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craftbeerpi4-pione/venv3/lib/python3.7/site-packages/pandas/tests/frame/test_analytics.py

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"fix cli main_dir error"
2021-03-03 23:49:41 +01:00
from datetime import timedelta
from decimal import Decimal
import operator
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import (
Categorical,
DataFrame,
MultiIndex,
Series,
Timestamp,
date_range,
isna,
notna,
to_datetime,
to_timedelta,
)
import pandas._testing as tm
import pandas.core.algorithms as algorithms
import pandas.core.nanops as nanops
def assert_stat_op_calc(
opname,
alternative,
frame,
has_skipna=True,
check_dtype=True,
check_dates=False,
rtol=1e-5,
atol=1e-8,
skipna_alternative=None,
):
"""
Check that operator opname works as advertised on frame
Parameters
----------
opname : string
Name of the operator to test on frame
alternative : function
Function that opname is tested against; i.e. "frame.opname()" should
equal "alternative(frame)".
frame : DataFrame
The object that the tests are executed on
has_skipna : bool, default True
Whether the method "opname" has the kwarg "skip_na"
check_dtype : bool, default True
Whether the dtypes of the result of "frame.opname()" and
"alternative(frame)" should be checked.
check_dates : bool, default false
Whether opname should be tested on a Datetime Series
rtol : float, default 1e-5
Relative tolerance.
atol : float, default 1e-8
Absolute tolerance.
skipna_alternative : function, default None
NaN-safe version of alternative
"""
f = getattr(frame, opname)
if check_dates:
expected_warning = FutureWarning if opname in ["mean", "median"] else None
df = DataFrame({"b": date_range("1/1/2001", periods=2)})
with tm.assert_produces_warning(expected_warning):
result = getattr(df, opname)()
assert isinstance(result, Series)
df["a"] = range(len(df))
with tm.assert_produces_warning(expected_warning):
result = getattr(df, opname)()
assert isinstance(result, Series)
assert len(result)
if has_skipna:
def wrapper(x):
return alternative(x.values)
skipna_wrapper = tm._make_skipna_wrapper(alternative, skipna_alternative)
result0 = f(axis=0, skipna=False)
result1 = f(axis=1, skipna=False)
tm.assert_series_equal(
result0,
frame.apply(wrapper),
check_dtype=check_dtype,
rtol=rtol,
atol=atol,
)
# HACK: win32
tm.assert_series_equal(
result1,
frame.apply(wrapper, axis=1),
check_dtype=False,
rtol=rtol,
atol=atol,
)
else:
skipna_wrapper = alternative
result0 = f(axis=0)
result1 = f(axis=1)
tm.assert_series_equal(
result0,
frame.apply(skipna_wrapper),
check_dtype=check_dtype,
rtol=rtol,
atol=atol,
)
if opname in ["sum", "prod"]:
expected = frame.apply(skipna_wrapper, axis=1)
tm.assert_series_equal(
result1, expected, check_dtype=False, rtol=rtol, atol=atol,
)
# check dtypes
if check_dtype:
lcd_dtype = frame.values.dtype
assert lcd_dtype == result0.dtype
assert lcd_dtype == result1.dtype
# bad axis
with pytest.raises(ValueError, match="No axis named 2"):
f(axis=2)
# all NA case
if has_skipna:
all_na = frame * np.NaN
r0 = getattr(all_na, opname)(axis=0)
r1 = getattr(all_na, opname)(axis=1)
if opname in ["sum", "prod"]:
unit = 1 if opname == "prod" else 0 # result for empty sum/prod
expected = pd.Series(unit, index=r0.index, dtype=r0.dtype)
tm.assert_series_equal(r0, expected)
expected = pd.Series(unit, index=r1.index, dtype=r1.dtype)
tm.assert_series_equal(r1, expected)
def assert_stat_op_api(opname, float_frame, float_string_frame, has_numeric_only=False):
"""
Check that API for operator opname works as advertised on frame
Parameters
----------
opname : string
Name of the operator to test on frame
float_frame : DataFrame
DataFrame with columns of type float
float_string_frame : DataFrame
DataFrame with both float and string columns
has_numeric_only : bool, default False
Whether the method "opname" has the kwarg "numeric_only"
"""
# make sure works on mixed-type frame
getattr(float_string_frame, opname)(axis=0)
getattr(float_string_frame, opname)(axis=1)
if has_numeric_only:
getattr(float_string_frame, opname)(axis=0, numeric_only=True)
getattr(float_string_frame, opname)(axis=1, numeric_only=True)
getattr(float_frame, opname)(axis=0, numeric_only=False)
getattr(float_frame, opname)(axis=1, numeric_only=False)
def assert_bool_op_calc(opname, alternative, frame, has_skipna=True):
"""
Check that bool operator opname works as advertised on frame
Parameters
----------
opname : string
Name of the operator to test on frame
alternative : function
Function that opname is tested against; i.e. "frame.opname()" should
equal "alternative(frame)".
frame : DataFrame
The object that the tests are executed on
has_skipna : bool, default True
Whether the method "opname" has the kwarg "skip_na"
"""
f = getattr(frame, opname)
if has_skipna:
def skipna_wrapper(x):
nona = x.dropna().values
return alternative(nona)
def wrapper(x):
return alternative(x.values)
result0 = f(axis=0, skipna=False)
result1 = f(axis=1, skipna=False)
tm.assert_series_equal(result0, frame.apply(wrapper))
tm.assert_series_equal(
result1, frame.apply(wrapper, axis=1), check_dtype=False
) # HACK: win32
else:
skipna_wrapper = alternative
wrapper = alternative
result0 = f(axis=0)
result1 = f(axis=1)
tm.assert_series_equal(result0, frame.apply(skipna_wrapper))
tm.assert_series_equal(
result1, frame.apply(skipna_wrapper, axis=1), check_dtype=False
)
# bad axis
with pytest.raises(ValueError, match="No axis named 2"):
f(axis=2)
# all NA case
if has_skipna:
all_na = frame * np.NaN
r0 = getattr(all_na, opname)(axis=0)
r1 = getattr(all_na, opname)(axis=1)
if opname == "any":
assert not r0.any()
assert not r1.any()
else:
assert r0.all()
assert r1.all()
def assert_bool_op_api(
opname, bool_frame_with_na, float_string_frame, has_bool_only=False
):
"""
Check that API for boolean operator opname works as advertised on frame
Parameters
----------
opname : string
Name of the operator to test on frame
float_frame : DataFrame
DataFrame with columns of type float
float_string_frame : DataFrame
DataFrame with both float and string columns
has_bool_only : bool, default False
Whether the method "opname" has the kwarg "bool_only"
"""
# make sure op works on mixed-type frame
mixed = float_string_frame
mixed["_bool_"] = np.random.randn(len(mixed)) > 0.5
getattr(mixed, opname)(axis=0)
getattr(mixed, opname)(axis=1)
if has_bool_only:
getattr(mixed, opname)(axis=0, bool_only=True)
getattr(mixed, opname)(axis=1, bool_only=True)
getattr(bool_frame_with_na, opname)(axis=0, bool_only=False)
getattr(bool_frame_with_na, opname)(axis=1, bool_only=False)
class TestDataFrameAnalytics:
# ---------------------------------------------------------------------
# Reductions
def test_stat_op_api(self, float_frame, float_string_frame):
assert_stat_op_api(
"count", float_frame, float_string_frame, has_numeric_only=True
)
assert_stat_op_api(
"sum", float_frame, float_string_frame, has_numeric_only=True
)
assert_stat_op_api("nunique", float_frame, float_string_frame)
assert_stat_op_api("mean", float_frame, float_string_frame)
assert_stat_op_api("product", float_frame, float_string_frame)
assert_stat_op_api("median", float_frame, float_string_frame)
assert_stat_op_api("min", float_frame, float_string_frame)
assert_stat_op_api("max", float_frame, float_string_frame)
assert_stat_op_api("mad", float_frame, float_string_frame)
assert_stat_op_api("var", float_frame, float_string_frame)
assert_stat_op_api("std", float_frame, float_string_frame)
assert_stat_op_api("sem", float_frame, float_string_frame)
assert_stat_op_api("median", float_frame, float_string_frame)
try:
from scipy.stats import kurtosis, skew # noqa:F401
assert_stat_op_api("skew", float_frame, float_string_frame)
assert_stat_op_api("kurt", float_frame, float_string_frame)
except ImportError:
pass
def test_stat_op_calc(self, float_frame_with_na, mixed_float_frame):
def count(s):
return notna(s).sum()
def nunique(s):
return len(algorithms.unique1d(s.dropna()))
def mad(x):
return np.abs(x - x.mean()).mean()
def var(x):
return np.var(x, ddof=1)
def std(x):
return np.std(x, ddof=1)
def sem(x):
return np.std(x, ddof=1) / np.sqrt(len(x))
def skewness(x):
from scipy.stats import skew # noqa:F811
if len(x) < 3:
return np.nan
return skew(x, bias=False)
def kurt(x):
from scipy.stats import kurtosis # noqa:F811
if len(x) < 4:
return np.nan
return kurtosis(x, bias=False)
assert_stat_op_calc(
"nunique",
nunique,
float_frame_with_na,
has_skipna=False,
check_dtype=False,
check_dates=True,
)
# GH#32571 check_less_precise is needed on apparently-random
# py37-npdev builds and OSX-PY36-min_version builds
# mixed types (with upcasting happening)
assert_stat_op_calc(
"sum",
np.sum,
mixed_float_frame.astype("float32"),
check_dtype=False,
rtol=1e-3,
)
assert_stat_op_calc(
"sum", np.sum, float_frame_with_na, skipna_alternative=np.nansum
)
assert_stat_op_calc("mean", np.mean, float_frame_with_na, check_dates=True)
assert_stat_op_calc(
"product", np.prod, float_frame_with_na, skipna_alternative=np.nanprod
)
assert_stat_op_calc("mad", mad, float_frame_with_na)
assert_stat_op_calc("var", var, float_frame_with_na)
