You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
422 lines
13 KiB
422 lines
13 KiB
"""
|
|
Module responsible for execution of NDFrame.describe() method.
|
|
|
|
Method NDFrame.describe() delegates actual execution to function describe_ndframe().
|
|
"""
|
|
from __future__ import annotations
|
|
|
|
from abc import (
|
|
ABC,
|
|
abstractmethod,
|
|
)
|
|
from typing import (
|
|
TYPE_CHECKING,
|
|
Any,
|
|
Callable,
|
|
Hashable,
|
|
Sequence,
|
|
cast,
|
|
)
|
|
import warnings
|
|
|
|
import numpy as np
|
|
|
|
from pandas._libs.tslibs import Timestamp
|
|
from pandas._typing import NDFrameT
|
|
from pandas.util._exceptions import find_stack_level
|
|
from pandas.util._validators import validate_percentile
|
|
|
|
from pandas.core.dtypes.common import (
|
|
is_bool_dtype,
|
|
is_datetime64_any_dtype,
|
|
is_numeric_dtype,
|
|
is_timedelta64_dtype,
|
|
)
|
|
|
|
from pandas.core.reshape.concat import concat
|
|
|
|
from pandas.io.formats.format import format_percentiles
|
|
|
|
if TYPE_CHECKING:
|
|
from pandas import (
|
|
DataFrame,
|
|
Series,
|
|
)
|
|
|
|
|
|
def describe_ndframe(
|
|
*,
|
|
obj: NDFrameT,
|
|
include: str | Sequence[str] | None,
|
|
exclude: str | Sequence[str] | None,
|
|
datetime_is_numeric: bool,
|
|
percentiles: Sequence[float] | np.ndarray | None,
|
|
) -> NDFrameT:
|
|
"""Describe series or dataframe.
|
|
|
|
Called from pandas.core.generic.NDFrame.describe()
|
|
|
|
Parameters
|
|
----------
|
|
obj: DataFrame or Series
|
|
Either dataframe or series to be described.
|
|
include : 'all', list-like of dtypes or None (default), optional
|
|
A white list of data types to include in the result. Ignored for ``Series``.
|
|
exclude : list-like of dtypes or None (default), optional,
|
|
A black list of data types to omit from the result. Ignored for ``Series``.
|
|
datetime_is_numeric : bool, default False
|
|
Whether to treat datetime dtypes as numeric.
|
|
percentiles : list-like of numbers, optional
|
|
The percentiles to include in the output. All should fall between 0 and 1.
|
|
The default is ``[.25, .5, .75]``, which returns the 25th, 50th, and
|
|
75th percentiles.
|
|
|
|
Returns
|
|
-------
|
|
Dataframe or series description.
|
|
"""
|
|
percentiles = refine_percentiles(percentiles)
|
|
|
|
describer: NDFrameDescriberAbstract
|
|
if obj.ndim == 1:
|
|
describer = SeriesDescriber(
|
|
obj=cast("Series", obj),
|
|
datetime_is_numeric=datetime_is_numeric,
|
|
)
|
|
else:
|
|
describer = DataFrameDescriber(
|
|
obj=cast("DataFrame", obj),
|
|
include=include,
|
|
exclude=exclude,
|
|
datetime_is_numeric=datetime_is_numeric,
|
|
)
|
|
|
|
result = describer.describe(percentiles=percentiles)
|
|
return cast(NDFrameT, result)
|
|
|
|
|
|
class NDFrameDescriberAbstract(ABC):
|
|
"""Abstract class for describing dataframe or series.
|
|
|
|
Parameters
|
|
----------
|
|
obj : Series or DataFrame
|
|
Object to be described.
|
|
datetime_is_numeric : bool
|
|
Whether to treat datetime dtypes as numeric.
