5915 lines
208 KiB
Plaintext
5915 lines
208 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "d0b99ebe",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd"
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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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"id": "18b71e0d",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 2 main datatypes\n",
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"series = pd.Series([\"BMW\", \"Toyota\", \"Honda\"])"
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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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"id": "5fd5c8ea",
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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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"0 BMW\n",
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"1 Toyota\n",
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"2 Honda\n",
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"dtype: object"
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]
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},
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"execution_count": 3,
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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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"series"
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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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"id": "8293df83",
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"metadata": {},
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"outputs": [],
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"source": [
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"# series = 1-dimensional"
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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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"id": "7ce01316",
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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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"0 Red\n",
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"1 Blue\n",
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"2 White\n",
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"dtype: object"
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]
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},
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"execution_count": 5,
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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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"colours = pd.Series([\"Red\", \"Blue\", \"White\"])\n",
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"colours"
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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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"id": "3df244ef",
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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>Car make</th>\n",
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" <th>Colur</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>BMW</td>\n",
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" <td>Red</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>Toyota</td>\n",
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" <td>Blue</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>Honda</td>\n",
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" <td>White</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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" Car make Colur\n",
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"0 BMW Red\n",
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"1 Toyota Blue\n",
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"2 Honda White"
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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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"# DataFrame = 2-dimensional\n",
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"car_data = pd.DataFrame({\"Car make\": series, \"Colur\": colours})\n",
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"car_data"
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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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"id": "e6269eb7",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Import data\n",
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"car_sales = pd.read_csv(\"car-sales.csv\")"
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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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"id": "5ed55160",
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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>Make</th>\n",
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" <th>Colour</th>\n",
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" <th>Odometer (KM)</th>\n",
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" <th>Doors</th>\n",
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" <th>Price</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>Toyota</td>\n",
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" <td>White</td>\n",
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" <td>150043</td>\n",
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" <td>4</td>\n",
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" <td>$4,000.00</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>Honda</td>\n",
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" <td>Red</td>\n",
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" <td>87899</td>\n",
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" <td>4</td>\n",
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" <td>$5,000.00</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>Toyota</td>\n",
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" <td>Blue</td>\n",
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" <td>32549</td>\n",
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" <td>3</td>\n",
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" <td>$7,000.00</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>BMW</td>\n",
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" <td>Black</td>\n",
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" <td>11179</td>\n",
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" <td>5</td>\n",
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" <td>$22,000.00</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>Nissan</td>\n",
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" <td>White</td>\n",
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" <td>213095</td>\n",
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" <td>4</td>\n",
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" <td>$3,500.00</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>Toyota</td>\n",
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" <td>Green</td>\n",
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" <td>99213</td>\n",
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" <td>4</td>\n",
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" <td>$4,500.00</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>Honda</td>\n",
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" <td>Blue</td>\n",
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" <td>45698</td>\n",
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" <td>4</td>\n",
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" <td>$7,500.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>7</th>\n",
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" <td>Honda</td>\n",
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" <td>Blue</td>\n",
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" <td>54738</td>\n",
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" <td>4</td>\n",
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" <td>$7,000.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>8</th>\n",
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" <td>Toyota</td>\n",
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" <td>White</td>\n",
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" <td>60000</td>\n",
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" <td>4</td>\n",
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" <td>$6,250.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>9</th>\n",
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" <td>Nissan</td>\n",
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" <td>White</td>\n",
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" <td>31600</td>\n",
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" <td>4</td>\n",
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" <td>$9,700.00</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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" Make Colour Odometer (KM) Doors Price\n",
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"0 Toyota White 150043 4 $4,000.00\n",
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"1 Honda Red 87899 4 $5,000.00\n",
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"2 Toyota Blue 32549 3 $7,000.00\n",
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"3 BMW Black 11179 5 $22,000.00\n",
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"4 Nissan White 213095 4 $3,500.00\n",
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"5 Toyota Green 99213 4 $4,500.00\n",
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"6 Honda Blue 45698 4 $7,500.00\n",
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"7 Honda Blue 54738 4 $7,000.00\n",
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"8 Toyota White 60000 4 $6,250.00\n",
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"9 Nissan White 31600 4 $9,700.00"
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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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"car_sales"
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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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"id": "118c9363",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Exporting a dataframe\n",
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"# car_sales.to_csv(\"exported.csv\", index=False)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0be3c88e",
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"metadata": {},
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"source": [
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"## Describe data"
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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": 10,
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"id": "f6ae0796",
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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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"Make object\n",
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"Colour object\n",
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"Odometer (KM) int64\n",
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"Doors int64\n",
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"Price object\n",
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"dtype: object"
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]
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},
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"execution_count": 10,
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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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"# Attribute\n",
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"car_sales.dtypes\n",
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"\n",
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"# Function\n",
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"#car_sales.to_csv()"
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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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"id": "d55320ea",
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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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"Index(['Make', 'Colour', 'Odometer (KM)', 'Doors', 'Price'], dtype='object')"
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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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"car_sales.columns"
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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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"id": "92b983d1",
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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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"Index(['Make', 'Colour', 'Odometer (KM)', 'Doors', 'Price'], dtype='object')"
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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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"car_columns = car_sales.columns\n",
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"car_columns"
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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": 14,
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"id": "92937e49",
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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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"RangeIndex(start=0, stop=10, step=1)"
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]
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},
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"execution_count": 14,
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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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"car_sales.index"
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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": 15,
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"id": "922a7259",
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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>Make</th>\n",
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" <th>Colour</th>\n",
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" <th>Odometer (KM)</th>\n",
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" <th>Doors</th>\n",
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" <th>Price</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>Toyota</td>\n",
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" <td>White</td>\n",
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" <td>150043</td>\n",
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" <td>4</td>\n",
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" <td>$4,000.00</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>Honda</td>\n",
|
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" <td>Red</td>\n",
|
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" <td>87899</td>\n",
|
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" <td>4</td>\n",
|
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" <td>$5,000.00</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>Toyota</td>\n",
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" <td>Blue</td>\n",
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" <td>32549</td>\n",
|
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" <td>3</td>\n",
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" <td>$7,000.00</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>BMW</td>\n",
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" <td>Black</td>\n",
|
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" <td>11179</td>\n",
|
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" <td>5</td>\n",
|
