FM / pandas

/usr/local/lib64/python3.6/site-packages/pandas/

/usr/local/lib64/python3.6/site-packages/pandas

Name	Size	Mode	Actions
api/	-	0755	rm
arrays/	-	0755	rm
compat/	-	0755	rm
core/	-	0755	rm
errors/	-	0755	rm
io/	-	0755	rm
plotting/	-	0755	rm
tests/	-	0755	rm
tseries/	-	0755	rm
util/	-	0755	rm
_config/	-	0755	rm
_libs/	-	0755	rm
__pycache__/	-	0755	rm
conftest.py	31345	0644	edit dl rm
testing.py	312	0644	edit dl rm
_testing.py	90736	0644	edit dl rm
_typing.py	3745	0644	edit dl rm
_version.py	545	0644	edit dl rm
__init__.py	10637	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/pandas/__init__.py (10637B)

# flake8: noqa

__docformat__ = "restructuredtext"

# Let users know if they're missing any of our hard dependencies
hard_dependencies = ("numpy", "pytz", "dateutil")
missing_dependencies = []

for dependency in hard_dependencies:
    try:
        __import__(dependency)
    except ImportError as e:
        missing_dependencies.append(f"{dependency}: {e}")

if missing_dependencies:
    raise ImportError(
        "Unable to import required dependencies:\n" + "\n".join(missing_dependencies)
    )
del hard_dependencies, dependency, missing_dependencies

# numpy compat
from pandas.compat.numpy import (
    _np_version_under1p16,
    _np_version_under1p17,
    _np_version_under1p18,
    _is_numpy_dev,
)

try:
    from pandas._libs import hashtable as _hashtable, lib as _lib, tslib as _tslib
except ImportError as e:  # pragma: no cover
    # hack but overkill to use re
    module = str(e).replace("cannot import name ", "")
    raise ImportError(
        f"C extension: {module} not built. If you want to import "
        "pandas from the source directory, you may need to run "
        "'python setup.py build_ext --inplace --force' to build the C extensions first."
    ) from e

from pandas._config import (
    get_option,
    set_option,
    reset_option,
    describe_option,
    option_context,
    options,
)

# let init-time option registration happen
import pandas.core.config_init

from pandas.core.api import (
    # dtype
    Int8Dtype,
    Int16Dtype,
    Int32Dtype,
    Int64Dtype,
    UInt8Dtype,
    UInt16Dtype,
    UInt32Dtype,
    UInt64Dtype,
    CategoricalDtype,
    PeriodDtype,
    IntervalDtype,
    DatetimeTZDtype,
    StringDtype,
    BooleanDtype,
    # missing
    NA,
    isna,
    isnull,
    notna,
    notnull,
    # indexes
    Index,
    CategoricalIndex,
    Int64Index,
    UInt64Index,
    RangeIndex,
    Float64Index,
    MultiIndex,
    IntervalIndex,
    TimedeltaIndex,
    DatetimeIndex,
    PeriodIndex,
    IndexSlice,
    # tseries
    NaT,
    Period,
    period_range,
    Timedelta,
    timedelta_range,
    Timestamp,
    date_range,
    bdate_range,
    Interval,
    interval_range,
    DateOffset,
    # conversion
    to_numeric,
    to_datetime,
    to_timedelta,
    # misc
    Grouper,
    factorize,
    unique,
    value_counts,
    NamedAgg,
    array,
    Categorical,
    set_eng_float_format,
    Series,
    DataFrame,
)

from pandas.core.arrays.sparse import SparseDtype

from pandas.tseries.api import infer_freq
from pandas.tseries import offsets

from pandas.core.computation.api import eval

from pandas.core.reshape.api import (
    concat,
    lreshape,
    melt,
    wide_to_long,
    merge,
    merge_asof,
    merge_ordered,
    crosstab,
    pivot,
    pivot_table,
    get_dummies,
    cut,
    qcut,
)

import pandas.api
from pandas.util._print_versions import show_versions

from pandas.io.api import (
    # excel
    ExcelFile,
    ExcelWriter,
    read_excel,
    # parsers
    read_csv,
    read_fwf,
    read_table,
    # pickle
    read_pickle,
    to_pickle,
    # pytables
    HDFStore,
    read_hdf,
    # sql
    read_sql,
    read_sql_query,
    read_sql_table,
    # misc
    read_clipboard,
    read_parquet,
    read_orc,
    read_feather,
    read_gbq,
    read_html,
    read_json,
    read_stata,
    read_sas,
    read_spss,
)

from pandas.io.json import _json_normalize as json_normalize

from pandas.util._tester import test
import pandas.testing
import pandas.arrays

# use the closest tagged version if possible
from ._version import get_versions

v = get_versions()
__version__ = v.get("closest-tag", v["version"])
__git_version__ = v.get("full-revisionid")
del get_versions, v

