• <i id='AW9Ji'><tr id='AW9Ji'><dt id='AW9Ji'><q id='AW9Ji'><span id='AW9Ji'><b id='AW9Ji'><form id='AW9Ji'><ins id='AW9Ji'></ins><ul id='AW9Ji'></ul><sub id='AW9Ji'></sub></form><legend id='AW9Ji'></legend><bdo id='AW9Ji'><pre id='AW9Ji'><center id='AW9Ji'></center></pre></bdo></b><th id='AW9Ji'></th></span></q></dt></tr></i><div id='AW9Ji'><tfoot id='AW9Ji'></tfoot><dl id='AW9Ji'><fieldset id='AW9Ji'></fieldset></dl></div>

      <small id='AW9Ji'></small><noframes id='AW9Ji'>

      • <bdo id='AW9Ji'></bdo><ul id='AW9Ji'></ul>
      <legend id='AW9Ji'><style id='AW9Ji'><dir id='AW9Ji'><q id='AW9Ji'></q></dir></style></legend>

      <tfoot id='AW9Ji'></tfoot>

        Python pandas 根据标题值匹配VLOOKUP列

        Python Pandas Match Vlookup columns based on header values(Python pandas 根据标题值匹配VLOOKUP列)

        1. <small id='z2yNx'></small><noframes id='z2yNx'>

          <i id='z2yNx'><tr id='z2yNx'><dt id='z2yNx'><q id='z2yNx'><span id='z2yNx'><b id='z2yNx'><form id='z2yNx'><ins id='z2yNx'></ins><ul id='z2yNx'></ul><sub id='z2yNx'></sub></form><legend id='z2yNx'></legend><bdo id='z2yNx'><pre id='z2yNx'><center id='z2yNx'></center></pre></bdo></b><th id='z2yNx'></th></span></q></dt></tr></i><div id='z2yNx'><tfoot id='z2yNx'></tfoot><dl id='z2yNx'><fieldset id='z2yNx'></fieldset></dl></div>
          <tfoot id='z2yNx'></tfoot>
            <tbody id='z2yNx'></tbody>

        2. <legend id='z2yNx'><style id='z2yNx'><dir id='z2yNx'><q id='z2yNx'></q></dir></style></legend>

                <bdo id='z2yNx'></bdo><ul id='z2yNx'></ul>
                  本文介绍了Python pandas 根据标题值匹配VLOOKUP列的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着跟版网的小编来一起学习吧!

                  问题描述

                  我有以下数据帧DF:

                  Customer_ID | 2015 | 2016 |2017 | Year_joined_mailing
                  ABC            5      6     10     2015
                  BCD            6      7     3      2016        
                  DEF            10     4     5      2017
                  GHI            8      7     10     2016
                  

                  我要查找客户在加入邮件列表时的价值,并将其保存在新列中。

                  输出将为:

                  Customer_ID | 2015 | 2016 |2017 | Year_joined_mailing | Purchases_1st_year
                  ABC            5      6     10     2015                       5
                  BCD            6      7     3      2016                       7       
                  DEF            10     4     5      2017                       5
                  GHI            8      9     10     2016                       9
                  

                  我为python中的Match VLOOKUP找到了一些解决方案,但没有一个可以使用其他列的标题。

                  推荐答案

                  弃用通知lookup为deprecated in v1.2.0

                  使用pd.DataFrame.lookup
                  请记住,我假设Customer_ID是索引。

                  df.lookup(df.index, df.Year_joined_mailing)
                  
                  array([5, 7, 5, 7])
                  

                  df.assign(
                      Purchases_1st_year=df.lookup(df.index, df.Year_joined_mailing)
                  )
                  
                               2015  2016  2017  Year_joined_mailing  Purchases_1st_year
                  Customer_ID                                                           
                  ABC             5     6    10                 2015                   5
                  BCD             6     7     3                 2016                   7
                  DEF            10     4     5                 2017                   5
                  GHI             8     7    10                 2016                   7
                  

                  但是,在比较列名中可能的字符串和第一年列中的整数时必须小心.

                  确保遵守类型比较的核心选项。

                  df.assign(
                      Purchases_1st_year=df.rename(columns=str).lookup(
                          df.index, df.Year_joined_mailing.astype(str)
                      )
                  )
                  
                               2015  2016  2017  Year_joined_mailing  Purchases_1st_year
                  Customer_ID                                                           
                  ABC             5     6    10                 2015                   5
                  BCD             6     7     3                 2016                   7
                  DEF            10     4     5                 2017                   5
                  GHI             8     7    10                 2016                   7
                  

                  这篇关于Python pandas 根据标题值匹配VLOOKUP列的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持跟版网!

                  本站部分内容来源互联网,如果有图片或者内容侵犯了您的权益,请联系我们,我们会在确认后第一时间进行删除!

                  相关文档推荐

                  groupby multiple coords along a single dimension in xarray(在xarray中按单个维度的多个坐标分组)
                  Group by and Sum in Pandas without losing columns(Pandas中的GROUP BY AND SUM不丢失列)
                  Is there a way of group by month in Pandas starting at specific day number?( pandas 有从特定日期开始的按月分组的方式吗?)
                  Group by + New Column + Grab value former row based on conditionals(GROUP BY+新列+基于条件的前一行抓取值)
                  Groupby and interpolate in Pandas(PANDA中的Groupby算法和插值算法)
                  Pandas - Group Rows based on a column and replace NaN with non-null values(PANAS-基于列对行进行分组,并将NaN替换为非空值)
                      <tbody id='fR6vR'></tbody>
                        <bdo id='fR6vR'></bdo><ul id='fR6vR'></ul>
                        <legend id='fR6vR'><style id='fR6vR'><dir id='fR6vR'><q id='fR6vR'></q></dir></style></legend>

                        <small id='fR6vR'></small><noframes id='fR6vR'>

                        1. <i id='fR6vR'><tr id='fR6vR'><dt id='fR6vR'><q id='fR6vR'><span id='fR6vR'><b id='fR6vR'><form id='fR6vR'><ins id='fR6vR'></ins><ul id='fR6vR'></ul><sub id='fR6vR'></sub></form><legend id='fR6vR'></legend><bdo id='fR6vR'><pre id='fR6vR'><center id='fR6vR'></center></pre></bdo></b><th id='fR6vR'></th></span></q></dt></tr></i><div id='fR6vR'><tfoot id='fR6vR'></tfoot><dl id='fR6vR'><fieldset id='fR6vR'></fieldset></dl></div>
                          1. <tfoot id='fR6vR'></tfoot>