pandas.DataFrame.from_dict不使用OrderedDict保留订单 [英] pandas.DataFrame.from_dict not preserving order using OrderedDict

问题描述

我想将荷兰统计局(CBS)的OData XML数据提要导入到我们的数据库中.我认为使用lxml和pandas应该很简单.通过使用OrderDict，我想保留列的顺序以提高可读性，但是由于某种原因我无法正确处理.

I want to import OData XML datafeeds from the Dutch Bureau of Statistics (CBS) into our database. Using lxml and pandas I thought this should be straigtforward. By using OrderDict I want to preserve the order of the columns for readability, but somehow I can't get it right.

from collections import OrderedDict
from lxml import etree
import requests
import pandas as pd


# CBS URLs
base_url = 'http://opendata.cbs.nl/ODataFeed/odata'
datasets = ['/37296ned', '/82245NED']

feed = requests.get(base_url + datasets[1] + '/TypedDataSet')
root = etree.fromstring(feed.content)

# all record entries start at tag m:properties, parse into data dict
data = []
for record in root.iter('{{{}}}properties'.format(root.nsmap['m'])):
    row = OrderedDict()
    for element in record:
        row[element.tag.split('}')[1]] = element.text
    data.append(row)

df = pd.DataFrame.from_dict(data)
df.columns

检查data，OrderDict的顺序正确.但是，查看df.head()时，列是否已按CAPS的字母顺序进行排序?

Inspecting data, the OrderDict is in the right order. But looking at df.head() the columns have been sorted alphabetically with CAPS first?

有帮助吗?

推荐答案

示例中的内容似乎不一致，因为data是list而不是dict，但假设您确实有OrderedDict :

Something in your example seems to be inconsistent, as data is a list and no dict, but assuming you really have an OrderedDict:

尝试在创建DataFrame时显式指定列顺序:

Try to explicitly specify your column order when you create your DataFrame:

# ... all your data collection
df = pd.DataFrame(data, columns=data.keys())

这应该为您的DataFrame提供与OrderedDict中完全相同的排序顺序的列(通过data.keys()生成的列表)

This should give you your DataFrame with the columns ordered just in exact the way they are in the OrderedDict (via the data.keys() generated list)

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