",
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       "...                                 ...                               ...   \n",
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       "\n",
       "       customer_zip_code_prefix          customer_city customer_state  \n",
       "0                         14409                 franca             SP  \n",
       "1                          9790  sao bernardo do campo             SP  \n",
       "2                          1151              sao paulo             SP  \n",
       "3                          8775        mogi das cruzes             SP  \n",
       "4                         13056               campinas             SP  \n",
       "...                         ...                    ...            ...  \n",
       "99436                      3937              sao paulo             SP  \n",
       "99437                      6764        taboao da serra             SP  \n",
       "99438                     60115              fortaleza             CE  \n",
       "99439                     92120                 canoas             RS  \n",
       "99440                      6703                  cotia             SP  \n",
       "\n",
       "[99441 rows x 5 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clientes = pd.read_csv('olist_customers_dataset.csv')\n",
    "df_clientes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a4cd2430-a9b3-4808-bfd9-4354de98b163",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99441 entries, 0 to 99440\n",
      "Data columns (total 5 columns):\n",
      " #   Column                    Non-Null Count  Dtype \n",
      "---  ------                    --------------  ----- \n",
      " 0   customer_id               99441 non-null  object\n",
      " 1   customer_unique_id        99441 non-null  object\n",
      " 2   customer_zip_code_prefix  99441 non-null  int64 \n",
      " 3   customer_city             99441 non-null  object\n",
      " 4   customer_state            99441 non-null  object\n",
      "dtypes: int64(1), object(4)\n",
      "memory usage: 3.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df_clientes.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "725ff3aa-695c-43c6-923f-3d16f5e6ee01",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>customer_id</th>\n",
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       "      <td>60115</td>\n",
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       "                            customer_id                customer_unique_id  \\\n",
       "0      06b8999e2fba1a1fbc88172c00ba8bc7  861eff4711a542e4b93843c6dd7febb0   \n",
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       "...                                 ...                               ...   \n",
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       "\n",
       "       customer_zip_code_prefix          customer_city customer_state  \n",
       "0                         14409                 franca             SP  \n",
       "1                          9790  sao bernardo do campo             SP  \n",
       "2                          1151              sao paulo             SP  \n",
       "3                          8775        mogi das cruzes             SP  \n",
       "4                         13056               campinas             SP  \n",
       "...                         ...                    ...            ...  \n",
       "99436                      3937              sao paulo             SP  \n",
       "99437                      6764        taboao da serra             SP  \n",
       "99438                     60115              fortaleza             CE  \n",
       "99439                     92120                 canoas             RS  \n",
       "99440                      6703                  cotia             SP  \n",
       "\n",
       "[99441 rows x 5 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Agora iremos filtrar somente as cidades em que os clientes mais realizam pedidos dentro do estado de SP.\n",
    "\n",
    "df_clientes['customer_state'].unique()\n",
    "df_clientes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c82299e7-c94b-4077-aab1-b5a1a763f019",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SP', 'RJ', 'MG', 'RS', 'PR', 'SC', 'BA', 'DF', 'ES', 'GO', 'PE', 'CE',\n",
       "       'PA', 'MT', 'MA', 'MS', 'PB', 'PI', 'RN', 'AL', 'SE', 'TO', 'RO', 'AM',\n",
       "       'AC', 'AP', 'RR'],\n",
       "      dtype='object', name='customer_state')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ordem crescente dos estados \n",
    "df_clientes.customer_state.value_counts().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "389244cd-23fa-415b-a110-476d4065ccf5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "                        customer_id                customer_unique_id  \\\n",
       "0  06b8999e2fba1a1fbc88172c00ba8bc7  861eff4711a542e4b93843c6dd7febb0   \n",
       "1  18955e83d337fd6b2def6b18a428ac77  290c77bc529b7ac935b93aa66c333dc3   \n",
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       "3  b2b6027bc5c5109e529d4dc6358b12c3  259dac757896d24d7702b9acbbff3f3c   \n",
