I’m working with a CSV file that contains transportation data. The file has stop identifiers that are mostly long numbers followed by letters, but some entries are just numeric values.
When I load this data using pandas, the purely numeric identifiers get automatically converted to scientific notation format. Here’s what my dataframe looks like:
The column data type shows as ‘object’, but I need these identifiers to remain as their original numeric format so I can join them with other datasets. Since the column contains mixed data types, I can’t simply convert the entire column to integer or float.
What’s the best approach to handle this issue and keep the original number format intact?
Had this exact problem with utility meter IDs a few months back. Happens when pandas tries to guess data types during CSV parsing. Instead of forcing everything to strings, try keep_default_na=False with selective dtype specification. Stops pandas from converting large numbers to floats right off the bat. Also worked well: pd.read_csv with low_memory=False so it checks the whole column for type inference, not just the first few rows. For your transportation data, since you need exact format for joins, read those columns as strings first. Then make a separate numeric version only when you need calculations.
I encountered a similar situation when working with large identifiers in a CSV file. To prevent pandas from converting your stop identifiers to scientific notation, explicitly define the data type for those columns when loading the CSV. You can achieve this by using the dtype parameter in pd.read_csv. For example, set it to dtype={'stop_from': 'str', 'stop_to': 'str'}. This approach ensures that pandas treats these columns as strings, preserving the original format. It also allows for flexible joins with other datasets, and if numeric calculations are needed later, you can convert specific items back to numbers with pd.to_numeric, ensuring they remain intact during the process.
This happens because pandas sees what looks like numbers and automatically converts them to floats during CSV loading. Don’t bother with external tools or converting everything to strings - just fix it at the source with the float_precision parameter. Try pd.read_csv(filename, float_precision='round_trip') to keep the original number format. But honestly, since these are identifiers and not actual numbers, just use dtype=str for those columns. Handle any real numeric columns separately. I’ve dealt with this tons of times in transportation datasets. These IDs aren’t meant for math - they’re just labels. Keep them as strings from start to finish and your joins won’t break when you move between systems.
Been there with transportation datasets. Scientific notation is the worst when matching IDs across systems.
Sure, converting everything to strings works, but then you can’t do numeric operations on actual numbers. Use pandas converters parameter instead - way cleaner.
I actually automated this whole mess with Latenode. Set up a workflow that preprocesses CSV files before they hit pandas. It auto-detects mixed identifier columns and applies the right formatting.
The best part? Latenode handles the joins too. No more wrestling with pandas merges on mixed data types. It fixes data type consistency across datasets before joining.
Runs automatically when new CSVs arrive. Five minutes to set up, saves hours of debugging.
try pd.read_csv(file, dtype=object) - it stops pandas from auto-converting data types. saved me tons of headaches with transit data that mixed strings and large numbers.