I'm working with a large set of data(over 10GB), and my current approach with Pandas is causing memory issues. I've heard that Dask can handle larger datasets more efficiently, but I'm not sure how to get started with it or what to watch out for.
Here's an exsample of what I'm doing with Pandas:
python
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import pandas as pd
df = pd.read_csv('large_dataset.csv')
df['new_column'] = df['column1'] + df['column2']
df.to_csv('updated_dataset.csv')
This works fine with smaller datasets, but with my 10GB dataset, I'm getting a MemoryError.
I've looked into Dask and tried
python
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import dask.dataframe as dd
df = dd.read_csv('large_dataset.csv')
df['new_column'] = df['column1'] + df['column2']
df.to_csv('updated_dataset.csv')
This doesn't give a MemoryError, but it's also not giving me the expected results. I'm not sure what I'm missing.
What are the key differences in handling large datasets between Pandas and Dask? What modifications should I make to my Dask code to get the same results as my Pandas code?
By default dask.dataframe will write one file per partition. If you are expecting the same output as pandas, then a relevant kwarg is single_file which should be set to True:
df.to_csv('updated_dataset.csv', single_file=True)