Jovian
Sign In
Learn practical skills, build real-world projects, and advance your career
3 years ago
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import scipy
data = pd.read_csv("SampleSuperstore.csv")
data.head()
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 9994 entries, 0 to 9993 Data columns (total 13 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Ship Mode 9994 non-null object 1 Segment 9994 non-null object 2 Country 9994 non-null object 3 City 9994 non-null object 4 State 9994 non-null object 5 Postal Code 9994 non-null int64 6 Region 9994 non-null object 7 Category 9994 non-null object 8 Sub-Category 9994 non-null object 9 Sales 9994 non-null float64 10 Quantity 9994 non-null int64 11 Discount 9994 non-null float64 12 Profit 9994 non-null float64 dtypes: float64(3), int64(2), object(8) memory usage: 1015.1+ KB 
for i in data.columns:
    print(i)
    print(data[i].unique())
    print("-"*50)
Ship Mode ['Second Class' 'Standard Class' 'First Class' 'Same Day'] -------------------------------------------------- Segment ['Consumer' 'Corporate' 'Home Office'] -------------------------------------------------- Country ['United States'] -------------------------------------------------- City ['Henderson' 'Los Angeles' 'Fort Lauderdale' 'Concord' 'Seattle' 'Fort Worth' 'Madison' 'West Jordan' 'San Francisco' 'Fremont' 'Philadelphia' 'Orem' 'Houston' 'Richardson' 'Naperville' 'Melbourne' 'Eagan' 'Westland' 'Dover' 'New Albany' 'New York City' 'Troy' 'Chicago' 'Gilbert' 'Springfield' 'Jackson' 'Memphis' 'Decatur' 'Durham' 'Columbia' 'Rochester' 'Minneapolis' 'Portland' 'Saint Paul' 'Aurora' 'Charlotte' 'Orland Park' 'Urbandale' 'Columbus' 'Bristol' 'Wilmington' 'Bloomington' 'Phoenix' 'Roseville' 'Independence' 'Pasadena' 'Newark' 'Franklin' 'Scottsdale' 'San Jose' 'Edmond' 'Carlsbad' 'San Antonio' 'Monroe' 'Fairfield' 'Grand Prairie' 'Redlands' 'Hamilton' 'Westfield' 'Akron' 'Denver' 'Dallas' 'Whittier' 'Saginaw' 'Medina' 'Dublin' 'Detroit' 'Tampa' 'Santa Clara' 'Lakeville' 'San Diego' 'Brentwood' 'Chapel Hill' 'Morristown' 'Cincinnati' 'Inglewood' 'Tamarac' 'Colorado Springs' 'Belleville' 'Taylor' 'Lakewood' 'Arlington' 'Arvada' 'Hackensack' 'Saint Petersburg' 'Long Beach' 'Hesperia' 'Murfreesboro' 'Layton' 'Austin' 'Lowell' 'Manchester' 'Harlingen' 'Tucson' 'Quincy' 'Pembroke Pines' 'Des Moines' 'Peoria' 'Las Vegas' 'Warwick' 'Miami' 'Huntington Beach' 'Richmond' 'Louisville' 'Lawrence' 'Canton' 'New Rochelle' 'Gastonia' 'Jacksonville' 'Auburn' 'Norman' 'Park Ridge' 'Amarillo' 'Lindenhurst' 'Huntsville' 'Fayetteville' 'Costa Mesa' 'Parker' 'Atlanta' 'Gladstone' 'Great Falls' 'Lakeland' 'Montgomery' 'Mesa' 'Green Bay' 'Anaheim' 'Marysville' 'Salem' 'Laredo' 'Grove City' 'Dearborn' 'Warner Robins' 'Vallejo' 'Mission Viejo' 'Rochester Hills' 'Plainfield' 'Sierra Vista' 'Vancouver' 'Cleveland' 'Tyler' 'Burlington' 'Waynesboro' 'Chester' 'Cary' 'Palm Coast' 'Mount Vernon' 'Hialeah' 'Oceanside' 'Evanston' 'Trenton' 'Cottage Grove' 'Bossier City' 'Lancaster' 'Asheville' 'Lake Elsinore' 'Omaha' 'Edmonds' 'Santa Ana' 'Milwaukee' 'Florence' 'Lorain' 'Linden' 'Salinas' 'New Brunswick' 'Garland' 'Norwich' 'Alexandria' 'Toledo' 'Farmington' 'Riverside' 'Torrance' 'Round Rock' 'Boca