{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Lame Cars Are a Better Deal #" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "\n", "In 2019 the global used car market was estimated to be a whopping 1332 billion US dollars (1). This was prior to the ubiquitous supply chain disruptions that are now part of our daily lives and the expectations of a 13% to 32% decline in global trade (2)(3). The turnover in used car inventory for August and September was the highest in six years (4). One large sector of used car sales comes from the eBay Vehicle Market. They have over 7.4 million users per month. A car or truck is sold every 3 minutes (5). That's of lot of interactions!\n", "\n", "The main conclusion of this study is unpopular cars might be a better deal when using mileage as a metric. For the money spent they have lower mileage.\n", "\n", "Let's take a quick look at a selection of 50,000 rows from a dataset created in 2015 for the German eBay marketplace, eBay Kleinanzeigen. The original data set can be found here.\n", "https://data.world/data-society/used-cars-data\n", "\n", "Citations\n", "1. Grand View Research. Used Car Market Size, Share & Trends Analysis Report By Vehicle Type (Hybrid, Conventional, Electric), By Vendor Type, By Fuel Type, By Size, By Region, By Sales Channel, And Segment Forecasts, 2020 - 2027. Sept 2020.\n", "https://www.grandviewresearch.com/industry-analysis/used-car-market\n", "2. Li, X., Ghadami, A., Drake, J.M. et al. Mathematical model of the feedback between global supply chain disruption and COVID-19 dynamics. Sci Rep 11, 15450 (2021).\n", "https://doi.org/10.1038/s41598-021-94619-1\n", "3. World Trade Organization. Trade set to plunge as COVID-19 pandemic upends global economy. April 2020.\n", "https://www.wto.org/english/news_e/pres20_e/pr855_e.htm\n", "4. Eric Rosenbaum. The used car boom is one of the hottest, and trickiest, coronavirus markets for consumers. CNBC October 2020.\n", "https://www.cnbc.com/2020/10/15/used-car-boom-is-one-of-hottest-coronavirus-markets-for-consumers.html\n", "5. Welcome to the eBay Vehicle Seller Center. eBay. Sept 2021.\n", "https://pages.ebay.com/sellerinformation/vehiclesellercenter.html" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Open and Examine Data Set\n", "* An initial look at the data set shows 50,000 entries with 20 columns of information.\n", "* Five of the columns are missing entries and in some the data needs to be converted to the correct type.\n", "* Some of the entries are in German and need to be translated." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 50000 entries, 0 to 49999\n", "Data columns (total 20 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 dateCrawled 50000 non-null object\n", " 1 name 50000 non-null object\n", " 2 seller 50000 non-null object\n", " 3 offerType 50000 non-null object\n", " 4 price 50000 non-null object\n", " 5 abtest 50000 non-null object\n", " 6 vehicleType 44905 non-null object\n", " 7 yearOfRegistration 50000 non-null int64 \n", " 8 gearbox 47320 non-null object\n", " 9 powerPS 50000 non-null int64 \n", " 10 model 47242 non-null object\n", " 11 odometer 50000 non-null object\n", " 12 monthOfRegistration 50000 non-null int64 \n", " 13 fuelType 45518 non-null object\n", " 14 brand 50000 non-null object\n", " 15 notRepairedDamage 40171 non-null object\n", " 16 dateCreated 50000 non-null object\n", " 17 nrOfPictures 50000 non-null int64 \n", " 18 postalCode 50000 non-null int64 \n", " 19 lastSeen 50000 non-null object\n", "dtypes: int64(5), object(15)\n", "memory usage: 7.6+ MB\n" ] } ], "source": [ "# import libraries and read csv\n", "import emoji\n", "import pandas as pd\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import pprint\n", "autos = pd.read_csv(\"autos.csv\", encoding=\"Latin-1\")\n", "# get info about dataset\n", "autos.info()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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dateCrawlednamesellerofferTypepriceabtestvehicleTypeyearOfRegistrationgearboxpowerPSmodelodometermonthOfRegistrationfuelTypebrandnotRepairedDamagedateCreatednrOfPicturespostalCodelastSeen
count5000050000500005000050000500004490550000.0000004732050000.000000472425000050000.0000004551850000401715000050000.050000.00000050000
unique482133875422235728NaN2NaN24513NaN740276NaNNaN39481
top2016-03-30 17:37:35Ford_FiestaprivatAngebot$0testlimousineNaNmanuellNaNgolf150,000kmNaNbenzinvolkswagennein2016-04-03 00:00:00NaNNaN2016-04-07 06:17:27
freq378499994999914212575612859NaN36993NaN402432424NaN3010710687352321946NaNNaN8
meanNaNNaNNaNNaNNaNNaNNaN2005.073280NaN116.355920NaNNaN5.723360NaNNaNNaNNaN0.050813.627300NaN
stdNaNNaNNaNNaNNaNNaNNaN105.712813NaN209.216627NaNNaN3.711984NaNNaNNaNNaN0.025779.747957NaN
minNaNNaNNaNNaNNaNNaNNaN1000.000000NaN0.000000NaNNaN0.000000NaNNaNNaNNaN0.01067.000000NaN
25%NaNNaNNaNNaNNaNNaNNaN1999.000000NaN70.000000NaNNaN3.000000NaNNaNNaNNaN0.030451.000000NaN
50%NaNNaNNaNNaNNaNNaNNaN2003.000000NaN105.000000NaNNaN6.000000NaNNaNNaNNaN0.049577.000000NaN
75%NaNNaNNaNNaNNaNNaNNaN2008.000000NaN150.000000NaNNaN9.000000NaNNaNNaNNaN0.071540.000000NaN
maxNaNNaNNaNNaNNaNNaNNaN9999.000000NaN17700.000000NaNNaN12.000000NaNNaNNaNNaN0.099998.000000NaN
\n", "
" ], "text/plain": [ " dateCrawled name seller offerType price abtest \\\n", "count 50000 50000 50000 50000 50000 50000 \n", "unique 48213 38754 2 2 2357 2 \n", "top 2016-03-30 17:37:35 Ford_Fiesta privat Angebot $0 test \n", "freq 3 78 49999 49999 1421 25756 \n", "mean NaN NaN NaN NaN NaN NaN \n", "std NaN NaN NaN NaN NaN NaN \n", "min NaN NaN NaN NaN NaN NaN \n", "25% NaN NaN NaN NaN NaN NaN \n", "50% NaN NaN NaN NaN NaN NaN \n", "75% NaN NaN NaN NaN NaN NaN \n", "max NaN NaN NaN NaN NaN NaN \n", "\n", " vehicleType yearOfRegistration gearbox powerPS model \\\n", "count 44905 50000.000000 47320 50000.000000 47242 \n", "unique 8 NaN 2 NaN 245 \n", "top limousine NaN manuell NaN golf \n", "freq 12859 NaN 36993 NaN 4024 \n", "mean NaN 2005.073280 NaN 116.355920 NaN \n", "std NaN 105.712813 NaN 209.216627 NaN \n", "min NaN 1000.000000 NaN 0.000000 NaN \n", "25% NaN 1999.000000 NaN 70.000000 NaN \n", "50% NaN 2003.000000 NaN 105.000000 NaN \n", "75% NaN 2008.000000 NaN 150.000000 NaN \n", "max NaN 9999.000000 NaN 17700.000000 NaN \n", "\n", " odometer monthOfRegistration fuelType brand notRepairedDamage \\\n", "count 50000 50000.000000 45518 50000 40171 \n", "unique 13 NaN 7 40 2 \n", "top 150,000km NaN benzin volkswagen nein \n", "freq 