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sitc/ml21/preprocessing/09_String_Data.ipynb
Carlos A. Iglesias 86114b4a56
Added preprocessing notebooks
2024-04-03 22:50:36 +02:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"![](images/EscUpmPolit_p.gif \"UPM\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"# Course Notes for Learning Intelligent Systems"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © Carlos A. Iglesias"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"## [Introduction to Preprocessing](00_Intro_Preprocessing.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# String Data\n",
"It is widespread to clean string columns to follow a predefined format (e.g., emails, URLs, ...).\n",
"\n",
"We can do it using regular expressions or specific libraries."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Beautifier\n",
"A simple [library](https://github.com/labtocat/beautifier) to cleanup and prettify URL patterns, domains, and so on. The library helps to clean Unicode, special characters, and unnecessary redirection patterns from the URLs and gives you a clean date.\n",
"\n",
"Install with **'pip install beautifier'**."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Email cleanup"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"from beautifier import Email\n",
"email = Email('me@imsach.in')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"'imsach.in'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"email.domain"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"'me'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"email.username"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"email.is_free_email"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"email2 = Email('This my address')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"email2.is_valid"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"email3 = Email('pepe@gmail.com')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"email3.is_valid"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"email3.is_free_email"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## URL cleanup"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"from beautifier import Url\n",
"url = Url('https://in.linkedin.com/in/sachinphilip?authtoken=887nasdadasd6hasdtg21&secret=98jy766yhhuhnjk')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"'https://in.linkedin.com/in/sachinphilip'"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"url.cleanup"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"'in.linkedin.com'"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"url.domain"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"['authtoken=887nasdadasd6hasdtg21', 'secret=98jy766yhhuhnjk']"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"url.param"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"'authtoken=887nasdadasd6hasdtg21&secret=98jy766yhhuhnjk'"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"url.parameters"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"'sachinphilip'"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"url.username"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Unicode\n",
"Problem: Some unicode code has been broken. We see the character in a different character dataset.\n",
"\n",
"A **mojibake** is a character displayed in an unintended character encoding. Example: \"<22>\").\n",
"\n",
"We will use the library **ftfy** (fixed text for you) to fix it.\n",
"\n",
"First, you should install the library: **conda install ftfy** (or **pip install ftfy**)."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"¯\\_(ツ)_/¯\n",
"Party\n",
"I'm\n"
]
}
],
"source": [
"import ftfy\n",
"foo = '&macr;\\\\_(ã\\x83\\x84)_/&macr;'\n",
"bar = '\\ufeffParty'\n",
"baz = '\\001\\033[36;44mI&#x92;m'\n",
"print(ftfy.fix_text(foo))\n",
"print(ftfy.fix_text(bar))\n",
"print(ftfy.fix_text(baz))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"We can understand which heuristics ftfy is using."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"U+0026 & [Po] AMPERSAND\n",
"U+006D m [Ll] LATIN SMALL LETTER M\n",
"U+0061 a [Ll] LATIN SMALL LETTER A\n",
"U+0063 c [Ll] LATIN SMALL LETTER C\n",
"U+0072 r [Ll] LATIN SMALL LETTER R\n",
"U+003B ; [Po] SEMICOLON\n",
"U+005C \\ [Po] REVERSE SOLIDUS\n",
"U+005F _ [Pc] LOW LINE\n",
"U+0028 ( [Ps] LEFT PARENTHESIS\n",
"U+00E3 ã [Ll] LATIN SMALL LETTER A WITH TILDE\n",
"U+0083 \\x83 [Cc] <unknown>\n",
"U+0084 \\x84 [Cc] <unknown>\n",
"U+0029 ) [Pe] RIGHT PARENTHESIS\n",
"U+005F _ [Pc] LOW LINE\n",
"U+002F / [Po] SOLIDUS\n",
"U+0026 & [Po] AMPERSAND\n",
"U+006D m [Ll] LATIN SMALL LETTER M\n",
"U+0061 a [Ll] LATIN SMALL LETTER A\n",
"U+0063 c [Ll] LATIN SMALL LETTER C\n",
"U+0072 r [Ll] LATIN SMALL LETTER R\n",
"U+003B ; [Po] SEMICOLON\n"
]
}
],
"source": [
"ftfy.explain_unicode(foo)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Dates\n",
"Sometimes we want to extract date from text. We can use regular expressions or handy packages, such as [**python-dateutil**](https://dateutil.readthedocs.io/en/stable/). An alternative is [arrow](https://arrow.readthedocs.io/en/latest/).\n",
"\n",
"Install the library: **pip install python-dateutil**."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2019-08-22 10:22:46+00:00\n"
]
}
],
"source": [
"from dateutil.parser import parse\n",
"now = parse(\"Thu Aug 22 10:22:46 UTC 2019\")\n",
"print(now)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2019-08-08 10:20:00\n"
]
}
],
"source": [
"dt = parse(\"Today is Thursday 8, 2019 at 10:20:00AM\", fuzzy=True)\n",
"print(dt)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"# References\n",
"* [Cleaning and Prepping Data with Python for Data Science — Best Practices and Helpful Packages](https://medium.com/@rrfd/cleaning-and-prepping-data-with-python-for-data-science-best-practices-and-helpful-packages-af1edfbe2a3), DeFilippi, 2019, \n",
"* [Data Preprocessing for Machine learning in Python, GeeksForGeeks](https://www.geeksforgeeks.org/data-preprocessing-machine-learning-python/), , A. Sharma, 2018.\n",
"* [Beautifier](https://github.com/labtocat/beautifier) package\n",
"* [Ftfy](https://ftfy.readthedocs.io/en/latest/) package\n",
"* [python-dateutil](https://dateutil.readthedocs.io/en/stable/)package"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"## Licence\n",
"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": {
"celltoolbar": "Slideshow",
"datacleaner": {
"position": {
"top": "50px"
},
"python": {
"varRefreshCmd": "try:\n print(_datacleaner.dataframe_metadata())\nexcept:\n print([])"
},
"window_display": false
},
"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": "3.10.13"
},
"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"
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"labels_anchors": false,
"latex_user_defs": false,
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"user_envs_cfg": false
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"nbformat_minor": 4
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