Along with CSV, JSON is another commonly found format for datasets, especially when extracting data from web APIs.
- To create a Pandas DataFrame from a JSON file, first import the Python libraries that you need:
import pandas as pd
- Next, define a variable for the JSON file and enter the full path to the file:
customer_json_file = 'customer_data.json'
- Next, create a DataFrame from the JSON file using the
read_json()method provided by Pandas. Note that the dates in our JSON file are stored in the ISO format, so we're going to tell theread_json()method to convert dates:customers_json = pd.read_json(customer_json_file, convert_dates=True)
- Finally, use the
head()command to see the top five rows of data:customers_json.head()
After importing Pandas and defining a variable for the full path to our JSON file, we use the read_json() method provided by Pandas to create a DataFrame from our JSON file.
read_json() takes a number of arguments, but here we keep it simple and use two: the file path variable and convert_dates. convert_dates is a list of columns to parse for dates that, when set to True, attempts to parse date-like columns.
See the official Pandas documentation for all possible arguments.
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