32424 NaN 30107 10687 35232 \n", "mean NaN 5.723360 NaN NaN NaN \n", "std NaN 3.711984 NaN NaN NaN \n", "min NaN 0.000000 NaN NaN NaN \n", "25% NaN 3.000000 NaN NaN NaN \n", "50% NaN 6.000000 NaN NaN NaN \n", "75% NaN 9.000000 NaN NaN NaN \n", "max NaN 12.000000 NaN NaN NaN \n", "\n", " dateCreated nrOfPictures postalCode lastSeen \n", "count 50000 50000.0 50000.000000 50000 \n", "unique 76 NaN NaN 39481 \n", "top 2016-04-03 00:00:00 NaN NaN 2016-04-07 06:17:27 \n", "freq 1946 NaN NaN 8 \n", "mean NaN 0.0 50813.627300 NaN \n", "std NaN 0.0 25779.747957 NaN \n", "min NaN 0.0 1067.000000 NaN \n", "25% NaN 0.0 30451.000000 NaN \n", "50% NaN 0.0 49577.000000 NaN \n", "75% NaN 0.0 71540.000000 NaN \n", "max NaN 0.0 99998.000000 NaN " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# initial stats\n", "autos.describe(include=\"all\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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dateCrawlednamesellerofferTypepriceabtestvehicleTypeyearOfRegistrationgearboxpowerPSmodelodometermonthOfRegistrationfuelTypebrandnotRepairedDamagedateCreatednrOfPicturespostalCodelastSeen
02016-03-26 17:47:46Peugeot_807_160_NAVTECH_ON_BOARDprivatAngebot$5,000controlbus2004manuell158andere150,000km3lpgpeugeotnein2016-03-26 00:00:000795882016-04-06 06:45:54
12016-04-04 13:38:56BMW_740i_4_4_Liter_HAMANN_UMBAU_Mega_OptikprivatAngebot$8,500controllimousine1997automatik2867er150,000km6benzinbmwnein2016-04-04 00:00:000710342016-04-06 14:45:08
22016-03-26 18:57:24Volkswagen_Golf_1.6_UnitedprivatAngebot$8,990testlimousine2009manuell102golf70,000km7benzinvolkswagennein2016-03-26 00:00:000353942016-04-06 20:15:37
32016-03-12 16:58:10Smart_smart_fortwo_coupe_softouch/F1/Klima/Pan...privatAngebot$4,350controlkleinwagen2007automatik71fortwo70,000km6benzinsmartnein2016-03-12 00:00:000337292016-03-15 03:16:28
42016-04-01 14:38:50Ford_Focus_1_6_Benzin_TÜV_neu_ist_sehr_gepfleg...privatAngebot$1,350testkombi2003manuell0focus150,000km7benzinfordnein2016-04-01 00:00:000392182016-04-01 14:38:50
\n", "
" ], "text/plain": [ " dateCrawled name \\\n", "0 2016-03-26 17:47:46 Peugeot_807_160_NAVTECH_ON_BOARD \n", "1 2016-04-04 13:38:56 BMW_740i_4_4_Liter_HAMANN_UMBAU_Mega_Optik \n", "2 2016-03-26 18:57:24 Volkswagen_Golf_1.6_United \n", "3 2016-03-12 16:58:10 Smart_smart_fortwo_coupe_softouch/F1/Klima/Pan... \n", "4 2016-04-01 14:38:50 Ford_Focus_1_6_Benzin_TÜV_neu_ist_sehr_gepfleg... \n", "\n", " seller offerType price abtest vehicleType yearOfRegistration \\\n", "0 privat Angebot $5,000 control bus 2004 \n", "1 privat Angebot $8,500 control limousine 1997 \n", "2 privat Angebot $8,990 test limousine 2009 \n", "3 privat Angebot $4,350 control kleinwagen 2007 \n", "4 privat Angebot $1,350 test kombi 2003 \n", "\n", " gearbox powerPS model odometer monthOfRegistration fuelType \\\n", "0 manuell 158 andere 150,000km 3 lpg \n", "1 automatik 286 7er 150,000km 6 benzin \n", "2 manuell 102 golf 70,000km 7 benzin \n", "3 automatik 71 fortwo 70,000km 6 benzin \n", "4 manuell 0 focus 150,000km 7 benzin \n", "\n", " brand notRepairedDamage dateCreated nrOfPictures \\\n", "0 peugeot nein 2016-03-26 00:00:00 0 \n", "1 bmw nein 2016-04-04 00:00:00 0 \n", "2 volkswagen nein 2016-03-26 00:00:00 0 \n", "3 smart nein 2016-03-12 00:00:00 0 \n", "4 ford nein 2016-04-01 00:00:00 0 \n", "\n", " postalCode lastSeen \n", "0 79588 2016-04-06 06:45:54 \n", "1 71034 2016-04-06 14:45:08 \n", "2 35394 2016-04-06 20:15:37 \n", "3 33729 2016-03-15 03:16:28 \n", "4 39218 2016-04-01 14:38:50 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# first five rows\n", "autos.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Begin Cleaning Data Set\n", "* The `price` and `odometer` columns need to be converted to integers.\n", "* The column titles could be more clear and use snake_case.\n", "* It would be nice if the `name`, `brand`, `model`, and `vehicle_type` columns were grouped together." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The data type for the price column is : int64\n", "The data type for the odometer_km column is : int64\n" ] } ], "source": [ "# clean the price and odometer columns\n", "autos[\"price\"] = autos[\"price\"].str.replace(\"$\",\"\").str.replace(\",\",\"\")\n", "autos[\"price\"] = autos[\"price\"].astype(int)\n", "autos[\"odometer\"] = autos[\"odometer\"].str.replace(\",\",\"\").str.replace(\"km\",\"\")\n", "autos[\"odometer\"] = autos[\"odometer\"].astype(int)\n", "autos.rename(columns={\"odometer\" : \"odometer_km\"}, inplace=True)\n", "print(\"The data type for the price column is : \", autos[\"price\"].dtypes)\n", "print(\"The data type for the odometer_km column is : \", autos[\"odometer_km\"].dtypes)\n", "# change the column names\n", "new_cols = ['date_crawled', 'name', 'seller', 'offer_type', 'price', 'ab_test',\n", " 'vehicle_type', 'year_of_registration', 'gearbox', 'powerPS', 'model',\n", " 'odometer_km', 'month_of_registration', 'fuel_type', 'brand',\n", " 'unrepaired_damage', 'ad_created', 'num_of_pictures', 'postal_code',\n", " 'last_seen']\n", "autos.columns = new_cols\n", "# reorder columns\n", "autos = autos.reindex(columns=[\"date_crawled\", \"price\", \"name\", \"brand\", \"model\", \"vehicle_type\", \"ab_test\",\n", " \"odometer_km\", \"month_of_registration\", \"year_of_registration\", \"gearbox\",\"powerPS\",\n", " \"fuel_type\", \"unrepaired_damage\", \"ad_created\", \"postal_code\", \"last_seen\",\n", " \"seller\", \"offer_type\", \"num_of_pictures\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Removing outliers and unnecessary columns\n", "## Part 1 of 4\n", "Looking at the `price` and `odometer` columns.\n", "* The majority of the values in the price column are less than 500,000 but a small number are close to or well over 1,000,000! The rows for these prices do not appear to be unique in other ways nor are they related to the three columns being considered for removal. Two of the entries are for an expensive brand, but they are not representative of either group. Most of the expensive cars are priced a suspicious \\\\$12,345,678🤔. The high number of vehicles (1421) with a price of 0 will skew the results as well. Keeping all rows with prices between \\\\$1 and \\\\$500,00 seems to make sense.\n", "* The odometer column doesn't contain any outlier values. It is broken into a group of 13 buckets. Notably, 64% of the entries are in the 150,000km or more category. There doesn't seem to be any reason to remove any of the rows based on the odometer entries." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Here's the index and price for the 15 most expensive vehicles:\n", " 39705 99999999\n", "42221 27322222\n", "39377 12345678\n", "47598 12345678\n", "27371 12345678\n", "2897 11111111\n", "24384 11111111\n", "11137 10000000\n", "47634 3890000\n", "7814 1300000\n", "22947 1234566\n", "43049 999999\n", "514 999999\n", "37585 999990\n", "36818 350000\n", "Name: price, dtype: int64\n", "Here's the index and price for the 10 least expensive vehicles:\n", " 31332 0\n", "35821 0\n", "8438 0\n", "43925 0\n", "38832 0\n", "8445 0\n", "29499 0\n", "15225 0\n", "43923 0\n", "18089 0\n", "Name: price, dtype: int64\n" ] } ], "source": [ "# isolate the price column and reveal outlier values\n", "price_series = autos[\"price\"]\n", "print(\"Here's the index and price for the 15 most expensive vehicles:\\n\", price_series.sort_values(ascending=False).head(n=15))\n", "print(\"Here's the index and price for the 10 least expensive vehicles:\\n\", price_series.sort_values(ascending=False).tail(n=10))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The number of cars between $1 and $100: 475\n", "The number of cars more than $500,000: 14\n", "The number of cars that are free: 1421\n", "This chart shows the most and least expensive cars.\n" ] } ], "source": [ "# examine rows with outlier prices\n", "cheap_cars = autos[autos[\"price\"].between(1,100)]\n", "print(\"The number of cars between $1 and $100: \", len(cheap_cars))\n", "expensive_cars = autos[autos[\"price\"]>500000]\n", "print(\"The number of cars more than $500,000: \", len(expensive_cars))\n", "free_cars = autos[autos[\"price\"] == 0]\n", "print(\"The number of cars that are free: \", len(free_cars))\n", "outlier_bool = (autos[\"price\"] < 1) | (autos[\"price\"] > 500000)\n", "outlier_rows = autos[outlier_bool]\n", "outlier_rows.sort_values(by=\"price\", axis=0, ascending=False)\n", "print(\"This chart shows the most and least expensive cars.\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Length: 50000\n", "Length: 48565\n" ] } ], "source": [ "# remove rows with outlier prices\n", "print(\"Length: \", len(autos))\n", "autos = autos[autos[\"price\"].between(1, 500000)]\n", "print(\"Length: \", len(autos))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(13,)\n", "\n" ] }, { "data": { "text/plain": [ "150000 31414\n", "125000 5057\n", "100000 2115\n", "90000 1734\n", "80000 1415\n", "70000 1217\n", "60000 1155\n", "50000 1012\n", "40000 815\n", "30000 780\n", "20000 762\n", "10000 253\n", "5000 836\n", "Name: odometer_km, dtype: int64" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# isolate the odometer column and reveal outlier values\n", "odometer_series = autos[\"odometer_km\"]\n", "print(odometer_series.unique().shape)\n", "print(odometer_series.describe)\n", "odometer_series.value_counts().sort_index(ascending=False).head(n=15)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Removing outliers and unnecessary columns\n", "### Part 2 of 4\n", "We can probably remove these three columns and make a note in the conclusion.\n", "* The `seller` and `offer_type` columns have only two values in each column. In each, one value represents all of the entries except for one entry.\n", "* The `num_of_pictures` column only has one entry, \"0\"." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "privat 48564\n", "gewerblich 1\n", "Name: seller, dtype: int64\n", "Angebot 48565\n", "Name: offer_type, dtype: int64\n", "0 48565\n", "Name: num_of_pictures, dtype: int64\n" ] } ], "source": [ "# close look at value counts in three columns\n", "print(autos[\"seller\"].value_counts())\n", "print(autos[\"offer_type\"].value_counts())\n", "print(autos[\"num_of_pictures\"].value_counts())" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# drop the seller, offer_type, and num_of_pictures columns\n", "autos = autos.drop([\"seller\", \"offer_type\", \"num_of_pictures\"], axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Removing outliers and unnecessary columns\n", "### Part 3 of 4\n", "The date values are in string format and should be converted but an initial look shows some interesting things.\n", "* The `date_crawled` column shows a normal distribution for when the ads were put into the data set. There are three low points that could be due to problems with the bot or network, or some other factor.\n", "* The `date_created` column is heavily skewed to the left. However, the tail is very shallow and the vast majority of entries occurs under a normal distribution. Creating a frequency table shows some ads with much older `date_created` values than the rest of the set. This is the only one of the three with dates prior to March 2016. Again, there are three low points that interestingly appear to correspond to the low points in the date_crawled series. Removing any dates prior to the earliest date for the `date_crawled` column removes 203 entries. Looking at a line graph for `date_crawled` and `date_created` confirms that the dips occur at the same times.\n", "* The `last_seen` column aligns more closely with `date_crawled` than the `date_created` column but there is a huge increase on April 4-6. These three days represent over 50% of the values in this column.\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# isolate the date portion of the date_crawled column and count the number of entries per day\n", "date_crawled_dist = autos[\"date_crawled\"].str[:10].value_counts().sort_index()\n", "# create a line plot showing crawl volume for each day\n", "date_crawled_dist.plot.line()\n", "plt.ylabel(\"Number of entries\")\n", "plt.xlabel(\"Date