|
|
"""
|
|
|
|
def __init__(self, obj: DataFrame | Series, datetime_is_numeric: bool):
|
|
self.obj = obj
|
|
self.datetime_is_numeric = datetime_is_numeric
|
|
|
|
@abstractmethod
|
|
def describe(self, percentiles: Sequence[float] | np.ndarray) -> DataFrame | Series:
|
|
"""Do describe either series or dataframe.
|
|
|
|
Parameters
|
|
----------
|
|
percentiles : list-like of numbers
|
|
The percentiles to include in the output.
|
|
"""
|
|
|
|
|
|
class SeriesDescriber(NDFrameDescriberAbstract):
|
|
"""Class responsible for creating series description."""
|
|
|
|
obj: Series
|
|
|
|
def describe(self, percentiles: Sequence[float] | np.ndarray) -> Series:
|
|
describe_func = select_describe_func(
|
|
self.obj,
|
|
self.datetime_is_numeric,
|
|
)
|
|
return describe_func(self.obj, percentiles)
|
|
|
|
|
|
class DataFrameDescriber(NDFrameDescriberAbstract):
|
|
"""Class responsible for creating dataobj description.
|
|
|
|
Parameters
|
|
----------
|
|
obj : DataFrame
|
|
DataFrame to be described.
|
|
include : 'all', list-like of dtypes or None
|
|
A white list of data types to include in the result.
|
|
exclude : list-like of dtypes or None
|
|
A black list of data types to omit from the result.
|
|
datetime_is_numeric : bool
|
|
Whether to treat datetime dtypes as numeric.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
obj: DataFrame,
|
|
*,
|
|
include: str | Sequence[str] | None,
|
|
exclude: str | Sequence[str] | None,
|
|
datetime_is_numeric: bool,
|
|
):
|
|
self.include = include
|
|
self.exclude = exclude
|
|
|
|
if obj.ndim == 2 and obj.columns.size == 0:
|
|
raise ValueError("Cannot describe a DataFrame without columns")
|
|
|
|
super().__init__(obj, datetime_is_numeric=datetime_is_numeric)
|
|
|
|
def describe(self, percentiles: Sequence[float] | np.ndarray) -> DataFrame:
|
|
data = self._select_data()
|
|
|
|
ldesc: list[Series] = []
|
|
for _, series in data.items():
|
|
describe_func = select_describe_func(series, self.datetime_is_numeric)
|
|
ldesc.append(describe_func(series, percentiles))
|
|
|
|
col_names = reorder_columns(ldesc)
|
|
d = concat(
|
|
[x.reindex(col_names, copy=False) for x in ldesc],
|
|
axis=1,
|
|
sort=False,
|
|
)
|
|
d.columns = data.columns.copy()
|
|
return d
|
|
|
|
def _select_data(self):
|
|
"""Select columns to be described."""
|
|
if (self.include is None) and (self.exclude is None):
|
|
# when some numerics are found, keep only numerics
|
|
default_include = [np.number]
|
|
if self.datetime_is_numeric:
|
|
# error: Argument 1 to "append" of "list" has incompatible type "str";
|
|
# expected "Type[number[Any]]"
|
|
default_include.append("datetime") # type: ignore[arg-type]
|
|
data = self.obj.select_dtypes(include=default_include)
|
|
if len(data.columns) == 0:
|
|
data = self.obj
|
|
elif self.include == "all":
|
|
if self.exclude is not None:
|
|
msg = "exclude must be None when include is 'all'"
|
|
raise ValueError(msg)
|
|
data = self.obj
|
|
else:
|
|
data = self.obj.select_dtypes(
|
|
include=self.include,
|
|
exclude=self.exclude,
|
|
)
|
|
return data
|
|
|
|
|
|
def reorder_columns(ldesc: Sequence[Series]) -> list[Hashable]:
|
|
"""Set a convenient order for rows for display."""
