![]() Knowing the precise details of the CSV format used by Excel. It allows programmers to say, “write this data in the format preferredīy Excel,” or “read data from this file which was generated by Excel,” without The csv module implements classes to read and write tabular data in CSVįormat. Similar enough that it is possible to write a single module which canĮfficiently manipulate such data, hiding the details of reading and writing the Still, while the delimiters and quoting characters vary, the overall format is Theseĭifferences can make it annoying to process CSV files from multiple sources. Often exist in the data produced and consumed by different applications. The lack of a well-defined standard means that subtle differences Years prior to attempts to describe the format in a standardized way in I'm looking for a more elegant solution to achieve this with fewer lines of code.Īttached is a sample output JSON for reference.The so-called CSV (Comma Separated Values) format is the most common import andĮxport format for spreadsheets and databases. Creating separate code for each of these columns seems redundant and inefficient. ![]() # Apply the function to each row of the DataFrameĭf_final = pd.concat(, axis=1)ĭf_final.to_csv('resul.csv', index=False)Īlthough I have successfully created separate columns for some nested data, there are still other nested values within these columns. # Custom function to extract values from JSON objects Max_objects = max(len(item_list) for item_list in df_item) # Determine the maximum number of JSON objects in a cell My goal is to get a csv with all the columns inside the json, and to do this I have to create single columns for each nested index inside the JSON.Ĭredentials = " for i in range(max_items) for key in keys] I have a nested json output from an API request.
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