Raton' 'Virginia Beach' 'Murrieta' 'Olympia' 'Washington' 'Jefferson City' 'Saint Peters' 'Rockford' 'Brownsville' 'Yonkers' 'Oakland' 'Clinton' 'Encinitas' 'Roswell' 'Jonesboro' 'Antioch' 'Homestead' 'La Porte' 'Lansing' 'Cuyahoga Falls' 'Reno' 'Harrisonburg' 'Escondido' 'Royal Oak' 'Rockville' 'Coral Springs' 'Buffalo' 'Boynton Beach' 'Gulfport' 'Fresno' 'Greenville' 'Macon' 'Cedar Rapids' 'Providence' 'Pueblo' 'Deltona' 'Murray' 'Middletown' 'Freeport' 'Pico Rivera' 'Provo' 'Pleasant Grove' 'Smyrna' 'Parma' 'Mobile' 'New Bedford' 'Irving' 'Vineland' 'Glendale' 'Niagara Falls' 'Thomasville' 'Westminster' 'Coppell' 'Pomona' 'North Las Vegas' 'Allentown' 'Tempe' 'Laguna Niguel' 'Bridgeton' 'Everett' 'Watertown' 'Appleton' 'Bellevue' 'Allen' 'El Paso' 'Grapevine' 'Carrollton' 'Kent' 'Lafayette' 'Tigard' 'Skokie' 'Plano' 'Suffolk' 'Indianapolis' 'Bayonne' 'Greensboro' 'Baltimore' 'Kenosha' 'Olathe' 'Tulsa' 'Redmond' 'Raleigh' 'Muskogee' 'Meriden' 'Bowling Green' 'South Bend' 'Spokane' 'Keller' 'Port Orange' 'Medford' 'Charlottesville' 'Missoula' 'Apopka' 'Reading' 'Broomfield' 'Paterson' 'Oklahoma City' 'Chesapeake' 'Lubbock' 'Johnson City' 'San Bernardino' 'Leominster' 'Bozeman' 'Perth Amboy' 'Ontario' 'Rancho Cucamonga' 'Moorhead' 'Mesquite' 'Stockton' 'Ormond Beach' 'Sunnyvale' 'York' 'College Station' 'Saint Louis' 'Manteca' 'San Angelo' 'Salt Lake City' 'Knoxville' 'Little Rock' 'Lincoln Park' 'Marion' 'Littleton' 'Bangor' 'Southaven' 'New Castle' 'Midland' 'Sioux Falls' 'Fort Collins' 'Clarksville' 'Sacramento' 'Thousand Oaks' 'Malden' 'Holyoke' 'Albuquerque' 'Sparks' 'Coachella' 'Elmhurst' 'Passaic' 'North Charleston' 'Newport News' 'Jamestown' 'Mishawaka' 'La Quinta' 'Tallahassee' 'Nashville' 'Bellingham' 'Woodstock' 'Haltom City' 'Wheeling' 'Summerville' 'Hot Springs' 'Englewood' 'Las Cruces' 'Hoover' 'Frisco' 'Vacaville' 'Waukesha' 'Bakersfield' 'Pompano Beach' 'Corpus Christi' 'Redondo Beach' 'Orlando' 'Orange' 'Lake Charles' 'Highland Park' 'Hempstead' 'Noblesville' 'Apple Valley' 'Mount Pleasant' 'Sterling Heights' 'Eau Claire' 'Pharr' 'Billings' 'Gresham' 'Chattanooga' 'Meridian' 'Bolingbrook' 'Maple Grove' 'Woodland' 'Missouri City' 'Pearland' 'San Mateo' 'Grand Rapids' 'Visalia' 'Overland Park' 'Temecula' 'Yucaipa' 'Revere' 'Conroe' 'Tinley Park' 'Dubuque' 'Dearborn Heights' 'Santa Fe' 'Hickory' 'Carol Stream' 'Saint Cloud' 'North Miami' 'Plantation' 'Port Saint Lucie' 'Rock Hill' 'Odessa' 'West Allis' 'Chula Vista' 'Manhattan' 'Altoona' 'Thornton' 'Champaign' 'Texarkana' 'Edinburg' 'Baytown' 'Greenwood' 'Woonsocket' 'Superior' 'Bedford' 'Covington' 'Broken Arrow' 'Miramar' 'Hollywood' 'Deer Park' 'Wichita' 'Mcallen' 'Iowa City' 'Boise' 'Cranston' 'Port Arthur' 'Citrus Heights' 'The Colony' 'Daytona Beach' 'Bullhead City' 'Portage' 'Fargo' 'Elkhart' 'San Gabriel' 'Margate' 'Sandy Springs' 'Mentor' 'Lawton' 'Hampton' 'Rome' 'La Crosse' 'Lewiston' 'Hattiesburg' 'Danville' 'Logan' 'Waterbury' 'Athens' 'Avondale' 'Marietta' 'Yuma' 'Wausau' 'Pasco' 'Oak Park' 'Pensacola' 'League City' 'Gaithersburg' 'Lehi' 'Tuscaloosa' 'Moreno Valley' 'Georgetown' 'Loveland' 'Chandler' 'Helena' 'Kirkwood' 'Waco' 'Frankfort' 'Bethlehem' 'Grand Island' 'Woodbury' 'Rogers' 'Clovis' 'Jupiter' 'Santa Barbara' 