From 62cda138c7f3889f36cfd0ef9476dc905e3d63d5 Mon Sep 17 00:00:00 2001 From: Natalia Rosa Date: Wed, 25 Oct 2023 15:33:43 -0300 Subject: [PATCH] exercicio_11 --- exercicios/para-casa/exercicio.ipynb | 0 .../para-casa}/titanic.csv | 1784 +++---- material/aula_s11.ipynb | 4280 ++++++++++++++++- 3 files changed, 5125 insertions(+), 939 deletions(-) create mode 100644 exercicios/para-casa/exercicio.ipynb rename {material => exercicios/para-casa}/titanic.csv (98%) diff --git a/exercicios/para-casa/exercicio.ipynb b/exercicios/para-casa/exercicio.ipynb new file mode 100644 index 0000000..e69de29 diff --git a/material/titanic.csv b/exercicios/para-casa/titanic.csv similarity index 98% rename from material/titanic.csv rename to exercicios/para-casa/titanic.csv index 63b68ab..5cc466e 100644 --- a/material/titanic.csv +++ b/exercicios/para-casa/titanic.csv @@ -1,892 +1,892 @@ -PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked -1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S -2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C -3,1,3,"Heikkinen, Miss. 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Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S +890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C +891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q diff --git a/material/aula_s11.ipynb b/material/aula_s11.ipynb index 8193734..d2eb3df 100644 --- a/material/aula_s11.ipynb +++ b/material/aula_s11.ipynb @@ -9,6 +9,83 @@ "Pandas é uma biblioteca Python de análise e manipulação de dados que otimiza o trabalho com dados relacionais, tornando-se uma ferramenta essencial dentro da área de Ciências de Dados pois permite limpeza e tratamento de dados, análise exploratória de dados (EDA), suporte em atividades de Machine Learning, consultas e queries em bancos de dados relacionais, visualização de dados, webscraping, etc." ] }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting pandasNote: you may need to restart the kernel to use updated packages.\n", + "\n", + " Downloading pandas-2.1.1-cp311-cp311-win_amd64.whl.metadata (18 kB)\n", + "Collecting numpy>=1.23.2 (from pandas)\n", + " Downloading numpy-1.26.1-cp311-cp311-win_amd64.whl.metadata (61 kB)\n", + " ---------------------------------------- 0.0/61.2 kB ? 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eta -:--:--\n", + " --------------------------------------- 501.8/502.5 kB 7.8 MB/s eta 0:00:01\n", + " ---------------------------------------- 502.5/502.5 kB 3.9 MB/s eta 0:00:00\n", + "Installing collected packages: pytz, tzdata, numpy, pandas\n", + "Successfully installed numpy-1.26.1 pandas-2.1.1 pytz-2023.3.post1 tzdata-2023.3\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " WARNING: The script f2py.exe is installed in 'c:\\Users\\nathi\\AppData\\Local\\Programs\\Python\\Python311\\Scripts' which is not on PATH.\n", + " Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\n" + ] + } + ], + "source": [ + "%pip install pandas" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -26,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -55,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -63,7 +140,30 @@ "id": "2AqXNR4HyYPi", "outputId": "8d969379-0cb2-41eb-f281-87d8f26a075a" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 Patrícia\n", + "1 Camila\n", + "2 Gabi\n", + "3 Anna\n", + "4 Tamires\n", + "dtype: object\n" + ] + }, + { + "data": { + "text/plain": [ + "pandas.core.series.Series" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "lista = [\"Patrícia\", \"Camila\", \"Gabi\", \"Anna\", \"Tamires\"]\n", "\n", @@ -74,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -82,7 +182,30 @@ "id": "MZ6CTY79ycKR", "outputId": "518d60e5-e35d-4e97-aea9-2c677c75766a" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a Patrícia\n", + "b Camila\n", + "c Gabi\n", + "d Anna\n", + "e Tamires\n", + "dtype: object\n" + ] + }, + { + "data": { + "text/plain": [ + "pandas.core.series.Series" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dicionario = {\"a\": \"Patrícia\", \"b\": \"Camila\", \"c\": \"Gabi\", \"d\": \"Anna\", \"e\": \"Tamires\"}\n", "\n", @@ -93,7 +216,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: numpy in c:\\users\\nathi\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (1.26.1)Note: you may need to restart the kernel to use updated packages.\n", + "\n" + ] + } + ], + "source": [ + "%pip install numpy" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -101,7 +251,21 @@ "id": "x_I2XcYCycMU", "outputId": "7ea30b52-93a3-4787-b461-1f09448dc340" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[11 7 17 7 18]\n", + "0 11\n", + "1 7\n", + "2 17\n", + "3 7\n", + "4 18\n", + "dtype: int32\n" + ] + } + ], "source": [ "array_numpy = np.random.randint(5, 25, size=5)\n", "print(array_numpy)\n", @@ -109,7 +273,7 @@ "serie_do_array = pd.Series(array_numpy)\n", "\n", "serie_do_array\n", - "type(serie_do_array)" + "print(serie_do_array)" ] }, { @@ -123,7 +287,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -132,14 +296,33 @@ "id": "nrAyRyd32H8S", "outputId": "14540136-0b05-44e8-80d8-07f4803af26e" }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\nathi\\AppData\\Local\\Temp\\ipykernel_7684\\3066844099.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " serie_do_dicionario[0]\n" + ] + }, + { + "data": { + "text/plain": [ + "'Patrícia'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "serie_do_dicionario[0]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -148,7 +331,18 @@ "id": "I91ASKbz2H-s", "outputId": "7e5f4fc8-19e0-481e-bfac-da9340dc87b3" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'Anna'" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "serie_do_dicionario[\"d\"]" ] @@ -190,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -201,18 +395,415 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
.......................................
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
88688702Montvila, Rev. Juozasmale27.00021153613.00NaNS
88788811Graham, Miss. Margaret Edithfemale19.00011205330.00B42S
88888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.45NaNS
88989011Behr, Mr. Karl Howellmale26.00011136930.00C148C
89089103Dooley, Mr. Patrickmale32.0003703767.75NaNQ
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PassengerIdSurvivedPclassAgeSibSpParchFare
count891.000000891.000000891.000000714.000000891.000000891.000000891.000000
mean446.0000000.3838382.30864229.6991180.5230080.38159432.204208
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min1.0000000.0000001.0000000.4200000.0000000.0000000.000000
25%223.5000000.0000002.00000020.1250000.0000000.0000007.910400
50%446.0000000.0000003.00000028.0000000.0000000.00000014.454200
75%668.5000001.0000003.00000038.0000001.0000000.00000031.000000
max891.0000001.0000003.00000080.0000008.0000006.000000512.329200
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" + ], + "text/plain": [ + " PassengerId Survived Pclass Age SibSp \\\n", + "count 891.000000 891.000000 891.000000 714.000000 891.000000 \n", + "mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n", + "std 257.353842 0.486592 0.836071 14.526497 1.102743 \n", + "min 1.000000 0.000000 1.000000 0.420000 0.000000 \n", + "25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n", + "50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n", + "75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n", + "max 891.000000 1.000000 3.000000 80.000000 8.000000 \n", + "\n", + " Parch Fare \n", + "count 891.000000 891.000000 \n", + "mean 0.381594 32.204208 \n", + "std 0.806057 49.693429 \n", + "min 0.000000 0.000000 \n", + "25% 0.000000 7.910400 \n", + "50% 0.000000 14.454200 \n", + "75% 0.000000 31.000000 \n", + "max 6.000000 512.329200 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# resumo estatístico dos dados\n", "df.describe()" @@ -250,9 +1130,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "PassengerId int64\n", + "Survived int64\n", + "Pclass int64\n", + "Name object\n", + "Sex object\n", + "Age float64\n", + "SibSp int64\n", + "Parch int64\n", + "Ticket object\n", + "Fare float64\n", + "Cabin object\n", + "Embarked object\n", + "dtype: object" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Quais os tipos dos dados?\n", "df.dtypes" @@ -260,9 +1163,250 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
036813Moussa, Mrs. (Mantoura Boulos)femaleNaN0026267.2292NaNC
181903Holm, Mr. John Fredrik Alexandermale43.000C 70756.4500NaNS
211013Moran, Miss. BerthafemaleNaN1037111024.1500NaNQ
376103Garfirth, Mr. JohnmaleNaN0035858514.5000NaNS
425711Thorne, Mrs. Gertrude MaybellefemaleNaN00PC 1758579.2000NaNC
.......................................