assert_stat_op_calc("std", std, float_frame_with_na)
assert_stat_op_calc("sem", sem, float_frame_with_na)
assert_stat_op_calc(
"count",
count,
float_frame_with_na,
has_skipna=False,
check_dtype=False,
check_dates=True,
)
try:
from scipy import kurtosis, skew # noqa:F401
assert_stat_op_calc("skew", skewness, float_frame_with_na)
assert_stat_op_calc("kurt", kurt, float_frame_with_na)
except ImportError:
pass
# TODO: Ensure warning isn't emitted in the first place
@pytest.mark.filterwarnings("ignore:All-NaN:RuntimeWarning")
def test_median(self, float_frame_with_na, int_frame):
def wrapper(x):
if isna(x).any():
return np.nan
return np.median(x)
assert_stat_op_calc("median", wrapper, float_frame_with_na, check_dates=True)
assert_stat_op_calc(
"median", wrapper, int_frame, check_dtype=False, check_dates=True
)
@pytest.mark.parametrize(
"method", ["sum", "mean", "prod", "var", "std", "skew", "min", "max"]
)
def test_stat_operators_attempt_obj_array(self, method):
# GH#676
data = {
"a": [
-0.00049987540199591344,
-0.0016467257772919831,
0.00067695870775883013,
],
"b": [-0, -0, 0.0],
"c": [
0.00031111847529610595,
0.0014902627951905339,
-0.00094099200035979691,
],
}
df1 = DataFrame(data, index=["foo", "bar", "baz"], dtype="O")
df2 = DataFrame({0: [np.nan, 2], 1: [np.nan, 3], 2: [np.nan, 4]}, dtype=object)
for df in [df1, df2]:
assert df.values.dtype == np.object_
result = getattr(df, method)(1)
expected = getattr(df.astype("f8"), method)(1)
if method in ["sum", "prod"]:
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("op", ["mean", "std", "var", "skew", "kurt", "sem"])
def test_mixed_ops(self, op):
# GH#16116
df = DataFrame(
{
"int": [1, 2, 3, 4],
"float": [1.0, 2.0, 3.0, 4.0],
"str": ["a", "b", "c", "d"],
}
)
result = getattr(df, op)()
assert len(result) == 2
with pd.option_context("use_bottleneck", False):
result = getattr(df, op)()
assert len(result) == 2
def test_reduce_mixed_frame(self):
# GH 6806
df = DataFrame(
{
"bool_data": [True, True, False, False, False],
"int_data": [10, 20, 30, 40, 50],
"string_data": ["a", "b", "c", "d", "e"],
}
)
df.reindex(columns=["bool_data", "int_data", "string_data"])
test = df.sum(axis=0)
tm.assert_numpy_array_equal(
test.values, np.array([2, 150, "abcde"], dtype=object)
)
tm.assert_series_equal(test, df.T.sum(axis=1))
def test_nunique(self):
df = DataFrame({"A": [1, 1, 1], "B": [1, 2, 3], "C": [1, np.nan, 3]})
tm.assert_series_equal(df.nunique(), Series({"A": 1, "B": 3, "C": 2}))
tm.assert_series_equal(
df.nunique(dropna=False), Series({"A": 1, "B": 3, "C": 3})
)
tm.assert_series_equal(df.nunique(axis=1), Series({0: 1, 1: 2, 2: 2}))
tm.assert_series_equal(
df.nunique(axis=1, dropna=False), Series({0: 1, 1: 3, 2: 2})
)
@pytest.mark.parametrize("tz", [None, "UTC"])
def test_mean_mixed_datetime_numeric(self, tz):
# https://github.com/pandas-dev/pandas/issues/24752
df = pd.DataFrame({"A": [1, 1], "B": [pd.Timestamp("2000", tz=tz)] * 2})
with tm.assert_produces_warning(FutureWarning):
result = df.mean()
expected = pd.Series([1.0], index=["A"])
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("tz", [None, "UTC"])
def test_mean_excludes_datetimes(self, tz):
# https://github.com/pandas-dev/pandas/issues/24752
# Our long-term desired behavior is unclear, but the behavior in
# 0.24.0rc1 was buggy.
df = pd.DataFrame({"A": [pd.Timestamp("2000", tz=tz)] * 2})
with tm.assert_produces_warning(FutureWarning):
result = df.mean()
expected = pd.Series(dtype=np.float64)
tm.assert_series_equal(result, expected)
def test_mean_mixed_string_decimal(self):
# GH 11670
# possible bug when calculating mean of DataFrame?
d = [
{"A": 2, "B": None, "C": Decimal("628.00")},
{"A": 1, "B": None, "C": Decimal("383.00")},
{"A": 3, "B": None, "C": Decimal("651.00")},
{"A": 2, "B": None, "C": Decimal("575.00")},
{"A": 4, "B": None, "C": Decimal("1114.00")},
{"A": 1, "B": "TEST", "C": Decimal("241.00")},
{"A": 2, "B": None, "C": Decimal("572.00")},
{"A": 4, "B": None, "C": Decimal("609.00")},
{"A": 3, "B": None, "C": Decimal("820.00")},
{"A": 5, "B": None, "C": Decimal("1223.00")},
]
df = pd.DataFrame(d)
result = df.mean()
expected = pd.Series([2.7, 681.6], index=["A", "C"])
tm.assert_series_equal(result, expected)
def test_var_std(self, datetime_frame):
result = datetime_frame.std(ddof=4)
expected = datetime_frame.apply(lambda x: x.std(ddof=4))
tm.assert_almost_equal(result, expected)
result = datetime_frame.var(ddof=4)
expected = datetime_frame.apply(lambda x: x.var(ddof=4))
tm.assert_almost_equal(result, expected)
arr = np.repeat(np.random.random((1, 1000)), 1000, 0)
result = nanops.nanvar(arr, axis=0)
assert not (result < 0).any()
with pd.option_context("use_bottleneck", False):
result = nanops.nanvar(arr, axis=0)
assert not (result < 0).any()
@pytest.mark.parametrize("meth", ["sem", "var", "std"])
def test_numeric_only_flag(self, meth):
# GH 9201
df1 = DataFrame(np.random.randn(5, 3), columns=["foo", "bar", "baz"])
# set one entry to a number in str format
df1.loc[0, "foo"] = "100"
df2 = DataFrame(np.random.randn(5, 3), columns=["foo", "bar", "baz"])
# set one entry to a non-number str
df2.loc[0, "foo"] = "a"
result = getattr(df1, meth)(axis=1, numeric_only=True)
expected = getattr(df1[["bar", "baz"]], meth)(axis=1)
tm.assert_series_equal(expected, result)
result = getattr(df2, meth)(axis=1, numeric_only=True)
expected = getattr(df2[["bar", "baz"]], meth)(axis=1)
tm.assert_series_equal(expected, result)
# df1 has all numbers, df2 has a letter inside
msg = r"unsupported operand type\(s\) for -: 'float' and 'str'"
with pytest.raises(TypeError, match=msg):
getattr(df1, meth)(axis=1, numeric_only=False)
msg = "could not convert string to float: 'a'"
with pytest.raises(TypeError, match=msg):
getattr(df2, meth)(axis=1, numeric_only=False)
def test_sem(self, datetime_frame):
result = datetime_frame.sem(ddof=4)
expected = datetime_frame.apply(lambda x: x.std(ddof=4) / np.sqrt(len(x)))
tm.assert_almost_equal(result, expected)
arr = np.repeat(np.random.random((1, 1000)), 1000, 0)
result = nanops.nansem(arr, axis=0)
assert not (result < 0).any()
with pd.option_context("use_bottleneck", False):
result = nanops.nansem(arr, axis=0)
assert not (result < 0).any()
@td.skip_if_no_scipy
def test_kurt(self):
index = MultiIndex(
levels=[["bar"], ["one", "two", "three"], [0, 1]],
codes=[[0, 0, 0, 0, 0, 0], [0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]],
)
df = DataFrame(np.random.randn(6, 3), index=index)
kurt = df.kurt()
kurt2 = df.kurt(level=0).xs("bar")
tm.assert_series_equal(kurt, kurt2, check_names=False)
assert kurt.name is None
assert kurt2.name == "bar"
@pytest.mark.parametrize(
"dropna, expected",
[
(
True,
{
"A": [12],
"B": [10.0],
"C": [1.0],
"D": ["a"],
"E": Categorical(["a"], categories=["a"]),
"F": to_datetime(["2000-1-2"]),
"G": to_timedelta(["1 days"]),
},
),
(
False,
{
"A": [12],
"B": [10.0],
"C": [np.nan],
"D": np.array([np.nan], dtype=object),
"E": Categorical([np.nan], categories=["a"]),
"F": [pd.NaT],
"G": to_timedelta([pd.NaT]),
},
),
(
True,
{
"H": [8, 9, np.nan, np.nan],
"I": [8, 9, np.nan, np.nan],
"J": [1, np.nan, np.nan, np.nan],
"K": Categorical(["a", np.nan, np.nan, np.nan], categories=["a"]),
"L": to_datetime(["2000-1-2", "NaT", "NaT", "NaT"]),
"M": to_timedelta(["1 days", "nan", "nan", "nan"]),
"N": [0, 1, 2, 3],
},
),
(
False,
{
"H": [8, 9, np.nan, np.nan],
"I": [8, 9, np.nan, np.nan],
"J": [1, np.nan, np.nan, np.nan],
"K": Categorical([np.nan, "a", np.nan, np.nan], categories=["a"]),
"L": to_datetime(["NaT", "2000-1-2", "NaT", "NaT"]),
"M": to_timedelta(["nan", "1 days", "nan", "nan"]),
"N": [0, 1, 2, 3],
},
),
],
)
def test_mode_dropna(self, dropna, expected):
df = DataFrame(
{
"A": [12, 12, 19, 11],
"B": [10, 10, np.nan, 3],
"C": [1, np.nan, np.nan, np.nan],
"D": [np.nan, np.nan, "a", np.nan],
"E": Categorical([np.nan, np.nan, "a", np.nan]),
"F": to_datetime(["NaT", "2000-1-2", "NaT", "NaT"]),
"G": to_timedelta(["1 days", "nan", "nan", "nan"]),
"H": [8, 8, 9, 9],
"I": [9, 9, 8, 8],
"J": [1, 1, np.nan, np.nan],
"K": Categorical(["a", np.nan, "a", np.nan]),
"L": to_datetime(["2000-1-2", "2000-1-2", "NaT", "NaT"]),
"M": to_timedelta(["1 days", "nan", "1 days", "nan"]),
"N": np.arange(4, dtype="int64"),
}
)
result = df[sorted(expected.keys())].mode(dropna=dropna)
expected = DataFrame(expected)
tm.assert_frame_equal(result, expected)
def test_mode_sortwarning(self):
# Check for the warning that is raised when the mode
# results cannot be sorted
df = DataFrame({"A": [np.nan, np.nan, "a", "a"]})
expected = DataFrame({"A": ["a", np.nan]})
with tm.assert_produces_warning(UserWarning, check_stacklevel=False):
result = df.mode(dropna=False)
result = result.sort_values(by="A").reset_index(drop=True)
tm.assert_frame_equal(result, expected)
def test_operators_timedelta64(self):
df = DataFrame(
dict(
A=date_range("2012-1-1", periods=3, freq="D"),
B=date_range("2012-1-2", periods=3, freq="D"),
C=Timestamp("20120101") - timedelta(minutes=5, seconds=5),
)
)
diffs = DataFrame(dict(A=df["A"] - df["C"], B=df["A"] - df["B"]))
# min
result = diffs.min()
