I have a pandas DataFrame, df_test. It contains a column 'size' which represents size in bytes. I've calculated KB, MB, and GB using the following code: df_test = pd.DataFrame([ {'dir': '/Users/uname1', 'size': 994933}, {'dir': '/Users/uname2', 'size': 109338711}, ]) df_test['size_kb'] = df_test['size'].astype(int).apply(lambda x: locale.format("%.1f", x / 1024.0, grouping=True) + ' KB') df_test['size_mb'] = df_test['size'].astype(int).apply(lambda x: locale.format("%.1f", x / 1024.0 ** 2, grouping=True) + ' MB') df_test['size_gb'] = df_test['size'].astype(int).apply(lambda x: locale.format("%.1f", x / 1024.0 ** 3, grouping=True) + ' GB') df_test dir size size_kb size_mb size_gb 0 /Users/uname1 994933 971.6 KB 0.9 MB 0.0 GB 1 /Users/uname2 109338711 106,776.1 KB 104.3 MB 0.1 GB [2 rows x 5 columns] I've run this over 120,000 rows and time it takes about 2.97 seconds per column * 3 = ~9 seconds according to %timeit. Is there anyway I can make this faster? For example, can I instead of returning one column at a time from apply and running it 3 times, can I return all three columns in one pass to insert back into the original dataframe? The other questions I've found all want to take multiple values and return a single value. I want to take a single value and return multiple columns. Continue reading...