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" <td>$22,000.00</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>Nissan</td>\n",
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" <td>White</td>\n",
|
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" <td>213095</td>\n",
|
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" <td>4</td>\n",
|
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" <td>$3,500.00</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>Toyota</td>\n",
|
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" <td>Green</td>\n",
|
|
" <td>99213</td>\n",
|
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" <td>4</td>\n",
|
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" <td>$4,500.00</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>Honda</td>\n",
|
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" <td>Blue</td>\n",
|
|
" <td>45698</td>\n",
|
|
" <td>4</td>\n",
|
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" <td>$7,500.00</td>\n",
|
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>7</th>\n",
|
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" <td>Honda</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>54738</td>\n",
|
|
" <td>4</td>\n",
|
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" <td>$7,000.00</td>\n",
|
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>8</th>\n",
|
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" <td>Toyota</td>\n",
|
|
" <td>White</td>\n",
|
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" <td>60000</td>\n",
|
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" <td>4</td>\n",
|
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" <td>$6,250.00</td>\n",
|
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>9</th>\n",
|
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" <td>Nissan</td>\n",
|
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" <td>White</td>\n",
|
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" <td>31600</td>\n",
|
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" <td>4</td>\n",
|
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" <td>$9,700.00</td>\n",
|
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" </tr>\n",
|
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" </tbody>\n",
|
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"</table>\n",
|
|
"</div>"
|
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],
|
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"text/plain": [
|
|
" Make Colour Odometer (KM) Doors Price\n",
|
|
"0 Toyota White 150043 4 $4,000.00\n",
|
|
"1 Honda Red 87899 4 $5,000.00\n",
|
|
"2 Toyota Blue 32549 3 $7,000.00\n",
|
|
"3 BMW Black 11179 5 $22,000.00\n",
|
|
"4 Nissan White 213095 4 $3,500.00\n",
|
|
"5 Toyota Green 99213 4 $4,500.00\n",
|
|
"6 Honda Blue 45698 4 $7,500.00\n",
|
|
"7 Honda Blue 54738 4 $7,000.00\n",
|
|
"8 Toyota White 60000 4 $6,250.00\n",
|
|
"9 Nissan White 31600 4 $9,700.00"
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]
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},
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"execution_count": 15,
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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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"car_sales"
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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": 16,
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"id": "f46a652c",
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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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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>Odometer (KM)</th>\n",
|
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" <th>Doors</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
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" <tr>\n",
|
|
" <th>count</th>\n",
|
|
" <td>10.000000</td>\n",
|
|
" <td>10.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>mean</th>\n",
|
|
" <td>78601.400000</td>\n",
|
|
" <td>4.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>std</th>\n",
|
|
" <td>61983.471735</td>\n",
|
|
" <td>0.471405</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>min</th>\n",
|
|
" <td>11179.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>25%</th>\n",
|
|
" <td>35836.250000</td>\n",
|
|
" <td>4.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>50%</th>\n",
|
|
" <td>57369.000000</td>\n",
|
|
" <td>4.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>75%</th>\n",
|
|
" <td>96384.500000</td>\n",
|
|
" <td>4.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>max</th>\n",
|
|
" <td>213095.000000</td>\n",
|
|
" <td>5.000000</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
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"text/plain": [
|
|
" Odometer (KM) Doors\n",
|
|
"count 10.000000 10.000000\n",
|
|
"mean 78601.400000 4.000000\n",
|
|
"std 61983.471735 0.471405\n",
|
|
"min 11179.000000 3.000000\n",
|
|
"25% 35836.250000 4.000000\n",
|
|
"50% 57369.000000 4.000000\n",
|
|
"75% 96384.500000 4.000000\n",
|
|
"max 213095.000000 5.000000"
|
|
]
|
|
},
|
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"execution_count": 16,
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"metadata": {},
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"output_type": "execute_result"
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}
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"source": [
|
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"car_sales.describe()"
|
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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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"id": "79387319",
|
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"metadata": {},
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"outputs": [
|
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{
|
|
"name": "stdout",
|
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"output_type": "stream",
|
|
"text": [
|
|
"<class 'pandas.core.frame.DataFrame'>\n",
|
|
"RangeIndex: 10 entries, 0 to 9\n",
|
|
"Data columns (total 5 columns):\n",
|
|
" # Column Non-Null Count Dtype \n",
|
|
"--- ------ -------------- ----- \n",
|
|
" 0 Make 10 non-null object\n",
|
|
" 1 Colour 10 non-null object\n",
|
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" 2 Odometer (KM) 10 non-null int64 \n",
|
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" 3 Doors 10 non-null int64 \n",
|
|
" 4 Price 10 non-null object\n",
|
|
"dtypes: int64(2), object(3)\n",
|
|
"memory usage: 528.0+ bytes\n"
|
|
]
|
|
}
|
|
],
|
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"source": [
|
|
"car_sales.info()"
|
|
]
|
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},
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{
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"cell_type": "code",
|
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"execution_count": 21,
|
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"id": "cbfd8da3",
|
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"metadata": {},
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"outputs": [
|
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{
|
|
"name": "stderr",
|
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"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_18146/4073448239.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n",
|
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" car_sales.mean()\n"
|
|
]
|
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},
|
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{
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"data": {
|
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"text/plain": [
|
|
"Odometer (KM) 78601.4\n",
|
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"Doors 4.0\n",
|
|
"dtype: float64"
|
|
]
|
|
},
|
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"execution_count": 21,
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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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"car_sales.mean()"
|
|
]
|
|
},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 22,
|
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"id": "73ea13e4",
|
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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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"376500.0"
|
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]
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},
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"execution_count": 22,
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"metadata": {},
|
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"output_type": "execute_result"
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}
|
|
],
|
|
"source": [
|
|
"car_prices = pd.Series([3000, 1500, 1125000])\n",
|
|
"car_prices.mean()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
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"execution_count": 23,
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"id": "8b05884d",
|
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"metadata": {},
|
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"outputs": [
|
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{
|
|
"data": {
|
|
"text/plain": [
|
|
"Make ToyotaHondaToyotaBMWNissanToyotaHondaHondaToyo...\n",
|
|
"Colour WhiteRedBlueBlackWhiteGreenBlueBlueWhiteWhite\n",
|
|
"Odometer (KM) 786014\n",
|
|
"Doors 40\n",
|
|
"Price $4,000.00$5,000.00$7,000.00$22,000.00$3,500.00...\n",
|
|
"dtype: object"
|
|
]
|
|
},
|
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"execution_count": 23,
|
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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": [
|
|
"car_sales.sum()"
|
|
]
|
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},
|
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{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
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"id": "4ddbed66",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"text/plain": [
|
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"40"
|
|
]
|
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},
|
|
"execution_count": 24,
|
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"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"car_sales[\"Doors\"].sum()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
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"execution_count": 25,
|
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"id": "0fdb1df3",
|
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"metadata": {},
|
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"outputs": [
|
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{
|
|
"data": {
|
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"text/plain": [
|
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"10"
|
|
]
|
|
},
|
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"execution_count": 25,
|
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"metadata": {},
|
|
"output_type": "execute_result"
|
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}
|
|
],
|
|
"source": [
|
|
"len(car_sales)"
|
|
]
|
|
},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 26,
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"id": "72af5003",
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"<div>\n",
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"</style>\n",
|
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|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>Make</th>\n",
|
|
" <th>Colour</th>\n",
|
|
" <th>Odometer (KM)</th>\n",
|
|
" <th>Doors</th>\n",
|
|
" <th>Price</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>150043</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>$4,000.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>Honda</td>\n",
|
|
" <td>Red</td>\n",
|
|
" <td>87899</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>$5,000.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>32549</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>$7,000.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>BMW</td>\n",
|
|
" <td>Black</td>\n",
|
|
" <td>11179</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>$22,000.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>Nissan</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>213095</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>$3,500.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>Green</td>\n",
|
|
" <td>99213</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>$4,500.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>Honda</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>45698</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>$7,500.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>Honda</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>54738</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>$7,000.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>60000</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>$6,250.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>Nissan</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>31600</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>$9,700.00</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Make Colour Odometer (KM) Doors Price\n",
|
|
"0 Toyota White 150043 4 $4,000.00\n",
|
|
"1 Honda Red 87899 4 $5,000.00\n",
|
|
"2 Toyota Blue 32549 3 $7,000.00\n",
|
|
"3 BMW Black 11179 5 $22,000.00\n",
|
|
"4 Nissan White 213095 4 $3,500.00\n",
|
|
"5 Toyota Green 99213 4 $4,500.00\n",
|
|
"6 Honda Blue 45698 4 $7,500.00\n",
|
|
"7 Honda Blue 54738 4 $7,000.00\n",
|
|
"8 Toyota White 60000 4 $6,250.00\n",
|
|
"9 Nissan White 31600 4 $9,700.00"
|
|
]
|
|
},
|
|
"execution_count": 26,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"car_sales"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "33cfa487",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Viewing and selecting data"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
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"id": "23567f48",
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"metadata": {},
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|
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{
|
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"<div>\n",
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" vertical-align: middle;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>Make</th>\n",
|
|
" <th>Colour</th>\n",
|
|
" <th>Odometer (KM)</th>\n",
|
|
" <th>Doors</th>\n",
|
|
" <th>Price</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>150043</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>$4,000.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>Honda</td>\n",
|
|
" <td>Red</td>\n",
|
|
" <td>87899</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>$5,000.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>32549</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>$7,000.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>BMW</td>\n",
|
|
" <td>Black</td>\n",
|
|
" <td>11179</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>$22,000.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>Nissan</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>213095</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>$3,500.00</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Make Colour Odometer (KM) Doors Price\n",
|
|
"0 Toyota White 150043 4 $4,000.00\n",
|
|
"1 Honda Red 87899 4 $5,000.00\n",
|
|
"2 Toyota Blue 32549 3 $7,000.00\n",
|
|
"3 BMW Black 11179 5 $22,000.00\n",
|
|
"4 Nissan White 213095 4 $3,500.00"
|
|
]
|
|
},
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
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}
|
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],
|
|
"source": [
|
|
"car_sales.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"id": "fe1ea0d4",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"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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" .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",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>Make</th>\n",
|
|
" <th>Colour</th>\n",
|
|
" <th>Odometer (KM)</th>\n",
|
|
" <th>Doors</th>\n",
|
|
" <th>Price</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>150043</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>$4,000.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>Honda</td>\n",
|
|
" <td>Red</td>\n",
|
|
" <td>87899</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>$5,000.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>32549</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>$7,000.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>BMW</td>\n",
|
|
" <td>Black</td>\n",
|
|
" <td>11179</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>$22,000.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>Nissan</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>213095</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>$3,500.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>Green</td>\n",
|
|
" <td>99213</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>$4,500.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>Honda</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>45698</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>$7,500.00</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Make Colour Odometer (KM) Doors Price\n",
|
|
"0 Toyota White 150043 4 $4,000.00\n",
|
|
"1 Honda Red 87899 4 $5,000.00\n",
|
|
"2 Toyota Blue 32549 3 $7,000.00\n",
|
|
"3 BMW Black 11179 5 $22,000.00\n",
|
|
"4 Nissan White 213095 4 $3,500.00\n",
|
|
"5 Toyota Green 99213 4 $4,500.00\n",
|
|
"6 Honda Blue 45698 4 $7,500.00"
|
|
]
|
|
},
|
|
"execution_count": 28,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"car_sales.head(7)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"id": "a05981ea",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"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",
|
|
" }\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",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>Make</th>\n",
|
|
" <th>Colour</th>\n",
|
|
" <th>Odometer (KM)</th>\n",
|
|
" <th>Doors</th>\n",
|
|
" <th>Price</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>Green</td>\n",
|
|
" <td>99213</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>$4,500.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>Honda</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>45698</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>$7,500.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>Honda</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>54738</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>$7,000.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>60000</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>$6,250.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>Nissan</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>31600</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>$9,700.00</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Make Colour Odometer (KM) Doors Price\n",
|
|
"5 Toyota Green 99213 4 $4,500.00\n",
|
|
"6 Honda Blue 45698 4 $7,500.00\n",
|
|
"7 Honda Blue 54738 4 $7,000.00\n",
|
|
"8 Toyota White 60000 4 $6,250.00\n",
|
|
"9 Nissan White 31600 4 $9,700.00"
|
|
]
|
|
},
|
|
"execution_count": 29,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"car_sales.tail()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"id": "d3f41528",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# .loc & .iloc\n",
|
|
"animals = pd.Series([\"cat\", \"dog\", \"bird\", \"panda\", \"snake\"],\n",
|
|
" index=[0,3, 9, 8, 3])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"id": "b849ece1",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0 cat\n",
|
|
"3 dog\n",
|
|
"9 bird\n",
|
|
"8 panda\n",
|
|
"3 snake\n",
|
|
"dtype: object"
|
|
]
|
|
},
|
|
"execution_count": 31,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"animals"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"id": "7aaabb07",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"3 dog\n",
|
|
"3 snake\n",
|
|
"dtype: object"
|
|
]
|
|
},
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"animals.loc[3]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"id": "d3305a05",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'bird'"
|
|
]
|
|
},
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"animals.loc[9]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"id": "bc0c43a5",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Make BMW\n",
|
|
"Colour Black\n",
|
|
"Odometer (KM) 11179\n",
|
|
"Doors 5\n",
|
|
"Price $22,000.00\n",
|
|
"Name: 3, dtype: object"
|
|
]
|
|
},
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# loc refers to index\n",
|
|
"car_sales.loc[3]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"id": "c0600348",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'panda'"
|
|
]
|
|
},
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# .iloc refers to position\n",
|
|
"animals.iloc[3]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"id": "f77b2a57",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0 cat\n",
|
|
"3 dog\n",
|
|
"9 bird\n",
|
|
"8 panda\n",
|
|
"3 snake\n",
|
|
"dtype: object"
|
|
]
|
|
},
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"animals"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"id": "99050e3c",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0 cat\n",
|
|
"3 dog\n",
|
|
"9 bird\n",
|
|
"dtype: object"
|
|
]
|
|
},
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"animals.iloc[:3]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"id": "a9e018ad",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .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>Make</th>\n",
|
|
" <th>Colour</th>\n",
|
|
" <th>Odometer (KM)</th>\n",
|
|
" <th>Doors</th>\n",
|
|
" <th>Price</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>150043</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>$4,000.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>Honda</td>\n",
|
|
" <td>Red</td>\n",
|
|
" <td>87899</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>$5,000.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>32549</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>$7,000.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>BMW</td>\n",
|
|
" <td>Black</td>\n",
|
|
" <td>11179</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>$22,000.00</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Make Colour Odometer (KM) Doors Price\n",
|
|
"0 Toyota White 150043 4 $4,000.00\n",
|
|
"1 Honda Red 87899 4 $5,000.00\n",
|
|
"2 Toyota Blue 32549 3 $7,000.00\n",
|
|
"3 BMW Black 11179 5 $22,000.00"
|
|
]
|
|
},
|
|
"execution_count": 17,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"car_sales.loc[:3]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"id": "cedd32fb",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .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>Make</th>\n",
|
|
" <th>Colour</th>\n",
|
|
" <th>Odometer (KM)</th>\n",
|
|
" <th>Doors</th>\n",
|
|
" <th>Price</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>150043</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>$4,000.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>Honda</td>\n",
|
|
" <td>Red</td>\n",
|
|
" <td>87899</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>$5,000.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>32549</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>$7,000.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>BMW</td>\n",
|
|
" <td>Black</td>\n",
|
|
" <td>11179</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>$22,000.00</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Make Colour Odometer (KM) Doors Price\n",
|
|
"0 Toyota White 150043 4 $4,000.00\n",
|
|
"1 Honda Red 87899 4 $5,000.00\n",
|
|
"2 Toyota Blue 32549 3 $7,000.00\n",
|
|
"3 BMW Black 11179 5 $22,000.00"
|
|
]
|
|
},
|
|
"execution_count": 19,
|
|
"metadata": {},
|
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"output_type": "execute_result"
|
|
}
|
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],
|
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"source": [
|
|
"car_sales.head(4)"
|
|
]
|
|
},
|
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{
|
|
"cell_type": "code",
|
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"execution_count": 20,
|
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"id": "1d2be05c",
|
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"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0 Toyota\n",
|
|
"1 Honda\n",
|
|
"2 Toyota\n",
|
|
"3 BMW\n",
|
|
"4 Nissan\n",
|
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"5 Toyota\n",
|
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"6 Honda\n",
|
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"7 Honda\n",
|
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"8 Toyota\n",
|
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"9 Nissan\n",
|
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"Name: Make, dtype: object"
|
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]
|
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},
|
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"execution_count": 20,
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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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"car_sales[\"Make\"]"
|
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]
|
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},
|
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{
|
|
"cell_type": "code",
|
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"execution_count": 21,
|
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"id": "4962a1fc",
|
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"metadata": {},
|
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"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0 White\n",
|
|
"1 Red\n",
|
|
"2 Blue\n",
|
|
"3 Black\n",
|
|
"4 White\n",
|
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"5 Green\n",
|
|
"6 Blue\n",
|
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"7 Blue\n",
|
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"8 White\n",
|
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"9 White\n",
|
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"Name: Colour, dtype: object"
|
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]
|
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},
|
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"execution_count": 21,
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"metadata": {},
|
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"output_type": "execute_result"
|
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}
|
|
],
|
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"source": [
|
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"car_sales[\"Colour\"]"
|
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]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
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"execution_count": 22,
|
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"id": "d4043529",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0 Toyota\n",
|
|
"1 Honda\n",
|
|
"2 Toyota\n",
|
|
"3 BMW\n",
|
|
"4 Nissan\n",
|
|
"5 Toyota\n",
|
|
"6 Honda\n",
|
|
"7 Honda\n",
|
|
"8 Toyota\n",
|
|
"9 Nissan\n",
|
|
"Name: Make, dtype: object"
|
|
]
|
|
},
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"car_sales[\"Make\"]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"id": "7acbc784",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0 Toyota\n",
|
|
"1 Honda\n",
|
|
"2 Toyota\n",
|
|
"3 BMW\n",
|
|
"4 Nissan\n",
|
|
"5 Toyota\n",
|
|
"6 Honda\n",
|
|
"7 Honda\n",
|
|
"8 Toyota\n",
|
|
"9 Nissan\n",
|
|
"Name: Make, dtype: object"
|
|
]
|
|
},
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"car_sales.Make"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"id": "f6d2bca3",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
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"<div>\n",
|
|
"<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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|
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" }\n",
|
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"</style>\n",
|
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|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>Make</th>\n",
|
|
" <th>Colour</th>\n",
|
|
" <th>Odometer (KM)</th>\n",
|
|
" <th>Doors</th>\n",
|
|
" <th>Price</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>150043</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>$4,000.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>32549</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>$7,000.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>Green</td>\n",
|
|
" <td>99213</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>$4,500.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>60000</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>$6,250.00</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Make Colour Odometer (KM) Doors Price\n",
|
|
"0 Toyota White 150043 4 $4,000.00\n",
|
|
"2 Toyota Blue 32549 3 $7,000.00\n",
|
|
"5 Toyota Green 99213 4 $4,500.00\n",
|
|
"8 Toyota White 60000 4 $6,250.00"
|
|
]
|
|
},
|
|
"execution_count": 25,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"car_sales[car_sales[\"Make\"] == \"Toyota\"]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"id": "a9bbcefc",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
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" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
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" }\n",
|
|
"\n",
|
|
" .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>Make</th>\n",
|
|
" <th>Colour</th>\n",
|
|
" <th>Odometer (KM)</th>\n",
|
|
" <th>Doors</th>\n",
|
|
" <th>Price</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>150043</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>$4,000.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>Nissan</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>213095</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>$3,500.00</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Make Colour Odometer (KM) Doors Price\n",
|
|
"0 Toyota White 150043 4 $4,000.00\n",
|
|
"4 Nissan White 213095 4 $3,500.00"
|
|
]
|
|
},
|
|
"execution_count": 27,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"car_sales[car_sales[\"Odometer (KM)\"] > 100000]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"id": "b2a8ee80",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
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"<div>\n",
|
|
"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
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" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .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>Doors</th>\n",
|
|
" <th>3</th>\n",
|
|
" <th>4</th>\n",
|
|
" <th>5</th>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>Make</th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>BMW</th>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>Honda</th>\n",
|
|
" <td>0</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>Nissan</th>\n",
|
|
" <td>0</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>Toyota</th>\n",
|
|
" <td>1</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
"Doors 3 4 5\n",
|
|
"Make \n",
|
|
"BMW 0 0 1\n",
|
|
"Honda 0 3 0\n",
|
|
"Nissan 0 2 0\n",
|
|
"Toyota 1 3 0"
|
|
]
|
|
},
|
|
"execution_count": 28,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"pd.crosstab(car_sales[\"Make\"], car_sales[\"Doors\"])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"id": "aa0d76c3",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .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>Odometer (KM)</th>\n",
|
|
" <th>Doors</th>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>Make</th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>BMW</th>\n",