assert result[0] == diffs.loc[0, "A"]
assert result[1] == diffs.loc[0, "B"]
result = diffs.min(axis=1)
assert (result == diffs.loc[0, "B"]).all()
# max
result = diffs.max()
assert result[0] == diffs.loc[2, "A"]
assert result[1] == diffs.loc[2, "B"]
result = diffs.max(axis=1)
assert (result == diffs["A"]).all()
# abs
result = diffs.abs()
result2 = abs(diffs)
expected = DataFrame(dict(A=df["A"] - df["C"], B=df["B"] - df["A"]))
tm.assert_frame_equal(result, expected)
tm.assert_frame_equal(result2, expected)
# mixed frame
mixed = diffs.copy()
mixed["C"] = "foo"
mixed["D"] = 1
mixed["E"] = 1.0
mixed["F"] = Timestamp("20130101")
# results in an object array
result = mixed.min()
expected = Series(
[
pd.Timedelta(timedelta(seconds=5 * 60 + 5)),
pd.Timedelta(timedelta(days=-1)),
"foo",
1,
1.0,
Timestamp("20130101"),
],
index=mixed.columns,
)
tm.assert_series_equal(result, expected)
# excludes numeric
result = mixed.min(axis=1)
expected = Series([1, 1, 1.0], index=[0, 1, 2])
tm.assert_series_equal(result, expected)
# works when only those columns are selected
result = mixed[["A", "B"]].min(1)
expected = Series([timedelta(days=-1)] * 3)
tm.assert_series_equal(result, expected)
result = mixed[["A", "B"]].min()
expected = Series(
[timedelta(seconds=5 * 60 + 5), timedelta(days=-1)], index=["A", "B"]
)
tm.assert_series_equal(result, expected)
# GH 3106
df = DataFrame(
{
"time": date_range("20130102", periods=5),
"time2": date_range("20130105", periods=5),
}
)
df["off1"] = df["time2"] - df["time"]
assert df["off1"].dtype == "timedelta64[ns]"
df["off2"] = df["time"] - df["time2"]
df._consolidate_inplace()
assert df["off1"].dtype == "timedelta64[ns]"
assert df["off2"].dtype == "timedelta64[ns]"
def test_sum_corner(self):
empty_frame = DataFrame()
axis0 = empty_frame.sum(0)
axis1 = empty_frame.sum(1)
assert isinstance(axis0, Series)
assert isinstance(axis1, Series)
assert len(axis0) == 0
assert len(axis1) == 0
@pytest.mark.parametrize("method, unit", [("sum", 0), ("prod", 1)])
def test_sum_prod_nanops(self, method, unit):
idx = ["a", "b", "c"]
df = pd.DataFrame(
{"a": [unit, unit], "b": [unit, np.nan], "c": [np.nan, np.nan]}
)
# The default
result = getattr(df, method)
expected = pd.Series([unit, unit, unit], index=idx, dtype="float64")
# min_count=1
result = getattr(df, method)(min_count=1)
expected = pd.Series([unit, unit, np.nan], index=idx)
tm.assert_series_equal(result, expected)
# min_count=0
result = getattr(df, method)(min_count=0)
expected = pd.Series([unit, unit, unit], index=idx, dtype="float64")
tm.assert_series_equal(result, expected)
result = getattr(df.iloc[1:], method)(min_count=1)
expected = pd.Series([unit, np.nan, np.nan], index=idx)
tm.assert_series_equal(result, expected)
# min_count > 1
df = pd.DataFrame({"A": [unit] * 10, "B": [unit] * 5 + [np.nan] * 5})
result = getattr(df, method)(min_count=5)
expected = pd.Series(result, index=["A", "B"])
tm.assert_series_equal(result, expected)
result = getattr(df, method)(min_count=6)
expected = pd.Series(result, index=["A", "B"])
tm.assert_series_equal(result, expected)
def test_sum_nanops_timedelta(self):
# prod isn't defined on timedeltas
idx = ["a", "b", "c"]
df = pd.DataFrame({"a": [0, 0], "b": [0, np.nan], "c": [np.nan, np.nan]})
df2 = df.apply(pd.to_timedelta)
# 0 by default
result = df2.sum()
expected = pd.Series([0, 0, 0], dtype="m8[ns]", index=idx)
tm.assert_series_equal(result, expected)
# min_count=0
result = df2.sum(min_count=0)
tm.assert_series_equal(result, expected)
# min_count=1
result = df2.sum(min_count=1)
expected = pd.Series([0, 0, np.nan], dtype="m8[ns]", index=idx)
tm.assert_series_equal(result, expected)
def test_sum_object(self, float_frame):
values = float_frame.values.astype(int)
frame = DataFrame(values, index=float_frame.index, columns=float_frame.columns)
deltas = frame * timedelta(1)
deltas.sum()
def test_sum_bool(self, float_frame):
# ensure this works, bug report
bools = np.isnan(float_frame)
bools.sum(1)
bools.sum(0)
def test_sum_mixed_datetime(self):
# GH#30886
df = pd.DataFrame(
{"A": pd.date_range("2000", periods=4), "B": [1, 2, 3, 4]}
).reindex([2, 3, 4])
result = df.sum()
expected = pd.Series({"B": 7.0})
tm.assert_series_equal(result, expected)
def test_mean_corner(self, float_frame, float_string_frame):
# unit test when have object data
the_mean = float_string_frame.mean(axis=0)
the_sum = float_string_frame.sum(axis=0, numeric_only=True)
tm.assert_index_equal(the_sum.index, the_mean.index)
assert len(the_mean.index) < len(float_string_frame.columns)
