问题描述
我有一个pandas数据框,其中的管道分隔列包含任意数量的元素,称为零件".这些管道字符串中的元素数量从0到超过10不等.所有管道字符串中包含的唯一元素的数量并不比行数小得多(这使我无法在同时手动指定所有元素创建新列).
I have a pandas dataframe with a pipe delimited column with an arbitrary number of elements, called Parts. The number of elements in these pipe-strings varies from 0 to over 10. The number of unique elements contained in all pipe-strings is not much smaller than the number of rows (which makes it impossible for me to manually specify all of them while creating new columns).
对于每一行,我想创建一个新列,用作管道分隔列表的每个元素的指示符变量.例如,如果该行
For each row, I want to create a new column that acts as an indicator variable for each element of the pipe delimited list. For instance, if the row
... 'Parts'
...
... '12|34|56'
应转换为
... 'Part_12' 'Part_34' 'Part_56'
...
... 1 1 1
...
由于它们有很多独特的部分,因此这些列显然将是稀疏的-多数为零,因为每一行仅包含一小部分独特的部分.
Because they are a lot of unique parts, these columns are obviously going to be sparse - mostly zeros since each row only contains a small fraction of unique parts.
我没有找到不需要手动指定列的任何方法(例如,).我还研究了熊猫的融化,但我认为这不是合适的工具.
I haven't found any approach that doesn't require manually specifying the columns (for instance, Pandas Dataframe: split column into multiple columns, right-align inconsistent cell entries).I've also looked at pandas' melt, but I don't think that's the appropriate tool.
我知道如何解决该问题的方法是将原始CSV管道传输到另一个python脚本并逐个字符地处理它,但是我需要在现有的脚本中工作,因为我将处理数百个CSV格式.
The way I know how to solve it would be to pipe the raw CSV to another python script and deal with it on a char-by-char basis, but I need to work within my existing script since I will be processing hundreds of CSVs in this manner.
这是数据的更好说明
ID YEAR AMT PARTZ
1202 2007 99.34
9321 1988 1012.99 2031|8942
2342 2012 381.22 1939|8321|Amx3
推荐答案
您可以使用get_dummies
和add_prefix
:
df.Parts.str.get_dummies().add_prefix('Part_')
输出:
Part_12 Part_34 Part_56
0 1 1 1
编辑以评论和计算重复项.
df = pd.DataFrame({'Parts':['12|34|56|12']}, index=[0])
pd.get_dummies(df.Parts.str.split('|',expand=True).stack()).sum(level=0).add_prefix('Part_')
输出:
Part_12 Part_34 Part_56
0 2 1 1
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