{ "cells": [ { "cell_type": "markdown", "id": "c6157cb0-073b-45fb-a9a6-84948c7266ab", "metadata": {}, "source": [ "# Calidad del dato 2" ] }, { "cell_type": "markdown", "id": "ecf98755-7ce8-4c73-b5c5-7fed88eff1f0", "metadata": {}, "source": [ "## Caso titanic" ] }, { "cell_type": "code", "execution_count": 1, "id": "b1dc6675-54dc-41c8-ba7c-a19ed223513a", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "pd.options.display.float_format = '{:.2f}'.format #Desactivar notación científica en pandas:\n", "np.set_printoptions(suppress=True) #Desactivar notación científica en numpy:\n", "pd.set_option('display.max_columns', None) #comando para mostrar todas las columnas" ] }, { "cell_type": "code", "execution_count": 57, "id": "f6f68f41-931e-482c-b399-3dc13ad5a67a", "metadata": {}, "outputs": [], "source": [ "df1 = pd.read_csv(\"./datos/calidad_2.csv\", sep=\";\", decimal=\".\", encoding=\"WINDOWS-1252\")" ] }, { "cell_type": "code", "execution_count": 5, "id": "fefa83bc-d1fb-490f-bbc4-3d29d14105d3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.0010A/5 211717.25NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.0010PC 1759971.28C85C
2313Heikkinen, Miss. Lainafemale26.0000STON/O2. 31012827.92NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.001011380353.10C123S
4503Allen, Mr. William Henrymale35.00003734508.05NaNS
\n", "
" ], "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 22.00 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.00 1 \n", "2 Heikkinen, Miss. Laina female 26.00 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.00 1 \n", "4 Allen, Mr. William Henry male 35.00 0 \n", "\n", " Parch Ticket Fare Cabin Embarked \n", "0 0 A/5 21171 7.25 NaN S \n", "1 0 PC 17599 71.28 C85 C \n", "2 0 STON/O2. 3101282 7.92 NaN S \n", "3 0 113803 53.10 C123 S \n", "4 0 373450 8.05 NaN S " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.head()" ] }, { "cell_type": "code", "execution_count": 6, "id": "fc328191-94cd-4f98-a96d-a799caf9b456", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 891 entries, 0 to 890\n", "Data columns (total 12 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 PassengerId 891 non-null int64 \n", " 1 Survived 891 non-null int64 \n", " 2 Pclass 891 non-null int64 \n", " 3 Name 891 non-null object \n", " 4 Sex 891 non-null object \n", " 5 Age 714 non-null float64\n", " 6 SibSp 891 non-null int64 \n", " 7 Parch 891 non-null int64 \n", " 8 Ticket 891 non-null object \n", " 9 Fare 891 non-null float64\n", " 10 Cabin 204 non-null object \n", " 11 Embarked 889 non-null object \n", "dtypes: float64(2), int64(5), object(5)\n", "memory usage: 83.7+ KB\n" ] } ], "source": [ "df1.info()" ] }, { "cell_type": "code", "execution_count": 7, "id": "f79db2b3-48aa-4910-858d-0a679ea8859f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
PassengerIdSurvivedPclassAgeSibSpParchFare
count891.00891.00891.00714.00891.00891.00891.00
mean446.000.382.3129.700.520.3832.20
std257.350.490.8414.531.100.8149.69
min1.000.001.000.420.000.000.00
25%223.500.002.0020.120.000.007.91
50%446.000.003.0028.000.000.0014.45
75%668.501.003.0038.001.000.0031.00
max891.001.003.0080.008.006.00512.33
\n", "
