Join nested JSON dataframe and another dataframe - json

Question

I am trying to join a dataframe1 generated by the JSON with dataframe2 using the field order_id, then assign the "status" from dataframe2 to the "status" of dataframe1. Anyone knows how to do this. Many thanks for your help.
dataframe1
[{
"client_id": 1,
"name": "Test01",
"olist": [{
"order_id": 10000,
"order_dt_tm": "2012-12-01",
"status": "" <== use "status" from dataframe2 to populate this field
},
{
"order_id": 10000,
"order_dt_tm": "2012-12-01",
"status": ""
}
]
},
{
"client_id": 2,
"name": "Test02",
"olist": [{
"order_id": 10002,
"order_dt_tm": "2012-12-01",
"status": ""
},
{
"order_id": 10003,
"order_dt_tm": "2012-12-01",
"status": ""
}
]
}
]
dataframe2
order_id status
10002 "Delivered"
10001 "Ordered"

Answer 1

Here is your raw dataset as a json string:
d = """[{
"client_id": 1,
"name": "Test01",
"olist": [{
"order_id": 10000,
"order_dt_tm": "2012-12-01",
"status": ""
},
{
"order_id": 10000,
"order_dt_tm": "2012-12-01",
"status": ""
}
]
},
{
"client_id": 2,
"name": "Test02",
"olist": [{
"order_id": 10002,
"order_dt_tm": "2012-12-01",
"status": ""
},
{
"order_id": 10003,
"order_dt_tm": "2012-12-01",
"status": ""
}
]
}
]"""
Firstly, I would load it as json:
import json
data = json.loads(d)
Then, I would turn it into a Pandas dataframe, notice that I remove status field as it will be populated by the join step :
df1 = pd.json_normalize(data, 'olist')[['order_id', 'order_dt_tm']]
Then, from the second dataframe sample, I would do a left join using merge function:
data = {'order_id':[10002, 10001],'status':['Delivered', 'Ordered']}
df2 = pd.DataFrame(data)
result = df1.merge(df2, on='order_id', how='left')
Good luck
UPDATE
# JSON to Dataframe
df1 = pd.json_normalize(data)
# Sub JSON to dataframe
df1['sub_df'] = df1['olist'].apply(lambda x: pd.json_normalize(x).drop('status', axis=1))
# Build second dataframe
data2 = {'order_id':[10002, 10001],'status':['Delivered', 'Ordered']}
df2 = pd.DataFrame(data2)
# Populates status in sub dataframes
df1['sub_df'] = df1['sub_df'].apply(lambda x: x.merge(df2, on='order_id', how='left').fillna(''))
# Sub dataframes back to JSON
def back_to_json_str(df):
# turns a df back to string json
return str(df.to_json(orient="records", indent=4))
df1['olist'] = df1['sub_df'].apply(lambda x: back_to_json_str(x))
# Global DF back to JSON string
parsed = str(df1.drop('sub_df', axis=1).to_json(orient="records", indent=4))
parsed = parsed.replace(r'\n', '\n')
parsed = parsed.replace(r'\"', '\"')
# Print result
print(parsed)
UPDATE 2
here is a way to add index colum to a dataframe:
df1['index'] = [e for e in range(df1.shape[0])]

Answer 2

This is my code assigning title values from a dataframe back to the JSON object. The assignment operation takes a bit time if the number records in the JSON object is 100000. Anyone knows how to improve the performance of this code. Many thanks.
import json
import random
import pandas as pd
import pydash as _
data = [{"pid":1,"name":"Test1","title":""},{"pid":2,"name":"Test2","title":""}] # 5000 records
# dataframe1
df = pd.json_normalize(data)
# dataframe2
pid = [x for x in range(1, 5000)]
title_set = ["Boss", "CEO", "CFO", "PMO", "Team Lead"]
titles = [title_set[random.randrange(0, 5)] for x in range(1, 5000)]
df2 = pd.DataFrame({'pid': pid, 'title': titles})
#left join dataframe1 and dataframe2
df3 = df.merge(df2, on='pid', how='left')
#assign title values from dataframe back to the json object
for row in df3.iterrows():
idx = _.find_index(data, lambda x: x['pid'] == row[1]['pid'])
data[idx]['title'] = row[1]['title_y']
print(data)