# xs sum mixed type, just want to know it works...
the_mean = float_string_frame.mean(axis=1)
the_sum = float_string_frame.sum(axis=1, numeric_only=True)
tm.assert_index_equal(the_sum.index, the_mean.index)
# take mean of boolean column
float_frame["bool"] = float_frame["A"] > 0
means = float_frame.mean(0)
assert means["bool"] == float_frame["bool"].values.mean()
def test_mean_datetimelike(self):
# GH#24757 check that datetimelike are excluded by default, handled
# correctly with numeric_only=True
df = pd.DataFrame(
{
"A": np.arange(3),
"B": pd.date_range("2016-01-01", periods=3),
"C": pd.timedelta_range("1D", periods=3),
"D": pd.period_range("2016", periods=3, freq="A"),
}
)
result = df.mean(numeric_only=True)
expected = pd.Series({"A": 1.0})
tm.assert_series_equal(result, expected)
with tm.assert_produces_warning(FutureWarning):
# in the future datetime columns will be included
result = df.mean()
expected = pd.Series({"A": 1.0, "C": df.loc[1, "C"]})
tm.assert_series_equal(result, expected)
def test_mean_datetimelike_numeric_only_false(self):
df = pd.DataFrame(
{
"A": np.arange(3),
"B": pd.date_range("2016-01-01", periods=3),
"C": pd.timedelta_range("1D", periods=3),
}
)
# datetime(tz) and timedelta work
result = df.mean(numeric_only=False)
expected = pd.Series({"A": 1, "B": df.loc[1, "B"], "C": df.loc[1, "C"]})
tm.assert_series_equal(result, expected)
# mean of period is not allowed
df["D"] = pd.period_range("2016", periods=3, freq="A")
with pytest.raises(TypeError, match="mean is not implemented for Period"):
df.mean(numeric_only=False)
def test_mean_extensionarray_numeric_only_true(self):
# https://github.com/pandas-dev/pandas/issues/33256
arr = np.random.randint(1000, size=(10, 5))
df = pd.DataFrame(arr, dtype="Int64")
result = df.mean(numeric_only=True)
expected = pd.DataFrame(arr).mean()
tm.assert_series_equal(result, expected)
def test_stats_mixed_type(self, float_string_frame):
# don't blow up
float_string_frame.std(1)
float_string_frame.var(1)
float_string_frame.mean(1)
float_string_frame.skew(1)
def test_sum_bools(self):
df = DataFrame(index=range(1), columns=range(10))
bools = isna(df)
assert bools.sum(axis=1)[0] == 10
# ----------------------------------------------------------------------
# Index of max / min
def test_idxmin(self, float_frame, int_frame):
frame = float_frame
frame.iloc[5:10] = np.nan
frame.iloc[15:20, -2:] = np.nan
for skipna in [True, False]:
for axis in [0, 1]:
for df in [frame, int_frame]:
result = df.idxmin(axis=axis, skipna=skipna)
expected = df.apply(Series.idxmin, axis=axis, skipna=skipna)
tm.assert_series_equal(result, expected)
msg = "No axis named 2 for object type DataFrame"
with pytest.raises(ValueError, match=msg):
frame.idxmin(axis=2)
def test_idxmax(self, float_frame, int_frame):
frame = float_frame
frame.iloc[5:10] = np.nan
frame.iloc[15:20, -2:] = np.nan
for skipna in [True, False]:
for axis in [0, 1]:
for df in [frame, int_frame]:
result = df.idxmax(axis=axis, skipna=skipna)
expected = df.apply(Series.idxmax, axis=axis, skipna=skipna)
tm.assert_series_equal(result, expected)
msg = "No axis named 2 for object type DataFrame"
with pytest.raises(ValueError, match=msg):
frame.idxmax(axis=2)
# ----------------------------------------------------------------------
# Logical reductions
@pytest.mark.parametrize("opname", ["any", "all"])
def test_any_all(self, opname, bool_frame_with_na, float_string_frame):
assert_bool_op_calc(
opname, getattr(np, opname), bool_frame_with_na, has_skipna=True
)
assert_bool_op_api(
opname, bool_frame_with_na, float_string_frame, has_bool_only=True
)
def test_any_all_extra(self):
df = DataFrame(
{
"A": [True, False, False],
"B": [True, True, False],
"C": [True, True, True],
},
index=["a", "b", "c"],
)
result = df[["A", "B"]].any(1)
expected = Series([True, True, False], index=["a", "b", "c"])
tm.assert_series_equal(result, expected)
result = df[["A", "B"]].any(1, bool_only=True)
tm.assert_series_equal(result, expected)
result = df.all(1)
expected = Series([True, False, False], index=["a", "b", "c"])
tm.assert_series_equal(result, expected)
result = df.all(1, bool_only=True)
tm.assert_series_equal(result, expected)
# Axis is None
result = df.all(axis=None).item()
assert result is False
result = df.any(axis=None).item()
assert result is True
result = df[["C"]].all(axis=None).item()
assert result is True
def test_any_datetime(self):
# GH 23070
float_data = [1, np.nan, 3, np.nan]
datetime_data = [
pd.Timestamp("1960-02-15"),
pd.Timestamp("1960-02-16"),
pd.NaT,
pd.NaT,
]
df = DataFrame({"A": float_data, "B": datetime_data})
result = df.any(1)
expected = Series([True, True, True, False])
tm.assert_series_equal(result, expected)
def test_any_all_bool_only(self):
# GH 25101
df = DataFrame(
{"col1": [1, 2, 3], "col2": [4, 5, 6], "col3": [None, None, None]}
)
result = df.all(bool_only=True)
expected = Series(dtype=np.bool_)
tm.assert_series_equal(result, expected)
df = DataFrame(
{
"col1": [1, 2, 3],
"col2": [4, 5, 6],
"col3": [None, None, None],
"col4": [False, False, True],
}
)
result = df.all(bool_only=True)
expected = Series({"col4": False})
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"func, data, expected",
[
(np.any, {}, False),
(np.all, {}, True),
(np.any, {"A": []}, False),
(np.all, {"A": []}, True),
(np.any, {"A": [False, False]}, False),
(np.all, {"A": [False, False]}, False),
(np.any, {"A": [True, False]}, True),
(np.all, {"A": [True, False]}, False),
(np.any, {"A": [True, True]}, True),
(np.all, {"A": [True, True]}, True),
(np.any, {"A": [False], "B": [False]}, False),
(np.all, {"A": [False], "B": [False]}, False),
(np.any, {"A": [False, False], "B": [False, True]}, True),
(np.all, {"A": [False, False], "B": [False, True]}, False),
# other types
(np.all, {"A": pd.Series([0.0, 1.0], dtype="float")}, False),
(np.any, {"A": pd.Series([0.0, 1.0], dtype="float")}, True),
(np.all, {"A": pd.Series([0, 1], dtype=int)}, False),
(np.any, {"A": pd.Series([0, 1], dtype=int)}, True),
pytest.param(
np.all,
{"A": pd.Series([0, 1], dtype="M8[ns]")},
False,
marks=[td.skip_if_np_lt("1.15")],
),
pytest.param(
np.any,
{"A": pd.Series([0, 1], dtype="M8[ns]")},
True,
marks=[td.skip_if_np_lt("1.15")],
),
pytest.param(
np.all,
{"A": pd.Series([1, 2], dtype="M8[ns]")},
True,
marks=[td.skip_if_np_lt("1.15")],
),
pytest.param(
np.any,
{"A": pd.Series([1, 2], dtype="M8[ns]")},
True,
marks=[td.skip_if_np_lt("1.15")],
),
pytest.param(
np.all,
{"A": pd.Series([0, 1], dtype="m8[ns]")},
False,
marks=[td.skip_if_np_lt("1.15")],
),
pytest.param(
np.any,
{"A": pd.Series([0, 1], dtype="m8[ns]")},
True,
marks=[td.skip_if_np_lt("1.15")],
),
pytest.param(
np.all,
{"A": pd.Series([1, 2], dtype="m8[ns]")},
True,
marks=[td.skip_if_np_lt("1.15")],
),
pytest.param(
np.any,
{"A": pd.Series([1, 2], dtype="m8[ns]")},
True,
marks=[td.skip_if_np_lt("1.15")],
),
(np.all, {"A": pd.Series([0, 1], dtype="category")}, False),
(np.any, {"A": pd.Series([0, 1], dtype="category")}, True),
(np.all, {"A": pd.Series([1, 2], dtype="category")}, True),
(np.any, {"A": pd.Series([1, 2], dtype="category")}, True),
# Mix GH#21484
pytest.param(
np.all,
{
"A": pd.Series([10, 20], dtype="M8[ns]"),
"B": pd.Series([10, 20], dtype="m8[ns]"),
},
True,
# In 1.13.3 and 1.14 np.all(df) returns a Timedelta here
marks=[td.skip_if_np_lt("1.15")],
),
],
)
def test_any_all_np_func(self, func, data, expected):
# GH 19976
data = DataFrame(data)
result = func(data)
assert isinstance(result, np.bool_)
assert result.item() is expected
# method version
result = getattr(DataFrame(data), func.__name__)(axis=None)
assert isinstance(result, np.bool_)
assert result.item() is expected
def test_any_all_object(self):
# GH 19976
result = np.all(DataFrame(columns=["a", "b"])).item()
assert result is True
result = np.any(DataFrame(columns=["a", "b"])).item()
assert result is False
@pytest.mark.parametrize("method", ["any", "all"])
def test_any_all_level_axis_none_raises(self, method):
df = DataFrame(
{"A": 1},
index=MultiIndex.from_product(
[["A", "B"], ["a", "b"]], names=["out", "in"]
),
)
xpr = "Must specify 'axis' when aggregating by level."
