titanicc-checkpoint.ipynb

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   "execution_count": 1,
   "id": "db11c709-1319-4a99-8927-d07892948b37",
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   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "dados = pd.read_csv('titanic.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "22f11a37-a411-432d-bebf-29105152c751",
   "metadata": {
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   "outputs": [],
   "source": [
    "dados = dados.sort_values('Name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4bc3853f-5961-469e-8a66-e63dd37b8d68",
   "metadata": {},
   "outputs": [],
   "source": [
    "dados = dados[['Name', 'Age', 'Sex', 'Pclass', 'Survived']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "264fd02a-8ff3-4049-8587-4aefb2e854ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "dados = dados.loc[(dados['Sex'] == 'female') & (dados['Pclass']==1) & (dados['Survived'] ==1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7f98dd88-7cb0-4d1f-9735-d2988928dd85",
   "metadata": {},
   "outputs": [],
   "source": [
    "dados.to_csv('titan1c_results.csv', index = False)"
   ]
  }
 ],
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