24532312Slayter, Miss. Hilda Maryfemale30.00023481812.3500NaNQ
24683113Yasbeck, Mrs. Antoni (Selini Alexander)female15.010265914.4542NaNC
24781703Heininen, Miss. Wendla Mariafemale23.000STON/O2. 31012907.9250NaNS
2485103Panula, Master. Juha Niilomale7.041310129539.6875NaNS
24910713Salkjelsvik, Miss. Anna Kristinefemale21.0003431207.6500NaNS
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250 rows × 12 columns

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" + ], + "text/plain": [ + " PassengerId Survived Pclass Name \\\n", + "0 368 1 3 Moussa, Mrs. (Mantoura Boulos) \n", + "1 819 0 3 Holm, Mr. John Fredrik Alexander \n", + "2 110 1 3 Moran, Miss. Bertha \n", + "3 761 0 3 Garfirth, Mr. John \n", + "4 257 1 1 Thorne, Mrs. Gertrude Maybelle \n", + ".. ... ... ... ... \n", + "245 323 1 2 Slayter, Miss. Hilda Mary \n", + "246 831 1 3 Yasbeck, Mrs. Antoni (Selini Alexander) \n", + "247 817 0 3 Heininen, Miss. Wendla Maria \n", + "248 51 0 3 Panula, Master. Juha Niilo \n", + "249 107 1 3 Salkjelsvik, Miss. Anna Kristine \n", + "\n", + " Sex Age SibSp Parch Ticket Fare Cabin Embarked \n", + "0 female NaN 0 0 2626 7.2292 NaN C \n", + "1 male 43.0 0 0 C 7075 6.4500 NaN S \n", + "2 female NaN 1 0 371110 24.1500 NaN Q \n", + "3 male NaN 0 0 358585 14.5000 NaN S \n", + "4 female NaN 0 0 PC 17585 79.2000 NaN C \n", + ".. ... ... ... ... ... ... ... ... \n", + "245 female 30.0 0 0 234818 12.3500 NaN Q \n", + "246 female 15.0 1 0 2659 14.4542 NaN C \n", + "247 female 23.0 0 0 STON/O2. 3101290 7.9250 NaN S \n", + "248 male 7.0 4 1 3101295 39.6875 NaN S \n", + "249 female 21.0 0 0 343120 7.6500 NaN S \n", + "\n", + "[250 rows x 12 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Caso queiremos reduzir o número de amostras, podemos usar a função sample\n", "df_menor = df.sample(250)\n", @@ -275,9 +1419,152 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "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
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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", + " 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", + " 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 " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Slicing: obter apenas uma parte do dataset\n", "df[:5]" @@ -285,9 +1572,242 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "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
5603Moran, Mr. JamesmaleNaN003308778.4583NaNQ
6701McCarthy, Mr. Timothy Jmale54.0001746351.8625E46S
7803Palsson, Master. Gosta Leonardmale2.03134990921.0750NaNS
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.00234774211.1333NaNS
91012Nasser, Mrs. Nicholas (Adele Achem)female14.01023773630.0708NaNC
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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", + "5 6 0 3 \n", + "6 7 0 1 \n", + "7 8 0 3 \n", + "8 9 1 3 \n", + "9 10 1 2 \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", + "5 Moran, Mr. James male NaN 0 \n", + "6 McCarthy, Mr. Timothy J male 54.0 0 \n", + "7 Palsson, Master. Gosta Leonard male 2.0 3 \n", + "8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 \n", + "9 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 \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", + "5 0 330877 8.4583 NaN Q \n", + "6 0 17463 51.8625 E46 S \n", + "7 1 349909 21.0750 NaN S \n", + "8 2 347742 11.1333 NaN S \n", + "9 0 237736 30.0708 NaN C " + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "mini_df = df[:10]\n", "mini_df" @@ -295,9 +1815,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n", + " 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n", + " dtype='object')" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Como