我有一堆分类的词性数据，我想将它们折叠成更少的类别。np.where() 不行，因为我想在末尾有 6 个类别：名词、动词、形容词、副词、介词和其他。
我发现我可以将 pandas.replace() 与字典结合使用来执行此操作。
所以，我制作了以下字典：
mappings = {"NN" : "noun", "NNS" : "noun", "NNP" : "noun",
"VB" : "verb", "VBD" : "verb", "VBG" : "verb", "VBN" : "verb", "VBP" : "verb", "VBZ" : "verb",
"JJ" : "adj", "JJR" : "adj", "JJS" : "adj",
"RB" : "adv", "RBR" : "adv", "RBS" : "adv",
"IN" : "prep"}
问题是，数据中存在更多的词性。有没有办法让我将所有其他词性都推到“其他”类别中，还是我必须手动输入所有其他可能的词性？
你可以使用numpy select 函数
你需要适应，但它会是这样的：
nouns = ["NN","NNS","NNP"]
verbs = ["VB","VBD","VBG","VBN","VBP","VBZ"]
adjs = ["JJ","JJR","JJS"]
advs = ["RB","RBR","RBS"]
preps = ["IN"]
condlist = [
df.my_colum.isin(nouns),
df.my_colum.isin(verbs),
df.my_colum.isin(adjs),
df.my_colum.isin(advs),
df.my_colum.isin(preps),
]
choicelist = ["noun","verb","adj","adv","prep"]
df["gruop"] = np.select(condlist= condlist, choicelist= choicelist, default = "other")