What is the fastest and generic way to flatten deeply nested JSON to get a Dataframe?

Question

I am trying to do some descriptives on my own location data that I got from my Google timeline. But when trying to get some workable data, to convert it from a JSON-file to a DataFrame. It brought up some questions that I would like to have some answers to because when trying to convert the JSON-file to a DataFrame it felt for me that I was going to do it in an inefficient way.

To give a description of what my JSON looks like. It is a JSON of 3 levels deep and has around 4.5 million lines. A small example of the JSON:

"locations" : [ 
{
  "timestampMs" : "1489591483",
  "latitudeE7" : -21.61909,
  "longitudeE7" : 121.65283,
  "accuracy" : 23,
  "velocity" : 18,
  "heading" : 182,
  "altitude" : 55,
  "activity" : [ {
    "timestampMs" : "1489591507",
    "activity" : [ {
      "type" : "IN_VEHICLE",
      "confidence" : 49
    }, {
      "type" : "UNKNOWN",
      "confidence" : 17
    }, {
      "type" : "ON_BICYCLE",
      "confidence" : 15
    }, {
      "type" : "ON_FOOT",
      "confidence" : 9
    }, {
      "type" : "STILL",
      "confidence" : 9
    }, {
      "type" : "WALKING",
      "confidence" : 9
    } ]
  } ]
},
...
]

To convert it to a DataFrame I want to flatten those 3 levels down to 0 levels. I have seen some implementations with json_normalize in combination with .apply or .append but therefore you still needed to know the key to the value, which I would have rather seen to be more generic (so without knowing the key). And it also required to manually iterate over the values. Now what I would like to know is: "Is there a method that automatically flattens the JSON down to 0 levels without using apply or append?" If there isn't such a method, what would be the preferred way of flatting JSON and converting it to a DataFrame?

---

Edit: Added an example of what the DataFrame should look like and a better example of the JSON.

---

To give a small example of what the DataFrame should look like, see the image below:

To include a better example of what the JSON looks like I have included a Pastebin URL below: tiny location history sample

Answer 1

Use json_normalize, specifying the record_path and meta_path.

df = pd.io.json.json_normalize(d, ['locations', 'activity', 'activity'], 
                         ['locations', ['locations', 'activity', 'timestampMs']])
df = df.drop('locations', 1).add_prefix('activity.')
v = pd.DataFrame(df['locations'].tolist()).drop('activity', 1)    

pd.concat([df, v], 1)


   activity.confidence activity.type activity.locations.activity.timestampMs  \
0                   49    IN_VEHICLE                              1489591507   
1                   17       UNKNOWN                              1489591507   
2                   15    ON_BICYCLE                              1489591507   
3                    9       ON_FOOT                              1489591507   
4                    9         STILL                              1489591507   
5                    9       WALKING                              1489591507   

   accuracy  altitude  heading  latitudeE7  longitudeE7 timestampMs  velocity  
0        23        55      182   -21.61909    121.65283  1489591483        18  
1        23        55      182   -21.61909    121.65283  1489591483        18  
2        23        55      182   -21.61909    121.65283  1489591483        18  
3        23        55      182   -21.61909    121.65283  1489591483        18  
4        23        55      182   -21.61909    121.65283  1489591483        18  
5        23        55      182   -21.61909    121.65283  1489591483        18