mirror of
https://github.com/gsi-upm/sitc
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592 lines
13 KiB
Plaintext
592 lines
13 KiB
Plaintext
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{
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"cells": [
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "skip"
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}
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},
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"source": [
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"![](images/EscUpmPolit_p.gif \"UPM\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "skip"
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}
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},
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"source": [
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"# Course Notes for Learning Intelligent Systems"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "skip"
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}
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},
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"source": [
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"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © Carlos A. Iglesias"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "skip"
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}
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},
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"source": [
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"## [Introduction to Preprocessing](00_Intro_Preprocessing.ipynb)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"# Unknown values\n",
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"\n",
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"Two possible approaches are **remove** these rows or **fill** them. It depends on every case."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"## Filling NaN values\n",
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"If we need to fill errors or blanks, we can use the methods **fillna()** or **dropna()**.\n",
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"\n",
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"* For **string** fields, we can fill NaN with **' '**.\n",
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"\n",
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"* For **numbers**, we can fill with the **mean** or **median** value. \n"
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]
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},
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{
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"cell_type": "raw",
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"# Fill NaN with ' '\n",
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"df['col'] = df['col'].fillna(' ')\n",
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"# Fill NaN with 99\n",
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"df['col'] = df['col'].fillna(99)\n",
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"# Fill NaN with the mean of the column\n",
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"df['col'] = df['col'].fillna(df['col'].mean())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"## Propagate non-null values forward or backward\n",
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"You can also **propagate** non-null values with these methods:\n",
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"\n",
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"* **ffill**: Fill values by propagating the last valid observation to the next valid.\n",
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"* **bfill**: Fill values using the following valid observation to fill the gap.\n",
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"* **interpolate**: Fill NaN values using interpolation.\n",
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"\n",
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"It will fill the next value in the dataframe with the previous non-NaN value. \n",
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"\n",
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"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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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"outputs": [],
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"source": [
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"df = pd.DataFrame(data={'col1':[np.nan, np.nan, 2,3,4, np.nan, np.nan]})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>col1</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>2.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>3.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>4.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" col1\n",
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"0 NaN\n",
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"1 NaN\n",
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"2 2.0\n",
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"3 3.0\n",
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"4 4.0\n",
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"5 NaN\n",
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"6 NaN"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We fill forward the value 4.0 and fill the next one (limit = 1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>col1</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>2.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>3.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>4.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>4.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" col1\n",
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"0 NaN\n",
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"1 NaN\n",
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"2 2.0\n",
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"3 3.0\n",
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"4 4.0\n",
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"5 4.0\n",
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"6 NaN"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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" df.ffill(limit = 1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df.ffill()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"We can also backfilling with **bfill**. Since we do not include *limit*, we fill all the values."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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|
"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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|
"</style>\n",
|
||
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|
"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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||
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>col1</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>2.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>2.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>2.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>3.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>4.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" col1\n",
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"0 2.0\n",
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"1 2.0\n",
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"2 2.0\n",
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"3 3.0\n",
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"4 4.0\n",
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"5 NaN\n",
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"6 NaN"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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||
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"source": [
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"df.bfill()"
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||
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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||
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"## Removing NaN values\n",
|
||
|
|
"We can remove them by row or column (use inplace=True if you want to modify the DataFrame)."
|
||
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]
|
||
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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||
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
|
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}
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},
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||
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"outputs": [
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||
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{
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"data": {
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"text/html": [
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||
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"<div>\n",
|
||
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"<style scoped>\n",
|
||
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|
" .dataframe tbody tr th:only-of-type {\n",
|
||
|
|
" vertical-align: middle;\n",
|
||
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|
" }\n",
|
||
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"\n",
|
||
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
|
||
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" }\n",
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||
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"\n",
|
||
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" .dataframe thead th {\n",
|
||
|
|
" text-align: right;\n",
|
||
|
|
" }\n",
|
||
|
|
"</style>\n",
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" <th>col1</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>2</th>\n",
|
||
|
|
" <td>2.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>3</th>\n",
|
||
|
|
" <td>3.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>4</th>\n",
|
||
|
|
" <td>4.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" col1\n",
|
||
|
|
"2 2.0\n",
|
||
|
|
"3 3.0\n",
|
||
|
|
"4 4.0"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 26,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"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/)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"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": {
|
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|
|
"celltoolbar": "Slideshow",
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|
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"position": {
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|
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"top": "50px"
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||
|
|
},
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|
|
"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
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||
|
|
},
|
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|
|
"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,
|
||
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|
"eqLabelWithNumbers": true,
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|
|
"eqNumInitial": 1,
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|
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"hotkeys": {
|
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|
|
"equation": "Ctrl-E",
|
||
|
|
"itemize": "Ctrl-I"
|
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|
|
},
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|
|
"labels_anchors": false,
|
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|
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"latex_user_defs": false,
|
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"report_style_numbering": false,
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|
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"user_envs_cfg": false
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}
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|
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},
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|
|
"nbformat": 4,
|
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|
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"nbformat_minor": 4
|
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