       "4  4f2d8ab171c80ec8364f7c12e35b23ad  345ecd01c38d18a9036ed96c73b8d066   \n",
       "\n",
       "   customer_zip_code_prefix          customer_city customer_state  \n",
       "0                     14409                 franca             SP  \n",
       "1                      9790  sao bernardo do campo             SP  \n",
       "2                      1151              sao paulo             SP  \n",
       "3                      8775        mogi das cruzes             SP  \n",
       "4                     13056               campinas             SP  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Imprime as 5 primeiras cidade de SP\n",
    "\n",
    "df_clientes.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f63b560d-bf03-4ee6-85e9-984fad7e3f4b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Vida me surpreenda\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\Vida me surpreenda\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Seus dados\n",
    "data = {\n",
    "  \n",
    "    'customer_zip_code_prefix': [14409, 9790, 1151, 8775, 13056],\n",
    "    'customer_city': ['franca', 'sao bernardo do campo', 'sao paulo', 'mogi das cruzes', 'campinas'],\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# Contagem das cidades\n",
    "cidades = df['customer_city'].value_counts().head(5)\n",
    "\n",
    "# Filtrando o DataFrame para considerar apenas as top 5 cidades\n",
    "cidades_df = df[df['customer_city'].isin(cidades.index)]\n",
    "\n",
    "# Criando o countplot usando Seaborn\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(y='customer_city', data=cidades_df, order=cidades.index, palette='viridis')\n",
    "plt.title('As 5 Cidades de SP que Mais Solicitam Pedidos')\n",
    "plt.xlabel('Número de Pedidos')\n",
    "plt.ylabel('Cidade')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5fc2c969-e492-42f2-844b-a632fb769faf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>geolocation_zip_code_prefix</th>\n",
       "      <th>geolocation_lat</th>\n",
       "      <th>geolocation_lng</th>\n",
       "      <th>geolocation_city</th>\n",
       "      <th>geolocation_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1037</td>\n",
       "      <td>-23.545621</td>\n",
       "      <td>-46.639292</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1046</td>\n",
       "      <td>-23.546081</td>\n",
       "      <td>-46.644820</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1046</td>\n",
       "      <td>-23.546129</td>\n",
       "      <td>-46.642951</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1041</td>\n",
       "      <td>-23.544392</td>\n",
       "      <td>-46.639499</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1035</td>\n",
       "      <td>-23.541578</td>\n",
       "      <td>-46.641607</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000158</th>\n",
       "      <td>99950</td>\n",
       "      <td>-28.068639</td>\n",
       "      <td>-52.010705</td>\n",
       "      <td>tapejara</td>\n",
       "      <td>RS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000159</th>\n",
       "      <td>99900</td>\n",
       "      <td>-27.877125</td>\n",
       "      <td>-52.224882</td>\n",
       "      <td>getulio vargas</td>\n",
       "      <td>RS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000160</th>\n",
       "      <td>99950</td>\n",
       "      <td>-28.071855</td>\n",
       "      <td>-52.014716</td>\n",
       "      <td>tapejara</td>\n",
       "      <td>RS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000161</th>\n",
       "      <td>99980</td>\n",
       "      <td>-28.388932</td>\n",
       "      <td>-51.846871</td>\n",
       "      <td>david canabarro</td>\n",
       "      <td>RS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000162</th>\n",
       "      <td>99950</td>\n",
       "      <td>-28.070104</td>\n",
       "      <td>-52.018658</td>\n",
       "      <td>tapejara</td>\n",
       "      <td>RS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000163 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         geolocation_zip_code_prefix  geolocation_lat  geolocation_lng  \\\n",
       "0                               1037       -23.545621       -46.639292   \n",
       "1                               1046       -23.546081       -46.644820   \n",
       "2                               1046       -23.546129       -46.642951   \n",
       "3                               1041       -23.544392       -46.639499   \n",
       "4                               1035       -23.541578       -46.641607   \n",
       "...                              ...              ...              ...   \n",
       "1000158                        99950       -28.068639       -52.010705   \n",
       "1000159                        99900       -27.877125       -52.224882   \n",
       "1000160                        99950       -28.071855       -52.014716   \n",
       "1000161                        99980       -28.388932       -51.846871   \n",
       "1000162                        99950       -28.070104       -52.018658   \n",
       "\n",
       "        geolocation_city geolocation_state  \n",
       "0              sao paulo                SP  \n",
