data_json_to_csv.py

import json
import pandas as pd
from operator import itemgetter
from datetime import timezone
import math
from datetime import datetime
import os

def add_unix_time_to_data(prs_metric_data):
    new_data = []
    for data in prs_metric_data:
        if data['time'] == None:
            data['time'] = '00:00'
        data['timestamp'] = int(datetime.strptime('{} {}'.format(data['date'], data['time']), '%d/%m/%Y %H:%M').timestamp())
        new_data.append(data)
    new_data = sorted(new_data, key=itemgetter('timestamp'))
    return new_data

def metric_data_to_df(prs_metric_data):
    """
    @gagan: TODO
    this function takes a list of dicts (each dict is the output of getMetricData from pms)
    and converts it into
    a df to be used in the above ml functions
    """
    data_value_list = []
    prs_metric_data = sorted(prs_metric_data, key=itemgetter('timestamp'))
    for metric_data in prs_metric_data:
        for kpi in metric_data['kpiValues']:
            if metric_data['kpiValues'][kpi]!=None and not(math.isnan(metric_data['kpiValues'][kpi])):
                data_value_list.append({
                    'value': metric_data['kpiValues'][kpi],
                    'timestamp': metric_data['timestamp']
                })
    prs_metric_df = pd.DataFrame(data_value_list)
    return prs_metric_df


#%%

from glob import glob

files = glob('./json/*.json')
content = []
for file in files:
    with open(file, 'r') as f:
        content = json.load(f)
        content = add_unix_time_to_data(content)
        df = metric_data_to_df(content)
        df.to_csv('./json_to_csv/'+file.split('/')[-1].split('.')[0]+'.csv', index=False)