pandas dataframe在其他列值上使用多个条件添加具有值的列问题的回答

pandas dataframe在其他列值上使用多个条件添加具有值的列

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我有熊猫完整的数据框叫'调整'。我想在'fyear'和'conm'上添加具有新值条件的'stage'列。在 <pre><code> fyear conm indadjsg 1 1999 1-800-FLOWERS.COM 26.646086 2 2000 1-800-FLOWERS.COM 22.727175 3 2001 1-800-FLOWERS.COM 7.312014 4 2002 1-800-FLOWERS.COM 4.948308 5 2003 1-800-FLOWERS.COM 6.278798 23 1996 ABERCROMBIE & FITCH -CL A 34.831691 24 1997 ABERCROMBIE & FITCH -CL A 48.053137 25 1998 ABERCROMBIE & FITCH -CL A 48.918326 26 1999 ABERCROMBIE & FITCH -CL A 46.956456 27 2000 ABERCROMBIE & FITCH -CL A 33.91436 28 2001 ABERCROMBIE & FITCH -CL A 67.23423 29 2002 ABERCROMBIE & FITCH -CL A 99.09342 11929 2006 CLIFTON BANCORP INC 0.236418 11930 2007 CLIFTON BANCORP INC -1.366626 11931 2008 CLIFTON BANCORP INC 8.564019 11932 2009 CLIFTON BANCORP INC -4.966110 11933 2010 CLIFTON BANCORP INC -4.359552 11934 2011 CLIFTON BANCORP INC -16.313852 11935 2012 CLIFTON BANCORP INC -18.193550 11936 2013 CLIFTON BANCORP INC -10.126603 11937 2014 CLIFTON BANCORP INC 4.718584 11938 2015 CLIFTON BANCORP INC -11.889065 11940 2015 CLIPPER REALTY INC 70.945767 11941 2016 CLIPPER REALTY INC 3.776001 11980 2014 CM FINANCE INC 205.894048 11981 2015 CM FINANCE INC 68.518555 121247 2009 VCA INC -5.552030 121248 2010 VCA INC -3.357275 121249 2011 VCA INC -0.930798 121250 2012 VCA INC 5.974914 121256 2007 VIASPACE INC -50.966869 121257 2008 VIASPACE INC 149.957403 121258 2009 VIASPACE INC 197.776855 121259 2010 VIASPACE INC -25.201733 121260 2011 VIASPACE INC 77.082624 121261 2012 VIASPACE INC 78.034233 121266 2005 YASHENG GROUP -3.728098 121267 2006 YASHENG GROUP -2.233927 121268 2007 YASHENG GROUP 0.349349 121279 2009 YUHE INTERNATIONAL INC 27.995324 121280 2010 YUHE INTERNATIONAL INC 34.375630 </code></pre> 1）如果唯一公司的年度数等于或小于5，我想填写“开始”。在 ^{pr2}$ 2）在我用“start”条件填充完剩余数据后，我想填充另一个值。计算出的唯一公司平均值。在 <pre><code>mask2 = adjusted.groupby(by=['conm'])['indadjsg'] countsg = mask2.mean().to_frame().reset_index() c = countsg.dropna() </code></pre> 数据框“c” <pre><code> conm indadjsg 0 1-800-FLOWERS.COM 3.291539 1 ABERCROMBIE & FITCH -CL A 105.335324 2 CLIFTON BANCORP INC 22.920683 3 CLIPPER REALTY INC 36.784677 4 CM FINANCE INC 1.605919 5 VCA INC 3.116871 6 VIASPACE INC -106.153789 7 YASHENG GROUP -2.676296 8 YUHE INTERNATIONAL INC 12.306557 </code></pre> 我想给出的条件是： <pre><code> indadjsg < 0, 'decline' 0 <= indadjsg <= 15, 'revival' 15< indadjsg <= 100, 'mature' 100< indajsg , 'growth' </code></pre> 我要制作的最终数据帧是这样的 <pre><code> fyear conm indadjsg stage 1 1999 1-800-FLOWERS.COM 26.646086 start 2 2000 1-800-FLOWERS.COM 22.727175 start 3 2001 1-800-FLOWERS.COM 7.312014 start 4 2002 1-800-FLOWERS.COM 4.948308 start 5 2003 1-800-FLOWERS.COM 6.278798 start 23 1996 ABERCROMBIE & FITCH -CL A 34.831691 growth 24 1997 ABERCROMBIE & FITCH -CL A 48.053137 growth 25 1998 ABERCROMBIE & FITCH -CL A 48.918326 growth 26 1999 ABERCROMBIE & FITCH -CL A 46.956456 growth 27 2000 ABERCROMBIE & FITCH -CL A 33.91436 growth 28 2001 ABERCROMBIE & FITCH -CL A 67.23423 growth 29 2002 ABERCROMBIE & FITCH -CL A 99.09342 growth 11929 2006 CLIFTON BANCORP INC 0.236418 mature 11930 2007 CLIFTON BANCORP INC -1.366626 mature 11931 2008 CLIFTON BANCORP INC 8.564019 mature 11932 2009 CLIFTON BANCORP INC -4.966110 mature 11933 2010 CLIFTON BANCORP INC -4.359552 mature 11934 2011 CLIFTON BANCORP INC -16.313852 mature 11935 2012 CLIFTON BANCORP INC -18.193550 mature 11936 2013 CLIFTON BANCORP INC -10.126603 mature 11937 2014 CLIFTON BANCORP INC 4.718584 mature 11938 2015 CLIFTON BANCORP INC -11.889065 mature 11940 2015 CLIPPER REALTY INC 70.945767 start 11941 2016 CLIPPER REALTY INC 3.776001 start 11980 2014 CM FINANCE INC 205.894048 start 11981 2015 CM FINANCE INC 68.518555 start 121247 2009 VCA INC -5.552030 start 121248 2010 VCA INC -3.357275 start 121249 2011 VCA INC -0.930798 start 121250 2012 VCA INC 5.974914 start 121256 2007 VIASPACE INC -50.966869 decline 121257 2008 VIASPACE INC 149.957403 decline 121258 2009 VIASPACE INC 197.776855 decline 121259 2010 VIASPACE INC -25.201733 decline 121260 2011 VIASPACE INC 77.082624 decline 121261 2012 VIASPACE INC 78.034233 decline 121266 2005 YASHENG GROUP -3.728098 start 121267 2006 YASHENG GROUP -2.233927 start 121268 2007 YASHENG GROUP 0.349349 start 121279 2009 YUHE INTERNATIONAL INC 27.995324 start 121280 2010 YUHE INTERNATIONAL INC 34.375630 start </code></pre> 有什么办法我能马上做吗？我只能考虑做单独的列并合并它。你能帮我有效地思考吗？提前谢谢你。在