       "1              sao paulo                SP  \n",
       "2              sao paulo                SP  \n",
       "3              sao paulo                SP  \n",
       "4              sao paulo                SP  \n",
       "...                  ...               ...  \n",
       "1000158         tapejara                RS  \n",
       "1000159   getulio vargas                RS  \n",
       "1000160         tapejara                RS  \n",
       "1000161  david canabarro                RS  \n",
       "1000162         tapejara                RS  \n",
       "\n",
       "[1000163 rows x 5 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_localizacao = pd.read_csv('olist_geolocation_dataset.csv')\n",
    "df_localizacao"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "90307462-75e3-4040-b7ce-34f9f389c4e9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geolocation_zip_code_prefix</th>\n",
       "      <th>geolocation_city</th>\n",
       "      <th>geolocation_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1037</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
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       "    <tr>\n",
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       "      <td>1046</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1046</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1041</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1035</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1000158</th>\n",
       "      <td>99950</td>\n",
       "      <td>tapejara</td>\n",
       "      <td>RS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000159</th>\n",
       "      <td>99900</td>\n",
       "      <td>getulio vargas</td>\n",
       "      <td>RS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000160</th>\n",
       "      <td>99950</td>\n",
       "      <td>tapejara</td>\n",
       "      <td>RS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000161</th>\n",
       "      <td>99980</td>\n",
       "      <td>david canabarro</td>\n",
       "      <td>RS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000162</th>\n",
       "      <td>99950</td>\n",
       "      <td>tapejara</td>\n",
       "      <td>RS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000163 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         geolocation_zip_code_prefix geolocation_city geolocation_state\n",
       "0                               1037        sao paulo                SP\n",
       "1                               1046        sao paulo                SP\n",
       "2                               1046        sao paulo                SP\n",
       "3                               1041        sao paulo                SP\n",
       "4                               1035        sao paulo                SP\n",
       "...                              ...              ...               ...\n",
       "1000158                        99950         tapejara                RS\n",
       "1000159                        99900   getulio vargas                RS\n",
       "1000160                        99950         tapejara                RS\n",
       "1000161                        99980  david canabarro                RS\n",
       "1000162                        99950         tapejara                RS\n",
       "\n",
       "[1000163 rows x 3 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filtrando somente as colunas que que irei utilizar\n",
    "\n",
    "columns_drop = ['geolocation_lat', 'geolocation_lng']\n",
    "df_localizacao= df_localizacao.drop(columns_drop, axis=1)\n",
    "df_localizacao\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8897b2b1-50f0-466f-9586-160b733757ce",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000163 entries, 0 to 1000162\n",
      "Data columns (total 3 columns):\n",
      " #   Column                       Non-Null Count    Dtype \n",
      "---  ------                       --------------    ----- \n",
      " 0   geolocation_zip_code_prefix  1000163 non-null  int64 \n",
      " 1   geolocation_city             1000163 non-null  object\n",
      " 2   geolocation_state            1000163 non-null  object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 22.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df_localizacao.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "86d4948e-fe8d-44b5-ae35-d61d8cfa297f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "geolocation_zip_code_prefix\n",
       "24220    1146\n",
       "24230    1102\n",
       "38400     965\n",
       "35500     907\n",
       "11680     879\n",
       "         ... \n",
       "71750       1\n",
       "71742       1\n",
       "26475       1\n",
       "26357       1\n",
       "29826       1\n",
       "Name: count, Length: 19015, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# No estado em que mais houve vendas, qual CEP mais realizaram pedidos?\n",
    "\n",
    "df_localizacao = df_localizacao['geolocation_zip_code_prefix'].value_counts()\n",
    "df_localizacao"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5fb1c2b5-2800-4e60-af8b-70cce13d2bea",
   "metadata": {
    "tags": []
   },
   "outputs": [
