PyConJP 2016: pandas による 時系列データ処理

Transcript

1. pandas ʹΑΔ
   
   ࣌ܥྻσʔλॲཧ
   
   @PyConJP 2016
   
   sinhrks
   

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2. ࣗݾ঺հ
   • @sinhrks
   
   • ۀ຿: σʔλ෼ੳ
   
   • OSS׆ಈ:
   
   PyData Development Team (pandas)
   
   Dask Development Team (Dask)
   
   • GitHub: https://github.com/sinhrks
   

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3. ໨త
   • ໨త:
   
   • ࣌ܥྻσʔλ෼ੳͷͨΊͷޮ཰తͳॲཧΛ஌Δ
   
   • ࣌ܥྻϞσϧͷΠϯτϩμΫγϣϯ
   

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4. ໨࣍
   • pandasͱ͸
   
   • ࣌ܥྻσʔλͷॲཧ
   
   • ࣌ܥྻσʔλͷ౷ܭϞσϧ
   
   • ͓·͚:
   
   • ։ൃϩʔυϚοϓ
   

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5. pandasͱ͸?
   

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6. pandasͱ͸
   • σʔλ෼ੳͷͨΊͷσʔλߏ଄ͱɺσʔλͷલॲཧ / ूܭʹ͓
   ͍ͯศརͳؔ਺ / ϝιουΛఏڙ
   
   • Rͷ “data.frame” + α
   
   • ࡞ऀ: Wes McKinney
   
   • ϥΠηϯε: BSD
   
   • ҙຯ: PANel DAta System
   
   • GitHub: 7000↑⭐️
   

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7. pandasΛ࢖͏ϝϦοτ
   • ݱ࣮ͷ(Ԛ͍)σʔλʹରԠ
   
   • ௚ײతͳૢ࡞
   
   • ߴ଎
   
   • ࢀߟ: pandas internals @PyConJP 2015
   

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8. pandasͷσʔλߏ଄
   • σʔλͷ࣍ݩ͝ͱʹఆٛ
   4FSJFT
   ࣍ݩ
   
   %BUB'SBNF
   ࣍ݩ
   
   1BOFM
   ࣍ݩ
   
   ৭෇͖ͷηϧ͸ϥϕϧ
   ࣍ݩҎ্ͷσʔλߏ଄͸WͰඇਪ঑
   

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9. DataFrame
   • 2࣍ݩͷσʔλߏ଄:
   
   • ߦ (index) ͱ ྻ(columns) ʹϥϕϧΛ࣋ͭ
   
   • ྻ͝ͱʹܕΛ࣋ͭ
   $PMVNOT
   *OEFY
   JOUܕ PCKFDUܕ
   

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10. import pandas as pd
    df = pd.read_csv(‘adult.csv’)
    df
    DataFrame
    "EVMU%BUBTFUUBLFOGSPN6$*.-3FQPTJUPSZ
    -JDINBO .
    6$*.BDIJOF-FBSOJOH3FQPTJUPSZ

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11. DataFrame
    df[['age', 'marital-status']]
    df.groupby('income')['hours-per-week'].mean()
    άϧʔϓԽ ྻબ୒ ू໿ ฏۉ
    
    ྻͷબ୒
    

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12. pandasͷػೳ
    • ϕΫτϧԽ͞Εͨܭࢉ
    
    • άϧʔϓԽ ὎ ू໿ (split-apply-combine)
    
    • มܗ (merge, join, concat…)
    
    • ଟ༷ͳೖग़ྗ (SQL, CSV, Excel, …)
    
    • ॊೈͳ࣌ܥྻσʔλॲཧ
    
    • ՄࢹԽ
    

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13. ؀ڥ
    • όʔδϣϯ
    
    • Python 3.5.2
    
    • pandas 0.19.0rc1
    
    • statsmodels 0.8.0rc1
    
    • ໊લۭؒ
    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt
    import statsmodels.api as sm
    

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14. pandasʹΑΔ
    
    ࣌ܥྻσʔλॲཧ
    

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15. ࣌ܥྻσʔλͱ͸
    • ݱ࣮ͷσʔλ͸ʁ
    ͋Δݱ৅ͷ࣌ؒతͳมԽΛɺ࿈ଓతʹʢ·ͨ͸ҰఆִؒΛ͓͍ͯ
    ෆ࿈ଓʹʣ؍ଌͯ͠ಘΒΕͨ஋ͷܥྻʢҰ࿈ͷ஋ʣ XJLJQFEJBΑΓ
    
    

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16. ݱ࣮ͷσʔλ͸
    • ඞཁͳपظ͕ҟͳΔ
    
    • ೔࣍σʔλΛ݄࣍Ͱ෼ੳ͍ͨ͠
    
    • पظతͰͳ͍
    
    • Πϕϯτͷൃੜ͝ͱʹه࿥͞ΕͨϩάΛ෼ੳ͍ͨ͠
    
    • ࣌ؒͰϥϕϧ෇͚͞Ε͍ͯͳ͍
    
    • ೔࣌Λྻͱؚͯ͠ΉੜσʔλΛɺ͋Δपظ͝ͱʹूܭͯ͠෼ੳ͍ͨ͠
    
    ԿΒ͔ͷલॲཧΛߦ͍ѻ͍΍͍͢ܗʹ͢Δ
    ੜσʔλ ࣌ܥྻσʔλ
    

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17. ࣌ܥྻσʔλͷ४උ
    values = [datetime.datetime(2001, 1, 1),
    datetime.datetime(2001, 2, 1),
    datetime.datetime(2001, 3, 1)]
    s = pd.Series(np.arange(3), index=values)
    s
    2001-01-01 0
    2001-02-01 1
    2001-03-01 2
    dtype: int64
    ೔࣌ͷϦετ
    ೔࣌Λϥϕϧͱ͢Δ
    Ұ࣍ݩσʔλ 4FSJFT
    
    df = pd.DataFrame({'঎඼A': [25, 27, 30],
    '঎඼B': [10, 15, 17]},
    index=values)
    df
    ೔࣌Λϥϕϧͱ͢Δ
    ೋ࣍ݩσʔλ %BUB'SBNF
    
    

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18. ࣌ܥྻσʔλͷϥϕϧ
    df.index
    DatetimeIndex(['2001-01-01', '2001-02-01', '2001-03-01'],
    dtype='datetime64[ns]', freq=None)
    df
    df['঎඼A']
    2001-01-01 25
    2001-02-01 27
    2001-03-01 30
    Name: ঎඼A, dtype: int64
    ϥϕϧ͸೔࣌ͷܕΛ࣋ͭ
    %BUFUJNF*OEFY
    
    ϥϕϧ JOEFY
    
    ྻͷબ୒
    

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19. σʔλͷ४උ
    • ΍Γ͍ͨ͜ͱ
    
    • 1. σʔλʹ೔࣌ͷϥϕϧΛ͚͍ͭͨ
    
    • 2. ೚ҙͷ೔࣌ϑΥʔϚοτΛύʔε͍ͨ͠
    

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20. γʔέϯεͷੜ੒ (pd.date_range)
    s = pd.Series(np.arange(10))
    s
    0 0
    1 1
    2 2
    dtype: int64
    s.index = pd.date_range('2001-01-01', freq='M', periods=3)
    s
    2001-01-31 0
    2001-02-28 1
    2001-03-31 2
    Freq: M, dtype: int64
    ϥϕϧ JOEFY
    Λ্ॻ͖
    pd.date_range('2001-01-01', freq='M', periods=3)
    DatetimeIndex(['2001-01-31', '2001-02-28', '2001-03-31'],
    dtype='datetime64[ns]', freq='M')
    ͔Β݄࣍Ͱݸ
    ͷσʔλΛ࡞੒
    ೔࣌ͷϥϕϧ͕ͳ͍σʔλ
    

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21. Frequency String
    • ੜ੒͢Δ࣌ܥྻͷपظΛࢦఆ͢Δ
    
    • ଞɺશ25छྨ
    'SFRVFODZ4USJOH ҙຯ
    " ೥຤
    . ݄຤
    8 ि
    % ೔
    ) ࣌
    5 ෼
    4 ඵ
    

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22. Frequency String
    pd.date_range('2016-01-01', freq='M', periods=3)
    DatetimeIndex(['2016-01-31', '2016-02-29', ‘2016-03-31’],
    dtype='datetime64[ns]', freq='M')
    pd.date_range('2016-01-01', freq='MS', periods=3)
    DatetimeIndex(['2016-01-01', '2016-02-01', '2016-03-01'],
    dtype='datetime64[ns]', freq='MS')
    pd.date_range('2016-01-01', freq='W', periods=3)
    DatetimeIndex(['2016-01-03', '2016-01-10', '2016-01-17'],
    dtype='datetime64[ns]', freq='W-SUN')
    pd.date_range('2016-01-01', freq='W-TUE', periods=3)
    DatetimeIndex(['2016-01-05', '2016-01-12', '2016-01-19'],
    dtype='datetime64[ns]', freq='W-TUE')
    856&
    ि Ր༵࢝·Γ
    
    8ि
    .4݄ॳ
    .݄຤
    

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23. ೔࣌ͷύʔε (pd.to_datetime)
    • ೔࣌จࣈྻΛߴ଎ʹύʔε
    
    • C Parser ὎ ਖ਼نදݱ ὎ dateutil
    pd.to_datetime(['2016-09-22', '2016-09-23'])
    DatetimeIndex(['2016-09-22', ‘2016-09-23'],
    dtype='datetime64[ns]', freq=None)
    pd.to_datetime(['September 22nd, 2016', 'September 22nd, 2016'])
    DatetimeIndex(['2016-09-22', ‘2016-09-22’],
    dtype='datetime64[ns]', freq=None)
    pd.to_datetime(['22 Sep 2016', '23 Sep 2016'])
    DatetimeIndex(['2016-09-22', ‘2016-09-23'],
    dtype='datetime64[ns]', freq=None)
    

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24. ೔࣌ͷύʔε (pd.to_datetime)
    • ϑΥʔϚοτࢦఆʹΑΔॊೈͳύʔε΋Մೳ
    pd.to_datetime(['2016೥9݄22೔', '2016೥9݄23೔'])
    ValueError: Unknown string format
    pd.to_datetime(['2016೥9݄22೔', '2016೥9݄23೔'], format='%Y೥%m݄%d೔')
    DatetimeIndex(['2016-09-22', ‘2016-09-23'],
    dtype='datetime64[ns]', freq=None)
    

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25. σʔλબ୒
    • ΍Γ͍ͨ͜ͱ
    
    • 1. ͋Δ೔࣌Λબ୒͍ͨ͠
    
    • 2. ͋ΔظؒΛબ୒͍ͨ͠
    
    • 3. ͋Δ৚݅Λຬͨ͢೔࣌Λબ୒͍ͨ͠
    

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26. σʔλબ୒
    idx = pd.date_range('2016-01-01', freq='D', periods=366)
    df = pd.DataFrame({'঎඼A': np.random.randint(100, size=366),
    '঎඼B': np.random.randint(100, size=366)},
    index=idx)
    df.loc[datetime.datetime(2016, 1, 2)]
    ঎඼A 12
    ঎඼B 64
    Name: 2016-01-02 00:00:00, dtype: int64
    ͷߦΛબ୒
    ݁Ռ͸4FSJFT
    df.loc['2016-01-02']
    ঎඼A 12
    ঎඼B 64
    Name: 2016-01-02 00:00:00, dtype: int64
    จࣈྻ͸೔࣌ͱͯ͠ѻΘΕΔ
    df
    

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27. εϥΠεʹΑΔબ୒
    Ҏ߱Λબ୒
    df.loc['2016-09-22':]
    df
    df.loc['2016-09-01':'2016-09-30':2]
    ʙ·Ͱ
    ೔͓͖ʹબ୒
    

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28. ෦෼จࣈྻʹΑΔબ୒
    df['2016-03']
    df
    df['2016-03':'2016-05']
    ݄ʙ݄ͷσʔλΛ
    બ୒
    จࣈྻ͕೔෇Λؚ·ͳ͍
    ݄ͷσʔλΛબ୒
    

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29. ৚݅ʹΑΔબ୒
    df.index.month
    df.loc[(df.index.month == 1) | (df.index.month == 3)]
    array([ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    ...
    12, 12, 12, 12, 12, 12, 12, 12, 12], dtype=int32)
    ϕΫτϧԽ͞Εͨ
    
    ϓϩύςΟΞΫηε
    (df.index.month == 1) | (df.index.month == 3)
    array([ True, True, True, True, True, True, True,
    ...
    False, False, False, False, False, False], dtype=bool)
    

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30. ϓϩύςΟΞΫηε
    • ೔࣌ͷଐੑʹԠͨ͡ॲཧ͕؆୯ʹॻ͚Δ
    ϓϩύςΟ ϓϩύςΟ
    ZFBS EBUF
    NPOUI UJNF
    EBZ EBZPGZFBS
    IPVS XFFLPGZFBS
    NJOVUF XFFL
    TFDPOE EBZPGXFFL
    NJDSPTFDPOE XFFLEBZ
    OBOPTFDPOE XFFLEBZ@OBNF
    RVBSUFS
    

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31. ೔࣌σʔλͷલॲཧ
    • ΍Γ͍ͨ͜ͱ
    
    • 1. ೔࣌ͷपظΛม͍͑ͨ
    
    • 2. ܽଛ஋Λิ׬͍ͨ͠
    
    • 3. લޙͷ஋ͱൺֱ / ܭࢉ͍ͨ͠
    

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32. ϦαϯϓϦϯά (.resample)
    • αϯϓϧσʔλ
    
    • ਖ਼نཚ਺ͷྦྷੵ࿨ (ϥϯμϜ΢ΥʔΫ)
    idx = pd.date_range('2016-09-22', freq='H', periods=50)
    df = pd.DataFrame({'val': np.random.randn(50)}, index=idx)
    df = df.cumsum()
    df
    

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33. ϦαϯϓϦϯά (.resample)
    df.resample('6H').mean()
    df.resample('30T').interpolate()
    μ΢ϯαϯϓϦϯά
    ΞοϓαϯϓϦϯά
    

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34. ϦαϯϓϦϯά (.resample)
    • ༷ʑͳू໿͕Մೳ
    ू໿ϝιου ू໿ϝιου
    ⒏MM NFEJBO
    CBDLpMM NJO
    QBE PIMD
    pMMOB QSPE
    JOUFSQPMBUF TJ[F
    DPVOU TFN
    OVOJRVF TUE
    pSTU
    MB
    TVN
    MBTU WBS
    NBY
    

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35. ิ׬ (.interpolate)
    • αϯϓϧσʔλ
    
    • ஋ʹܽଛ (NaN) ΛؚΉ
    indexer = np.random.randint(4, size=50) == 1
    df.loc[indexer] = np.nan
    df
    ܽଛ
    ܽଛ
    

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36. ิ׬ (.interpolate)
    • ܽଛ஋ͷิ׬
    
    • ಺෦Ͱ scipy.interpolate Λར༻
    df.interpolate()
    

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37. ΢Οϯυ΢ؔ਺ (.rolling)
    • .resample ͱಉ͘͡ɺू໿ϝιουΛνΣΠϯͰ
    ͖Δ
    df.rolling(3).mean()
    

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38. γϑτ (.shift)
    • ஋Λࢦఆ͞Εͨ periods ͚ͩͣΒ͢
    
    • લޙͷ஋ͱͷൺֱ / ܭࢉΛ͢Δࡍʹศར
    df.shift(periods=1)
    

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39. ࠩ෼ (.diff)
    • ࢦఆ͞Εͨ periods ͱͷࠩΛͱΔ
    
    • df - df.shift() ͱಉ͡
    df.diff(periods=1)
    

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40. γϑτͷ࢖͍ํ (ྫ)
    idx = pd.date_range('2016-09-22 10:00', freq='T', periods=50)
    df = pd.DataFrame({'val': np.repeat([0, 1, 0, 1], [10, 20, 10, 10])},
    index=idx)
    df
    df.index[df['val'] != df[‘val'].shift()]
    DatetimeIndex(['2016-09-22 10:00:00', '2016-09-22 10:10:00',
    '2016-09-22 10:30:00', '2016-09-22 10:40:00'],
    dtype='datetime64[ns]', freq=None)
    

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41. ूܭ
    • ΍Γ͍ͨ͜ͱ
    
    • ೔࣌ΛؚΉੜσʔλΛूܭ͍ͨ͠
    

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42. ೔࣌σʔλͷूܭ
    • αϯϓϧσʔλ
    
    • ঎඼ͷൃ஫σʔλ
    df = pd.DataFrame({'਺ྔ': np.random.randint(100, size=1000),
    '঎඼໊': np.random.choice(list('ABC'), 1000),
    'ൃ஫೔': np.random.choice(idx, 1000)})
    df
    

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43. ೔࣌σʔλͷूܭ
    • pd.Grouper
    
    • ྻ໊ͱपظΛࢦఆͨ͠άϧʔϓԽ
    df.groupby([pd.Grouper(key='ൃ஫೔', freq='M'), '঎඼໊']).sum()
    

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44. ϓϩύςΟΞΫηε (.dt)
    df['ൃ஫೔'].dt.weekday
    df.groupby(df['ൃ஫೔'].dt.weekday).sum()
    0 6
    1 4
    2 6
    ..
    997 6
    998 5
    999 2
    Name: ൃ஫೔, dtype: int64
    EUϓϩύςΟΛ௨ͯ͡ɺ
    ೔࣌ϓϩύςΟ΁ͷΞΫηε͕Մೳ
    

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45. Ϋϩεूܭ (pd.pivot_table)
    • pd.pivot_table +
    
    • pd.Grouper
    
    • ϓϩύςΟΞΫηε
    pd.pivot_table(df, index=pd.Grouper(key='ൃ஫೔', freq='M'),
    columns='঎඼໊', values='਺ྔ', aggfunc='sum')
    

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46. ΧϨϯμʔ
    • pandas.tseries.offsets.CustomBusinessDay
    
    • ॕ೔Λߟྀͨ͠ॲཧ
    
    • japandas (https://github.com/sinhrks/japandas)
    
    • JapaneseHolidayCalendar
    from pandas.tseries.offsets import CustomBusinessDay
    import japandas
    cal = japandas.JapaneseHolidayCalendar()
    cbd = CustomBusinessDay(calendar=cal)
    idx = pd.DatetimeIndex(['2016-09-20', '2016-09-21', ‘2016-09-22'])
    idx + cbd
    DatetimeIndex(['2016-09-21', '2016-09-23', '2016-09-23'],
    dtype='datetime64[ns]', freq=None)
    ͸ॕ೔
    

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47. ՄࢹԽ
    • ࣌ܥྻσʔλͷपظΛࣗಈͰௐ੔ͯ͠ϓϩοτ
    idx1 = pd.date_range('2016-09-01', freq='D', periods=50)
    df1 = pd.DataFrame({'val1': np.random.randn(50)}, index=idx1)
    df1.plot()
    

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48. ՄࢹԽ
    • ࣌ܥྻσʔλͷपظΛࣗಈͰௐ੔ͯ͠ϓϩοτ
    
    • पظ͕ҟͳΔ৔߹΋ࣗಈௐ੔
    idx2 = pd.date_range('2016-09-01', freq='M', periods=3)
    df2 = pd.DataFrame({'val1': np.random.randn(3)}, index=idx2)
    ax = df1.plot()
    df2.plot(ax=ax)
    

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49. ࣌ܥྻσʔλͷ
    
    ౷ܭϞσϧ
    

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50. ࣌ܥྻσʔλͷ౷ܭϞσϧ
    • ໨త
    
    • ࣌ܥྻͷؔ܎Λௐ΂͍ͨ
    
    • কདྷͷ༧ଌΛ͍ͨ͠
    
    • มԽ఺ / ҟৗ஋Λݕ஌͍ͨ͠
    
    • …
    
    • ࣌ܥྻσʔλͷཹҙ఺
    
    • ͋Δ࣌఺Ҏલͷσʔλ͔ΒͷӨڹ͕͋Δ͔ʁ
    
    • τϨϯυ΍قઅੑ͕͋Δ͔ʁ
    

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51. ࣌ܥྻϞσϧΛؚΈPythonύοέʔδ
    • ར༻͍ͨ͠Ϟσϧʹ߹ΘͤͯύοέʔδΛબͿ
    
    • ඞཁʹԠ͡ R Λར༻ (rpy2, pypeR)
    4UBUT.PEFMT 1Z'MVY
    ౷ܭྔݕఆ ✅
    "3*." ✅ ✅
    7"3 ✅ ✅
    ("3$)
    ✅
    TBOECPY
    
    ✅
    ("4 ✅
    4UBUF4QBDF
    ✅
    SD
    
    ✅
    

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52. αϯϓϧσʔλ
    • AirPassengers
    
    • ݄࣍ͷࠃࡍઢ౥৐ਓ਺ (ઍਓ)
    
    • ୯มྔɺτϨϯυͱقઅੑΛ࣋ͭ
    df = pd.read_csv('airpassengers.csv', index_col=0, parse_dates=[0])
    df
    

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53. • ࣌ܥྻΛτϨϯυɺقઅੑɺ࢒ࠩʹ෼ղ
    ࣌ܥྻͷ੒෼෼ղ
    res = sm.tsa.seasonal_decompose(df)
    fig = res.plot();
    ݩσʔλ
    τϨϯυ
    قઅੑ
    ࢒ࠩ
    

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54. ౷ܭྔ
    • ඪຊࣗݾ૬ؔ (ACF)
    
    • ҟ࣌఺ؒͷڞ෼ࢄΛඪ४Խͨ͠΋ͷ
    
    • ඪຊภࣗݾ૬ؔ (PACF)
    fig, axes = plt.subplots(1, 2)
    sm.tsa.graphics.plot_acf(df, ax=axes[0]);
    sm.tsa.graphics.plot_pacf(df, ax=axes[1]);
    

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55. ౷ܭྔ
    • ਖ਼نཚ਺ (ϗϫΠτϊΠζ) ͷ৔߹
    
    • աڈͷ஋ͱ૬͕ؔͳ͍
    wn = pd.Series(np.random.randn(100))
    fig, axes = plt.subplots(1, 2)
    sm.tsa.graphics.plot_acf(wn, ax=axes[0]);
    sm.tsa.graphics.plot_pacf(wn, ax=axes[1]);
    

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56. SARIMAϞσϧ
    • قઅతࣗݾճؼ࿨෼ҠಈฏۉϞσϧ
    
    • ࣗݾճؼ࿨෼ҠಈฏۉϞσϧ (ARIMA)
    
    • + قઅมಈ (ARIMA)
    
    • ARIMA (p, d, q)
    
    • d֊ࠩ෼Λͱͬͨ࣌ܥྻ yt ͕
    
    • (ऑ)ఆৗ (ฏۉɺࣗݾڞ෼ࢄ͕࣌ؒʹΑΒͣҰఆ)
    
    • ҎԼͷաఔʹै͏
    
    • yt = c + φ1yt-1 + … + φpyp + εt + θ1εt-1 + … + θqεt-q
    

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57. τϨϯυͷআڈ
    df.plot()
    df.diff().plot()
    ෼ࢄ͕େ͖͘ͳ͍ͬͯΔ
    ֊ࠩ
    

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58. ର਺ม׵
    ldf = np.log(df)
    ldf.plot()
    ldf.diff().plot()
    ର਺ม׵
    

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59. قઅੑͷআڈ
    res = sm.tsa.seasonal_decompose(ldf)
    seasonal_adjust = (ldf - res.seasonal)
    seasonal_adjust.plot()
    قઅ੒෼ΛҾ͘
    

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60. ୯Ґࠜݕఆ
    • Augmented Dickey-Fullerݕఆ
    sm.tsa.adfuller(df['Air passengers'])[1]
    0.99188024343764114
    sm.tsa.adfuller(ldf['Air passengers'])[1]
    0.42236677477038415
    sm.tsa.adfuller(ldf['Air passengers'].diff().dropna())[1]
    0.071120548150854057
    ݩσʔλ
    ର਺Խ
    ର਺Խ֊ࠩ
    sm.tsa.adfuller(seasonal_adjust['Air passengers'].diff().dropna())[1]
    8.0990048658604878e-09
    ର਺Խقઅੑআڈ֊ࠩ
    

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61. SARIMAϞσϧͷਪఆ
    mod_seasonal = sm.tsa.SARIMAX(ldf, trend='c', order=(1, 1, 1),
    seasonal_order=(0, 1, 2, 12))
    res_seasonal = mod_seasonal.fit()
    res_seasonal.summary()
    ʜ
    ʜ
    "3*."قઅ੒෼ͷύϥϝʔλ
    SD͕ඞཁ
    

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62. Ϟσϧ͔Βͷ༧ଌ
    pred = res_seasonal.forecast(36)
    pred
    1961-01-01 6.110548
    1961-02-01 6.052912
    1961-03-01 6.174690
    ...
    1963-10-01 6.388955
    1963-11-01 6.242262
    1963-12-01 6.345214
    Freq: MS, dtype: float64
    ax = ldf.plot()
    pred.plot(ax=ax)
    ظઌΛ༧ଌ
    ݩσʔλ༧ଌ஋Λϓϩοτ
    

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63. ·ͱΊ
    • pandas Λ࢖ͬͯ࣌ܥྻ·ΘΓͷॲཧΛ͢Δํ๏
    
    • PythonͰ࣌ܥྻϞσϧΛѻ͏ํ๏ (ͷ৮Γ)
    

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64. ։ൃϩʔυϚοϓ
    • ܭը
    
    • 0.19 (ݱࡏrc) ὎ 0.20 ὎ 1.0 ΛϦϦʔε
    
    • pandas 1.0
    
    • API ౚ݁
    
    • Long Time Support
    
    • pandas 2.0 (under discussion)
    
    • Python 3.xͷΈΛαϙʔτ
    
    • 2࣍ݩҎԼͷσʔλʹಛԽ
    
    • όοΫΤϯυΛ C++ ʹҠߦ (Apache Arrow)
    

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