Here we save the data to a local file named people.csv and also confirm some file metadata after the process including created time and filesize. We can output our DataFrame to a CSV format file using the to_csv method as follows: import os Fortunately, Pandas has methods to both load and save CSV data. This is achieved as follows: # set indexįirst_name last_name age location.City location.Stateĥ Pat Anderson 60 Miami Florida Step 3: Export to CSV FileĪt this point, our data is ready to be exported to a CSV file. This data will produce an extra column if exported directly to CSV (the index column.) There are several ways in which one could approach handling this, but a convenient means is the setindex method - it will create a visual representation of the change in the DataFrame as well. Here we see the keys of the location field automatically being converted into dot-delimited column names. Id first_name last_name age location.City location.StateĢ 3 Alice Jacobs 18 Los Angeles California # load JSON data and parse into Dictionary object # Load via context manager and read_json() method As such, we need to first load the JSON data as a dict as such: import json However, this function takes a dict object as an argument. To load nested JSON as a DataFrame we need to take advantage of the json_normalize function. This takes the raw JSON data and loads it directly into a DataFrame. In the first step, we loaded our data directly via the read_json function in the Pandas library. To approach the first issue, we’ll have to modify the approach by which we loaded our data.
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