別の列の値に基づいて列のモード値を取得しますか？

Question

私は見つけることができるようにしたいどのような

>>> df 
           song      artist year \ 
0      (Iwas)BornToCry       Dion 1962 
1  (LastNight)IDidn'tGetToSleepAtAll    The5thDimension 1972 
2    (Sittin'On)TheDockOfTheBay     OtisRedding 1968 
3  (You'reSoSquare)Baby,IDon'tCare     JoniMitchell 1982 
4         20-75    WillieMitchell 1964 
5     50WaysToLeaveYourLover      PaulSimon 1976 
6         Abacab      Genesis 1982 
7     Abraham,MartinAndJohn       Dion 1968 
8      AbsolutelyRight   FiveManElectricalBand 1971 
9    ACowboy'sWorkIsNeverDone     Sonny&Cher 1972 
10      AddictedtoLove     RobertPalmer 1986 
11     ADreamGoesOnForever     ToddRundgren 1974 
12     AfterTheLoveHasGone    Earth,Wind&Fire 1979 
13      AftertheLovin'   EngelbertHumperdinck 1977 
14      AgainstTheWind      BobSeger 1980 
15     AHazyShadeOfWinter    SimonAndGarfunel 1966 
16      Ain'tNoSunshine     BillWithers 1971 
17     Ain'tTooProudToBeg    TheTemptations 1966 
18      ALessoninLeavin     DottieWest 1980 
19       AliveAgain      Chicago 1978 
20       AllAloneAmI      BrendaLee 1962 
21      AllIEverNeedIsYou     Sonny&Cher 1971 
22       Allshewantsis     DuranDuran 1989 
23       AllThisTime       Sting 1991 
24     AllThroughTheNight     CyndiLauper 1984 
25     AlmostbyBeingInLove    MichaelJohnson 1978 
26       AlmostGrown     ChuckBerry 1959 
27      AlongComesAWoman      Chicago 1985 
28        ALoveSong     AnneMurray 1974 
29       AlreadyGone      TheEagles 1974 
..         ...       ... ... 
700       WildHorses     RollingStones 1971 
701     WillieAndTheHandJive     EricClapton 1974 
702       WillieNelson    BlueEyesCryin' 1975 
703     WillTheWolfSurvive      LosLobos 1985 
704    WillYouLoveMeTomorrow     TheShirelles 1961 
705     WishSomeoneWouldCare     IrmaThomas 1964 
706   WithALittleHelpFromMyFriends      JoeCocker 1968 
707      WithaLittleLuck     PaulMcCartney 1978 
708      WithOrWithoutYou       U2 1987 
709     WithYouI'mBornAgain     BillyPreston 1979 
710       WomanToWoman     ShirleyBrown 1975 
711  WonderfulWorld,BeautifulPeople     JimmyCliff 1969 
712      WorldInMyEyes     DepecheMode 1990 
713       WorriedGuy    JohnnyTillotson 1964 
714  WouldItMakeAnyDifferenceToYou      EttaJames 1963 
715        YearsAgo    GeorgeHarrison 1981 
716       YearsFromNow      Dr.Hook 1980 
717 You'reTheFirst,TheLast,MyEverything     BarryWhite 1974 
718      You'vegotaFriend RobertaFlackandDonnyHathaway 1971 
719   You'veGotAnotherThingComin'     JudasPriest 1982 
720   YouCan'tJudgeABookByTheCover      BoDiddley 1962 
721 YouCan'tRollerSkateInABuffaloHerd     RogerMiller 1966 
722      YouCanCallMeAl      PaulSimon 1986 
723     YouDecoratedMyLife     KennyRogers 1980 
724      YouDon'tOwnMe     LesleyGore 1964 
725    YouMakeMeFeelLikeDancing      LeoSayer 1977 
726     YoungHeartsRunFree     CandiStaton 1976 
727   YourLoveHasLiftedMeHigher     RitaCoolidge 1977 
728  YouTookTheWordsRightOutOfMyMouth      MeatLoaf 1978 
729      YvonneElliman    IfICan'tHaveYou 1978 

