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        拆分(分解) pandas 数据框字符串条目以分隔行

        Split (explode) pandas dataframe string entry to separate rows(拆分(分解) pandas 数据框字符串条目以分隔行)

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                • 本文介绍了拆分(分解) pandas 数据框字符串条目以分隔行的处理方法,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!


                  I have a pandas dataframe in which one column of text strings contains comma-separated values. I want to split each CSV field and create a new row per entry (assume that CSV are clean and need only be split on ','). For example, a should become b:

                  In [7]: a
                      var1  var2
                  0  a,b,c     1
                  1  d,e,f     2
                  In [8]: b
                    var1  var2
                  0    a     1
                  1    b     1
                  2    c     1
                  3    d     2
                  4    e     2
                  5    f     2

                  So far, I have tried various simple functions, but the .apply method seems to only accept one row as return value when it is used on an axis, and I can't get .transform to work. Any suggestions would be much appreciated!

                  Example data:

                  from pandas import DataFrame
                  import numpy as np
                  a = DataFrame([{'var1': 'a,b,c', 'var2': 1},
                                 {'var1': 'd,e,f', 'var2': 2}])
                  b = DataFrame([{'var1': 'a', 'var2': 1},
                                 {'var1': 'b', 'var2': 1},
                                 {'var1': 'c', 'var2': 1},
                                 {'var1': 'd', 'var2': 2},
                                 {'var1': 'e', 'var2': 2},
                                 {'var1': 'f', 'var2': 2}])

                  I know this won't work because we lose DataFrame meta-data by going through numpy, but it should give you a sense of what I tried to do:

                  def fun(row):
                      letters = row['var1']
                      letters = letters.split(',')
                      out = np.array([row] * len(letters))
                      out['var1'] = letters
                  a['idx'] = range(a.shape[0])
                  z = a.groupby('idx')


                  How about something like this:

                  In [55]: pd.concat([Series(row['var2'], row['var1'].split(','))              
                                      for _, row in a.iterrows()]).reset_index()
                    index  0
                  0     a  1
                  1     b  1
                  2     c  1
                  3     d  2
                  4     e  2
                  5     f  2

                  Then you just have to rename the columns

                  这篇关于拆分(分解) pandas 数据框字符串条目以分隔行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!



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