遍历列表列并根据列表顺序附加其他列

Question

cats = {'lesson_name': {0: 'Mutt',
  1: 'Ragdoll',
  2: 'Black',
  3: 'Calico',
  4: 'Tortoise',
  5: 'Mainecoon'},
 'tag_list': {0: ['Orange', 'Black', 'White'],
  1: ['Grey', 'White'],
  2: ['Black','Brown'],
  3: ['Orange','Grey','White'],
  4: ['Orange', 'Brown','White'],
  5: ['Grey','White']},
 'Orange': {0: '',
  1: '',
  2: '',
  3: '',
  4: '',
  5: ''},
 'Black': {0: '',
  1: '',
  2: '',
  3: '',
  4: '',
  5: ''},
 'White': {0: '',
  1: '',
  2: '',
  3: '',
  4: '',
  5: ''},
 'Grey': {0: '',
  1: '',
  2: '',
  3: '',
  4: '',
  5: ''},
 'Brown': {0: '',
  1: '',
  2: '',
  3: '',
  4: '',
  5: ''}}

cats_frame = pd.DataFrame(cats)

我正在尝试根据“tag_list”列中列表的顺序创建一个函数来附加颜色列（橙色、黑色、白色、灰色、棕色）。例如，如果“橙色”是 tag_list 中的第一种颜色，我希望该行在橙色列中有一个“1”，如果列表中的第二种颜色是“黑色”，我希望该行有一个“ 2' 在黑色列中。

我正在考虑为每种颜色制作一个函数并尝试过：

def black(df):
        
    for (df['tag_list'],j) in df.iteritems():
        
        if [j][0] == 'Black':
            return 1
        
        if [j][1] == 'Black':
            return 2
        
        if [j][2] == 'Black':
            return 3

        else:
            return 0
        
cats_frame['Black'] = cats_frame.apply(black,1)

Answer 1

这是解决问题的一种方法：

tag = pd.DataFrame(cats_frame['tag_list'].tolist())
tag_order = tag.stack().reset_index(level=1)\
               .set_index(0, append=True)['level_1'].add(1).unstack(fill_value=0)

cats_frame.update(tag_order)

详情：

>>> tag

        0      1      2
0  Orange  Black  White
1    Grey  White   None
2   Black  Brown   None
3  Orange   Grey  White
4  Orange  Brown  White
5    Grey  White   None

>> tag_order

0  Black  Brown  Grey  Orange  White
0      2      0     0       1      3
1      0      0     1       0      2
2      1      2     0       0      0
3      0      0     2       1      3
4      0      2     0       1      3
5      0      0     1       0      2

>> cats_frame

  lesson_name                tag_list Orange Black White Grey Brown
0        Mutt  [Orange, Black, White]      1     2     3    0     0
1     Ragdoll           [Grey, White]      0     0     2    1     0
2       Black          [Black, Brown]      0     1     0    0     2
3      Calico   [Orange, Grey, White]      1     0     3    2     0
4    Tortoise  [Orange, Brown, White]      1     0     3    0     2
5   Mainecoon           [Grey, White]      0     0     2    1     0

Answer 2

我会警告这一点，可能有更有效的方法可以做到这一点，但除非您的数据框很大，否则这是使用df.iterrows() 完成它的合理方法

#iterate over the rows
for idx, cols in cats_frame.iterrows():
    #iterate over the columns containing color names
    for c in cats_frame.columns[2:]:
        #Check whether the column name appears in the list
        if c in cols['tag_list']:
            #If it does, find the index in the list, add 1 due to zero-indexing, and assign the value
            cols[c]=cols['tag_list'].index(c)+1
        else:
            #otherwise, add 0
            cols[c]=0

    lesson_name tag_list                Orange  Black   White   Grey    Brown
0   Mutt        [Orange, Black, White]  1       2       3       0       0
1   Ragdoll     [Grey, White]           0       0       2       1       0
2   Black       [Black, Brown]          0       1       0       0       2
3   Calico      [Orange, Grey, White]   1       0       3       2       0
4   Tortoise    [Orange, Brown, White]  1       0       3       0       2
5   Mainecoon   [Grey, White]           0       0       2       1       0

Answer 3

# i holds index e.g. 1; a_list is for example [Grey, White] in the second turn
for i, a_list in enumerate(cats_frame.tag_list):
    # j becomes the index number (starting at 1), color becomes e.g. "Grey"
    for j, color in enumerate(a_list, start=1):
        # put numbers according to tag list to the position
        # where i points to the row and color points to the column
        cats_frame.loc[i, color] = str(j)

然后

  lesson_name                tag_list Orange Black White Grey Brown
0        Mutt  [Orange, Black, White]      1     2     3
1     Ragdoll           [Grey, White]                  2    1
2       Black          [Black, Brown]            1                2
3      Calico   [Orange, Grey, White]      1           3    2
4    Tortoise  [Orange, Brown, White]      1           3          2
5   Mainecoon           [Grey, White]                  2    1