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      ],
      "text/plain": [
       "                                order_id  order_item_id  \\\n",
       "0       00010242fe8c5a6d1ba2dd792cb16214              1   \n",
       "1       00018f77f2f0320c557190d7a144bdd3              1   \n",
       "2       000229ec398224ef6ca0657da4fc703e              1   \n",
       "3       00024acbcdf0a6daa1e931b038114c75              1   \n",
       "4       00042b26cf59d7ce69dfabb4e55b4fd9              1   \n",
       "...                                  ...            ...   \n",
       "112645  fffc94f6ce00a00581880bf54a75a037              1   \n",
       "112646  fffcd46ef2263f404302a634eb57f7eb              1   \n",
       "112647  fffce4705a9662cd70adb13d4a31832d              1   \n",
       "112648  fffe18544ffabc95dfada21779c9644f              1   \n",
       "112649  fffe41c64501cc87c801fd61db3f6244              1   \n",
       "\n",
       "                              product_id                         seller_id  \\\n",
       "0       4244733e06e7ecb4970a6e2683c13e61  48436dade18ac8b2bce089ec2a041202   \n",
       "1       e5f2d52b802189ee658865ca93d83a8f  dd7ddc04e1b6c2c614352b383efe2d36   \n",
       "2       c777355d18b72b67abbeef9df44fd0fd  5b51032eddd242adc84c38acab88f23d   \n",
       "3       7634da152a4610f1595efa32f14722fc  9d7a1d34a5052409006425275ba1c2b4   \n",
       "4       ac6c3623068f30de03045865e4e10089  df560393f3a51e74553ab94004ba5c87   \n",
       "...                                  ...                               ...   \n",
       "112645  4aa6014eceb682077f9dc4bffebc05b0  b8bc237ba3788b23da09c0f1f3a3288c   \n",
       "112646  32e07fd915822b0765e448c4dd74c828  f3c38ab652836d21de61fb8314b69182   \n",
       "112647  72a30483855e2eafc67aee5dc2560482  c3cfdc648177fdbbbb35635a37472c53   \n",
       "112648  9c422a519119dcad7575db5af1ba540e  2b3e4a2a3ea8e01938cabda2a3e5cc79   \n",
       "112649  350688d9dc1e75ff97be326363655e01  f7ccf836d21b2fb1de37564105216cc1   \n",
       "\n",
       "        shipping_limit_date   price  freight_value  \n",
       "0       2017-09-19 09:45:35   58.90          13.29  \n",
       "1       2017-05-03 11:05:13  239.90          19.93  \n",
       "2       2018-01-18 14:48:30  199.00          17.87  \n",
       "3       2018-08-15 10:10:18   12.99          12.79  \n",
       "4       2017-02-13 13:57:51  199.90          18.14  \n",
       "...                     ...     ...            ...  \n",
       "112645  2018-05-02 04:11:01  299.99          43.41  \n",
       "112646  2018-07-20 04:31:48  350.00          36.53  \n",
       "112647  2017-10-30 17:14:25   99.90          16.95  \n",
       "112648  2017-08-21 00:04:32   55.99           8.72  \n",
       "112649  2018-06-12 17:10:13   43.00          12.79  \n",
       "\n",
       "[112650 rows x 7 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_produtos = pd.read_csv('olist_order_items_dataset.csv')\n",
    "df_produtos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "41c14764",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 112650 entries, 0 to 112649\n",
      "Data columns (total 7 columns):\n",
      " #   Column               Non-Null Count   Dtype  \n",
      "---  ------               --------------   -----  \n",
      " 0   order_id             112650 non-null  object \n",
      " 1   order_item_id        112650 non-null  int64  \n",
      " 2   product_id           112650 non-null  object \n",
      " 3   seller_id            112650 non-null  object \n",
      " 4   shipping_limit_date  112650 non-null  object \n",
      " 5   price                112650 non-null  float64\n",
      " 6   freight_value        112650 non-null  float64\n",
      "dtypes: float64(2), int64(1), object(4)\n",
      "memory usage: 6.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df_produtos.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1965a298",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "      <th>2</th>\n",
       "      <td>000229ec398224ef6ca0657da4fc703e</td>\n",
       "      <td>5b51032eddd242adc84c38acab88f23d</td>\n",
       "      <td>199.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00024acbcdf0a6daa1e931b038114c75</td>\n",
       "      <td>9d7a1d34a5052409006425275ba1c2b4</td>\n",
       "      <td>12.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00042b26cf59d7ce69dfabb4e55b4fd9</td>\n",
       "      <td>df560393f3a51e74553ab94004ba5c87</td>\n",
       "      <td>199.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112645</th>\n",
       "      <td>fffc94f6ce00a00581880bf54a75a037</td>\n",
       "      <td>b8bc237ba3788b23da09c0f1f3a3288c</td>\n",
       "      <td>299.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112646</th>\n",
       "      <td>fffcd46ef2263f404302a634eb57f7eb</td>\n",
       "      <td>f3c38ab652836d21de61fb8314b69182</td>\n",
       "      <td>350.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112647</th>\n",
       "      <td>fffce4705a9662cd70adb13d4a31832d</td>\n",
       "      <td>c3cfdc648177fdbbbb35635a37472c53</td>\n",
       "      <td>99.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112648</th>\n",
       "      <td>fffe18544ffabc95dfada21779c9644f</td>\n",
