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Nested Json Using pyspark

We have to build nested json using below structure in pyspark and i have added data that need to feed using this
Input Data structure
Data
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('sparkdf').getOrCreate()
a1=["DA_STinf","DA_Stinf_NA","DA_Stinf_city","DA_Stinf_NA_ID","DA_Stinf_NA_ID_GRANT","DA_country"]
a2=["data.studentinfo","data.studentinfo.name","data.studentinfo.city","data.studentinfo.name.id","data.studentinfo.name.id.grant","data.country"]
columns = ["data","action"]
df = spark.createDataFrame(zip(a1, a2), columns)
#Input data for json structure
a1=["Pune"]
a2=["YES"]
a3=["India"]
col=["DA_Stinf_city","DA_Stinf_NA_ID_GRANT","DA_country"]
data=spark.createDataFrame(zip(a1, a2,a3), col)
Expected result based on above data
{
"data": {
"studentinfo": {
"city": "Pune",
"name": {
"id": {
"grant": "YES"
}
}
},
"country": "india"
}
}
we have tried using F.struct function in manually but we have find dynamic way to build this json using df dataframe having data and action column
data.select(
F.struct(
F.struct(
F.struct(F.col("DA_Stinf_city")).alias("city"),
F.struct(
F.struct(F.col("DA_Stinf_NA_ID_GRANT")).alias("id")
).alias("name"),
).alias("studentinfo"),
F.struct(F.col("DA_country")).alias("country")
).alias("data")
)

The approach below should give the correct structure (with the wrong key names - if you are happy with the approach, which doesn't use DataFrame operations but rather works in the underlying RDD, then I can flesh it out):
def build_json(input, running={}):
new_input = {}
for hierarchy, value in input:
key = hierarchy.pop(0)
if len(hierarchy) == 0:
running[key] = value
else:
new_input[key] = new_input.get(key, []) + [(hierarchy, value)]
for key in new_input:
print(new_input[key])
running[key] = build_json(new_input[key], running={})
return running
data.rdd.map(
lambda x: build_json(
[(column.split("_"), value) for column, value in x.asDict().items()]
)
)
The basic idea is to get a set of tuples from the underlying RDD consisting of the column name broken into its json hierarchy and the value to insert into the hierarchy. Then the function build_json inserts the value into its correct place in the json hierarchy, while building out the json object recursively.

How to access nested attribute without passing parent attribute in pyspark json

I am trying to access inner attributes of following json using pyspark
[
{
"432": [
{
"atttr1": null,
"atttr2": "7DG6",
"id":432,
"score": 100
}
]
},
{
"238": [
{
"atttr1": null,
"atttr2": "7SS8",
"id":432,
"score": 100
}
]
}
]
In the output, I am looking for something like below in form of csv
atttr1, atttr2,id,score
null,"7DG6",432,100
null,"7SS8",238,100
I understand I can get these details like below but I don't want to pass 432 or 238 in lambda expression as in bigger json this(italic one) will vary. I want to iterate over all available values.
print(inputDF.rdd.map(lambda x:(x['*432*'])).first())
print(inputDF.rdd.map(lambda x:(x['*238*'])).first())
I also tried registering a temp table with the name "test" but it gave an error with message element._id doesn't exist.
inputDF.registerTempTable("test")
srdd2 = spark.sql("select element._id from test limit 1")
Any help will be highly appreciated. I am using spark 2.4

Without using pyspark features, you can do it like this:
data = json.loads(json_str) # or whatever way you're getting the data
columns = 'atttr1 atttr2 id score'.split()
print(','.join(columns)) # headers
for item in data:
for obj in list(item.values())[0]: # since each list has only one object
print(','.join(str(obj[col]) for col in columns))
Output:
atttr1,atttr2,id,score
None,7DG6,432,100
None,7SS8,432,100
Or
for item in data:
obj = list(item.values())[0][0] # since the object is the one and only item in list
print(','.join(str(obj[col]) for col in columns))
FYI, you can store those in a variable or write it out to csv instead of/and also printing it.
And if you're just looking to dump that to csv, see this answer.