obter as colunas do dataframe:\n", "df.columns" @@ -324,9 +1857,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Valores nulos por coluna do dataframe:\n", + "PassengerId 0\n", + "Survived 0\n", + "Pclass 0\n", + "Name 0\n", + "Sex 0\n", + "Age 177\n", + "SibSp 0\n", + "Parch 0\n", + "Ticket 0\n", + "Fare 0\n", + "Cabin 687\n", + "Embarked 2\n", + "dtype: int64\n" + ] + } + ], "source": [ "# Checar valores nulos no dataset\n", "print(\"Valores nulos por coluna do dataframe:\")\n", @@ -335,9 +1889,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(712, 12)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Remove entradas (linhas) contendo valores nulos em colunas específicas\n", "df = df.dropna(subset=['Age', 'Embarked'])\n", @@ -346,9 +1911,254 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\nathi\\AppData\\Local\\Temp\\ipykernel_7684\\730007117.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['Sex'] = df['Sex'].replace({'male': 'M', 'female': 'F'})\n" + ] + }, + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen HarrisM22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...F38.010PC 1759971.2833C85C
2313Heikkinen, Miss. LainaF26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)F35.01011380353.1000C123S
4503Allen, Mr. William HenryM35.0003734508.0500NaNS
6701McCarthy, Mr. Timothy JM54.0001746351.8625E46S
7803Palsson, Master. Gosta LeonardM2.03134990921.0750NaNS
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)F27.00234774211.1333NaNS
91012Nasser, Mrs. Nicholas (Adele Achem)F14.01023773630.0708NaNC
101113Sandstrom, Miss. Marguerite RutF4.011PP 954916.7000G6S
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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", + "6 7 0 1 \n", + "7 8 0 3 \n", + "8 9 1 3 \n", + "9 10 1 2 \n", + "10 11 1 3 \n", + "\n", + " Name Sex Age SibSp Parch \\\n", + "0 Braund, Mr. Owen Harris M 22.0 1 0 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... F 38.0 1 0 \n", + "2 Heikkinen, Miss. Laina F 26.0 0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) F 35.0 1 0 \n", + "4 Allen, Mr. William Henry M 35.0 0 0 \n", + "6 McCarthy, Mr. Timothy J M 54.0 0 0 \n", + "7 Palsson, Master. Gosta Leonard M 2.0 3 1 \n", + "8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) F 27.0 0 2 \n", + "9 Nasser, Mrs. Nicholas (Adele Achem) F 14.0 1 0 \n", + "10 Sandstrom, Miss. Marguerite Rut F 4.0 1 1 \n", + "\n", + " Ticket Fare Cabin Embarked \n", + "0 A/5 21171 7.2500 NaN S \n", + "1 PC 17599 71.2833 C85 C \n", + "2 STON/O2. 3101282 7.9250 NaN S \n", + "3 113803 53.1000 C123 S \n", + "4 373450 8.0500 NaN S \n", + "6 17463 51.8625 E46 S \n", + "7 349909 21.0750 NaN S \n", + "8 347742 11.1333 NaN S \n", + "9 237736 30.0708 NaN C \n", + "10 PP 9549 16.7000 G6 S " + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Vamos substituir os valores contidos na coluna 'Sex' por 'F' e 'M'\n", "df['Sex'] = df['Sex'].replace({'male': 'M', 'female': 'F'})\n", @@ -358,9 +2168,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(712, 12)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Removendo entradas duplicadas\n", "df = df.drop_duplicates()\n", @@ -384,9 +2205,263 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen HarrisM0.27117410A/5 211710.014151NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...F0.47222910PC 175990.139136C85C
2313Heikkinen, Miss. LainaF0.32143800STON/O2. 31012820.015469NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)F0.434531101138030.103644C123S
4503Allen, Mr. William HenryM0.434531003734500.015713NaNS
.......................................
