Basic EDA Using the Vehicles Database

myDF.head()

myDF.tail()

mydf.info()

<class 'pandas.core.frame.DataFrame'>
Index: 426880 entries, 7222695916 to 7301591129
Data columns (total 25 columns):
 #   Column        Non-Null Count   Dtype
---  ------        --------------   -----
 0   url           426880 non-null  object
 1   region        426880 non-null  object
 2   region_url    426880 non-null  object
 3   price         426880 non-null  int64
 4   year          425675 non-null  float64
 5   manufacturer  409234 non-null  object
 6   model         421603 non-null  object
 7   condition     252776 non-null  object
 8   cylinders     249202 non-null  object
 9   fuel          423867 non-null  object
 10  odometer      422480 non-null  float64
 11  title_status  418638 non-null  object
 12  transmission  424324 non-null  object
 13  VIN           265838 non-null  object
 14  drive         296313 non-null  object
 15  size          120519 non-null  object
 16  type          334022 non-null  object
 17  paint_color   296677 non-null  object
 18  image_url     426812 non-null  object
 19  description   426810 non-null  object
 20  county        0 non-null       float64
 21  state         426880 non-null  object
 22  lat           420331 non-null  float64
 23  long          420331 non-null  float64
 24  posting_date  426812 non-null  object
dtypes: float64(5), int64(1), object(19)
memory usage: 84.7+ MB

rows, columns = mydf.shape
print(f'DataFrame has {rows} rows')
print(f'DataFrame has {columns} columns')

DataFrame has 426880 rows
DataFrame has 25 columns

columns = mydf.columns
print(f"DataFrame's column name list: {columns.tolist()}")

DataFrame's column name list: ['url', 'region', 'region_url', 'price', 'year', 'manufacturer', 'model', 'condition', 'cylinders', 'fuel', 'odometer', 'title_status', 'transmission', 'VIN', 'drive', 'size', 'type', 'paint_color', 'image_url', 'description', 'county', 'state', 'lat', 'long', 'posting_date']

rows,columns = mydf[mydf['price']>6000].shape
print(f'{rows} cars are more than $6000 in the list')

312633 cars are more than $6000 in the list

<class 'pandas.core.frame.DataFrame'>
Index: 312633 entries, 7218891961 to 7301591129
Data columns (total 25 columns):
 #   Column        Non-Null Count   Dtype
---  ------        --------------   -----
 0   url           312633 non-null  object
 1   region        312633 non-null  object
 2   region_url    312633 non-null  object
 3   price         312633 non-null  int64
 4   year          311494 non-null  float64
 5   manufacturer  301006 non-null  object
 6   model         309569 non-null  object
 7   condition     191086 non-null  object
 8   cylinders     179159 non-null  object
 9   fuel          310303 non-null  object
 10  odometer      310804 non-null  float64
 11  title_status  307086 non-null  object
 12  transmission  311016 non-null  object
 13  VIN           214441 non-null  object
 14  drive         220821 non-null  object
 15  size          79013 non-null   object
 16  type          253846 non-null  object
 17  paint_color   227459 non-null  object
 18  image_url     312595 non-null  object
 19  description   312593 non-null  object
 20  county        0 non-null       float64
 21  state         312633 non-null  object
 22  lat           309371 non-null  float64
 23  long          309371 non-null  float64
 24  posting_date  312595 non-null  object
dtypes: float64(5), int64(1), object(19)
memory usage: 62.0+ MB

mydf['state']= mydf['state'].str.lower()
rows,columns = mydf[mydf['state']=='in'].shape
print(f'{rows} cars are from indiana in the list')

5704 cars are from indiana in the list

rows,columns = mydf[mydf['state']=='tx'].shape
print(f'{rows} cars are from texas in the list')

22945 cars are from texas in the list

mydf['region'].unique().tolist()