" ], "text/plain": [ " PassengerId Survived Pclass Age SibSp Parch Fare\n", "count 891.00 891.00 891.00 714.00 891.00 891.00 891.00\n", "mean 446.00 0.38 2.31 29.70 0.52 0.38 32.20\n", "std 257.35 0.49 0.84 14.53 1.10 0.81 49.69\n", "min 1.00 0.00 1.00 0.42 0.00 0.00 0.00\n", "25% 223.50 0.00 2.00 20.12 0.00 0.00 7.91\n", "50% 446.00 0.00 3.00 28.00 0.00 0.00 14.45\n", "75% 668.50 1.00 3.00 38.00 1.00 0.00 31.00\n", "max 891.00 1.00 3.00 80.00 8.00 6.00 512.33" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.describe()" ] }, { "cell_type": "code", "execution_count": 10, "id": "b717654b-1d08-429d-9c51-fd341489b80a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "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" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# ¿Cuántos valores nulos tenemos por columna?\n", "df1.isnull().sum()" ] }, { "cell_type": "markdown", "id": "684e156a-4377-4025-9e3d-048aa1d79e0a", "metadata": {}, "source": [ "Se observan 177 NAs (valores perdidos) en la variable \"age\"\n", "La variable \"Survived\" esta como variable continua, pero debería ser una categoría igual que Pclass" ] }, { "cell_type": "code", "execution_count": 11, "id": "8f447c9c-85f9-4f38-95ad-af8885846536", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "B96 B98 4\n", "G6 4\n", "C23 C25 C27 4\n", "C22 C26 3\n", "F33 3\n", " ..\n", "E34 1\n", "C7 1\n", "C54 1\n", "E36 1\n", "C148 1\n", "Name: Cabin, Length: 147, dtype: int64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Comprobamos las variables Cabin y Embarked\n", "df1.Cabin.value_counts()" ] }, { "cell_type": "code", "execution_count": 33, "id": "3544a871-d7a5-4411-ae6a-241c17aa9201", "metadata": { "scrolled": true, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "array([nan, 'C85', nan, 'C123', nan, nan, 'E46', nan, nan, nan, 'G6',\n", " 'C103', nan, nan, nan, nan, nan, nan, nan, nan, nan, 'D56', nan,\n", " 'A6', nan, nan, nan, 'C23 C25 C27', nan, nan, nan, 'B78', nan, nan,\n", " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n", " nan, nan, nan, nan, nan, 'D33', nan, 'B30', 'C52', nan, nan, nan,\n", " nan, nan, 'B28', 'C83', nan, nan, nan, 'F33', nan, nan, nan, nan,\n", " nan, nan, nan, nan, 'F G73', nan, nan, nan, nan, nan, nan, nan,\n", " nan, nan, nan, nan, nan, 'C23 C25 C27', nan, nan, nan, 'E31', nan,\n", " nan, nan, 'A5', 'D10 D12', nan, nan, nan, nan, 'D26', nan, nan,\n", " nan, nan, nan, nan, nan, 'C110', nan, nan, nan, nan, nan, nan, nan,\n", " 'B58 B60', nan, nan, nan, nan, 'E101', 'D26', nan, nan, nan,\n", " 'F E69', nan, nan, nan, nan, nan, nan, nan, 'D47', 'C123', nan,\n", " 'B86', nan, nan, nan, nan, nan, nan, nan, nan, 'F2', nan, nan,\n", " 'C2', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n", " nan, nan, 'E33', nan, nan, nan, 'B19', nan, nan, nan, 'A7', nan,\n", " nan, 'C49', nan, nan, nan, nan, nan, 'F4', nan, 'A32', nan, nan,\n", " nan, nan, nan, nan, nan, 'F2', 'B4', 'B80', nan, nan, nan, nan,\n", " nan, nan, nan, nan, nan, 'G6', nan, nan, nan, 'A31', nan, nan, nan,\n", " nan, nan, 'D36', nan, nan, 'D15', nan, nan, nan, nan, nan, 'C93',\n", " nan, nan, nan, nan, nan, 'C83', nan, nan, nan, nan, nan, nan, nan,\n", " nan, nan, nan, nan, nan, nan, nan, 'C78', nan, nan, 'D35', nan,\n", " nan, 'G6', 'C87', nan, nan, nan, nan, 'B77', nan, nan, nan, nan,\n", " 'E67', 'B94', nan, nan, nan, nan, 'C125', 'C99', nan, nan, nan,\n", " 