Crawled\")\n", "plt.xticks(rotation=30)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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mfn3Bsbx03mRUlb+s3Mw3bnqcVy2cNuTgdpAjZk/gwyfP55Dp4wa1sLAvzLMwDCM0OrNDm+9PJWJ85JXzmTdAB7jGZLx86mxXgeZ038ZCFVY+v3vQeopFpStfHLZnAZCMx3j5/CnctmZb2cWHNzyykVQ8xvUXvoyXzpsMuGm5r77+MLpyRb7yp8dY8fwuErHBB7d78/FXLRiRVwG+sai9ZxFmD+4rRGSriKwMjH1RRJ4XkUe8x2sC710sImtFZI2InBIYP1pEVnjvfV+GO/FnGGOIztzQ0k4HS1MqUX4aKlegsY9pKP+ufMXzO3uM/2bZen5xz7qy+/j9G0biWQCcePBUNu/O8HivkiOqyk0rN/Py+VMY39jT25nX1sKFJx7EHx/dyG8f3MDB08cNOdBeSdKJ+KjPhvo5cGqZ8e+p6hLvcSOAiCwEzgUWefv8UET8b+dHwAXAfO9R7piGYXjkCkVyBR3xhbYcfU1DdWbzpRXevZnckmbmhEYeDQS5l2/Yyad/t4Kr7nm27D6d2fLrNobK8Qe3AXDbmp5xi1Ubd/P8zk5OXbTv9BTA+0+Yx7y2Zrbt6RrWFFQlSSdH+TSUqt4BbB/k5mcC16hql6o+A6wFjhGRGUCrqt6jzo+8Cnh9KIINY5TQ2ccCuUrQmCrfWrVjgHUdR8weX6p6m8kV+NivH6FQ1D57evvnGOkd/bTWBhbOaOW2NVt7jN+0chPxmPCqhdPK7pdOxPnaWYcjAkcNM+W1UqQTYzd19oMistybpvK/hZnA+sA2G7yxmd7z3uNlEZELRGSZiCzbtq3vDAjDGM1kKnRXXo6mZKLUuyJIZ7ZAUz81jw6fOYHntnewoz3LN256nKe2tXPI9HFljwXdhfMq4R2dcHAby57dwe6MazXrT0EdO28SE5tTfe537LzJ3P6JE3nDUbNGrGEkpPvw5qpNtY3Fj4ADgSXAJuA73ni5OIT2M14WVb1MVZeq6tK2trYRSjWM+qRcnaZK0ZCK01lm/ry9n2kocGU/AH58+1P8/O51vPNf53L8grY+PYtSf4wKGIsTD5lKoajc9eQLAKzdupent7X3OQUVZM7kJuJl0oiryagPcJdDVbeoakFVi8BPgGO8tzYAwbq9s4CN3visMuOGYfRBZwXvynvTmIz10Smv/zURi7x5/0vveJp5bc1cdOohNKUSdOWLZVuhVipmAXDk7Am0NiT4hzcVddPKzYi4tRj1wJichvJiED5nAX6m1A3AuSKSFpEDcIHs+1V1E7BHRI71sqDOB/5QTc2GUW90d62r/DKqplSCjlwf01D9XNjHNyaZN6WZeEz43puW0JiKBzrvlTleBeMuiXiMV8xv4/YntpXWURw1ZyJTWxtGfOxq4GpD1X4aKrRFeSJyNXACMEVENgBfAE4QkSW4qaR1wPsAVHWViFwLrAbywIWq6n86H8BlVjUCN3kPwzD6oJLz/b1pSMbpzPa8y83mi+SLOuC01ydPOZiufLFUL6op3b0ivPdivUr/DScc3MafV2zi5lVbWL1pN587/dCKHLcauGyo2nsWoRkLVT2vzPDl/Wx/CXBJmfFlwGEVlGYYo5owYxaNyfg+wVbfM+ir3IfPab0Wp/XX07vSU2l+Cu2X/7gKqJ8pKHBlVrryRVR12PWlKoGt4DaMUUal0k7L0ZSK05HN91gRPVzj5BuXcqXEfe+lUhldU8c1cNjMVjbuynDYzNYh95WoJX63vGyFmzoNFTMWhjHK6Czd6YezzqKoPS9cHcMMRvu9L8qv23B/QyUN3gkLpgJw2mEjK79RbfzS6LUuJmjGwjBGGZVMO+2Nf/HOBOIW3f23hzar7RuXcp5FGHGXM5fsx0FTWzjjiP0qdsxqkE74rVVrG+S2qrOGMcoIcwV3Kc6QyzMeF5RuL3XJG6Jnke4/ZpGICalE5e5n508bx60fP75ix6sWJWNhnoVhGJWkM1tApPsiU0lK3fICF/jhroloSrp71bLGIlsMxTOqR9Le51DrjCgzFoYxyujMFmhKxkPJnPGnoYJxho5hTkN1p86WX2fREIJnVI9EZRrKjIVhjDJG0mFuIPxpqKBn0THMgHp/qbND6fQ32mkwz8IwjDDIDLL/9nAo14d7uDGShkQcEegomzprxsLH9yxqXUxwSMZCRGIi0hqWGMMwRk5/LU5HSrmYRXuXe948xGmoWExoTMb7bKZk01CO7mmoiHsWIvIrEWkVkWZcOY41IvLJ8KUZhjEcOvvpWjdSynoW2TwirjHSUGlKJWgvNw2VLdA4jOONRvx1FvWQDbVQVXfjmg7dCMwB3h6mKMMwhk9niBfacp6F78kMJ6DelIqXFhEGcW1hLbMfuldw10OAOykiSZyx+IOq5uinp4RhGLUlzAttY7lsqBH0+25Kxct6Fp0W4C5RN9NQwKW4CrHNwB0isj+wO0xRhmEMn45sPryYRZkMpo6u/LCNk/Msyq2zKIRS26oeqZtsKFX9vqrOVNXXqONZ4MQqaDMMYxhkcsXQsqHSiRgiPTNzBuq/3R/N6URpBXiQTK5AY8piFhBcwR3xaSgRmSYil4vITd7rhcA7QldmGMawCNOzEHEZTD1WcI9gXUfvY/mEmdFVb5QC3FH3LHCNh24G/OpbTwAfDUmPYRgjpHMEMYTB0JSK0xGiZ6GqFrMIkIwLInXgWQBTVPVaoAigqnmg9j3+DMPYh2JRyeSKoc73NyTjPfpwOy9g+DGLjq6elxP/Djqs9N96Q0Rca9U68CzaRWQyXgaUiBwL7BpoJxG5QkS2isjKwNi3RORxEVkuIteLyARvfK6IdIrII97jx4F9jhaRFSKyVkS+L7VsFWUYESeTD69Lnk9jMt6rNlS+VEF2qLhmSj2NRakwoa2zKJH2uuXVksF8Gx8HbgAOFJG7gKuADw1iv58Dp/YauwU4TFUX46azLg6895SqLvEe7w+M/wi4AJjvPXof0zAMj+E2IhoKvS/wI5mGakol6MwVKBa7s/HDLLFerzjPIuL9LFT1IRE5HjgYEGCNt9ZioP3uEJG5vcb+Gnh5L3BOf8cQkRlAq6re472+Crfe46aBzm8YY5Huu/Jwp6F6ruAe2TQUOAPRnO7ZOc9SZ7tpSMaju4JbRE7y/j0bOANnLBYAr/PGRsq76XnRP0BEHhaR20XkFd7YTGBDYJsN3lhfmi8QkWUismzbtm0VkGgY9UU17sqbUvFS6qyq0pHND9+z8AxEMMhdDYNXb6QTsdIUY63o73bgeODvwOvKvKfA74Z7UhH5LJAHfukNbQLmqOqLInI08HsRWYTzZMqduyyqehlwGcDSpUttlbkx5uhucRpizCIVp2OHO09XvkhRh2+cmso1U7JpqH1IJ2M19yz6NBaq+gURiQE3edlQFUFE3gG8FjhZVdU7VxfQ5T1/UESewnkxG4BZgd1nARsrpcUwRht+LCHsbCj/4u6fb6gtVX38wHh7V89pLTDPIkjkA9yqWgQ+WKmTicipwEXAGaraERhvE5G493weLpD9tKpuAvaIyLFeFtT5wB8qpccwRhv+9FCYRfiC01DdjY+Gdz4/PbYzF5iGMs9iH6IQ4B5MNtQtIvIJEZktIpP8x0A7icjVwD3AwSKyQUTeA/wAGOcdM5giexywXEQeBX4LvF9Vt3vvfQD4KbAWeAoLbhtGn3RU4a482INiuP23fXyPJOhZ+IbIPItuorDOYjC3A+/2/r0wMKbAvP52UtXzygxf3se21wHX9fHeMuCwgWUahjHcFqdDwV9n4YLbI4uRlCtMOFIDNBqJQjbUYIzFoaqaCQ6ISENIegzDGAGZKqSd+lNHXfliKYtpuNNQfne9jmyZaSjzLErUyzTU3YMcMwyjxoz0Tn8w+CurO7KFEWdfNZXxLKoRpK830ok4mah6FiIyHbemoVFEjqQ7jbUVaKqCNsMwhkg17sqDrVVHapz8dRZBzyKTKyDSXZrb8FJnI7zO4hTgnbh01e8GxvcAnwlRk2EYw6QzWyCdiBGLhVdCrZTBFPAsRlKiHPZNnR1um9bRSqQD3Kp6JXCliLzBC0AbhhFxwi5PDj37cI80ZhGPCQ3JWM/yIVX4G+qNKKyzGMw3/CcReQswN7i9qn45LFGGYQyPajQNCvbhrkSMpDmVoL2rZ4Db4hU9SSdiFIpKvlAkEa/N9NxgjMUfcCXJH8RbZW0YRjQZSde6wRKMWXRmC8RGGF9o7NWHO2ONj/Yh2Ic7ysZilqpaWXDDqAM6s1UwFqVpqLxXnjwxovhCcyqxTyFBW2PRk7SXgZYJVOetNoNKnRWRw0NXYhjGoLlr7Qsc/ZVb2NXZs1tAZ7ZA0zDLhQ+WHp5FLj/iC3tjmf4YNg3VE99zq2XcYjDG4uXAgyKyxutwt0JEloctzDCMvlm7dS8vtmd59sX2HuMduQINId+Vl3pQZIu0dxWGXUTQpznd01jYNNS+pBPd01C1YjC3IKeFrsIwjCHhr9TevCvD4kBd5ky2wIzWcAss+Hf9Hd401Eh7ZTcmE2xv7yy97swV2M+moXrQ7VnUbq3FgJ6Fqj4LzAZO8p53DGY/wzDCw0813bK7RyUeOiowLTQQ/l1/xpuGGmmaq/MsemZDmWfREz9mUcv6UANe9EXkC7iy4n6/7CTwf2GKMgyjf/zSD5t7GYvObDF0Y5FKxEjEhI5sYUT9t3169/TuzBZDn0qrN6IwDTUYD+EsXFvVdgBV3YgrM24YRo3onobqmc3emc1X5a7crzzb0TVyL6AplaCjq2e5D/MsetKQrINpKCDrdbRTABFpDleSYRgD4RuLrXu6PQtVrdrq5wavAVJHLj/iVM6mVJwOr+S539PbjEVPfM+ilsUEB2MsrhWRS4EJIvJe4FbgJ+HKMgyjPzoDAW4fvx92NdJO/amjSqyJaEolUHUXwmxhZD29RytRCHAPeEugqt8WkVcBu4GDgc+r6i2hKzMMo09K01CBmEV3S9UqTUP5MYsRT0N5xQSzeZIxd1G0dRY9KcUsIu5ZoKq3qOonVfUTgzUUInKFiGwVkZWBsUkicouIPOn9OzHw3sUistZbz3FKYPxob23HWhH5vlgpSsOg07to7MnkS5lE1Wip6tPgtVatxLRX97qNQsljskKCPSllQ0U8wD1cfg70LhPyaeBvqjof+Jv3GhFZCJwLLPL2+aGI+L+WHwEXAPO9h5UeMcY8mUD2kD8VVeplUYULbVMqzo6OLKrdPSmGix/zaM/mrUteH0RhGio0Y6GqdwDbew2fCVzpPb8SeH1g/BpV7VLVZ4C1wDEiMgNoVdV7vCD7VYF9DGPMkskXShky/lRUZxU9i8ZknBf3ZoGRewHBPtz+32DTUD0JFhKsFX0aCxH5m/fvf1bwfNNUdROA9+9Ub3wmsD6w3QZvbKb3vPd4X5ovEJFlIrJs27ZtFZRtGNGiM1tg7mSXmOgvzOuewgm/0FxDKs72dmcsRmqcSn24uwpV9Y7qiVS8u5BgrejPs5ghIscDZ4jIkSJyVPBRYR3l4hDaz3hZVPUyVV2qqkvb2toqJs4wokYmX2D/ya67sb/WohSzSIVfYKEpGSdbcHe5IzVO3X2481X1juqJWExIxWvbLa+/b/nzuJhC77aq4C7YJw3jfFtEZIaqbvKmmLZ64xtwJUV8ZgEbvfFZZcYNY0zTmS0yqTnNuHSi27PwAt2NIVedhZ53/pUKcHdkC6VS52Ys9iWdiEUzG0pVf6uqpwHfVNUTez2GYygAbgDe4T1/B66xkj9+roikReQAXCD7fm+qao+IHOtlQZ0f2Mcwxixd3irnaeMbahLgDl7MR24svGmorE1D9Uc6GYv8OouviMgZwHHe0G2q+qeB9hORq4ETgCkisgH4AvAN3CK/9wDPAW/0zrFKRK4FVgN54EJV9T+VD+AyqxqBm7yHYYxpXOvRGNNbG9jireKuRIvTwdLTsxjhNFS6exoqEZN9jm84at2He8BvWUS+DhwD/NIb+oiIvExVL+5nN1T1vD7eOrmP7S8BLikzvgw4bCCdhjFWyBWK5ItKYzLO1NY09z61F6CqmURBz2LEK7iT3dNQSS+Qa9NQ+5JORDdm4XM6sERViwAiciXwMN1VaA3DqCJ+RkxDMs701ga27umiWNSSsai+ZzGy8yXiMVKJGO3ZPKmEGYu+SCfjdEU0GyrIhMDz8SHoMAxjkPjF5BpScaaPbyBfVF5o76IzVyARk9LdeZgEL+bNFUjVbUrFXeqsZ/D8RWhGN+lEjEzEPYuvAw+LyD9wqazHYV6FYdSMkmeRiNHamARgy64ur2tdde7Ig+epxDmbUwlPv4vFxGJW1ac3Lhsq2gHuq0XkNuAlOGNxkapuDluYYRjlyQQyhqZ7LVQ3785UtQ+Ef55ETEpTRyM6Xsp1y2vKxm0Kqg/SyTi7O3M1O/+g/EcvhfWGkLUYhjEIOkuehZuGAmcsKtG1brD43kSlPJnmVLAwYfjrROqRWge4bWLQMOqM0irnVJwpLWniMWHLroyXTltdz6JSxqkplXAruHPdNa+MnjhjEf0At2EYEcEPcjYkY8RjQltLms27M3TWwLOoRHAbupspZaoYd6k3GpLxaK7gBhCRWLAfhWEYtaf3eopp4xvYstt5FtW60DZ5JUUqdb6mdKI0DWUxi/JE2rPw1lY8KiJzqqTHMIwB8C8Y/kV1emuazbtczKIadaEAGrxihRWbhkq6AHdHtnpTafVGOlFbz2Iwv6wZwCoRuR9o9wdV9YzQVBmG0Se9PYvprQ3c89SLTG5JVy91NukHuCs0DZV26ywyuQJTx6UrcszRhqsNFW1j8aXQVRiGMWgyvbrJTRvfwO6Mqzg70n7Yg6UU4K7Q+ZpScTpyhapmdNUb6USMbKFIsag1WYcymHUWt4vI/sB8Vb1VRJoA+zYNo0b4/beDngXA7ky+ap5FIh4jFY+VigCOlKZUgkJR2dWZswB3H6QT7nPJFoo0xKr/GQ2YDSUi7wV+C1zqDc0Efh+iJsMw+sH3LPySGL6xgOpWa21tTDKhMVWRY/nexK7OnMUs+qDUh7tGcYvBTENdiKs6ex+Aqj4pIlP738UwjLDI5AqkE90lMaaNDxiLKl5or3jn0tKiwJESTMG1bKjydPfhLgDJqp9/MMaiS1WzfgcrEUnQT2tTwzDCJdMrRTboWVRzvn/xrAkVO1aPWlNmLMriexaZGnkWg1mUd7uIfAZoFJFXAb8B/hiuLMMw+qIzV6AhEaj6mk4wLu3u++p1Cqc5EPuwmEV50t7K9lqttRiMsfg0sA1YAbwPuBH4XJiiDMPom0yuuM8F1Z+KqtdMouD6kHo1eGHjB7hrlT47mGyootfw6D7c9NMaVR32NJSIHAz8OjA0D/g8rmfGe3GGCeAzqnqjt8/FwHuAAvBhVb15uOc3jHqn04tZBJne2sDarXvrdgon6FnUq8ELm1KAu0aexWDaqp4O/Bh4Clei/AAReZ+qDqsXtqquAZZ4x44DzwPXA+8Cvqeq3+51/oXAucAiYD/gVhFZEOjRbRhjit4xC4BpXtyiXqdwmizAPSD1kA31HeBEVV0LICIHAn8GhmUsenEy8JSqPusH0MtwJnCNqnYBz4jIWlx21j0VOL9h1B2ZXjELgOnj3arner3QBr2Jhjo1eGGTTtZ2GmowMYutvqHweBrYWqHznwtcHXj9QRFZLiJXiMhEb2wmsD6wzQZvzDDGJOUKBvqeRb32grDU2YHxS7dnatQtr09jISJni8jZuLpQN4rIO0XkHbhMqAdGemIRSQFn4LKrAH4EHIibotqE82jATX31pmzMREQuEJFlIrJs27Zt5TYxjLonkyvu0/PhqDkTmTmhkVkTG2ukamRY6uzARDnA/brA8y3A8d7zbcDEfTcfMqcBD6nqFgD/XwAR+QnwJ+/lBmB2YL9ZwMZyB1TVy4DLAJYuXWprQYxRSWeZyqyHzRzPXZ8+qUaKRk4qESMZF3IFrdu4S9hENsCtqu8K+dznEZiCEpEZXvtWgLMAv4/GDcCvROS7uAD3fOD+kLUZRmTpyo/OMt6NyTi5Qt48iz7oNhbR8ywAEJEDgA8Bc4Pbj6REuVeM8FW4dRs+3xSRJbgppnX+e6q6SkSuBVYDeeBCy4QyxjKd2dHZIKg5nahqMcR6oxTgjnA21O+By3GxioqoVNUOYHKvsbf3s/0lwCWVOLdh1DOqSia/b8xiNOAbidFoCCtBZKehAmRU9fuhKzEMY0ByBaVQ1FF5QfUzokbjFFslSMZdz/XITkMB/y0iXwD+CnT5g6r6UGiqDMMoSybfs0veaKIxFSeVcBdEozzpRKxmqbODMRaHA28HTqJ7Gkq914ZhVJFMdvQai+ZUfFR6TJUknahda9XBGIuzgHmqmg1bjGEY/eOXpx6NF9WmVGJU/l2VJJ2IRzrA/SiuyF+lVm0bhjFMOnOj17M47fDpHDi1pdYyIk06GYt0gHsa8LiIPEDPmMWwU2cNwxge/nx1Y2r0ZUO9dvF+tZYQeaI+DfWF0FUYhjEoSp5FYvR5FsbApBPx6BoLVb29GkIMwxgY37Owyqxjk4ZkhLOhRGQP3YX7UrhO4e2q2hqmMMMw9iVjnsWYJp2Il7zLajMYz2Jc8LWIvB7XT8IwjCrTWYpZmLEYi6QTMXZ21iYxdchRMlX9PbbGwjBqgp86OxrLfRgDk07Gops66/W08IkBS+mjn4RhGOHS6S3Ks/UIY5NIB7jp2dcij6sIe2YoagzD6JfRXO7DGBiXOhvdmEXYfS0MwxgkfrkPvwKpMbZoSSfY1ZlDVRGpbg2tPo2FiHy+n/1UVb8Sgh7DMPrBL09e7QuFEQ3axqXJ5Iq0Zwu0pKvbb72/25P2Mg+A9wAXhazLMIwyjNbGR8bgaBuXBmDbnq4Btqw8/bVV/Y7/XETGAR8B3gVcA3ynr/0MwwiPTG50tlQ1BodvLF7Y28UBU5qreu5+Jz5FZJKIfBVYjjMsR6nqRao6oqKCIrJORFaIyCMisixwrltE5Env34mB7S8WkbUiskZEThnJuQ2jnunMmWcxlqmlZ9GnsRCRbwEPAHuAw1X1i6q6o4LnPlFVl6jqUu/1p4G/qep84G/ea0RkIXAusAg4FfihiNj/FmNMkskVS72YjbFHW0sEjQXw78B+wOeAjSKy23vsEZHdIWg5E7jSe34l8PrA+DWq2qWqzwBrsRXkxhglkyvQaAvyxiwTm1LEYxK5mEWYv0gF/ioiClyqqpcB01R1k3fuTSIy1dt2JnBvYN8N3phhjDkyuYKV+hjDxGLC5OZUtIxFyLxMVTd6BuEWEXm8n23L5QiWXUEuIhcAFwDMmTNn5CoNI2J05gqMb0zWWoZRQ9rGpXlhb7SmoUJDVTd6/24FrsdNK20RkRkA3r9+EH0DMDuw+yxgYx/HvUxVl6rq0ra2trDkG0bN6MwVrDz5GKdtXJptY8FYiEizl4qLiDQDrwZWAjcA7/A2ewfwB+/5DcC5IpIWkQOA+cD91VVtGNGgK1e08uRjnLaW9JiZhpoGXO+tQE0Av1LVv3htW68VkfcAzwFvBFDVVSJyLbAaV5vqQlWtTXEUw6gxnbnCqGypagyeKd40VLGoxGLVW8lfdWOhqk8DR5QZfxE4uY99LgEuCVmaYUSeTK5gnsUYp60lTa6g7OrMMbE5VbXz2i2KYdQJqup5FmYsxjKlhXlVjluYsTCMOiFbKKJq5cnHOqWSH1WOW5ixMIw6IZP1u+SZsRjLmGdhGEa/dDc+sv+2Y5kpNSr5Yb86w6gTrKWqAdDakCCViJmxMAyjPNZS1QAQkZqstTBjYRh1gnkWhk8tVnGbsTCMOiGTcwHutMUsxjxt48yzMAyjDzI58ywMRy2KCZqxMIw6wTcWFrMwprSkebE9S75QrNo5zVgYRp3QaZ6F4dE2Lo0qbG/PVu2cZiwMo07oNM/C8PDbq26tYtzCjIVh1Al+gNs8C6MWq7jNWBhGnVCKWViJ8jHP1BrUh7JfnWHUCZlcARFIxe2/7VinVPLDPAvDMHrTmS3QmIzjNQ4zxjCNqTgt6URV11qYsTCMOiGTL1hw2yhR7YV5ZiwMo07ozBYtuG2UqHZ9qKobCxGZLSL/EJHHRGSViHzEG/+iiDwvIo94j9cE9rlYRNaKyBoROaXamg0jCmTyBSv1YZSodn2oqvfgBvLAv6vqQyIyDnhQRG7x3vueqn47uLGILATOBRYB+wG3isgCVS1UVbVh1JiMF7MwDHDG4p9PjmLPQlU3qepD3vM9wGPAzH52ORO4RlW7VPUZYC1wTPhKDSNaWMzCCDKlJcXuTL6UUh02NfVpRWQucCRwnzf0QRFZLiJXiMhEb2wmsD6w2wb6MC4icoGILBORZdu2bQtLtmHUhE7zLIwApV7cVZqKqpmxEJEW4Drgo6q6G/gRcCCwBNgEfMfftMzuWu6YqnqZqi5V1aVtbW2VF20YNSSTK1pLVaNEaRV3lYLcNfnliUgSZyh+qaq/A1DVLapaUNUi8BO6p5o2ALMDu88CNlZTr2FEgUzOpqGMbtpaGoBRbCzErSi6HHhMVb8bGJ8R2OwsYKX3/AbgXBFJi8gBwHzg/mrpNYyoYMbCCFLt+lC1yIZ6GfB2YIWIPOKNfQY4T0SW4KaY1gHvA1DVVSJyLbAal0l1oWVCGWORzpzFLIxuJrekAHhhT3XKlFfdWKjqnZSPQ9zYzz6XAJeEJsow6oDOXMFiFkaJZDzGxKYk2/ZmqnI+++UZRh2gqmRytoLb6Ek1S36YsTCMOqAr73pZpM1YGAHMWBiG0YOMtVQ1ytDWUr2SH2YsDKMOsJaqRjmmtjawZXdXVVZxm7EwjDqg1FLVuuQZAU5Y0EY2X+SmlZtCP5f98gyjDujMep5FwjwLo5tj501m7uQmrr5v/cAbjxAzFoZRB2Tyfv9tMxZGN7GYcN4xc7h/3XbWbt0T7rlCPbphGBUhk7UAt1GeNxw9i2RcuPr+cL0LMxaGUQeUPAszFkYvprSkefWi6Vz30IZQA91mLAyjDujMegFuMxZGGd5yzBx2duT4y8rNoZ3DjIVh1AGZUuqs/Zc19uVf5k1m/8lN/Or+50I7h/3yDKMO6LRFeUY/lALdz2xn7da94ZwjlKMahlExOrJ5/vH4VkSgKV2LQtFGPXBOKdAdjndhxsIwIszT2/Zy1v/ezd/XbOWiUw+hxYyF0QdTWtK8emF4gW775RlGRPnLyk184jfLScaFq959DK+Yb62Cjf45/1/2Z2prOpRGWWYsDCNiqCr/8/e1fPeWJzhi9gR++NajmDmhsdayjDrgpfMm89J5k0M5thkLw4gQqso3bnqcS+94mrOPmsnXzz6ctJX4MCJA3cQsRORUEVkjImtF5NO11mMYlaZYVD7/h1VcesfTvP3Y/fn2OUeYoTAiQ114FiISB/4XeBWwAXhARG5Q1dW1VWYYkC8UeWFvli27M4FHF9lCkeZUgpaGBC3pOK0NSSY2p5jUnGJCU5KWdALxOgwXVfnCDav47YMbeN9x8/j0aYcgUq77sGHUhrowFsAxwFpVfRpARK4BzgQqbiy+ffMatuyuTk9bn6JCtlAkly+SLRTJF5VUXEjGY6QSMZLxWNmm5f0drzOXZ29Xgb2ZHB3ZAulEjOZ0guZ0gpZ0gkSs+4iKu+DlCkpXvkiuUKSo2uOY8ZiQisdIJmKk4zHisaFdyAqqZPNFst7x88WexxcRUnEhlYiRisdI9Pqbiwq5gts/W3DH6Aoer6AkvP2TcXeMXKF723xBSSfjtKTjNKcSNKXitGcL7GjPsr0jy66OHPGYeBf2BM2pBIqSK2jpHL5G/3vZ25Vn864ML+ztotefQzwmJGJS6nA3WD72ygV8+OSDzFAYkaNejMVMIFglawPw0t4bicgFwAUAc+bMGdaJHnpuB+teaB/WvsNFpPsimUwI8ViMfPDCOMQLDrh8fGcY4kxuSZPNF2nvyrO9vYO9XXmKva5uiXiMZFxIJeKk4kKslzEoFLWkJ5sv7rP/YP7GdKLb+MVjQvB6WFRKxjJX5m/2P6NkL4MwriFBOuGOly9oSV9nrkAiJrSkE57xETpz7jN4cW8H7dk8zakEk5pTHDqjlQmNSQpFZW9Xnr1dedq78ghCYzJOa0OCZDyGQsngdeWLTGhKccj0cUxrbSg9prc2MK01zeSWNPGYkCsU6egqsDebZ1dHjp0dzjjtaM/Snu2Z3jhvSjOvXjR9SJ+rYVSLejEW5W6z9rlaqeplwGUAS5cuHdrVzONX7z12OLsZRlmS8Rjjm2KMb0paRpNR19RLgHsDMDvwehawsUZaDMMwxhz1YiweAOaLyAEikgLOBW6osSbDMIwxQ11MQ6lqXkQ+CNwMxIErVHVVjWUZhmGMGerCWACo6o3AjbXWYRiGMRapl2kowzAMo4aYsTAMwzAGxIyFYRiGMSBmLAzDMIwBEdVhrV2LPCKyDXh2mLtPAV6ooJwwiLrGqOuD6GuMuj6Ivsao64PoadxfVfdpnjJqjcVIEJFlqrq01jr6I+oao64Poq8x6vog+hqjrg/qQyPYNJRhGIYxCMxYGIZhGANixqI8l9VawCCIusao64Poa4y6Poi+xqjrg/rQaDELwzAMY2DMszAMwzAGxIyFYRiGMSBmLAzDMIwBMWNRBUQk7v0b2cbKInK2iEyutY6+EJH5ItJQax39ISIHikhk2+GJyGIRaam1jv6Iusao64PwfodmLEJERN4pIg8DH6m1lv4QkS8APwHeXGstvRGRM0XkKeDLwE9FZFKtNfVGRN4qIquAbwHXiUikSv97+pYDXwJ+7TUQixRR1xh1fRD+79CMRUiIyCHA/wP+BBwnIvNUVUUkMp95QEsHcCVwiIgc3eu9muEZhn8D3qKq5wFbgc+KyILaKutGRM4E3ge8R1XPBpqA93rvReEzPA2n7wOqehZwIPA6771IeLpR1xh1fVCd32HNf8yjCX+6CUBVHwfeBnwPWA180Bsv1kadI6gR8POm48BeXK/z10HtdIrIuN5DgK/lGuANwGtqeWfXaxriUeAdqnqv9/r7wOshMp/hrap6nKreJSLjgae9bURrmDff63d4W9Q09vqOb4maPu/8we859N+hGYsKISJfBj4vIqUCXKr6hKpuB64HDhSR47xta/K5l9EY8+6MWoFfATcBM0TkeyJyag30fRp4WES+KSJv8T67FcA7RGQisBRYBkwHZlZbn6fxU8BtIvItETlPVdfRs2DlPODuWmiDfT7D81U1JyIxEZmG6zS5E2dwv+V5v7XQ2Pt32OWNR0Jjr+/4bV5b56h9hsHv+a3e7/C5wCYV/x2asRghIpIWkYuBdwCLgSMD7/ku6mPAP3BuIqpa7HVnVRONqlrw7owUZzAOB84ETgfWVEufp/Ek4DXAq3BG6zvedNOPgSzwf8DLgC8Ax9LtFVVL32QR+TnOYP0bcB/wURGZ6X2fSW/T6Xh3ntWmzGf4nyKyWFWLqroFOF1V34LzchfgLijV1NfX77Do/bsFeE2tNPbxHX/I/45rrS+gs/f3/G3vey6E+Ts0YzFycri4xELgXuBEETkAIOCitgO/BfaKyFdE5JvA3ChoFJdhlAR+DnwU+Jq37fFV1Ien4WFVfUZV/wH8D/AdVX1WVT+Om4t9q6quwk2XVTvQ3Q78VVXfpKqPAH/DeT2zAFQ15213OPBPEVkgIl+U6maY9f4M/xv4hv+mqu70/t2Oi/9MrKI26