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pandas.DataFrame.from_dict not preserving order using OrderedDict

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

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

Help, anyone?

Solution

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

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

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

    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)