How To Append A Dictionary To A Pandas Dataframe?

I have a set of urls containing json files and an empty pandas dataframe with columns representing the attributes of the jsnon files. Not all json files have all the attributes in

Solution 1:

For me below code works:

row = -1for i in links:
    row = row + 1data = urllib2.urlopen(str(i)).read()
    data = json.loads(data)
    for key indata.keys():
        df.loc[row,key] = data[key]

You have mixed order of arguments in .loc() and have one to much []

Solution 2:

Assuming that df is empty and has the same columns as the url dictionary keys, i.e.

list(df)
#[u'alternate_product_code',# u'availability',# u'boz',# ...

len(df)
#0

then you can use pandas.append

for url in links:
    url_data = urllib2.urlopen(str(url)).read()
    url_dict = json.loads(url_data)
    a_dict   = { k:pandas.Series([str(v)], index=[0]) for k,v in url_dict.iteritems() }
    new_df = pandas.DataFrame.from_dict(a_dict)
    df.append(new_df, ignore_index=True)

Not too sure why your code won't work, but consider the following few edits which should clean things up, should you still want to use it:

for row,url in enumerate(links):
    data      = urllib2.urlopen(str(url)).read()
    data_dict = json.loads(data)
    for key,val in data_dict.items():
        if key in list(df):
            df.ix[row,key] = val

I used enumerate to iterate over the index and value of links array, in this way you dont need an index counter (row in your code) and then I used the .items dictionary method, so I can iterate over key and values at once. I believe pandas will automatically handle the empty dataframe entries.