本文介绍了在Pandas DataFrame中按顺序重复编号的处理方法,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小编来一起学习吧!

问题描述

我有一个Pandas DataFrame,它的列基本上是外键,如下所示:

I have a Pandas DataFrame that has a column that is basically a foreign key, as below:

Index   |  f_key  |    values
  0     |    1    |     red
  1     |    2    |     blue
  2     |    1    |     green
  3     |    2    |     yellow
  4     |    3    |     orange
  5     |    1    |     violet

我想添加一列,按顺序标记重复的外键,如下面的"dup_number"所示:

What I would like is to add a column that labels the repeated foreign keys sequentially, as in "dup_number" below:

Index   | dup_number |  f_key  |    values
  0     |     1      |    1    |     red
  1     |     1      |    2    |     blue
  2     |     2      |    1    |     green
  3     |     2      |    2    |     yellow
  4     |     1      |    3    |     orange
  5     |     3      |    1    |     violet

可以根据需要对行进行重新排序,我只需要在其中获取"dup_number"键即可.我编写了以下代码,效果很好,它给了我一个系列,然后可以将其添加到DataFrame中,但是它非常慢(for循环会浪费时间),而且我觉得它比所需的方法更复杂:

The rows can be reordered if needed, I just need to get the "dup_number" keys in there. I wrote following code, which works fine, it gives me a Series which I can then add into the DataFrame, but it is very slow (that for loop is what kills the time), and I feel like it's way more complicated than is needed:

df = pd.DataFrame({'f_key': [1,2,1,2,3,1], 'values': ['red', 'blue', 'green', 'yellow', 'orange', 'violet']})
df_unique = df['f_key'].drop_duplicates().reset_index(drop=True)
dup_number = pd.DataFrame(columns = ['dup_number', 'temp_index'])
for n in np.arange(len(df_unique)):
    sub_df = df.loc[df['f_key'] == df_unique[n]].reset_index()
    dup_index = pd.DataFrame({'dup_number': sub_df.index.values[:]+1, 'temp_index': sub_df['index']})
    dup_number = dup_number.append(dup_index)
dup_number = dup_number.set_index(dup_number['temp_index'].astype(int))
dup_number = dup_number['dup_number'].sort_index()

任何对更快,更简单的方法的建议,将不胜感激!

Any suggestions on faster/simpler ways to do this are appreciated!

推荐答案

您可以使用 cumcount()

df['dup_number'] = df.groupby(['f_key']).cumcount()+1

           f_key  values  dup_number
    0      1     red           1
    1      2    blue           1
    2      1   green           2
    3      2  yellow           2
    4      3  orange           1
    5      1  violet           3

这篇关于在Pandas DataFrame中按顺序重复编号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!

07-23 15:48