From 116dcfcdd6330609e2dbd101ac968513aeed9f00 Mon Sep 17 00:00:00 2001
From: spbeach46 <spbeacg46@gmail.com>
Date: Sat, 3 Oct 2020 22:19:01 -0700
Subject: [PATCH] done w/complete single item dataframe conversion using json
 and pandas

---
 shopping_test.py | 22 +++++++++++-----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/shopping_test.py b/shopping_test.py
index 862bccf..904d5a4 100644
--- a/shopping_test.py
+++ b/shopping_test.py
@@ -5,23 +5,23 @@ import pandas as pd
 
 params = {
     "callname":"GetMultipleItems",
-    "appid":"scottbea-xlister-PRD-6796e0ff6-14862949", 
+    "appid":"scottbea-xlister-PRD-6796e0ff6-14862949",
     "version":"671",
     "responseencoding":"JSON",
     "ItemID":'184228756721', # you pass in a list? If not then maybe a comma-separated
     "IncludeSelector":"ItemSpecifics",
     }
 
-response = requests.get("https://open.api.ebay.com/shopping?", params=params) # 
+response = requests.get("https://open.api.ebay.com/shopping?", params=params)
 data = response.json()
 pretty_data = json.dumps(data, indent=2)
 
-df = pd.read_json(data, 'Item')
-nvl = df.iloc[0]['ItemSpecifics.NameValueList']
-nvl_dict = {}
-for item in nvl:
-    nvl['Name']
-# departments = ["3034","93427"]
-# Best plan of action is probably json-->pandas df. You can decide how to clean later, but it 
-# will prolly be easier if you clean in pandas because you can drop columns easily that you don't 
-# need. Figure out how to "flatten" your json data into the df first. 
+names = []
+values = []
+nvl = data['Item'][0]['ItemSpecifics']['NameValueList']
+for nvl_dict in nvl:
+    names.append(nvl_dict['Name'])
+    values.append(nvl_dict['Value'])
+nvl_dict = dict(zip(names, values))
+data.update(nvl_dict)
+