[FIXED] Is this the best way to parse a Json output from Google Ads Stream

Issue

Is this the best way to parse a Json output from Google Ads Stream. I am parsing the json with pandas & it is taking too much time

record counts is around 700K

[{
  "results": [
    {
      "customer": {
        "resourceName": "customers/12345678900",
        "id": "12345678900",
        "descriptiveName": "ABC"
      },
      "campaign": {
        "resourceName": "customers/12345678900/campaigns/12345",
        "name": "Search_Google_Generic",
        "id": "12345"
      },
      "adGroup": {
        "resourceName": "customers/12345678900/adGroups/789789",
        "id": "789789",
        "name": "adgroup_details"
      },
      "metrics": {
        "clicks": "500",
        "conversions": 200,
        "costMicros": "90000000",
        "allConversionsValue": 5000.6936,
        "impressions": "50000"
      },
      "segments": {
        "device": "DESKTOP",
        "date": "2022-10-28"
      }
    }
  ],
  "fieldMask": "segments.date,customer.id,customer.descriptiveName,campaign.id,campaign.name,adGroup.id,adGroup.name,segments.device,metrics.costMicros,metrics.impressions,metrics.clicks,metrics.conversions,metrics.allConversionsValue",
  "requestId": "fdhfgdhfgjf"
}
]

This is the sample json.I am saving the stream in json file and then reading using pandas and trying to dump in csv file

I want to convert it to CSV format, Like

with open('Adgroups.json', encoding='utf-8') as inputfile:
    df = pd.read_json(inputfile)

df_new = pd.DataFrame(columns= ['Date', 'Account_ID', 'Account', 'Campaign_ID','Campaign',
                                    'Ad_Group_ID', 'Ad_Group','Device',
                                    'Cost', 'Impressions', 'Clicks', 'Conversions', 'Conv_Value'])

for i in range(len(df['results'])):
  results = df['results'][i]
  for result in results:
    new_row = pd.Series({ 'Date': result['segments']['date'],
                                  'Account_ID': result['customer']['id'],
                                  'Account': result['customer']['descriptiveName'],
                                  'Campaign_ID': result['campaign']['id'],
                                  'Campaign': result['campaign']['name'],
                                  'Ad_Group_ID': result['adGroup']['id'],
                                  'Ad_Group': result['adGroup']['name'],
                                  'Device': result['segments']['device'],
                                  'Cost': result['metrics']['costMicros'],
                                  'Impressions': result['metrics']['impressions'],
                                  'Clicks': result['metrics']['clicks'],
                                  'Conversions': result['metrics']['conversions'],
                                  'Conv_Value': result['metrics']['allConversionsValue']
                        })
    df_new = df_new.append(new_row, ignore_index = True)
  
df_new.to_csv('Adgroups.csv', encoding='utf-8', index=False)

Solution

Don't use df.append. It's very slow because it has to copy the dataframe over and over again. I think it's being deprecated for this reason.

You can build the rows using list comprehension before constructing the data frame:

import json

with open("Adgroups.json") as fp:
    data = json.load(fp)

columns = [
    "Date",
    "Account_ID",
    "Account",
    "Campaign_ID",
    "Campaign",
    "Ad_Group_ID",
    "Ad_Group",
    "Device",
    "Cost",
    "Impressions",
    "Clicks",
    "Conversions",
    "Conv_Value",
]
records = [
    (
        r["segments"]["date"],
        r["customer"]["id"],
        r["customer"]["descriptiveName"],
        r["campaign"]["id"],
        r["campaign"]["name"],
        r["adGroup"]["id"],
        r["adGroup"]["name"],
        r["segments"]["device"],
        r["metrics"]["costMicros"],
        r["metrics"]["impressions"],
        r["metrics"]["clicks"],
        r["metrics"]["conversions"],
        r["metrics"]["allConversionsValue"],
    )
    for d in data
    for r in d["results"]
]
df = pd.DataFrame(records, columns=columns)

Answered By - Code Different