JSON in python from mysql with additional key value pairs - mysql

This is the code used to fetch data from DB
import pymysql
import json
conn = pymysql.connect(host='localhost', port=3306, user='root', passwd='', db='test', charset='utf8mb4', cursorclass=pymysql.cursors.DictCursor)
cursor = conn.cursor()
cursor.execute("SELECT * FROM user")
rows = []
for row in cursor:
rows += [row]
print(json.dumps(rows, sort_keys=False, indent=4, separators=(',', ': ')))
cursor.close()
conn.close()
Output in json is -
[
{
"name": "John",
"id": 50
},
{
"name": "Mark",
"id": 57
}
]
But I want the output in this format -
{
"version": "5.2",
"user_type": "online",
"user":
[
{
"name": "John",
"id": 50
},
{
"name": "Mark",
"id": 57
}
]
}
where the version and user_type can be manually entered or appended to the result.

Simply wrap the result set in a dict of your liking then.
# ...
cursor.execute("SELECT * FROM user")
response = {
"version": "5.2",
"user_type": "online",
"user": list(cursor), # This is equivalent to iterating over the cursor yourself.
}
print(json.dumps(response, sort_keys=False, indent=4, separators=(',', ': ')))
# ...

You can create a dict with the version, the user type, and the user (where for the key 'user' you enter rows as the value). Then convert that to json using json.dump or json.dumps:
data = { "version": "5.2", "user_type": "online", "user":rows }
print(json.dumps(data, sort_keys=False, indent=4, separators=(',', ': ')))

Related

Combine multiple JSON files, and parse into CSV

I have about 100 JSON files, all titled with different dates and I need to merge them into one CSV file that has headers "date", "real_name", "text".
There are no dates listed in the JSON itself, and the real_name is nested. I haven't worked with JSON in a while and am a little lost.
The basic structure of the JSON looks more or less like this:
Filename: 2021-01-18.json
[
{
"client_msg_id": "xxxx",
"type": "message",
"text": "THIS IS THE TEXT I WANT TO PULL",
"user": "XXX",
"user_profile": {
"first_name": "XXX",
"real_name": "THIS IS THE NAME I WANT TO PULL",
"display_name": "XXX",
"is_restricted": false,
"is_ultra_restricted": false
},
"blocks": [
{
"type": "rich_text",
"block_id": "yf=A9",
}
]
}
]
So far I have
import glob
read_files = glob.glob("*.json")
output_list = []
all_items = []
for f in read_files:
with open(f, "rb") as infile:
output_list.append(json.load(infile))
data = {}
for obj in output_list[]
data['date'] = f
data['text'] = 'text'
data['real_name'] = 'real_name'
all_items.append(data)
Once you've read the JSON object, just index into the dictionaries for the data. You might need obj[0]['text'], etc., if your JSON data is really in a list in each file, but that seems odd and I'm assuming your data was pasted from output_list after you'd collected the data. So assuming your file content is exactly like below:
{
"client_msg_id": "xxxx",
"type": "message",
"text": "THIS IS THE TEXT I WANT TO PULL",
"user": "XXX",
"user_profile": {
"first_name": "XXX",
"real_name": "THIS IS THE NAME I WANT TO PULL",
"display_name": "XXX",
"is_restricted": false,
"is_ultra_restricted": false
},
"blocks": [
{
"type": "rich_text",
"block_id": "yf=A9",
}
]
}
test.py:
import json
import glob
from pathlib import Path
read_files = glob.glob("*.json")
output_list = []
all_items = []
for f in read_files:
with open(f, "rb") as infile:
output_list.append(json.load(infile))
data = {}
for obj in output_list:
data['date'] = Path(f).stem
data['text'] = obj['text']
data['real_name'] = obj['user_profile']['real_name']
all_items.append(data)
print(all_items)
Output:
[{'date': '2021-01-18', 'text': 'THIS IS THE TEXT I WANT TO PULL', 'real_name': 'THIS IS THE NAME I WANT TO PULL'}]