Python dataframe to nested json file

I have a python dataframe as below.
python dataframe:-
Emp_No Name Project Task
1 ABC P1 T1
1 ABC P2 T2
2 DEF P3 T3
3 IJH Null Null
I need to convert it to json file and save it to disk as below
Json File
{
"Records"[
{
"Emp_No":"1",
"Project_Details":[
{
"Project":"P1",
"Task":"T1"
},
{
"Project":"P2",
"Task":"T2"
}
],
"Name":"ÄBC"
},
{
"Emp_No":"2",
"Project_Details":[
{
"Project":"P2",
"Task":"T3"
}
],
"Name":"DEF"
},
{
"Emp_No":"3",
"Project_Details":[
],
"Name":"IJH"
}
]
}

I feel like this post is not a doubt per se, but a cheecky atempt to avoid formatting the data, hahaha. But, since i'm trying to get used to the dataframe structure and the different ways of handling it, here you go!
import pandas as pd
asutosh_data = {'Emp_No':["1","1","2","3"], 'Name':["ABC","ABC","DEF","IJH"], 'Project':["P1","P2","P3","Null"], 'Task':["T1","T2","T3","Null"]}
df = pd.DataFrame(data=asutosh_data)
records = []
dif_emp_no = df['Emp_No'].unique()
for emp_no in dif_emp_no :
emp_data = df.loc[df['Emp_No'] == emp_no]
emp_project_details = []
for index,data in emp_data.iterrows():
if data["Project"]!="Null":
emp_project_details.append({"Project":data["Project"],"Task":data["Task"]})
records.append({"Emp_No":emp_data.iloc[0]["Emp_No"], "Project_Details":emp_project_details , "Name":emp_data.iloc[0]["Name"]})
final_data = {"Records":records}
print(final_data)
If you have any question about the code above, feel free to ask. I'll also leave below the documentation i've used to solve your problem (you may wanna check that):
unique : https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.unique.html
loc : https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html
iloc : https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.iloc.html

Extract JSON | API | Pandas DataFrame

I am using the Facebook API (v2.10) to which I've extracted the data I need, 95% of which is perfect. My problem is the 'actions' metric which returns as a dictionary within a list within another dictionary.
At present, all the data is in a DataFrame, however, the 'actions' column is a list of dictionaries that contain each individual action for that day.
{
"actions": [
{
"action_type": "offsite_conversion.custom.xxxxxxxxxxx",
"value": "7"
},
{
"action_type": "offsite_conversion.custom.xxxxxxxxxxx",
"value": "3"
},
{
"action_type": "offsite_conversion.custom.xxxxxxxxxxx",
"value": "144"
},
{
"action_type": "offsite_conversion.custom.xxxxxxxxxxx",
"value": "34"
}]}
All this appears in one cell (row) within the DataFrame.
What is the best way to:
Get the action type, create a new column and use the Use "action_type" as the column name?
List the correct value under this column
It looks like JSON but when I look at the type, it's a panda series (stored as an object).
For those willing to help (thank you, I greatly appreciate it) - can you either point me in the direction of the right material and I will read it and work it out on my own (I'm not entirely sure what to look for) or if you decide this is an easy problem, explain to me how and why you solved it this way. Don't just want the answer
I have tried the following (with help from a friend) and it kind of works, but I have issues with this running in my script. IE: if it runs within a bigger code block, I get the following error:
for i in range(df.shape[0]):
line = df.loc[i, 'Conversions']
L = ast.literal_eval(line)
for l in L:
cid = l['action_type']
value = l['value']
df.loc[i, cid] = value
If I save the DF as a csv, call it using pd.read_csv...it executes properly, but not within the script. No idea why.
Error:
ValueError: malformed node or string: [{'value': '1', 'action_type': 'offsite_conversion.custom.xxxxx}]
Any help would be greatly appreciated.
Thanks,
Adrian

You can use json_normalize:
In [11]: d # e.g. dict from json.load OR instead pass the json path to json_normalize
Out[11]:
{'actions': [{'action_type': 'offsite_conversion.custom.xxxxxxxxxxx',
'value': '7'},
{'action_type': 'offsite_conversion.custom.xxxxxxxxxxx', 'value': '3'},
{'action_type': 'offsite_conversion.custom.xxxxxxxxxxx', 'value': '144'},
{'action_type': 'offsite_conversion.custom.xxxxxxxxxxx', 'value': '34'}]}
In [12]: pd.io.json.json_normalize(d, record_path="actions")
Out[12]:
action_type value
0 offsite_conversion.custom.xxxxxxxxxxx 7
1 offsite_conversion.custom.xxxxxxxxxxx 3
2 offsite_conversion.custom.xxxxxxxxxxx 144
3 offsite_conversion.custom.xxxxxxxxxxx 34

