On the Right Hand Side, we break up the 'stats' column using apply to make a data frame out of each key/value pair. Each element in the stats column is a dictionary. On the Left Hand Side, we get the new column names from the keys of the element of the stats column.
This will only work for regular data (the json object needs to have 3 values or at least missing values need to be handled in our CustomParser) df.keys())] = df.apply(pandas.Series)
CSV files may be opened with ease in standard spreadsheet programs such as Microsoft Excel, OpenOffice, LibreOffice Calc. This utility can convert any JSON data to CSV or spreadsheet format. The data format conversion can be beneficial for REST-based service automation testing. This will make the stats column a dictįrom here, we can use a little hack to directly append these columns in one step with the appropriate column names. This is a free online tool for converting JSON to a CSV file format. We are telling read_csv to read the data in the standard way, but for the stats column use our custom parsers. I am working with CSV files where several of the columns have a simple json object (several key value pairs) while other columns are normal.
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