从多个小纸条创建一个熊猫数据目录[重复]

Question

Some file:
01Jhon      Smith     555-1234                                        
03Cow            Bos primigenius taurus        00401                  
01Jannette  Jhonson           00100000000                             
...


field    start  length   
type         1       2   *common to all records, example: 01 = person, 03 = animal
name         3      10
surname     13      10
phone       23       8
credit      31      11
fill of spaces

person1 = {'type': 01, 'name': = 'Jhon', 'surname': = 'Smith', 'phone': '555-1234'}
person2 = {'type': 01, 'name': 'Jannette', 'surname': 'Jhonson', 'credit': 1000000.00}
animal1 = {'type': 03, 'cname': 'cow', 'sciname': 'Bos....', 'legs': 4, 'tails': 1 }

Answer 1

若要从字典生成数据目录，可以传递一个字典列表：

>>> person1 = {'type': 01, 'name': 'Jhon', 'surname': 'Smith', 'phone': '555-1234'}
>>> person2 = {'type': 01, 'name': 'Jannette', 'surname': 'Jhonson', 'credit': 1000000.00}
>>> animal1 = {'type': 03, 'cname': 'cow', 'sciname': 'Bos....', 'legs': 4, 'tails': 1 }
>>> pd.DataFrame([person1])
   name     phone surname  type
0  Jhon  555-1234   Smith     1
>>> pd.DataFrame([person1, person2])
    credit      name     phone  surname  type
0      NaN      Jhon  555-1234    Smith     1
1  1000000  Jannette       NaN  Jhonson     1
>>> pd.DataFrame.from_dict([person1, person2])
    credit      name     phone  surname  type
0      NaN      Jhon  555-1234    Smith     1
1  1000000  Jannette       NaN  Jhonson     1

对于两个格式不同的文件混合的更基本的问题，假设文件不太大，我们无法读取它们并将它们存储在内存中，我将使用stringio创建一个有点像文件的对象，但它只有我们想要的行，然后使用read_fwf（固定宽度文件）。例如：

from StringIO import StringIO

def get_filelike_object(filename, line_prefix):
    s = StringIO()
    with open(filename, "r") as fp:
        for line in fp:
            if line.startswith(line_prefix):
                s.write(line)
    s.seek(0)
    return s

后来呢

>>> type01 = get_filelike_object("animal.dat", "01")
>>> df = pd.read_fwf(type01, names="type name surname phone credit".split(), 
                     widths=[2, 10, 10, 8, 11], header=None)
>>> df
   type      name  surname     phone     credit
0     1      Jhon    Smith  555-1234        NaN
1     1  Jannette  Jhonson       NaN  100000000