mirror of
https://github.com/gsi-upm/sitc
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276 lines
7.4 KiB
Plaintext
276 lines
7.4 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"![](images/EscUpmPolit_p.gif \"UPM\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Course Notes for Learning Intelligent Systems"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © Carlos A. Iglesias"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## [Introduction to Machine Learning II](3_0_0_Intro_ML_2.ipynb)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Table of Contents\n",
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"\n",
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"* [The Titanic dataset](#The-Titanic-dataset)\n",
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"* [Reading Data](#Reading-Data)\n",
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"* [Reading Data from a File](#Reading-Data-from-a-File)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# The Titanic dataset"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In this session we will work with the Titanic dataset. This dataset is provided by [Kaggle](http://www.kaggle.com). Kaggle is a crowdsourcing platform that organizes competitions where researchers and companies post their data and users compete to obtain the best models.\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"The main objective is predicting which passengers survived the sinking of the Titanic.\n",
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"\n",
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"The data is available [here](https://www.kaggle.com/c/titanic/data). There are two files, one for training ([train.csv](files/data-titanic/train.csv)) and another file for testing [test.csv](files/data-titanic/test.csv). A local copy has been included in this notebook under the folder *data-titanic*.\n",
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"\n",
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"\n",
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"Here follows a description of the variables.\n",
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"\n",
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"|Variable | Description| Values|\n",
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"|-------------------------------|\n",
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"| survival| Survival| (0 = No; 1 = Yes)|\n",
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"|Pclass |Name | |\n",
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"|Sex |Sex | male, female|\n",
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"|Age |Age|\n",
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"|SibSp |Number of Siblings/Spouses Aboard||\n",
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"|Parch |Number of Parents/Children Aboard||\n",
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"|Ticket|Ticket Number||\n",
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"|Fare |Passenger Fare||\n",
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"|Cabin |Cabin||\n",
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"|Embarked |Port of Embarkation| (C = Cherbourg; Q = Queenstown; S = Southampton)|\n",
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"\n",
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"\n",
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"The definitions used for SibSp and Parch are:\n",
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"* *Sibling*: Brother, Sister, Stepbrother, or Stepsister of Passenger Aboard Titanic\n",
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"* *Spouse*: Husband or Wife of Passenger Aboard Titanic (Mistresses and Fiances Ignored)\n",
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"* *Parent*: Mother or Father of Passenger Aboard Titanic\n",
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"* *Child*: Son, Daughter, Stepson, or Stepdaughter of Passenger Aboard Titanic"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Reading Data"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In the previous dataset we load a bundle dataset in scikit-learn. In this notebook we are going to learn how to read from a file or a url using the Pandas library."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Reading Data from a File"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"from pandas import Series, DataFrame\n",
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"\n",
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"df = pd.read_csv('data-titanic/train.csv')\n",
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"df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# we can get the number of samples and features\n",
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"df.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#I can read only a number of rows and tell where the header is, among other options.\n",
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"df = df = pd.read_csv('data-titanic/train.csv', header=0, nrows=5)\n",
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"df"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Pandas provides methods for reading other formats, such as Excel (*read_excel()*), JSON (*read_json()*), or HTML (*read_html()*), look at the [documentation](http://pandas.pydata.org/pandas-docs/stable/api.html#input-output) for more details."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Reading data from a URL"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"#We get a URL with raw content (not HTML one)\n",
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"url = \"https://raw.githubusercontent.com/gsi-upm/sitc/master/ml2/data-titanic/train.csv\"\n",
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"df = pd.read_csv(url)\n",
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"df"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"An alternative option is reading the file with the library *requests* and then use *pandas*."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# First we open the file\n",
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"import pandas as pd\n",
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"import io\n",
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"import requests\n",
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"url = \"https://raw.githubusercontent.com/gsi-upm/sitc/master/ml2/data-titanic/train.csv\"\n",
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"s = requests.get(url, stream=True).content\n",
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"#Print the first 320 characters for understanding how it works\n",
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"s[:320]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.read_csv(io.StringIO(s.decode('utf-8')))\n",
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"df"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## References"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"* [Pandas API input-output](http://pandas.pydata.org/pandas-docs/stable/api.html#input-output)\n",
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"* [Pandas API - pandas.read_csv](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html)\n",
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"* [DataFrame](http://pandas.pydata.org/pandas-docs/stable/dsintro.html)\n",
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"* [An introduction to NumPy and Scipy](https://sites.engineering.ucsb.edu/~shell/che210d/numpy.pdf)\n",
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"* [NumPy tutorial](https://numpy.org/doc/stable/)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Licence"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n",
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"\n",
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"© Carlos A. Iglesias, Universidad Politécnica de Madrid."
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.1"
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},
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"latex_envs": {
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"LaTeX_envs_menu_present": true,
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"autocomplete": true,
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"bibliofile": "biblio.bib",
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"cite_by": "apalike",
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"current_citInitial": 1,
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"eqLabelWithNumbers": true,
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"eqNumInitial": 1,
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"hotkeys": {
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"equation": "Ctrl-E",
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"itemize": "Ctrl-I"
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},
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"labels_anchors": false,
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"latex_user_defs": false,
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"report_style_numbering": false,
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"user_envs_cfg": false
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}
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