88588603Rice, Mrs. William (Margaret Norton)F0.484795053826520.056848NaNQ
88688702Montvila, Rev. JuozasM0.334004002115360.025374NaNS
88788811Graham, Miss. Margaret EdithF0.233476001120530.058556B42S
88989011Behr, Mr. Karl HowellM0.321438001113690.058556C148C
89089103Dooley, Mr. PatrickM0.396833003703760.015127NaNQ
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712 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", + "885 886 0 3 \n", + "886 887 0 2 \n", + "887 888 1 1 \n", + "889 890 1 1 \n", + "890 891 0 3 \n", + "\n", + " Name Sex Age SibSp \\\n", + "0 Braund, Mr. Owen Harris M 0.271174 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... F 0.472229 1 \n", + "2 Heikkinen, Miss. Laina F 0.321438 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) F 0.434531 1 \n", + "4 Allen, Mr. William Henry M 0.434531 0 \n", + ".. ... .. ... ... \n", + "885 Rice, Mrs. William (Margaret Norton) F 0.484795 0 \n", + "886 Montvila, Rev. Juozas M 0.334004 0 \n", + "887 Graham, Miss. Margaret Edith F 0.233476 0 \n", + "889 Behr, Mr. Karl Howell M 0.321438 0 \n", + "890 Dooley, Mr. Patrick M 0.396833 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "0 0 A/5 21171 0.014151 NaN S \n", + "1 0 PC 17599 0.139136 C85 C \n", + "2 0 STON/O2. 3101282 0.015469 NaN S \n", + "3 0 113803 0.103644 C123 S \n", + "4 0 373450 0.015713 NaN S \n", + ".. ... ... ... ... ... \n", + "885 5 382652 0.056848 NaN Q \n", + "886 0 211536 0.025374 NaN S \n", + "887 0 112053 0.058556 B42 S \n", + "889 0 111369 0.058556 C148 C \n", + "890 0 370376 0.015127 NaN Q \n", + "\n", + "[712 rows x 12 columns]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Normalização de colunas numéricas \n", "\n", @@ -402,7 +2477,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -411,6 +2486,36 @@ "df['Embarked'] = df['Embarked'].fillna(df['Embarked'].mode().iloc[0])" ] }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "df['Embarked'] = df['Embarked'].fillna(df['Embarked'].mode().iloc[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 S\n", + "Name: Embarked, dtype: object" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['Embarked'].mode()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -430,19 +2535,2100 @@ " - Bônus II: pesquisar sobre outras formas de processamento de dados além das vistas em sala de aula" ] }, + { + "cell_type": "code", + "execution_count": 42, + "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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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
5603Moran, Mr. JamesmaleNaN003308778.4583NaNQ
6701McCarthy, Mr. Timothy Jmale54.0001746351.8625E46S
7803Palsson, Master. Gosta Leonardmale2.03134990921.0750NaNS