'Cedar Hill' 'Norfolk' 'Draper' 'Ann Arbor' 'La Mesa' 'Pocatello' 'Holland' 'Milford' 'Buffalo Grove' 'Lake Forest' 'Redding' 'Chico' 'Utica' 'Conway' 'Cheyenne' 'Owensboro' 'Caldwell' 'Kenner' 'Nashua' 'Bartlett' 'Redwood City' 'Lebanon' 'Santa Maria' 'Des Plaines' 'Longview' 'Hendersonville' 'Waterloo' 'Cambridge' 'Palatine' 'Beverly' 'Eugene' 'Oxnard' 'Renton' 'Glenview' 'Delray Beach' 'Commerce City' 'Texas City' 'Wilson' 'Rio Rancho' 'Goldsboro' 'Montebello' 'El Cajon' 'Beaumont' 'West Palm Beach' 'Abilene' 'Normal' 'Saint Charles' 'Camarillo' 'Hillsboro' 'Burbank' 'Modesto' 'Garden City' 'Atlantic City' 'Longmont' 'Davis' 'Morgan Hill' 'Clifton' 'Sheboygan' 'East Point' 'Rapid City' 'Andover' 'Kissimmee' 'Shelton' 'Danbury' 'Sanford' 'San Marcos' 'Greeley' 'Mansfield' 'Elyria' 'Twin Falls' 'Coral Gables' 'Romeoville' 'Marlborough' 'Laurel' 'Bryan' 'Pine Bluff' 'Aberdeen' 'Hagerstown' 'East Orange' 'Arlington Heights' 'Oswego' 'Coon Rapids' 'San Clemente' 'San Luis Obispo' 'Springdale' 'Lodi' 'Mason'] -------------------------------------------------- State ['Kentucky' 'California' 'Florida' 'North Carolina' 'Washington' 'Texas' 'Wisconsin' 'Utah' 'Nebraska' 'Pennsylvania' 'Illinois' 'Minnesota' 'Michigan' 'Delaware' 'Indiana' 'New York' 'Arizona' 'Virginia' 'Tennessee' 'Alabama' 'South Carolina' 'Oregon' 'Colorado' 'Iowa' 'Ohio' 'Missouri' 'Oklahoma' 'New Mexico' 'Louisiana' 'Connecticut' 'New Jersey' 'Massachusetts' 'Georgia' 'Nevada' 'Rhode Island' 'Mississippi' 'Arkansas' 'Montana' 'New Hampshire' 'Maryland' 'District of Columbia' 'Kansas' 'Vermont' 'Maine' 'South Dakota' 'Idaho' 'North Dakota' 'Wyoming' 'West Virginia'] -------------------------------------------------- Postal Code [42420 90036 33311 90032 28027 98103 76106 53711 84084 94109 68025 19140 84057 90049 77095 75080 77041 60540 32935 55122 48185 19901 47150 10024 12180 90004 60610 85234 22153 10009 49201 38109 77070 35601 94122 27707 60623 29203 55901 55407 97206 55106 80013 28205 60462 10035 50322 43229 37620 19805 61701 85023 95661 64055 91104 43055 53132 85254 95123 98105 98115 73034 90045 19134 88220 78207 77036 62521 71203 6824 75051 92374 45011 7090 19120 44312 80219 75220 37064 90604 48601 44256 43017 48227 38401 33614 95051 55044 92037 77506 94513 27514 7960 45231 94110 90301 33319 80906 7109 48180 8701 22204 80004 7601 33710 19143 90805 92345 37130 84041 78745 1852 31907 6040 78550 85705 62301 2038 33024 98198 61604 89115 2886 33180 28403 92646 40475 80027 1841 39212 48187 10801 28052 32216 47201 13021 73071 94521 60068 79109 11757 90008 92024 77340 14609 72701 92627 80134 30318 64118 59405 48234 33801 36116 85204 60653 54302 45503 92804 98270 97301 78041 75217 43123 10011 48126 31088 94591 92691 48307 7060 85635 98661 60505 76017 40214 75081 44105 75701 27217 22980 19013 27511 32137 10550 48205 33012 11572 92105 60201 48183 55016 71111 50315 93534 23223 28806 92530 68104 98026 92704 53209 41042 44052 7036 93905 8901 17602 3301 21044 75043 6360 22304 43615 87401 92503 90503 78664 92054 33433 23464 92563 28540 52601 98502 20016 65109 63376 61107 33142 78521 10701 94601 28110 20735 30076 72401 47374 94509 33030 46350 48911 44221 89502 22801 92025 48073 20852 33065 14215 33437 39503 93727 27834 11561 35630 31204 52402 2908 