"What's the best way to read pd dataframe from local in Python?"
Hey everyone!
I’ve been trying to read pd dataframe from local files (CSV, Excel, etc.) in Python, but I’m not sure if I’m doing it the most efficient way.
Right now, I’m just using `pd.read_csv()` for CSVs, but my files are kinda big, and it feels slow.
Are there faster methods to read pd dataframe from local? Maybe some tricks with chunks or different libraries?
Also, what about other formats like Excel or JSON—any tips for those?
Thanks in advance!
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*PS: If you’ve got any gotchas to watch out for, lmk!*
For CSV files, pd.read_csv() is solid, but if speed's an issue, try the `engine='c'` param (default in pandas).
For bigger files, chunks help—use `chunksize=10000` to read pd dataframe from local in batches.
Also, check out `dask.dataframe` if you're dealing with massive datasets—it's like pandas but lazy-loaded.
For Excel, `pd.read_excel()` works, but it's slower. Maybe export to CSV first if possible?
JSON? `pd.read_json()` is fine, but nested data can be messy—flatten it first!
If you're reading pd dataframe from local files often, consider parquet format!
`pd.read_parquet()` is way faster than CSV for large datasets, and it keeps dtypes intact.
PyArrow backend (`engine='pyarrow'`) can also speed things up.
Downside? Not as human-readable as CSV, but worth it for performance.
Yo, CSV is slow af for big files.
Try `pd.read_csv(..., usecols=[...])` to only load the columns you need. Saves memory + time.
Also, set `dtypes` manually if you know them—stops pandas from guessing (which is slow).
For Excel, openpyxl is faster than xlrd for newer .xlsx files.
For reading pd dataframe from local files efficiently, here’s a pro tip:
Pre-allocate memory by specifying `dtype` for each column. Pandas wastes time inferring types otherwise.
Also, `pd.read_feather()` is insanely fast for medium-sized data—way quicker than CSV/Excel.
Downside? Feather’s not as widely supported, but great for intermediate storage.
Don’t sleep on `pd.read_sql()` if your data’s in a local SQLite DB!
Even for CSV/Excel, dumping to SQLite first can make querying faster later.
For Excel, `pd.ExcelFile()` lets you read sheets without reloading the file each time.
Also, `memory_map=True` in `pd.read_csv()` can help with huge files.
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Wow, thanks for all the tips!
I tried `pd.read_parquet()` and it’s *way* faster for my big datasets. Also, the `usecols` trick saved me so much time.
One follow-up—anyone know if `modin` works well with `chunksize`? Or is it better to just let it parallelize the whole load?
Thanks again, y’all are legends!
If you're stuck with CSV but need speed, try `modin.pandas` instead of regular pandas.
It parallelizes the read pd dataframe from local ops—can be a game-changer for big files.
Just `pip install modin` and replace `import pandas as pd` with `import modin.pandas as pd`.
Works like a drop-in replacement for most pandas functions!
For JSON, `pd.read_json()` is fine, but if it’s nested, try `json_normalize()` to flatten it first.
Also, if you’re reading pd dataframe from local files repeatedly, cache it!
`df.to_pickle()` + `pd.read_pickle()` is faster for re-reading the same data later.
Just don’t use pickle for long-term storage—it’s not version-stable.
Big files? Try `polars` instead of pandas.
`pl.read_csv()` is way faster for reading pd dataframe from local CSVs, and the syntax is similar.
Also handles lazy evaluation, so you can filter/process before loading everything.
Downside? Fewer pandas-like features, but worth it for raw speed.