|
|
" <td>11179.000000</td>\n",
|
|
" <td>5.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>Honda</th>\n",
|
|
" <td>62778.333333</td>\n",
|
|
" <td>4.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>Nissan</th>\n",
|
|
" <td>122347.500000</td>\n",
|
|
" <td>4.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>Toyota</th>\n",
|
|
" <td>85451.250000</td>\n",
|
|
" <td>3.75</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Odometer (KM) Doors\n",
|
|
"Make \n",
|
|
"BMW 11179.000000 5.00\n",
|
|
"Honda 62778.333333 4.00\n",
|
|
"Nissan 122347.500000 4.00\n",
|
|
"Toyota 85451.250000 3.75"
|
|
]
|
|
},
|
|
"execution_count": 29,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Groupby\n",
|
|
"car_sales.groupby([\"Make\"]).mean()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 33,
|
|
"id": "52390ea4",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"%matplotlib inline\n",
|
|
"import matplotlib.pyplot as plt"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 34,
|
|
"id": "e7fdeb36",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<AxesSubplot:>"
|
|
]
|
|
},
|
|
"execution_count": 34,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"car_sales[\"Odometer (KM)\"].plot()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 35,
|
|
"id": "fc72e078",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<AxesSubplot:>"
|
|
]
|
|
},
|
|
"execution_count": 35,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "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\n",
|
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"text/plain": [
|
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"<Figure size 432x288 with 1 Axes>"
|
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]
|
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
|
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"car_sales[\"Odometer (KM)\"].hist()"
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]
|
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},
|
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{
|
|
"cell_type": "code",
|
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"execution_count": 36,
|
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"id": "b30e787b",
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"metadata": {},
|
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"outputs": [
|
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{
|
|
"ename": "TypeError",
|
|
"evalue": "no numeric data to plot",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
|
|
"Input \u001b[0;32mIn [36]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mcar_sales\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mPrice\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
|
|
"File \u001b[0;32m~/Documents/learning/machine_learning_course/sample_project/env/lib/python3.10/site-packages/pandas/plotting/_core.py:972\u001b[0m, in \u001b[0;36mPlotAccessor.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 969\u001b[0m label_name \u001b[38;5;241m=\u001b[39m label_kw \u001b[38;5;129;01mor\u001b[39;00m data\u001b[38;5;241m.\u001b[39mcolumns\n\u001b[1;32m 970\u001b[0m data\u001b[38;5;241m.\u001b[39mcolumns \u001b[38;5;241m=\u001b[39m label_name\n\u001b[0;32m--> 972\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mplot_backend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkind\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkind\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
|
"File \u001b[0;32m~/Documents/learning/machine_learning_course/sample_project/env/lib/python3.10/site-packages/pandas/plotting/_matplotlib/__init__.py:71\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(data, kind, **kwargs)\u001b[0m\n\u001b[1;32m 69\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124max\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(ax, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mleft_ax\u001b[39m\u001b[38;5;124m\"\u001b[39m, ax)\n\u001b[1;32m 70\u001b[0m plot_obj \u001b[38;5;241m=\u001b[39m PLOT_CLASSES[kind](data, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 71\u001b[0m \u001b[43mplot_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 72\u001b[0m plot_obj\u001b[38;5;241m.\u001b[39mdraw()\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m plot_obj\u001b[38;5;241m.\u001b[39mresult\n",
|
|
"File \u001b[0;32m~/Documents/learning/machine_learning_course/sample_project/env/lib/python3.10/site-packages/pandas/plotting/_matplotlib/core.py:327\u001b[0m, in \u001b[0;36mMPLPlot.generate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 325\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgenerate\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 326\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_args_adjust()\n\u001b[0;32m--> 327\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_compute_plot_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 328\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_setup_subplots()\n\u001b[1;32m 329\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_plot()\n",
|
|
"File \u001b[0;32m~/Documents/learning/machine_learning_course/sample_project/env/lib/python3.10/site-packages/pandas/plotting/_matplotlib/core.py:506\u001b[0m, in \u001b[0;36mMPLPlot._compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 504\u001b[0m \u001b[38;5;66;03m# no non-numeric frames or series allowed\u001b[39;00m\n\u001b[1;32m 505\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_empty:\n\u001b[0;32m--> 506\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mno numeric data to plot\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 508\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;241m=\u001b[39m numeric_data\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_convert_to_ndarray)\n",
|
|
"\u001b[0;31mTypeError\u001b[0m: no numeric data to plot"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"car_sales[\"Price\"].plot()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 37,
|
|
"id": "bb9b5864",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_4212/3108854531.py:1: FutureWarning: The default value of regex will change from True to False in a future version.\n",
|
|
" car_sales[\"Price\"] = car_sales[\"Price\"].str.replace('[\\$\\,\\.]','').astype(int)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"car_sales[\"Price\"] = car_sales[\"Price\"].str.replace('[\\$\\,\\.]','').astype(int)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 38,
|
|
"id": "37128899",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0 400000\n",
|
|
"1 500000\n",
|
|
"2 700000\n",
|
|
"3 2200000\n",
|
|
"4 350000\n",
|
|
"5 450000\n",
|
|
"6 750000\n",
|
|
"7 700000\n",
|
|
"8 625000\n",
|
|
"9 970000\n",
|
|
"Name: Price, dtype: int64"
|
|
]
|
|
},
|
|
"execution_count": 38,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"car_sales[\"Price\"]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 39,
|
|
"id": "962db850",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<AxesSubplot:>"
|
|
]
|
|
},
|
|
"execution_count": 39,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"car_sales[\"Price\"].plot()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "4413ac95",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Manipulating Data"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 40,
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|
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|
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{
|
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|
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"0 toyota\n",
|
|
"1 honda\n",
|
|
"2 toyota\n",
|
|
"3 bmw\n",
|
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"4 nissan\n",
|
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|
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|
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|
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"Name: Make, dtype: object"
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
|
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|
|
" <td>Toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>150043</td>\n",
|
|
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|
|
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|
|
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|
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|
|
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|
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|
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|
|
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|
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|
|
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|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
" <th>6</th>\n",
|
|
" <td>Honda</td>\n",
|
|
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|
|
" <td>45698</td>\n",
|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
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|
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|
|
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|
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|
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|
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|
|
"0 Toyota White 150043 4 400000\n",
|
|
"1 Honda Red 87899 4 500000\n",
|
|
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|
|
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|
|
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|
|
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|
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|
|
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|
|
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|
|
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|
|
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|
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|
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"source": [
|
|
"car_sales[\"Make\"] = car_sales[\"Make\"].str.lower()"
|
|
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|
|
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|
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|
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|
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|
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|
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|
|
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|
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|
|
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|
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|
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|
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|
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|
|
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|
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|
|
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|
|
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|
|
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|
|
" <th>2</th>\n",
|
|
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|
|
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|
|
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|
|
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|
|
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|
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|
|
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|
|
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|
|
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|
|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
" <td>honda</td>\n",
|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
" <td>nissan</td>\n",
|
|
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|
|
" <td>31600</td>\n",
|
|
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|
|
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|
|
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|
|
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|
|
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|
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|
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|
|
"text/plain": [
|
|
" Make Colour Odometer (KM) Doors Price\n",
|
|
"0 toyota White 150043 4 400000\n",
|
|
"1 honda Red 87899 4 500000\n",
|
|
"2 toyota Blue 32549 3 700000\n",
|
|
"3 bmw Black 11179 5 2200000\n",
|
|
"4 nissan White 213095 4 350000\n",
|
|
"5 toyota Green 99213 4 450000\n",
|
|
"6 honda Blue 45698 4 750000\n",
|
|
"7 honda Blue 54738 4 700000\n",
|
|
"8 toyota White 60000 4 625000\n",
|
|
"9 nissan White 31600 4 970000"
|
|
]
|
|
},
|
|
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|
|
"metadata": {},
|
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|
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|
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|
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|
|
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|
|
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|
|
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|
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|
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|
|
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|
|
"id": "65b2f115",
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|
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|
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|
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|
|
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|
|
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|
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|
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|
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|
|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
" <th>1</th>\n",
|
|
" <td>honda</td>\n",
|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
" <th>2</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>32549</td>\n",
|
|
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|
|
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|
|
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|
|
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|
|
" <th>3</th>\n",
|
|
" <td>bmw</td>\n",
|
|
" <td>Black</td>\n",
|
|
" <td>11179</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>2200000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>nissan</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>213095</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>350000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>Green</td>\n",
|
|
" <td>99213</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>450000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>honda</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>45698</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>750000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>honda</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>54738</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>700000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>60000</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>625000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>nissan</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>31600</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>970000</td>\n",
|
|
" </tr>\n",
|
|
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|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Make Colour Odometer (KM) Doors Price\n",
|
|
"0 toyota White 150043 4 400000\n",
|
|
"1 honda Red 87899 4 500000\n",
|
|
"2 toyota Blue 32549 3 700000\n",
|
|
"3 bmw Black 11179 5 2200000\n",
|
|
"4 nissan White 213095 4 350000\n",
|
|
"5 toyota Green 99213 4 450000\n",
|
|
"6 honda Blue 45698 4 750000\n",
|
|
"7 honda Blue 54738 4 700000\n",
|
|
"8 toyota White 60000 4 625000\n",
|
|
"9 nissan White 31600 4 970000"
|
|
]
|
|
},
|
|
"execution_count": 44,
|
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"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
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],