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
383903Vander Planke, Miss. Augusta Mariafemale18.02034576418.0000NaNS
394013Nicola-Yarred, Miss. Jamilafemale14.010265111.2417NaNC
404103Ahlin, Mrs. Johan (Johanna Persdotter Larsson)female40.01075469.4750NaNS
414202Turpin, Mrs. William John Robert (Dorothy Ann ...female27.0101166821.0000NaNS
424303Kraeff, Mr. TheodormaleNaN003492537.8958NaNC
434412Laroche, Miss. Simonne Marie Anne Andreefemale3.012SC/Paris 212341.5792NaNC
444513Devaney, Miss. Margaret Deliafemale19.0003309587.8792NaNQ
454603Rogers, Mr. William JohnmaleNaN00S.C./A.4. 235678.0500NaNS
464703Lennon, Mr. DenismaleNaN1037037115.5000NaNQ
474813O'Driscoll, Miss. BridgetfemaleNaN00143117.7500NaNQ
484903Samaan, Mr. YoussefmaleNaN20266221.6792NaNC
495003Arnold-Franchi, Mrs. Josef (Josefine Franchi)female18.01034923717.8000NaNS
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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", + "5 Moran, Mr. James male NaN 0 \n", + "6 McCarthy, Mr. Timothy J male 54.0 0 \n", + "7 Palsson, Master. Gosta Leonard male 2.0 3 \n", + "8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 \n", + "9 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 \n", + "10 Sandstrom, Miss. Marguerite Rut female 4.0 1 \n", + "11 Bonnell, Miss. Elizabeth female 58.0 0 \n", + "12 Saundercock, Mr. William Henry male 20.0 0 \n", + "13 Andersson, Mr. Anders Johan male 39.0 1 \n", + "14 Vestrom, Miss. Hulda Amanda Adolfina female 14.0 0 \n", + "15 Hewlett, Mrs. (Mary D Kingcome) female 55.0 0 \n", + "16 Rice, Master. Eugene male 2.0 4 \n", + "17 Williams, Mr. Charles Eugene male NaN 0 \n", + "18 Vander Planke, Mrs. Julius (Emelia Maria Vande... female 31.0 1 \n", + "19 Masselmani, Mrs. Fatima female NaN 0 \n", + "20 Fynney, Mr. Joseph J male 35.0 0 \n", + "21 Beesley, Mr. Lawrence male 34.0 0 \n", + "22 McGowan, Miss. Anna \"Annie\" female 15.0 0 \n", + "23 Sloper, Mr. William Thompson male 28.0 0 \n", + "24 Palsson, Miss. Torborg Danira female 8.0 3 \n", + "25 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... female 38.0 1 \n", + "26 Emir, Mr. Farred Chehab male NaN 0 \n", + "27 Fortune, Mr. Charles Alexander male 19.0 3 \n", + "28 O'Dwyer, Miss. Ellen \"Nellie\" female NaN 0 \n", + "29 Todoroff, Mr. Lalio male NaN 0 \n", + "30 Uruchurtu, Don. Manuel E male 40.0 0 \n", + "31 Spencer, Mrs. William Augustus (Marie Eugenie) female NaN 1 \n", + "32 Glynn, Miss. Mary Agatha female NaN 0 \n", + "33 Wheadon, Mr. Edward H male 66.0 0 \n", + "34 Meyer, Mr. Edgar Joseph male 28.0 1 \n", + "35 Holverson, Mr. Alexander Oskar male 42.0 1 \n", + "36 Mamee, Mr. Hanna male NaN 0 \n", + "37 Cann, Mr. Ernest Charles male 21.0 0 \n", + "38 Vander Planke, Miss. Augusta Maria female 18.0 2 \n", + "39 Nicola-Yarred, Miss. Jamila female 14.0 1 \n", + "40 Ahlin, Mrs. Johan (Johanna Persdotter Larsson) female 40.0 1 \n", + "41 Turpin, Mrs. William John Robert (Dorothy Ann ... female 27.0 1 \n", + "42 Kraeff, Mr. Theodor male NaN 0 \n", + "43 Laroche, Miss. Simonne Marie Anne Andree female 3.0 1 \n", + "44 Devaney, Miss. Margaret Delia female 19.0 0 \n", + "45 Rogers, Mr. William John male NaN 0 \n", + "46 Lennon, Mr. Denis male NaN 1 \n", + "47 O'Driscoll, Miss. Bridget female NaN 0 \n", + "48 Samaan, Mr. Youssef male NaN 2 \n", + "49 Arnold-Franchi, Mrs. Josef (Josefine Franchi) female 18.0 1 \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", + "5 0 330877 8.4583 NaN Q \n", + "6 0 17463 51.8625 E46 S \n", + "7 1 349909 21.0750 NaN S \n", + "8 2 347742 11.1333 NaN S \n", + "9 0 237736 30.0708 NaN C \n", + "10 1 PP 9549 16.7000 G6 S \n", + "11 0 113783 26.5500 C103 S \n", + "12 0 A/5. 2151 8.0500 NaN S \n", + "13 5 347082 31.2750 NaN S \n", + "14 0 350406 7.8542 NaN S \n", + "15 0 248706 16.0000 NaN S \n", + "16 1 382652 29.1250 NaN Q \n", + "17 0 244373 13.0000 NaN S \n", + "18 0 345763 18.0000 NaN S \n", + "19 0 2649 7.2250 NaN C \n", + "20 0 239865 26.0000 NaN S \n", + "21 0 248698 13.0000 D56 S \n", + "22 0 330923 8.0292 NaN Q \n", + "23 0 113788 35.5000 A6 S \n", + "24 1 349909 21.0750 NaN S \n", + "25 5 347077 31.3875 NaN S \n", + "26 0 2631 7.2250 NaN C \n", + "27 2 19950 263.0000 C23 C25 C27 S \n", + "28 0 330959 7.8792 NaN Q \n", + "29 0 349216 7.8958 NaN S \n", + "30 0 PC 17601 27.7208 NaN C \n", + "31 0 PC 17569 146.5208 B78 C \n", + "32 0 335677 7.7500 NaN Q \n", + "33 0 C.A. 24579 10.5000 NaN S \n", + "34 0 PC 17604 82.1708 NaN C \n", + "35 0 113789 52.0000 NaN S \n", + "36 0 2677 7.2292 NaN C \n", + "37 0 A./5. 2152 8.0500 NaN S \n", + "38 0 345764 18.0000 NaN S \n", + "39 0 2651 11.2417 NaN C \n", + "40 0 7546 9.4750 NaN S \n", + "41 0 11668 21.0000 NaN S \n", + "42 0 349253 7.8958 NaN C \n", + "43 2 SC/Paris 2123 41.5792 NaN C \n", + "44 0 330958 7.8792 NaN Q \n", + "45 0 S.C./A.4. 23567 8.0500 NaN S \n", + "46 0 370371 15.5000 NaN Q \n", + "47 0 14311 7.7500 NaN Q \n", + "48 0 2662 21.6792 NaN C \n", + "49 0 349237 17.8000 NaN S " + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#diminuir numero de amostras para 50 via slicing\n", + "df_exercicio[:50]" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
6701McCarthy, Mr. Timothy Jmale54.0001746351.8625E46S
101113Sandstrom, Miss. Marguerite Rutfemale4.011PP 954916.7000G6S
111211Bonnell, Miss. Elizabethfemale58.00011378326.5500C103S
.......................................
87187211Beckwith, Mrs. Richard Leonard (Sallie Monypeny)female47.0111175152.5542D35S
87287301Carlsson, Mr. Frans Olofmale33.0006955.0000B51 B53 B55S
87988011Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)female56.0011176783.1583C50C
88788811Graham, Miss. Margaret Edithfemale19.00011205330.0000B42S
88989011Behr, Mr. Karl Howellmale26.00011136930.0000C148C
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204 rows × 12 columns

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" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "1 2 1 1 \n", + "3 4 1 1 \n", + "6 7 0 1 \n", + "10 11 1 3 \n", + "11 12 1 1 \n", + ".. ... ... ... \n", + "871 872 1 1 \n", + "872 873 0 1 \n", + "879 880 1 1 \n", + "887 888 1 1 \n", + "889 890 1 1 \n", + "\n", + " Name Sex Age SibSp \\\n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", + "6 McCarthy, Mr. Timothy J male 54.0 0 \n", + "10 Sandstrom, Miss. Marguerite Rut female 4.0 1 \n", + "11 Bonnell, Miss. Elizabeth female 58.0 0 \n", + ".. ... ... ... ... \n", + "871 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47.0 1 \n", + "872 Carlsson, Mr. Frans Olof male 33.0 0 \n", + "879 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56.0 0 \n", + "887 Graham, Miss. Margaret Edith female 19.0 0 \n", + "889 Behr, Mr. Karl Howell male 26.0 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "1 0 PC 17599 71.2833 C85 C \n", + "3 0 113803 53.1000 C123 S \n", + "6 0 17463 51.8625 E46 S \n", + "10 1 PP 9549 16.7000 G6 S \n", + "11 0 113783 26.5500 C103 S \n", + ".. ... ... ... ... ... \n", + "871 1 11751 52.5542 D35 S \n", + "872 0 695 5.0000 B51 B53 B55 S \n", + "879 1 11767 83.1583 C50 C \n", + "887 0 112053 30.0000 B42 S \n", + "889 0 111369 30.0000 C148 C \n", + "\n", + "[204 rows x 12 columns]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#retirar nulos da coluna 'Cabin'\n", + "df_exercicio = df_exercicio.dropna(subset=['Cabin'])\n", + "\n", + "df_exercicio" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "\n", + " - obter métricas\n", + " - Organizar valores via coluna 'Fare'\n", + " - copiar o DF\n", + " - Bônus: normalizar coluna (qualquer uma)\n", + " - Bônus II: pesquisar sobre outras formas de processamento de dados além das vistas em sala de aula" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "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
5603Moran, Mr. JamesmaleNaN003308778.4583NaNQ
6701McCarthy, Mr. Timothy Jmale54.0001746351.8625E46S
7803Palsson, Master. Gosta Leonardmale2.03134990921.0750NaNS
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
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PassengerIdSurvivedPclassAgeSibSpParchFare
count204.000000204.000000204.000000185.000000204.000000204.000000204.000000
mean455.4019610.6666671.19607835.8292970.4411760.43627576.141504
std251.3792790.4725640.52596115.6793560.6289180.72997674.391749
min2.0000000.0000001.0000000.9200000.0000000.0000000.000000
25%261.7500000.0000001.00000024.0000000.0000000.00000029.453125
50%457.5000001.0000001.00000036.0000000.0000000.00000055.220850
75%684.0000001.0000001.00000048.0000001.0000001.00000089.328150
max890.0000001.0000003.00000080.0000003.0000004.000000512.329200
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" + ], + "text/plain": [ + " PassengerId Survived Pclass Age SibSp \\\n", + "count 204.000000 204.000000 204.000000 185.000000 204.000000 \n", + "mean 455.401961 0.666667 1.196078 35.829297 0.441176 \n", + "std 251.379279 0.472564 0.525961 15.679356 0.628918 \n", + "min 2.000000 0.000000 1.000000 0.920000 0.000000 \n", + "25% 261.750000 0.000000 1.000000 24.000000 0.000000 \n", + "50% 457.500000 1.000000 1.000000 36.000000 0.000000 \n", + "75% 684.000000 1.000000 1.000000 48.000000 1.000000 \n", + "max 890.000000 1.000000 3.000000 80.000000 3.000000 \n", + "\n", + " Parch Fare \n", + "count 204.000000 204.000000 \n", + "mean 0.436275 76.141504 \n", + "std 0.729976 74.391749 \n", + "min 0.000000 0.000000 \n", + "25% 0.000000 29.453125 \n", + "50% 0.000000 55.220850 \n", + "75% 1.000000 89.328150 \n", + "max 4.000000 512.329200 " + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#obter métricas\n", + "df_exercicio.describe()\n" + ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "#Organizar valores via coluna 'Fare'\n", + "\n", + "df_exercicio['Fare'] = sorted" + ] } ], "metadata": { @@ -464,7 +4650,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.11.4" } }, "nbformat": 4,