diff --git a/exercicios/para-casa/exercicio_12.ipynb b/exercicios/para-casa/exercicio_12.ipynb new file mode 100644 index 0000000..7925a11 --- /dev/null +++ b/exercicios/para-casa/exercicio_12.ipynb @@ -0,0 +1,937 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
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
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
.......................................
88688702Montvila, Rev. Juozasmale27.00021153613.0000NaNS
88788811Graham, Miss. Margaret Edithfemale19.00011205330.0000B42S
88888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.4500NaNS
88989011Behr, Mr. Karl Howellmale26.00011136930.0000C148C
89089103Dooley, Mr. Patrickmale32.0003703767.7500NaNQ
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891 rows × 12 columns

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" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "0 1 0 3 \n", + "1 2 1 1 \n", + "2 3 1 3 \n", + "3 4 1 1 \n", + "4 5 0 3 \n", + ".. ... ... ... \n", + "886 887 0 2 \n", + "887 888 1 1 \n", + "888 889 0 3 \n", + "889 890 1 1 \n", + "890 891 0 3 \n", + "\n", + " Name Sex Age SibSp \\\n", + "0 Braund, Mr. Owen Harris male 22.0 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", + "2 Heikkinen, Miss. Laina female 26.0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", + "4 Allen, Mr. William Henry male 35.0 0 \n", + ".. ... ... ... ... \n", + "886 Montvila, Rev. Juozas male 27.0 0 \n", + "887 Graham, Miss. Margaret Edith female 19.0 0 \n", + "888 Johnston, Miss. Catherine Helen \"Carrie\" female NaN 1 \n", + "889 Behr, Mr. Karl Howell male 26.0 0 \n", + "890 Dooley, Mr. Patrick male 32.0 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "0 0 A/5 21171 7.2500 NaN S \n", + "1 0 PC 17599 71.2833 C85 C \n", + "2 0 STON/O2. 3101282 7.9250 NaN S \n", + "3 0 113803 53.1000 C123 S \n", + "4 0 373450 8.0500 NaN S \n", + ".. ... ... ... ... ... \n", + "886 0 211536 13.0000 NaN S \n", + "887 0 112053 30.0000 B42 S \n", + "888 2 W./C. 6607 23.4500 NaN S \n", + "889 0 111369 30.0000 C148 C \n", + "890 0 370376 7.7500 NaN Q \n", + "\n", + "[891 rows x 12 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv(\"titanic.csv\")\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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indexPassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
084584603Abbing, Mr. Anthonymale42.000C.A. 55477.5500NaNS
174674703Abbott, Mr. Rossmore Edwardmale16.011C.A. 267320.2500NaNS
227928013Abbott, Mrs. Stanton (Rosa Hunt)female35.011C.A. 267320.2500NaNS
330830902Abelson, Mr. Samuelmale30.010P/PP 338124.0000NaNC
487487512Abelson, Mrs. Samuel (Hannah Wizosky)female28.010P/PP 338124.0000NaNC
..........................................
88628628713de Mulder, Mr. Theodoremale30.0003457749.5000NaNS
88728228303de Pelsmaeker, Mr. Alfonsmale16.0003457789.5000NaNS
88836136202del Carlo, Mr. Sebastianomale29.010SC/PARIS 216727.7208NaNC
88915315403van Billiard, Mr. Austin Blylermale40.502A/5. 85114.5000NaNS
89086886903van Melkebeke, Mr. PhilemonmaleNaN003457779.5000NaNS
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891 rows × 13 columns

