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sitc/ml21/preprocessing/5_Duplicated_Values.ipynb
Carlos A. Iglesias 86114b4a56
Added preprocessing notebooks
2024-04-03 22:50:36 +02:00

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"source": [
"![](images/EscUpmPolit_p.gif \"UPM\")"
]
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{
"cell_type": "markdown",
"metadata": {
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"source": [
"# Course Notes for Learning Intelligent Systems"
]
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"source": [
"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © Carlos A. Iglesias"
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"cell_type": "markdown",
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"source": [
"## [Introduction to Preprocessing](00_Intro_Preprocessing.ipynb)"
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"slide_type": "subslide"
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"source": [
"# Duplicated values\n",
"\n",
"There are two possible approaches: **remove** these rows or **filling** them. It depends on every case.\n",
"\n",
"\n"
]
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"source": [
"import pandas as pd\n",
"import numpy as np"
]
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"source": [
"## Filling NaN values\n",
"If we need to fill errors or blanks, we can use the methods **fillna()** or **dropna()**.\n",
"\n",
"* For **string** fields, we can fill NaN with **' '**.\n",
"\n",
"* For **numbers**, we can fill with the **mean** or **median** value. \n"
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"# Fill NaN with ' '\n",
"df['col'] = df['col'].fillna(' ')\n",
"# Fill NaN with 99\n",
"df['col'] = df['col'].fillna(99)\n",
"# Fill NaN with the mean of the column\n",
"df['col'] = df['col'].fillna(df['col'].mean())"
]
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"source": [
"## Propagate non-null values forward or backwards\n",
"You can also propagate non-null values forward or backwards by putting\n",
"method=’pad’ as the method argument. It will fill the next value in the\n",
"dataframe with the previous non-NaN value. Maybe you just want to fill one\n",
"value ( limit=1 )or you want to fill all the values."
]
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"df = pd.DataFrame(data={'col1':[np.nan, np.nan, 2,3,4, np.nan, np.nan]})"
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"# We fill forward the value 4.0 and fill the next one (limit = 1)\n",
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"We can also backfilling with **bfill**."
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"# Fill the first two NaN values with the first available value\n",
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"source": [
"## Removing NaN values\n",
"We can remove them by row or column."
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"/# Drop any rows which have any nans\n",
"df.dropna()\n",
"/# Drop columns that have any nans\n",
"df.dropna(axis=1)\n",
"/# Only drop columns which have at least 90% non-NaNs\n",
"df.dropna(thresh=int(df.shape[0] * .9), axis=1)"
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"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/)"
]
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"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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}
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