fbjson2table 1.1.0

Parse Facebook archive JSON files to tables

fb-json2table

Parse Facebook archive JSON files to tables.

requirement

pandas >= 0.24.1

Setup

1. clone this repo

   git clone https://github.com/numbersprotocol/fb-json2table.git

2. export path

   export PYTHONPATH=$PWD/fb-json2table/:$PYTHONPATH

or you want to use virtualenv to keep environment clean

1. git clone https://github.com/numbersprotocol/fb-json2table.git

2. pip3 install virtualenv

3. virtualenv -p python3 env

4. source env/bin/activate

5. (env) pip3 install pandas==0.24.1

6. (env) python3 setup.py bdist_wheel

7. (env) pip3 install dist/fbjson2table-1.0.0-py3-none-any.whl

(if you want to run example/examply.py)

8. (env) pip3 install tabulate

TL;DR

from fbjson2table.func_lib import parse_fb_json
from fbjson2table.table_class import TempDFs


json_content = parse_fb_json($PATH_OF_JSON)
temp_dfs = TempDFs(json_content)

for df in temp_dfs.df_list:
    print(df)

and you will find that all the content in json turned to table-like(DataFrame)

Introduction

This repo is based on the requierments of Numbers.

The final goal of this repo is to automate the parsing process of json in downloaded Facebook data.

Thus, if you also want to analyze your own facebook data or you have some json that structure is like facebook data,

this repo can help you to turn the difficult to analyze json to easier to analyze table.

Note: what I mean of structure is like Facebook json:

[
  {
    feature_1: feature_1_of_record_1,
    feature_2: feature_2_of_record_1,
    ...
   },
   {
    feature_1: feature_1_of_record_2,
    feature_2: feature_2_of_record_2,
    ...
   },
   ...
]

or you can refer to how this repo turn dict and list combination to table: link

why Facebook json is not friendly to analyze?

here is an example of Facebook json: example_facebook_json

We can find that if we want to analyze the relationship between reaction type ("LIKE" or "WOW") and time by using python,

we have to write code like:

timestamp = [x["timestamp"] for x in example_json["reactions"]]
reaction_type = [x["data"][0]["reactions"]["reactions"] for x in example_json["reactions"]]

We have to specify many keys in our code. Furthermore, make things more difficult is that, if one record does not have one feature, in Facebook json, it will not display instead of displaying "null" , and we do not really know how many features Facebook records.

Or at least, I do not find a formal document writed kinds of data recorded by Facebook.

Take a look of example_json_content, this is example of your_post.json, we can find that if one post does not have photo, that post will not have features of photo. If the json is too long for naked eyes, we may ignore some interesting data recorded by Facebook!

Finally, the most bothering and making automation almost impossible is that, the structure of Facebook json may change, and have changed! And the worst is that,

!!!!Facebook will not notify you!!!!

For example, the data I download long time ago, I can find posts in "posts/your_posts.json/", and the content is like:

{
  "status_updates": [
    {
      "timestamp": 1415339550,
      ...

The data I download recently, if I want to find posts, I should go to "posts/your_posts_1.json/", the filename have changed, and the content is like:

[
  {
    "timestamp": 1575375973,
    ...

We can find that in new structure, we do not have to and shoud not to specify "status_updates", and if we load new json into our old code, it will raise many "KeyError". Furthermore, there may be other changing in the json content.

the goal of this repo

1. turn the Facebook json to table

2. decrease the things should be speficfied

3. make the code robust to changing of json structure, or make it easier to fix when json structure changes

How to use

1. load json

   you can load json by your own method, or use the function we write for Facebook json to handling mojibake.

   from fbjson2table.func_lib import parse_fb_json
   
   json_content = parse_fb_json($PATH_OF_JSON)
   

2. feed it into "TempDFs", and take a look of "TempDFs.df_list" and "TempDFs.table_name_list",

   from tabulate import tabulate
   from fbjson2table.table_class import TempDFs
   
   temp_dfs = TempDFs(json_content)
   for df, table_name in zip(temp_dfs.df_list, temp_dfs.table_name_list):
       print(table_name, ':')
       print(tabulate(df, headers='keys', tablefmt='psql'), '\n')
   

   here is example of json_content

   here is example of TempDFs.df_list and TempDFs.table_name_list

   explanation:

   Every df has its own name, the first df is default named with "temp", for the follownig dfs will concat "__DICT_KEY " as suffix.

   Every df has id of its own "depth(peeling)", and all ids of connected upper layer. The id of first depth is always named "id_0", and the following id is named with "id_DICT_KEY_DEPTH", example: "id_attachment_1".

   With the ids, we can do the "join" operation. For example, if we want to put "uri" of "media" and "timestamp" of posts in same table, the code will like:

   top_df = temp_posts_dfs[0].set_index("id_0", drop=False)
   append_df = temp_posts_df[4].set_index("id_0", drop=False)
   
   wanted_df = top_df.join(append_df) # What we want
   

   If you are lazy to find where is the data you want, and you confirm that the data is one-to-one relationship with "top_df", you can use "merge_one_to_one_sub_df."

   example:

   one_to_one_df = temp_dfs.merge_one_to_one_sub_df(
                   temp_dfs.df_list,
                   temp_dfs.table_name_list,
                   temp_dfs.id_column_names_list,
                   start_peeling=0) # start_peeling is the index of df we want to set as "top_df" in df_list
   

   note: in the "one_to_one_df", all column names of sub dfs will concat its depth dict key as prefix. For example, "id_media_3" => "media_id_media_3".

explanation of terms

depth(peeling)

In the json, every dict will add one depth(peeling). We count depth from 0.

For example,

dummy_dict = {"a": "b", "c":{"aa": "bb", "cc": "d"}},

"a" is at depth 0, "aa" is at depth 1.

Because in normal method, if we want to get "b" or "bb", we should write dummy_dict["a"] or dummy_dict["c"]["aa"]. We have to specify 1 or 2 keys, so the depth is 0 or 1.

top_df, sub_df

Sub_df are those dfs with table name containing the table name of the specifics. For example, "temp__attachments__data", "temp__attachments__data__media" and so on, are sub_dfs of "temp__attachments".

The sub_df can be viewd as one column but recording mutilple value of one df.

Take "dummy_dict" as example, this repo will turn it into,

temp
id_0| a |
----+---+
  0 | b |      
----+---+

temp_c(table name)
id_0|id_c_1| aa | cc |
----+------+----+----+
  0 |  0   | bb | d  |
----+------+----+----+

but it can be viewed as

temp
id_0| a | c                      |
----+---+------------------------+
  0 | b |{"aa": "bb", "cc": "d"} |    
----+---+------------------------+

The "temp_c" is like something growing from "temp", so I call it sub_df.

Top_df refers to the base df when we want to merge sub_df.

Automation

TL;DR

For most case of Facebook json

from fbjson2table.func_lib import parse_fb_json
from fbjson2table.table_class import TempDFs


json_content = parse_fb_json($PATH_OF_JSON)
temp_dfs = TempDFs(json_content)
one_to_one_df, _ = temp_dfs.temp_to_wanted_df(
              wanted_columns=[]
              ) 

Take a look of one_to_one_df, and determine which columns we want.

print(one_to_one_df.columns)

wanted_columns = LIST_OF_WANTED_COLUMNS

df, top_id = temp_dfs.temp_to_wanted_df(
         wanted_columns=wanted_columns
     )

Then "df" is what we want.

Take a look of temp_to_wanted_df

For the reason that Facebook may change json structure, we developed a series of methods to handle this problem.

The core concept is that, "putting all things in one table, then from the table take what we want."

Take a look of: link

The first thing we do is get_routed_dfs.

=> Because that "df" in "df_list" may diverge, we have to get the dfs have the same "route" of "top_df".

Second, we do get_start_peeling.

=> We have to find the index of top_df in df_list for next step.

Then, we do merge_one_to_one_sub_df.

=> Merge all one-to-one sub_dfs.

Finally, we do get_wanted_columns.

=> Extract the columns what we want, and return NaN when the column do not exist.

In temp_to_wanted_df, there are four parameters we should specify, temp_to_wanted_df, wanted_columns,route_by_table_name, start_by, and regex.

So, how to input these value?

The wanted_columns is a list of names of columns we want, or we can input [], it will return all columns.

The route_by_table_name is the last table name suffix of top_df.

It is be used when we want to choose one df as top_df, and that df is in a diverging branch of df_list.

Take example_df_list for example,

If we plot the relationship of df_list:

We can find there are two route in the picture. When we want to merge temp__attachments__data__media and its sub_dfs, we have to extract them in df_list. Or temp__data may be a problem when we do the join operation.

When we set route_by_table_name eqauls "media", we will get the route like:

The start_by is the unique column name of top_df in routed_df_list.

When we do join operation, we have to choose one df in df_list as our top_df. Take the example_df_list, if we want to set "temp__attachments__data__media" as top_df, we have to find the unique column name of it. For example, "uri". By the way, the "title" will not work because "title" also appear in "temp."

The regex is whether to use "regex" when find start_by.

Thus, if we want get the "creation_timestamp", "description", "title", "uri", and "upload_ip" of photos of posts in one table, the whole process will be like:

from fbjson2table.func_lib import parse_fb_json
from fbjson2table.table_class import TempDFs


json_content = parse_fb_json($PATH_OF_JSON)
temp_dfs = TempDFs(json_content)
one_to_one_df, _ = temp_dfs.temp_to_wanted_df(
                   route_by_table_name='media',
                   strat_by='uri'
                   )
print(one_to_one_df.columns)

get

Index(['creation_timestamp', 'description', 'id_0', 'id_attachments_1',
    'id_data_2', 'id_media_3', 'title', 'uri', 'media_metadata_id_0',
    'media_metadata_id_attachments_1', 'media_metadata_id_data_2',
    'media_metadata_id_media_metadata_4', 'photo_metadata_id_0',
    'photo_metadata_id_attachments_1', 'photo_metadata_id_data_2',
    'photo_metadata_id_media_metadata_4',
    'photo_metadata_id_photo_metadata_5', 'photo_metadata_upload_ip'],
   dtype='object')

Then,

wanted_columns = ['creation_timestamp', 'description', 'title', 'uri',
                  'photo_metadata_upload_ip']
photo_df, _ = temp_dfs.temp_to_wanted_df(
                       wanted_columns=wanted_columns,
                       route_by_table_name='media',
                       strat_by='uri'
                   )

The photo_df is what we want.