问如何将嵌套字典列表转换为DataFrame

EN

{
  "archives" : [ {
    "ads_account" : {
      "account_name" : "@BradleyByrne - U.S. Political Campaigning",
      "user_name" : "BradleyByrne",
      "bio_url" : "https://twitter.com/ZpdrcK6Met",
      "billing_information" : {
        "insertion_order" : [ ],
        "credit_card" : [ {
          "city" : "Arlington",
          "spend" : 3.5845999999999995E-4,
          "postal_code" : "22209",
          "region" : "va",
          "credit_card_full_name" : "Targeted Victory"
        } ]
      }
    },
    "tweets" : [ {
      "impressions" : 0,
      "spend" : 0.0,
      "ad_campaigns" : [ {
        "targeting" : [ {
          "target" : "Montgomery AL- US",
          "target_type" : "GEO",
          "impressions" : 895
        }, {
          "target" : "13-54",
          "target_type" : "AGE_BUCKET",
          "impressions" : 5721
        }, {
          "target" : "Dothan AL- US",
          "target_type" : "GEO",
          "impressions" : 189
        }, {
          "target" : "13-29",
          "target_type" : "AGE_BUCKET",
          "impressions" : 3009
        }, {
          "target" : "Chattanooga TN- US",
          "target_type" : "GEO",
          "impressions" : 2
        }, {
          "target" : "English",
          "target_type" : "LANGUAGE",
          "impressions" : 8568
        }, {
          "target" : "Orlando-Daytona Beach-Melbourne FL- US",
          "target_type" : "GEO",
          "impressions" : 13
        }, {
          "target" : "21-54",
          "target_type" : "AGE_BUCKET",
          "impressions" : 4297
        }, {
          "target" : "Thai",
          "target_type" : "LANGUAGE",
          "impressions" : 1
        }, {
          "target" : "20 and up",
          "target_type" : "AGE_BUCKET",
          "impressions" : 6598
        },


"ads_account" : {
  "account_name" : "@club4growth - U.S. Political Campaigning - Bask Digital Media",
  "user_name" : "club4growth",
  "bio_url" : "http://twitter.com/wEF8OWW5zn",
  "billing_information" : {
    "insertion_order" : [ ],
    "credit_card" : [ ]
  }
},
"tweets" : [ {
  "impressions" : 466501,
  "spend" : 2993.5,
  "ad_campaigns" : [ {
    "targeting" : [ {
      "target" : "13 and up",
      "target_type" : "AGE_BUCKET",
      "impressions" : 144460
    }, {
      "target" : "20-34",
      "target_type" : "AGE_BUCKET",
      "impressions" : 78242
    }, {
      "target" : "Korean",
      "target_type" : "LANGUAGE",
      "impressions" : 160
    }, {
      "target" : "13-54",
      "target_type" : "AGE_BUCKET",
      "impressions" : 131703
    }, {
      "target" : "30-39",
      "target_type" : "AGE_BUCKET",
      "impressions" : 42685
    }, {
      "target" : "Pennsylvania- US",
      "target_type" : "GEO",
      "impressions" : 2
    }, {
      "target" : "25-54",
      "target_type" : "AGE_BUCKET",
      "impressions" : 86998
    }, {
      "target" : "South Dakota- US",
      "target_type" : "GEO",
      "impressions" : 1
    }, {
      "target" : "20-29",
      "target_type" : "AGE_BUCKET",
      "impressions" : 61090
    }, {
      "target" : "Dutch",
      "target_type" : "LANGUAGE",
      "impressions" : 41
    }, {
      "target" : "Unknown",
      "target_type" : "GENDER",
      "impressions" : 214
    }, {
      "target" : "Washington DC- US",
      "target_type" : "GEO",
      "impressions" : 144356
    }, {
      "target" : "French",
      "target_type" : "LANGUAGE",
      "impressions" : 420
    }, {
      "target" : "German",
      "target_type" : "LANGUAGE",
      "impressions" : 71
    }, {
      "target" : "New Jersey- US",
      "target_type" : "GEO",
      "impressions" : 1
    }, {
      "target" : "Female",
      "target_type" : "GENDER",
      "impressions" : 57736
    },

import json
from pandas.io.json import json_normalize
file = 'issue.txt'
with open(file) as train_file:
    dict_train = json.load(train_file)


train = pd.DataFrame.from_dict(dict_train, orient='index')
train.reset_index(level=0, inplace=True)
train