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How to Run Parallel Data Analysis in Python using Dask Data frames
Dask provides high-level Array, Bag, and DataFrame collections that mimic NumPy, lists, and Pandas but can operate in parallel on datasets that don't fit into main memory. Dask's high-level collections are alternatives to NumPy and Pandas for large datasets.
- Manipulating large datasets, even when those datasets don't fit in memory
- Accelerating long computations by using many cores
- Distributed computing on large datasets with standard Pandas operations like group, join, and time series computations
- Arithmetic operations (multiplying or adding to a Series)
- Common aggregations (mean, min, max, sum, etc.)
- Calling apply (as long as it's along the index -that is, not after a group('y') where 'y' is not the index-)
- Calling value_counts(), drop_duplicates() or corr()
- Filtering with loc, isin, and row-wise selection
204.313940048 seconds for get_big_mean
39.7543280125 seconds for get_big_mean_old
131.600986004 seconds for get_big_max
43.7621600628 seconds for get_big_max_old
120.027213097 seconds for get_big_sum
7.49701309204 seconds for get_big_sum_old
0.581165790558 seconds for filter_df
226.700095892 seconds for filter_df_old
157.643756866 seconds for apply_random_old
3.08352184296 seconds for get_big_mean
1.3314101696 seconds for get_big_max
1.21639800072 seconds for get_big_sum
0.228978157043 seconds for filter_df
112.135010004 seconds for apply_random
50.2007009983 seconds for value_count_test