Projeto2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ce6a0f34-b289-4d71-b331-859f5e5e4446",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1433ccf3-b668-44fa-810c-3da8b0f4a9d4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#lendo o arquivo csv com seu separador e encoding\n",
    "\n",
    "df = pd.read_csv(\"Indústria_da_pesca_por_país.csv \", sep=\",\" , encoding= 'UTF-8') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4f2c513c-34fd-4956-9873-80658dc8f63d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "# Mostrando que tipo é o df\n",
    "\n",
    "print(type(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ace108bf-e3c9-4d25-b239-e3e57fd802f9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "País           0\n",
      "Capture        0\n",
      "Aquicultura    0\n",
      "Total          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#contar nulos\n",
    "import pandas as pd\n",
    "\n",
    "# Carregar os dados do arquivo 'energia_brasil.csv' em um DataFrame\n",
    "df = pd.read_csv('Indústria_da_pesca_por_país.csv', encoding='UTF-8')\n",
    "\n",
    "# Especificar as colunas para contar os valores nulos\n",
    "colunas = ['País', 'Capture', 'Aquicultura', 'Total']\n",
    "\n",
    "# Contar os valores nulos nas colunas especificadas\n",
    "contagem_nulos = df[colunas].isnull().sum()\n",
    "\n",
    "# Imprimir a contagem de valores nulos\n",
    "print(contagem_nulos)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f14c08f5-07a9-4ac2-b279-7208c418cdb2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Indústria_da_pesca_por_país.csv\", sep=\",\" , encoding= 'UTF-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0eccb653-9cd0-4923-b068-a0f4dc6829ea",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "País           object\n",
       "Capture         int64\n",
       "Aquicultura     int64\n",
       "Total           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "74159bed-d5fc-4118-bf05-d573b44001db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 217 entries, 0 to 216\n",
      "Data columns (total 4 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   País         217 non-null    object\n",
      " 1   Capture      217 non-null    int64 \n",
      " 2   Aquicultura  217 non-null    int64 \n",
      " 3   Total        217 non-null    int64 \n",
      "dtypes: int64(3), object(1)\n",
      "memory usage: 19.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.info(memory_usage=\"deep\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7022920b-2f38-4d66-aa2e-13f92a775acc",
   "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>País</th>\n",
       "      <th>Capture</th>\n",
       "      <th>Aquicultura</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>China</td>\n",
       "      <td>17800000</td>\n",
       "      <td>63700000</td>\n",
       "      <td>81500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>6584419</td>\n",
       "      <td>16600000</td>\n",
       "      <td>23200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>India</td>\n",
       "      <td>5082332</td>\n",
       "      <td>5703002</td>\n",
       "      <td>10800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>2785940</td>\n",
       "      <td>3634531</td>\n",
       "      <td>6420471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United States</td>\n",
       "      <td>4931017</td>\n",
       "      <td>444369</td>\n",
       "      <td>5375386</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>212</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>Liechtenstein</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>San Marino</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>Total (2018)</td>\n",
       "      <td>93736944</td>\n",
       "      <td>101084799</td>\n",
       "      <td>199741129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>217 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              País   Capture  Aquicultura      Total\n",
       "0            China  17800000     63700000   81500000\n",
       "1        Indonesia   6584419     16600000   23200000\n",
       "2            India   5082332      5703002   10800000\n",
       "3          Vietnam   2785940      3634531    6420471\n",
       "4    United States   4931017       444369    5375386\n",
       "..             ...       ...          ...        ...\n",
       "212        Andorra         0            0          0\n",
       "213  Liechtenstein         0            0          0\n",
       "214     Luxembourg         0            0          0\n",
       "215     San Marino         0            0          0\n",
       "216   Total (2018)  93736944    101084799  199741129\n",
       "\n",
       "[217 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Imprime as 10 primeiras\n",
    "\n",
    "df.head(217)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2c3dd390-62f4-4c21-ab45-0e3f241a263e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(217, 4)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape #Mostra as informações do data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "83771723-cf76-4f1c-96d7-be811eed2ebc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Conte os valores na coluna 'Total' e pegue os 10 países com maior produção de frutos do mar.\n",
    "contagem = df[['País', 'Total']].sort_values(by='Total', ascending=False).head(11)\n",
    "\n",
    "# Crie um gráfico de barras para os 10 países com maior produção de frutos do mar.\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.bar(contagem['País'], contagem['Total'])\n",
    "plt.xlabel('País')\n",
    "plt.ylabel('Produção Total de Frutos do Mar')\n",
    "plt.title('OS 10 PAÍSES COM MAIOR PRODUÇÃO DE FRUTOS DO MAR NO MUNDO')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77fdc5f9-39da-44de-91e2-ef0360a19c02",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07ce3b4c-54f5-4181-8e8f-9663cbebccd4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}