Pandas: access fields within field in a DataFrame Suppose I have such a JSON file:

[
  {
    "id": "0",
    "name": "name0", 
    "first_sent": "date0",  
    "analytics": [
        {
            "a": 1,
            ...
        }, 
        {
            "a": 2, 
            ...
        }
    ]
  }
]


and I want to parse it with Pandas. So I load it with

df = pd.read_json('file.son')


It's all good until I try to access and count the number of dictionaries in the "analytics" field for each item, for which task I haven't found any better way than 

for i in range(df.shape[0]):
    num = len(df[i:i+1]['analytics'][i])


But this looks totally non-elegant and it's missing the point of using Pandas in the first place. I need to be able to access the fields within "analytics" for each item.
The question is how to use Pandas to access fields within a field (which maps to a Series object), without reverting to non-Pandas approaches.

A head of the DataFrame looks like this (only fields 'id' and 'analytics' reported):

0    [{u'a': 0.0, u'b...
1    [{u'a': 0.01, u'b...
2    [{u'a': 0.4, u'b...
3    [{u'a': 0.2, u'b...
Name: analytics, dtype: object
0      '0'
1      '1'
2      '2'
3      '3'


The first number is obviously the index, the string is the 'id', and it is clear that 'analytics' appears as a Series.
Multi-indexing might be helpful. See this.
But the below was the immediate solution that came to mind. I think it's a little more elegant than what you came up with (fewer obscure numbers, more interpretable natural language):
import pandas as pd
df = pd.read_json('test_file.json')
df = df.append(df) # just to give us an extra row to loop through below
df.reset_index(inplace=True) # not really important except to distinguish your rows
for _ , row in df.iterrows():
    currNumbDict = len(row['analytics'])
    print(currNumbDict)