是否有一种从列表字典创建虚拟变量数据帧的方法？

Question

在熊猫中我有一个类似下面的词典：

DECLARE @SearchStr nvarchar(100)
SET @SearchStr =''

    CREATE TABLE #Results (ColumnName nvarchar(370), ColumnValue nvarchar(3630))--,TableName varchar(200))

    SET NOCOUNT ON

    DECLARE @TableName nvarchar(256), @ColumnName nvarchar(128), @SearchStr2 nvarchar(110)
    SET  @TableName = ''
    SET @SearchStr2 = QUOTENAME('' + @SearchStr + '','''')

    WHILE @TableName IS NOT NULL

    BEGIN
        SET @ColumnName = ''
        SET @TableName = 
        (
            SELECT MIN(QUOTENAME(TABLE_SCHEMA) + '.' + QUOTENAME(TABLE_NAME))
            FROM     INFORMATION_SCHEMA.TABLES
            WHERE         

                TABLE_SCHEMA = 'SAMPLE SCHEMA HERE'
                AND TABLE_TYPE = 'BASE TABLE'
                AND    QUOTENAME(TABLE_SCHEMA) + '.' + QUOTENAME(TABLE_NAME) > @TableName
                AND    OBJECTPROPERTY(
                        OBJECT_ID(
                            QUOTENAME(TABLE_SCHEMA) + '.' + QUOTENAME(TABLE_NAME)
                             ), 'IsMSShipped'
                            ) = 0
        )

        WHILE (@TableName IS NOT NULL) AND (@ColumnName IS NOT NULL)

        BEGIN
            SET @ColumnName =
            (
                SELECT MIN(QUOTENAME(COLUMN_NAME))
                FROM     INFORMATION_SCHEMA.COLUMNS
                WHERE         TABLE_SCHEMA    = PARSENAME(@TableName, 2)
                    AND    TABLE_NAME    = PARSENAME(@TableName, 1)
                    AND    DATA_TYPE IN ('char', 'varchar', 'nchar', 'nvarchar', 'int', 'decimal')
                    AND    QUOTENAME(COLUMN_NAME) > @ColumnName
            )

            IF @ColumnName IS NOT NULL

            BEGIN
                INSERT INTO #Results
                EXEC
                (
                    'SELECT ''' + @TableName + '.' + @ColumnName + ''', LEFT(' + @ColumnName + ', 3630) FROM ' + @TableName + ' (NOLOCK) ' +
                    ' WHERE ' + @ColumnName + '=' + @SearchStr2 +
                    ' OR' + @ColumnName + ' IS NULL '
                )
            END
        END   
    END

        SELECT  DISTINCT left(columnName, charindex('.', ColumnName, charindex('.',ColumnName )+1)-1) as tableName,ColumnName FROM #Results Order by tableName;


                DROP TABLE #Results

我想将其转换为DataFrame，您可以在其中看到动物类型可能吃的东西。所以它看起来与下图类似：

当试图生成这样一个表时，我感觉我是以不正确的方式这样做的，因为我需要相当多的代码行。所以我的问题是，是否有一个很好的函数将这个字典映射到一个DataFrame，所以它看起来像上面的表？

Answer 1

最简单的方式
使用pd.str.get_dummies

dct = {
    'Anemones & allies': ['Carnivore'],
    'Ants, bees & wasps': ['Omnivore',  'Herbivore',  'Nectar',  'Insects', 'Parasite'],
    'Beetles & bugs': ['Herbivore', 'Carnivore', 'Nectar', 'Insects'],
    'Birds': ['Carnivore'],
    'Fishes': ['Carnivore', 'Plankton or Particles']
}

pd.Series(dct).str.join('|').str.get_dummies()

                    Carnivore  Herbivore  Insects  Nectar  Omnivore  Parasite  Plankton or Particles
Anemones & allies           1          0        0       0         0         0                      0
Ants, bees & wasps          0          1        1       1         1         1                      0
Beetles & bugs              1          1        1       1         0         0                      0
Birds                       1          0        0       0         0         0                      0
Fishes                      1          0        0       0         0         0                      1

更复杂
但可能推荐

from sklearn.preprocessing import MultiLabelBinarizer

dct = {
    'Anemones & allies': ['Carnivore'],
    'Ants, bees & wasps': ['Omnivore',  'Herbivore',  'Nectar',  'Insects', 'Parasite'],
    'Beetles & bugs': ['Herbivore', 'Carnivore', 'Nectar', 'Insects'],
    'Birds': ['Carnivore'],
    'Fishes': ['Carnivore', 'Plankton or Particles']
}

s = pd.Series(dct)

mlb = MultiLabelBinarizer()

d = mlb.fit_transform(s)
c = mlb.classes_
pd.DataFrame(d, s.index, c)

                    Carnivore  Herbivore  Insects  Nectar  Omnivore  Parasite  Plankton or Particles
Anemones & allies           1          0        0       0         0         0                      0
Ants, bees & wasps          0          1        1       1         1         1                      0
Beetles & bugs              1          1        1       1         0         0                      0
Birds                       1          0        0       0         0         0                      0
Fishes                      1          0        0       0         0         0                      1

Answer 2

从DataFrame（d）创建dict，然后使用get_dummies

pd.get_dummies(pd.DataFrame(dict([ (k,pd.Series(v)) for k,v in d.items() ])).stack()).sum(level=1)
Out[130]: 
                    Carnivore  Herbivore  Insects  Nectar  Omnivore  Parasite  \
Anemones & allies           1          0        0       0         0         0   
Ants, bees & wasps          0          1        1       1         1         1   
Beetles & bugs              1          1        1       1         0         0   
Birds                       1          0        0       0         0         0   
Fishes                      1          0        0       0         0         0   
                    Plankton or Particles  
Anemones & allies                       0  
Ants, bees & wasps                      0  
Beetles & bugs                          0  
Birds                                   0  
Fishes                                  1