df.to_dict()转化为字典数据

df.to_dict()里面参数可选（‘dict’, ‘list’, ‘series’, ‘split’, ‘records’, ‘index’）

df = pd.DataFrame({\'col1\': [1, 2],
                   \'col2\': [0.5, 0.75]},
                  index=[\'row1\', \'row2\'])
print(df)
\'\'\'
      col1  col2
row1     1  0.50
row2     2  0.75
\'\'\'
df.to_dict(\'dict\') #默认是这种形式，根据columns划分
#{\'col1\': {\'row1\': 1, \'row2\': 2}, \'col2\': {\'row1\': 0.5, \'row2\': 0.75}}

df.to_dict(\'list\')  #和index没有关系
#{\'col1\': [1, 2], \'col2\': [0.5, 0.75]}

df.to_dict(\'series\') 
\'\'\'
{\'col1\': row1    1
 row2    2
 Name: col1, dtype: int64,
 \'col2\': row1    0.50
 row2    0.75
 Name: col2, dtype: float64}
\'\'\'

df.to_dict(\'split\')  #分为index和columns以及data
\'\'\'
{\'index\': [\'row1\', \'row2\'],
 \'columns\': [\'col1\', \'col2\'],
 \'data\': [[1, 0.5], [2, 0.75]]}
\'\'\'

df.to_dict(\'records\')  #根据index划分的
#[{\'col1\': 1, \'col2\': 0.5}, {\'col1\': 2, \'col2\': 0.75}]

df.to_dict(\'index\')   #根据index划分且多了index
#{\'row1\': {\'col1\': 1, \'col2\': 0.5}, \'row2\': {\'col1\': 2, \'col2\': 0.75}}

df.to_dict()  #默认形式
#{\'col1\': {\'row1\': 1, \'row2\': 2}, \'col2\': {\'row1\': 0.5, \'row2\': 0.75}}