       "      <td>2b3e4a2a3ea8e01938cabda2a3e5cc79</td>\n",
       "      <td>55.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112649</th>\n",
       "      <td>fffe41c64501cc87c801fd61db3f6244</td>\n",
       "      <td>f7ccf836d21b2fb1de37564105216cc1</td>\n",
       "      <td>43.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>112650 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                order_id                         seller_id  \\\n",
       "0       00010242fe8c5a6d1ba2dd792cb16214  48436dade18ac8b2bce089ec2a041202   \n",
       "1       00018f77f2f0320c557190d7a144bdd3  dd7ddc04e1b6c2c614352b383efe2d36   \n",
       "2       000229ec398224ef6ca0657da4fc703e  5b51032eddd242adc84c38acab88f23d   \n",
       "3       00024acbcdf0a6daa1e931b038114c75  9d7a1d34a5052409006425275ba1c2b4   \n",
       "4       00042b26cf59d7ce69dfabb4e55b4fd9  df560393f3a51e74553ab94004ba5c87   \n",
       "...                                  ...                               ...   \n",
       "112645  fffc94f6ce00a00581880bf54a75a037  b8bc237ba3788b23da09c0f1f3a3288c   \n",
       "112646  fffcd46ef2263f404302a634eb57f7eb  f3c38ab652836d21de61fb8314b69182   \n",
       "112647  fffce4705a9662cd70adb13d4a31832d  c3cfdc648177fdbbbb35635a37472c53   \n",
       "112648  fffe18544ffabc95dfada21779c9644f  2b3e4a2a3ea8e01938cabda2a3e5cc79   \n",
       "112649  fffe41c64501cc87c801fd61db3f6244  f7ccf836d21b2fb1de37564105216cc1   \n",
       "\n",
       "         price  \n",
       "0        58.90  \n",
       "1       239.90  \n",
       "2       199.00  \n",
       "3        12.99  \n",
       "4       199.90  \n",
       "...        ...  \n",
       "112645  299.99  \n",
       "112646  350.00  \n",
       "112647   99.90  \n",
       "112648   55.99  \n",
       "112649   43.00  \n",
       "\n",
       "[112650 rows x 3 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filtrando somente as colunas que que irei utilizar\n",
    "\n",
    "columns_drop = ['order_item_id', 'product_id', 'shipping_limit_date', 'freight_value']\n",
    "df_produtos= df_produtos.drop(columns_drop, axis=1)\n",
    "df_produtos\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "113cb166-9cb7-40f2-a9c6-a00fc821a8e9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Os 10 vendedores que mais venderam e o total vendido por cada um:\n",
      "                             seller_id      price\n",
      "857   4869f7a5dfa277a7dca6462dcf3b52b2  229472.63\n",
      "1013  53243585a1d6dc2643021fd1853d8905  222776.05\n",
      "881   4a3ca9315b744ce9f8e9374361493884  200472.92\n",
      "3024  fa1c13f2614d7b5c4749cbc52fecda94  194042.03\n",
      "1535  7c67e1448b00f6e969d365cea6b010ab  187923.89\n",
      "1560  7e93a43ef30c4f03f38b393420bc753a  176431.87\n",
      "2643  da8622b14eb17ae2831f4ac5b9dab84a  160236.57\n",
      "1505  7a67c85e85bb2ce8582c35f2203ad736  141745.53\n",
      "192   1025f0e2d44d7041d6cf58b6550e0bfa  138968.55\n",
      "1824  955fee9216a65b617aa5c0531780ce60  135171.70\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Carregar os dados do arquivo CSV\n",
    "df_produtos = pd.read_csv('olist_order_items_dataset.csv')\n",
    "\n",
    "# Agrupar por vendedor e somar o total de vendas\n",
    "vendas_por_vendedor = df_produtos.groupby('seller_id')['price'].sum().reset_index()\n",
    "\n",
    "# Ordenar os vendedores pelo total de vendas em ordem decrescente\n",
    "vendedores = vendas_por_vendedor.nlargest(10, 'price')\n",
    "\n",
    "# Exibir os resultados\n",
    "print(\"Os 10 vendedores que mais venderam e o total vendido por cada um:\")\n",
    "print(vendedores)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "30e8e00c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Dados dos 10 vendedores que mais venderam e o total vendido por cada um.\n",
    "data = {\n",
    "    'seller_id': [\n",
    "        '4869f7a5dfa277a7dca6462dcf3b52b2',\n",
    "        '53243585a1d6dc2643021fd1853d8905',\n",
    "        '4a3ca9315b744ce9f8e9374361493884',\n",
    "        'fa1c13f2614d7b5c4749cbc52fecda94',\n",
    "        '7c67e1448b00f6e969d365cea6b010ab',\n",
    "        '7e93a43ef30c4f03f38b393420bc753a',\n",
    "        'da8622b14eb17ae2831f4ac5b9dab84a',\n",
    "        '7a67c85e85bb2ce8582c35f2203ad736',\n",
    "        '1025f0e2d44d7041d6cf58b6550e0bfa',\n",
    "        '955fee9216a65b617aa5c0531780ce60'\n",
    "    ],\n",
    "    'price': [\n",
    "        229472.63,\n",
    "        222776.05,\n",
    "        200472.92,\n",
    "        194042.03,\n",
    "        187923.89,\n",
    "        176431.87,\n",
    "        160236.57,\n",
    "        141745.53,\n",
    "        138968.55,\n",
    "        135171.70\n",
    "    ]\n",
    "}\n",
    "\n",
    "# Criando um DataFrame com os dados\n",
    "df_dez_vendedores = pd.DataFrame(data)\n",
    "\n",
    "# Criando o gráfico de barras com os 10 vendedores que mais venderam\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.barh(df_dez_vendedores['seller_id'], df_dez_vendedores['price'], color='brown')\n",
    "plt.xlabel('Total Vendido')\n",
    "plt.ylabel('ID do Vendedor')\n",
    "plt.title('Os 10 Vendedores que Mais Venderam no País')\n",
