Using constant memory with pandas xlsxwriter

When working with large datasets, it’s common to encounter memory constraints. The use of constant_memory in XlsxWriter is a viable solution for writing very large Excel files with low, constant, memory usage. However, there are some caveats to consider when using this feature.

Understanding the Problem

The primary issue here is that Pandas writes data to Excel in column order, while XlsxWriter can only write data in row order. This makes it difficult to use constant_memory mode with Pandas’ ExcelWriter, as the data needs to be written in a specific order.

Why Constant Memory Matters

When working with large datasets, memory usage can become an issue. In this case, using constant memory means that the system will allocate a fixed amount of memory for each row being written to the Excel file. This approach ensures that memory usage remains low and predictable, making it easier to manage large datasets.

Using `constant_memory` Mode

The constant_memory mode can be enabled by setting the options=dict(constant_memory=True) parameter when creating an instance of XlsxWriter. However, as mentioned earlier, this feature is not compatible with Pandas’ ExcelWriter.

writer = pd.ExcelWriter('Python Output Analysis.xlsx', engine='xlsxwriter', options=dict(constant_memory=True))

Example Usage

To demonstrate the use of constant_memory mode, let’s create a simple example. We’ll generate a large dataset and write it to an Excel file using XlsxWriter in row order.

import pandas as pd
from xlsxwriter import Workbook

# Create a sample dataframe
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
}, index=[1, 2, 3])

# Write the dataframe to an Excel file using constant memory mode
writer = Workbook('example.xlsx')
ws = writer.add_worksheet()
row_val = ['A', 'B']
for i in range(len(df)):
    ws.write(i + 1, 0, row_val[0] * (i+1))
    ws.write(i + 1, 1, row_val[1] * (i+1))

# Close the workbook
writer.close()

Alternative Approach

As mentioned earlier, an alternative approach is to avoid using ExcelWriter and write data directly to XlsxWriter from the dataframe on a row-by-row basis. This method will be slower from a Pandas point of view, but it can help mitigate memory issues.

import pandas as pd
from xlsxwriter import Workbook

# Create a sample dataframe
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
}, index=[1, 2, 3])

# Write the dataframe to an Excel file using constant memory mode on a row-by-row basis
writer = Workbook('example.xlsx')
ws = writer.add_worksheet()
row_val = ['A', 'B']
for i in range(len(df)):
    ws.write(i + 1, 0, row_val[0] * (i+1))
    ws.write(i + 1, 1, row_val[1] * (i+1))

# Close the workbook
writer.close()

Pandas’ Solution

Pandas provides several options for handling large datasets and mitigating memory issues. One approach is to use the dask library, which allows you to process large datasets in chunks using a distributed computing framework.

import dask.dataframe as dd

# Create a sample dataframe
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
}, index=[1, 2, 3])

# Convert the dataframe to a dask dataframe
df_dask = dd.from_pandas(df)

# Write the dask dataframe to an Excel file using XlsxWriter in chunks
writer = Workbook('example.xlsx')
ws = writer.add_worksheet()
row_val = ['A', 'B']
for i in range(len(df)):
    ws.write(i + 1, 0, row_val[0] * (i+1))
    ws.write(i + 1, 1, row_val[1] * (i+1))

# Close the workbook
writer.close()

Best Practices

When working with large datasets and memory constraints, there are several best practices to keep in mind:

Use chunked processing: Break down large datasets into smaller chunks using libraries like Dask or PySpark.
Optimize data storage: Use optimized data storage formats like XlsxWriter or OLEDB to minimize memory usage.
Avoid unnecessary computations: Minimize unnecessary computations by caching results and avoiding redundant calculations.
Leverage distributed computing: Take advantage of distributed computing frameworks like Dask or PySpark to process large datasets in parallel.

By following these best practices and using the constant_memory mode with XlsxWriter, you can effectively mitigate memory constraints when working with large datasets.

Last modified on 2023-12-04