Od3GNC4q4Ya+/qOZ0dEn0+57/nrnq7QfodmLEaId1f0hKq2A7/GXTyOEZE0uCknz2hkcF/gB4BtqvpUBDQ2qGoGeAC4RFWPUdXvA48A66qlz6MJmOwZL1T1a8BMEXmz93qziMwWkf/FTUFV1fPxPqc/BobywBJgkz8gInNwRuxLuPjKC6r6YhVl9v4MvwFMF5E3evriIjJJRL7taX+gitoG839FvH9rorGf73hjcLtafoYe5b7nGYHv+UBC+B2asRgCIvJ2ETm897iqdnn/rgPuxN2VH+KN+cGl/wRWAXNU9VshaiybndGHxoXe2G9U9ZeBzX+hqreFpG9c4Hnp96eq1+OyTF4b2PybOG/H5we4YPzp3gUnFPrRuCew2WRgq6oG54nTuL9hK/AyVf1BSPomBZ4P5jP8uPd8Ee4inQSOV9UnwtA3gMY+/6942y4Grg1bYz/6+v2OReSIaujzznVoufF+vud/957HcNNjlf0dqqo9BngAR+CyDX4PHBEYFyDmPY97/7biplDegsuGOscbbwxZ45m49NclvcYHo/Esf9sQ9Z2Gi9tcBXw2MB4H0t7zc4E7gLne6znA/wIt3uumkD/D/jT2/gxfDvzSe34KcBzuAjIjRH2nep/PVbgpOn88NsBn+ENPWwswJeTPsD+N/f0O3w681htvi4i+fb5j73mon6F3jv8BnvG/xyF8zwmgLYzfYah/8Gh5ABcDF/Tz/jxgQuD1h4EdwFpcQCwsXX7V4BOB5cCDuGmuicH3a6URZ6jiwPtx7vprgJfi5q3fXeYzFLzFd8CFwF+AH4f83Q5V4wTv+WeA3wA/Au4CXhGyvgtw8/xneheG24DTIvYZDlZjrX6HQ9JXre/Y19jr9S+Bh3CB9vQgv+dLQ/2ewzx4vT7KfHFXAmd4zz+Guzvxf1AXAZtxd6WCc6mfBj5TLY24YPkM4CRcoPr4wHsx4NO4ufWqaeyl7zXA/MDrjwIf957HPX3bgFcA43FZT1cCn6ziZzgYjZtwU2D+f+bngI9USd9CIOE9n4qbCplD913wZyPwGQ5GYy1/h4PVV7XvuIxGX8uHcIvq/gEcFnj/Ily/7qp+z6pKpMoSRAER+SBwsojcDvxGVZ/HBbimisj1OMPwUuA9IvJVYBdwqKru8PZfBxyu4c6p+xrvAK5WN/8LsElETgGOF5G1nvbpnsaF1dIY0PdP4CpVvdELriZUNQ8cCjzubT7V07fA1wfcJSL3qmohDH0j0LgwoPFq4EL1MoxC1HcHcI2qrvbGj8RNzSVwF45O4BPU9jMcisZa/A6HpY+Qv+NeGm8HrlXVjeIWnJ6KSzGeAZwrIvfj1vPsxt3UVO17LhG2NaqnB3AWbiriROBnuB/UHNz84N+Bb/nW33v9usC+iRpp/AE94yhH4NYknFVm39A19qFvSfD8OO/nX8vsGyfEuEmFNCZr+R3jpiDmeM9bcBe4IyPyGQ5WY61+h4PVF/p33I/Go7z3vuT9ex7OQDwGTK729xx8WDZUT14K/Ehd7vIXcZb8YlW9BudRJEVkmjorfjcuI8Ff9p+vkcZnCBQqVNVHcT/Aw0XkJG+lZzU1ltP3YU9b3kuTnA08KCKzRMSvXyPavUgwyhpzfRwzbH0f9fQ9rV52jqruxWU3TQroq+VnOFiNtfodDlZfNb7jvjT+P++90z2v9yLgD7hYS3tAY7W+5xJmLOiRbvo0LjMDVX0WuAFoE5GX4yo55oCLReQ/gHOA271tQ//S+tH4Z6BZRM4IbH41LjD2a1z6X+gMQt+Z3vuHeJo+gvt8q7a4LuoaB9DX1Os7RkQ+h0uHXeVtW+vfYc01Rl3fIDROEJF/wS20u1tVl6jq23HTyYdWS2M5xqSxEJG5wdeBD/+3QEfgorEJN930r6r6MG6V5OO4RTGv9MbC0rhURKYOUuNtwEJxtOB+aCuAxar6yV7710rfod5/knm4H/0BuEDif4ahrx40Duc79vY7TUTuxJWbOEdVN1dSVz1pjLq+YWj8Oy4N+5eqelHgMGeFeb0ZDGPKWIjIUSJyK/BlCdRmClj6Hbiifx/wXL1dQLP3QFW3q+qPVfViVd0QksZFInI3rgbShEFqbAEavB9hBpe9cbqqbqLCjEBfo6dvLfByVf1AGPrqQeNI9HnvPwa8X1XPj+JnWA2NUdc3Ao3NuP/LRXEJFzEorS6vKWPCWHh33J/FTc9c4/1ACt57fjkOcD+km3EW/jIR2Q9X7CxbRbkfAa5X1deptzp0kBpz4ObcVXVrhPWtUNWwSyREXeNw9WU9fetUdWWI+upBY9T1jURj3tNY0Bq3NAgyJoyF9+WkgDtV9afg0ufEdZJS7/VXcFZ+Gm7Z/BZc2e6dBIqxhYV3FzHJ0/MDb+wsEZmFm/ZCXKpuTTRWQN/Xw9RXDxqj/h3Xg8ao66sXjcNBtDaxktARkeOBjKre571uBq7DBbKOw305u3ArNG8FLgM+r6prA8doUtWOKmpsAB7G5XyfB0zB3XVkcNk6V1ZTY9T11YPGqOurB41R11cvGkeMVjFPtxoPYBzwO2A7cAVe6QvvvbfiDINf4+V9uPzm/QPbxGqs8VO4iq9v917PxNXVP7laGqOurx40Rl1fPWiMur560Vipx2ichsriMgrehlt5/Ub/DXWVVd+kqnd4Q7fi0iJzUJpPrMYcYZ8accXAGnGrhlG3Cvt2XCG4ammMur560Bh1ffWgMer66kVjZai1taqQdT8fV+p4gve6ARejOB83vbTAG+9d8+n9uEqyrVHR6L33JlyV2yW4woAPAQePZX31oDHq+upBY9T11YvGMB51G7Pw0s+m44JCReApXNrZR1T1BW+b+bj6KhlV/ao3FsOVHv5vXJGwi1T18X3PUDuN3vibcaU7FuEKra0aa/rqQWPU9dWDxqjrqxeNoVNrazVMy+5XZlwA/J/3PIGrAX9dr23PwrmDB+HuAARXpuOMiGpsxqtNA6H2l4i0vnrQGHV99aAx6vrqRWM1HnVVddZLdf0yEBeRG3HNUwpQqunzYWCjiByvqn4pjuvFdZz6C25Rzknqqk+G0ta0QhpPBB5T71c2lvTVg8ao66sHjVHXVy8aq0qtrdVgH7g5wkdxjUjei+sSdSpuKumYwHYfAP4ReP1GXAGunwBTx7LGqOurB41R11cPGqOur140VvtRcwFD+PJegZeC5r3+ofdFvRN40BuL4eYVrwUOCOwXWoeretIYdX31oDHq+upBY9T11YvGaj/qKXX2QeBa6a7pdBeuJv3PcW7ih9Sloc0CCqr6DICq/lNV/2ka60JfPWiMur560Bh1ffWisarUjbFQ1Q5V7dLujlCvwrWRBHgXrmLon3D1nx4yjfWnrx40Rl1fPWiMur560Vht6irADa7uCq7myjRcrwGAPbjm6ocBz6hb/FIzoq4x6vog+hqjrg+irzHq+qA+NFaLuvEsAhRxKyBfABZ71v0/gKKq3hmRLy7qGqOuD6KvMer6IPoao64P6kNjdah10GQ4D+BY3Jd4J/CeWuupR41R11cPGqOurx40Rl1fvWisxqMuV3CLK/X7duC7qtpVaz3liLrGqOuD6GuMuj6Ivsao64P60FgN6tJYGIZhGNWlHmMWhmEYRpUxY2EYhmEMiBkLwzAMY0DMWBiGYRgDYsbCGHWISEFEHhGRVSLyqIh83Otj0t8+c0XkLcM831kioiJySD/b3CYiS8uMJ0XkGyLypIisFJH7ReS04egoc+yPikjTEPc5wVtLYBg9MGNhjEY6VXWJqi7ClWl4DfCFAfaZCwzLWADn4XLwzx3Gvl8BZgCHqephwOtwfZ17EKhRNBQ+CgzJWBhGX5ixMEY1qroVuAD4oDjmisg/ReQh7/Gv3qbfAF7heSQfE5G4iHxLRB4QkeUi8r5yxxeRFuBlwHsIGAsRaRSRa7x9f43rxdx73yZc+esP+fn7qrpFVa/13t8rIl8WkfuAfxGRt3mexyMicqlvQETkRyKyzPOkvuSNfRjYD/iHiPzDG3u1iNzj/d2/8bQjIqeKyOMicidw9sg+cWPUUutVgfawR6UfwN4yYztw9X2agAZvbD6wzHt+AvCnwPYXAJ/znqeBZXhlqHsd923A5d7zu4GjvOcfB67wni8G8sDSXvsuBh7u5+9Q4E3e80OBP9Ldee2HwPne80nev3HgNmCx93odMMV7PgXXk6HZe30R8Hlc98j13mchuHLbf+pLkz3G7qPuCgkaxjAR798k8AMRWYLreragj+1fjasFdI73ejzugvpMr+3OA/7Le36N9/oh4Djg+wCqulxElg9DcwG4znt+MnA08ICIgPNUtnrvvUlELsAVBp0BLAR6n+9Yb/wub/8UcA9wCK4Y3pMAIvJ/OENpGD0wY2GMekRkHu7CuxUXu9gCHIGbhs30tRtueujmfo47GTgJOExEFHdnryLyKW+TgcojrAXmiMg4Vd1T5v2MdpfIFuBKVb24l4YDgE8AL1HVHSLyc5y3UO7vuUVVz+u1/5JB6DQMi1kYoxsRaQN+DPxAVRXnIWxS17jm7bgLPLiy08HA8s3AB0Qk6R1ngYg09zr8OcBVqrq/qs5V1dk4z+PluCmft3r7HoabcuqBqnYAlwPfF5GUt+0MEXlbmT/lb8A5IjLV226SiOyP6wvdDuwSkWlAMJMq+DfdC7xMRA7y9m8SkQXA48ABInKgt10PY2IYPmYsjNFIo586C9wK/BX4kvfeD4F3iMi9uCmodm98OZD3Um0/BvwUWA08JCIrgUvZ1xM/D7i+19h1uKyqHwEt3vTTp4D7+9D6OVxTndXeeX5Pd5OdEqq62tv2r94xbwFmqOqjwMPAKuAKXEc3n8uAm0TkH6q6DdcS9Gpv/3uBQ1Q1g5t2+rMX4H62D53GGMcKCRqGYRgDYp6FYRiGMSBmLAzDMIwBMWNhGIZhDIgZC8MwDGNAzFgYhmEYA2LGwjAMwxgQMxaGYRjGgJixMAzDMAbk/wNT7z/S8TyxNQAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# isolate the date portion of the ad_created column and count the number of entries per day\n", "ad_created_dist = autos[\"ad_created\"].str[:10].value_counts().sort_index()\n", "# create a line plot showing day the ad was created\n", "ad_created_dist.plot.line()\n", "plt.ylabel(\"Number of entries\")\n", "plt.xlabel(\"Date Ad Created\")\n", "plt.xticks(rotation=30)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "48362\n", "48362\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# remove early entries so ad_created start date matches dates for date_crawled column\n", "autos = autos[autos[\"ad_created\"].str[:4] != \"2015\"]\n", "autos = autos[autos[\"ad_created\"].str[:7] != \"2016-01\"]\n", "autos = autos[autos[\"ad_created\"].str[:7] != \"2016-02\"]\n", "autos = autos[autos[\"ad_created\"].str[:10] != \"2016-03-01\"]\n", "autos = autos[autos[\"ad_created\"].str[:10] != \"2016-03-02\"]\n", "autos = autos[autos[\"ad_created\"].str[:10] != \"2016-03-03\"]\n", "autos = autos[autos[\"ad_created\"].str[:10] != \"2016-03-04\"]\n", "print(len(autos))\n", "# create a line plot showing day the ad was created\n", "print(len(autos))\n", "ad_created_dist = autos[\"ad_created\"].str[:10].value_counts().sort_index()\n", "ad_created_dist.plot.line()\n", "plt.ylabel(\"Number of entries\")\n", "plt.xlabel(\"Date Ad Created\")\n", "plt.xticks(rotation=30)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# create a line plot that shows date_crawled and ad_created\n", "distributions = [date_crawled_dist, ad_created_dist]\n", "for distribution in distributions:\n", " distribution.plot.line()\n", "plt.legend()\n", "plt.xticks(rotation=30)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# isolate the date portion of the last_seen column and count the number of entries per day\n", "last_seen_dist = autos[\"last_seen\"].str[:10].value_counts().sort_index()\n", "# create a line plot showing day the ad was removed\n", "last_seen_dist.plot.line()\n", "plt.ylabel(\"Number of entries\")\n", "plt.xlabel(\"Date Ad Removed\")\n", "plt.xticks(rotation=30)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "2016-03-05 0.097184\n", "2016-03-06 0.403209\n", "2016-03-07 0.525206\n", "2016-03-08 0.734047\n", "2016-03-09 0.942889\n", "2016-03-10 1.056615\n", "2016-03-11 1.228237\n", "2016-03-12 2.367561\n", "2016-03-13 0.876721\n", "2016-03-14 1.261321\n", "2016-03-15 1.585956\n", "2016-03-16 1.647988\n", "2016-03-17 2.799719\n", "2016-03-18 0.736115\n", "2016-03-19 1.585956\n", "2016-03-20 2.063604\n", "2016-03-21 2.065671\n", "2016-03-22 2.131839\n", "2016-03-23 1.848559\n", "2016-03-24 1.982962\n", "2016-03-25 1.922997\n", "2016-03-26 1.683140\n", "2016-03-27 1.571482\n", "2016-03-28 2.092552\n", "2016-03-29 2.243497\n", "2016-03-30 2.479219\n", "2016-03-31 2.384103\n", "2016-04-01 2.280716\n", "2016-04-02 2.501964\n", "2016-04-03 2.528845\n", "2016-04-04 2.454406\n", "2016-04-05 12.478806\n", "2016-04-06 22.221992\n", "2016-04-07 13.214921\n", "Name: last_seen, dtype: float64" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# create a frequency table for the last_seen column\n", "# this allows for a more precise measure for the volume in the last three days\n", "last_seen_freq = autos[\"last_seen\"].str[:10].value_counts(normalize=True, dropna=False)*100\n", "last_seen_freq = last_seen_freq.sort_index()\n", "last_seen_freq" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Removing outliers and unnecessary columns\n", "### Part 4 of 4\n", "The `year_of_registration` column has 5 entries prior to 1900 and 1879 after 2016. It's unlikely that any of these dates are correct. It would take far to long to look up the true values for these entries and using a mean value would not be appropriate for what this column represents. Removing them might allow a more accurate view of the distribution. Interestingly, while this does affect the minimum, maximum, and standard deviation values, it results in little change to the upper ends of the first, second, and third quartiles." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "count 48362.000000\n", "mean 2004.757371\n", "std 88.828659\n", "min 1000.000000\n", "25% 1999.000000\n", "50% 2004.000000\n", "75% 2008.000000\n", "max 9999.000000\n", "Name: year_of_registration, dtype: float64\n" ] } ], "source": [ "# isolate the year_of_registration column and get initial stats\n", "registration_series = autos[\"year_of_registration\"]\n", "print(registration_series.describe())" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5\n", "1870\n", "The length of the data set before removing outlier registration year values is: 48362\n", "The length of the data set after removing outlier registration year values is: 46487\n" ] } ], "source": [ "# remove year_of_registration outliers\n", "print(len(autos[autos[\"year_of_registration\"] < 1900]))\n", "print(len(autos[autos[\"year_of_registration\"] > 2016]))\n", "print(\"The length of the data set before removing outlier registration year values is: \", len(autos))\n", "autos = autos[autos[\"year_of_registration\"].between(1900, 2016)]\n", "print(\"The length of the data set after removing outlier registration year values is: \", len(autos))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "count 46487.000000\n", "mean 2002.907480\n", "std 7.188417\n", "min 1910.000000\n", "25% 1999.000000\n", "50% 2003.000000\n", "75% 2008.000000\n", "max 2016.000000\n", "Name: year_of_registration, dtype: float64\n", "The earliest year for a vehicle to be registered is: 1910\n", "The latest year for a vehicle to be registered is: 2016\n" ] } ], "source": [ "# isolate the year_of_registration column and get final stats\n", "registration_series = autos[\"year_of_registration\"]\n", "print(registration_series.describe())\n", "print(\"The earliest year for a vehicle to be registered is:\", registration_series.min())\n", "print(\"The latest year for a vehicle to be registered is:\", registration_series.max())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Focusing on the Brand\n", "At this point the dataset is prepared for an initial analysis. Vehicle brand is an easy way for consumers to evaluate a purchase. Using this as a starting point leads to a few coclusions.\n", "* The three most popular brands (Volkswagen, BMW, and Opel respectively) represent slightly more than 50% of the vehicles in the data set. Un-commenting \"# print(brand_value_counts)\" displays the frequency table.\n", "* Of the five least most popular vehicles, Ladas and Trabants have very low mileage. Their unpopularity is probably due to quality perceptions.\n", "* The price_mileage_ratio column shows the relationship between mean_price and mean_odomkm. The closer the number to zero, the lower the vehicle mileage per dollar spent. So, amongst the five most popular brands, audis had the lowest mileage per dollar in the mean price.\n", "* Additionally, this allows a comparison of value compared to popularity. The mean price for the five most popular cars is \\$6935 and for the five least popular it is \\$2101. The price to mileage ratio for the popular cars is 0.05 and for the unpopular cars it is 0.114. So not only is an unpopular car less expensive it is also a better deal if mileage is the only criteria.\n", "* Coding a dashboard to combine different columns and row slices to create new ratios would possibly show more unexpected results.\n", "* Lastly, it would be interesting to examine the rows with outlier prices. If the price is controled for in some way would that group reveal similar paterns?" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# isolate the brand column and look at totals and relative frequencies.\n", "brand_series = autos[\"brand\"]\n", "# brand_value_counts = brand_series.value_counts()\n", "brand_value_counts = brand_series.value_counts(normalize=True, dropna=False)*100\n", "# print(brand_value_counts)\n", "# create a line plot showing volume of sales by brand\n", "# how do i get volkswagen on the last tick?