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sitc/ml21/preprocessing/04_Unknown_Values.ipynb

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{
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"source": [
"![](images/EscUpmPolit_p.gif \"UPM\")"
]
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"cell_type": "markdown",
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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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"source": [
"## [Introduction to Preprocessing](00_Intro_Preprocessing.ipynb)"
]
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"source": [
"# Unknown values\n",
"\n",
"Two possible approaches are **remove** these rows or **fill** them. It depends on every case."
]
},
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"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {
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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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"source": [
"# 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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"## Propagate non-null values forward or backward\n",
"You can also **propagate** non-null values with these methods:\n",
"\n",
"* **ffill**: Fill values by propagating the last valid observation to the next valid.\n",
"* **bfill**: Fill values using the following valid observation to fill the gap.\n",
"* **interpolate**: Fill NaN values using interpolation.\n",
"\n",
"It will fill the next value in the dataframe with the previous non-NaN value. \n",
"\n",
"You may want to fill in one value (**limit=1**) or all the values. You can also indicate inplace=True to fill in-place."
]
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"source": [
"df = pd.DataFrame(data={'col1':[np.nan, np.nan, 2,3,4, np.nan, np.nan]})"
]
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"cell_type": "code",
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"We fill forward the value 4.0 and fill the next one (limit = 1)"
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" df.ffill(limit = 1)"
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"df.ffill()"
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"source": [
"We can also backfilling with **bfill**. Since we do not include *limit*, we fill all the values."
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"df.bfill()"
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"source": [
"## Removing NaN values\n",
"We can remove them by row or column (use inplace=True if you want to modify the DataFrame)."
]
},
{
"cell_type": "code",
"execution_count": 26,
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"source": [
"# Drop any rows which have any nans\n",
"df1 = df.dropna()\n",
"# Drop columns that have any nans (axis = 1 -> drop columns, axis = 0 -> drop rows)\n",
"df2 = df.dropna(axis=1)\n",
"# Only drop columns which have at least 90% non-NaNs \n",
"df3 = df.dropna(thresh=int(df.shape[0] * .9), axis=1)\n",
"df1"
]
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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/)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
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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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