     c1 c2 c3 c4 c5 c6 c7 c8 c9 c10 c11 c12 c13 \ 
0 1.0 2.0 2.0 2.0 1.0 1.0 1.0 3.0 6.0 3.0 1.0 4.0 8.0 
1 1.0 2.0 1.0 4.0 4.0 5.0 2.0 8.0 2.0 7.0 6.0 8.0 13.0 
2 1.0 1.0 1.0 1.0 5.0 2.0 6.0 4.0 9.0 8.0 9.0 2.0 12.0 
3 1.0 1.0 2.0 2.0 1.0 1.0 1.0 3.0 6.0 1.0 1.0 4.0 10.0 
4 1.0 2.0 3.0 3.0 2.0 3.0 5.0 5.0 5.0 2.0 11.0 7.0 11.0 
5 1.0 1.0 2.0 2.0 1.0 1.0 1.0 3.0 6.0 3.0 1.0 4.0 8.0 
6 1.0 1.0 1.0 1.0 5.0 4.0 3.0 1.0 7.0 6.0 8.0 1.0 7.0 
7 1.0 1.0 2.0 2.0 3.0 6.0 7.0 6.0 8.0 5.0 7.0 9.0 2.0 
8 1.0 2.0 3.0 3.0 2.0 3.0 5.0 5.0 5.0 2.0 11.0 7.0 11.0 
9 1.0 2.0 1.0 4.0 1.0 1.0 1.0 3.0 6.0 3.0 10.0 11.0 9.0 
10 1.0 1.0 2.0 2.0 3.0 6.0 4.0 2.0 3.0 9.0 3.0 10.0 3.0 
11 1.0 2.0 3.0 3.0 2.0 3.0 5.0 5.0 4.0 10.0 2.0 5.0 5.0 
12 1.0 2.0 3.0 4.0 4.0 5.0 2.0 8.0 2.0 9.0 3.0 8.0 3.0 
13 1.0 2.0 3.0 3.0 2.0 3.0 5.0 5.0 4.0 10.0 2.0 5.0 5.0 
14 1.0 1.0 2.0 2.0 3.0 6.0 7.0 6.0 8.0 5.0 7.0 9.0 2.0 
15 1.0 2.0 1.0 1.0 5.0 4.0 3.0 1.0 7.0 6.0 8.0 1.0 7.0 
16 1.0 1.0 2.0 2.0 3.0 6.0 7.0 6.0 8.0 5.0 10.0 9.0 2.0 
17 1.0 1.0 2.0 2.0 3.0 6.0 4.0 2.0 3.0 1.0 4.0 3.0 10.0 
18 1.0 2.0 1.0 4.0 4.0 5.0 2.0 8.0 2.0 7.0 6.0 8.0 13.0 
19 1.0 2.0 1.0 4.0 1.0 1.0 1.0 3.0 6.0 3.0 5.0 6.0 3.0 
20 1.0 2.0 3.0 3.0 2.0 3.0 5.0 5.0 5.0 2.0 11.0 7.0 11.0 
21 1.0 2.0 3.0 3.0 2.0 3.0 5.0 5.0 4.0 10.0 2.0 5.0 5.0 
22 1.0 1.0 1.0 1.0 5.0 4.0 6.0 4.0 9.0 6.0 4.0 2.0 12.0 
23 1.0 2.0 2.0 2.0 1.0 1.0 1.0 3.0 6.0 3.0 1.0 4.0 8.0 
24 1.0 2.0 3.0 3.0 2.0 3.0 5.0 8.0 5.0 2.0 11.0 7.0 11.0 
25 1.0 1.0 1.0 1.0 5.0 4.0 3.0 1.0 7.0 6.0 8.0 1.0 4.0 
26 1.0 1.0 2.0 2.0 3.0 6.0 7.0 6.0 8.0 5.0 7.0 9.0 2.0 
27 1.0 2.0 1.0 4.0 4.0 5.0 2.0 8.0 2.0 7.0 10.0 11.0 9.0 
28 1.0 2.0 3.0 3.0 2.0 3.0 5.0 5.0 5.0 2.0 11.0 7.0 11.0 
29 1.0 1.0 2.0 2.0 3.0 6.0 7.0 6.0 8.0 5.0 7.0 9.0 2.0 
.. ... ... ... ... ... ... ... ... ... ... ... ... ... 
700 1.0 1.0 2.0 2.0 3.0 6.0 7.0 6.0 8.0 5.0 7.0 9.0 2.0 
701 1.0 2.0 1.0 4.0 1.0 1.0 1.0 3.0 6.0 3.0 1.0 4.0 8.0 
702 1.0 2.0 2.0 2.0 1.0 1.0 1.0 3.0 6.0 3.0 1.0 4.0 8.0 
703 1.0 1.0 2.0 2.0 3.0 6.0 7.0 6.0 8.0 5.0 7.0 9.0 2.0 
704 1.0 2.0 1.0 4.0 4.0 5.0 2.0 8.0 2.0 7.0 6.0 8.0 13.0 
705 1.0 2.0 1.0 4.0 4.0 5.0 2.0 8.0 2.0 7.0 6.0 8.0 13.0 
706 1.0 2.0 3.0 3.0 2.0 3.0 5.0 5.0 5.0 9.0 3.0 10.0 3.0 
707 1.0 1.0 2.0 2.0 3.0 6.0 4.0 2.0 3.0 1.0 4.0 3.0 10.0 
708 1.0 1.0 2.0 2.0 3.0 6.0 7.0 6.0 8.0 5.0 10.0 11.0 9.0 
709 1.0 2.0 3.0 3.0 2.0 3.0 5.0 5.0 5.0 2.0 11.0 7.0 11.0 
710 1.0 1.0 1.0 1.0 5.0 4.0 6.0 4.0 9.0 6.0 4.0 2.0 12.0 
711 1.0 2.0 2.0 2.0 1.0 1.0 1.0 3.0 6.0 3.0 1.0 4.0 8.0 
712 1.0 1.0 2.0 2.0 3.0 6.0 4.0 2.0 3.0 1.0 4.0 3.0 10.0 
713 1.0 2.0 1.0 4.0 4.0 5.0 2.0 8.0 2.0 7.0 6.0 8.0 13.0 
714 1.0 2.0 1.0 4.0 4.0 5.0 2.0 7.0 1.0 4.0 5.0 6.0 6.0 
715 1.0 2.0 3.0 3.0 2.0 3.0 5.0 5.0 4.0 10.0 2.0 5.0 5.0 
716 1.0 2.0 3.0 3.0 2.0 3.0 5.0 5.0 5.0 2.0 11.0 11.0 11.0 
717 1.0 2.0 3.0 3.0 2.0 3.0 5.0 5.0 4.0 10.0 2.0 5.0 5.0 
718 1.0 2.0 3.0 3.0 2.0 3.0 5.0 5.0 4.0 10.0 2.0 5.0 5.0 
719 1.0 2.0 2.0 2.0 1.0 1.0 1.0 3.0 6.0 9.0 3.0 12.0 1.0 
720 1.0 1.0 2.0 2.0 3.0 6.0 7.0 6.0 8.0 5.0 7.0 9.0 2.0 
721 1.0 2.0 3.0 3.0 2.0 3.0 5.0 5.0 5.0 2.0 11.0 7.0 11.0 
722 1.0 2.0 1.0 4.0 4.0 5.0 2.0 7.0 1.0 4.0 10.0 11.0 9.0 
723 1.0 1.0 2.0 2.0 3.0 6.0 7.0 6.0 8.0 5.0 7.0 9.0 2.0 
724 1.0 2.0 1.0 4.0 4.0 5.0 2.0 8.0 2.0 7.0 6.0 8.0 13.0 
725 1.0 2.0 3.0 3.0 2.0 3.0 5.0 5.0 4.0 10.0 2.0 5.0 5.0 
726 1.0 2.0 2.0 2.0 1.0 1.0 1.0 3.0 6.0 3.0 1.0 4.0 8.0 
727 1.0 1.0 1.0 1.0 5.0 2.0 6.0 4.0 9.0 8.0 9.0 2.0 12.0 
728 1.0 2.0 1.0 4.0 1.0 1.0 1.0 7.0 6.0 3.0 5.0 6.0 6.0 
729 1.0 1.0 2.0 2.0 3.0 6.0 4.0 2.0 3.0 9.0 3.0 10.0 3.0 

