A "Memory Error" in Pandas when trying to fill NaN values with 0s typically occurs when your DataFrame is too large to fit into memory with the new changes. Here are a few strategies you can consider to mitigate this issue:
1. **Reduce DataFrame Size**: If your DataFrame is too large, consider filtering or downsizing your data before attempting to fill NaN values with 0s. This might involve selecting a subset of columns or rows.
2. **Use Data Types Wisely**: Make sure you are using appropriate data types for your columns. Using more memory-efficient data types can help reduce memory consumption.
3. **Chunk Processing**: If you're working with an extremely large dataset, you can process it in smaller chunks. You can read your data in chunks using `pd.read_csv()` or another appropriate method, fill NaNs with 0s for each chunk, and then concatenate the results.
4. **Sparse Data**: If your dataset has a lot of missing values, consider using Pandas' sparse data structures, which can save memory when dealing with sparse data.
5. **Use Dask**: Dask is a parallel computing library that can handle larger-than-memory data. You can use Dask DataFrames to perform operations similar to Pandas on larger datasets.
Here's an example of how to fill NaN values with 0s using Dask DataFrames:
```python
import dask.dataframe as dd
# Read data with Dask
ddf = dd.read_csv('your_large_file.csv')
# Fill NaNs with 0s
ddf = ddf.fillna(0)
# Compute the result when needed
result = ddf.compute()
```
These strategies should help you deal with the "Memory Error" issue when filling NaN values with 0s in a large Pandas DataFrame.