\n", "brand_value_counts.plot.line()\n", "plt.ylabel(\"Number of entries\")\n", "plt.xlabel(\"Brand\")\n", "plt.xticks(rotation=45)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "# a function to create a dictionary and mean column value for part of a series\n", "def autos_converter(c_series, mean_column, start_slice=0, stop_slice=5):\n", " \"\"\"\n", " Takes a series index and returns that index and the mean of a column in the autos dataframe.\n", " Can be sliced. Default = 0:5\n", " Example:\n", " >>> autos_converter(c_series=brand_value_counts, mean_column=\"price\", stop_slice=2)\n", " {'volkswagen': 5404.27828123409, 'bmw': 8337.549843627834}\n", " \"\"\"\n", " series_slice = c_series.iloc[start_slice:stop_slice]\n", " series_dict = {}\n", " for index in series_slice.index:\n", " index_mean = autos.loc[autos[\"brand\"] == index, mean_column].mean()\n", " series_dict[index] = index_mean\n", " return series_dict" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "# create dictionary with brand and mean price for top five selling brands\n", "t5_mean_price = autos_converter(c_series=brand_value_counts, mean_column=\"price\")\n", "# create dictionary with brand and mean odometer_km for top five selling brands\n", "t5_mean_odomkm = autos_converter(c_series=brand_value_counts, mean_column=\"odometer_km\")" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "# i could have made another fundtion but i have to get onto the next project!\n", "# transform t5_mean_price dictionary to dataframe\n", "t5_mean_price_series = pd.Series(t5_mean_price)\n", "t5_df = pd.DataFrame(t5_mean_price_series, columns=[\"mean_price\"])\n", "# add columns for mean odometer_km and relative frequency in original df\n", "t5_mean_odomkm_series = pd.Series(t5_mean_odomkm)\n", "t5_df[\"mean_odomkm\"] = t5_mean_odomkm_series\n", "t5_df[\"%_in_autos\"] = brand_value_counts[:5]\n", "t5_df = t5_df.astype(int)\n", "t5_df = t5_df.reset_index()\n", "t5_df = t5_df.rename(columns={'index': 'brand'})" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "# create dictionary with brand and mean price for five least selling brands\n", "l5_mean_price = autos_converter(c_series=brand_value_counts, mean_column=\"price\", start_slice=-5, stop_slice=None)\n", "# create dictionary with brand and mean odometer_km for five least selling brands\n", "l5_mean_odomkm = autos_converter(c_series=brand_value_counts, mean_column=\"odometer_km\", start_slice=-5, stop_slice=None)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "# transform l5_mean_price dictionary to dataframe\n", "l5_mean_price_series = pd.Series(l5_mean_price)\n", "l5_df = pd.DataFrame(l5_mean_price_series, columns=[\"mean_price\"])\n", "# add columns for mean odometer_km and relative frequency in original df\n", "l5_mean_odomkm_series = pd.Series(l5_mean_odomkm)\n", "l5_df[\"mean_odomkm\"] = l5_mean_odomkm_series\n", "l5_df[\"%_in_autos\"] = brand_value_counts[-5:]\n", "l5_df = l5_df.astype(int)\n", "l5_df = l5_df.reset_index()\n", "l5_df = l5_df.rename(columns={'index': 'brand'})" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "t5_df[\"price_mileage_ratio\"] = 0\n", "t5_df[\"price_mileage_ratio\"] = t5_df[\"mean_price\"] / t5_df[\"mean_odomkm\"]\n", "l5_df[\"price_mileage_ratio\"] = 0\n", "l5_df[\"price_mileage_ratio\"] = l5_df[\"mean_price\"] / l5_df[\"mean_odomkm\"]" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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brandmean_pricemean_odomkm%_in_autosprice_mileage_ratio
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3mercedes_benz862513078690.066
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" ], "text/plain": [ " brand mean_price mean_odomkm %_in_autos price_mileage_ratio\n", "0 volkswagen 5404 128695 21 0.042 \n", "1 bmw 8337 132600 11 0.063 \n", "2 opel 2966 129425 10 0.023 \n", "3 mercedes_benz 8625 130786 9 0.066 \n", "4 audi 9341 129118 8 0.072 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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brandmean_pricemean_odomkm%_in_autosprice_mileage_ratio
0daewoo104912164200.009
1trabant17905453800.033
2rover160213766100.012
3lancia337612190000.028
4lada26888351800.032
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" ], "text/plain": [ " brand mean_price mean_odomkm %_in_autos price_mileage_ratio\n", "0 daewoo 1049 121642 0 0.009 \n", "1 trabant 1790 54538 0 0.033 \n", "2 rover 1602 137661 0 0.012 \n", "3 lancia 3376 121900 0 0.028 \n", "4 lada 2688 83518 0 0.032 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# pretty print dataframes for five most sold brands and five least sold brands\n", "pd.set_option('display.max_rows', None)\n", "pd.set_option('display.max_columns', None)\n", "pd.set_option('display.width', 1000)\n", "pd.set_option('display.colheader_justify', 'center')\n", "pd.set_option('display.precision', 3)\n", "display(t5_df)\n", "display(l5_df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Looking for More Correlations" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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priceodometer_kmmonth_of_registrationyear_of_registrationpowerPSpostal_code
price1.000-3.858e-015.484e-020.3120.1910.072
odometer_km-0.3861.000e+002.708e-04-0.266-0.019-0.023
month_of_registration0.0552.708e-041.000e+000.0700.0470.011
year_of_registration0.312-2.662e-017.039e-021.0000.0770.036
powerPS0.191-1.940e-024.706e-020.0771.0000.027
postal_code0.072-2.283e-021.061e-020.0360.0271.000
\n", "
" ], "text/plain": [ " price odometer_km month_of_registration year_of_registration powerPS postal_code\n", "price 1.000 -3.858e-01 5.484e-02 0.312 0.191 0.072 \n", "odometer_km -0.386 1.000e+00 2.708e-04 -0.266 -0.019 -0.023 \n", "month_of_registration 0.055 2.708e-04 1.000e+00 0.070 0.047 0.011 \n", "year_of_registration 0.312 -2.662e-01 7.039e-02 1.000 0.077 0.036 \n", "powerPS 0.191 -1.940e-02 4.706e-02 0.077 1.000 0.027 \n", "postal_code 0.072 -2.283e-02 1.061e-02 0.036 0.027 1.000 " ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# run a pairwise correlation of columns\n", "autos.corr()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# is this a relationship between a group of the most expensive cars and the zip_code?\n", "# odometer_km is/might be irrelevant\n", "sns.relplot(data=autos, x=\"postal_code\", y=\"price\", hue=\"odometer_km\", palette=\"PiYG\")\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }