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Merge branch 'master' of https://github.com/pandas-dev/pandas into div_zero2
2 parents 0277d9f + 24d9509 commit 78de1a4

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‎ci/requirements-3.6_WIN.run

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@@ -12,5 +12,6 @@ numexpr
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pytables
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matplotlib
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blosc
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thrift=0.10*
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fastparquet
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pyarrow

‎doc/source/developer.rst

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@@ -153,7 +153,7 @@ Libraries can use the decorators
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pandas objects. All of these follow a similar convention: you decorate a class, providing the name of attribute to add. The
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class's `__init__` method gets the object being decorated. For example:
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.. ipython:: python
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.. code-block:: python
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@pd.api.extensions.register_dataframe_accessor("geo")
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class GeoAccessor(object):

‎doc/source/whatsnew/v0.23.0.txt

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- :func:`Timestamp.replace` will now handle Daylight Savings transitions gracefully (:issue:`18319`)
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- Bug in tz-aware :class:`DatetimeIndex` where addition/subtraction with a :class:`TimedeltaIndex` or array with ``dtype='timedelta64[ns]'`` was incorrect (:issue:`17558`)
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- Bug in :func:`DatetimeIndex.insert` where inserting ``NaT`` into a timezone-aware index incorrectly raised (:issue:`16357`)
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- Bug in the :class:`DataFrame` constructor, where tz-aware Datetimeindex and a given column name will result in an empty ``DataFrame`` (:issue:`19157`)
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Offsets
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^^^^^^^
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- Bug in :func:`DataFrame.to_latex()` where pairs of braces meant to serve as invisible placeholders were escaped (:issue:`18667`)
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- Bug in :func:`read_json` where large numeric values were causing an ``OverflowError`` (:issue:`18842`)
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- Bug in :func:`DataFrame.to_parquet` where an exception was raised if the write destination is S3 (:issue:`19134`)
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- :class:`Interval` now supported in :func:`DataFrame.to_excel` for all Excel file types (:issue:`19242`)
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- :class:`Timedelta` now supported in :func:`DataFrame.to_excel` for xls file type (:issue:`19242`, :issue:`9155`)
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-
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Plotting
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Sparse
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^^^^^^
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-
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- Bug in which creating a ``SparseDataFrame`` from a dense ``Series`` or an unsupported type raised an uncontrolled exception (:issue:`19374`)
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-
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-
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‎pandas/_libs/algos.pyx

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# cython: profile=False
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cimport numpy as np
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import numpy as np
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cimport cython
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from cython cimport Py_ssize_t
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np.import_array()
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cdef float64_t FP_ERR = 1e-13
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cimport util
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from libc.stdlib cimport malloc, free
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from libc.string cimport memmove
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from libc.math cimport fabs, sqrt
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import numpy as np
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cimport numpy as cnp
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from numpy cimport (ndarray,
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NPY_INT64, NPY_UINT64, NPY_INT32, NPY_INT16, NPY_INT8,
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NPY_FLOAT32, NPY_FLOAT64,
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NPY_OBJECT,
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int8_t, int16_t, int32_t, int64_t, uint8_t, uint16_t,
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uint32_t, uint64_t, float32_t, float64_t,
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double_t)
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cnp.import_array()
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cdef double NaN = <double> np.NaN
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cdef double nan = NaN
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from libc.math cimport fabs, sqrt
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# this is our util.pxd
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cimport util
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from util cimport numeric, get_nat
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import missing
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cdef float64_t FP_ERR = 1e-13
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cdef double NaN = <double> np.NaN
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cdef double nan = NaN
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cdef int64_t iNaT = get_nat()
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cdef:

‎pandas/_libs/algos_rank_helper.pxi.in

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@@ -50,7 +50,7 @@ def rank_1d_{{dtype}}(object in_arr, ties_method='average', ascending=True,
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ndarray[float64_t] ranks
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ndarray[int64_t] argsorted
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ndarray[np.uint8_t, cast=True] sorted_mask
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ndarray[uint8_t, cast=True] sorted_mask
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{{if dtype == 'uint64'}}
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{{ctype}} val

‎pandas/_libs/hashtable.pyx

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# cython: profile=False
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from cpython cimport PyObject, Py_INCREF, PyList_Check, PyTuple_Check
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cimport cython
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from cpython cimport (PyObject, Py_INCREF, PyList_Check, PyTuple_Check,
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PyMem_Malloc, PyMem_Realloc, PyMem_Free,
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PyString_Check, PyBytes_Check,
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PyUnicode_Check)
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from libc.stdlib cimport malloc, free
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import numpy as np
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cimport numpy as cnp
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from numpy cimport ndarray, uint8_t, uint32_t
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cnp.import_array()
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cdef extern from "numpy/npy_math.h":
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double NAN "NPY_NAN"
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from khash cimport (
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khiter_t,
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kh_put_pymap, kh_resize_pymap)
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from numpy cimport ndarray, uint8_t, uint32_t
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from libc.stdlib cimport malloc, free
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from cpython cimport (PyMem_Malloc, PyMem_Realloc, PyMem_Free,
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PyString_Check, PyBytes_Check,
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PyUnicode_Check)
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from util cimport _checknan
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cimport util
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import numpy as np
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nan = np.nan
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cdef extern from "numpy/npy_math.h":
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double NAN "NPY_NAN"
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cimport cython
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cimport numpy as cnp
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from missing cimport checknull
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cnp.import_array()
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cnp.import_ufunc()
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nan = np.nan
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cdef int64_t iNaT = util.get_nat()
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_SIZE_HINT_LIMIT = (1 << 20) + 7

‎pandas/_libs/index.pyx

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# cython: profile=False
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from datetime import datetime, timedelta, date
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from numpy cimport (ndarray, float64_t, int32_t, int64_t, uint8_t, uint64_t,
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NPY_DATETIME, NPY_TIMEDELTA)
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cimport cython
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cimport numpy as cnp
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from cpython cimport PyTuple_Check, PyList_Check
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from cpython.slice cimport PySlice_Check
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import numpy as np
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cimport numpy as cnp
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from numpy cimport ndarray, float64_t, int32_t, int64_t, uint8_t, uint64_t
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cnp.import_array()
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cnp.import_ufunc()
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cimport util
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cdef extern from "numpy/arrayobject.h":
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# These can be cimported directly from numpy in cython>=0.27.3
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cdef enum NPY_TYPES:
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NPY_DATETIME
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NPY_TIMEDELTA
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import numpy as np
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cimport util
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from tslibs.conversion cimport maybe_datetimelike_to_i8
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from pandas._libs import algos, hashtable as _hash
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from pandas._libs.tslibs import period as periodlib
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from pandas._libs.tslib import Timestamp, Timedelta
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from datetime import datetime, timedelta, date
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from cpython cimport PyTuple_Check, PyList_Check
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from cpython.slice cimport PySlice_Check
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cdef int64_t iNaT = util.get_nat()
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‎pandas/_libs/internals.pyx

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Py_ssize_t PY_SSIZE_T_MAX
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import numpy as np
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cimport numpy as np
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from numpy cimport int64_t
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cdef extern from "compat_helper.h":

‎pandas/_libs/interval.pyx

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cimport numpy as np
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cimport numpy as cnp
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import numpy as np
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cimport util

‎pandas/_libs/join.pyx

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# cython: profile=False
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cimport numpy as np
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import numpy as np
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cimport cython
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from cython cimport Py_ssize_t
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np.import_array()
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import numpy as np
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cimport numpy as cnp
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from numpy cimport (ndarray,
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int8_t, int16_t, int32_t, int64_t, uint8_t, uint16_t,
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uint32_t, uint64_t, float32_t, float64_t)
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cnp.import_array()
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cdef double NaN = <double> np.NaN
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cdef double nan = NaN

‎pandas/_libs/lib.pyx

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from cython cimport Py_ssize_t
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import numpy as np
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cimport numpy as np
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cimport numpy as cnp
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from numpy cimport (ndarray, PyArray_NDIM, PyArray_GETITEM,
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PyArray_ITER_DATA, PyArray_ITER_NEXT, PyArray_IterNew,
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flatiter, NPY_OBJECT,
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int64_t,
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float32_t, float64_t,
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uint8_t, uint64_t,
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complex128_t)
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# initialize numpy
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np.import_array()
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np.import_ufunc()
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cnp.import_array()
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from cpython cimport (Py_INCREF, PyTuple_SET_ITEM,
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"""
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return (np.PyArray_IsAnyScalar(val)
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return (cnp.PyArray_IsAnyScalar(val)
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# As of numpy-1.9, PyArray_IsAnyScalar misses bytearrays on Py3.
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or PyBytes_Check(val)
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# We differ from numpy (as of 1.10), which claims that None is
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for i in range(n):
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v = obj[i]
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if not (PyList_Check(v) or np.PyArray_Check(v) or hasattr(v, '_data')):
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if not (PyList_Check(v) or util.is_array(v) or hasattr(v, '_data')):
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all_arrays = 0
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break
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‎pandas/_libs/missing.pyx

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from cython cimport Py_ssize_t
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import numpy as np
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cimport numpy as np
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cimport numpy as cnp
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np.import_array()
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cnp.import_array()
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cimport util
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‎pandas/_libs/reduction.pyx

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import numpy as np
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cimport numpy as np
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cimport numpy as cnp
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from numpy cimport (ndarray,
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int64_t,
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PyArray_SETITEM,
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PyArray_ITER_NEXT, PyArray_ITER_DATA, PyArray_IterNew,
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flatiter)
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np.import_array()
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cimport util
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from lib import maybe_convert_objects

‎pandas/_libs/reshape.pyx

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# cython: profile=False
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cimport numpy as np
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import numpy as np
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cimport cython
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np.import_array()
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import numpy as np
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cimport numpy as cnp
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from numpy cimport (ndarray,
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int8_t, int16_t, int32_t, int64_t, uint8_t, uint16_t,
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uint32_t, uint64_t, float32_t, float64_t)
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cnp.import_array()
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cdef double NaN = <double> np.NaN
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cdef double nan = NaN

‎pandas/_libs/skiplist.pyx

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from libc.math cimport log
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import numpy as np
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cimport numpy as cnp
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from numpy cimport double_t
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cnp.import_array()
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cdef double Log2(double x):
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return log(x) / log(2.)
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cimport numpy as np
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import numpy as np
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from numpy cimport double_t
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from random import random
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# initialize numpy
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np.import_array()
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cdef class Node:

‎pandas/_libs/sparse.pyx

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from numpy cimport (ndarray, uint8_t, int64_t, int32_t, int16_t, int8_t,
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float64_t, float32_t)
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cimport numpy as np
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# -*- coding: utf-8 -*-
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import operator
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import sys
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cimport cython
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import numpy as np
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import operator
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import sys
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cimport numpy as cnp
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from numpy cimport (ndarray, uint8_t, int64_t, int32_t, int16_t, int8_t,
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float64_t, float32_t)
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cnp.import_array()
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from distutils.version import LooseVersion
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_np_version_under1p10 = LooseVersion(_np_version) < LooseVersion('1.10')
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_np_version_under1p11 = LooseVersion(_np_version) < LooseVersion('1.11')
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np.import_array()
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np.import_ufunc()
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# -----------------------------------------------------------------------------
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# Preamble stuff

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