The journey to convert nested JSON structures into structured Pandas DataFrames can be daunting, but with the right tools and techniques, it becomes a seamless task. Let's explore the options available for this transformation.
JSON Normalization: A Straightforward Approach
json_normalize provides a powerful solution for flattening nested JSON objects. As we embark on this approach:
<code class="python">import json
with open('myJson.json') as data_file:
data = json.load(data_file)
df = pd.json_normalize(data, 'locations', ['date', 'number', 'name'],
record_prefix='locations_')
print (df)</code>Output:
locations_arrTime locations_arrTimeDiffMin locations_depTime \ 0 06:32 1 06:37 1 06:40 2 08:24 1 locations_depTimeDiffMin locations_name locations_platform \ 0 0 Spital am Pyhrn Bahnhof 2 1 0 Windischgarsten Bahnhof 2 2 Linz/Donau Hbf 1A-B locations_stationIdx locations_track number name date 0 0 R 3932 R 3932 01.10.2016 1 1 R 3932 01.10.2016 2 22 R 3932 01.10.2016
Parsing Name and Grouping for Concatenation
However, if flattening is not your ultimate goal, you can embrace an alternative approach:
<code class="python">df = pd.read_json("myJson.json")
df.locations = pd.DataFrame(df.locations.values.tolist())['name']
df = df.groupby(['date','name','number'])['locations'].apply(','.join).reset_index()
print (df)</code>Output:
date name locations 0 2016-01-10 R 3932 Spital am Pyhrn Bahnhof,Windischgarsten Bahnho...
This technique enables you to concatenate the locations without compromising the nesting structure.
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