我想找到第一個數據幀的最后一個有效索引，并用它來索引第二個數據幀。所以，假設我有以下數據框（df1）： Site 1 Site 2 Site 3 Site 4 Site 5 Site 6Date 2000-01-01 13.0 28.0 76.0 45 90.0 58.02001-01-01 77.0 75.0 57.0 3 41.0 24.02002-01-01 50.0 29.0 2.0 65 48.0 21.02003-01-01 7.0 48.0 14.0 63 12.0 66.02004-01-01 11.0 90.0 11.0 5 47.0 6.02005-01-01 50.0 4.0 31.0 1 40.0 79.02006-01-01 30.0 98.0 91.0 96 43.0 39.02007-01-01 50.0 20.0 54.0 65 NaN 47.02008-01-01 24.0 84.0 52.0 84 NaN 81.02009-01-01 56.0 61.0 57.0 25 NaN 36.02010-01-01 87.0 45.0 68.0 65 NaN 71.02011-01-01 22.0 50.0 92.0 91 NaN 48.02012-01-01 12.0 44.0 79.0 77 NaN 25.02013-01-01 1.0 22.0 34.0 57 NaN 25.02014-01-01 94.0 NaN 86.0 97 NaN 91.02015-01-01 2.0 NaN 98.0 44 NaN 79.02016-01-01 81.0 NaN 35.0 87 NaN 32.02017-01-01 59.0 NaN 95.0 32 NaN 58.02018-01-01 NaN NaN 3.0 14 NaN NaN2019-01-01 NaN NaN 48.0 9 NaN NaN2020-01-01 NaN NaN NaN 49 NaN NaN現在我可以使用“first_valid_index()”找到每列的最后一個有效索引：lvi = df.apply(lambda series: series.last_valid_index())哪個產量：Site 1 2017-01-01Site 2 2013-01-01Site 3 2019-01-01Site 4 2020-01-01Site 5 2006-01-01Site 6 2017-01-01我如何將它應用到另一個 Dataframe，我使用這個索引來切片另一個 Dataframe 的時間序列。Dataframe 的另一個示例可以使用以下方法創建：import pandas as pdimport numpy as npfrom numpy import randomrandom.seed(30)idx = pd.date_range(start='2000-01-01', end='2020-01-01',freq ='AS')df2 = df2.set_index(idx)我如何使用那個“lvi”變量來索引 df2？要手動執行此操作，我可以使用：df_s1 = df['Site 1'].loc['2000-01-01':'2017-01-01']有沒有更好的方法來解決這個問題？另外，每一列本質上都必須是自己的數據框才能工作嗎？任何幫助是極大的贊賞！
3 回答
largeQ
TA貢獻2039條經驗 獲得超8個贊
這可能有點慣用：
df2[df.notna()]
甚至
df2.where(df.notna())
請注意，在這些情況下（和df1*0 + df2），操作是為了匹配df和的索引值而完成的df2。例如，df2[df.reset_index(drop=True).notna()]將返回所有nan，因為沒有公共索引值。
溫溫醬
TA貢獻1752條經驗 獲得超4個贊
這似乎工作得很好：
In [34]: d
Out[34]:
x y
Date
2020-01-01 1.0 2.0
2020-01-02 1.0 2.0
2020-01-03 1.0 2.0
2020-01-04 1.0 2.0
2020-01-05 1.0 2.0
2020-01-06 1.0 NaN
2020-01-07 1.0 NaN
2020-01-08 1.0 NaN
2020-01-09 1.0 NaN
2020-01-10 1.0 NaN
2020-01-11 NaN NaN
2020-01-12 NaN NaN
2020-01-13 NaN NaN
2020-01-14 NaN NaN
2020-01-15 NaN NaN
2020-01-16 NaN NaN
2020-01-17 NaN NaN
2020-01-18 NaN NaN
2020-01-19 NaN NaN
2020-01-20 NaN NaN
In [35]: d.apply(lambda col: col.last_valid_index())
Out[35]:
x 2020-01-10
y 2020-01-05
dtype: datetime64[ns]
進而： In [15]: d.apply(lambda col: col.last_valid_index()).apply(lambda date: df2.loc[date]) Out[15]: z x 0.940396 y 0.564007
精慕HU
TA貢獻1845條經驗 獲得超8個贊
好吧，所以在考慮了一段時間并嘗試想出一個涉及 for 循環等的詳細過程之后，我得出的結論是這個簡單的數學運算就可以解決問題?；旧希依昧?pandas 中 Dataframes 之間的數學計算方式。
output = df1*0 + df2
這給出了 df2 的輸出，它將采用 df1 的 NaN 值，如下所示：
Site 1 Site 2 Site 3 Site 4 Site 5 Site 6
Date
2000-01-01 0.690597 0.443933 0.787931 0.659639 0.363606 0.922373
2001-01-01 0.388669 0.577734 0.450225 0.021592 0.554249 0.305546
2002-01-01 0.578212 0.927848 0.361426 0.840541 0.626881 0.545491
2003-01-01 0.431668 0.128282 0.893351 0.783488 0.122182 0.666194
2004-01-01 0.151491 0.928584 0.834474 0.945401 0.590830 0.802648
2005-01-01 0.113477 0.398326 0.649955 0.202538 0.485927 0.127925
2006-01-01 0.521906 0.458672 0.923632 0.948696 0.638754 0.552753
2007-01-01 0.266599 0.839047 0.099069 0.000928 NaN 0.018146
2008-01-01 0.819810 0.809779 0.706223 0.247780 NaN 0.759691
2009-01-01 0.441574 0.020291 0.702551 0.468862 NaN 0.341191
2010-01-01 0.277030 0.130573 0.906697 0.589474 NaN 0.819986
2011-01-01 0.795344 0.103121 0.846405 0.589916 NaN 0.564411
2012-01-01 0.697255 0.599767 0.206482 0.718980 NaN 0.731366
2013-01-01 0.891771 0.001944 0.703132 0.751986 NaN 0.845933
2014-01-01 0.672579 NaN 0.466981 0.466770 NaN 0.618069
2015-01-01 0.767219 NaN 0.702156 0.370905 NaN 0.481971
2016-01-01 0.315264 NaN 0.793531 0.754920 NaN 0.091432
2017-01-01 0.431651 NaN 0.974520 0.708074 NaN 0.870077
2018-01-01 NaN NaN 0.408743 0.430576 NaN NaN
2019-01-01 NaN NaN 0.751509 0.755521 NaN NaN
2020-01-01 NaN NaN NaN 0.518533 NaN NaN
我基本上是想將 NaN 值從一個 Dataframe 印到另一個 Dataframe 上。我不敢相信我做這件事有多困難。只要我的 Dataframes 大小相同，這就可以很好地滿足我的需求。
現在我應該能夠從這里開始計算每個最后有效數據點的百分比變化。謝謝大家的投入！
編輯：
只是為了向大家展示我最終想要完成的事情，這是我在大家的幫助和建議下制作的最終代碼！
原來的 df 最初看起來像：
Site 1 Site 2 Site 3 Site 4 Site 5 Site 6
Date
2000-01-01 13.0 28.0 76.0 45 90.0 58.0
2001-01-01 77.0 75.0 57.0 3 41.0 24.0
2002-01-01 50.0 29.0 2.0 65 48.0 21.0
2003-01-01 7.0 48.0 14.0 63 12.0 66.0
2004-01-01 11.0 90.0 11.0 5 47.0 6.0
2005-01-01 50.0 4.0 31.0 1 40.0 79.0
2006-01-01 30.0 98.0 91.0 96 43.0 39.0
2007-01-01 50.0 20.0 54.0 65 NaN 47.0
2008-01-01 24.0 84.0 52.0 84 NaN 81.0
2009-01-01 56.0 61.0 57.0 25 NaN 36.0
2010-01-01 87.0 45.0 68.0 65 NaN 71.0
2011-01-01 22.0 50.0 92.0 91 NaN 48.0
2012-01-01 12.0 44.0 79.0 77 NaN 25.0
2013-01-01 1.0 22.0 34.0 57 NaN 25.0
2014-01-01 94.0 NaN 86.0 97 NaN 91.0
2015-01-01 2.0 NaN 98.0 44 NaN 79.0
2016-01-01 81.0 NaN 35.0 87 NaN 32.0
2017-01-01 59.0 NaN 95.0 32 NaN 58.0
2018-01-01 NaN NaN 3.0 14 NaN NaN
2019-01-01 NaN NaN 48.0 9 NaN NaN
2020-01-01 NaN NaN NaN 49 NaN NaN
然后我想出了第二個完整的數據框（df2）：
df2 = pd.DataFrame({
"Site 1": np.random.rand(21),
"Site 2": np.random.rand(21),
"Site 3": np.random.rand(21),
"Site 4": np.random.rand(21),
"Site 5": np.random.rand(21),
"Site 6": np.random.rand(21)})
idx = pd.date_range(start='2000-01-01', end='2020-01-01',freq ='AS')
df2 = df2.set_index(idx)
現在，我將 df2 中的 nan 值替換為 df 中的 nan 值：
dfr = df2[df.notna()]
然后我反轉數據框：
dfr = dfr[::-1]
valid_first = dfr.apply(lambda col: col.first_valid_index())
valid_last = dfr.apply(lambda col: col.last_valid_index())
現在我想要計算從最后一個有效數據點開始的百分比變化，每個列都是固定的。這給了我從現在到過去的百分比變化，相對于最近（或最后有效）的數據點。
new = []
for j in dfr:
m = dfr[j].loc[valid_first[j]:valid_last[j]]
pc = m / m.iloc[0]-1
new.append(pc)
final = pd.concat(new,axis=1)
print(final)
這給了我：
Site 1 Site 2 Site 3 Site 4 Site 5 Site 6
2000-01-01 0.270209 -0.728445 -0.636105 0.380330 41.339081 -0.462147
2001-01-01 0.854952 -0.827804 -0.703568 -0.787391 40.588791 -0.884806
2002-01-01 -0.677757 -0.120482 -0.208255 -0.982097 54.348094 -0.483415
2003-01-01 -0.322010 -0.061277 -0.382602 1.025088 5.440808 -0.602661
2004-01-01 1.574451 -0.768251 -0.543260 1.210434 50.494788 -0.859331
2005-01-01 -0.412226 -0.866441 -0.055027 -0.168267 1.346869 -0.385080
2006-01-01 1.280867 -0.640899 0.354513 1.086703 0.000000 0.108504
2007-01-01 1.121585 -0.741675 -0.735990 -0.768578 NaN -0.119436
2008-01-01 -0.210467 -0.376884 -0.575106 -0.779147 NaN 0.055949
2009-01-01 1.864107 -0.966827 0.566590 1.003121 NaN -0.214482
2010-01-01 0.571762 -0.311459 -0.518113 1.036950 NaN -0.513911
2011-01-01 -0.122525 -0.178137 -0.641642 0.197481 NaN 0.033141
2012-01-01 0.403578 -0.829402 0.161753 -0.438578 NaN -0.996595
2013-01-01 0.383481 0.000000 -0.305824 0.602079 NaN -0.057711
2014-01-01 -0.699708 NaN -0.515074 -0.277157 NaN -0.840873
2015-01-01 0.422364 NaN -0.759708 1.230037 NaN -0.663253
2016-01-01 -0.418945 NaN 0.197396 -0.445260 NaN -0.299741
2017-01-01 0.000000 NaN -0.897428 0.669791 NaN 0.000000
2018-01-01 NaN NaN 0.138997 0.486961 NaN NaN
2019-01-01 NaN NaN 0.000000 0.200771 NaN NaN
2020-01-01 NaN NaN NaN 0.000000 NaN NaN
我知道很多時候這些問題沒有上下文，所以這里是由于您的輸入而獲得的最終輸出。再次感謝大家的幫助！
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