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匿名 1天前

　擅长：python、mysql、java

我相信您可以通过<a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.cut.html" rel="nofollow noreferrer">pd.cut</a>和<a href="https://docs.scipy.org/doc/numpy-1.12.0/reference/generated/numpy.where.html" rel="nofollow noreferrer">np.where</a>来实现这一点： <pre><code>adjusted # copied text from your example Out[86]: fyear conm indadjsg 0 1999 1-800-FLOWERS.COM 26.64609 1 2000 1-800-FLOWERS.COM 22.72717 2 2001 1-800-FLOWERS.COM 7.31201 3 2002 1-800-FLOWERS.COM 4.94831 4 2003 1-800-FLOWERS.COM 6.27880 5 1996 ABERCROMBIE 34.83169 6 1997 ABERCROMBIE 48.05314 7 1998 ABERCROMBIE 48.91833 8 1999 ABERCROMBIE 46.95646 9 2000 ABERCROMBIE 33.91436 10 2001 ABERCROMBIE 67.23423 11 2002 ABERCROMBIE 99.09342 .. ... ... ... 25 2015 CM 68.51856 26 2009 VCA -5.55203 27 2010 VCA -3.35728 28 2011 VCA -0.93080 29 2012 VCA 5.97491 30 2007 VIASPACE -50.96687 31 2008 VIASPACE 149.95740 32 2009 VIASPACE 197.77686 33 2010 VIASPACE -25.20173 34 2011 VIASPACE 77.08262 35 2012 VIASPACE 78.03423 36 2005 YASHENG -3.72810 byyr = adjusted.groupby(by='conm')['fyear'].count().to_frame() start = byyr.fyear[adjusted.conm] indadjsg = adjusted.groupby(by='conm')['indadjsg'].mean().to_frame() px = indadjsg.indadjsg[adjusted.conm] categories = pd.cut(px.values.reshape((len(px), )), bins= [-np.inf, 0, 15, 100, np.inf], labels=['decline', 'revival', 'mature', 'growth']) adjusted.loc[:, 'stage'] = np.where(start <= 5, 'start', categories) adjusted # result Out[130]: fyear conm indadjsg stage 0 1999 1-800-FLOWERS.COM 26.64609 start 1 2000 1-800-FLOWERS.COM 22.72717 start 2 2001 1-800-FLOWERS.COM 7.31201 start 3 2002 1-800-FLOWERS.COM 4.94831 start 4 2003 1-800-FLOWERS.COM 6.27880 start 5 1996 ABERCROMBIE 34.83169 mature 6 1997 ABERCROMBIE 48.05314 mature 7 1998 ABERCROMBIE 48.91833 mature 8 1999 ABERCROMBIE 46.95646 mature 9 2000 ABERCROMBIE 33.91436 mature 10 2001 ABERCROMBIE 67.23423 mature 11 2002 ABERCROMBIE 99.09342 mature .. ... ... ... ... 25 2015 CM 68.51856 start 26 2009 VCA -5.55203 start 27 2010 VCA -3.35728 start 28 2011 VCA -0.93080 start 29 2012 VCA 5.97491 start 30 2007 VIASPACE -50.96687 mature 31 2008 VIASPACE 149.95740 mature 32 2009 VIASPACE 197.77686 mature 33 2010 VIASPACE -25.20173 mature 34 2011 VIASPACE 77.08262 mature 35 2012 VIASPACE 78.03423 mature 36 2005 YASHENG -3.72810 start </code></pre> 打开pd.切割，请确保使用<code>right=True</code>或<code>right=False.</code>指定容器的边缘

pandas dataframe在其他列值上使用多个条件添加具有值的列

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