in [ ]result out[ ]{ 'name' 'wes' 'pet'noneu'places_lived'[ 'united states' 'spain' 'germany'] 'siblings'[{ 'age' 'name' 'scott' 'pet' 'zuko'}{ 'age' 'name' 'katie' 'pet' 'cisco'}]json dumps on the other hand converts python object back to jsonin [ ]asjson json dumps(resulthow you convert json object or list of objects to dataframe or some other data structure for analysis will be up to you convenientlyyou can pass list of json objects to the dataframe constructor and select subset of the data fieldsin [ ]siblings dataframe(result['siblings']columns=['name''age']in [ ]siblings out[ ]name age scott katie for an extended example of reading and manipulating json data (including nested records)see the usda food database example in the next an effort is underway to add fast native json export (to_jsonand decoding (from_jsonto pandas this was not ready at the time of writing xml and htmlweb scraping python has many libraries for reading and writing data in the ubiquitous html and xml formats lxml (parsing very large files lxml has multiple programmer interfacesfirst 'll show using lxml html for htmlthen parse some xml using lxml objectify many websites make data available in html tables for viewing in browserbut not downloadable as an easily machine-readable format like jsonhtmlor xml noticed that this was the case with yahoofinance' stock options data if you aren' familiar with this dataoptions are derivative contracts giving you the right to buy (call optionor sell (put optiona company' stock at some particular price (the strikebetween now and some fixed point in the future (the expirypeople trade both call and put options across many strikes and expiriesthis data can all be found together in tables on yahoofinance data loadingstorageand file formats www it-ebooks info