英文:
How can i remove all duplicated elements from list, but updating first entrys value in other column, if not the same
问题
我有这样特性的虚构数据框架：
df = pd.DataFrame({'brand': ['Yum Yum', 'ByRice', 'LuxSoba', 'Indomie', 'Indomie'],'style': ['cup', 'cup', 'cup', 'pack', 'pack'],'flavour': [chili, chicken, chili, beef, cheese]})dfbrand style flavour0 Yum Yum cup chili1 ByRice cup chicken2 LuxSoba cup chili3 Indomie pack beef4 Indomie pack cheese
我的目标是以这种方式更改数据框架，即删除所有品牌的重复条目，但如果有多种口味，将它们全部附加到第一个条目的一个列中。因此，数据框架应该如下所示：
brand style flavour0 Yum Yum cup chili1 ByRice cup chicken2 LuxSoba cup chili3 Indomie pack beef, cheese
我不确定如何解决这个问题。
英文:
I have an imaginary dataframe of such nature:
df = pd.DataFrame({'brand': ['Yum Yum', 'ByRice', 'LuxSoba', 'Indomie', 'Indomie'],'style': ['cup', 'cup', 'cup', 'pack', 'pack'],'flavour': [chili, chicken, chili, beef, cheese]})dfbrand style flavour0 Yum Yum cup chili1 ByRice cup chicken2 LuxSoba cup chili3 Indomie pack beef4 Indomie pack cheese
My goal is to change dataframe in such manner, that all duplicate entries of brands are deleted, but if there are several flavours, they all are appended into one column, to the first entry. So dataframe should look like this:
brand style flavour0 Yum Yum cup chili1 ByRice cup chicken2 LuxSoba cup chili3 Indomie pack beef, cheese
I'm not sure how to approach this problem.
答案1
得分: 1
你可以这样做：
df = pd.DataFrame({'品牌': ['Yum Yum', 'ByRice', 'LuxSoba', 'Indomie', 'Indomie'],'风味': ['杯装', '杯装', '杯装', '包装', '包装'],'口味': ['辣椒', '鸡肉', '辣椒', '牛肉', '芝士']})df2 = df.groupby(['品牌', '风味'])['口味'].agg(lambda x: ', '.join(x)).reset_index()
结果：
英文:
You can do this:
df = pd.DataFrame({'brand': ['Yum Yum', 'ByRice', 'LuxSoba', 'Indomie', 'Indomie'],'style': ['cup', 'cup', 'cup', 'pack', 'pack'],'flavour': ['chili', 'chicken', 'chili', 'beef', 'cheese']})df2 = df.groupby(['brand', 'style'])['flavour'].agg(lambda x: ', '.join(x)).reset_index()
Result:
答案2
得分: 0
你可以使用 groupby_agg：
>>> df.groupby(['brand', 'style'], sort=False, as_index=False)['flavour'].agg(', '.join)brand style flavour0 Yum Yum 杯子 辣椒味1 ByRice 杯子 鸡肉味2 LuxSoba 杯子 辣椒味3 Indomie 包装袋 牛肉, 芝士味
英文:
You can use groupby_agg:
>>> df.groupby(['brand', 'style'], sort=False, as_index=False)['flavour'].agg(', '.join)brand style flavour0 Yum Yum cup chili1 ByRice cup chicken2 LuxSoba cup chili3 Indomie pack beef, cheese
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