From b0f2dcbff7df4c7b90494c75de087261dc6ffcda Mon Sep 17 00:00:00 2001 From: Nalla Cloviel Date: Mon, 30 Oct 2023 23:35:08 -0300 Subject: [PATCH 1/2] Exercicios --- exercicios/para-casa/S12-Nargylla(casa).ipynb | 1024 +++++++++++++++++ 1 file changed, 1024 insertions(+) create mode 100644 exercicios/para-casa/S12-Nargylla(casa).ipynb diff --git a/exercicios/para-casa/S12-Nargylla(casa).ipynb b/exercicios/para-casa/S12-Nargylla(casa).ipynb new file mode 100644 index 0000000..97d9f0f --- /dev/null +++ b/exercicios/para-casa/S12-Nargylla(casa).ipynb @@ -0,0 +1,1024 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**BREVE HISTÓRIA DA ANÁLISE DE DADOS:**\n", + "\n", + "O dataframe traz uma análise abrangente de um levantamento sobre os fatores que podem contribuir para o aumento no nível de estresse dos alunos. Fatores sociais, psicológicos, fisiológicos e etc. são trazidos para se compreender como eles se interrelacionam contribuindo agravar ou amenizar problemas de saúde dos estudantes. A análise desses dados, seguirá um foco nos fatores psicológicos: depressão e ansiedade; fatores acadêmicos: tempo de estudo e desempenho acadêmico; fatores fisiológicos: qualidade de sono." + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " anxiety_level self_esteem mental_health_history depression headache \\\n", + "0 14 20 0 11 2 \n", + "1 15 8 1 15 5 \n", + "2 12 18 1 14 2 \n", + "3 16 12 1 15 4 \n", + "4 16 28 0 7 2 \n", + "... ... ... ... ... ... \n", + "1095 11 17 0 14 3 \n", + "1096 9 12 0 8 0 \n", + "1097 4 26 0 3 1 \n", + "1098 21 0 1 19 5 \n", + "1099 18 6 1 15 3 \n", + "\n", + " blood_pressure sleep_quality breathing_problem noise_level \\\n", + "0 1 2 4 2 \n", + "1 3 1 4 3 \n", + "2 1 2 2 2 \n", + "3 3 1 3 4 \n", + "4 3 5 1 3 \n", + "... ... ... ... ... \n", + "1095 1 3 2 2 \n", + "1096 3 0 0 0 \n", + "1097 2 5 2 2 \n", + "1098 3 1 4 3 \n", + "1099 3 0 3 3 \n", + "\n", + " living_conditions ... basic_needs academic_performance study_load \\\n", + "0 3 ... 2 3 2 \n", + "1 1 ... 2 1 4 \n", + "2 2 ... 2 2 3 \n", + "3 2 ... 2 2 4 \n", + "4 2 ... 3 4 3 \n", + "... ... ... ... ... ... \n", + "1095 2 ... 3 2 2 \n", + "1096 1 ... 4 0 1 \n", + "1097 3 ... 4 5 1 \n", + "1098 1 ... 1 2 5 \n", + "1099 0 ... 3 3 4 \n", + "\n", + " teacher_student_relationship future_career_concerns social_support \\\n", + "0 3 3 2 \n", + "1 1 5 1 \n", + "2 3 2 2 \n", + "3 1 4 1 \n", + "4 1 2 1 \n", + "... ... ... ... \n", + "1095 2 3 3 \n", + "1096 1 1 1 \n", + "1097 4 1 3 \n", + "1098 1 4 1 \n", + "1099 3 3 1 \n", + "\n", + " peer_pressure extracurricular_activities bullying stress_level \n", + "0 3 3 2 1 \n", + "1 4 5 5 2 \n", + "2 3 2 2 1 \n", + "3 4 4 5 2 \n", + "4 5 0 5 1 \n", + "... ... ... ... ... \n", + "1095 2 3 3 1 \n", + "1096 3 4 3 2 \n", + "1097 1 2 1 0 \n", + "1098 4 4 4 2 \n", + "1099 5 1 4 2 \n", + "\n", + "[1100 rows x 21 columns]" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv(\"C:\\\\Users\\\\nargy\\\\Documents\\\\Estudos\\\\Semana-12\\\\on26-python-s12-pandas-numpy-II\\\\material\\\\StressLevelDataset.csv\")\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "perguntas norteadoras:\n", + "\n", + "- quantos alunos estão no conjunto de dados?\n", + "\n", + "- qual o tempo médio de estudo dos alunos?\n", + "\n", + "- o tempo médio de estudo dos alunos está relacionado com a depressão?\n", + "\n", + "- o tempo médio de estudo dos alunos está relacionado com a ansiedade?\n", + "\n", + "- quantos estudantes classificam a qualidade do sono como ruim e isso se relaciona com a ansiedade?" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1100, 21)" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [], + "source": [ + "#selecionando fatores das perguntas norteadoras\n", + "df_filter = df.loc[:, [\"anxiety_level\", \"depression\", \"sleep_quality\", \"academic_performance\", \"study_load\"]]" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " anxiety_level depression sleep_quality academic_performance study_load\n", + "0 14 11 2 3 2\n", + "1 15 15 1 1 4\n", + "2 12 14 2 2 3\n", + "3 16 15 1 2 4\n", + "4 16 7 5 4 3\n", + ".. ... ... ... ... ...\n", + "95 19 11 3 5 5\n", + "96 15 19 1 1 4\n", + "97 17 23 1 2 3\n", + "98 6 1 5 5 1\n", + "99 9 13 3 3 3\n", + "\n", + "[100 rows x 5 columns]" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_head = df_filter.head(100)\n", + "df_head" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ansiedade max 21, min 0\n", + "Depressão max 27, min 0\n", + "Sono max 5, min 0\n", + "Academic max 5, min 0\n", + "Study max 5, min 0\n" + ] + } + ], + "source": [ + "#descobrindo os valores máximos e mínimos das colunas:\n", + "valor_max_anxiety = df_head[\"anxiety_level\"].max()\n", + "valor_min_anxiety = df_head[\"anxiety_level\"].min()\n", + "\n", + "valor_max_depression = df_head[\"depression\"].max()\n", + "valor_min_depression = df_head[\"depression\"].min()\n", + "\n", + "valor_max_sleep = df_head[\"sleep_quality\"].max()\n", + "valor_min_sleep = df_head[\"sleep_quality\"].min()\n", + "\n", + "valor_max_academic = df_head[\"academic_performance\"].max()\n", + "valor_min_academic = df_head[\"academic_performance\"].min()\n", + "\n", + "valor_max_study = df_head[\"study_load\"].max()\n", + "valor_min_study = df_head[\"study_load\"].min()\n", + "\n", + "print(f'Ansiedade max {valor_max_anxiety}, min {valor_min_anxiety}')\n", + "print(f'Depressão max {valor_max_depression}, min {valor_min_depression}')\n", + "print(f'Sono max {valor_max_sleep}, min {valor_min_sleep}')\n", + "print(f'Academic max {valor_max_academic}, min {valor_min_academic}')\n", + "print(f'Study max {valor_max_study}, min {valor_min_study}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(100, 5)" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_nao_nulos = df_head.dropna()\n", + "df_nao_nulos.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Possui 100 alunos.\n" + ] + } + ], + "source": [ + "#Qual o número de alunos do dataset?\n", + "\n", + "num_alunos = len(df_head)\n", + "print(\"Possui\", num_alunos, \"alunos.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "O tempo médio de estudo dos alunos é:\n", + "3 horas\n" + ] + } + ], + "source": [ + "#Qual o tempo médio de estudo dos alunos?\n", + "media_estudo = df_head[\"study_load\"].median()\n", + "print(\"O tempo médio de estudo dos alunos é:\")\n", + "media_arredondada = print(round(media_estudo), \"horas\")" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "study_load\n", + "0 3\n", + "1 15\n", + "2 31\n", + "3 28\n", + "4 13\n", + "5 10\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#contagem de estudos dos alunos\n", + "cont_estudo = df_head[\"study_load\"].value_counts().sort_index()\n", + "cont_estudo" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "depression\n", + "0 5\n", + "1 4\n", + "2 3\n", + "3 5\n", + "4 2\n", + "5 6\n", + "6 3\n", + "7 3\n", + "8 4\n", + "9 2\n", + "10 5\n", + "11 4\n", + "12 5\n", + "13 5\n", + "14 8\n", + "15 3\n", + "16 1\n", + "17 1\n", + "18 4\n", + "19 2\n", + "20 4\n", + "21 3\n", + "22 4\n", + "23 3\n", + "24 3\n", + "25 2\n", + "26 2\n", + "27 4\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#contagem dos níveis de depressão\n", + "media_depressao = df_head[\"depression\"].value_counts().sort_index()\n", + "media_depressao" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "cont_estudo = df_head[\"study_load\"].value_counts().sort_index()\n", + "media_depressao = df_head[\"depression\"].value_counts().sort_index()\n", + "plt.scatter(data['study_load'], data['depression'], color='pink', label='Relação Tempo de Estudo x Depressão')\n", + "plt.xlabel('Nível de depressão')\n", + "plt.ylabel('Tempo de estudo')\n", + "plt.xticks(np.arange(0, 30, 3))\n", + "plt.grid()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "É possível notar, a partir do gráfico de dispersão, que há uma tendencia de diminuição no tempo de estudo, a medida que o nível