    "plt.gca().invert_yaxis()  # Inverter eixo y para que o maior valor fique no topo\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "55f67817",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'seller_zip_code_prefix'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_19800\\3476860138.py\u001b[0m in \u001b[0;36m?\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf_olist\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_clientes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf_localizacao\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mon\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'seller_zip_code_prefix'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mdf_olist\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Vida me surpreenda\\anaconda3\\Lib\\site-packages\\pandas\\core\\reshape\\merge.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[0;32m    165\u001b[0m             \u001b[0mvalidate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalidate\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    166\u001b[0m             \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    167\u001b[0m         )\n\u001b[0;32m    168\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 169\u001b[1;33m         op = _MergeOperation(\n\u001b[0m\u001b[0;32m    170\u001b[0m             \u001b[0mleft_df\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    171\u001b[0m             \u001b[0mright_df\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    172\u001b[0m             \u001b[0mhow\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mhow\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Vida me surpreenda\\anaconda3\\Lib\\site-packages\\pandas\\core\\reshape\\merge.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, indicator, validate)\u001b[0m\n\u001b[0;32m    787\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mright_join_keys\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    788\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin_names\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    789\u001b[0m             \u001b[0mleft_drop\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    790\u001b[0m             \u001b[0mright_drop\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 791\u001b[1;33m         ) = self._get_merge_keys()\n\u001b[0m\u001b[0;32m    792\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    793\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mleft_drop\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    794\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mleft\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_drop_labels_or_levels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mleft_drop\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Vida me surpreenda\\anaconda3\\Lib\\site-packages\\pandas\\core\\reshape\\merge.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1265\u001b[0m                         \u001b[1;31m# Then we're either Hashable or a wrong-length arraylike,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1266\u001b[0m                         \u001b[1;31m#  the latter of which will raise\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1267\u001b[0m                         \u001b[0mrk\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcast\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mHashable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1268\u001b[0m                         \u001b[1;32mif\u001b[0m \u001b[0mrk\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1269\u001b[1;33m                             \u001b[0mright_keys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mright\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_label_or_level_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1270\u001b[0m                         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1271\u001b[0m                             \u001b[1;31m# work-around for merge_asof(right_index=True)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1272\u001b[0m                             \u001b[0mright_keys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mright\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Vida me surpreenda\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1840\u001b[0m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mother_axes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1841\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_level_reference\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1842\u001b[0m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_level_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1843\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1844\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1845\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1846\u001b[0m         \u001b[1;31m# Check for duplicates\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1847\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'seller_zip_code_prefix'"
     ]
    }
   ],
   "source": [
    "df_olist = pd.merge(df_clientes, df_localizacao, on='seller_zip_code_prefix')\n",
    "df_olist"
   ]
  }
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