with pytest.raises(ValueError, match=xpr):
getattr(df, method)(axis=None, level="out")
# ---------------------------------------------------------------------
# Matrix-like
def test_matmul(self):
# matmul test is for GH 10259
a = DataFrame(
np.random.randn(3, 4), index=["a", "b", "c"], columns=["p", "q", "r", "s"]
)
b = DataFrame(
np.random.randn(4, 2), index=["p", "q", "r", "s"], columns=["one", "two"]
)
# DataFrame @ DataFrame
result = operator.matmul(a, b)
expected = DataFrame(
np.dot(a.values, b.values), index=["a", "b", "c"], columns=["one", "two"]
)
tm.assert_frame_equal(result, expected)
# DataFrame @ Series
result = operator.matmul(a, b.one)
expected = Series(np.dot(a.values, b.one.values), index=["a", "b", "c"])
tm.assert_series_equal(result, expected)
# np.array @ DataFrame
result = operator.matmul(a.values, b)
assert isinstance(result, DataFrame)
assert result.columns.equals(b.columns)
assert result.index.equals(pd.Index(range(3)))
expected = np.dot(a.values, b.values)
tm.assert_almost_equal(result.values, expected)
# nested list @ DataFrame (__rmatmul__)
result = operator.matmul(a.values.tolist(), b)
expected = DataFrame(
np.dot(a.values, b.values), index=["a", "b", "c"], columns=["one", "two"]
)
tm.assert_almost_equal(result.values, expected.values)
# mixed dtype DataFrame @ DataFrame
a["q"] = a.q.round().astype(int)
result = operator.matmul(a, b)
expected = DataFrame(
np.dot(a.values, b.values), index=["a", "b", "c"], columns=["one", "two"]
)
tm.assert_frame_equal(result, expected)
# different dtypes DataFrame @ DataFrame
a = a.astype(int)
result = operator.matmul(a, b)
expected = DataFrame(
np.dot(a.values, b.values), index=["a", "b", "c"], columns=["one", "two"]
)
tm.assert_frame_equal(result, expected)
# unaligned
df = DataFrame(np.random.randn(3, 4), index=[1, 2, 3], columns=range(4))
df2 = DataFrame(np.random.randn(5, 3), index=range(5), columns=[1, 2, 3])
with pytest.raises(ValueError, match="aligned"):
operator.matmul(df, df2)
# ---------------------------------------------------------------------
# Unsorted
def test_series_broadcasting(self):
# smoke test for numpy warnings
# GH 16378, GH 16306
df = DataFrame([1.0, 1.0, 1.0])
df_nan = DataFrame({"A": [np.nan, 2.0, np.nan]})
s = Series([1, 1, 1])
s_nan = Series([np.nan, np.nan, 1])
with tm.assert_produces_warning(None):
df_nan.clip(lower=s, axis=0)
for op in ["lt", "le", "gt", "ge", "eq", "ne"]:
getattr(df, op)(s_nan, axis=0)
class TestDataFrameReductions:
def test_min_max_dt64_with_NaT(self):
# Both NaT and Timestamp are in DataFrame.
df = pd.DataFrame({"foo": [pd.NaT, pd.NaT, pd.Timestamp("2012-05-01")]})
res = df.min()
exp = pd.Series([pd.Timestamp("2012-05-01")], index=["foo"])
tm.assert_series_equal(res, exp)
res = df.max()
exp = pd.Series([pd.Timestamp("2012-05-01")], index=["foo"])
tm.assert_series_equal(res, exp)
# GH12941, only NaTs are in DataFrame.
df = pd.DataFrame({"foo": [pd.NaT, pd.NaT]})
res = df.min()
exp = pd.Series([pd.NaT], index=["foo"])
tm.assert_series_equal(res, exp)
res = df.max()
exp = pd.Series([pd.NaT], index=["foo"])
tm.assert_series_equal(res, exp)
def test_min_max_dt64_api_consistency_with_NaT(self):
# Calling the following sum functions returned an error for dataframes but
# returned NaT for series. These tests check that the API is consistent in
# min/max calls on empty Series/DataFrames. See GH:33704 for more
# information
df = pd.DataFrame(dict(x=pd.to_datetime([])))
expected_dt_series = pd.Series(pd.to_datetime([]))
# check axis 0
assert (df.min(axis=0).x is pd.NaT) == (expected_dt_series.min() is pd.NaT)
assert (df.max(axis=0).x is pd.NaT) == (expected_dt_series.max() is pd.NaT)
# check axis 1
tm.assert_series_equal(df.min(axis=1), expected_dt_series)
tm.assert_series_equal(df.max(axis=1), expected_dt_series)
def test_min_max_dt64_api_consistency_empty_df(self):
# check DataFrame/Series api consistency when calling min/max on an empty
# DataFrame/Series.
df = pd.DataFrame(dict(x=[]))
expected_float_series = pd.Series([], dtype=float)
# check axis 0
assert np.isnan(df.min(axis=0).x) == np.isnan(expected_float_series.min())
assert np.isnan(df.max(axis=0).x) == np.isnan(expected_float_series.max())
# check axis 1
tm.assert_series_equal(df.min(axis=1), expected_float_series)
tm.assert_series_equal(df.min(axis=1), expected_float_series)
@pytest.mark.parametrize(
"initial",
["2018-10-08 13:36:45+00:00", "2018-10-08 13:36:45+03:00"], # Non-UTC timezone
)
@pytest.mark.parametrize("method", ["min", "max"])
def test_preserve_timezone(self, initial: str, method):
# GH 28552
initial_dt = pd.to_datetime(initial)
expected = Series([initial_dt])
df = DataFrame([expected])
result = getattr(df, method)(axis=1)
tm.assert_series_equal(result, expected)
def test_mixed_frame_with_integer_sum():
# https://github.com/pandas-dev/pandas/issues/34520
df = pd.DataFrame([["a", 1]], columns=list("ab"))
df = df.astype({"b": "Int64"})
result = df.sum()
expected = pd.Series(["a", 1], index=["a", "b"])
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("numeric_only", [True, False, None])
@pytest.mark.parametrize("method", ["min", "max"])
def test_minmax_extensionarray(method, numeric_only):
# https://github.com/pandas-dev/pandas/issues/32651
int64_info = np.iinfo("int64")
ser = Series([int64_info.max, None, int64_info.min], dtype=pd.Int64Dtype())
df = DataFrame({"Int64": ser})
result = getattr(df, method)(numeric_only=numeric_only)
expected = Series(
[getattr(int64_info, method)], index=pd.Index(["Int64"], dtype="object")
)
tm.assert_series_equal(result, expected)
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