de depressão aumenta. " + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "anxiety_level\n", + "0 3\n", + "1 3\n", + "2 3\n", + "3 3\n", + "4 5\n", + "5 5\n", + "6 5\n", + "7 6\n", + "8 5\n", + "9 8\n", + "10 2\n", + "11 5\n", + "12 5\n", + "13 7\n", + "14 1\n", + "15 5\n", + "16 3\n", + "17 9\n", + "18 4\n", + "19 4\n", + "20 4\n", + "21 5\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#contagem dos níveis de ansiedade\n", + "cont_ansiedade = df_head[\"anxiety_level\"].value_counts().sort_index()\n", + "cont_ansiedade" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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rHagI2zRwUulrwaZPybUxc0X8LjITJ04M2IMfOHBAQ4YMUW5uruLi4tS2bVutWLFCvXr1Cthj1CYhoSFKuixJ2/65TUmX2bnBAD/HNg2cFBIaojO7n+l0DL/YmNkbv4tMaGiocnNz1ahRozLjeXl5atSokYqLi31e1+zZs/19eAAAAA+/f32q6GrtoqIihYeHVzsQAACAr3zeI/P0009Lklwul1588UXVrVvXM6+4uFjr1q3z+xwZAACA6vC5yEydOlXSyT0yM2fOLHODyPDwcJ155pmaOXNm4BMCAABUwOcis3v3bknSFVdcoaVLl6pevXo1FgoAAMAXfp8js3r16jIlpri4WFu3btWhQ4cCGgwAAKAqfheZUaNGea42Ki4uVrdu3dShQwclJiZqzZo1gc4HAABQIb+LzJIlS9SuXTtJ0ptvvqmvv/5a27dv1913361777034AEBAAAq4neRycvLU5MmTSRJ//znP3XjjTeqZcuWGj58uD799NOABwQAAKiI30WmcePG+vzzz1VcXKysrCzPp/AWFhaWuZIJAACgpvn9yb7Dhg1TcnKy4uPj5XK51LNnT0nShx9+yOfIAACAoDqley2df/752rt3r2688UZFRERIOnnrgnHjxgU8IAAAQEX8LjKSdMMNN5QbS01NrXYYAAAAf/h8jky/fv2Un5/vmZ4yZYoOHz7smc7Ly9O5554b0HAAAACV8bnIrFixQkVFRZ7pSZMm6eDBg57pEydOaMeOHYFNBwAAUAmfi8wv73pd0V2wAQAAgsXvy68BAABqC5+LjMvlksvlKjcGAADgFJ+vWjLGaOjQoZ7LrY8fP64//elPOu200ySpzPkzAAAAweBzkfnl5dW33HJLuWWGDBlS/UQAAAA+8rnIzJ07tyZzAAAA+I2TfQEAgLUoMgAAwFoUGQAAYC2KDAAAsBZFBgAAWOuUiszLL7+sSy+9VAkJCfrmm28kSdOmTdPrr78e0HAAAACV8bvIPPfcc0pPT1e/fv10+PBhFRcXS5JOP/10TZs2LdD5AAAAKuR3kXnmmWc0a9Ys3XvvvQoNDfWMd+rUSZ9++mlAwwEAAFTG7yKze/dutW/fvtx4RESEjh49GpBQAAAAvvC7yDRv3lxbt24tN56VlaU2bdoEIhMAAIBPfL5FQan09HSlpaXp+PHjMsZo48aNWrhwoSZPnqwXX3yxJjICAAB45XeRufXWWxUVFaX77rtPhYWFSklJUUJCgqZPn66bb765JjICAAB45XeRkaTBgwdr8ODBKiwsVEFBgRo1ahToXAAAAFU6pSJTKjo6WtHR0YHKAgAA4Befikz79u3lcrl8WuHmzZurFQgAAMBXPhWZAQMGeP5+/PhxPfvsszr33HPVtWtXSdKGDRv02Wef6fbbb6+RkAAAAN74VGQmTJjg+futt96qP//5z3rooYfKLbN3797ApgMAAKiE358js2TJEg0ZMqTc+C233KJ//OMfAQkFAADgC7+LTFRUlN57771y4++9954iIyMDEgoAAMAXfl+1NGrUKI0cOVKbN29Wly5dJEkffvih5syZo/vvvz/gAQEAACrid5EZN26czjrrLE2fPl1///vfJUlt2rTR3LlzlZycHPCAAAAAFTmlz5FJTk6mtAAAAMf5fY4MAABAbUGRAQAA1qLIAAAAa1FkAACAtSgyAADAWj5dtZSenu7zCp966qlTDgMAAOAPn4rMli1bfFqZr3fIBgAACASfiszq1atrOgcAAIDfTvkcmV27dmnFihU6duyYJMkYE7BQAAAAvvC7yOTl5alHjx5q2bKl+vXrp9zcXEnSiBEj9Je//CXgAQEAACrid5G5++67FRYWpj179ig6OtozftNNNykrKyug4QAAACrj972W3nnnHa1YsUJNmzYtM37OOefom2++CVgwAACAqvi9R+bo0aNl9sSUOnjwoCIiIgISCgAAwBd+F5nf//73eumllzzTLpdLJSUleuyxx3TFFVcENBwAAEBl/D609Nhjj6lHjx7Kzs7WTz/9pDFjxuizzz7TwYMH9d5779VERgAAAK/83iNz/vnna+fOnbrsssvUv39/HT16VAMHDtSWLVvUokWLmsgIAADgld97ZCQpLi5O9957b6CzAAAA+MXvPTJnn322Jk6cqC+++KIm8gAAAPjM7yKTlpamt99+W61atVLnzp01ffp0fffddzWRDQAAoFKn9IF4H330kbZv365+/fppxowZSkxMVO/evctczeSLyZMnq3PnzoqJiVGjRo00YMAA7dixw99IAADgN+qU77XUsmVLZWRkaOfOnfr3v/+t//znPxo2bJhf61i7dq3S0tK0YcMGrVy5Um63W71799bRo0dPNRYAAPgNOaWTfUtt3LhRmZmZWrx4sY4cOaIbb7zRr+//5S0N5s2bp0aNGmnTpk3q1q1bdaIBAIDfAL+LzM6dO7VgwQItXLhQu3fv1pVXXqlHH31UAwcOVN26dasVJj8/X5JUv359r/OLiopUVFTkmT5y5Igkye12y+12V+uxg6U0py15JTIHk425yRwcZA4OMgdHILO6jDHGn28ICQlR586dlZKSoptvvlmNGzcOSJCSkhJde+21Onz4sNavX+91mYkTJyojI6PceGZmptfbJgAAgNqnsLBQKSkpys/PV2xsbLXWVWWR+fjjj9W2bVu5XC5J0hdffKFzzjmnWg/qzciRI7V8+XKtX7++3A0pS3nbI5OYmKjc3Fw1aNAg4Jlqgtvt1sqVK9WrVy+FhYU5HccnZA4eG3OTOTjIHBxkDo68vDzFx8cHpMhUeWjp3Xff1YQJE7Ro0SJFRkbWSIm544479NZbb2ndunUVlhhJioiI8HpjyrCwMGt+eKXIHBw2ZpbszE3m4CBzcJC5ZgUyZ5VFJj09XXl5eerZs6fWr1+vevXqefbOeHPw4EGfH9wYozvvvFPLli3TmjVr1Lx5c5+/FwAAoMoi43K5NGnSJL3++uuSpKlTp1ZaZPyRlpamzMxMvf7664qJifF8sF5cXJyioqIC8hgAAODXy+erlvr37y9JGjp0aMAe/LnnnpMkde/evcz43LlzA/o4AADg16lanyNTXX5eMAUAAFCGz0UmJCSkykNKLpdLJ06cqHYoAAAAX/hcZJYtW1bhvA8++EBPP/20SkpKAhIKAADAF36fI/NzO3bs0Lhx4/Tmm29q8ODBevDBBwMaDgAAoDKndNPI/fv367bbbtMFF1ygEydOaOvWrZo/f76aNWsW6HwAAAAV8qvI5Ofna+zYsTr77LP12WefadWqVXrzzTd1/vnn11Q+AACACvl8aOmxxx7To48+qiZNmmjhwoVeDzUBAAAEk89FZty4cYqKitLZZ5+t+fPna/78+V6XW7p0acDCAQAAVMbnIjNkyJCAfaIvAABAIPhcZObNm1eDMQAAAPx3SlctAQAA1AYUGQAAYC2KDAAAsBZFBgAAWIsiAwAArEWRAQAA1qLIAAAAa1FkAACAtSgyAADAWhQZAABgLYoMAACwFkUGAABYiyIDAACsRZEBAADWosgAAABrUWQAAIC1KDIAAMBaFBkAAGAtigwAALAWRQYAAFiLIgMAAKxFkQEAANaiyAAAAGtRZAAAgLUoMgAAwFoUGQAAYC2KDAAAsBZFBgAAWIsiAwAArEWRAQAA1qLIAAAAa1FkAACAtSgyAADAWhQZAABgLYoMAACwFkUGAABYiyIDAACsRZEBAADWosgAAABrUWQAAIC1KDIAAMBaFBkAAGAtigwAALAWRQYAAFiLIgMAAKxFkQEAANaiyAAAAGtRZAAAgLUoMgAAwFoUGQAAYC2KDAAAsBZFBgAAWIsiAwAArOVokVm3bp2uueYaJSQkyOVy6bXXXnMyDgAAsIyjRebo0aNq166dZsyY4WQMAABgqTpOPnjfvn3Vt29fn5cvKipSUVGRZ/rIkSOSJLfbLbfbHfB8NaE0py15JTIHk425yRwcZA4OMgdHILO6jDEmYGurBpfLpWXLlmnAgAEVLjNx4kRlZGSUG8/MzFR0dHQNpgMAAIFSWFiolJQU5efnKzY2tlrrsqrIeNsjk5iYqNzcXDVo0CAIKavP7XZr5cqV6tWrl8LCwpyO4xMyB4+NuckcHGQODjIHR15enuLj4wNSZBw9tOSviIgIRURElBsPCwuz5odXiszBYWNmyc7cZA4OMgcHmWtWIHNy+TUAALAWRQYAAFjL0UNLBQUF2rVrl2d69+7d2rp1q+rXr6+kpCQHkwEAABs4WmSys7N1xRVXeKbT09MlSampqZo3b55DqQAAgC0cLTLdu3dXLbloCgAAWIhzZAAAgLUoMgAAwFoUGQAAYC2KDAAAsBZFBgAAWIsiAwAArEWRAQAA1qLIAAAAa1FkAACAtSgyAADAWhQZAABgLYoMAACwFkUGAABYiyIDAACsRZEBAADWosgAAABrUWQAAIC1KDIAAMBaFBkAAGAtigwAALAWRQYAAFiLIgMAAKxFkQEAANaiyAAAAGtRZAAAgLUoMgAAwFoUGQAAYC2KDAAAsBZFBgAAWIsiAwAArEWRAQAA1qLIAAAAa1FkAACAtSgyAADAWhQZAABgLYoMAACwFkUGAABYiyIDAACsRZEBAADWosgAAABrUWQAAIC1KDIAAMBaFBkAAGAtigwAALAWRQYAAFiLIgMAAKxFkQEAANaiyAAAAGtRZAAAgLUoMgAAwFoUGQAAYC2KDAAAsBZFBgAAWIsiAwAArEWRAQAA1qLIAAAAa1FkAACAtSgyAADAWhQZAABgLYoMAACwFkUGAABYq1YUmRkzZujMM89UZGSkLrroIm3cuNHpSAAAwAKOF5nFixcrPT1dEyZM0ObNm9WuXTtdddVVOnDggNPRAABALed4kXnqqad02223adiwYTr33HM1c+ZMRUdHa86cOU5HAwAAtVwdJx/8p59+0qZNmzR+/HjPWEhIiHr27KkPPvig3PJFRUUqKiryTOfn50uSDh48WPNhA8TtdquwsFB5eXkKCwtzOo5PyBw8NuYmc3CQOTjIHByl79vGmGqvy9Ei88MPP6i4uFiNGzcuM964cWNt37693PKTJ09WRkZGufGWLVvWWEYAAFAz8vLyFBcXV611OFpk/DV+/Hilp6d7pg8fPqxmzZppz5491X4iguXIkSNKTEzU3r17FRsb63Qcn5A5eGzMTebgIHNwkDk48vPzlZSUpPr161d7XY4WmTPOOEOhoaH6/vvvy4x///33atKkSbnlIyIiFBERUW48Li7Omh9eqdjYWDIHgY2ZJTtzkzk4yBwcZA6OkJDqn6rr6Mm+4eHh6tixo1atWuUZKykp0apVq9S1a1cHkwEAABs4fmgpPT1dqamp6tSpk7p06aJp06bp6NGjGjZsmNPRAABALed4kbnpppv0n//8Rw888IC+++47XXjhhcrKyip3ArA3ERERmjBhgtfDTbUVmYPDxsySnbnJHBxkDg4yB0cgM7tMIK59AgAAcIDjH4gHAABwqigyAADAWhQZAABgLYoMAACwltVFZsaMGTrzzDMVGRmpiy66SBs3bnQ6UoWee+45tW3b1vOBRV27dtXy5cudjlWlffv26ZZbblGDBg0UFRWlCy64QNnZ2U7HqtSPP/6oUaNGqVmzZoqKitIll1yijz76yOlYHuvWrdM111yjhIQEuVwuvfbaa555brdbY8eO1QUXXKDTTjtNCQkJGjJkiPbv3+9cYFWeWZKGDh0ql8tV5qtPnz7OhP2vqjIXFBTojjvuUNOmTRUVFeW5aa2TJk+erM6dOysmJkaNGjXSgAEDtGPHjjLLvPDCC+revbtiY2Plcrl0+PBhZ8L+ly+ZSxlj1LdvX68/j2CqKvPXX39dbnsu/VqyZIkjmat6Dzl+/LjS0tLUoEED1a1bV9dff325D5sNtqoy/+///q9atGihqKgoNWzYUP379/d6e6KqWFtkFi9erPT0dE2YMEGbN29Wu3btdNVVV+nAgQNOR/OqadOmmjJlijZt2qTs7GxdeeWV6t+/vz777DOno1Xo0KFDuvTSSxUWFqbly5fr888/15NPPql69eo5Ha1St956q1auXKmXX35Zn376qXr37q2ePXtq3759TkeTJB09elTt2rXTjBkzys0rLCzU5s2bdf/992vz5s1aunSpduzYoWuvvdaBpP+vssyl+vTpo9zcXM/XwoULg5iwvKoyp6enKysrS3//+9+Vk5OjUaNG6Y477tAbb7wR5KT/b+3atUpLS9OGDRu0cuVKud1u9e7dW0ePHvUsU1hYqD59+uiee+5xLOfP+ZK51LRp0+RyuRxIWVZVmRMTE8tsy7m5ucrIyFDdunXVt29fRzJX9R5y9913680339SSJUu0du1a7d+/XwMHDnQkq6+ZO3bsqLlz5yonJ0crVqyQMUa9e/dWcXGxfw9kLNWlSxeTlpbmmS4uLjYJCQlm8uTJDqbyT7169cyLL77odIwKjR071lx22WVOx/BLYWGhCQ0NNW+99VaZ8Q4dOph7773XoVQVk2SWLVtW6TIbN240ksw333wTnFBV8JY5NTXV9O/f35E8vvCW+bzzzjMPPvhgmbHatp0cOHDASDJr164tN2/16tVGkjl06FDwg1Wiosxbtmwxv/vd70xubq5P230wVfY8l7rwwgvN8OHDg5iqaqXvIYcPHzZhYWFmyZIlnnk5OTlGkvnggw8cTFheZe97H3/8sZFkdu3a5dc6rdwj89NPP2nTpk3q2bOnZywkJEQ9e/bUBx984GAy3xQXF2vRokU6evRorb4VwxtvvKFOnTrpxhtvVKNGjdS+fXvNmjXL6ViVOnHihIqLixUZGVlmPCoqSuvXr3coVfXk5+fL5XLp9NNPdzpKpdasWaNGjRqpVatWGjlypPLy8pyOVKlLLrlEb7zxhvbt2ydjjFavXq2dO3eqd+/eTkfzyM/Pl6SA3FgvWLxlLiwsVEpKimbMmOH1PnpOq+p53rRpk7Zu3aoRI0YEM1aFfvkesmnTJrnd7jLvia1bt1ZSUlKteU+s6n3v6NGjmjt3rpo3b67ExET/Vh6AghV0+/btM5LM+++/X2Z89OjRpkuXLg6lqtonn3xiTjvtNBMaGmri4uLM22+/7XSkSkVERJiIiAgzfvx4s3nzZvP888+byMhIM2/ePKejVapr167m8ssvN/v27TMnTpwwL7/8sgkJCTEtW7Z0Olo5quI302PHjpkOHTqYlJSU4IWqgrfMCxcuNK+//rr55JNPzLJly0ybNm1M586dzYkTJ5wJ+QveMh8/ftwMGTLESDJ16tQx4eHhZv78+c4E9KK4uNhcffXV5tJLL/U6vzbukako8x//+EczYsQIz3RV230wVfU8G2PMyJEjTZs2bYKYyruK3kMWLFhgwsPDyy3fuXNnM2bMmGDHLKOq970ZM2aY0047zUgyrVq18ntvjDHGOH6Lgt+SVq1aaevWrcrPz9err76q1NRUrV27Vueee67T0bwqKSlRp06dNGnSJElS+/bttW3bNs2cOVOpqakOp6vYyy+/rOHDh+t3v/udQkND1aFDBw0aNEibNm1yOppf3G63kpOTZYzRc88953ScSt18882ev19wwQVq27atWrRooTVr1qhHjx4OJqvYM888ow0bNuiNN95Qs2bNtG7dOqWlpSkhIaHMb7ZOSUtL07Zt26zak+gt8xtvvKF//etf2rJli4PJKlbV83zs2DFlZmbq/vvvD3Ky8ip6D6nNqnrfGzx4sHr16qXc3Fw98cQTSk5O1nvvvVdur3qlAlS6gqqoqMiEhoaWa/RDhgwx1157rTOhTkGPHj3MH//4R6djVCgpKanMb1HGGPPss8+ahIQEhxL5p6CgwOzfv98YY0xycrLp16+fw4nKUwW/mf70009mwIABpm3btuaHH34IfrBKVJT5l8444wwzc+bMmg/kg19mLiwsNGFhYeXOpRoxYoS56qqrgpyuvLS0NNO0aVPz1VdfVbhMbdsjU1Hmu+66y7hcLhMaGur5kmRCQkLM5Zdf7kzY//LleX7ppZdMWFiYOXDgQBCT+ab0PWTVqlVet4WkpCTz1FNPOROuApW97xUVFZno6GiTmZnp1zqtPEcmPDxcHTt21KpVqzxjJSUlWrVqVa0+5+SXSkpKVFRU5HSMCl166aXlLqPcuXOnmjVr5lAi/5x22mmKj4/XoUOHtGLFCvXv39/pSD4p3RPzxRdf6N1331WDBg2cjuS3b7/9Vnl5eYqPj3c6ildut1tut1shIWX/CwwNDVVJSYlDqU5ennzHHXdo2bJl+te//qXmzZs7lsVXVWUeN26cPvnkE23dutXzJUlTp07V3LlzHUjs3/M8e/ZsXXvttWrYsGEQE/qm9D2kY8eOCgsLK/OeuGPHDu3Zs6fWvSdW9r5njJExxu/3RWsPLaWnpys1NVWdOnVSly5dNG3aNB09elTDhg1zOppX48ePV9++fZWUlKQff/xRmZmZWrNmjVasWOF0tArdfffduuSSSzRp0iQlJydr48aNeuGFF/TCCy84Ha1SpZfxtWrVSrt27dLo0aPVunXrWrNtFBQUaNeuXZ7p3bt3a+vWrapfv77i4+N1ww03aPPmzXrrrbdUXFys7777TtLJExHDw8NrXeb69esrIyND119/vZo0aaIvv/xSY8aM0dlnn62rrrrKkbxVZU5KStLll1+u0aNHKyoqSs2aNdPatWv10ksv6amnnnIsc1pamjIzM/X6668rJibG87OPi4tTVFSUJOm7777Td9995/m3ffrpp4qJiVFSUpIjJwVXlblJkyZeT/BNSkpyrKj58jxL0q5du7Ru3Tr985//dCTnz1X2HhIXF6cRI0YoPT1d9evXV2xsrO6880517dpVF198ca3M/NVXX2nx4sXq3bu3GjZsqG+//VZTpkxRVFSU+vXr598DVW8nkbOeeeYZk5SUZMLDw02XLl3Mhg0bnI5UoeHDh5tmzZqZ8PBw07BhQ9OjRw/zzjvvOB2rSm+++aY5//zzTUREhGndurV54YUXnI5UpcWLF5uzzjrLhIeHmyZNmpi0tDRz+PBhp2N5lB4S+OVXamqq2b17t9d5kszq1atrZebCwkLTu3dv07BhQxMWFmaaNWtmbrvtNvPdd985lreqzMYYk5uba4YOHWoSEhJMZGSkadWqlXnyySdNSUmJY5kr+tnPnTvXs8yECROqXKa2Zfb2PU6e7Otr5vHjx5vExERTXFzsTNCfqeo95NixY+b222839erVM9HR0ea6664zubm5DiauPPO+fftM3759TaNGjUxYWJhp2rSpSUlJMdu3b/f7cVzGGONf9QEAAKgdrDxHBgAAQKLIAAAAi1FkAACAtSgyAADAWhQZAABgLYoMAACwFkUGAABYiyIDWK6kpERPPPFErb0pHwDUJIoMYLn77rtP69atU9u2bQOyvu7du2vUqFF+fc/EiRN14YUXBuTxa9K8efN0+umnV3s9LpdLr732WrXWYctzBtR2FBmgFho6dKhcLpemTJlSZvy1116Ty+UqM71mzRotWrRIoaGhwY5ZIz744AOFhobq6quvDvi6b7rpJu3cuTPg6wXgHIoMUEtFRkbq0Ucf1aFDhypcZsCAAXr//fcVHR0dxGQ1a/bs2brzzju1bt067d+/P6DrjoqKUqNGjQK6TgDOosgAtVTPnj3VpEkTTZ48ucJlfn6oZOfOnXK5XNq+fXuZZaZOnaoWLVp4prdt26a+ffuqbt26aty4sf7nf/5HP/zwg1/ZpkyZosaNGysmJkYjRozQ8ePHyy3z4osvqk2bNoqMjFTr1q317LPPVrnegoICLV68WCNHjtTVV1+tefPmlZm/Zs0auVwurVq1Sp06dVJ0dLQuueQS7dixw7PMxx9/rCuuuEIxMTGKjY1Vx44dlZ2dLcn7oaXXX39dHTp0UGRkpM466yxlZGToxIkTnvlffPGFunXrpsjISJ177rlauXJludxjx45Vy5YtFR0drbPOOkv333+/3G53UJ4z4LeOIgPUUqGhoZo0aZKeeeYZffvtt1Uu37JlS3Xq1EkLFiwoM75gwQKlpKRIkg4fPqwrr7xS7du3V3Z2trKysvT9998rOTnZ51yvvPKKJk6cqEmTJik7O1vx8fHl3nAXLFigBx54QI888ohycnI0adIk3X///Zo/f36V627durVatWqlW265RXPmzJG3+9ree++9evLJJ5Wdna06depo+PDhnnmDBw9W06ZN9dFHH2nTpk0aN26cwsLCvD7ev//9bw0ZMkR33XWXPv/8cz3//POaN2+eHnnkEUknT6QeOHCgwsPD9eGHH2rmzJkaO3ZsufXExMRo3rx5+vzzzzV9+nTNmjVLU6dODcpzBvzmBfSe3QACIjU11fTv398YY8zFF19shg8fbowxZtmyZebnL9u5c+eauLg4z/TUqVNNixYtPNM7duwwkkxOTo4xxpiHHnrI9O7du8xj7d2710gyO3bsMMYYc/nll5u77rqrwmxdu3Y1t99+e5mxiy66yLRr184z3aJFC5OZmVlmmYceesh07dq10n/3JZdcYqZNm2aMMcbtdpszzjjDrF692jN/9erVRpJ59913PWNvv/22kWSOHTtmjDEmJibGzJs3z+v6f/l89ejRw0yaNKnMMi+//LKJj483xhizYsUKU6dOHbNv3z7P/OXLlxtJZtmyZRX+Ox5//HHTsWNHz3RNPmfAbx17ZIBa7tFHH9X8+fOVk5NT5bI333yzvv76a23YsEHSyd/yO3TooNatW0s6edhl9erVqlu3ruerdN6XX37pU56cnBxddNFFZca6du3q+fvRo0f15ZdfasSIEWUe5+GHH670MXbs2KGNGzdq0KBBkqQ6deropptu0uzZs8st+/MrtOLj4yVJBw4ckCSlp6fr1ltvVc+ePTVlypRKH/Pjjz/Wgw8+WCbnbbfdptzcXBUWFionJ0eJiYlKSEjw+m8ttXjxYl166aVq0qSJ6tatq/vuu0979uyp8ecMgFTH6QAAKtetWzddddVVGj9+vIYOHVrpsk2aNNGVV16pzMxMXXzxxcrMzNTIkSM98wsKCnTNNdfo0UcfLfe9pYWgugoKCiRJs2bNKvfmXdmVVbNnz9aJEyfKlAZjjCIiIvS3v/1NcXFxnvGfHyoqvYqrpKRE0snLmlNSUvT2229r+fLlmjBhghYtWqTrrrvOa9aMjAwNHDiw3LzIyEhf/rn64IMPNHjwYGVkZOiqq65SXFycFi1apCeffNKn7y/NIfn/nAGgyABWmDJlii688EK1atWqymUHDx6sMWPGaNCgQfrqq6908803e+Z16NBB//jHP3TmmWeqTp1Te/m3adNGH374oYYMGeIZK90DJEmNGzdWQkKCvvrqKw0ePNindZ44cUIvvfSSnnzySfXu3bvMvAEDBmjhwoX605/+5HPGli1bqmXLlrr77rs1aNAgzZ0712uR6dChg3bs2KGzzz7b63ratGmjvXv3Kjc311P0fv5vlaT3339fzZo107333usZ++abb8qtJ9DPGYCTKDKABS644AINHjxYTz/9dJXLDhw4UCNHjtTIkSN1xRVXlNnDkZaWplmzZmnQoEEaM2aM6tevr127dmnRokV68cUXffrt/6677tLQoUPVqVMnXXrppVqwYIE+++wznXXWWZ5lMjIy9Oc//1lxcXHq06ePioqKlJ2drUOHDik9Pb3cOt966y0dOnRII0aMKLPnRZKuv/56zZ4926cic+zYMY0ePVo33HCDmjdvrm+//VYfffSRrr/+eq/LP/DAA/rDH/6gpKQk3XDDDQoJCdHHH3+sbdu26eGHH1bPnj3VsmVLpaam6vHHH9eRI0fKFBZJOuecc7Rnzx4tWrRInTt31ttvv61ly5bV+HMG4L+cPkkHQHk/P9m31O7du014eHilJ/uWSk5ONpLMnDlzys3buXOnue6668zpp59uoqKiTOvWrc2oUaNMSUmJMabqk32NMeaRRx4xZ5xxhqlbt65JTU01Y8aMKXPiqjHGLFiwwFx44YUmPDzc1KtXz3Tr1s0sXbrU6/r+8Ic/mH79+nmd9+GHHxpJ5uOPP/ac7Hvo0CHP/C1bthhJZvfu3aaoqMjcfPPNJjEx0YSHh5uEhARzxx13eE4E9vZ8ZWVlmUsuucRERUWZ2NhY06VLF/PCCy945u/YscNcdtllJjw83LRs2dJkZWWVO9l39OjRpkGDBqZu3brmpptuMlOnTi33OIF+zgCc5DLGy7WNAAAAFuCqJQAAYC2KDAAAsBZFBgAAWIsiAwAArEWRAQAA1qLIAAAAa1FkAACAtSgyAADAWhQZAABgLYoMAACwFkUGAABY6/8A1QTpnQ8iyM4AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#ANSIEDADE E TEMPO DE ESTUDO\n", + "study_load = cont_estudo = df_head[\"study_load\"].value_counts().sort_index()\n", + "anxiety_level = cont_ansiedade = df_head[\"anxiety_level\"].value_counts().sort_index()\n", + "plt.scatter(data['study_load'],data['anxiety_level'], color='purple')\n", + "plt.xlabel('Nível de Ansiedade')\n", + "plt.xticks(np.arange(0, 35, 3))\n", + "plt.yticks(np.arange(0, 6, 1))\n", + "plt.ylabel('Nível de Estudo')\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "O nível de ansiedade de alunos não é um fator impactante no seu tempo de estudo, uma vez que mesmo nos níveis mais baixo, se mantém na média de 3h" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Relação ansiedade e sono" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "51\n" + ] + } + ], + "source": [ + "#Sono de má qualidade\n", + "sono_ruim = df_head[df_head['sleep_quality'] < 3]\n", + "num_sono_ruim = len(sono_ruim)\n", + "print(num_sono_ruim)" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "52\n" + ] + } + ], + "source": [ + "#alunos com nível de ansiedade elevados\n", + "ansiedade = df_head[df_head['anxiety_level'] > 10]\n", + "num_anxiedade = len(ansiedade)\n", + "print(num_anxiedade)" + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "contagem = [num_sono_ruim, num_anxiedade]\n", + "categoria = ['sleep_quality', 'anxiety_level']\n", + "plt.bar(categoria, contagem, color=['#CC0099', '#33A1C9'], edgecolor = \"black\",)\n", + "plt.xlabel(\"\")\n", + "plt.ylabel(\"\")\n", + "plt.yticks(np.arange(0, 55, 5))\n", + "plt.title(\"Relação sono ruim x ansiedade\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "É possível notar uma semelhaça entre a quantidade de alunos que possuem um sono ruim, abaixo do nível 3 e a quantidade de alunos com alto nível de ansiedade, acima do nível 10. Podemos, logo, supor que há uma grande relação entre a ansiedade e a falta de sono." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 9df5a528ecd8ef2b5f10e761eb70adfce70f1320 Mon Sep 17 00:00:00 2001 From: Nalla Cloviel Date: Mon, 30 Oct 2023 23:35:41 -0300 Subject: [PATCH 2/2] Aula 12 --- exercicios/para-sala/aula12.ipynb | 4596 +++++++++++++++++++++++++++++ 1 file changed, 4596 insertions(+) create mode 100644 exercicios/para-sala/aula12.ipynb diff --git a/exercicios/para-sala/aula12.ipynb b/exercicios/para-sala/aula12.ipynb new file mode 100644 index 0000000..9e0e09a --- /dev/null +++ b/exercicios/para-sala/aula12.ipynb @@ -0,0 +1,4596 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "df = df = pd.read_csv(\"C:\\\\Users\\\\nargy\\\\Documents\\\\estudos\\\\Semana-12\\\\on26-python-s12-pandas-numpy-II\\\\material\\\\titanic.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
.......................................
