I’m working with an API that returns data in JSON format, and I need to convert this response into an Excel file using Python. Some of the fields in the JSON are nested (lists and dictionaries).
What is the most efficient way to handle this transformation so the data can be flattened and exported properly to Excel?
Are there recommended libraries or approaches (for example using pandas) that work best for this type of task?
Avatar A common approach is to parse the JSON response and convert it into a DataFrame, then export it to Excel.
In Python, this is usually done with requests and pandas:
import requests
import pandas as pd
url = "https://api.example.com/data"
response = requests.get(url)
data = response.json()
df = pd.json_normalize(data)
df.to_excel("output.xlsx", index=False)
pd.json_normalize() helps flatten nested JSON structures into a tabular format, which makes it easier to export to Excel.
For more complex APIs with deeply nested fields, you may need to select specific keys or further transform the JSON before creating the DataFrame.
A simple way is to fetch the API response as JSON and convert it to a DataFrame, then export it to Excel.
import requests
import pandas as pd
url = "https://api.example.com/data"
response = requests.get(url)
data = response.json()
df = pd.json_normalize(data)
df.to_excel("output.xlsx", index=False)
requests gets the API data, pandas converts the JSON into a structured table, and to_excel() saves it as an Excel file. This works well when the JSON response is fairly structured.
