Minimizing Memory Usage in Pandas DataFrames: A Guide to Float16 and Sparse Data Types

Smallest Float Dtype for Pandas/Minimizing Size of Transform

When working with large datasets in pandas, one common issue is the size of the transformed data. Specifically, when performing operations that result in a lot of floating-point numbers, the memory usage can quickly become excessive. In this blog post, we’ll explore how to minimize the size of the transformed data using the smallest possible float data type.

Understanding Float Data Types

In Python’s NumPy library, there are several float data types available: float16, float32, and float64. The choice of which one to use depends on the specific requirements of your project. Here’s a brief overview of each:

Float16: This is the smallest floating-point data type in NumPy, with a precision of 16 bits (approximately 1/2^16). It’s useful when working with large datasets where memory usage is critical.
Float32: With a precision of 32 bits (approximately 1/2^32), this data type offers better accuracy than float16. However, it still uses less memory compared to float64.
Float64: This is the most common floating-point data type used in Python and NumPy. It has a precision of 64 bits (approximately 1/2^64) and is suitable for most numerical computations.

Minimizing Memory Usage

In the question provided, the user is dividing each column of the dataframe by the sum of the column values. To minimize memory usage, we can use NumPy’s float16 data type, which is the smallest possible float data type.

However, there are a few issues with using float16 directly:

TypeError: As shown in the question, if we try to set the dtype of an operation to float16, we get a TypeError. This is because most NumPy operations don’t support float16.
Limited precision: While float16 offers better memory usage compared to other float data types, its limited precision can lead to issues with calculations.

Converting Columns to Sparse Data Type

One possible solution to minimize memory usage is to convert columns with low density (i.e., most values are zero) to sparse data types. This approach works because NumPy’s sparse data structures use significantly less memory than dense arrays.

Here’s an example of how we can achieve this:

# Generate a binary dense matrix with low density (~90% are zeros)
import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.randint(low=0, high=50, size=(50_000, 17_000)))
df[df > 5] = 0

# Convert columns to sparse data type
sdf = df.copy()
for col in sdf.columns:
    if sdf[col].dtype == np.float16 and sdf[col].count() < 100:  # adjust the threshold value as needed
        sdf[col] = pd.SparseDtype(dtype='int8', fill_value=0).astype(sdf[col])

This approach can significantly reduce memory usage, especially for columns with low density. However, it may also affect performance due to the added complexity of sparse data structures.

Example Use Case

Here’s an example use case that demonstrates how to use float16 and sparse data types to minimize memory usage:

# Generate a large dataframe with 17000 columns and 50000 rows
import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.randint(low=0, high=50, size=(50_000, 17_000)))

To minimize memory usage, we can convert columns to float16 using sparse data types:

# Convert columns to sparse data type
sdf = df.copy()
for col in sdf.columns:
    if sdf[col].dtype == np.float32 and sdf[col].count() < 100:  # adjust the threshold value as needed
        sdf[col] = pd.SparseDtype(dtype='int8', fill_value=0).astype(sdf[col])

We can then perform operations on the sparse dataframe:

# Perform operation using float16 data type
start = time.time()
result = sdf.div(sdf.sum(axis=1), axis=0, dtype=np.float16) * (10**6)
print(f"Time elapsed: {time.time() - start:.2f} seconds")

By using float16 and sparse data types, we can minimize memory usage while maintaining acceptable performance.

Conclusion

Minimizing the size of transformed data is crucial when working with large datasets. By using NumPy’s smallest float data type (float16) and converting columns to sparse data types, we can significantly reduce memory usage while maintaining acceptable performance. However, it’s essential to carefully evaluate the trade-offs between memory usage and performance for each specific use case.

References

Note: The references provided are subject to change, and it’s always best to check the official NumPy and Pandas documentation for the most up-to-date information.

Last modified on 2024-08-27