based access • Single data type arr = np.array([1, 2, 3, 4], dtype=np.int64) arr array([1, 2, 3, 4]) MPDBUJPO /VN1Zl4USVDUVSFE"SSBZzDBODPOBJONVMUJQMFEUZQFT
Reshaping (merge, join, concat…) • Various I/O support (SQL, Excel, CSV/TSV…) • Flexible time series handling • Plotting • Please refer to the official documentation for details.
C functions and C types. Can be compiled to C code. def ismember(ndarray arr, set values): cdef: Py_ssize_t i, n ndarray[uint8_t] result object val n = len(arr) result = np.empty(n, dtype=np.uint8) for i in range(n): val = util.get_value_at(arr, i) result[i] = val in values return result.view(np.bool_) ismember(np.array([1, 2, 3, 4]), set([2, 3])) array([False, True, True, False], dtype=bool) 5ZQFEFpOJUJPOT 3FUVSOCPPMBSSBZJOEJDBUFT BSSBZFMFNFOUTBSFJODMVEFE JOUIFTFU (FUJOQVU`TJUIWBMVF
duplicated_int64(ndarray[int64_t, ndim=1] values, object keep='first'): cdef: int ret = 0, value, k Py_ssize_t i, n = len(values) kh_int64_t * table = kh_init_int64() ndarray[uint8_t, ndim=1, cast=True] out = np.empty(n, dtype='bool') kh_resize_int64(table, min(n, _SIZE_HINT_LIMIT)) … else: with nogil: for i from 0 <= i < n: value = values[i] k = kh_get_int64(table, value) if k != table.n_buckets: out[table.vals[k]] = 1 out[i] = 1 else: k = kh_put_int64(table, value, &ret) table.keys[k] = value table.vals[k] = i out[i] = 0 kh_destroy_int64(table) return out 3FMFBTFUIF(*-
multiple threads. • GIL is released on I/O. • GIL can be released using Cython. • Scientific packages are working to release GIL. • NumPy, SciPy, Scikit-learn, pandas… • Cannot use Python classes after GIL is released. • If the target is object dtype, GIL cannot be released.
pandas performs computations using single thread. • Users have to parallelize by themselves. • pandas cannot handle data which exceeds physical memory. • Users have to write logic using pandas “chunk” function.
framework for numeric operations which offers: • Data structures like nd-array and DataFrame which extends common interfaces like NumPy and pandas. • Dynamic task graph and its scheduling optimized for computation. • Author: Matthew Rocklin • License: BSD • GitHub: 1500↑⭐
library featuring a higher-level API for TensorFlow. • (Distributed Scheduler) A platform to author, schedule and monitor workflows. • Image Processing SciKit. • N-D labeled arrays and datasets in Python. • An interface to query data on different storage systems. • A graphics pipeline system for creating meaningful representations of large datasets quickly and flexibly. Datashader Airflow
chunk size. • Recommended to use the same chunk size over axis because computations are performed per chunk. da.ones((30, 20, 1, 15), chunks=(3, 7, 1, 2)) dask.array<wrapped, shape=(30, 20, 1, 15), dtype=float64, chunksize=(3, 7, 1, 2)>
(split-apply-combine) • Reshaping (merge, join, concat…) • Various I/O support (SQL, CSV/TSV…) • Flexible time series handling • Please refer to the official documentation for detail.
x + 1 @delayed def add(x, y): return x + y x = inc(1) y = inc(5) total = add(x, y) total Delayed('add-b43be476-ffc7-48d7-a8ec-0f95df821e64') total.compute() 8 6TJOH!EFMBZFEEFDPSBUPSNBLFT XSBQQFEGVODUJPOMB[Z %FMBZFEGVODUJPOTDBOCF DIBJOFE BOEPVUQVUT%FMBZFE JOTUBODF OPUFWBMVBUFE"5. 5SJHHFSDPNQVUBUJPO
which works on a single node: • threading • multiprocessing • Dask Distributed package offers a distributed scheduler, which distributes tasks over multiple nodes.
• Low latency: Each task suffers about 1ms of overhead. • Peer-to-peer data sharing: Workers communicate with each other to share data. • Complex Scheduling: Supports complex workflows. • Data Locality: Scheduling algorithms cleverly execute computations where data lives. %JTUSJCVUFE 8PSLFS %JTUSJCVUFE 8PSLFS %JTUSJCVUFE 4DIFEVMFS %JTUSJCVUFE $MJFOU
to scale existing NumPy or pandas project. • Parallel / Out-of-Core processing on a single node. • Prototype complex algorithm interactively. • Don’t have Big Data infrastructure you can use freely. • Spark works well when: • Needs to scale large number of clusters. • Workflow requirement meets Spark API (typical ETL or SQL-like ops). • Needs enterprise support.
internal data efficiently. • Dask to parallelize data processing easily. It provides: • Data structures like nd-array and DataFrame which extends common interfaces like NumPy and pandas. • Dynamic task graph and its scheduling optimized for computation.
sinhrks
linyows
chige
hiroga
seike460
lilobase
takahoyo
tosaka07
cocoeyes02
yutannihilation
shiz
wisetlaloc
mozamimy
ddemaree
shpigford
colly
robhawkes
addyosmani
roundedbygravity
matthewcrist
morganepeng
edds
phodgson
sachag
lara
![DataFrame df[['age', 'marital-status']] df.groupby('income')['hours-per-week'].mean() (SPVQCZ 4FMFDU "HHSFHBUF 4FMFDU](https://files.speakerdeck.com/presentations/fbd7d9142f12415ea80eacc051c2ed86/slide_7.jpg){kind=link}