'C118', nan, 'D7', nan, nan, nan, nan, nan, nan, nan, nan, 'A19',\n", " nan, nan, nan, nan, nan, nan, 'B49', 'D', nan, nan, nan, nan,\n", " 'C22 C26', 'C106', 'B58 B60', nan, nan, nan, 'E101', nan,\n", " 'C22 C26', nan, 'C65', nan, 'E36', 'C54', 'B57 B59 B63 B66', nan,\n", " nan, nan, nan, nan, nan, 'C7', 'E34', nan, nan, nan, nan, nan,\n", " 'C32', nan, 'D', nan, 'B18', nan, 'C124', 'C91', nan, nan, nan,\n", " 'C2', 'E40', nan, 'T', 'F2', 'C23 C25 C27', nan, nan, nan, 'F33',\n", " nan, nan, nan, nan, nan, 'C128', nan, nan, nan, nan, 'E33', nan,\n", " nan, nan, nan, nan, nan, nan, nan, nan, 'D37', nan, nan, 'B35',\n", " 'E50', nan, nan, nan, nan, nan, nan, 'C82', nan, nan, nan, nan,\n", " nan, nan, nan, nan, nan, nan, nan, nan, 'B96 B98', nan, nan, 'D36',\n", " 'G6', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n", " nan, nan, nan, nan, nan, 'C78', nan, nan, nan, nan, nan, nan, nan,\n", " nan, nan, nan, nan, nan, nan, nan, nan, nan, 'E10', 'C52', nan,\n", " nan, nan, 'E44', 'B96 B98', nan, nan, 'C23 C25 C27', nan, nan, nan,\n", " nan, nan, nan, 'A34', nan, nan, nan, 'C104', nan, nan, 'C111',\n", " 'C92', nan, nan, 'E38', 'D21', nan, nan, 'E12', nan, 'E63', nan,\n", " nan, nan, nan, nan, nan, nan, nan, nan, nan, 'D', nan, 'A14', nan,\n", " nan, nan, nan, nan, nan, nan, nan, 'B49', nan, 'C93', 'B37', nan,\n", " nan, nan, nan, 'C30', nan, nan, nan, 'D20', nan, 'C22 C26', nan,\n", " nan, nan, nan, nan, 'B79', 'C65', nan, nan, nan, nan, nan, nan,\n", " 'E25', nan, nan, 'D46', 'F33', nan, nan, nan, 'B73', nan, nan,\n", " 'B18', nan, nan, nan, 'C95', nan, nan, nan, nan, nan, nan, nan,\n", " nan, 'B38', nan, nan, 'B39', 'B22', nan, nan, nan, 'C86', nan, nan,\n", " nan, nan, nan, 'C70', nan, nan, nan, nan, nan, 'A16', nan, 'E67',\n", " nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'C101',\n", " 'E25', nan, nan, nan, nan, 'E44', nan, nan, nan, 'C68', nan, 'A10',\n", " nan, 'E68', nan, 'B41', nan, nan, nan, 'D20', nan, nan, nan, nan,\n", " nan, nan, nan, 'A20', nan, nan, nan, nan, nan, nan, nan, nan, nan,\n", " 'C125', nan, nan, nan, nan, nan, nan, nan, nan, 'F4', nan, nan,\n", " 'D19', nan, nan, nan, 'D50', nan, 'D9', nan, nan, 'A23', nan,\n", " 'B50', nan, nan, nan, nan, nan, nan, nan, nan, 'B35', nan, nan,\n", " nan, 'D33', nan, 'A26', nan, nan, nan, nan, nan, nan, nan, nan,\n", " nan, nan, nan, 'D48', nan, nan, 'E58', nan, nan, nan, nan, nan,\n", " nan, 'C126', nan, 'B71', nan, nan, nan, nan, nan, nan, nan,\n", " 'B51 B53 B55', nan, 'D49', nan, nan, nan, nan, nan, nan, nan, 'B5',\n", " 'B20', nan, nan, nan, nan, nan, nan, nan, 'C68', 'F G63',\n", " 'C62 C64', 'E24', nan, nan, nan, nan, nan, 'E24', nan, nan, 'C90',\n", " 'C124', 'C126', nan, nan, 'F G73', 'C45', 'E101', nan, nan, nan,\n", " nan, nan, nan, 'E8', nan, nan, nan, nan, nan, 'B5', nan, nan, nan,\n", " nan, nan, nan, 'B101', nan, nan, 'D45', 'C46', 'B57 B59 B63 B66',\n", " nan, nan, 'B22', nan, nan, 'D30', nan, nan, 'E121', nan, nan, nan,\n", " nan, nan, nan, nan, 'B77', nan, nan, nan, 'B96 B98', nan, 'D11',\n", " nan, nan, nan, nan, nan, nan, 'E77', nan, nan, nan, 'F38', nan,\n", " nan, 'B3', nan, 'B20', 'D6', nan, nan, nan, nan, nan, nan,\n", " 'B82 B84', nan, nan, nan, nan, nan, nan, 'D17', nan, nan, nan, nan,\n", " nan, 'B96 B98', nan, nan, nan, 'A36', nan, nan, 'E8', nan, nan,\n", " nan, nan, nan, 'B102', nan, nan, nan, nan, 'B69', nan, nan, 'E121',\n", " nan, nan, nan, nan, nan, 'B28', nan, nan, nan, nan, nan, 'E49',\n", " nan, nan, nan, 'C47', nan, nan, nan, nan, nan, nan, nan, nan, nan,\n", " 'C92', nan, nan, nan, 'D28', nan, nan, nan, 'E17', nan, nan, nan,\n", " nan, 'D17', nan, nan, nan, nan, 'A24', nan, nan, nan, 'D35',\n", " 'B51 B53 B55', nan, nan, nan, nan, nan, nan, 'C50', nan, nan, nan,\n", " nan, nan, nan, nan, 'B42', nan, 'C148', nan], dtype=object)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Como no vemos todos los elementos, modificamos la configuración.\n", "pd.set_option('display.max_rows', 150)\n", "df1.Cabin.values" ] }, { "cell_type": "code", "execution_count": 35, "id": "bf69f080-b171-42be-9683-086bfebcdc44", "metadata": { "scrolled": true, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "array(['S', 'C', 'S', 'S', 'S', 'Q', 'S', 'S', 'S', 'C', 'S', 'S', 'S',\n", " 'S', 'S', 'S', 'Q', 'S', 'S', 'C', 'S', 'S', 'Q', 'S', 'S', 'S',\n", " 'C', 'S', 'Q', 'S', 'C', 'C', 'Q', 'S', 'C', 'S', 'C', 'S', 'S',\n", " 'C', 'S', 'S', 'C', 'C', 'Q', 'S', 'Q', 'Q', 'C', 'S', 'S', 'S',\n", " 'C', 'S', 'C', 'S', 'S', 'C', 'S', 'S', 'C', nan, 'S', 'S', 'C',\n", " 'C', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'C', 'S', 'S', 'S', 'S',\n", " 'S', 'S', 'S', 'S', 'Q', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S',\n", " 'S', 'S', 'S', 'S', 'S', 'C', 'C', 'S', 'S', 'S', 'S', 'S', 'S',\n", " 'S', 'S', 'S', 'S', 'S', 'Q', 'S', 'C', 'S', 'S', 'C', 'S', 'Q',\n", " 'S', 'C', 'S', 'S', 'S', 'C', 'S', 'S', 'C', 'Q', 'S', 'C', 'S',\n", " 'C', 'S', 'S', 'S', 'S', 'C', 'S', 'S', 'S', 'C', 'C', 'S', 'S',\n", " 'Q', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'C',\n", " 'Q', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S',\n", " 'S', 'S', 'Q', 'S', 'S', 'C', 'S', 'S', 'C', 'S', 'S', 'S', 'C',\n", " 'S', 'S', 'S', 'S', 'Q', 'S', 'Q', 'S', 'S', 'S', 'S', 'S', 'C',\n", " 'C', 'Q', 'S', 'Q', 'S', 'S', 'S', 'S', 'C', 'S', 'S', 'S', 'C',\n", " 'Q', 'C', 'S', 'S', 'S', 'S', 'Q', 'C', 'S', 'S', 'C', 'S', 'S',\n", " 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S',\n", " 'S', 'S', 'S', 'S', 'S', 'S', 'C', 'Q', 'S', 'S', 'C', 'Q', 'S',\n", " 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'C', 'C', 'S', 'C', 'S',\n", " 'Q', 'S', 'S', 'S', 'Q', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S',\n", " 'C', 'Q', 'S', 'S', 'S', 'Q', 'S', 'Q', 'S', 'S', 'S', 'S', 'C',\n", " 'S', 'S', 'S', 'Q', 'S', 'C', 'C', 'S', 'S', 'C', 'C', 'S', 'S',\n", " 'C', 'Q', 'Q', 'S', 'Q', 'S', 'S', 'C', 'C', 'C', 'C', 'C', 'C',\n", " 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'C', 'S', 'S', 'Q', 'S', 'S',\n", " 'C', 'S', 'S', 'S', 'C', 'Q', 'S', 'S', 'S', 'S', 'S', 'S', 'C',\n", " 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S',\n", " 'S', 'C', 'S', 'C', 'S', 'S', 'S', 'Q', 'Q', 'S', 'C', 'C', 'S',\n", " 'Q', 'S', 'C', 'C', 'Q', 'C', 'C', 'S', 'S', 'C', 'S', 'C', 'S',\n", " 'C', 'C', 'S', 'C', 'C', 'S', 'S', 'S', 'S', 'S', 'S', 'Q', 'C',\n", " 'S', 'S', 'S', 'C', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S',\n", " 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'Q', 'Q', 'S', 'S', 'S',\n", " 'S', 'S', 'S', 'S', 'C', 'Q', 'S', 'S', 'S', 'S', 'S', 'S', 'Q',\n", " 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S',\n", " 'S', 'S', 'S', 'S', 'S', 'S', 'C', 'S', 'S', 'S', 'C', 'C', 'S',\n", " 'C', 'S', 'S', 'S', 'Q', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S',\n", " 'Q', 'C', 'S', 'S', 'S', 'C', 'S', 'S', 'S', 'S', 'S', 'S', 'S',\n", " 'S', 'S', 'S', 'C', 'S', 'S', 'C', 'S', 'S', 'S', 'S', 'S', 'C',\n", " 'S', 'C', 'C', 'S', 'S', 'S', 'S', 'Q', 'Q', 'S', 'S', 'C', 'S',\n", " 'S', 'S', 'S', 'Q', 'S', 'S', 'C', 'S', 'S', 'S', 'Q', 'S', 'S',\n", " 'S', 'S', 'C', 'C', 'C', 'Q', 'S', 'S', 'S', 'S', 'S', 'C', 'C',\n", " 'C', 'S', 'S', 'S', 'C', 'S', 'C', 'S', 'S', 'S', 'S', 'C', 'S',\n", " 'S', 'C', 'S', 'S', 'C', 'S', 'Q', 'C', 'S', 'S', 'C', 'C', 'S',\n", " 'S', 'Q', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'C', 'S', 'S', 'S',\n", " 'S', 'Q', 'S', 'S', 'S', 'S', 'C', 'S', 'S', 'C', 'S', 'C', 'C',\n", " 'S', 'S', 'C', 'S', 'S', 'S', 'C', 'S', 'Q', 'S', 'S', 'S', 'S',\n", " 'C', 'C', 'S', 'S', 'S', 'S', 'C', 'S', 'S', 'S', 'C', 'S', 'S',\n", " 'S', 'Q', 'Q', 'S', 'S', 'S', 'S', 'S', 'S', 'C', 'S', 'C', 'S',\n", " 'S', 'S', 'Q', 'S', 'S', 'Q', 'S', 'S', 'C', 'S', 'S', 'S', 'S',\n", " 'S', 'S', 'S', 'S', 'C', 'S', 'S', 'C', 'C', 'S', 'C', 'S', 'S',\n", " 'S', 'S', 'S', 'Q', 'Q', 'S', 'S', 'Q', 'S', 'C', 'S', 'C', 'S',\n", " 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S',\n", " 'S', 'S', 'S', 'C', 'Q', 'C', 'S', 'S', 'S', 'C', 'S', 'S', 'S',\n", " 'S', 'S', 'C', 'S', 'C', 'S', 'S', 'S', 'Q', 'C', 'S', 'C', 'S',\n", " 'C', 'Q', 'S', 'S', 'S', 'S', 'S', 'C', 'C', 'S', 'S', 'S', 'S',\n", " 'S', 'C', 'S', 'Q', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'Q',\n", " 'S', 'S', 'S', 'C', 'S', 'S', 'S', 'S', 'S', 'C', 'S', 'S', 'S',\n", " 'S', 'C', 'S', 'S', 'S', 'S', 'S', 'S', 'Q', 'S', 'S', 'S', 'S',\n", " 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'C', 'S', 'S', 'S', 'C',\n", " 'Q', 'Q', 'S', 'S', 'S', 'S', 'C', 'S', 'S', 'Q', 'S', 'Q', 'S',\n", " 'C', 'S', 'S', 'S', 'S', 'S', 'S', 'Q', 'S', 'C', 'Q', 'S', 'S',\n", " 'C', 'S', 'S', 'S', 'S', 'C', 'S', 'S', 'S', 'S', 'C', 'S', 'S',\n", " 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'C', 'S',\n", " 'S', 'S', 'S', 'S', 'S', 'S', 'Q', 'S', 'C', 'Q', nan, 'C', 'S',\n", " 'C', 'S', 'S', 'C', 'S', 'S', 'S', 'C', 'S', 'S', 'C', 'C', 'S',\n", " 'S', 'S', 'C', 'S', 'C', 'S', 'S', 'C', 'S', 'S', 'S', 'S', 'S',\n", " 'C', 'C', 'S', 'S', 'S', 'S', 'S', 'S', 'C', 'S', 'S', 'S', 'S',\n", " 'S', 'S', 'S', 'C', 'C', 'S', 'S', 'S', 'C', 'S', 'S', 'S', 'S',\n", " 'S', 'Q', 'S', 'S', 'S', 'C', 'Q'], dtype=object)" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.Embarked.values" ] }, { "cell_type": "markdown", "id": "693c37e4-3199-40c0-b6df-40b253476cca", "metadata": {}, "source": [ "Acordaros de que podíamos completar los NAN con .fillna('nuevo valor')" ] }, { "cell_type": "code", "execution_count": 36, "id": "7e798a49-ef55-4b91-9037-aa5391ee39d3", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b5dd972a8e4d4c39b6a731327b97dce3", "version_major": 2, "version_minor": 0 }, "text/plain": [ " | | [ 0%] 00:00 -> (? left)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import sweetviz\n", "informeTitanic = sweetviz.analyze(df1)" ] }, { "cell_type": "code", "execution_count": 30, "id": "17b3a585-5be0-415a-932a-93961808ffb9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Report SWEETVIZ_REPORT.html was generated! NOTEBOOK/COLAB USERS: the web browser MAY not pop up, regardless, the report IS saved in your notebook/colab files.