PyConJP 2015: pandas internals

Transcript

1. pandas internals
   

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2. Introduction
   • Data Analyst
   
   • OSS Contributions:
   
   • PyData Development Team (pandas)
   
   • Blaze Development Team (Dask)
   
   • GitHub: https://github.com/sinhrks
   

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3. Goal
   • Understand:
   
   • How pandas handles internal data efficiently.
   
   • Some basic rules to get the most out of
   pandas.
   

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4. Agenda
   • What is pandas?
   
   • pandas internals
   
   • Tips for performance
   

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5. What is pandas?
   

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6. What is pandas?
   • “pandas” provides high-performance, easy-to-use
   data structures for data analysis.
   
   • Known as “DataFrame” in R language.
   
   • Author: Wes McKinney
   
   • License: BSD
   
   • Etymology: PANel DAta System
   
   • GitHub: 5000↑⭐️
   

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7. import pandas as pd
   df = pd.read_csv(‘adult.csv’)
   df
   DataFrame
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8. DataFrame
   df[['age', 'marital-status']]
   df.groupby('income')['hours-per-week'].mean()
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9. Why pandas?
   • Capable for real-world (messy) data
   
   • Provides intuitive data structures
   
   • Batteries included for data wrangling
   

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10. NumPy
    • N-dimensional array (ndarray) and matrix
    
    • Index (location) based access
    
    • Single data type
    arr = np.array([1, 2, 3, 4], dtype=np.int64)
    arr
    array([1, 2, 3, 4])
    
    
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11. pandas
    • In addition to NumPy capabilities:
    
    • Label based access (Index / Columns)
    
    • Mixed data types
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12. Data Structures
    • Defined per dimension.
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13. Functionality
    • Vectorized computations
    
    • Group by (split-apply-combine)
    
    • Reshaping (merge, join, concat…)
    
    • Various I/O support (SQL, Excel, …)
    
    • Flexible time series handling
    
    • Plotting
    
    • Please refer to the official documentation for details.
    

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14. • Cooperative with various scientific packages.
    PyData Stacks
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15. • Is functionality everything?
    
    • Performance
    
    • What developers do.
    
    • What users can.
    

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16. pandas internals
    

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17. pandas internals
    • Introduce some typical techniques used in
    pandas internally.
    
    • Intend to clarify the basics, rather than
    explaining algorithm detail.
    
    • Expect to be useful to achieve better
    performance in your program.
    

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18. DataFrame Internals
    • Consists of Type-based “Block”.
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19. Cython (Language)
    • A superset of the Python additionally supports 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)
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20. Cython
    • Performance critical functions are written in Cython or C.
    
    • Cited from “Cython: A guide for Python programmers” by
    Kurt W.Smith”
    
    • NOTE: C and Fortran are not included in the table.
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21. Code Example: Reindex
    df = pd.DataFrame({'X': [1, 2, 3],
    'Y': [4, 5, 6],
    'Z': [True, False, True]},
    index=['a', 'b', 'c'])
    df
    df.reindex(['b', 'a', 'c'])
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22. Code Example: Reindex
    • Pseudo code (logic is simplified):
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    def reindex(self, given_index):
    if [given_index is equal to self.index]:
    return self.copy()
    if len(given_index) == 0:
    return [empty]
    if self.index.is_unique:
    return [unique index logic]
    else:
    return [non-unique index logic]
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23. array([[1, 4, True],
    [2, 5, False],
    [3, 6, True]], dtype=object)
    df.index.get_indexer(['b', 'a', ‘c'])
    • Step by step:
    df.values
    Code Example: Reindex
    np.take(df.values, [1, 0, 2], axis=0)
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    array([[2, 5, False],
    [1, 4, True],
    [3, 6, True]], dtype=object)
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24. Code Example: get_indexer
    • Utilize Cython and C hash table (klib/khash.h)
    cdef class Int64Engine(IndexEngine):
    cdef initialize(self):
    …
    self.mapping = _hash.Int64HashTable(…)
    def get_indexer(self, values):
    …
    return self.mapping.lookup(values)
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25. Code Example: Cache
    • Index is immutable and can cache some
    computations.
    class Index(…):
    @cache_readonly(…)
    def is_unique(self):
    return self._engine.is_unique
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26. Code Example: Type-Optimized Cython
    Python script to generate Cython functions for
    each type using templates.
    left_join_template = """
    def left_join_indexer_%(name)s(ndarray[%(c_type)s] left,
    ndarray[%(c_type)s] right):
    '''
    Two-pass algorithm for monotonic indexes. Handles many-
    to-one merges
    '''
    cdef:
    Py_ssize_t i, j, k, nright, nleft, count
    %(c_type)s lval, rval
    ndarray[int64_t] lindexer, rindexer
    ndarray[%(c_type)s] result
    …
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27. Release the GIL
    • For parallelism (pandas 0.17.0-).
    def 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
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28. Further Reading
    • “A look inside pandas design and development”
    by Wes McKinney
    

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29. Performance Testing
    • airspeed velocity
    
    • pandas now has 650↑ benchmarks
    All benchmarks:
    before after ratio
    [5049b5 ] [53ac28 ]
    293.20ns 290.10ns 0.99 attrs_caching.getattr_dataframe_index.time_getattr_dataframe_index
    3.13μs 3.08μs 0.98 attrs_caching.setattr_dataframe_index.time_setattr_dataframe_index
    7.45ms 7.23ms 0.97 binary_ops.frame_add.time_frame_add
    4.14ms 4.09ms 0.99 binary_ops.frame_add_no_ne.time_frame_add_no_ne
    4.28ms 4.40ms 1.03 binary_ops.frame_add_st.time_frame_add_st
    21.67ms 21.58ms 1.00 binary_ops.frame_float_div.time_frame_float_div
    5.74ms 5.84ms 1.02 binary_ops.frame_float_div_by_zero.time_frame_float_div_by_zero
    17.90ms 17.81ms 0.99 binary_ops.frame_float_floor_by_zero.time_frame_float_floor_by_zero
    10.49ms 9.97ms 0.95 binary_ops.frame_float_mod.time_frame_float_mod
    5.95ms 6.14ms 1.03 binary_ops.frame_int_div_by_zero.time_frame_int_div_by_zero
    10.64ms 10.64ms 1.00 binary_ops.frame_int_mod.time_frame_int_mod
    7.26ms 7.31ms 1.01 binary_ops.frame_mult.time_frame_mult
    4.14ms 4.10ms 0.99 binary_ops.frame_mult_no_ne.time_frame_mult_no_ne
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30. Performance Testing
    • airspeed velocity
    
    • Changes with the passage of the time
    

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31. Tips for performance
    

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32. Tips for performance
    • Introduce basic rules which can be applied to most
    cases in performance point of view.
    
    • Some functions intends user’s convenience,
    rather than performance.
    
    • Environment
    
    • AWS EC2: c4.2xlarge (vCPU: 8, Memory: 15 GiB)
    
    • Python 3.5.0
    
    • DISCLAIMER: Performance is mostly depending on actual data
    and operations. Be sure to profile the effectiveness.
    

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33. 1. Installation
    • Link NumPy to linear algebra libraries.
    
    • BLAS/ATLAS, LAPACK
    
    • Install pandas optional dependencies:
    #PUUMFOFDL A collection of fast NumPy array functions.
    /VNFYQS A fast numerical expression evaluator.
    

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34. 1. Installation
    • Confirm NumPy links
    sysinfo.get_info('lapack')
    {'language': 'f77',
    'libraries': ['openblas'],
    'library_dirs': ['/home/ec2-user/miniconda/lib']}
    import numpy.distutils.system_info as sysinfo
    sysinfo.get_info('atlas')
    {'define_macros': [('ATLAS_INFO', '"\\"3.8.4\\""')],
    'include_dirs': ['/home/ec2-user/miniconda/include'],
    'language': 'f77',
    'libraries': ['lapack', 'f77blas', 'cblas', 'atlas'],
    'library_dirs': ['/home/ec2-user/miniconda/lib']}
    

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35. 1. Installation
    • Confirm pandas-related environment.
    pd.show_versions()
    INSTALLED VERSIONS
    ------------------
    python: 3.5.0.final.0
    …
    pandas: 0.17.0
    numpy: 1.10.0
    bottleneck: 1.0.0
    numexpr: 2.4.4
    …
    

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36. 2. Built-in Functions / Methods
    • Check API doc before writing user defined
    functions (UDF) by yourself.
    
    • Some functions may be faster than NumPy
    depending on conditions.
    
    • Example: Uniquify
    np.unique([1, 2, 2, 3, 2, 4])
    array([1, 2, 3, 4])
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37. 2. Built-in Functions / Methods
    %timeit np.unique(values)
    10 loops, best of 3: 42.2 ms per loop
    %timeit pd.unique(values)
    100 loops, best of 3: 7.1 ms per loop
    np.random.seed(71)
    values = np.random.randint(1, 1000, 1000000)
    values
    array([108, 942, 12, ..., 308, 897, 40])
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38. 2. Built-in Functions / Methods
    • Especially, avoid “apply”.
    
    • Example: String concatenation
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39. 2. Built-in Functions / Methods
    def f1(s):
    return s['b'] + s['c']
    %timeit df.apply(f1, axis=1)
    1 loops, best of 3: 14.3 s per loop
    %timeit df['b'] + df['c']
    10 loops, best of 3: 92.5 ms per loop
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    import pandas.util.testing as tm
    chars1 = tm.rands_array(5, 100)
    chars2 = tm.rands_array(5, 10000)
    n = 1000000
    df = pd.DataFrame({'a': np.random.randn(n),
    'b': tm.choice(chars1, size=n),
    'c': tm.choice(chars2, size=n))
    df
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40. 3. Repeated Ops
    • pandas methods basically returns a copy (not
    view).
    
    • Use single vectorized operation to avoid
    repeated copies.
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41. 3. Repeated Ops
    • Example: Arithmetic
    %timeit df['a'] + 2 - 1
    1000 loops, best of 3: 1.03 ms per loop
    %timeit df['a'] + 1
    1000 loops, best of 3: 475 µs per loop
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42. 4. Data Types
    • Avoid to use “object” dtype.
    
    • Note that “str” is regarded as “object” dtype.
    
    • Example: Group-by → mean
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43. 4. Data Types
    • Object dtype.
    
    • Convert the grouping column to “Categorical”.
    %timeit df.groupby('b').mean()
    10 loops, best of 3: 59.7 ms per loop
    df['b'] = df['b'].astype('category')
    %timeit df.groupby('b').mean()
    100 loops, best of 3: 17.2 ms per loop
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44. 4. Data Types
    • Categorical, what?
    c = pd.Categorical(list(‘ababcabaca'))
    c
    [a, b, a, b, c, a, b, a, c, a]
    Categories (3, object): [a, b, c]
    c.categories
    Index(['a', 'b', 'c'], dtype='object')
    c.codes
    array([0, 1, 0, 1, 2, 0, 1, 0, 2, 0], dtype=int8)
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45. 5. Index
    • Index is immutable and cache some calculated
    results.
    
    • Better to be sorted, unique and without
    missing values (NaN).
    
    • Example: Left-outer join by Index
    left.join(right)
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46. 5. Index
    np.random.seed(71)
    df_left = pd.DataFrame({'a': np.random.randn(n),
    'b': np.random.randn(n)})
    n_right = 10000
    df_right = pd.DataFrame({'c': np.random.randint(1, 100,
    n_right)})
    %timeit df_left.join(df_right)
    100 loops, best of 3: 6.88 ms per loop
    df_right_shuffled = df_right.sample(n=len(df_right))
    %timeit df_left.join(df_right_shuffled)
    100 loops, best of 3: 18.7 ms per loop
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47. 6. I/O
    • No single solution (read/write, types…)
    • Cited from “Efficiently Store Pandas DataFrames” by
    Matthew Rocklin
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48. 6. I/O
    • Parsing datetime likely to be a bottleneck (load).
    
    • ISO-8601
    iso_8641_fmt = '2011-{0:02d}-{1:02d} 00:00:00'
    values = [iso_8601_fmt.format(m, d) for m, d in zip([1, 2], [3, 4])]
    values
    ['2011-01-03 00:00:00', '2011-02-04 00:00:00']
    pd.to_datetime(values)
    DatetimeIndex(['2011-01-03', '2011-02-04'], dtype='datetime64[ns]',
    freq=None, tz=None)
    

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49. 6. I/O
    • Flexible format (parsed by dateutil)
    mdy_fmt = '{0:02d}/{1:02d}/2011'
    values = [mdy_fmt.format(m, d) for m, d in zip([1, 2], [3, 4])]
    values
    ['01/03/2011', '02/04/2011']
    pd.to_datetime(values)
    DatetimeIndex(['2011-01-03', '2011-02-04'], dtype='datetime64[ns]',
    freq=None, tz=None)
    

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50. 6. I/O
    N = 10000
    months = np.random.randint(1, 12, N)
    days = np.random.randint(1, 28, N)
    100 loops, best of 3: 2.26 ms per loop
    dates = [mdy_fmt.format(m, d) for m, d in zip(months, days)]
    %timeit pd.to_datetime(dates)
    1 loops, best of 3: 805 ms per loop
    dates = [iso_8601_fmt.format(m, d) for m, d in zip(months, days)]
    %timeit pd.to_datetime(dates)
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    %timeit pd.to_datetime(dates, format='%m/%d/%Y')
    10 loops, best of 3: 26.1 ms per loop
    *40
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51. Limitations
    • pandas cannot control:
    
    • Internal data alignment & consolidation
    
    • Internal data copy triggered by NumPy
    
    • Alternatives:
    
    • Use NumPy directly
    
    • Use languages can control lower-levels
    

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52. Other Tools
    • Computation time
    
    • Cython
    
    • Numba
    
    • Parallelize
    
    • Dask
    
    • PySpark
    

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53. Conclusions
    • Understand:
    
    • How pandas handles internal data efficiently.
    
    • Some basic rules to get the most out of
    pandas.
    

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54. Interested?
    • Let’s start contribution!
    
    • https://github.com/pydata/pandas
    

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