Question

我想转换这个DF

pd.DataFrame({"l1": [["fr en","en"]],
              "l2": [["fr en","in","it"]],
              "l3": [["he","es","fi"]],
              "l4": [["es"]]}).T
>> l1  [fr en, en]
   ...
   l4  [es]

此DTM：

data = [[1,1,0,0,0,0,0], [1,0,1,1,0,0,0], [0,0,0,0,1,1,1], [0,0,0,0,0,1,1]]
pd.DataFrame(index=["l1","l2","l3","l4"], data=data, columns=["fr en","en","in","it","he","es","fi"])
>>      fr en en in it he es fi
    l1  1     1  0  0  0  0  0
    ... ...

我效率不高的方法是先chain，然后将所有可能的值都计算成

langs = set(chain(*df["lang"]))
pd.DataFrame(data=df["lang"].apply(lambda x: [1 if lang in x else 0 for lang in langs]).tolist(), columns=langs)

PS：我不想" ".join()列出这些列表，因为您可能会在fr en中看到这些列表，这可能代表信息丢失

Answer 1

我认为需要MultiLabelBinarizer：

from sklearn.preprocessing import MultiLabelBinarizer

mlb = MultiLabelBinarizer()
df = pd.DataFrame(mlb.fit_transform(df[0]),columns=mlb.classes_, index=df.index)
print (df)
    en  es  fi  fr en  he  in  it
l1   1   0   0      1   0   0   0
l2   0   0   0      1   0   1   1
l3   0   1   1      0   1   0   0
l4   0   1   0      0   0   0   0

或者如果数据中不存在分隔符，则可以通过|使用较慢的连接解决方案：

df = df[0].str.join('|').str.get_dummies()
print (df)
    en  es  fi  fr en  he  in  it
l1   1   0   0      1   0   0   0
l2   0   0   0      1   0   1   1
l3   0   1   1      0   1   0   0
l4   0   1   0      0   0   0   0

Pandas Python-将列表列表列转换为多列

1 个答案: