Slightly better than the read_excel method, but still slow. The first file well work with is a compilation of all the car accidents in England from 1979-2004, to extract all accidents that.
In my case, I have given project name ReadCSVFileInSpark and have selected 2.10.4 as scala version. Once it opened, Go to File -> New -> Project -> Choose SBT Click next and provide all the details like Project name and choose scala version. The above command took my computer 11 minutes 8 seconds to load. The first step is to create a spark project with IntelliJ IDE with SBT. Load as generator wb = openpyxl.load_workbook(filename=data_path, read_only=True) ws = wb.active # Load the rows rows = ws.rows first_row = # Load the data data = for row in rows: record = for key, cell in zip(first_row, row): record = cell.value data.append(record) # Convert to a df df = pd.DataFrame(data) The above command took my computer 11 minutes 44 seconds to load.
Load sheet directly wb = openpyxl.load_workbook(filename=data_path, read_only=True) ws = wb.active # Convert to a df df = pd.DataFrame(ws) Openpyxl Documentation: Memory use is fairly high in comparison with other libraries and applications and is approximately 50 times the original file size. Still slow but a tiny drop faster than Pandas. You can refer to this link: C serialize and deserialize json to txt file. The file is loaded to memory but data is loaded through a generator which allows mapped-retrieval of values. In what form do you need to import the json format content into the txt file.
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