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" + ], + "text/plain": [ + " index PassengerId Survived Pclass \\\n", + "0 845 846 0 3 \n", + "1 746 747 0 3 \n", + "2 279 280 1 3 \n", + "3 308 309 0 2 \n", + "4 874 875 1 2 \n", + ".. ... ... ... ... \n", + "886 286 287 1 3 \n", + "887 282 283 0 3 \n", + "888 361 362 0 2 \n", + "889 153 154 0 3 \n", + "890 868 869 0 3 \n", + "\n", + " Name Sex Age SibSp Parch \\\n", + "0 Abbing, Mr. Anthony male 42.0 0 0 \n", + "1 Abbott, Mr. Rossmore Edward male 16.0 1 1 \n", + "2 Abbott, Mrs. Stanton (Rosa Hunt) female 35.0 1 1 \n", + "3 Abelson, Mr. Samuel male 30.0 1 0 \n", + "4 Abelson, Mrs. Samuel (Hannah Wizosky) female 28.0 1 0 \n", + ".. ... ... ... ... ... \n", + "886 de Mulder, Mr. Theodore male 30.0 0 0 \n", + "887 de Pelsmaeker, Mr. Alfons male 16.0 0 0 \n", + "888 del Carlo, Mr. Sebastiano male 29.0 1 0 \n", + "889 van Billiard, Mr. Austin Blyler male 40.5 0 2 \n", + "890 van Melkebeke, Mr. Philemon male NaN 0 0 \n", + "\n", + " Ticket Fare Cabin Embarked \n", + "0 C.A. 5547 7.5500 NaN S \n", + "1 C.A. 2673 20.2500 NaN S \n", + "2 C.A. 2673 20.2500 NaN S \n", + "3 P/PP 3381 24.0000 NaN C \n", + "4 P/PP 3381 24.0000 NaN C \n", + ".. ... ... ... ... \n", + "886 345774 9.5000 NaN S \n", + "887 345778 9.5000 NaN S \n", + "888 SC/PARIS 2167 27.7208 NaN C \n", + "889 A/5. 851 14.5000 NaN S \n", + "890 345777 9.5000 NaN S \n", + "\n", + "[891 rows x 13 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.sort_values(by=['Name']).reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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indexPassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
080380413Thomas, Master. Assad Alexandermale0.420126258.5167NaNC
175575612Hamalainen, Master. Viljomale0.671125064914.5000NaNS
246947013Baclini, Miss. Helene Barbarafemale0.7521266619.2583NaNC
364464513Baclini, Miss. Eugeniefemale0.7521266619.2583NaNC
4787912Caldwell, Master. Alden Gatesmale0.830224873829.0000NaNS
..........................................
88685986003Razi, Mr. RaihedmaleNaN0026297.2292NaNC
88786386403Sage, Miss. Dorothy Edith \"Dolly\"femaleNaN82CA. 234369.5500NaNS
88886886903van Melkebeke, Mr. PhilemonmaleNaN003457779.5000NaNS
88987887903Laleff, Mr. KristomaleNaN003492177.8958NaNS
89088888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.4500NaNS
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891 rows × 13 columns

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" + ], + "text/plain": [ + " index PassengerId Survived Pclass \\\n", + "0 803 804 1 3 \n", + "1 755 756 1 2 \n", + "2 469 470 1 3 \n", + "3 644 645 1 3 \n", + "4 78 79 1 2 \n", + ".. ... ... ... ... \n", + "886 859 860 0 3 \n", + "887 863 864 0 3 \n", + "888 868 869 0 3 \n", + "889 878 879 0 3 \n", + "890 888 889 0 3 \n", + "\n", + " Name Sex Age SibSp Parch \\\n", + "0 Thomas, Master. Assad Alexander male 0.42 0 1 \n", + "1 Hamalainen, Master. Viljo male 0.67 1 1 \n", + "2 Baclini, Miss. Helene Barbara female 0.75 2 1 \n", + "3 Baclini, Miss. Eugenie female 0.75 2 1 \n", + "4 Caldwell, Master. Alden Gates male 0.83 0 2 \n", + ".. ... ... ... ... ... \n", + "886 Razi, Mr. Raihed male NaN 0 0 \n", + "887 Sage, Miss. Dorothy Edith \"Dolly\" female NaN 8 2 \n", + "888 van Melkebeke, Mr. Philemon male NaN 0 0 \n", + "889 Laleff, Mr. Kristo male NaN 0 0 \n", + "890 Johnston, Miss. Catherine Helen \"Carrie\" female NaN 1 2 \n", + "\n", + " Ticket Fare Cabin Embarked \n", + "0 2625 8.5167 NaN C \n", + "1 250649 14.5000 NaN S \n", + "2 2666 19.2583 NaN C \n", + "3 2666 19.2583 NaN C \n", + "4 248738 29.0000 NaN S \n", + ".. ... ... ... ... \n", + "886 2629 7.2292 NaN C \n", + "887 CA. 2343 69.5500 NaN S \n", + "888 345777 9.5000 NaN S \n", + "889 349217 7.8958 NaN S \n", + "890 W./C. 6607 23.4500 NaN S \n", + "\n", + "[891 rows x 13 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.sort_values(by=['Age', 'PassengerId']).reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def processamento_df(df):\n", + " \n", + " df_3_linhas = df.head(3)\n", + " \n", + " df_menor = df.sample(100)\n", + "\n", + " df = df.drop(columns='Cabin')\n", + "\n", + " df = df.sort_values(\"Age\")\n", + "\n", + " df = df.drop_duplicates()\n", + "\n", + " df = df.reset_index()\n", + "\n", + " colunas = list(df.columns) \n", + "\n", + " metricas = df.describe(include='all')\n", + " \n", + " return df_3_linhas, df_menor, df, colunas, metricas" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "df_3_linhas, df_menor, df_teste, colunas, metricas = processamento_df(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Gráficos" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df['Pclass'].value_counts().plot(kind='bar');" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(x=df['Age'], y=df['Fare'], color='black');\n", + "\n", + "plt.title('Idade e tarifas', color='Brown')\n", + "\n", + "plt.tick_params(axis='x', colors='red')\n", + "plt.tick_params(axis='y', colors='red')" + ] + } + ], + "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 +} diff --git a/material/titanic.csv b/exercicios/para-casa/titanic.csv similarity index 100% rename from material/titanic.csv rename to exercicios/para-casa/titanic.csv