Trouble extracting data from Airtable API: Empty fields and delimiters causing parsing issues

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1

Hey everyone,

I’ve been working on getting data from the Airtable API but I’m hitting a snag. The parsing is going okay for the most part, but I’m having trouble with empty fields and delimiters.

Here’s what’s happening:

sample_data = {
    'offset': 123,
    'records': [{
        'createdTime': '2023-05-01',
        'fields': {
            'Column1': 'value1',
            'Column2': '',
            'Column3': 'value3',
            'Column4': None
        }
    }]
}

When I try to put this into a DataFrame, the empty fields aren’t parsed correctly. This messes up the column alignment. I’ve tried various methods to parse the records, but nothing seems to work perfectly.

I know that PowerBI can handle this data properly, so there must be a solution. Any suggestions on how to fix this issue? I’m relatively new to API requests and handling complex DataFrames, so any assistance would be greatly appreciated.

Thanks!

2

I’ve dealt with this exact problem before when working with Airtable’s API. Here’s a solution that worked well for me:

Use the ‘fillna’ method in pandas after creating your DataFrame. This will replace any NaN values (which is what empty fields often become) with a value of your choice. Here’s an example:

import pandas as pd

df = pd.DataFrame(sample_data['records'])
df = df.fillna('Empty')

This approach maintains the structure of your data while ensuring all fields have a value. You can replace ‘Empty’ with any placeholder you prefer.

Also, make sure you’re flattening the ‘fields’ dictionary when creating your DataFrame. The ‘json_normalize’ function mentioned earlier is great for this.

Hope this helps solve your issue!

3

I’ve encountered similar issues when working with Airtable’s API. One approach that worked well for me was to pre-process the data before creating the DataFrame. Here’s what I did:

  1. Iterate through the records and replace empty strings and None values with a placeholder (e.g., ‘N/A’).

  2. Create a list of dictionaries, where each dictionary represents a row of data.

  3. Use pandas.DataFrame.from_records() to create the DataFrame.

Something like this:

import pandas as pd

processed_records = []
for record in sample_data['records']:
    fields = record['fields']
    for key, value in fields.items():
        if value == '' or value is None:
            fields[key] = 'N/A'
    processed_records.append(fields)

df = pd.DataFrame.from_records(processed_records)

This approach ensures that all fields are consistently populated, preventing misalignment issues. You can then replace ‘N/A’ with None or whatever you prefer in the resulting DataFrame if needed.

Hope this helps! Let me know if you need any clarification.

4

yo, ive had this issue too. its a pain! try using pandas.json_normalize() instead of DataFrame. it handles nested json better. something like:

import pandas as pd
df = pd.json_normalize(sample_data[‘records’])

this should keep ur columns aligned even w/ empty stuff. lmk if it works!