# GH 27101
# TODO: remove Panel compat in 1.0
if pandas.compat.PY37:

def __getattr__(name):
        import warnings

if name == "Panel":

warnings.warn(
                "The Panel class is removed from pandas. Accessing it "
                "from the top-level namespace will also be removed in the next version",
                FutureWarning,
                stacklevel=2,
            )

class Panel:
                pass

return Panel

elif name == "datetime":
            warnings.warn(
                "The pandas.datetime class is deprecated "
                "and will be removed from pandas in a future version. "
                "Import from datetime module instead.",
                FutureWarning,
                stacklevel=2,
            )

from datetime import datetime as dt

return dt

elif name == "np":

warnings.warn(
                "The pandas.np module is deprecated "
                "and will be removed from pandas in a future version. "
                "Import numpy directly instead",
                FutureWarning,
                stacklevel=2,
            )
            import numpy as np

return np

elif name in {"SparseSeries", "SparseDataFrame"}:
            warnings.warn(
                f"The {name} class is removed from pandas. Accessing it from "
                "the top-level namespace will also be removed in the next version",
                FutureWarning,
                stacklevel=2,
            )

return type(name, (), {})

elif name == "SparseArray":

warnings.warn(
                "The pandas.SparseArray class is deprecated "
                "and will be removed from pandas in a future version. "
                "Use pandas.arrays.SparseArray instead.",
                FutureWarning,
                stacklevel=2,
            )
            from pandas.core.arrays.sparse import SparseArray as _SparseArray

return _SparseArray

raise AttributeError(f"module 'pandas' has no attribute '{name}'")

else:

class Panel:
        pass

class SparseDataFrame:
        pass

class SparseSeries:
        pass

class __numpy:
        def __init__(self):
            import numpy as np
            import warnings

self.np = np
            self.warnings = warnings

def __getattr__(self, item):
            self.warnings.warn(
                "The pandas.np module is deprecated "
                "and will be removed from pandas in a future version. "
                "Import numpy directly instead",
                FutureWarning,
                stacklevel=2,
            )

try:
                return getattr(self.np, item)
            except AttributeError as err:
                raise AttributeError(f"module numpy has no attribute {item}") from err

np = __numpy()

class __Datetime(type):

from datetime import datetime as dt

datetime = dt

def __getattr__(cls, item):
            cls.emit_warning()

try:
                return getattr(cls.datetime, item)
            except AttributeError as err:
                raise AttributeError(
                    f"module datetime has no attribute {item}"
                ) from err

def __instancecheck__(cls, other):
            return isinstance(other, cls.datetime)

class __DatetimeSub(metaclass=__Datetime):
        def emit_warning(dummy=0):
            import warnings

warnings.warn(
                "The pandas.datetime class is deprecated "
                "and will be removed from pandas in a future version. "
                "Import from datetime instead.",
                FutureWarning,
                stacklevel=3,
            )

def __new__(cls, *args, **kwargs):
            cls.emit_warning()
            from datetime import datetime as dt

return dt(*args, **kwargs)

datetime = __DatetimeSub

class __SparseArray(type):

from pandas.core.arrays.sparse import SparseArray as sa

SparseArray = sa

def __instancecheck__(cls, other):
            return isinstance(other, cls.SparseArray)

class __SparseArraySub(metaclass=__SparseArray):
        def emit_warning(dummy=0):
            import warnings

def __new__(cls, *args, **kwargs):
            cls.emit_warning()
            from pandas.core.arrays.sparse import SparseArray as sa

return sa(*args, **kwargs)

SparseArray = __SparseArraySub

# module level doc-string
__doc__ = """
pandas - a powerful data analysis and manipulation library for Python
=====================================================================

**pandas** is a Python package providing fast, flexible, and expressive data
structures designed to make working with "relational" or "labeled" data both
easy and intuitive. It aims to be the fundamental high-level building block for
doing practical, **real world** data analysis in Python. Additionally, it has
the broader goal of becoming **the most powerful and flexible open source data
analysis / manipulation tool available in any language**. It is already well on
its way toward this goal.

Main Features
-------------
Here are just a few of the things that pandas does well:

- Easy handling of missing data in floating point as well as non-floating
    point data.
  - Size mutability: columns can be inserted and deleted from DataFrame and
    higher dimensional objects
  - Automatic and explicit data alignment: objects can be explicitly aligned
    to a set of labels, or the user can simply ignore the labels and let
    `Series`, `DataFrame`, etc. automatically align the data for you in
    computations.
  - Powerful, flexible group by functionality to perform split-apply-combine
    operations on data sets, for both aggregating and transforming data.
  - Make it easy to convert ragged, differently-indexed data in other Python
    and NumPy data structures into DataFrame objects.
  - Intelligent label-based slicing, fancy indexing, and subsetting of large
    data sets.
  - Intuitive merging and joining data sets.
  - Flexible reshaping and pivoting of data sets.
  - Hierarchical labeling of axes (possible to have multiple labels per tick).
  - Robust IO tools for loading data from flat files (CSV and delimited),
    Excel files, databases, and saving/loading data from the ultrafast HDF5
    format.
  - Time series-specific functionality: date range generation and frequency
    conversion, moving window statistics, date shifting and lagging.
"""

cmd: