title: Parsing Column in Pandas DataFrame with one column that contains a nested JSON string
body: <p>I have a DataFrame in Python that looks like the one below. Theres a column (referred to below as 'json') that has a large nested JSON string in it. How can I parse it out so I can have a nice clean data frame with many columns. just particularly need the cost and monthly amounts for each ID in separate columns. Ideally i'd have a table that looks like:</p>
  
  <p>id, name, cost, monthly</p>
  
  <p>10001, frank, 15.85, 15.85</p>
  
  <p>10002, mary, 30.86, 23.03</p>
  
  <pre><code>    d = {'id': ['10001', '10002'], 'json': ['{"costs":[{"cost":15.85}],"policies":[{"logo":"HLIF-transparent-inhouse.png","monthly":15.85,"rating":"A++","waiverOfPremium":1.74,"carrier":"companyabc","face":250000,"term":20,"newFace":null,"newMonthly":null,"isCompanyD":true,"carrierCode":"xyz","product":"XYZt"}],"agentSuggestion":{"costs":[{"cost":15.85}],"options":{"product":"XYZt","gender":"male","healthClass":"0","smoker":"false","age":32,"term":"20","faceAmount":250000,"waiverOfPremiumAmount":1.74,"includeWaiverOfPremium":false,"state":"CT"},"policies":[{"logo":"HLIF-transparent-inhouse.png","monthly":15.85,"rating":"A++","waiverOfPremium":1.74,"carrier":"companyabc","face":250000,"term":20,"newFace":null,"newMonthly":null,"isCompanyD":true,"carrierCode":"xyz","product":"XYZt"}]}}', '{"costs":[{"cost":30.86}],"policies":[{"logo":"HLIF-transparent-inhouse.png","monthly":23.03,"rating":"A++","waiverOfPremium":7.83,"carrier":"companyabc","face":1000000,"term":10,"newFace":null,"newMonthly":null,"isCompanyD":true,"carrierCode":"xyz","product":"XYZt"}],"agentSuggestion":{"costs":[{"cost":30.86}],"options":{"product":"XYZt","gender":"female","healthClass":"0","smoker":"false","age":35,"term":10,"faceAmount":1000000,"waiverOfPremiumAmount":7.83,"includeWaiverOfPremium":true,"state":"GA"},"policies":[{"logo":"HLIF-transparent-inhouse.png","monthly":23.03,"rating":"A++","waiverOfPremium":7.83,"carrier":"companyabc","face":1000000,"term":10,"newFace":null,"newMonthly":null,"isCompanyD":true,"carrierCode":"xyz","product":"XYZt"}]}}'], 'name':['frank','mary']}
  
     test = pd.DataFrame(data=d)
  </code></pre>