sorry for the long post! I'm a bit Python-illiterate, so please bear with me:
I am working on a project that uses extracted Fitbit resting heart-rate data to compare heart-rate values between a series of years. The fitbit data exports as a .json file that I am attempting to convert to .csv for further analysis. I pulled a script from github that converts .json files to .csv-formatted files, however when inputing the resting heart rate data I am running into a few troubles.
Sample lines from .json:
[{
"dateTime" : "09/30/16 00:00:00",
"value" : {
"date" : "09/30/16",
"value" : 76.83736383927637,
"error" : 2.737363838373737
}
Section of GitHub code that transforms nested frame into columns:
# reading json into dataframes
resting_hr_df = get_json_to_df(file_list=resting_hr_file_list).reset_index()
# Heart rate contains a sub json that are explicitly converted into column
resting_hr_df['date'] = resting_hr_df['value'].transform(lambda x: make_new_df_value(x, 'date'))
resting_hr_df['value'] = resting_hr_df['value'].transform(lambda x: make_new_df_value(x, 'value'))
resting_hr_df['error'] = resting_hr_df['value'].transform(lambda x: make_new_df_value(x, 'error'))
resting_hr_df = resting_hr_df.drop(['value', 'index'], axis=1)
There are two variables named 'value' and I think this is causing the issue.
When using the transform function in pandas to assign variable names for the nested dataframe keys, the second ‘value’ values store as 0 in the .csv file. How should I store the values?
The problem is that this is a nested json file. The solution is to load the json file with json and then load it into pandas with json_normalize
import json
import pandas as pd
with open('filename.json') as data_file:
data = json.load(data_file)
resting_hr_df = pd.json_normalize(data)
resting_hr_df
Output resting_hr_df:
| | dateTime | value.date | value.value | value.error |
|---:|:------------------|:-------------|--------------:|--------------:|
| 0 | 09/30/16 00:00:00 | 09/30/16 | 76.8374 | 2.73736 |
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