将字典列表转换为数据框 [英] Convert list of Dictionaries to a Dataframe

问题描述

我面临着一个基本的问题，即转换通过解析具有json格式的文本的列而获得的字典列表.以下是数据的简要快照:

I am facing a basic problem of converting a list of dictionaries obtained from parsing a column with text in json format. Below is the brief snapshot of data:

[{u'PAGE TYPE': u'used-serp.model.brand.city'},
 {u'BODY TYPE': u'MPV Cars',
  u'ENGINE CAPACITY': u'1461',
  u'FUEL TYPE': u' Diesel',
  u'MODEL NAME': u'Renault Lodgy',
  u'OEM NAME': u'Renault',
  u'PAGE TYPE': u'New-ModelPage.OverviewTab'},
 {u'PAGE TYPE': u'used-serp.brand.city'},
 {u'BODY TYPE': u'SUV Cars',
  u'ENGINE CAPACITY': u'2477',
  u'FUEL TYPE': u' Diesel',
  u'MODEL NAME': u'Mitsubishi Pajero',
  u'OEM NAME': u'Mitsubishi',
  u'PAGE TYPE': u'New-ModelPage.OverviewTab'},
 {u'BODY TYPE': u'Hatchback Cars',
  u'ENGINE CAPACITY': u'1198',
  u'FUEL TYPE': u' Petrol , Diesel',
  u'MODEL NAME': u'Volkswagen Polo',
  u'OEM NAME': u'Volkswagen',
  u'PAGE TYPE': u'New-ModelPage.GalleryTab'},

此外，我正在解析的代码如下:

Furthermore, the code i am using to parse is detailed below:

stdf_noncookie = []
stdf_noncookiejson = []

for index, row in df_noncookie.iterrows():
    try:
        loop_data = json.loads(row['attributes'])
        stdf_noncookie.append(loop_data)
    except ValueError:
        loop_nondata = row['attributes']
        stdf_noncookiejson.append(loop_nondata)

stdf_noncookie是我要转换为pandas数据框的词典列表. 属性"是带有json格式文本的列.我试图从此链接中获得一些学习，但是这无法实现解决我的问题.将字典列表转换为熊猫数据框的任何建议/提示都将很有帮助.

stdf_noncookie is the list of dictionaries i am trying to convert into a pandas dataframe. 'attributes' is the column with text in json format. I have tried to get some learning from this link, however this was not able to solve my problem. Any suggestion/tips for converting a list of dictionaries to panda dataframe will be helpful.

推荐答案

要将字典列表转换为熊猫数据框，请使用以下命令:

To convert your list of dicts to a pandas dataframe use the following:

stdf_noncookiejson = pd.DataFrame.from_records(data)

pandas.DataFrame .from_records

DataFrame.from_records (数据，索引=无，排除=无，列=无，coerce_float = False，nrows =无)

您可以在

如果您使用json，也可以使用read_json方法

If youre working with json you can also use the read_json method

stdf_noncookiejson = pd.read_json(data)

pandas.read_json

pandas.read_json (path_or_buf =无，orient =无，typ =帧"，dtype = True，convert_axes = True，convert_dates = True， keep_default_dates = True，numpy = False，precise_float = False， date_unit = None，编码= None，行= False)

pandas.read_json (path_or_buf=None, orient=None, typ='frame', dtype=True, convert_axes=True, convert_dates=True, keep_default_dates=True, numpy=False, precise_float=False, date_unit=None, encoding=None, lines=False)

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