     c14 c15 
0  8.0 7.0 
1  6.0 1.0 
2  1.0 3.0 
3 11.0 15.0 
4  4.0 10.0 
5  8.0 7.0 
6 10.0 8.0 
7  3.0 2.0 
8  4.0 10.0 
9 14.0 12.0 
10 7.0 6.0 
11 9.0 4.0 
12 7.0 6.0 
13 13.0 4.0 
14 5.0 2.0 
15 10.0 8.0 
16 3.0 8.0 
17 11.0 15.0 
18 5.0 1.0 
19 14.0 6.0 
20 4.0 10.0 
21 9.0 4.0 
22 14.0 5.0 
23 8.0 7.0 
24 4.0 10.0 
25 10.0 9.0 
26 3.0 2.0 
27 5.0 12.0 
28 4.0 10.0 
29 3.0 2.0 
.. ... ... 
700 3.0 2.0 
701 8.0 11.0 
702 8.0 7.0 
703 3.0 2.0 
704 6.0 1.0 
705 6.0 1.0 
706 7.0 6.0 
707 11.0 15.0 
708 3.0 7.0 
709 4.0 10.0 
710 14.0 5.0 
711 8.0 7.0 
712 11.0 15.0 
713 6.0 1.0 
714 12.0 11.0 
715 13.0 14.0 
716 4.0 12.0 
717 9.0 4.0 
718 9.0 4.0 
719 2.0 13.0 
720 3.0 2.0 
721 4.0 10.0 
722 14.0 12.0 
723 5.0 2.0 
724 6.0 1.0 
725 13.0 14.0 
726 8.0 7.0 
727 1.0 3.0 
728 5.0 11.0 
729 7.0 6.0 

、CSVファイルから読み込むデータフレームを持っているが「今年中に任意の値の列「C15」内の最も一般的な値です。どんな年であれ、c15の最も一般的な価値の小さな表がさらに良いでしょう。

これは十分に簡単だとわかりますが、私はオンラインで解決策を見つけることができません。

Answer 1

あなたはgroupbyを使用し、各グループ内で最も一般的なc15値を抽出するためにvalue_counts関数を適用することができます

df.groupby('year').apply(lambda x: x['c15'].value_counts().idxmax()) 

上記の出力は年によってインデックスを付け、最も一般的なc15値のシリーズです。