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4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
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3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
6701McCarthy, Mr. Timothy Jmale54.0001746351.8625E46S
111211Bonnell, Miss. Elizabethfemale58.00011378326.5500C103S
.......................................
87387403Vander Cruyssen, Mr. Victormale47.0003457659.0000NaNS
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IdPassageiroSobreviventeClasseNomeSexoIdadeIrmãosPais&FilhosBilheteTaxaCabineEmbarque
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.00234774211.1333NaNS
91012Nasser, Mrs. Nicholas (Adele Achem)female14.01023773630.0708NaNC
.......................................
88088112Shelley, Mrs. William (Imanita Parrish Hall)female25.00123043326.0000NaNS
88288303Dahlberg, Miss. Gerda Ulrikafemale22.000755210.5167NaNS
88588603Rice, Mrs. William (Margaret Norton)female39.00538265229.1250NaNQ
88788811Graham, Miss. Margaret Edithfemale19.00011205330.0000B42S
88888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.4500NaNS
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314 rows × 12 columns

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IdPassageiroSobreviventeClasseNomeSexoIdadeIrmãosPais&FilhosBilheteTaxaCabineEmbarque
7803Palsson, Master. Gosta Leonardmale2.003134990921.0750NaNS
161703Rice, Master. Eugenemale2.004138265229.1250NaNQ
505103Panula, Master. Juha Niilomale7.0041310129539.6875NaNS
596003Goodwin, Master. William Frederickmale11.0052CA 214446.9000NaNS
636403Skoog, Master. Haraldmale4.003234708827.9000NaNS
787912Caldwell, Master. Alden Gatesmale0.830224873829.0000NaNS
868703Ford, Mr. William Nealmale16.0013W./C. 660834.3750NaNS
12512613Nicola-Yarred, Master. Eliasmale12.0010265111.2417NaNC
13813903Osen, Mr. Olaf Elonmale16.000075349.2167NaNS
16316403Calic, Mr. Jovomale17.00003150938.6625NaNS
16416503Panula, Master. Eino Viljamimale1.0041310129539.6875NaNS
16516613Goldsmith, Master. Frank John William \"Frankie\"male9.000236329120.5250NaNS
17117203Rice, Master. Arthurmale4.004138265229.1250NaNQ
18218303Asplund, Master. Clarence Gustaf Hugomale9.004234707731.3875NaNS
18318412Becker, Master. Richard Fmale1.002123013639.0000F4S
19319412Navratil, Master. Michel Mmale3.001123008026.0000F2S
22022113Sunderland, Mr. Victor Francismale16.0000SOTON/OQ 3920898.0500NaNS
26126213Asplund, Master. Edvin Rojj Felixmale3.004234707731.3875NaNS
26626703Panula, Mr. Ernesti Arvidmale16.0041310129539.6875NaNS
27827903Rice, Master. Ericmale7.004138265229.1250NaNQ
28228303de Pelsmaeker, Mr. Alfonsmale16.00003457789.5000NaNS
30530611Allison, Master. Hudson Trevormale0.9212113781151.5500C22 C26S
33333403Vander Planke, Mr. Leo Edmondusmale16.002034576418.0000NaNS
34034112Navratil, Master. Edmond Rogermale2.001123008026.0000F2S
34834913Coutts, Master. William Loch \"William\"male3.0011C.A. 3767115.9000NaNS
35235303Elias, Mr. Tannousmale15.001126957.2292NaNC
38638703Goodwin, Master. Sidney Leonardmale1.0052CA 214446.9000NaNS
40740812Richards, Master. William Rowemale3.00112910618.7500NaNS
43343403Kallio, Mr. Nikolai Erlandmale17.0000STON/O 2. 31012747.1250NaNS
44544611Dodge, Master. Washingtonmale4.00023363881.8583A34S
48048103Goodwin, Master. Harold Victormale9.0052CA 214446.9000NaNS
48949013Coutts, Master. Eden Leslie \"Neville\"male9.0011C.A. 3767115.9000NaNS
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55055111Thayer, Mr. John Borland Jrmale17.000217421110.8833C70C
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72172203Jensen, Mr. Svend Lauritzmale17.00103500487.0542NaNS
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74674703Abbott, Mr. Rossmore Edwardmale16.0011C.A. 267320.2500NaNS
75175213Moor, Master. Meiermale6.000139209612.4750E121S
75575612Hamalainen, Master. Viljomale0.671125064914.5000NaNS
76476503Eklund, Mr. Hans Linusmale16.00003470747.7750NaNS
78778803Rice, Master. George Hughmale8.004138265229.1250NaNQ
78878913Dean, Master. Bertram Veremale1.0012C.A. 231520.5750NaNS
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80280311Carter, Master. William Thornton IImale11.0012113760120.0000B96 B98S
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85085103Andersson, Master. Sigvard Harald Eliasmale4.004234708231.2750NaNS
86987013Johnson, Master. Harold Theodormale4.001134774211.1333NaNS
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IdPassageiroSobreviventeClasseNomeSexoIdadeIrmãosPais&FilhosBilheteTaxaCabineEmbarque
161703Rice, Master. Eugenemale2.04138265229.125NaNQ
17117203Rice, Master. Arthurmale4.04138265229.125NaNQ
27827903Rice, Master. Ericmale7.04138265229.125NaNQ
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IdPassageiroSobreviventeClasseNomeSexoIdadeIrmãosPais&FilhosBilheteTaxaCabineEmbarque
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.00234774211.1333NaNS
91012Nasser, Mrs. Nicholas (Adele Achem)female14.01023773630.0708NaNC
101113Sandstrom, Miss. Marguerite Rutfemale4.011PP 954916.7000G6S
111211Bonnell, Miss. Elizabethfemale58.00011378326.5500C103S
121303Saundercock, Mr. William Henrymale20.000A/5. 21518.0500NaNS
131403Andersson, Mr. Anders Johanmale39.01534708231.2750NaNS
141503Vestrom, Miss. Hulda Amanda Adolfinafemale14.0003504067.8542NaNS
151612Hewlett, Mrs. (Mary D Kingcome)female55.00024870616.0000NaNS
161703Rice, Master. Eugenemale2.04138265229.1250NaNQ
171812Williams, Mr. Charles EugenemaleNaN0024437313.0000NaNS
181903Vander Planke, Mrs. Julius (Emelia Maria Vande...female31.01034576318.0000NaNS
192013Masselmani, Mrs. FatimafemaleNaN0026497.2250NaNC
202102Fynney, Mr. Joseph Jmale35.00023986526.0000NaNS
212212Beesley, Mr. Lawrencemale34.00024869813.0000D56S
222313McGowan, Miss. Anna \"Annie\"female15.0003309238.0292NaNQ
232411Sloper, Mr. William Thompsonmale28.00011378835.5000A6S
242503Palsson, Miss. Torborg Danirafemale8.03134990921.0750NaNS
252613Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...female38.01534707731.3875NaNS
262703Emir, Mr. Farred ChehabmaleNaN0026317.2250NaNC
272801Fortune, Mr. Charles Alexandermale19.03219950263.0000C23 C25 C27S
282913O'Dwyer, Miss. Ellen \"Nellie\"femaleNaN003309597.8792NaNQ
293003Todoroff, Mr. LaliomaleNaN003492167.8958NaNS
303101Uruchurtu, Don. Manuel Emale40.000PC 1760127.7208NaNC
313211Spencer, Mrs. William Augustus (Marie Eugenie)femaleNaN10PC 17569146.5208B78C
323313Glynn, Miss. Mary AgathafemaleNaN003356777.7500NaNQ
333402Wheadon, Mr. Edward Hmale66.000C.A. 2457910.5000NaNS
343501Meyer, Mr. Edgar Josephmale28.010PC 1760482.1708NaNC