81001 94533 32725 42071 6457 11520 90660 84604 84062 30080 24153 44134 36608 2740 75061 8360 85301 14304 27360 92683 38301 75019 91767 89031 18103 19711 85281 92677 8302 2149 13601 54915 98006 75002 79907 76051 75007 37167 98031 70506 97224 60076 75023 23434 46203 7002 28314 27405 21215 53142 66062 98002 74133 97756 27604 74403 6450 42104 46614 6010 89015 99207 76248 45014 32127 97504 22901 59801 33178 29501 97477 32712 19601 80020 65807 7501 73120 23320 79424 65203 37604 36830 92404 1453 59715 85345 44107 8861 91761 91730 56560 75150 95207 32174 94086 3820 17403 77840 63116 2169 95336 44240 76903 84106 35810 37918 72209 48146 43302 80122 5408 4401 38671 47362 48640 57103 80525 47905 37042 95823 91360 2148 1040 87105 89431 92236 60126 7055 29406 23602 14701 46544 43402 92253 32303 37211 98226 60098 76117 60090 29483 71901 80112 43130 88001 35244 75034 95687 84107 53186 93309 33068 45373 78415 90278 32839 7050 70601 60035 11550 46060 55124 29464 48310 54703 78577 59102 97030 37421 83642 92307 60440 55369 95695 77489 77581 94403 49505 93277 66212 92592 92399 2151 77301 60477 52001 48127 87505 28601 60188 56301 33161 46226 33317 34952 29730 79762 53214 91911 66502 16602 80229 61821 47401 71854 78539 77520 46142 90712 2895 54880 76021 98042 74012 33023 33021 77536 67212 78501 52240 83704 2920 61032 77642 95610 75056 98052 32114 86442 46368 58103 46514 91776 33063 30328 44060 73505 23666 13440 54601 83501 39401 94526 48858 84321 6708 30605 4240 61832 85323 30062 85364 54401 99301 60302 32503 77573 20877 84043 35401 92553 40324 80538 85224 59601 63122 76706 48066 60423 18018 55113 68801 55125 48237 72756 88101 33458 93101 75104 68701 84020 48104 91941 83201 49423 6460 60089 92630 96003 95928 13501 72032 82001 42301 83605 70065 3060 38134 94061 37087 93454 60016 98632 37075 50701 2138 60067 1915 97405 93030 98059 60025 33445 80022 77590 27893 87124 27534 98208 90640 92020 77705 33407 79605 61761 63301 60174 93010 97123 91505 95351 67846 8401 80501 95616 26003 95037 7011 53081 30344 57701 1810 34741 6484 6810 52302 32771 78666 80634 76063 44035 83301 33134 60441 1752 20707 77803 71603 57401 21740 7017 60004 60543 55433 92672 94568 93405 72762 95240 77571 45040 30188] -------------------------------------------------- Region ['South' 'West' 'Central' 'East'] -------------------------------------------------- Category ['Furniture' 'Office Supplies' 'Technology'] -------------------------------------------------- Sub-Category ['Bookcases' 'Chairs' 'Labels' 'Tables' 'Storage' 'Furnishings' 'Art' 'Phones' 'Binders' 'Appliances' 'Paper' 'Accessories' 'Envelopes' 'Fasteners' 'Supplies' 'Machines' 'Copiers'] -------------------------------------------------- Sales [261.96 731.94 14.62 ... 437.472 97.98 243.16 ] -------------------------------------------------- Quantity [ 2 3 5 7 4 6 9 1 8 14 11 13 10 12] -------------------------------------------------- Discount [0. 0.45 0.2 0.8 0.3 0.5 0.7 0.6 0.32 0.1 0.4 0.15] -------------------------------------------------- Profit [ 41.9136 219.582 6.8714 ... 16.124 4.1028 72.948 ] -------------------------------------------------- 
data[['Category', 'Profit']].groupby('Category').mean().plot.bar()
<matplotlib.axes._subplots.AxesSubplot at 0x22f71fbe040>
Notebook Image