Here are some tips to speed up your pandas projects:
Use an efficient data structure. pandas DataFrames are efficient, but consider alternatives like NumPy arrays or dictionaries for parts of your project that don't need the full DataFrame functionality.
Inspect your data types. Make sure strings, floats, ints, booleans, etc. are stored as the optimal data type. This avoids unnecessary type conversions.
Use vectorized operations. Vectorized calls like .apply(), .map(), and .fillna() are much faster than iterating through DataFrame rows.
Use .loc[] and .iloc[] instead of .ix[]. The .loc[] and .iloc[] indexing is more performant.
Use numexpr for complex calculations. The numexpr library can accelerate numerical operations on DataFrames.
Parallelize operations. You can parallelize some pandas operations using multiprocessing or dask.
Write to disk in batches. Writing to disk for each row is slow. Write in larger batches.
Cleanup frequently. Deleting variables and clearing the cache can improve performance over time.
Profile your code. Use cProfile, pandas's .profile_ Playground, or a tool like Line Profiler to find bottlenecks.
Optimize any bottlenecks you find. You may need to rewrite some logic, use a different algorithm, or switch to NumPy.
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