You can use df.join(pd.DataFrame(df['Conversions'].tolist()).pivot(columns='action_type', values='value').reset_index(drop=True)).
Explanation:
df['Conversions'].tolist() returns a list of dictionaries. This list is then transformed into a DataFrame using pd.DataFrame. Then, you can use the pivot function to pivot the table into the shape that you want.
Lastly, you can join the table with your original DataFrame. Note that this only works if you DataFrame's index is the default (i.e., integers starting from 0). If this is not the case, you can do this instead:
df2 = pd.DataFrame(df['Conversions'].tolist()).pivot(columns='action_type', values='value').reset_index(drop=True)
for col in df2.columns:
df[col] = df2[col]

Flattening nested JSON included embedded array in Python using Pandas

I have a JSON-array from a mongoexport containing data from the Beddit sleeptracker. Below is an example of one of the truncated documents (removed some unneeded detail).
{
"user" : "xxx",
"provider" : "beddit",
"date" : ISODate("2016-11-30T23:00:00.000Z"),
"data" : [
{
"end_timestamp" : 1480570804.26226,
"properties" : {
"sleep_efficiency" : 0.8772404,
"resting_heart_rate" : 67.67578,
"short_term_resting_heart_rate" : 61.36963,
"activity_index" : 50.51958,
"average_respiration_rate" : 16.25667,
"total_sleep_score" : 64,
},
"date" : "2016-12-01",
"session_range_start" : 1480545636.55059,
"start_timestamp" : 1480545636.55059,
"session_range_end" : 1480570804.26226,
"tags" : [
"not_enough_sleep",
"long_sleep_latency"
],
"updated" : 1480570805.25201
}
],
"__v" : 0
}
Several related questions like this and this do not seem to work for the data structure above. As recommended in other related questions I am trying to stay away from looping over each row for performance reasons (the full dataset is ~150MB). How would I flatten out the "data"-key with json_normalize so that each key is at the top-level? I would prefer one DataFrame where e.g. total_sleep_score is a column.
Any help is much appreciated! Even though I know how to 'prepare' the data using JavaScript, I would like to be able to understand and do it using Python.
edit (request from comment to show preferred structure):
{
"user" : "xxx",
"provider" : "beddit",
"date" : ISODate("2016-11-30T23:00:00.000Z"),
"end_timestamp" : 1480570804.26226,
"properties.sleep_efficiency" : 0.8772404,
"properties.resting_heart_rate" : 67.67578,
"properties.short_term_resting_heart_rate" : 61.36963,
"properties.activity_index" : 50.51958,
"properties.average_respiration_rate" : 16.25667,
"properties.total_sleep_score" : 64,
"date" : "2016-12-01",
"session_range_start" : 1480545636.55059,
"start_timestamp" : 1480545636.55059,
"session_range_end" : 1480570804.26226,
"updated" : 1480570805.25201,
"__v" : 0
}
The 'properties' append is not necessary but would be nice.

Try This algo for flatten:-
def flattenPattern(pattern):
newPattern = {}
if type(pattern) is list:
pattern = pattern[0]
if type(pattern) is not str:
for key, value in pattern.items():
if type(value) in (list, dict):
returnedData = flattenPattern(value)
for i,j in returnedData.items():
if key == "data":
newPattern[i] = j
else:
newPattern[key + "." + i] = j
else:
newPattern[key] = value
return newPattern
print(flattenPattern(dictFromJson))
OutPut:-
{
'session_range_start':1480545636.55059,
'start_timestamp':1480545636.55059,
'properties.average_respiration_rate':16.25667,
'session_range_end':1480570804.26226,
'properties.resting_heart_rate':67.67578,
'properties.short_term_resting_heart_rate':61.36963,
'updated':1480570805.25201,
'properties.total_sleep_score':64,
'properties.activity_index':50.51958,
'__v':0,
'user':'xxx',
'provider':'beddit',
'date':'2016-12-01',
'properties.sleep_efficiency':0.8772404,
'end_timestamp':1480570804.26226
}

Although not explicitly what I asked for, the following worked for me so far:
Step 1
Normalize the data record using json_normalize on the original dataset (not inside a Pandas DataFrame) and prefix the data.
beddit_data = pd.io.json.json_normalize(beddit, record_path='data', record_prefix='data.', meta='_id')
Step 2
The properties record was a Series with dicts so these can be 'formatted' with .apply(pd.Series)
beddit_data_properties = beddit_data['data.properties'].apply(pd.Series)
Step 3
Final step is to merge both DataFrames. In step 1, I kept the 'meta=_id' so that DataFrame can be merged with the original DataFrame from Bedit. I didn't include it in the final step yet because I can spend some time on the results from the results so far.
beddit_final = pd.concat([beddit_data_properties[:], beddit_data[:]], axis=1)
If anyone is interested, I can share the final Jupyter Notebook when it is ready :)