mirror of
https://github.com/PiBrewing/craftbeerpi4.git
synced 2024-09-20 06:17:29 +02:00
404 lines
10 KiB
Python
404 lines
10 KiB
Python
# 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
|
|
|
|
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=3,
|
|
)
|
|
|
|
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.
|
|
"""
|