['prescott',
 'fayetteville',
 'florida keys',
 'worcester / central MA',
 'greensboro',
 'hudson valley',
 'medford-ashland',
 'erie',
 'el paso',
 'bellingham',
 'skagit / island / SJI',
 'la crosse',
 'auburn',
 'birmingham',
 'dothan',
 'florence / muscle shoals',
 'gadsden-anniston',
 'huntsville / decatur',
 'mobile',
 'montgomery',
 'tuscaloosa',
 'anchorage / mat-su',
 'fairbanks',
 'kenai peninsula',
 'southeast alaska',
 'flagstaff / sedona',
 'mohave county',
 'phoenix',
 'show low',
 'sierra vista',
 'tucson',
 'yuma',
 'fort smith',
 'jonesboro',
 'little rock',
 'texarkana',
 'bakersfield',
 'chico',
 'fresno / madera',
 'gold country',
 'hanford-corcoran',
 'humboldt county',
 'imperial county',
 'inland empire',
 'los angeles',
 'mendocino county',
 'merced',
 'modesto',
 'monterey bay',
 'orange county',
 'palm springs',
 'redding',
 'reno / tahoe',
 'sacramento',
 'san diego',
 'san luis obispo',
 'santa barbara',
 'santa maria',
 'SF bay area',
 'siskiyou county',
 'stockton',
 'susanville',
 'ventura county',
 'visalia-tulare',
 'yuba-sutter',
 'boulder',
 'colorado springs',
 'denver',
 'eastern CO',
 'fort collins / north CO',
 'high rockies',
 'pueblo',
 'western slope',
 'eastern CT',
 'hartford',
 'new haven',
 'northwest CT',
 'washington, DC',
 'delaware',
 'daytona beach',
 'ft myers / SW florida',
 'gainesville',
 'heartland florida',
 'jacksonville',
 'lakeland',
 'north central FL',
 'ocala',
 'okaloosa / walton',
 'orlando',
 'panama city',
 'pensacola',
 'sarasota-bradenton',
 'south florida',
 'space coast',
 'st augustine',
 'tallahassee',
 'tampa bay area',
 'treasure coast',
 'albany',
 'athens',
 'atlanta',
 'augusta',
 'brunswick',
 'columbus',
 'macon / warner robins',
 'northwest GA',
 'savannah / hinesville',
 'statesboro',
 'valdosta',
 'hawaii',
 'boise',
 'east idaho',
 'lewiston / clarkston',
 'pullman / moscow',
 "spokane / coeur d'alene",
 'twin falls',
 'bloomington-normal',
 'champaign urbana',
 'chicago',
 'decatur',
 'la salle co',
 'mattoon-charleston',
 'peoria',
 'quad cities, IA/IL',
 'rockford',
 'southern illinois',
 'springfield',
 'st louis, MO',
 'western IL',
 'bloomington',
 'evansville',
 'fort wayne',
 'indianapolis',
 'kokomo',
 'lafayette / west lafayette',
 'muncie / anderson',
 'richmond',
 'south bend / michiana',
 'terre haute',
 'ames',
 'cedar rapids',
 'des moines',
 'dubuque',
 'fort dodge',
 'iowa city',
 'mason city',
 'omaha / council bluffs',
 'sioux city',
 'southeast IA',
 'waterloo / cedar falls',
 'kansas city, MO',
 'lawrence',
 'manhattan',
 'northwest KS',
 'salina',
 'southeast KS',
 'southwest KS',
 'topeka',
 'wichita',
 'bowling green',
 'eastern kentucky',
 'huntington-ashland',
 'lexington',
 'louisville',
 'owensboro',
 'western KY',
 'baton rouge',
 'central louisiana',
 'houma',
 'lafayette',
 'lake charles',
 'monroe',
 'new orleans',
 'shreveport',
 'maine',
 'annapolis',
 'baltimore',
 'cumberland valley',
 'eastern shore',
 'frederick',
 'southern maryland',
 'western maryland',
 'boston',
 'cape cod / islands',
 'south coast',
 'western massachusetts',
 'ann arbor',
 'battle creek',
 'central michigan',
 'detroit metro',
 'flint',
 'grand rapids',
 'holland',
 'jackson',
 'kalamazoo',
 'lansing',
 'muskegon',
 'northern michigan',
 'port huron',
 'saginaw-midland-baycity',
 'southwest michigan',
 'the thumb',
 'upper peninsula',
 'bemidji',
 'brainerd',
