extract data from dictionary with nested dictionaries that contain lists that contain dictionaries

Question

I have a response from an api that contains a data sump from a heating system, structured as dictionaries with nested dictionaries that contain lists that contain dictionaries.

eg

    sample = {"zoneType": "HEATING",
              "interval": {"from": "2020-10-23T22:45:00.000Z", "to": "2020-10-24T23:15:00.000Z"},
              "hoursInDay": 24,
              "measuredData": {
                  "measuringDeviceConnected": {
                      "timeSeriesType": "dataIntervals",
                      "valueType": "boolean",
                      "dataIntervals": [{
                          "from": "2020-10-23T22:45:00.000Z", "to": "2020-10-24T23:15:00.000Z", "value": True}]
                          },
                  "insideTemperature": {
                      "timeSeriesType": "dataPoints",
                      "valueType": "temperature",
                      "min": {
                          "celsius": 19.34,
                          "fahrenheit": 66.81},
                      "max": {
                          "celsius": 20.6,
                          "fahrenheit": 69.08},
                      "dataPoints": [
                          {"timestamp": "2020-10-23T22:45:00.000Z", "value": {"celsius": 20.6, "fahrenheit": 69.08}},
                          {"timestamp": "2020-10-23T23:00:00.000Z", "value": {"celsius": 20.55, "fahrenheit": 68.99}},
                          {"timestamp": "2020-10-23T23:15:00.000Z", "value": {"celsius": 20.53, "fahrenheit": 68.95}},
                          {"timestamp": "2020-10-23T23:30:00.000Z", "value": {"celsius": 20.51, "fahrenheit": 68.92}},
                          {"timestamp": "2020-10-23T23:45:00.000Z", "value": {"celsius": 20.48, "fahrenheit": 68.86}},
                          {"timestamp": "2020-10-24T00:00:00.000Z", "value": {"celsius": 20.48, "fahrenheit": 68.86}},
                          {"timestamp": "2020-10-24T00:15:00.000Z", "value": {"celsius": 20.44, "fahrenheit": 68.79}}]
                  },
                  "humidity": {
                      "timeSeriesType": "dataPoints",
                      "valueType": "percentage",
                      "percentageUnit": "UNIT_INTERVAL",
                      "min": 0.615,
                      "max": 0.627,
                      "dataPoints": [
                          {"timestamp": "2020-10-23T22:45:00.000Z", "value": 0.615},
                          {"timestamp": "2020-10-23T23:00:00.000Z", "value": 0.615},
                          {"timestamp": "2020-10-23T23:15:00.000Z", "value": 0.619},
                          {"timestamp": "2020-10-23T23:30:00.000Z", "value": 0.620},
                          {"timestamp": "2020-10-23T23:45:00.000Z", "value": 0.621},
                          {"timestamp": "2020-10-24T00:00:00.000Z", "value": 0.623},
                          {"timestamp": "2020-10-24T00:15:00.000Z", "value": 0.627}]
                  }
              }}

I want to extract the ['insideTemperature']['datapoints'] timestamp and celsius values from the above (actual data spans more periods) and place them as columns in a new pd.DataFrame along with other data from the 'humidity' key. In due course, I want to merge this with data from a separate API call that has a similar structure, though may not have consistent timestamp values.

A number of the top level dictionaries contain summary data (eg min and max values) so can be ignored. Equally, conversion from celsius to f etc, is easy to do if needed, so I don't want to pull this data.

What is the best way to cleanly create a DataFile that lists the timestamp, temperature in Celsius and humidity from this query that I can then join with other query outputs?

So far, I have been using the following:

import pandas as pd
df = pd.DataFrame(sample['measuredData']['insideTemperature']['dataPoints'])

## remove column that contains dictionary data, leaving time data
df.drop(labels='value', axis=1, inplace=True)

## get temp data into new column
input_data_point = sample['measuredData']['insideTemperature']['dataPoints']

temps = []

for i in input_data_point:
    temps.append(i['value']['celsius'])

df['inside_temp_c'] = pd.DataFrame(temps)

## repeat for humidity
input_data_point = sample['measuredData']['humidity']['dataPoints']

temps = []

for i in input_data_point:
    temps.append(i['value'])

df['humidity_pct'] = pd.DataFrame(temps)

Being new to coding in python, I am wondering if there is a far quicker way of extracting the data from the original downloaded data, straight into a clean Pandas DataFrame?? Grateful for any suggestions.

Answer 1

You can use json_normalize to get the data:

df1 = pd.json_normalize(sample,
                       record_path=['measuredData', 'insideTemperature', 'dataPoints'],
                       meta=['zoneType'])
print(df1)
df2 = pd.json_normalize(sample,
                       record_path=['measuredData', 'humidity', 'dataPoints'],
                       meta=['zoneType'])
print(df2)

df1:

                 timestamp  value.celsius  value.fahrenheit zoneType
0  2020-10-23T22:45:00.000Z          20.60             69.08  HEATING
1  2020-10-23T23:00:00.000Z          20.55             68.99  HEATING
2  2020-10-23T23:15:00.000Z          20.53             68.95  HEATING
3  2020-10-23T23:30:00.000Z          20.51             68.92  HEATING
4  2020-10-23T23:45:00.000Z          20.48             68.86  HEATING
5  2020-10-24T00:00:00.000Z          20.48             68.86  HEATING
6  2020-10-24T00:15:00.000Z          20.44             68.79  HEATING

df2:

                  timestamp  value zoneType
0  2020-10-23T22:45:00.000Z  0.615  HEATING
1  2020-10-23T23:00:00.000Z  0.615  HEATING
2  2020-10-23T23:15:00.000Z  0.619  HEATING