|
|
names: list[Hashable] = []
|
|
ldesc_indexes = sorted((x.index for x in ldesc), key=len)
|
|
for idxnames in ldesc_indexes:
|
|
for name in idxnames:
|
|
if name not in names:
|
|
names.append(name)
|
|
return names
|
|
|
|
|
|
def describe_numeric_1d(series: Series, percentiles: Sequence[float]) -> Series:
|
|
"""Describe series containing numerical data.
|
|
|
|
Parameters
|
|
----------
|
|
series : Series
|
|
Series to be described.
|
|
percentiles : list-like of numbers
|
|
The percentiles to include in the output.
|
|
"""
|
|
from pandas import Series
|
|
|
|
# error: Argument 1 to "format_percentiles" has incompatible type "Sequence[float]";
|
|
# expected "Union[ndarray, List[Union[int, float]], List[float], List[Union[str,
|
|
# float]]]"
|
|
formatted_percentiles = format_percentiles(percentiles) # type: ignore[arg-type]
|
|
|
|
stat_index = ["count", "mean", "std", "min"] + formatted_percentiles + ["max"]
|
|
d = (
|
|
[series.count(), series.mean(), series.std(), series.min()]
|
|
+ series.quantile(percentiles).tolist()
|
|
+ [series.max()]
|
|
)
|
|
return Series(d, index=stat_index, name=series.name)
|
|
|
|
|
|
def describe_categorical_1d(
|
|
data: Series,
|
|
percentiles_ignored: Sequence[float],
|
|
) -> Series:
|
|
"""Describe series containing categorical data.
|
|
|
|
Parameters
|
|
----------
|
|
data : Series
|
|
Series to be described.
|
|
percentiles_ignored : list-like of numbers
|
|
Ignored, but in place to unify interface.
|
|
"""
|
|
names = ["count", "unique", "top", "freq"]
|
|
objcounts = data.value_counts()
|
|
count_unique = len(objcounts[objcounts != 0])
|
|
if count_unique > 0:
|
|
top, freq = objcounts.index[0], objcounts.iloc[0]
|
|
dtype = None
|
|
else:
|
|
# If the DataFrame is empty, set 'top' and 'freq' to None
|
|
# to maintain output shape consistency
|
|
top, freq = np.nan, np.nan
|
|
dtype = "object"
|
|
|
|
result = [data.count(), count_unique, top, freq]
|
|
|
|
from pandas import Series
|
|
|
|
return Series(result, index=names, name=data.name, dtype=dtype)
|
|
|
|
|
|
def describe_timestamp_as_categorical_1d(
|
|
data: Series,
|
|
percentiles_ignored: Sequence[float],
|
|
) -> Series:
|
|
"""Describe series containing timestamp data treated as categorical.
|
|
|
|
Parameters
|
|
----------
|
|
data : Series
|
|
Series to be described.
|
|
percentiles_ignored : list-like of numbers
|
|
Ignored, but in place to unify interface.
|
|
"""
|
|
names = ["count", "unique"]
|
|
objcounts = data.value_counts()
|
|
count_unique = len(objcounts[objcounts != 0])
|
|
result = [data.count(), count_unique]
|
|
dtype = None
|
|
if count_unique > 0:
|
|
top, freq = objcounts.index[0], objcounts.iloc[0]
|
|
tz = data.dt.tz
|
|
asint = data.dropna().values.view("i8")
|
|
top = Timestamp(top)
|
|
if top.tzinfo is not None and tz is not None:
|
|
# Don't tz_localize(None) if key is already tz-aware
|
|
top = top.tz_convert(tz)
|
|
else:
|
|
top = top.tz_localize(tz)
|
|
names += ["top", "freq", "first", "last"]
|
|
result += [
|
|
top,
|
|
freq,
|
|
Timestamp(asint.min(), tz=tz),
|
|
Timestamp(asint.max(), tz=tz),
|
|
]
|
|
|
|
# If the DataFrame is empty, set 'top' and 'freq' to None
|
|
# to maintain output shape consistency
|
|
else:
|
|
names += ["top", "freq"]
|
|
result += [np.nan, np.nan]
|
|
dtype = "object"
|
|
|
|
from pandas import Series
|
|
|
|
return Series(result, index=names, name=data.name, dtype=dtype)
|
|
|
|
|
|
def describe_timestamp_1d(data: Series, percentiles: Sequence[float]) -> Series:
|
|
"""Describe series containing datetime64 dtype.
|
|
|
|
Parameters
|
|
----------
|
|
data : Series
|
|
Series to be described.
|
|
percentiles : list-like of numbers
|
|
The percentiles to include in the output.
|
|
"""
|
|
# GH-30164
|
|
from pandas import Series
|
|
|
|
# error: Argument 1 to "format_percentiles" has incompatible type "Sequence[float]";
|
|
# expected "Union[ndarray, List[Union[int, float]], List[float], List[Union[str,
|
|
# float]]]"
|
|
formatted_percentiles = format_percentiles(percentiles) # type: ignore[arg-type]
|
|
|
|
stat_index = ["count", "mean", "min"] + formatted_percentiles + ["max"]
|
|
d = (
|
|
[data.count(), data.mean(), data.min()]
|
|
+ data.quantile(percentiles).tolist()
|
|
+ [data.max()]
|
|
)
|
|
return Series(d, index=stat_index, name=data.name)
|
|
|
|
|
|
def select_describe_func(
|
|
data: Series,
|
|
datetime_is_numeric: bool,
|
|
) -> Callable:
|
|
"""Select proper function for describing series based on data type.
|
|
|
|
Parameters
|
|
----------
|
|
data : Series
|
|
Series to be described.
|
|
datetime_is_numeric : bool
|
|
Whether to treat datetime dtypes as numeric.
|
|
"""
|
|
if is_bool_dtype(data.dtype):
|
|
return describe_categorical_1d
|
|
elif is_numeric_dtype(data):
|
|
return describe_numeric_1d
|
|
elif is_datetime64_any_dtype(data.dtype):
|
|
if datetime_is_numeric:
|
|
return describe_timestamp_1d
|
|
else:
|
|
warnings.warn(
|
|
"Treating datetime data as categorical rather than numeric in "
|
|
"`.describe` is deprecated and will be removed in a future "
|
|
"version of pandas. Specify `datetime_is_numeric=True` to "
|
|
"silence this warning and adopt the future behavior now.",
|
|
FutureWarning,
|
|
stacklevel=find_stack_level(),
|
|
)
|
|
return describe_timestamp_as_categorical_1d
|
|
elif is_timedelta64_dtype(data.dtype):
|
|
return describe_numeric_1d
|
|
else:
|
|
return describe_categorical_1d
|
|
|
|
|
|
def refine_percentiles(
|
|
percentiles: Sequence[float] | np.ndarray | None,
|
|
) -> np.ndarray[Any, np.dtype[np.float64]]:
|
|
"""
|
|
Ensure that percentiles are unique and sorted.
|
|
|
|
Parameters
|
|
----------
|
|
percentiles : list-like of numbers, optional
|
|
The percentiles to include in the output.
|
|
"""
|
|
if percentiles is None:
|
|
return np.array([0.25, 0.5, 0.75])
|
|
|
|
# explicit conversion of `percentiles` to list
|
|
percentiles = list(percentiles)
|
|
|
|
# get them all to be in [0, 1]
|
|
validate_percentile(percentiles)
|
|
|
|
# median should always be included
|
|
if 0.5 not in percentiles:
|
|
percentiles.append(0.5)
|
|
|
|
percentiles = np.asarray(percentiles)
|
|
|
|
# sort and check for duplicates
|
|
unique_pcts = np.unique(percentiles)
|
|
assert percentiles is not None
|
|
if len(unique_pcts) < len(percentiles):
|
|
raise ValueError("percentiles cannot contain duplicates")
|
|
|
|
return unique_pcts
|
|
|