 'duluth / superior',
 'fargo / moorhead',
 'mankato',
 'minneapolis / st paul',
 'rochester',
 'southwest MN',
 'st cloud',
 'gulfport / biloxi',
 'hattiesburg',
 'meridian',
 'north mississippi',
 'southwest MS',
 'columbia / jeff city',
 'joplin',
 'kansas city',
 'kirksville',
 'lake of the ozarks',
 'southeast missouri',
 'st joseph',
 'st louis',
 'billings',
 'bozeman',
 'butte',
 'eastern montana',
 'great falls',
 'helena',
 'kalispell',
 'missoula',
 'asheville',
 'boone',
 'charlotte',
 'eastern NC',
 'hickory / lenoir',
 'outer banks',
 'raleigh / durham / CH',
 'wilmington',
 'winston-salem',
 'grand island',
 'lincoln',
 'north platte',
 'scottsbluff / panhandle',
 'elko',
 'las vegas',
 'central NJ',
 'jersey shore',
 'north jersey',
 'south jersey',
 'albuquerque',
 'clovis / portales',
 'farmington',
 'las cruces',
 'roswell / carlsbad',
 'santa fe / taos',
 'binghamton',
 'buffalo',
 'catskills',
 'chautauqua',
 'elmira-corning',
 'finger lakes',
 'glens falls',
 'ithaca',
 'long island',
 'new york city',
 'oneonta',
 'plattsburgh-adirondacks',
 'potsdam-canton-massena',
 'syracuse',
 'twin tiers NY/PA',
 'utica-rome-oneida',
 'watertown',
 'new hampshire',
 'bismarck',
 'grand forks',
 'north dakota',
 'akron / canton',
 'ashtabula',
 'chillicothe',
 'cincinnati',
 'cleveland',
 'dayton / springfield',
 'lima / findlay',
 'mansfield',
 'northern panhandle',
 'parkersburg-marietta',
 'sandusky',
 'toledo',
 'tuscarawas co',
 'youngstown',
 'zanesville / cambridge',
 'fort smith, AR',
 'lawton',
 'northwest OK',
 'oklahoma city',
 'stillwater',
 'texoma',
 'tulsa',
 'bend',
 'corvallis/albany',
 'east oregon',
 'eugene',
 'klamath falls',
 'oregon coast',
 'portland',
 'roseburg',
 'salem',
 'altoona-johnstown',
 'harrisburg',
 'lancaster',
 'lehigh valley',
 'meadville',
 'philadelphia',
 'pittsburgh',
 'poconos',
 'reading',
 'scranton / wilkes-barre',
 'state college',
 'williamsport',
 'york',
 'rhode island',
 'charleston',
 'columbia',
 'florence',
 'greenville / upstate',
 'hilton head',
 'myrtle beach',
 'northeast SD',
 'pierre / central SD',
 'rapid city / west SD',
 'sioux falls / SE SD',
 'south dakota',
 'chattanooga',
 'clarksville',
 'cookeville',
 'knoxville',
 'memphis',
 'nashville',
 'tri-cities',
 'abilene',
 'amarillo',
 'austin',
 'beaumont / port arthur',
 'brownsville',
 'college station',
 'corpus christi',
 'dallas / fort worth',
 'deep east texas',
 'del rio / eagle pass',
 'galveston',
 'houston',
 'killeen / temple / ft hood',
 'laredo',
 'lubbock',
 'mcallen / edinburg',
 'odessa / midland',
 'san angelo',
 'san antonio',
 'san marcos',
 'southwest TX',
 'tyler / east TX',
 'victoria',
 'waco',
 'wichita falls',
 'logan',
 'ogden-clearfield',
 'provo / orem',
 'salt lake city',
 'st george',
 'vermont',
 'charlottesville',
 'danville',
 'fredericksburg',
 'harrisonburg',
 'lynchburg',
 'new river valley',
 'norfolk / hampton roads',
 'roanoke',
 'southwest VA',
 'winchester',
 'kennewick-pasco-richland',
 'moses lake',
 'olympic peninsula',
 'seattle-tacoma',
 'wenatchee',
 'yakima',
 'eastern panhandle',
 'morgantown',
 'southern WV',
 'west virginia (old)',
 'appleton-oshkosh-FDL',
 'eau claire',
 'green bay',
 'janesville',
 'kenosha-racine',
 'madison',
 'milwaukee',
 'northern WI',
 'sheboygan',
 'wausau',
 'wyoming']

len(mydf['region'].unique().tolist())

404

mydf['region'].nunique()

404