\n" ] } ], "source": [ "informeTitanic.show_html()" ] }, { "cell_type": "code", "execution_count": 58, "id": "6fc91a34-8d3d-44ba-bd47-d7fb32ebbbb9", "metadata": {}, "outputs": [], "source": [ "# Tenemos varios elementos en Cabin y Embarked que aparecen como nan. \n", "# Modificamos la categoría Sex y la traducimos\n", "df1.Sex [df1.Sex == \"female\"] = 'Mujer'\n", "df1.Sex [df1.Sex == \"male\"] = 'Hombre'\n" ] }, { "cell_type": "code", "execution_count": 38, "id": "eb22dba1-22a1-4132-a6e8-1e22ab669a5c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Hombre 577\n", "Mujer 314\n", "Name: Sex, dtype: int64" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.Sex.value_counts()" ] }, { "cell_type": "code", "execution_count": 39, "id": "4c65e971-b5bb-4510-895d-7f4077bee02e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 Braund, Mr. Owen Harris\n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th...\n", "2 Heikkinen, Miss. Laina\n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel)\n", "4 Allen, Mr. William Henry\n", "Name: Name, dtype: object" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Dividimos el nombre en nombre y apellidos.\n", "df1.Name.head()" ] }, { "cell_type": "code", "execution_count": 59, "id": "6442bc19-851c-45f1-be40-133667a7df7e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 Braund\n", "1 Cumings\n", "2 Heikkinen\n", "3 Futrelle\n", "4 Allen\n", " ... \n", "886 Montvila\n", "887 Graham\n", "888 Johnston\n", "889 Behr\n", "890 Dooley\n", "Name: Apellido, Length: 891, dtype: object" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1['Apellido'] = df1.Name.str.split(\",\", expand=True)[0]\n", "df1.Apellido" ] }, { "cell_type": "code", "execution_count": 60, "id": "02d6a43b-a4db-4917-930b-ae6ff4c053f1", "metadata": {}, "outputs": [], "source": [ "# Dividimos el nombre para extraer el título\n", "df1['Titulo'] = df1.Name.str.split(\",\", expand=True)[1].str.split(\".\", expand=True)[0]\n", "df1['Nombre'] = df1.Name.str.split(\",\", expand=True)[1].str.split(\".\", expand=True)[1]" ] }, { "cell_type": "code", "execution_count": 55, "id": "1fba3a3c-0f83-4072-b856-dca32b255252", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedApellidoTituloNombre
003Braund, Mr. Owen HarrisHombre22.0010A/5 211717.25NaNSBraundMrOwen Harris
111Cumings, Mrs. John Bradley (Florence Briggs Th...Mujer38.0010PC 1759971.28C85CCumingsMrsJohn Bradley (Florence Briggs Thayer)
213Heikkinen, Miss. LainaMujer26.0000STON/O2. 31012827.92NaNSHeikkinenMissLaina
311Futrelle, Mrs. Jacques Heath (Lily May Peel)Mujer35.001011380353.10C123SFutrelleMrsJacques Heath (Lily May Peel)
403Allen, Mr. William HenryHombre35.00003734508.05NaNSAllenMrWilliam Henry
\n", "
" ], "text/plain": [ " Survived Pclass Name \\\n", "0 0 3 Braund, Mr. Owen Harris \n", "1 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... \n", "2 1 3 Heikkinen, Miss. Laina \n", "3 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) \n", "4 0 3 Allen, Mr. William Henry \n", "\n", " Sex Age SibSp Parch Ticket Fare Cabin Embarked \\\n", "0 Hombre 22.00 1 0 A/5 21171 7.25 NaN S \n", "1 Mujer 38.00 1 0 PC 17599 71.28 C85 C \n", "2 Mujer 26.00 0 0 STON/O2. 3101282 7.92 NaN S \n", "3 Mujer 35.00 1 0 113803 53.10 C123 S \n", "4 Hombre 35.00 0 0 373450 8.05 NaN S \n", "\n", " Apellido Titulo Nombre \n", "0 Braund Mr Owen Harris \n", "1 Cumings Mrs John Bradley (Florence Briggs Thayer) \n", "2 Heikkinen Miss Laina \n", "3 Futrelle Mrs Jacques Heath (Lily May Peel) \n", "4 Allen Mr William Henry " ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.head()" ] }, { "cell_type": "code", "execution_count": 63, "id": "c9afade5-fe59-4e1b-a415-bfb4404410e6", "metadata": {}, "outputs": [], "source": [ "# Tenemos varias variables que no aportan. Las borramos.\n", "del df1['PassengerId']\n", "df1.drop('Name', axis=1, inplace=True)" ] }, { "cell_type": "markdown", "id": "9d65cb4c-704e-469e-9fba-587da7025e0a", "metadata": {}, "source": [ "Ahora estan los datos organizados y limpios para poder aplicar tecnicas de reemplazo de valores perdidos.\n", "Tambien, podemos utilizar graficos para ver la distribucion de las variables" ] }, { "cell_type": "code", "execution_count": 67, "id": "7a27ca3e-4880-427b-bc12-2d0ea4ad6b7d", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "histograma = sns.histplot(df1.Age)\n", "histograma.set_title(\"Distribución edad\")\n", "histograma.set_ylabel(\"Frecuencia\")\n", "histograma.set_xlabel(\"Edad\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 74, "id": "16362705-5472-4fa3-9f29-727e8879c8be", "metadata": {}, "outputs": [], "source": [ "# La variable Sex es una variable categórica,la tipamos como tal\n", "df1['Sex'] = df1.Sex.astype('category')" ] }, { "cell_type": "code", "execution_count": 77, "id": "52337058-962e-43a6-8598-a74f75867db3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Distribución sexo')" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Otra forma de pintar los datos\n", "distSex = df1.Sex.value_counts().plot(kind='bar')\n", "distSex.set_title(\"Distribución sexo\")\n" ] }, { "cell_type": "code", "execution_count": 80, "id": "6418ed44-dfa5-485e-a83e-f706aed5f188", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# O con seaborn\n", "sns.countplot(df1.Sex)" ] }, { "cell_type": "code", "execution_count": 92, "id": "b4b08ae1-1c08-49bd-a7dd-4a7d147ce077", "metadata": {}, "outputs": [], "source": [ "#También podríamos hacer subsets de los datos, en vez de hacer imputaciones.\n", "#Nos quedamos con aquellos datos, donde la edad no es NA\n", "df2 = df1 [df1.Sex.notnull()]" ] }, { "cell_type": "code", "execution_count": 97, "id": "6e1bb98f-d8a9-48f6-8943-1842e18f21c6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2.Sex.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 98, "id": "ae37cd9f-c219-413a-a902-88031e2c3198", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2.Embarked.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 99, "id": "dcbcc8f7-6046-46b6-976b-f31c8c368a08", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df3 = df2 [df2.Embarked.notnull()]\n", "df3.Embarked.isnull().sum()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" } }, "nbformat": 4, "nbformat_minor": 5 }