Flatten JSON data to individual columns

I am working upon Twitter streaming data and I am having an output like this:
"data": {
"author_id": "1318123716522479616",
"created_at": "2020-11-05T04:18:21.000Z",
"entities": {
"hashtags": [
{
"end": 107,
"start": 86,
"tag": "MilliHesaplarYanyana"
}
],
"mentions": [
{
"end": 15,
"start": 3,
"username": "MilliTaakip"
}
]
},
"id": "1324204381177323520",
"lang": "tr",
"text": "RT #MilliTaakip: Milli hesaplar\u0131m\u0131z\u0131n g\u00fc\u00e7lenmesi i\u00e7in\nCumhurba\u015fkan\u0131m\u0131z\u0131n talimat\u0131yla,\n#MilliHesaplarYanyana \u00e7al\u0131\u015fmas\u0131n\u0131 destekliyoruz;\n\n\ud83c\uddf9\ud83c\uddf7\u2026"
}
}
I want to extract specific information like the hashtags from this data and store them in my database.
I tried using multiple ways like json.normalize ,flatten_json but it does not work. I get the following as my output
here's my code:
def connect_to_endpoint(url, headers):
response = requests.request("GET", url, headers=headers, stream=True, params=payload)
print(response.status_code)
for response_line in response.iter_lines():
if response_line:
# print(ndjson.dumps(json_response["data"]["text"], indent=4, sort_keys=True))
conn = psycopg2.connect(database="tweetData", user="postgres", password="pass", host="localhost", port="5432")
cur = conn.cursor()
# cc
try:
data = json.loads(response_line.decode('utf-8'))
index = 0
#for created at
var2 = json.loads(response_line.decode('utf-8'))["data"]["text"]
# define a list of keywords
keywords = ('biden', 'election', 'trump','stocks')
if any(keyword in var2.lower() for keyword in keywords):
df= pd.json_normalize(data)
dffinal=pd.DataFrame(df)
engine = create_engine('postgresql+psycopg2://postgres:root#localhost:5432/tweetData')
dffinal.to_sql("new-tweets", engine,if_exists='append',dtype = {'relevant_column':sqlalchemy.types.JSON})
print("loaded")
else:
print("none")
conn.commit()
index += 1
cur.close()
except IOError as io:
print("ERROR!")
if response.status_code != 200:
raise Exception(
"Request returned an error: {} {}".format(
response.status_code, response.text
)
)
Please advise on how should I proceed and what errors I have in my approach
EDIT:
Every time I try to retrieve the tweet data, in case there is no entities or no hashtags in the tweet data, it sends an error saying Key Error: 'entities'
In PostgreSQL you could use
SELECT value ->> 'tag'
FROM jsonb_array_elements(your_json #> '{data,entities,hashtags}') AS x(value);
to extract the tags.

Join nested JSON dataframe and another dataframe

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"
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])]
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)

How to Get JSON values Python

Learning Days
Code to the get the data in JSON Format
#...
cursor.execute("SELECT * FROM user")
response = {
"version": "5.2",
"user_type": "online",
"user": list(cursor),
}
response = json.dumps(response, sort_keys=False, indent=4, separators=(',', ': '))
print(response)
# ...
This produces output as
{
"version": "5.2",
"user_type": "online",
"user":
[
{
"name": "John",
"id": 50
},
{
"name": "Mark",
"id": 57
}
]
}
print(response["user"]) - TypeError: string indices must be integers
How do i access the values in JSON
json.dumps return a string, need a small conversion something like this, not sure is this the exact method to do
Solution:
response = JSONEncoder().encode(response )
response = JSONDecoder().decode(response )
response = json.loads(response )
print(response['user'[0]['id'])

How to get this type of JSON data in Python

This is the JSON I got from Mysql query - First Json:
[
{
"id": 121,
"name": "A"
},
{
"id": 122,
"name": "B"
},
{
"id": 123,
"name": "C"
},
{
"id": 124,
"name": "D"
}
]
Second Json : But I need in this format
{
"user_data":
[
{
"id": 121,
"name": "A"
},
{
"id": 122,
"name": "B"
},
{
"id": 123,
"name": "C"
},
{
"id": 124,
"name": "D"
}
]
}
So that I can identify this is user_data.
We can differentiate between two json.
The code used to generate the first json is below
import pymysql
import json
conn = pymysql.connect(host='localhost', port=3306, user='root', passwd='', db='test', charset='utf8mb4', cursorclass=pymysql.cursors.DictCursor)
cursor = conn.cursor()
cursor.execute("SELECT * FROM user")
rows = []
for row in cursor:
rows += [row]
print(json.dumps(rows, sort_keys=False, indent=4, separators=(',', ': ')))
cursor.close()
conn.close()
You can simply add this in dictionary object with key user_data.
user_data = json.dumps(rows, sort_keys=False, indent=4, separators=(',', ': '))
data = {
'user_data' : user_data
}