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"source": [
|
|
"car_sales"
|
|
]
|
|
},
|
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{
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"cell_type": "code",
|
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"execution_count": 46,
|
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"id": "bd67bc4a",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"car_sales_missing = pd.read_csv(\"car-sales-missing-data.csv\")"
|
|
]
|
|
},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 47,
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"id": "63f67964",
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|
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" <th>Make</th>\n",
|
|
" <th>Colour</th>\n",
|
|
" <th>Odometer</th>\n",
|
|
" <th>Doors</th>\n",
|
|
" <th>Price</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>150043.0</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>$4,000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>Honda</td>\n",
|
|
" <td>Red</td>\n",
|
|
" <td>87899.0</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>$5,000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>3.0</td>\n",
|
|
" <td>$7,000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>BMW</td>\n",
|
|
" <td>Black</td>\n",
|
|
" <td>11179.0</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>$22,000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>Nissan</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>213095.0</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>$3,500</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>Green</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>$4,500</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>Honda</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>$7,500</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>Honda</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>60000.0</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>31600.0</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>$9,700</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Make Colour Odometer Doors Price\n",
|
|
"0 Toyota White 150043.0 4.0 $4,000\n",
|
|
"1 Honda Red 87899.0 4.0 $5,000\n",
|
|
"2 Toyota Blue NaN 3.0 $7,000\n",
|
|
"3 BMW Black 11179.0 5.0 $22,000\n",
|
|
"4 Nissan White 213095.0 4.0 $3,500\n",
|
|
"5 Toyota Green NaN 4.0 $4,500\n",
|
|
"6 Honda NaN NaN 4.0 $7,500\n",
|
|
"7 Honda Blue NaN 4.0 NaN\n",
|
|
"8 Toyota White 60000.0 NaN NaN\n",
|
|
"9 NaN White 31600.0 4.0 $9,700"
|
|
]
|
|
},
|
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|
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|
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|
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|
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|
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|
|
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|
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|
|
},
|
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{
|
|
"cell_type": "code",
|
|
"execution_count": 48,
|
|
"id": "97464074",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"92302.66666666667"
|
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|
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|
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|
|
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|
|
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|
|
],
|
|
"source": [
|
|
"car_sales_missing[\"Odometer\"].mean()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 49,
|
|
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|
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|
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{
|
|
"data": {
|
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"text/plain": [
|
|
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|
|
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|
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|
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|
|
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|
|
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|
|
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|
|
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|
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|
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|
|
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|
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},
|
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"execution_count": 49,
|
|
"metadata": {},
|
|
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|
|
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|
|
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|
|
"source": [
|
|
"car_sales_missing[\"Odometer\"].fillna(car_sales_missing[\"Odometer\"].mean())"
|
|
]
|
|
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|
|
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|
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|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
" <td>Honda</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>60000.0</td>\n",
|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
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|
|
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|
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|
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|
|
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|
|
"text/plain": [
|
|
" Make Colour Odometer Doors Price\n",
|
|
"0 Toyota White 150043.0 4.0 $4,000\n",
|
|
"1 Honda Red 87899.0 4.0 $5,000\n",
|
|
"2 Toyota Blue NaN 3.0 $7,000\n",
|
|
"3 BMW Black 11179.0 5.0 $22,000\n",
|
|
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|
|
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|
|
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|
|
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|
|
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|
|
"9 NaN White 31600.0 4.0 $9,700"
|
|
]
|
|
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|
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|
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|
|
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|
|
"outputs": [],
|
|
"source": [
|
|
"car_sales_missing[\"Odometer\"].fillna(car_sales_missing[\"Odometer\"].mean(),\n",
|
|
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|
|
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|
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|
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|
|
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|
|
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|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
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|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
" <td>BMW</td>\n",
|
|
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|
|
" <td>11179.000000</td>\n",
|
|
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|
|
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|
|
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|
|
" <tr>\n",
|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
" <td>31600.000000</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>$9,700</td>\n",
|
|
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|
|
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|
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|
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|
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|
|
"text/plain": [
|
|
" Make Colour Odometer Doors Price\n",
|
|
"0 Toyota White 150043.000000 4.0 $4,000\n",
|
|
"1 Honda Red 87899.000000 4.0 $5,000\n",
|
|
"2 Toyota Blue 92302.666667 3.0 $7,000\n",
|
|
"3 BMW Black 11179.000000 5.0 $22,000\n",
|
|
"4 Nissan White 213095.000000 4.0 $3,500\n",
|
|
"5 Toyota Green 92302.666667 4.0 $4,500\n",
|
|
"6 Honda NaN 92302.666667 4.0 $7,500\n",
|
|
"7 Honda Blue 92302.666667 4.0 NaN\n",
|
|
"8 Toyota White 60000.000000 NaN NaN\n",
|
|
"9 NaN White 31600.000000 4.0 $9,700"
|
|
]
|
|
},
|
|
"execution_count": 53,
|
|
"metadata": {},
|
|
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|
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|
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|
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|
|
"car_sales_missing"
|
|
]
|
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|
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|
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|
|
" <th>Make</th>\n",
|
|
" <th>Colour</th>\n",
|
|
" <th>Odometer</th>\n",
|
|
" <th>Doors</th>\n",
|
|
" <th>Price</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>150043.000000</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>$4,000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>Honda</td>\n",
|
|
" <td>Red</td>\n",
|
|
" <td>87899.000000</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>$5,000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>92302.666667</td>\n",
|
|
" <td>3.0</td>\n",
|
|
" <td>$7,000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>BMW</td>\n",
|
|
" <td>Black</td>\n",
|
|
" <td>11179.000000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>$22,000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>Nissan</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>213095.000000</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>$3,500</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>Green</td>\n",
|
|
" <td>92302.666667</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>$4,500</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Make Colour Odometer Doors Price\n",
|
|
"0 Toyota White 150043.000000 4.0 $4,000\n",
|
|
"1 Honda Red 87899.000000 4.0 $5,000\n",
|
|
"2 Toyota Blue 92302.666667 3.0 $7,000\n",
|
|
"3 BMW Black 11179.000000 5.0 $22,000\n",
|
|
"4 Nissan White 213095.000000 4.0 $3,500\n",
|
|
"5 Toyota Green 92302.666667 4.0 $4,500"
|
|
]
|
|
},
|
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|
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|
|
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|
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|
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|
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|
|
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|
|
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|
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|
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|
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"cell_type": "code",
|
|
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|
|
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|
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|
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|
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|
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|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
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|
|
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|
|
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|
|
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|
|
" <td>$22,000</td>\n",
|
|
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|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>Nissan</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>213095.000000</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>$3,500</td>\n",
|
|
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|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
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|
|
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|
|
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|
|
" <td>$4,500</td>\n",
|
|
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|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>Honda</td>\n",
|
|
" <td>NaN</td>\n",
|
|
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|
|
" <td>4.0</td>\n",
|
|
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|
|
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|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>Honda</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>92302.666667</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>60000.000000</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
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|
|
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|
|
" <th>9</th>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>31600.000000</td>\n",
|
|
" <td>4.0</td>\n",
|
|
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|
|
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|
|
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|
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|
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|
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|
|
"text/plain": [
|
|
" Make Colour Odometer Doors Price\n",
|
|
"0 Toyota White 150043.000000 4.0 $4,000\n",
|
|
"1 Honda Red 87899.000000 4.0 $5,000\n",
|
|
"2 Toyota Blue 92302.666667 3.0 $7,000\n",
|
|
"3 BMW Black 11179.000000 5.0 $22,000\n",
|
|
"4 Nissan White 213095.000000 4.0 $3,500\n",
|
|
"5 Toyota Green 92302.666667 4.0 $4,500\n",
|
|
"6 Honda NaN 92302.666667 4.0 $7,500\n",
|
|
"7 Honda Blue 92302.666667 4.0 NaN\n",
|
|
"8 Toyota White 60000.000000 NaN NaN\n",
|
|
"9 NaN White 31600.000000 4.0 $9,700"
|
|
]
|
|
},
|
|
"execution_count": 55,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
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|
|
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|
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"source": [
|
|
"car_sales_missing"
|
|
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|
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|
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|
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|
|
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|
|
"id": "48a80b90",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"car_sales_missing.dropna(inplace=True)"
|
|
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|
|
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|
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{
|
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|
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|
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|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>Honda</td>\n",
|
|
" <td>Red</td>\n",
|
|
" <td>87899.000000</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>$5,000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>92302.666667</td>\n",
|
|
" <td>3.0</td>\n",
|
|
" <td>$7,000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>BMW</td>\n",
|
|
" <td>Black</td>\n",
|
|
" <td>11179.000000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>$22,000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>Nissan</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>213095.000000</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>$3,500</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>Green</td>\n",
|
|
" <td>92302.666667</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>$4,500</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
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|
|
"text/plain": [
|
|
" Make Colour Odometer Doors Price\n",
|
|
"0 Toyota White 150043.000000 4.0 $4,000\n",
|
|
"1 Honda Red 87899.000000 4.0 $5,000\n",
|
|
"2 Toyota Blue 92302.666667 3.0 $7,000\n",
|
|
"3 BMW Black 11179.000000 5.0 $22,000\n",
|
|
"4 Nissan White 213095.000000 4.0 $3,500\n",
|
|
"5 Toyota Green 92302.666667 4.0 $4,500"
|
|
]
|
|
},
|
|
"execution_count": 57,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
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|
|
"source": [
|
|
"car_sales_missing"
|
|
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|
|
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|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 68,
|
|
