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sitc/ml21/preprocessing/11_1_datacleaner.ipynb

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Added preprocessing notebooks
2024-04-03 20:50:36 +00:00
{
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{
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"metadata": {
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"slide_type": "skip"
}
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"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": [
"# Datacleaner\n",
"[Datacleaner](https://github.com/rhiever/datacleaner) supports:\n",
"\n",
"* drop rows with missing values\n",
"* replace missing values with the mode or median on a column-by-column basis\n",
"* encode non-numeric variables with numerical equivalents\n",
"\n",
"\n",
"Install with\n",
"\n",
"**pip install datacleaner**"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"slideshow": {
"slide_type": "slide"
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"\n",
"[891 rows x 12 columns]"
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},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"from datacleaner import autoclean\n",
"\n",
"df = pd.read_csv('https://raw.githubusercontent.com/gsi-upm/sitc/master/ml2/data-titanic/train.csv')\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"slideshow": {
"slide_type": "slide"
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" PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket \\\n",
"0 1 0 3 108 1 22.0 1 0 523 \n",
"1 2 1 1 190 0 38.0 1 0 596 \n",
"2 3 1 3 353 0 26.0 0 0 669 \n",
"3 4 1 1 272 0 35.0 1 0 49 \n",
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"889 890 1 1 81 1 26.0 0 0 8 \n",
"890 891 0 3 220 1 32.0 0 0 466 \n",
"\n",
" Fare Cabin Embarked \n",
"0 7.2500 47 2 \n",
"1 71.2833 81 0 \n",
"2 7.9250 47 2 \n",
"3 53.1000 55 2 \n",
"4 8.0500 47 2 \n",
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"\n",
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},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_clean = autoclean(df, copy=True)\n",
"df_clean"
]
},
{
"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",
"* [Handy Python Libraries for Formatting and Cleaning Data](https://mode.com/blog/python-data-cleaning-libraries), M. Bierly, 2016\n"
]
},
{
"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."
]
}
],
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"name": "python",
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"version": "3.11.7"
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