What strategies can you use to manage memory in Python big data projects?

1 Efficient Coding

Writing memory-efficient code is the first line of defense against high memory usage. Use built-in data types like sets, lists, and dictionaries judiciously, understanding their memory footprints. For example, using a set for membership tests is more efficient than a list. Also, consider generator expressions over list comprehensions for large datasets, as they yield items one by one, using less memory. Comprehend the space complexity of algorithms and optimize code to reduce unnecessary memory consumption.

2 Data Structures

Choosing the right data structure is pivotal in managing memory. Python collections such as `deque` can be more efficient than lists for queue and stack implementations. Also, the `array` module provides an array data structure that is more compact than lists for homogeneous data. For large numeric data sets, consider using `numpy` arrays, which are designed for high-performance operations and use less memory than native Python lists.

5 Data Processing

When processing big data, consider using lazy evaluation techniques such as generators and iterators that allow you to work with data incrementally without loading entire datasets into memory. Libraries like `pandas` and `dask` are optimized for data manipulation and can handle large volumes of data more efficiently than standard Python code. Use chunking techniques to process data in smaller, more manageable pieces.

6 External Storage

Sometimes, the best way to manage memory is not to use it at all. Offloading data to external storage systems or databases can be a smart move. For example, using a database to filter and aggregate data before it's loaded into Python can drastically reduce the in-memory size of your dataset. Consider serialization formats like JSON or Pickle to temporarily store data on disk when not in use.