"id": "07ef69f1",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"car_sales_missing = pd.read_csv(\"car-sales-missing-data.csv\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 69,
|
|
"id": "1392f357",
|
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|
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|
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|
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|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
" <th>0</th>\n",
|
|
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|
|
" <td>White</td>\n",
|
|
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|
|
" <td>4.0</td>\n",
|
|
" <td>$4,000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>Honda</td>\n",
|
|
" <td>Red</td>\n",
|
|
" <td>87899.0</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>$5,000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>3.0</td>\n",
|
|
" <td>$7,000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>BMW</td>\n",
|
|
" <td>Black</td>\n",
|
|
" <td>11179.0</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>$22,000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>Nissan</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>213095.0</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>$3,500</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>Green</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>$4,500</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>Honda</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>$7,500</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>Honda</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>60000.0</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>31600.0</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>$9,700</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Make Colour Odometer Doors Price\n",
|
|
"0 Toyota White 150043.0 4.0 $4,000\n",
|
|
"1 Honda Red 87899.0 4.0 $5,000\n",
|
|
"2 Toyota Blue NaN 3.0 $7,000\n",
|
|
"3 BMW Black 11179.0 5.0 $22,000\n",
|
|
"4 Nissan White 213095.0 4.0 $3,500\n",
|
|
"5 Toyota Green NaN 4.0 $4,500\n",
|
|
"6 Honda NaN NaN 4.0 $7,500\n",
|
|
"7 Honda Blue NaN 4.0 NaN\n",
|
|
"8 Toyota White 60000.0 NaN NaN\n",
|
|
"9 NaN White 31600.0 4.0 $9,700"
|
|
]
|
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},
|
|
"execution_count": 69,
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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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"car_sales_missing"
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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": 70,
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"id": "2c141c98",
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"metadata": {},
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"outputs": [],
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"source": [
|
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"car_sales_missing_dropped = car_sales_missing.dropna()"
|
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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": 71,
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"id": "620ef1af",
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"metadata": {},
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"outputs": [
|
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{
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"data": {
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|
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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>Make</th>\n",
|
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" <th>Colour</th>\n",
|
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" <th>Odometer</th>\n",
|
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" <th>Doors</th>\n",
|
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" <th>Price</th>\n",
|
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" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>Toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>150043.0</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>$4,000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>Honda</td>\n",
|
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|
|
" <td>87899.0</td>\n",
|
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" <td>4.0</td>\n",
|
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" <td>$5,000</td>\n",
|
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" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>BMW</td>\n",
|
|
" <td>Black</td>\n",
|
|
" <td>11179.0</td>\n",
|
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" <td>5.0</td>\n",
|
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" <td>$22,000</td>\n",
|
|
" </tr>\n",
|
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" <tr>\n",
|
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" <th>4</th>\n",
|
|
" <td>Nissan</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>213095.0</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>$3,500</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": [
|
|
" Make Colour Odometer Doors Price\n",
|
|
"0 Toyota White 150043.0 4.0 $4,000\n",
|
|
"1 Honda Red 87899.0 4.0 $5,000\n",
|
|
"3 BMW Black 11179.0 5.0 $22,000\n",
|
|
"4 Nissan White 213095.0 4.0 $3,500"
|
|
]
|
|
},
|
|
"execution_count": 71,
|
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"metadata": {},
|
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|
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"car_sales_missing_dropped"
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"cell_type": "code",
|
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"execution_count": 72,
|
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"id": "781e9bf0",
|
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"metadata": {},
|
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"outputs": [],
|
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"source": [
|
|
"car_sales_missing_dropped.to_csv(\"car-sales-missing-dropped.csv\")"
|
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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": 75,
|
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"id": "9bccd7ca",
|
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"metadata": {},
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"outputs": [
|
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|
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"data": {
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|
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|
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|
|
" <th></th>\n",
|
|
" <th>Make</th>\n",
|
|
" <th>Colour</th>\n",
|
|
" <th>Odometer (KM)</th>\n",
|
|
" <th>Doors</th>\n",
|
|
" <th>Price</th>\n",
|
|
" <th>Seats</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>150043</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>400000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>honda</td>\n",
|
|
" <td>Red</td>\n",
|
|
" <td>87899</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>500000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>32549</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>700000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>bmw</td>\n",
|
|
" <td>Black</td>\n",
|
|
" <td>11179</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>2200000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>nissan</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>213095</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>350000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>Green</td>\n",
|
|
" <td>99213</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>450000</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>honda</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>45698</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>750000</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>honda</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>54738</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>700000</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>60000</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>625000</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>nissan</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>31600</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>970000</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Make Colour Odometer (KM) Doors Price Seats\n",
|
|
"0 toyota White 150043 4 400000 5.0\n",
|
|
"1 honda Red 87899 4 500000 5.0\n",
|
|
"2 toyota Blue 32549 3 700000 5.0\n",
|
|
"3 bmw Black 11179 5 2200000 5.0\n",
|
|
"4 nissan White 213095 4 350000 5.0\n",
|
|
"5 toyota Green 99213 4 450000 NaN\n",
|
|
"6 honda Blue 45698 4 750000 NaN\n",
|
|
"7 honda Blue 54738 4 700000 NaN\n",
|
|
"8 toyota White 60000 4 625000 NaN\n",
|
|
"9 nissan White 31600 4 970000 NaN"
|
|
]
|
|
},
|
|
"execution_count": 75,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Column from series\n",
|
|
"seats_column = pd.Series([5, 5, 5, 5, 5,])\n",
|
|
"\n",
|
|
"# New column called seats\n",
|
|
"car_sales[\"Seats\"] = seats_column\n",
|
|
"car_sales"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 76,
|
|
"id": "c4f2c1e4",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"car_sales[\"Seats\"].fillna(5, inplace=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 77,
|
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"id": "8de131a3",
|
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"metadata": {},
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"outputs": [
|
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{
|
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"data": {
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|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>Make</th>\n",
|
|
" <th>Colour</th>\n",
|
|
" <th>Odometer (KM)</th>\n",
|
|
" <th>Doors</th>\n",
|
|
" <th>Price</th>\n",
|
|
" <th>Seats</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>150043</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>400000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>honda</td>\n",
|
|
" <td>Red</td>\n",
|
|
" <td>87899</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>500000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>32549</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>700000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>bmw</td>\n",
|
|
" <td>Black</td>\n",
|
|
" <td>11179</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>2200000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>nissan</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>213095</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>350000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>Green</td>\n",
|
|
" <td>99213</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>450000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>honda</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>45698</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>750000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>honda</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>54738</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>700000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>60000</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>625000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>nissan</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>31600</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>970000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Make Colour Odometer (KM) Doors Price Seats\n",
|
|
"0 toyota White 150043 4 400000 5.0\n",
|
|
"1 honda Red 87899 4 500000 5.0\n",
|
|
"2 toyota Blue 32549 3 700000 5.0\n",
|
|
"3 bmw Black 11179 5 2200000 5.0\n",
|
|
"4 nissan White 213095 4 350000 5.0\n",
|
|
"5 toyota Green 99213 4 450000 5.0\n",
|
|
"6 honda Blue 45698 4 750000 5.0\n",
|
|
"7 honda Blue 54738 4 700000 5.0\n",
|
|
"8 toyota White 60000 4 625000 5.0\n",
|
|
"9 nissan White 31600 4 970000 5.0"
|
|
]
|
|
},
|
|
"execution_count": 77,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"car_sales"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 80,
|
|
"id": "8b823fad",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"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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|
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|
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" text-align: right;\n",
|
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" }\n",
|
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"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>Make</th>\n",
|
|
" <th>Colour</th>\n",
|
|
" <th>Odometer (KM)</th>\n",
|
|
" <th>Doors</th>\n",
|
|
" <th>Price</th>\n",
|
|
" <th>Seats</th>\n",
|
|
" <th>Fuel per 100KM</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>150043</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>400000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>7.5</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>honda</td>\n",
|
|
" <td>Red</td>\n",
|
|
" <td>87899</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>500000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>9.2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>32549</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>700000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>bmw</td>\n",
|
|
" <td>Black</td>\n",
|
|
" <td>11179</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>2200000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>9.6</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>nissan</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>213095</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>350000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>8.7</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>Green</td>\n",
|
|
" <td>99213</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>450000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>4.7</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>honda</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>45698</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>750000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>7.6</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>honda</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>54738</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>700000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>8.7</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>60000</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>625000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>3.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>nissan</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>31600</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>970000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>4.5</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Make Colour Odometer (KM) Doors Price Seats Fuel per 100KM\n",
|
|
"0 toyota White 150043 4 400000 5.0 7.5\n",
|
|
"1 honda Red 87899 4 500000 5.0 9.2\n",
|
|
"2 toyota Blue 32549 3 700000 5.0 5.0\n",
|
|
"3 bmw Black 11179 5 2200000 5.0 9.6\n",
|
|
"4 nissan White 213095 4 350000 5.0 8.7\n",
|
|
"5 toyota Green 99213 4 450000 5.0 4.7\n",
|
|
"6 honda Blue 45698 4 750000 5.0 7.6\n",
|
|
"7 honda Blue 54738 4 700000 5.0 8.7\n",
|
|
"8 toyota White 60000 4 625000 5.0 3.0\n",
|
|
"9 nissan White 31600 4 970000 5.0 4.5"
|
|
]
|
|
},
|
|
"execution_count": 80,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Column from Python list\n",
|
|
"fuel_economy = [7.5, 9.2, 5.0, 9.6, 8.7, 4.7, 7.6, 8.7, 3.0, 4.5]\n",
|
|
"car_sales[\"Fuel per 100KM\"] = fuel_economy\n",
|
|
"car_sales"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 84,
|
|
"id": "9a181add",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"car_sales[\"Total fuel used (L)\"] = car_sales[\"Odometer (KM)\"]/100 * car_sales[\"Fuel per 100KM\"]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 85,
|
|
"id": "20131395",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .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>Make</th>\n",
