mirror of
https://github.com/gsi-upm/sitc
synced 2024-11-24 15:32:29 +00:00
276 lines
7.4 KiB
Plaintext
276 lines
7.4 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"![](images/EscUpmPolit_p.gif \"UPM\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Course Notes for Learning Intelligent Systems"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © Carlos A. Iglesias"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## [Introduction to Machine Learning II](3_0_0_Intro_ML_2.ipynb)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Table of Contents\n",
|
|
"\n",
|
|
"* [The Titanic dataset](#The-Titanic-dataset)\n",
|
|
"* [Reading Data](#Reading-Data)\n",
|
|
"* [Reading Data from a File](#Reading-Data-from-a-File)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# The Titanic dataset"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"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",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"The main objective is predicting which passengers survived the sinking of the Titanic.\n",
|
|
"\n",
|
|
"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",
|
|
"\n",
|
|
"\n",
|
|
"Here follows a description of the variables.\n",
|
|
"\n",
|
|
"|Variable | Description| Values|\n",
|
|
"|-------------------------------|\n",
|
|
"| survival| Survival| (0 = No; 1 = Yes)|\n",
|
|
"|Pclass |Name | |\n",
|
|
"|Sex |Sex | male, female|\n",
|
|
"|Age |Age|\n",
|
|
"|SibSp |Number of Siblings/Spouses Aboard||\n",
|
|
"|Parch |Number of Parents/Children Aboard||\n",
|
|
"|Ticket|Ticket Number||\n",
|
|
"|Fare |Passenger Fare||\n",
|
|
"|Cabin |Cabin||\n",
|
|
"|Embarked |Port of Embarkation| (C = Cherbourg; Q = Queenstown; S = Southampton)|\n",
|
|
"\n",
|
|
"\n",
|
|
"The definitions used for SibSp and Parch are:\n",
|
|
"* *Sibling*: Brother, Sister, Stepbrother, or Stepsister of Passenger Aboard Titanic\n",
|
|
"* *Spouse*: Husband or Wife of Passenger Aboard Titanic (Mistresses and Fiances Ignored)\n",
|
|
"* *Parent*: Mother or Father of Passenger Aboard Titanic\n",
|
|
"* *Child*: Son, Daughter, Stepson, or Stepdaughter of Passenger Aboard Titanic"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Reading Data"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"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."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Reading Data from a File"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import numpy as np\n",
|
|
"import pandas as pd\n",
|
|
"from pandas import Series, DataFrame\n",
|
|
"\n",
|
|
"df = pd.read_csv('data-titanic/train.csv')\n",
|
|
"df"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# we can get the number of samples and features\n",
|
|
"df.shape"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#I can read only a number of rows and tell where the header is, among other options.\n",
|
|
"df = df = pd.read_csv('data-titanic/train.csv', header=0, nrows=5)\n",
|
|
"df"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"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."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Reading data from a URL"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import pandas as pd\n",
|
|
"#We get a URL with raw content (not HTML one)\n",
|
|
"url = \"https://raw.githubusercontent.com/gsi-upm/sitc/master/ml2/data-titanic/train.csv\"\n",
|
|
"df = pd.read_csv(url)\n",
|
|
"df"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"An alternative option is reading the file with the library *requests* and then use *pandas*."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# First we open the file\n",
|
|
"import pandas as pd\n",
|
|
"import io\n",
|
|
"import requests\n",
|
|
"url = \"https://raw.githubusercontent.com/gsi-upm/sitc/master/ml2/data-titanic/train.csv\"\n",
|
|
"s = requests.get(url, stream=True).content\n",
|
|
"#Print the first 320 characters for understanding how it works\n",
|
|
"s[:320]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"df = pd.read_csv(io.StringIO(s.decode('utf-8')))\n",
|
|
"df"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## References"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"* [Pandas API input-output](http://pandas.pydata.org/pandas-docs/stable/api.html#input-output)\n",
|
|
"* [Pandas API - pandas.read_csv](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html)\n",
|
|
"* [DataFrame](http://pandas.pydata.org/pandas-docs/stable/dsintro.html)\n",
|
|
"* [An introduction to NumPy and Scipy](http://www.engr.ucsb.edu/~shell/che210d/numpy.pdf)\n",
|
|
"* [NumPy tutorial](https://docs.scipy.org/doc/numpy-dev/user/quickstart.html)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Licence"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n",
|
|
"\n",
|
|
"© Carlos A. Iglesias, Universidad Politécnica de Madrid."
|
|
]
|
|
}
|
|
],
|
|
"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": "3.7.1"
|
|
},
|
|
"latex_envs": {
|
|
"LaTeX_envs_menu_present": true,
|
|
"autocomplete": true,
|
|
"bibliofile": "biblio.bib",
|
|
"cite_by": "apalike",
|
|
"current_citInitial": 1,
|
|
"eqLabelWithNumbers": true,
|
|
"eqNumInitial": 1,
|
|
"hotkeys": {
|
|
"equation": "Ctrl-E",
|
|
"itemize": "Ctrl-I"
|
|
},
|
|
"labels_anchors": false,
|
|
"latex_user_defs": false,
|
|
"report_style_numbering": false,
|
|
"user_envs_cfg": false
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 1
|
|
}
|