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661 строка
36 KiB
Plaintext
661 строка
36 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np \n",
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"import sklearn.metrics.pairwise as pw\n"
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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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"outputs": [],
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"source": [
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"A = [[3, 4000, 5]]\n",
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"B = [[3, 4000, 4]]\n",
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"C = [[3, 4100, 5]]\n"
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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": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Euclidean: \t [[1.]]\n",
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"Cosine: \t [[0.99999997]]\n",
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"Manhattan: \t [[1.]]\n"
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]
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}
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],
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"source": [
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"print('Euclidean: \\t',pw.euclidean_distances(A, B))\n",
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"print('Cosine: \\t',pw.cosine_similarity(A, B))\n",
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"print('Manhattan: \\t',pw.manhattan_distances(A, B))"
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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": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Euclidean: \t [[100.]]\n",
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"Cosine: \t [[1.]]\n",
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"Manhattan: \t [[100.]]\n"
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]
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}
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],
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"source": [
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"print('Euclidean: \\t',pw.euclidean_distances(A, C))\n",
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"print('Cosine: \\t',pw.cosine_similarity(A, C))\n",
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"print('Manhattan: \\t',pw.manhattan_distances(A, C))"
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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": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Euclidean E-D: \t [[3.31662479]]\n",
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"Euclidean E-F: \t [[2.82842712]]\n",
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"\n",
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"Cosine: E-D \t [[0.99014754]]\n",
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"Cosine E-F: \t [[0.7592566]]\n",
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"\n",
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"Manhattan: E-D \t [[5.]]\n",
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"Manhattan E-F: \t [[6.]]\n"
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]
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}
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],
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"source": [
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"import sklearn.metrics.pairwise as pw\n",
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"\n",
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"D = [[6,0,0,3,3]]\n",
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"E = [[3,0,0,2,2]]\n",
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"F = [[1,1,1,1,1]]\n",
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"\n",
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"print('Euclidean E-D: \\t',pw.euclidean_distances(D, E))\n",
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"print('Euclidean E-F: \\t',pw.euclidean_distances(E, F))\n",
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"\n",
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"print('\\nCosine: E-D \\t',pw.cosine_similarity(D, E))\n",
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"print('Cosine E-F: \\t',pw.cosine_similarity(E, F))\n",
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"\n",
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"print('\\nManhattan: E-D \\t',pw.manhattan_distances(D, E))\n",
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"print('Manhattan E-F: \\t',pw.manhattan_distances(E, F))"
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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": 6,
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"metadata": {},
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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>A</th>\n",
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" <th>B</th>\n",
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" <th>C</th>\n",
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" <th>D</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>1.0</td>\n",
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" <td>red</td>\n",
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" <td>3300</td>\n",
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" <td>MSK</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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" <td>red</td>\n",
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" <td>1250</td>\n",
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" <td>SPB</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>NaN</td>\n",
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" <td>yellow</td>\n",
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" <td>4600</td>\n",
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" <td>EKB</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>2.0</td>\n",
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" <td>green</td>\n",
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" <td>4500</td>\n",
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" <td>MSK</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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" A B C D\n",
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"0 1.0 red 3300 MSK\n",
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"1 2.0 red 1250 SPB\n",
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"2 NaN yellow 4600 EKB\n",
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"3 2.0 green 4500 MSK"
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]
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},
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"execution_count": 6,
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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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"import pandas as pd\n",
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"dataset = pd.DataFrame({'A': [1 , 2, None, 2], \n",
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" 'B': ['red', 'red', 'yellow', 'green'], \n",
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" 'C': [3300, 1250, 4600, 4500],\n",
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" 'D': ['MSK', 'SPB', 'EKB', 'MSK']})\n",
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"dataset"
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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": 7,
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"metadata": {},
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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>A</th>\n",
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" <th>C</th>\n",
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" <th>D</th>\n",
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" <th>B_green</th>\n",
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" <th>B_red</th>\n",
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" <th>B_yellow</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>1.0</td>\n",
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" <td>3300</td>\n",
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" <td>MSK</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>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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" <td>1250</td>\n",
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" <td>SPB</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>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>NaN</td>\n",
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" <td>4600</td>\n",
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" <td>EKB</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>1</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>2.0</td>\n",
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" <td>4500</td>\n",
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" <td>MSK</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>0</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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" A C D B_green B_red B_yellow\n",
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"0 1.0 3300 MSK 0 1 0\n",
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"1 2.0 1250 SPB 0 1 0\n",
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"2 NaN 4600 EKB 0 0 1\n",
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"3 2.0 4500 MSK 1 0 0"
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]
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},
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"execution_count": 7,
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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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"# OHE encoding\n",
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"dataset = pd.get_dummies(dataset, columns = ['B'])\n",
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"dataset"
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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": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LabelEncoder()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LabelEncoder</label><div class=\"sk-toggleable__content\"><pre>LabelEncoder()</pre></div></div></div></div></div>"
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],
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"text/plain": [
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"LabelEncoder()"
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]
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},
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"execution_count": 8,
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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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"# Label encoding\n",
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"from sklearn import preprocessing\n",
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"le = preprocessing.LabelEncoder()\n",
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"le.fit(dataset['D'])"
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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": 9,
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"metadata": {},
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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>A</th>\n",
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" <th>C</th>\n",
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" <th>D</th>\n",
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" <th>B_green</th>\n",
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" <th>B_red</th>\n",
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" <th>B_yellow</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>1.0</td>\n",
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" <td>3300</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>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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" <td>1250</td>\n",
|
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" <td>2</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>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>NaN</td>\n",
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" <td>4600</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>1</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>2.0</td>\n",
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" <td>4500</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>0</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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" A C D B_green B_red B_yellow\n",
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"0 1.0 3300 1 0 1 0\n",
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"1 2.0 1250 2 0 1 0\n",
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"2 NaN 4600 0 0 0 1\n",
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"3 2.0 4500 1 1 0 0"
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]
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},
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"execution_count": 9,
|
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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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"dataset['D'] = le.transform(dataset['D'])\n",
|
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"dataset"
|
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]
|
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},
|
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{
|
|
"cell_type": "code",
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"metadata": {},
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"outputs": [
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" A C D B_green B_red B_yellow\n",
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"text/plain": [
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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": [
|
|
"dataset['C_normalized'] = (dataset['C'] - dataset['C'].min()) / (dataset['C'].max() - dataset['C'].min())\n",
|
|
"dataset['C_standardized'] = (dataset['C'] - dataset['C'].mean()) / dataset['C'].std()\n",
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"dataset"
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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": 22,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<Axes: >"
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]
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},
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"execution_count": 22,
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"metadata": {},
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\n",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"dataset.boxplot(['C_normalized','C_standardized'])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"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": "3.9.13"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|