353601Holverson, Mr. Alexander Oskarmale42.01011378952.0000NaNS
363713Mamee, Mr. HannamaleNaN0026777.2292NaNC
373803Cann, Mr. Ernest Charlesmale21.000A./5. 21528.0500NaNS
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" + ], + "text/plain": [ + " Sobrevivente Idade\n", + "7 0 2.00\n", + "16 0 2.00\n", + "50 0 7.00\n", + "59 0 11.00\n", + "63 0 4.00\n", + "78 1 0.83\n", + "86 0 16.00\n", + "125 1 12.00\n", + "138 0 16.00\n", + "163 0 17.00\n", + "164 0 1.00\n", + "165 1 9.00\n", + "171 0 4.00\n", + "182 0 9.00\n", + "183 1 1.00\n", + "193 1 3.00\n", + "220 1 16.00\n", + "261 1 3.00\n", + "266 0 16.00\n", + "278 0 7.00\n", + "282 0 16.00\n", + "305 1 0.92\n", + "333 0 16.00\n", + "340 1 2.00\n", + "348 1 3.00\n", + "352 0 15.00\n", + "386 0 1.00\n", + "407 1 3.00\n", + "433 0 17.00\n", + "445 1 4.00\n", + "480 0 9.00\n", + "489 1 9.00\n", + "500 0 17.00\n", + "532 0 17.00\n", + "549 1 8.00\n", + "550 1 17.00\n", + "574 0 16.00\n", + "683 0 14.00\n", + "686 0 14.00\n", + "721 0 17.00\n", + "731 0 11.00\n", + "746 0 16.00\n", + "751 1 6.00\n", + "755 1 0.67\n", + "764 0 16.00\n", + "787 0 8.00\n", + "788 1 1.00\n", + "791 0 16.00\n", + "802 1 11.00\n", + "803 1 0.42\n", + "819 0 10.00\n", + "824 0 2.00\n", + "827 1 1.00\n", + "831 1 0.83\n", + "841 0 16.00\n", + "844 0 17.00\n", + "850 0 4.00\n", + "869 1 4.00" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_filtrado1.loc[:, [\"Sobrevivente\", \"Idade\"]]" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Sobrevivente\n", + "0 35\n", + "1 23\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_criancas_sobrevivente = df_filtrado1[\"Sobrevivente\"].value_counts()\n", + "df_criancas_sobrevivente" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Classe\n", + "1 8.230000\n", + "2 4.757273\n", + "3 9.963256\n", + "Name: Idade, dtype: float64\n" + ] + } + ], + "source": [ + "dado_agrupado = df_filtrado1.groupby(\"Classe\")\n", + "media_idade = dado_agrupado['Idade'].mean()\n", + "\n", + "print(media_idade)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Exercício 04 - Parte 2" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sexo\n", + "female 27.915709\n", + "male 30.726645\n", + "Name: Idade, dtype: float64\n" + ] + } + ], + "source": [ + "dado_genero_agrupado = df.groupby(\"Sexo\")\n", + "media_idade_genero = dado_genero_agrupado[\"Idade\"].mean()\n", + "print(media_idade_genero)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sexo\n", + "female 110152\n", + "male 110413\n", + "Name: Bilhete, dtype: object\n", + "Sexo\n", + "female WE/P 5735\n", + "male WE/P 5735\n", + "Name: Bilhete, dtype: object\n" + ] + } + ], + "source": [ + "valor_max = dado_genero_agrupado[\"Bilhete\"].max()\n", + "valor_min = dado_genero_agrupado[\"Bilhete\"].min()\n", + "\n", + "\"O valor mínimo pago foi de:\"\n", + "print(valor_min)\n", + "\n", + "\"O valor máximo pago foi de:\"\n", + "print(valor_max)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Taxa \n", + " count mean median min max\n", + "Sexo \n", + "female 314 44.479818 23.0 6.75 512.3292\n", + "male 577 25.523893 10.5 0.00 512.3292" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby(\"Sexo\").agg({\"Taxa\": ['count', 'mean', 'median', 'min', 'max']})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "instalar o matpltlip" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting matplotlib\n", + " Obtaining dependency information for matplotlib from https://files.pythonhosted.org/packages/40/d9/c1784db9db0d484c8e5deeafbaac0d6ed66e165c6eb4a74fb43a5fa947d9/matplotlib-3.8.0-cp311-cp311-win_amd64.whl.metadata\n", + " Downloading matplotlib-3.8.0-cp311-cp311-win_amd64.whl.metadata (5.9 kB)\n", + "Collecting contourpy>=1.0.1 (from 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0:00:01\n", + " --------------------------- ----------- 71.7/103.1 kB 438.9 kB/s eta 0:00:01\n", + " -------------------------------------- 103.1/103.1 kB 496.8 kB/s eta 0:00:00\n", + "Installing collected packages: pyparsing, pillow, kiwisolver, fonttools, cycler, contourpy, matplotlib\n", + "Successfully installed contourpy-1.1.1 cycler-0.12.1 fonttools-4.43.1 kiwisolver-1.4.5 matplotlib-3.8.0 pillow-10.1.0 pyparsing-3.1.1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "[notice] A new release of pip is available: 23.2.1 -> 23.3.1\n", + "[notice] To update, run: C:\\Users\\nargy\\AppData\\Local\\Microsoft\\WindowsApps\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\python.exe -m pip install --upgrade pip\n" + ] + } + ], + "source": [ + "!pip install matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.plot([1, 2, 3, 4], [2, 4, 6, 8])" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Número de sobreviventes')" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "contagem_passageiros = df['Sobrevivente'].value_counts()\n", + "contagem_passageiros.plot(kind='bar')\n", + "\n", + "plt.xlabel('Sobrevivente')\n", + "plt.ylabel('Quantidade')\n", + "plt.title('Número de sobreviventes')" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "taxa_sob_sexo = df.groupby('Sexo')['Sobrevivente'].mean()\n", + "taxa_sob_sexo.plot.bar()\n", + "\n", + "plt.xlabel('Sexo')\n", + "plt.ylabel('Taxa de Sobrevivência')\n", + "plt.title('Taxa de Sobrevivência por Sexo')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [], + "source": [ + "df_filtrado1 = df[(df[\"Idade\"] < 11) & (df[\"Sexo\"] == \"male\")]\n", + "df_filtrado2 = df[(df['Idade'] > 11) & (df['Idade'] < 20) & (df[\"Sexo\"] == \"male\")]" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [], + "source": [ + "contagem_idades1 = df_filtrado1['Idade'].value_counts().sort_index()\n", + "contagem_idades2 = df_filtrado2['Idade'].value_counts().sort_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+/p2fpStXruj8+fOqXr26YmNjtWfPHod5fX199eKLL9qHPTw8VKlSpSSvY2q2L9mzZ1dMTIyWLFlyH8/2v5o3b678+fPbhytVqqTKlStr/vz5kqRTp05p69atat++vcOhy3LlyqlevXr2+e5093s0teLj47Vo0SI1b95cBQsWtI8vVapUsoeu71znly9f1vnz5xUeHq6DBw/aD+MvXbpUN27cUI8ePRy2RYl7Be80a9YsVa9eXTly5HB439etW1fx8fFavXp1mp6XlVBU0sH48eO1ZMkSrVixQrt27dLBgwftb+B9+/bJGKNixYopMDDQ4Wf37t06e/asJCksLEx9+vTR559/rty5cysiIkLjx493OD/l+eefV7Vq1fTyyy8rb968atWqlb7//nuHjeKRI0dUrFixJLuKS5UqZZ+eaN68eXryySfl5eWlnDlzKjAwUBMnTrzvx0yNNm3a2E/onDFjhlq1apViWbh48aJ69eqlvHnzytvbW4GBgQoLC5Mkh2wffPCBduzYoZCQEFWqVElDhgxx2KgmHoJ75JFH7iurM9y5QUuUI0cO/fPPP/bhhIQEjRkzRsWKFZOnp6dy586twMBAbd++3eF59u/fX76+vqpUqZKKFSumbt263deht0Rt2rTRrFmztH//fq1bty7FwyCJV5PUrl07yXt28eLF9vdsIi8vLwUGBt7zuR45ckT58uWTn5+fw3zJvS8l2V/vROfOndOlS5c0efLkJJk6dOggSfZcaV1fR44cUdGiRZO8L0uUKJHs+mnXrl2SLJ9//rni4uIcXr8H4e3tnex5KIknF9/5n2Jydu7cqaeffloBAQHy9/dXYGCgvYzcnbFAgQJJnntyr2Nqti9du3ZV8eLFFRkZqQIFCqhjx47JHmJMSbFixZKMK168uP1csMTHufu1ScySWBzvdPd7KrXOnTuna9euJZspucf/7bffVLduXft5M4GBgfZz+hLXeWL+u5cZGBioHDlyOIzbt2+fFi5cmOS9lnjbh7s/jxkR56ikg0qVKunxxx9PdlpCQoJsNpsWLFggd3f3JNPvvH/F6NGj1b59e82dO1eLFy9Wz549NWLECK1fv14FChSQt7e3Vq9erRUrVujXX3/VwoULNXPmTNWuXVuLFy9OdvkpWbNmjZo2baoaNWpowoQJCg4OVtasWTVlyhSHkw6d9ZiVK1dWkSJF7FdE3etcgZYtW2rdunXq16+fKlSoIF9fXyUkJKhBgwYOBally5aqXr26fvzxRy1evFgffvih3n//fc2ZM8d+TD01bDZbsses7z7h8H6ktF7ufJzhw4fr7bffVseOHfXuu+8qZ86ccnNzU+/evR2eZ6lSpbR3717NmzdPCxcu1A8//KAJEyZo8ODBGjp0aKoztW7dWgMGDFDnzp2VK1cu1a9fP9n5Eh972rRpCgoKSjL97stp7+d9l1p3/wecmOnFF19Uu3btkv2dcuXKSXLe+kpJYpYPP/xQFSpUSHYeZ92XJjg4WKdOnUoyPnFc4nlMybl06ZLCw8Pl7++vd955R0WKFJGXl5c2b96s/v37J/ljIzXv2dTKkyePtm7dqkWLFmnBggVasGCBpkyZorZt2yZ74m16+LdS5wwHDhxQnTp1VLJkSX300UcKCQmRh4eH5s+frzFjxqRpT1tCQoLq1aunN998M9npxYsXf9DYLkdRcbEiRYrIGKOwsLBUvaHKli2rsmXL6v/+7/+0bt06VatWTZMmTbLfsMvNzU116tRRnTp19NFHH2n48OEaNGiQVqxYobp16yo0NFTbt29XQkKCw189ibt5E+/38cMPP8jLy0uLFi1y2LU8ZcqUJJn+7TFTq3Xr1ho2bJhKlSqV4gb+n3/+0bJlyzR06FANHjzYPj6le0YEBwera9eu6tq1q86ePavHHntM7733niIjI+0n7u7YseOeOXPkyOGwJybR3X/lO9vs2bNVq1YtffHFFw7jL126pNy5czuMy5Ytm55//nk9//zzunHjhp555hm99957GjBgQKrvg1KwYEFVq1ZNK1eu1GuvvZbi/TsS11uePHnu6/W9l9DQUC1dulRXrlxx2Kty9/syJYGBgfLz81N8fHyqMqVlfYWGhmrHjh0yxjjsWdi7d6/DfInrx9/f32nrJyUVKlTQmjVrknyeN2zYIB8fn3tuU1auXKkLFy5ozpw5qlGjhn38nVcj3q/Ubl+k24eOmjRpoiZNmighIUFdu3bVp59+qrfffltFixa95+Mk93n/+++/7SfIJj7O3a9NYpbcuXMrW7Zs9/38khMYGChvb+9kM939+L/88ovi4uL0888/O+xVvftwaWL+ffv2qXDhwvbx586dc9iDJd1+v129evWhv9dciUM/LvbMM8/I3d1dQ4cOTfKXiTHGfsltdHS0bt265TC9bNmycnNzs+/6vXjxYpLlJ/6HnzhPw4YNdfr0aYf7lty6dUtjx46Vr6+vwsPDJd3+68lmsznsNTh8+LB++uknh+Wn5jFT6+WXX1ZUVJRGjx6d4jyJf9Xdva7uPrM9Pj4+ya7rPHnyKF++fPZcjz32mMLCwvTxxx/r0qVLDvPeufwiRYpoz549OnfunH3ctm3b0nR45X64u7sneZ6zZs3SiRMnHMbdfVm2h4eHSpcuLWOMbt68eV+POWzYMEVFRalHjx4pzhMRESF/f38NHz482eXfuZ5Sq2HDhoqPj9e4ceMcxo8ZM0Y2m+1f94C5u7vr2Wef1Q8//JDsZap3Zkrr+mrYsKFOnjzpcIl+bGysJk+e7DBfxYoVVaRIEY0aNUpXr169Z5YH1aJFC505c0Zz5syxjzt//rxmzZqlJk2a3PMeLMl9lm7cuKEJEyakOU9qty93vwZubm72PV6p2W789NNPDp+DP/74Qxs2bLC/T4KDg1WhQgV99dVXDp/tHTt2aPHixWrYsGGan+Pd3N3dFRERoZ9++klHjx61j9+9e7cWLVqUZF7JcZ1fvnw5yR+AdevWVdasWTV27FiHeZO7gqdly5b6/fffkzyWdPuPmrv/38iI2KPiYkWKFNGwYcM0YMAAHT58WM2bN5efn58OHTqkH3/8Ua+88or69u2r5cuXq3v37nruuedUvHhx3bp1S9OmTbNvoKXbd8BdvXq1GjVqpNDQUJ09e1YTJkxQgQIF7PcFeOWVV/Tpp5+qffv22rRpkwoVKqTZs2frt99+08cff2z/a7ZRo0b66KOP1KBBA7Vp00Znz57V+PHjVbRoUW3fvt2ePzWPmVqhoaEO35OTHH9/f9WoUUMffPCBbt68qfz582vx4sVJ/gq8cuWKChQooBYtWqh8+fLy9fXV0qVL9eeff9qLkJubmyZOnKgmTZqoQoUK6tChg4KDg7Vnzx7t3LnT/sHv2LGjPvroI0VERKhTp046e/asJk2apDJlyig6Ovq+nuP9aNy4sd555x116NBBVatW1V9//aXp06c7/IUlSfXr11dQUJCqVaumvHnzavfu3Ro3bpwaNWqU5JyPfxMeHm7/zyQl/v7+mjhxol566SU99thjatWqlQIDA3X06FH9+uuvqlatWpLC8W+aNGmiWrVqadCgQTp8+LDKly+vxYsXa+7cuerdu7d9L8W9jBw5UitWrFDlypXVuXNnlS5dWhcvXtTmzZu1dOlSe6lO6/rq3Lmzxo0bp7Zt22rTpk0KDg7WtGnTkpxw7Obmps8//1yRkZEqU6aMOnTooPz58+vEiRNasWKF/P399csvv9zzufzyyy/2++XcvHlT27dvt+81bdq0qf0/9RYtWujJJ59Uhw4dtGvXLuXOnVsTJkxQfHz8vx7Gqlq1qnLkyKF27dqpZ8+estlsmjZtWpoO5SRK7fbl5Zdf1sWLF1W7dm0VKFBAR44c0dixY1WhQgX7+Sz3UrRoUT311FN67bXXFBcXp48//li5cuVyOPzx4YcfKjIyUlWqVFGnTp3slycHBAT863bmfg0dOlQLFy5U9erV1bVrV3s5K1OmjMP2sn79+vY9Sa+++qquXr2qzz77THny5HE4hJd4r6ERI0aocePGatiwobZs2aIFCxYk2Zvar18//fzzz2rcuLH9FgcxMTH666+/NHv2bB0+fDjJ72Q46XuRUeaS3KW1Kfnhhx/MU089ZbJly2ayZctmSpYsabp162b27t1rjDHm4MGDpmPHjqZIkSLGy8vL5MyZ09SqVcssXbrUvoxly5aZZs2amXz58hkPDw+TL18+07p16ySXrZ05c8Z06NDB5M6d23h4eJiyZcuaKVOmJMn0xRdfmGLFihlPT09TsmRJM2XKFPulfPf7mMlJvDz5XpJbh8ePHzdPP/20yZ49uwkICDDPPfecOXnypMOlwXFxcaZfv36mfPnyxs/Pz2TLls2UL1/eTJgwIcljrF271tSrV88+X7ly5czYsWMd5vnmm29M4cKFjYeHh6lQoYJZtGjRA12enNzzvvsy6OvXr5s33njDBAcHG29vb1OtWjXz+++/J5nv008/NTVq1DC5cuUynp6epkiRIqZfv37m8uXL91y3d16efC93X56caMWKFSYiIsIEBAQYLy8vU6RIEdO+fXuzcePGf/3du99Hxty+7Pn11183+fLlM1mzZjXFihUzH374ocPlmcbcXscpXXZ+5swZ061bNxMSEmKyZs1qgoKCTJ06dczkyZPt86R1fRljzJEjR0zTpk2Nj4+PyZ07t+nVq5dZuHChw+XJibZs2WKeeeYZ++OEhoaali1bmmXLlv3r4yRe1p3cz92f1YsXL5pOnTqZXLlyGR8fHxMeHp6qbY4xxvz222/mySefNN7e3iZfvnzmzTffNIsWLUryfMLDw5O9pDu5z0Bqti+zZ8829evXN3ny5DEeHh6mYMGC5tVXXzWnTp26Z94737OjR482ISEhxtPT01SvXt1s27YtyfxLly411apVM97e3sbf3980adLE7Nq1y2GexPdiam+xkNzlycYYs2rVKlOxYkXj4eFhChcubCZNmpTs+/znn3825cqVM15eXqZQoULm/fffN19++WWS7UR8fLwZOnSo/fNfs2ZNs2PHDhMaGprkcvUrV66YAQMGmKJFixoPDw+TO3duU7VqVTNq1Chz48aNVD0vK7MZ8wD1GQCAdHL48GGFhYXpww8/VN++fV0dB+mEc1QAAIBlUVQAAIBlUVQAAIBlcY4KAACwLPaoAAAAy8rQ91FJSEjQyZMn5efn99C/RA4AADiHMUZXrlxRvnz5knw31N0ydFE5efKkQkJCXB0DAACkwbFjx1SgQIF7zpOhi0riXQ6PHTsmf39/F6cBAACpER0drZCQkFTdPTtDF5XEwz3+/v4UFQAAMpjUnLbBybQAAMCyKCoAAMCyKCoAAMCyKCoAAMCyKCoAAMCyKCoAAMCyKCoAAMCyKCoAAMCyKCoAAMCyKCoAAMCyKCoAAMCyXFpUhgwZIpvN5vBTsmRJV0YCAAAW4vIvJSxTpoyWLl1qH86SxeWRAACARbi8FWTJkkVBQUGujgEAACzI5UVl3759ypcvn7y8vFSlShWNGDFCBQsWTHbeuLg4xcXF2Yejo6PTKybwUFw+elSx5887bXk+uXMrIIXPD1yP1xu4fzZjjHHVgy9YsEBXr15ViRIldOrUKQ0dOlQnTpzQjh075Ofnl2T+IUOGaOjQoUnGX758Wf7+/ukRGXCay0ePanypUroZG+u0ZWb18VG33bv5z8uCeL2B/4qOjlZAQECq/v926R6VyMhI+7/LlSunypUrKzQ0VN9//706deqUZP4BAwaoT58+9uHo6GiFhISkS1bA2WLPn9fN2Fg9PegdBYaGPfDyzh05pB/fG6zY8+f5j8uCeL2BtHH5oZ87Zc+eXcWLF9f+/fuTne7p6SlPT890TgU8XIGhYQouztVumQWvN3B/LHUflatXr+rAgQMKDg52dRQAAGABLi0qffv21apVq3T48GGtW7dOTz/9tNzd3dW6dWtXxgIAABbh0kM/x48fV+vWrXXhwgUFBgbqqaee0vr16xUYGOjKWAAAwCJcWlS+++47Vz48AACwOEudowIAAHAnigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsigoAALAsyxSVkSNHymazqXfv3q6OAgAALMISReXPP//Up59+qnLlyrk6CgAAsJAsrg5w9epVvfDCC/rss880bNiwe84bFxenuLg4+3B0dPTDjgdkepePHlXs+fNOXaZP7twKKFjQacvLCBkBK8oInx2XF5Vu3bqpUaNGqlu37r8WlREjRmjo0KHplAzA5aNHNb5UKd2MjXXqcrP6+Kjb7t1O2ZhlhIyAFWWUz45Li8p3332nzZs3688//0zV/AMGDFCfPn3sw9HR0QoJCXlY8YBML/b8ed2MjdXTg95RYGiYU5Z57sgh/fjeYMWeP++UDVlGyAhYUUb57LisqBw7dky9evXSkiVL5OXllarf8fT0lKen50NOBuBugaFhCi5e0tUx7ikjZASsyOqfHZcVlU2bNuns2bN67LHH7OPi4+O1evVqjRs3TnFxcXJ3d3dVPAAAYAEuKyp16tTRX3/95TCuQ4cOKlmypPr3709JAQAArisqfn5+euSRRxzGZcuWTbly5UoyHgAAZE6WuI8KAABAclx+efKdVq5c6eoIAADAQtijAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALIuiAgAALCtLWn7p+vXrGjt2rFasWKGzZ88qISHBYfrmzZudEg4AAGRuaSoqnTp10uLFi9WiRQtVqlRJNpvN2bkAAADSVlTmzZun+fPnq1q1ag/04BMnTtTEiRN1+PBhSVKZMmU0ePBgRUZGPtByAQDA/4Y0naOSP39++fn5PfCDFyhQQCNHjtSmTZu0ceNG1a5dW82aNdPOnTsfeNkAACDjS1NRGT16tPr3768jR4480IM3adJEDRs2VLFixVS8eHG999578vX11fr16x9ouQAA4H9Dmg79PP7447p+/boKFy4sHx8fZc2a1WH6xYsX73uZ8fHxmjVrlmJiYlSlSpVk54mLi1NcXJx9ODo6+r4fBwAAZBxpKiqtW7fWiRMnNHz4cOXNm/eBTqb966+/VKVKFV2/fl2+vr768ccfVbp06WTnHTFihIYOHZrmxwIAABlLmorKunXr9Pvvv6t8+fIPHKBEiRLaunWrLl++rNmzZ6tdu3ZatWpVsmVlwIAB6tOnj304OjpaISEhD5wBAABYU5qKSsmSJXXt2jWnBPDw8FDRokUlSRUrVtSff/6pTz75RJ9++mmSeT09PeXp6emUxwUAANaXppNpR44cqTfeeEMrV67UhQsXFB0d7fDzIBISEhzOQwEAAJlXmvaoNGjQQJJUp04dh/HGGNlsNsXHx6dqOQMGDFBkZKQKFiyoK1euaMaMGVq5cqUWLVqUllgAAOB/TJqKyooVK5zy4GfPnlXbtm116tQpBQQEqFy5clq0aJHq1avnlOUDAICMLU1FJTw83CkP/sUXXzhlOQAA4H9Tmr89ec2aNXrxxRdVtWpVnThxQpI0bdo0rV271mnhAABA5pamovLDDz8oIiJC3t7e2rx5s/3k18uXL2v48OFODQgAADKvNBWVYcOGadKkSfrss88c7kpbrVo1bd682WnhAABA5pamorJ3717VqFEjyfiAgABdunTpQTMBAABISmNRCQoK0v79+5OMX7t2rQoXLvzAoQAAAKQ0FpXOnTurV69e2rBhg2w2m06ePKnp06erb9++eu2115ydEQAAZFJpujz5rbfeUkJCgurUqaPY2FjVqFFDnp6e6tu3r3r06OHsjAAAIJNKU1Gx2WwaNGiQ+vXrp/379+vq1asqXbq0fH19nZ0PAABkYmkqKok8PDyS/ZZjAAAAZ0hTUXn66adls9mSjLfZbPLy8lLRokXVpk0blShR4oEDAgCAzCtNJ9MGBARo+fLl2rx5s2w2m2w2m7Zs2aLly5fr1q1bmjlzpsqXL6/ffvvN2XkBAEAmkqY9KkFBQWrTpo3GjRsnN7fbXSchIUG9evWSn5+fvvvuO3Xp0kX9+/fnlvoAACDN0rRH5YsvvlDv3r3tJUWS3Nzc1KNHD02ePFk2m03du3fXjh07nBYUAABkPmkqKrdu3dKePXuSjN+zZ4/i4+MlSV5eXsmexwIAAJBaaTr089JLL6lTp04aOHCgnnjiCUnSn3/+qeHDh6tt27aSpFWrVqlMmTLOSwoAADKdNBWVMWPGKG/evPrggw905swZSVLevHn1+uuvq3///pKk+vXrq0GDBs5LCgAAMp00FRV3d3cNGjRIgwYNUnR0tCTJ39/fYZ6CBQs+eDoAAJCpPdAN36SkBQUAAMBZ0lxUZs+ere+//15Hjx7VjRs3HKZt3rz5gYMBAACk6aqf//znP+rQoYPy5s2rLVu2qFKlSsqVK5cOHjyoyMhIZ2cEAACZVJqKyoQJEzR58mSNHTtWHh4eevPNN7VkyRL17NlTly9fdnZGAACQSaWpqBw9elRVq1aVJHl7e+vKlSuSbl+2/O233zovHQAAyNTSVFSCgoJ08eJFSbev7lm/fr0k6dChQzLGOC8dAADI1NJUVGrXrq2ff/5ZktShQwe9/vrrqlevnp5//nk9/fTTTg0IAAAyrzRd9TN58mQlJCRIkrp166ZcuXJp3bp1atq0qV599VWnBgQAAJlXmorK8ePHFRISYh9u1aqVWrVqJWOMjh07xs3eAACAU6Tp0E9YWJjOnTuXZPzFixcVFhb2wKEAAACkNBYVY0yy34x89epVeXl5PXAoAAAA6T4P/fTp00eSZLPZ9Pbbb8vHx8c+LT4+Xhs2bFCFChWcGhAAAGRe91VUtmzZIun2HpW//vpLHh4e9mkeHh4qX768+vbt69yEAAAg07qvorJixQpJty9J/uSTT/hCQgAA8FCl6aqfKVOmODsHAABAEmkqKjExMRo5cqSWLVums2fP2u+pkujgwYNOCQcAADK3NBWVl19+WatWrdJLL72k4ODgZK8AAgAAeFBpKioLFizQr7/+qmrVqjk7DwAAgF2a7qOSI0cO5cyZ09lZAAAAHKSpqLz77rsaPHiwYmNjnZ0HAADALk2HfkaPHq0DBw4ob968KlSokLJmzeowffPmzU4JBwAAMrc0FZXmzZs7OQYAAEBSaSoqUVFRzs4BAACQRJqKSqJNmzZp9+7dkqQyZcro0UcfdUooAAAAKY1F5ezZs2rVqpVWrlyp7NmzS5IuXbqkWrVq6bvvvlNgYKAzMwIAgEwqTVf99OjRQ1euXNHOnTt18eJFXbx4UTt27FB0dLR69uzp7IwAACCTStMelYULF2rp0qUqVaqUfVzp0qU1fvx41a9f32nhAABA5pamPSoJCQlJLkmWpKxZsyb53h8AAIC0SlNRqV27tnr16qWTJ0/ax504cUKvv/666tSp47RwAAAgc0tTURk3bpyio6NVqFAhFSlSREWKFFFYWJiio6M1duxYZ2cEAACZVJrOUQkJCdHmzZu1dOlS7dmzR5JUqlQp1a1b16nhAABA5nZfe1SWL1+u0qVLKzo6WjabTfXq1VOPHj3Uo0cPPfHEEypTpozWrFnzsLICAIBM5r6Kyscff6zOnTvL398/ybSAgAC9+uqr+uijj5wWDgAAZG73VVS2bdumBg0apDi9fv362rRp0wOHAgAAkO6zqJw5cybZy5ITZcmSRefOnXvgUAAAANJ9FpX8+fNrx44dKU7fvn27goODHzgUAACAdJ9FpWHDhnr77bd1/fr1JNOuXbumqKgoNW7c2GnhAABA5nZflyf/3//9n+bMmaPixYure/fuKlGihCRpz549Gj9+vOLj4zVo0KCHEhQAAGQ+91VU8ubNq3Xr1um1117TgAEDZIyRJNlsNkVERGj8+PHKmzfvQwkKAAAyn/u+4VtoaKjmz5+vf/75R/v375cxRsWKFVOOHDkeRj4AAJCJpenOtJKUI0cOPfHEE87MAgAA4CBN3/UDAACQHigqAADAslxaVEaMGKEnnnhCfn5+ypMnj5o3b669e/e6MhIAALAQlxaVVatWqVu3blq/fr2WLFmimzdvqn79+oqJiXFlLAAAYBFpPpnWGRYuXOgwPHXqVOXJk0ebNm1SjRo1kswfFxenuLg4+3B0dPRDz4ikLh89qtjz5526TJ/cuRVQsKBTlwngf1NG2QY5O2dm3U66tKjc7fLly5KknDlzJjt9xIgRGjp0aHpGwl0uHz2q8aVK6WZsrFOXm9XHR912786UH0IAqZdRtkEPI2dm3U5apqgkJCSod+/eqlatmh555JFk5xkwYID69OljH46OjlZISEh6RYSk2PPndTM2Vk8PekeBoWFOWea5I4f043uDFXv+fKb7AAK4PxllG+TsnJl5O2mZotKtWzft2LFDa9euTXEeT09PeXp6pmMqpCQwNEzBxUu6OgaATCqjbIMySk4rs0RR6d69u+bNm6fVq1erQIECro4DAAAswqVFxRijHj166Mcff9TKlSsVFuac3XgAAOB/g0uLSrdu3TRjxgzNnTtXfn5+On36tCQpICBA3t7erowGAAAswKX3UZk4caIuX76smjVrKjg42P4zc+ZMV8YCAAAW4fJDPwAAACnhu34AAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlUVQAAIBlubSorF69Wk2aNFG+fPlks9n0008/uTIOAACwGJcWlZiYGJUvX17jx493ZQwAAGBRWVz54JGRkYqMjHRlBAAAYGEuLSr3Ky4uTnFxcfbh6OjoVP/u5aNHFXv+vFPz+OTOrYCCBZ26TGfnfBgZM4KM8noDVsNnB1aToYrKiBEjNHTo0Pv+vctHj2p8qVK6GRvr1DxZfXzUbfdup30AH0ZOZ2fMCDLK6w1YDZ8dWFGGKioDBgxQnz597MPR0dEKCQn519+LPX9eN2Nj9fSgdxQYGuaULOeOHNKP7w1W7PnzTvvwOTvnw8iYEWSU1xuwGj47sKIMVVQ8PT3l6emZ5t8PDA1TcPGSTkz0cGSUnFbHegTShs8OrIT7qAAAAMty6R6Vq1evav/+/fbhQ4cOaevWrcqZM6cKsosQAIBMz6VFZePGjapVq5Z9OPH8k3bt2mnq1KkuSgUAAKzCpUWlZs2aMsa4MgIAALAwzlEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWRVEBAACWZYmiMn78eBUqVEheXl6qXLmy/vjjD1dHAgAAFuDyojJz5kz16dNHUVFR2rx5s8qXL6+IiAidPXvW1dEAAICLubyofPTRR+rcubM6dOig0qVLa9KkSfLx8dGXX37p6mgAAMDFsrjywW/cuKFNmzZpwIAB9nFubm6qW7eufv/99yTzx8XFKS4uzj58+fJlSVJ0dPQ9H+fK1au6LunQ37t15do1p2Q/f+ywrv//ZWf7l8dPLWfnzAgZJefnzAgZJV5vXu8HkxEySrzevN7JS/x/2xjz7ws1LnTixAkjyaxbt85hfL9+/UylSpWSzB8VFWUk8cMPP/zwww8//wM/x44d+9eu4NI9KvdrwIAB6tOnj304ISFBFy9eVK5cuWSz2ZzyGNHR0QoJCdGxY8fk7+/vlGU6GxmdJyPkJKPzZIScZHSejJAzs2Y0xujKlSvKly/fv87r0qKSO3duubu768yZMw7jz5w5o6CgoCTze3p6ytPT02Fc9uzZH0o2f39/y75pEpHReTJCTjI6T0bISUbnyQg5M2PGgICAVM3n0pNpPTw8VLFiRS1btsw+LiEhQcuWLVOVKlVcmAwAAFiByw/99OnTR+3atdPjjz+uSpUq6eOPP1ZMTIw6dOjg6mgAAMDFXF5Unn/+eZ07d06DBw/W6dOnVaFCBS1cuFB58+Z1SR5PT09FRUUlOcRkJWR0noyQk4zOkxFyktF5MkJOMv47mzGpuTYIAAAg/bn8hm8AAAApoagAAADLoqgAAADLoqgAAADLoqgAAADLoqgAAADLoqhIOn78uK5evZpk/M2bN7V69WoXJPqvHj16aM2aNS7NkBq7d+/WlClTtGfPHknSnj179Nprr6ljx45avny5i9P917Vr17R27Vrt2rUrybTr16/r66+/dkEqIOO4+1vskXYZYV1aIqNzvgc5Yzp58qR54oknjJubm3F3dzcvvfSSuXLlin366dOnjZubmwsTGmOz2Yybm5spVqyYGTlypDl16pRL8yRnwYIFxsPDw+TMmdN4eXmZBQsWmMDAQFO3bl1Tu3Zt4+7ubpYtW+bqmGbv3r0mNDTUvk5r1KhhTp48aZ9uhdc7OdevXzfXr193dQwHO3fuNK+99pqpUKGCCQoKMkFBQaZChQrmtddeMzt37nR1vAwlI6zLxYsXm8jISJM9e3bj5uZm3NzcTPbs2U1kZKRZsmSJq+NlKBlhXVotY6beo/LWW2/Jzc1NGzZs0MKFC7Vr1y7VqlVL//zzj30eY4H74S1evFgNGzbUqFGjVLBgQTVr1kzz5s1TQkKCq6NJkt555x3169dPFy5c0JQpU9SmTRt17txZS5Ys0bJly9SvXz+NHDnS1THVv39/PfLIIzp79qz27t0rPz8/VatWTUePHnV1tCSWLFmihg0bKkeOHPLx8ZGPj49y5Mihhg0baunSpS7NtmDBAj366KPasmWLmjVrpsGDB2vw4MFq1qyZtm3bpscee0yLFi1yacZEu3btUteuXfXoo48qODhYwcHBevTRR9W1a9dk96qlt4ywLr/66is1bNhQAQEBGjNmjObNm6d58+ZpzJgxyp49uxo2bKhp06a5NGMiq7/eGWFdWjJjulcjC8mXL5/ZsGGDffj69eumSZMmpkKFCubChQuW+AvbZrOZM2fOGGOMuXHjhpk5c6aJiIgw7u7uJl++fGbgwIFm3759Ls3o7+9vzxAfH2+yZMliNm/ebJ/+119/mbx587oqnl2ePHnM9u3b7cMJCQmmS5cupmDBgubAgQOWeL2NMWbq1KkmS5YsplWrVmbKlClm/vz5Zv78+WbKlCmmdevWJmvWrObrr792Wb5y5cqZt99+O8XpUVFRpmzZsumYKHnz5883Hh4e5sknnzRRUVFmwoQJZsKECSYqKspUrVrVeHp6moULF7o0Y0ZYl8WKFTPjxo1Lcfr48eNN0aJF0zFR8jLC650R1qUVM2bqopItWzbz999/O4y7efOmad68uSlXrpzZvn27y//jurOo3OnIkSMmKirKhIaGujyjv7+/2b9/v33Y19fXHDhwwD58+PBh4+Xl5YpoDvz8/MyuXbuSjO/WrZspUKCAWb16tcvXpTHW3FDcycvLy+zZsyfF6Xv27LHE650RSkBGWJeenp6Wz2hMxni9M8K6tGLGTH3op3Dhwtq+fbvDuCxZsmjWrFkqXLiwGjdu7KJk/65gwYIaMmSIDh06pIULF7o0S6FChbRv3z778O+//66CBQvah48eParg4GBXRHNQsmRJbdy4Mcn4cePGqVmzZmratKkLUiV19OhR1a1bN8XpderU0fHjx9MxkaNChQrp119/TXH6r7/+qtDQ0HRMlLy///5bL7zwQorTW7du7fC+dYWMsC7LlCmjL774IsXpX375pUqXLp2OiZKXEV7vjLAurZjR5d+e7EqRkZGaPHmynn32WYfxiWXl2Wefdel/CJIUGhoqd3f3FKfbbDbVq1cvHRMl9dprryk+Pt4+/MgjjzhMX7BggWrXrp3esZJ4+umn9e233+qll15KMm3cuHFKSEjQpEmTXJDMUeKG4oMPPkh2uqs3Zu+8847atGmjlStXqm7duvZvOj9z5oyWLVumhQsXasaMGS7LlyixBJQoUSLZ6VYoARlhXY4ePVqNGzfWwoULk8148ODBe5at9JIRXu+MsC6tmDFTf3vyrVu3FBsbK39//xSnnzhxwuVvbmQuK1euVOPGjVW4cOF7bihq1Kjhsozr1q3Tf/7zH/3+++86ffq0JCkoKEhVqlRRr169VKVKFZdlSzRr1iy1adNGkZGR9ywBd/+hkt4ywro8fPiwJk6cqPXr1yfJ2KVLFxUqVMi1AZVxXu+MsC6tljFTF5WUHDp0SCEhIcqSxbo7nG7dumXpfImMMbLZbK6OkeFYbUORUWWEEgDn4fX+30RRSYaHh4e2bdumUqVKuTqKFi5cqPz586ts2bJKSEjQe++9p0mTJun06dMKDg5W9+7d1b9/f5eWgbi4OA0aNEh//PGHGjVqpP79+2vYsGH2S5KbNm2qSZMmpbjnKj1t27ZNo0eP1tq1a3Xq1Cm5ubmpcOHCat68ufr162eJjACA/8rUReWZZ55JdvzcuXNVu3Zt+fn5SZLmzJmTnrEclCxZUp999pmqV6+uESNGaPTo0Ro0aJBKlSqlvXv3asSIEXr99dfVv39/l2Xs06ePZs6cqdatW2v+/PmqVauW5s2bp+HDh8vNzU2DBw9WZGSk/vOf/7gsoyQtWrRITz/9tBo2bChvb2/NmTNHHTt2VLZs2fTDDz/IGKO1a9cqKCjIpTkzuoEDB+r06dP68ssvXR0lw8sI67Jdu3Y6duyYpe5AnVFlhHXpiozWP3bwEP3000+qUaOGwsLCkkzz9fVVQECAC1I5Onz4sP0cmRkzZmjixIl67rnnJEkNGjRQ0aJF1bt3b5cWldmzZ+urr75S3bp11bVrVxUrVkxz5sxRs2bNJEm5c+dW586dXV5U3nrrLX300Ufq0qWLJKlt27bq2bOndu/erXfffVeRkZEaMGCApkyZ4tKc/8bqG7MTJ07o2LFjro7xrzJCCcgI6zJ//vxyc7P+BaQZ4fXOCOvSJRnT9WJoi/n2229NgQIFzJdffukwPkuWLJa5dXVwcLD5/fffjTHG5M2b1+FGasYY8/fffxtvb29XRLPz9vY2R44csQ9nzZrV7Nixwz586NAh4+Pj44poDry8vMyhQ4fswwkJCSZr1qz22+ivXr3aBAYGuihd6r311lumffv2ro6R4b300kumVq1aro6RrISEBFdH+J/Ttm1by77euDdrV7eHrFWrVlqzZo2++OILPfvssw63zreKp59+Wu+9957i4+PVrFkzTZgwweG2/mPHjlWFChVcF1C37+ny+++/S5L+/PNP2Ww2/fHHH/bpGzZsUP78+V0Vzy5//vzau3evffjAgQNKSEhQrly5JEkFChRI9ssprWbEiBGW3+uTEXz99deW3Svl6emp3bt3uzrG/5SvvvrKsq+31Zw6dUqDBw9W7dq1VapUKZUpU0ZNmjTRF1984XArivSSqQ/9SLevvV+9erWGDh2q8uXL67PPPrPUVSrDhw9X3bp1VbJkSVWpUkWzZs3SkiVLVLx4ce3fv18XL150+XeBdOnSRe3bt9fnn3+uTZs2adSoURo4cKD27NkjNzc3TZw4UW+88YZLM0q3D/W8/PLLGjRokDw9PfXRRx+padOm8vDwkCRt3bo12cOAVnPs2DFFRUW5dBf2tWvXtGnTJuXMmTPJPV2uX7+u77//Xm3btnVRuv/avXu31q9frypVqqhkyZLas2ePPvnkE8XFxenFF190+f19+vTpk+z4+Ph4jRw50l6iP/roo/SM5WDz5s3KkSOH/bMxbdo0TZo0SUePHlVoaKi6d++uVq1auSxfoh49eqhly5aqXr26q6Pc07hx4/THH3+oYcOGatWqlaZNm6YRI0YoISFBzzzzjN555x2XXtG5ceNG1a1bV0WLFpW3t7f27dunNm3a6MaNG+rbt6++/PJLLVy40H4OZ7pw9S4dK1mzZo0JCwszbm5uljn0Y8zt7/iZOHGiadiwoSlZsqQpXry4CQ8PNwMHDjTHjh1zdTxjjDHTp0833bt3NzNmzDDGGLNixQpTvXp1U7FiRTNkyBATHx/v4oS3vx7hzTffNPny5TO5cuUybdq0MefOnbNP37Bhg1m1apULE6bO1q1bXXqr/4zyLdQZ4Vu9bTabqVChgqlZs6bDj81mM0888YSpWbOmyw9XlCtXzv6NuZ999pnx9vY2PXv2NBMnTjS9e/c2vr6+5osvvnBpRmMyxjfNv/vuu8bPz888++yzJigoyIwcOdLkypXLDBs2zAwfPtwEBgaawYMHuzRjtWrVzJAhQ+zD06ZNM5UrVzbGGHPx4kVToUIF07Nnz3TNRFG5y5UrV8zWrVtNXFycq6Mgk5o7d+49f8aMGePSItC8eXPTqFEjc+7cObNv3z7TqFEjExYWZj9PySpFpUqVKmbQoEHGmNvno+XIkcMMHDjQPv2tt94y9erVc1U8Y4wxI0aMMGFhYUkKk5XOk/P29jaHDx82xhjz6KOPmsmTJztMnz59uildurQrojmw2Wxm6dKlplevXiZ37twma9aspmnTpuaXX36xxB9KxhhTpEgR88MPPxhjbv/B4e7ubr755hv79Dlz5rj8Swm9vb0dvqstPj7eZM2a1Zw+fdoYY8zixYtNvnz50jUTRQWwmMS/DG02W4o/riwCGeVbqDPKt3r/8ccfpnjx4uaNN94wN27cMMZYq6jkypXLbNy40Rhz+7XfunWrw/T9+/e7/IR+YzLGN83/24UHhw8fdvmFB6GhoWbt2rX24ZMnTxqbzWZiY2ONMbcvjuBLCS1k4MCB6tixo6tj3BMZnccqOYODgzVnzhwlJCQk+7N582aX5rt27ZrDMXSbzaaJEyeqSZMmCg8P199//+3CdI4Szzdzc3OTl5eXwy0H/Pz8dPnyZVdFs3viiSe0adMmnTt3To8//rh27NhhqfPkIiMjNXHiRElSeHi4Zs+e7TD9+++/V9GiRV0RLUVZs2ZVy5YttXDhQh08eFCdO3fW9OnTU/weoPQSFBSkXbt2SZL27dun+Ph4+7Ak7dy5U3ny5HFVPElS8+bN1aVLFy1cuFArVqzQCy+8oPDwcHl7e0uS9u7dm+4XR2T6k2nv5fjx4y7/UsJ/Q0bnsUrOihUratOmTfb70NzNZrM5XPmV3hK/hfruOzePGzdOkizzLdSJ3+pdpEgRSdb9Vm/p9n2bvvrqK3333XeqW7euS66sSMn777+vatWqKTw8XI8//rhGjx6tlStX2m86uX79ev3444+ujpmixG+aj4qK0tKlS12a5YUXXlDbtm3VrFkzLVu2TG+++ab69u2rCxcuyGaz6b333lOLFi1cmnHYsGE6deqUmjRpovj4eFWpUkXffPONfbrNZtOIESPSNVOmvjMtYEVr1qxRTEyMGjRokOz0mJgYbdy4UeHh4emc7LYRI0ZozZo1mj9/frLTu3btqkmTJikhISGdkzmaNGmSQkJC1KhRo2SnDxw4UGfPntXnn3+ezsnu7fjx49q0aZPq1q2rbNmyuTqOJOnSpUsaOXKkfvnlFx08eFAJCQkKDg5WtWrV9Prrr+vxxx93dUSFhYVp48aN9iulrCghIUEjR47U77//rqpVq+qtt97SzJkz9eabbyo2NlZNmjTRuHHjLPG6X79+Xbdu3ZKvr6+ro1BUzp8/ry+//DLJl1hVrVpV7du3V2BgoIsTktGZMkpOAMBtmbqo/Pnnn4qIiJCPj0+yXwseGxurRYsWufSvBTJmvpwAgP/K1EXlySefVPny5TVp0qQkJ68ZY9SlSxdt377dftdVVyCj82SUnACA/8rURcXb21tbtmxRyZIlk52+Z88ePfroo7p27Vo6J/svMjpPRskJAPivTH15clBQkMN30tztjz/+sB8ecBUyOk9GyQkA+K9MfXly37599corr2jTpk2qU6dOknMWPvvsM40aNYqM/wMZpYyTEwBwh3S9vZwFfffdd6Zy5comS5Ys9rt+ZsmSxVSuXNnMnDnT1fGMMWR0poySEwBwW6Y+R+VON2/e1Pnz5yVJuXPnVtasWV2cKCkyOk9GyQkAmR1FBQAAWFamPpkWAABYG0UFAABYFkUFAABYFkUFAABYFkUFQLqrWbOmevfu/UDLWLlypWw2my5duuSUTACsiaICwOnat2+v5s2buzoGgP8BFBUAAGBZFBUAD1VMTIzatm0rX19fBQcHa/To0UnmmTZtmh5//HH5+fkpKChIbdq00dmzZx3mmT9/vooXLy5vb2/VqlVLhw8fTrKctWvXqnr16vL29lZISIh69uypmJiYh/XUAKQDigqAh6pfv35atWqV5s6dq8WLF2vlypXavHmzwzw3b97Uu+++q23btumnn37S4cOH1b59e/v0Y8eO6ZlnnlGTJk20detWvfzyy3rrrbcclnHgwAE1aNBAzz77rLZv366ZM2dq7dq16t69e3o8TQAPCXemBeB07du316VLl/TNN98oV65c+uabb/Tcc89Jki5evKgCBQrolVde0ccff5zs72/cuFFPPPGErly5Il9fXw0cOFBz587Vzp077fO89dZbev/99/XPP/8oe/bsevnll+Xu7q5PP/3UPs/atWsVHh6umJgYeXl5PdTnDODhYI8KgIfmwIEDunHjhipXrmwflzNnTpUoUcJhvk2bNqlJkyYqWLCg/Pz8FB4eLkk6evSoJGn37t0Oy5CkKlWqOAxv27ZNU6dOla+vr/0nIiJCCQkJOnTo0MN4egDSQRZXBwCQucXExCgiIkIRERGaPn26AgMDdfToUUVEROjGjRupXs7Vq1f16quvqmfPnkmmFSxY0JmRAaQjigqAh6ZIkSLKmjWrNmzYYC8L//zzj/7++2/7XpM9e/bowoULGjlypEJCQiTdPvRzp1KlSunnn392GLd+/XqH4ccee0y7du1S0aJFH9bTAeACHPoB8ND4+vqqU6dO6tevn5YvX64dO3aoffv2cnP776anYMGC8vDw0NixY3Xw4EH9/PPPevfddx2W06VLF+3bt0/9+vXT3r17NWPGDE2dOtVhnv79+2vdunXq3r27tm7dqn379mnu3LmcTAtkcBQVAA/Vhx9+qOrVq6tJkyaqW7eunnrqKVWsWNE+PTAwUFOnTtWsWbNUunRpjRw5UqNGjXJYRsGCBfXDDz/op59+Uvny5TVp0iQNHz7cYZ5y5cpp1apV+vvvv1W9enU9+uijGjx4sPLly5cuzxPAw8FVPwAAwLLYowIAACyLogIAACyLogIAACyLogIAACyLogIAACyLogIAACyLogIAACyLogIAACyLogIAACyLogIAACyLogIAACzr/wFoECSmhgquLwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "contagem_idades1.plot(kind= \"bar\", edgecolor=\"maroon\", color=\"pink\")\n", + "plt.xlabel(\"Idade\")\n", + "plt.ylabel(\"Contagem\")\n", + "plt.title(\"Pessoas Masculinas Menores de 10 anos por Idade\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "contagem_idades2.plot(kind='bar', edgecolor= \"black\", color = \"green\")\n", + "plt.xlabel(\"Idade\")\n", + "plt.ylabel(\"Contagem\")\n", + "plt.title(\"Pessoas Masculinas Menores de 20 anos por Idade\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#UM GRAFICO DE NOMES" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [], + "source": [ + "df = df = pd.read_csv(\"C:\\\\Users\\\\nargy\\\\Documents\\\\estudos\\\\Semana-12\\\\on26-python-s12-pandas-numpy-II\\\\material\\\\titanic.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": {}, + "outputs": [], + "source": [ + "df_male = df[df[\"Sex\"]==\"male\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": {}, + "outputs": [], + "source": [ + "df_filtered = df_male[~df_male['Name'].str.contains('Mr.')]\n", + "contagem_palavras = df_filtered['Name'].str.split().explode().value_counts()\n", + "filtro = contagem_palavras.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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