|
|
" <th>Colour</th>\n",
|
|
" <th>Odometer (KM)</th>\n",
|
|
" <th>Doors</th>\n",
|
|
" <th>Price</th>\n",
|
|
" <th>Seats</th>\n",
|
|
" <th>Fuel per 100KM</th>\n",
|
|
" <th>Total fuel used</th>\n",
|
|
" <th>Total fuel used (L)</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>150043</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>400000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>7.5</td>\n",
|
|
" <td>11253.225</td>\n",
|
|
" <td>11253.225</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>honda</td>\n",
|
|
" <td>Red</td>\n",
|
|
" <td>87899</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>500000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>9.2</td>\n",
|
|
" <td>8086.708</td>\n",
|
|
" <td>8086.708</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>32549</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>700000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>1627.450</td>\n",
|
|
" <td>1627.450</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>bmw</td>\n",
|
|
" <td>Black</td>\n",
|
|
" <td>11179</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>2200000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>9.6</td>\n",
|
|
" <td>1073.184</td>\n",
|
|
" <td>1073.184</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>nissan</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>213095</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>350000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>8.7</td>\n",
|
|
" <td>18539.265</td>\n",
|
|
" <td>18539.265</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>Green</td>\n",
|
|
" <td>99213</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>450000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>4.7</td>\n",
|
|
" <td>4663.011</td>\n",
|
|
" <td>4663.011</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>honda</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>45698</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>750000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>7.6</td>\n",
|
|
" <td>3473.048</td>\n",
|
|
" <td>3473.048</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>honda</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>54738</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>700000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>8.7</td>\n",
|
|
" <td>4762.206</td>\n",
|
|
" <td>4762.206</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>60000</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>625000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>3.0</td>\n",
|
|
" <td>1800.000</td>\n",
|
|
" <td>1800.000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>nissan</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>31600</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>970000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>4.5</td>\n",
|
|
" <td>1422.000</td>\n",
|
|
" <td>1422.000</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Make Colour Odometer (KM) Doors Price Seats Fuel per 100KM \\\n",
|
|
"0 toyota White 150043 4 400000 5.0 7.5 \n",
|
|
"1 honda Red 87899 4 500000 5.0 9.2 \n",
|
|
"2 toyota Blue 32549 3 700000 5.0 5.0 \n",
|
|
"3 bmw Black 11179 5 2200000 5.0 9.6 \n",
|
|
"4 nissan White 213095 4 350000 5.0 8.7 \n",
|
|
"5 toyota Green 99213 4 450000 5.0 4.7 \n",
|
|
"6 honda Blue 45698 4 750000 5.0 7.6 \n",
|
|
"7 honda Blue 54738 4 700000 5.0 8.7 \n",
|
|
"8 toyota White 60000 4 625000 5.0 3.0 \n",
|
|
"9 nissan White 31600 4 970000 5.0 4.5 \n",
|
|
"\n",
|
|
" Total fuel used Total fuel used (L) \n",
|
|
"0 11253.225 11253.225 \n",
|
|
"1 8086.708 8086.708 \n",
|
|
"2 1627.450 1627.450 \n",
|
|
"3 1073.184 1073.184 \n",
|
|
"4 18539.265 18539.265 \n",
|
|
"5 4663.011 4663.011 \n",
|
|
"6 3473.048 3473.048 \n",
|
|
"7 4762.206 4762.206 \n",
|
|
"8 1800.000 1800.000 \n",
|
|
"9 1422.000 1422.000 "
|
|
]
|
|
},
|
|
"execution_count": 85,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"car_sales"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 86,
|
|
"id": "d9f81e85",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .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>Make</th>\n",
|
|
" <th>Colour</th>\n",
|
|
" <th>Odometer (KM)</th>\n",
|
|
" <th>Doors</th>\n",
|
|
" <th>Price</th>\n",
|
|
" <th>Seats</th>\n",
|
|
" <th>Fuel per 100KM</th>\n",
|
|
" <th>Total fuel used</th>\n",
|
|
" <th>Total fuel used (L)</th>\n",
|
|
" <th>Number of wheels</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>150043</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>400000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>7.5</td>\n",
|
|
" <td>11253.225</td>\n",
|
|
" <td>11253.225</td>\n",
|
|
" <td>4</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>honda</td>\n",
|
|
" <td>Red</td>\n",
|
|
" <td>87899</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>500000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>9.2</td>\n",
|
|
" <td>8086.708</td>\n",
|
|
" <td>8086.708</td>\n",
|
|
" <td>4</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>32549</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>700000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>1627.450</td>\n",
|
|
" <td>1627.450</td>\n",
|
|
" <td>4</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>bmw</td>\n",
|
|
" <td>Black</td>\n",
|
|
" <td>11179</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>2200000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>9.6</td>\n",
|
|
" <td>1073.184</td>\n",
|
|
" <td>1073.184</td>\n",
|
|
" <td>4</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>nissan</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>213095</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>350000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>8.7</td>\n",
|
|
" <td>18539.265</td>\n",
|
|
" <td>18539.265</td>\n",
|
|
" <td>4</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>Green</td>\n",
|
|
" <td>99213</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>450000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>4.7</td>\n",
|
|
" <td>4663.011</td>\n",
|
|
" <td>4663.011</td>\n",
|
|
" <td>4</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>honda</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>45698</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>750000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>7.6</td>\n",
|
|
" <td>3473.048</td>\n",
|
|
" <td>3473.048</td>\n",
|
|
" <td>4</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>honda</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>54738</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>700000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>8.7</td>\n",
|
|
" <td>4762.206</td>\n",
|
|
" <td>4762.206</td>\n",
|
|
" <td>4</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>60000</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>625000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>3.0</td>\n",
|
|
" <td>1800.000</td>\n",
|
|
" <td>1800.000</td>\n",
|
|
" <td>4</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>nissan</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>31600</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>970000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>4.5</td>\n",
|
|
" <td>1422.000</td>\n",
|
|
" <td>1422.000</td>\n",
|
|
" <td>4</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Make Colour Odometer (KM) Doors Price Seats Fuel per 100KM \\\n",
|
|
"0 toyota White 150043 4 400000 5.0 7.5 \n",
|
|
"1 honda Red 87899 4 500000 5.0 9.2 \n",
|
|
"2 toyota Blue 32549 3 700000 5.0 5.0 \n",
|
|
"3 bmw Black 11179 5 2200000 5.0 9.6 \n",
|
|
"4 nissan White 213095 4 350000 5.0 8.7 \n",
|
|
"5 toyota Green 99213 4 450000 5.0 4.7 \n",
|
|
"6 honda Blue 45698 4 750000 5.0 7.6 \n",
|
|
"7 honda Blue 54738 4 700000 5.0 8.7 \n",
|
|
"8 toyota White 60000 4 625000 5.0 3.0 \n",
|
|
"9 nissan White 31600 4 970000 5.0 4.5 \n",
|
|
"\n",
|
|
" Total fuel used Total fuel used (L) Number of wheels \n",
|
|
"0 11253.225 11253.225 4 \n",
|
|
"1 8086.708 8086.708 4 \n",
|
|
"2 1627.450 1627.450 4 \n",
|
|
"3 1073.184 1073.184 4 \n",
|
|
"4 18539.265 18539.265 4 \n",
|
|
"5 4663.011 4663.011 4 \n",
|
|
"6 3473.048 3473.048 4 \n",
|
|
"7 4762.206 4762.206 4 \n",
|
|
"8 1800.000 1800.000 4 \n",
|
|
"9 1422.000 1422.000 4 "
|
|
]
|
|
},
|
|
"execution_count": 86,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Create a column from a single value\n",
|
|
"car_sales[\"Number of wheels\"] = 4\n",
|
|
"car_sales"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 87,
|
|
"id": "6859af53",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"car_sales[\"Passed road safety\"] = True"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 88,
|
|
"id": "a289dd85",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Make object\n",
|
|
"Colour object\n",
|
|
"Odometer (KM) int64\n",
|
|
"Doors int64\n",
|
|
"Price int64\n",
|
|
"Seats float64\n",
|
|
"Fuel per 100KM float64\n",
|
|
"Total fuel used float64\n",
|
|
"Total fuel used (L) float64\n",
|
|
"Number of wheels int64\n",
|
|
"Passed road safety bool\n",
|
|
"dtype: object"
|
|
]
|
|
},
|
|
"execution_count": 88,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"car_sales.dtypes"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 90,
|
|
"id": "a7c11bc0",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"car_sales = car_sales.drop(\"Total fuel used\", axis=1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 91,
|
|
"id": "15d32814",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"text/html": [
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|
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|
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|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>Make</th>\n",
|
|
" <th>Colour</th>\n",
|
|
" <th>Odometer (KM)</th>\n",
|
|
" <th>Doors</th>\n",
|
|
" <th>Price</th>\n",
|
|
" <th>Seats</th>\n",
|
|
" <th>Fuel per 100KM</th>\n",
|
|
" <th>Total fuel used (L)</th>\n",
|
|
" <th>Number of wheels</th>\n",
|
|
" <th>Passed road safety</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>150043</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>400000</td>\n",
|
|
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|
|
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|
|
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|
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|
|
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|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>honda</td>\n",
|
|
" <td>Red</td>\n",
|
|
" <td>87899</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>500000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
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|
|
" <td>8086.708</td>\n",
|
|
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|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>32549</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>700000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>1627.450</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>bmw</td>\n",
|
|
" <td>Black</td>\n",
|
|
" <td>11179</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>2200000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>9.6</td>\n",
|
|
" <td>1073.184</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>nissan</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>213095</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>350000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>8.7</td>\n",
|
|
" <td>18539.265</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>Green</td>\n",
|
|
" <td>99213</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>450000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>4.7</td>\n",
|
|
" <td>4663.011</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>honda</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>45698</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>750000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>7.6</td>\n",
|
|
" <td>3473.048</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>honda</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>54738</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>700000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>8.7</td>\n",
|
|
" <td>4762.206</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>60000</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>625000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
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|
|
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|
|
" <td>4</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>nissan</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>31600</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>970000</td>\n",
|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Make Colour Odometer (KM) Doors Price Seats Fuel per 100KM \\\n",
|
|
"0 toyota White 150043 4 400000 5.0 7.5 \n",
|
|
"1 honda Red 87899 4 500000 5.0 9.2 \n",
|
|
"2 toyota Blue 32549 3 700000 5.0 5.0 \n",
|
|
"3 bmw Black 11179 5 2200000 5.0 9.6 \n",
|
|
"4 nissan White 213095 4 350000 5.0 8.7 \n",
|
|
"5 toyota Green 99213 4 450000 5.0 4.7 \n",
|
|
"6 honda Blue 45698 4 750000 5.0 7.6 \n",
|
|
"7 honda Blue 54738 4 700000 5.0 8.7 \n",
|
|
"8 toyota White 60000 4 625000 5.0 3.0 \n",
|
|
"9 nissan White 31600 4 970000 5.0 4.5 \n",
|
|
"\n",
|
|
" Total fuel used (L) Number of wheels Passed road safety \n",
|
|
"0 11253.225 4 True \n",
|
|
"1 8086.708 4 True \n",
|
|
"2 1627.450 4 True \n",
|
|
"3 1073.184 4 True \n",
|
|
"4 18539.265 4 True \n",
|
|
"5 4663.011 4 True \n",
|
|
"6 3473.048 4 True \n",
|
|
"7 4762.206 4 True \n",
|
|
"8 1800.000 4 True \n",
|
|
"9 1422.000 4 True "
|
|
]
|
|
},
|
|
"execution_count": 91,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"car_sales"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 92,
|
|
"id": "34c60bf4",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
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|
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|
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|
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|
|
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|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>Make</th>\n",
|
|
" <th>Colour</th>\n",
|
|
" <th>Odometer (KM)</th>\n",
|
|
" <th>Doors</th>\n",
|
|
" <th>Price</th>\n",
|
|
" <th>Seats</th>\n",
|
|
" <th>Fuel per 100KM</th>\n",
|
|
" <th>Total fuel used (L)</th>\n",
|
|
" <th>Number of wheels</th>\n",
|
|
" <th>Passed road safety</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>honda</td>\n",
|
|
" <td>Red</td>\n",
|
|
" <td>87899</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>500000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
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|
|
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|
|
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|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>nissan</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>31600</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>970000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>4.5</td>\n",
|
|
" <td>1422.000</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>honda</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>54738</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>700000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>8.7</td>\n",
|
|
" <td>4762.206</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>32549</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>700000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>1627.450</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>bmw</td>\n",
|
|
" <td>Black</td>\n",
|
|
" <td>11179</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>2200000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>9.6</td>\n",
|
|
" <td>1073.184</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Make Colour Odometer (KM) Doors Price Seats Fuel per 100KM \\\n",
|
|
"1 honda Red 87899 4 500000 5.0 9.2 \n",
|
|
"9 nissan White 31600 4 970000 5.0 4.5 \n",
|
|
"7 honda Blue 54738 4 700000 5.0 8.7 \n",
|
|
"2 toyota Blue 32549 3 700000 5.0 5.0 \n",
|
|
"3 bmw Black 11179 5 2200000 5.0 9.6 \n",
|
|
"\n",
|
|
" Total fuel used (L) Number of wheels Passed road safety \n",
|
|
"1 8086.708 4 True \n",
|
|
"9 1422.000 4 True \n",
|
|
"7 4762.206 4 True \n",
|
|
"2 1627.450 4 True \n",
|
|
"3 1073.184 4 True "
|
|
]
|
|
},
|
|
"execution_count": 92,
|
|
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|
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|
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|
|
],
|
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"source": [
|
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"car_sales.sample(frac=0.5)"
|
|
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|
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|
|
{
|
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"cell_type": "code",
|
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"execution_count": 95,
|
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"id": "6ae2e87d",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"car_sales_shuffled = car_sales.sample(frac=1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 96,
|
|
"id": "46c3d98c",
|
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"metadata": {},
|
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"outputs": [
|
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|
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|
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|
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|
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|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
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|
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|
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|
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|
|
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|
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|
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|
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|
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|
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" Make Colour Odometer (KM) Doors Price Seats Fuel per 100KM \\\n",
|
|
"0 toyota White 150043 4 400000 5.0 7.5 \n",
|
|
"8 toyota White 60000 4 625000 5.0 3.0 \n",
|
|
"\n",
|
|
" Total fuel used (L) Number of wheels Passed road safety \n",
|
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|
|
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|
|
]
|
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|
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|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
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|
|
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|
|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
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|
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|
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|
|
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|
|
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|
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|
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|
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|
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|
|
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|
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|
|
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|
|
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|
|
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|
|
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|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
" <td>Blue</td>\n",
|
|
" <td>45698</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>750000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>7.6</td>\n",
|
|
" <td>3473.048</td>\n",
|
|
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|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
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|
|
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|
|
" <td>60000</td>\n",
|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
" <th>6</th>\n",
|
|
" <td>honda</td>\n",
|
|
" <td>Red</td>\n",
|
|
" <td>87899</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>500000</td>\n",
|
|
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|
|
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|
|
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|
|
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|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>bmw</td>\n",
|
|
" <td>Black</td>\n",
|
|
" <td>11179</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>2200000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>9.6</td>\n",
|
|
" <td>1073.184</td>\n",
|
|
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|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
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|
|
" <td>toyota</td>\n",
|
|
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|
|
" <td>150043</td>\n",
|
|
" <td>4</td>\n",
|
|
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|
|
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|
|
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|
|
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|
|
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|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>Green</td>\n",
|
|
" <td>99213</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>450000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>4.7</td>\n",
|
|
" <td>4663.011</td>\n",
|
|
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|
|
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|
|
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|
|
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|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Make Colour Odometer (KM) Doors Price Seats Fuel per 100KM \\\n",
|
|
"0 nissan White 213095 4 350000 5.0 8.7 \n",
|
|
"1 honda Blue 54738 4 700000 5.0 8.7 \n",
|
|
"2 nissan White 31600 4 970000 5.0 4.5 \n",
|
|
"3 toyota Blue 32549 3 700000 5.0 5.0 \n",
|
|
"4 honda Blue 45698 4 750000 5.0 7.6 \n",
|
|
"5 toyota White 60000 4 625000 5.0 3.0 \n",
|
|
"6 honda Red 87899 4 500000 5.0 9.2 \n",
|
|
"7 bmw Black 11179 5 2200000 5.0 9.6 \n",
|
|
"8 toyota White 150043 4 400000 5.0 7.5 \n",
|
|
"9 toyota Green 99213 4 450000 5.0 4.7 \n",
|
|
"\n",
|
|
" Total fuel used (L) Number of wheels Passed road safety \n",
|
|
"0 18539.265 4 True \n",
|
|
"1 4762.206 4 True \n",
|
|
"2 1422.000 4 True \n",
|
|
"3 1627.450 4 True \n",
|
|
"4 3473.048 4 True \n",
|
|
"5 1800.000 4 True \n",
|
|
"6 8086.708 4 True \n",
|
|
"7 1073.184 4 True \n",
|
|
"8 11253.225 4 True \n",
|
|
"9 4663.011 4 True "
|
|
]
|
|
},
|
|
"execution_count": 98,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"car_sales = car_sales_shuffled.reset_index(drop=True)\n",
|
|
"car_sales"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 99,
|
|
"id": "b9220ca6",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
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|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
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|
|
" <th>Make</th>\n",
|
|
" <th>Colour</th>\n",
|
|
" <th>Odometer (KM)</th>\n",
|
|
" <th>Doors</th>\n",
|
|
" <th>Price</th>\n",
|
|
" <th>Seats</th>\n",
|
|
" <th>Fuel per 100KM</th>\n",
|
|
" <th>Total fuel used (L)</th>\n",
|
|
" <th>Number of wheels</th>\n",
|
|
" <th>Passed road safety</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>nissan</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>213095</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>350000</td>\n",
|
|
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|
|
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|
|
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|
|
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|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>honda</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>54738</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>700000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>8.7</td>\n",
|
|
" <td>4762.206</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>nissan</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>31600</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>970000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>4.5</td>\n",
|
|
" <td>1422.000</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>32549</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>700000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>1627.450</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>honda</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>45698</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>750000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>7.6</td>\n",
|
|
" <td>3473.048</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>60000</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>625000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>3.0</td>\n",
|
|
" <td>1800.000</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>honda</td>\n",
|
|
" <td>Red</td>\n",
|
|
" <td>87899</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>500000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>9.2</td>\n",
|
|
" <td>8086.708</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>bmw</td>\n",
|
|
" <td>Black</td>\n",
|
|
" <td>11179</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>2200000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>9.6</td>\n",
|
|
" <td>1073.184</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>150043</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>400000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>7.5</td>\n",
|
|
" <td>11253.225</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>Green</td>\n",
|
|
" <td>99213</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>450000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>4.7</td>\n",
|
|
" <td>4663.011</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Make Colour Odometer (KM) Doors Price Seats Fuel per 100KM \\\n",
|
|
"0 nissan White 213095 4 350000 5.0 8.7 \n",
|
|
"1 honda Blue 54738 4 700000 5.0 8.7 \n",
|
|
"2 nissan White 31600 4 970000 5.0 4.5 \n",
|
|
"3 toyota Blue 32549 3 700000 5.0 5.0 \n",
|
|
"4 honda Blue 45698 4 750000 5.0 7.6 \n",
|
|
"5 toyota White 60000 4 625000 5.0 3.0 \n",
|
|
"6 honda Red 87899 4 500000 5.0 9.2 \n",
|
|
"7 bmw Black 11179 5 2200000 5.0 9.6 \n",
|
|
"8 toyota White 150043 4 400000 5.0 7.5 \n",
|
|
"9 toyota Green 99213 4 450000 5.0 4.7 \n",
|
|
"\n",
|
|
" Total fuel used (L) Number of wheels Passed road safety \n",
|
|
"0 18539.265 4 True \n",
|
|
"1 4762.206 4 True \n",
|
|
"2 1422.000 4 True \n",
|
|
"3 1627.450 4 True \n",
|
|
"4 3473.048 4 True \n",
|
|
"5 1800.000 4 True \n",
|
|
"6 8086.708 4 True \n",
|
|
"7 1073.184 4 True \n",
|
|
"8 11253.225 4 True \n",
|
|
"9 4663.011 4 True "
|
|
]
|
|
},
|
|
"execution_count": 99,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
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}
|
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],
|
|
"source": [
|
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"car_sales"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 100,
|
|
"id": "bdcc6512",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
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" vertical-align: middle;\n",
|
|
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|
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|
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|
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|
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|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>Make</th>\n",
|
|
" <th>Colour</th>\n",
|
|
" <th>Odometer (KM)</th>\n",
|
|
" <th>Doors</th>\n",
|
|
" <th>Price</th>\n",
|
|
" <th>Seats</th>\n",
|
|
" <th>Fuel per 100KM</th>\n",
|
|
" <th>Total fuel used (L)</th>\n",
|
|
" <th>Number of wheels</th>\n",
|
|
" <th>Passed road safety</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>nissan</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>133184.375</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>350000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>8.7</td>\n",
|
|
" <td>18539.265</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>honda</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>34211.250</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>700000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>8.7</td>\n",
|
|
" <td>4762.206</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>nissan</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>19750.000</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>970000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>4.5</td>\n",
|
|
" <td>1422.000</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>20343.125</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>700000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>1627.450</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>honda</td>\n",
|
|
" <td>Blue</td>\n",
|
|
" <td>28561.250</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>750000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>7.6</td>\n",
|
|
" <td>3473.048</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>37500.000</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>625000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>3.0</td>\n",
|
|
" <td>1800.000</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>honda</td>\n",
|
|
" <td>Red</td>\n",
|
|
" <td>54936.875</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>500000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>9.2</td>\n",
|
|
" <td>8086.708</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>bmw</td>\n",
|
|
" <td>Black</td>\n",
|
|
" <td>6986.875</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>2200000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>9.6</td>\n",
|
|
" <td>1073.184</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>White</td>\n",
|
|
" <td>93776.875</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>400000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>7.5</td>\n",
|
|
" <td>11253.225</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>toyota</td>\n",
|
|
" <td>Green</td>\n",
|
|
" <td>62008.125</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>450000</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>4.7</td>\n",
|
|
" <td>4663.011</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Make Colour Odometer (KM) Doors Price Seats Fuel per 100KM \\\n",
|
|
"0 nissan White 133184.375 4 350000 5.0 8.7 \n",
|
|
"1 honda Blue 34211.250 4 700000 5.0 8.7 \n",
|
|
"2 nissan White 19750.000 4 970000 5.0 4.5 \n",
|
|
"3 toyota Blue 20343.125 3 700000 5.0 5.0 \n",
|
|
"4 honda Blue 28561.250 4 750000 5.0 7.6 \n",
|
|
"5 toyota White 37500.000 4 625000 5.0 3.0 \n",
|
|
"6 honda Red 54936.875 4 500000 5.0 9.2 \n",
|
|
"7 bmw Black 6986.875 5 2200000 5.0 9.6 \n",
|
|
"8 toyota White 93776.875 4 400000 5.0 7.5 \n",
|
|
"9 toyota Green 62008.125 4 450000 5.0 4.7 \n",
|
|
"\n",
|
|
" Total fuel used (L) Number of wheels Passed road safety \n",
|
|
"0 18539.265 4 True \n",
|
|
"1 4762.206 4 True \n",
|
|
"2 1422.000 4 True \n",
|
|
"3 1627.450 4 True \n",
|
|
"4 3473.048 4 True \n",
|
|
"5 1800.000 4 True \n",
|
|
"6 8086.708 4 True \n",
|
|
"7 1073.184 4 True \n",
|
|
"8 11253.225 4 True \n",
|
|
"9 4663.011 4 True "
|
|
]
|
|
},
|
|
"execution_count": 100,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"car_sales[\"Odometer (KM)\"] = car_sales[\"Odometer (KM)\"].apply(lambda x: x / 1.6)\n",
|
|
"car_sales"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
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"execution_count": null,
|
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"id": "5d3da940",
|
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"metadata": {},
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