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Version:
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# coding=utf-8
# pylint: disable-msg=E1101,W0612
from itertools import product
from distutils.version import LooseVersion
import pytest
from numpy import nan
import numpy as np
import pandas as pd
from pandas import (Series, Categorical, DataFrame, isna, notna,
bdate_range, date_range, _np_version_under1p10)
from pandas.core.index import MultiIndex
from pandas.core.indexes.datetimes import Timestamp
from pandas.core.indexes.timedeltas import Timedelta
import pandas.core.nanops as nanops
from pandas.compat import lrange, range
from pandas import compat
from pandas.util.testing import (assert_series_equal, assert_almost_equal,
assert_frame_equal, assert_index_equal)
import pandas.util.testing as tm
from .common import TestData
class TestSeriesAnalytics(TestData):
@pytest.mark.parametrize("use_bottleneck", [True, False])
@pytest.mark.parametrize("method", ["sum", "prod"])
def test_empty(self, method, use_bottleneck):
with pd.option_context("use_bottleneck", use_bottleneck):
# GH 9422
# treat all missing as NaN
s = Series([])
result = getattr(s, method)()
assert isna(result)
result = getattr(s, method)(skipna=True)
assert isna(result)
s = Series([np.nan])
result = getattr(s, method)()
assert isna(result)
result = getattr(s, method)(skipna=True)
assert isna(result)
s = Series([np.nan, 1])
result = getattr(s, method)()
assert result == 1.0
s = Series([np.nan, 1])
result = getattr(s, method)(skipna=True)
assert result == 1.0
# GH #844 (changed in 9422)
df = DataFrame(np.empty((10, 0)))
assert (df.sum(1).isnull()).all()
@pytest.mark.parametrize(
"method", ['sum', 'mean', 'median', 'std', 'var'])
def test_ops_consistency_on_empty(self, method):
# GH 7869
# consistency on empty
# float
result = getattr(Series(dtype=float), method)()
assert isna(result)
# timedelta64[ns]
result = getattr(Series(dtype='m8[ns]'), method)()
assert result is pd.NaT
def test_nansum_buglet(self):
s = Series([1.0, np.nan], index=[0, 1])
result = np.nansum(s)
assert_almost_equal(result, 1)
@pytest.mark.parametrize("use_bottleneck", [True, False])
def test_sum_overflow(self, use_bottleneck):
with pd.option_context('use_bottleneck', use_bottleneck):
# GH 6915
# overflowing on the smaller int dtypes
for dtype in ['int32', 'int64']:
v = np.arange(5000000, dtype=dtype)
s = Series(v)
result = s.sum(skipna=False)
assert int(result) == v.sum(dtype='int64')
result = s.min(skipna=False)
assert int(result) == 0
result = s.max(skipna=False)
assert int(result) == v[-1]
for dtype in ['float32', 'float64']:
v = np.arange(5000000, dtype=dtype)
s = Series(v)
result = s.sum(skipna=False)
assert result == v.sum(dtype=dtype)
result = s.min(skipna=False)
assert np.allclose(float(result), 0.0)
result = s.max(skipna=False)
assert np.allclose(float(result), v[-1])
def test_sum(self):
self._check_stat_op('sum', np.sum, check_allna=True)
def test_sum_inf(self):
s = Series(np.random.randn(10))
s2 = s.copy()
s[5:8] = np.inf
s2[5:8] = np.nan
assert np.isinf(s.sum())
arr = np.random.randn(100, 100).astype('f4')
arr[:, 2] = np.inf
with pd.option_context("mode.use_inf_as_na", True):
assert_almost_equal(s.sum(), s2.sum())
res = nanops.nansum(arr, axis=1)
assert np.isinf(res).all()
def test_mean(self):
self._check_stat_op('mean', np.mean)
def test_median(self):
self._check_stat_op('median', np.median)
# test with integers, test failure
int_ts = Series(np.ones(10, dtype=int), index=lrange(10))
tm.assert_almost_equal(np.median(int_ts), int_ts.median())
def test_mode(self):
# No mode should be found.
exp = Series([], dtype=np.float64)
tm.assert_series_equal(Series([]).mode(), exp)
exp = Series([1], dtype=np.int64)
tm.assert_series_equal(Series([1]).mode(), exp)
exp = Series(['a', 'b', 'c'], dtype=np.object)
tm.assert_series_equal(Series(['a', 'b', 'c']).mode(), exp)
# Test numerical data types.
exp_single = [1]
data_single = [1] * 5 + [2] * 3
exp_multi = [1, 3]
data_multi = [1] * 5 + [2] * 3 + [3] * 5
for dt in np.typecodes['AllInteger'] + np.typecodes['Float']:
s = Series(data_single, dtype=dt)
exp = Series(exp_single, dtype=dt)
tm.assert_series_equal(s.mode(), exp)
s = Series(data_multi, dtype=dt)
exp = Series(exp_multi, dtype=dt)
tm.assert_series_equal(s.mode(), exp)
# Test string and object types.
exp = ['b']
data = ['a'] * 2 + ['b'] * 3
s = Series(data, dtype='c')
exp = Series(exp, dtype='c')
tm.assert_series_equal(s.mode(), exp)
exp = ['bar']
data = ['foo'] * 2 + ['bar'] * 3
for dt in [str, object]:
s = Series(data, dtype=dt)
exp = Series(exp, dtype=dt)
tm.assert_series_equal(s.mode(), exp)
# Test datetime types.
exp = Series(['1900-05-03', '2011-01-03',
'2013-01-02'], dtype='M8[ns]')
s = Series(['2011-01-03', '2013-01-02',
'1900-05-03'], dtype='M8[ns]')
tm.assert_series_equal(s.mode(), exp)
exp = Series(['2011-01-03', '2013-01-02'], dtype='M8[ns]')
s = Series(['2011-01-03', '2013-01-02', '1900-05-03',
'2011-01-03', '2013-01-02'], dtype='M8[ns]')
tm.assert_series_equal(s.mode(), exp)
# gh-5986: Test timedelta types.
exp = Series(['-1 days', '0 days', '1 days'], dtype='timedelta64[ns]')
s = Series(['1 days', '-1 days', '0 days'],
dtype='timedelta64[ns]')
tm.assert_series_equal(s.mode(), exp)
exp = Series(['2 min', '1 day'], dtype='timedelta64[ns]')
s = Series(['1 day', '1 day', '-1 day', '-1 day 2 min',
'2 min', '2 min'], dtype='timedelta64[ns]')
tm.assert_series_equal(s.mode(), exp)
# Test mixed dtype.
exp = Series(['foo'])
s = Series([1, 'foo', 'foo'])
tm.assert_series_equal(s.mode(), exp)
# Test for uint64 overflow.
exp = Series([2**63], dtype=np.uint64)
s = Series([1, 2**63, 2**63], dtype=np.uint64)
tm.assert_series_equal(s.mode(), exp)
exp = Series([1, 2**63], dtype=np.uint64)
s = Series([1, 2**63], dtype=np.uint64)
tm.assert_series_equal(s.mode(), exp)
# Test category dtype.
c = Categorical([1, 2])
exp = Categorical([1, 2], categories=[1, 2])
exp = Series(exp, dtype='category')
tm.assert_series_equal(Series(c).mode(), exp)
c = Categorical([1, 'a', 'a'])
exp = Categorical(['a'], categories=[1, 'a'])
exp = Series(exp, dtype='category')
tm.assert_series_equal(Series(c).mode(), exp)
c = Categorical([1, 1, 2, 3, 3])
exp = Categorical([1, 3], categories=[1, 2, 3])
exp = Series(exp, dtype='category')
tm.assert_series_equal(Series(c).mode(), exp)
def test_prod(self):
self._check_stat_op('prod', np.prod)
def test_min(self):
self._check_stat_op('min', np.min, check_objects=True)
def test_max(self):
self._check_stat_op('max', np.max, check_objects=True)
def test_var_std(self):
alt = lambda x: np.std(x, ddof=1)
self._check_stat_op('std', alt)
alt = lambda x: np.var(x, ddof=1)
self._check_stat_op('var', alt)
result = self.ts.std(ddof=4)
expected = np.std(self.ts.values, ddof=4)
assert_almost_equal(result, expected)
result = self.ts.var(ddof=4)
expected = np.var(self.ts.values, ddof=4)
assert_almost_equal(result, expected)
# 1 - element series with ddof=1
s = self.ts.iloc[[0]]
result = s.var(ddof=1)
assert isna(result)
result = s.std(ddof=1)
assert isna(result)
def test_sem(self):
alt = lambda x: np.std(x, ddof=1) / np.sqrt(len(x))
self._check_stat_op('sem', alt)
result = self.ts.sem(ddof=4)
expected = np.std(self.ts.values,
ddof=4) / np.sqrt(len(self.ts.values))
assert_almost_equal(result, expected)
# 1 - element series with ddof=1
s = self.ts.iloc[[0]]
result = s.sem(ddof=1)
assert isna(result)
def test_skew(self):
tm._skip_if_no_scipy()
from scipy.stats import skew
alt = lambda x: skew(x, bias=False)
self._check_stat_op('skew', alt)
# test corner cases, skew() returns NaN unless there's at least 3
# values
min_N = 3
for i in range(1, min_N + 1):
s = Series(np.ones(i))
df = DataFrame(np.ones((i, i)))
if i < min_N:
assert np.isnan(s.skew())
assert np.isnan(df.skew()).all()
else:
assert 0 == s.skew()
assert (df.skew() == 0).all()
def test_kurt(self):
tm._skip_if_no_scipy()
from scipy.stats import kurtosis
alt = lambda x: kurtosis(x, bias=False)
self._check_stat_op('kurt', alt)
index = MultiIndex(levels=[['bar'], ['one', 'two', 'three'], [0, 1]],
labels=[[0, 0, 0, 0, 0, 0], [0, 1, 2, 0, 1, 2],
[0, 1, 0, 1, 0, 1]])
s = Series(np.random.randn(6), index=index)
tm.assert_almost_equal(s.kurt(), s.kurt(level=0)['bar'])
# test corner cases, kurt() returns NaN unless there's at least 4
# values
min_N = 4
for i in range(1, min_N + 1):
s = Series(np.ones(i))
df = DataFrame(np.ones((i, i)))
if i < min_N:
assert np.isnan(s.kurt())
assert np.isnan(df.kurt()).all()
else:
assert 0 == s.kurt()
assert (df.kurt() == 0).all()
def test_describe(self):
s = Series([0, 1, 2, 3, 4], name='int_data')
result = s.describe()
expected = Series([5, 2, s.std(), 0, 1, 2, 3, 4],
name='int_data',
index=['count', 'mean', 'std', 'min', '25%',
'50%', '75%', 'max'])
tm.assert_series_equal(result, expected)
s = Series([True, True, False, False, False], name='bool_data')
result = s.describe()
expected = Series([5, 2, False, 3], name='bool_data',
index=['count', 'unique', 'top', 'freq'])
tm.assert_series_equal(result, expected)
s = Series(['a', 'a', 'b', 'c', 'd'], name='str_data')
result = s.describe()
expected = Series([5, 4, 'a', 2], name='str_data',
index=['count', 'unique', 'top', 'freq'])
tm.assert_series_equal(result, expected)
def test_argsort(self):
self._check_accum_op('argsort', check_dtype=False)
argsorted = self.ts.argsort()
assert issubclass(argsorted.dtype.type, np.integer)
# GH 2967 (introduced bug in 0.11-dev I think)
s = Series([Timestamp('201301%02d' % (i + 1)) for i in range(5)])
assert s.dtype == 'datetime64[ns]'
shifted = s.shift(-1)
assert shifted.dtype == 'datetime64[ns]'
assert isna(shifted[4])
result = s.argsort()
expected = Series(lrange(5), dtype='int64')
assert_series_equal(result, expected)
result = shifted.argsort()
expected = Series(lrange(4) + [-1], dtype='int64')
assert_series_equal(result, expected)
def test_argsort_stable(self):
s = Series(np.random.randint(0, 100, size=10000))
mindexer = s.argsort(kind='mergesort')
qindexer = s.argsort()
mexpected = np.argsort(s.values, kind='mergesort')
qexpected = np.argsort(s.values, kind='quicksort')
tm.assert_series_equal(mindexer, Series(mexpected),
check_dtype=False)
tm.assert_series_equal(qindexer, Series(qexpected),
check_dtype=False)
pytest.raises(AssertionError, tm.assert_numpy_array_equal,
qindexer, mindexer)
def test_cumsum(self):
self._check_accum_op('cumsum')
def test_cumprod(self):
self._check_accum_op('cumprod')
def test_cummin(self):
tm.assert_numpy_array_equal(self.ts.cummin().values,
np.minimum.accumulate(np.array(self.ts)))
ts = self.ts.copy()
ts[::2] = np.NaN
result = ts.cummin()[1::2]
expected = np.minimum.accumulate(ts.valid())
tm.assert_series_equal(result, expected)
def test_cummax(self):
tm.assert_numpy_array_equal(self.ts.cummax().values,
np.maximum.accumulate(np.array(self.ts)))
ts = self.ts.copy()
ts[::2] = np.NaN
result = ts.cummax()[1::2]
expected = np.maximum.accumulate(ts.valid())
tm.assert_series_equal(result, expected)
def test_cummin_datetime64(self):
s = pd.Series(pd.to_datetime(['NaT', '2000-1-2', 'NaT', '2000-1-1',
'NaT', '2000-1-3']))
expected = pd.Series(pd.to_datetime(['NaT', '2000-1-2', 'NaT',
'2000-1-1', 'NaT', '2000-1-1']))
result = s.cummin(skipna=True)
tm.assert_series_equal(expected, result)
expected = pd.Series(pd.to_datetime(
['NaT', '2000-1-2', '2000-1-2', '2000-1-1', '2000-1-1', '2000-1-1'
]))
result = s.cummin(skipna=False)
tm.assert_series_equal(expected, result)
def test_cummax_datetime64(self):
s = pd.Series(pd.to_datetime(['NaT', '2000-1-2', 'NaT', '2000-1-1',
'NaT', '2000-1-3']))
expected = pd.Series(pd.to_datetime(['NaT', '2000-1-2', 'NaT',
'2000-1-2', 'NaT', '2000-1-3']))
result = s.cummax(skipna=True)
tm.assert_series_equal(expected, result)
expected = pd.Series(pd.to_datetime(
['NaT', '2000-1-2', '2000-1-2', '2000-1-2', '2000-1-2', '2000-1-3'
]))
result = s.cummax(skipna=False)
tm.assert_series_equal(expected, result)
def test_cummin_timedelta64(self):
s = pd.Series(pd.to_timedelta(['NaT',
'2 min',
'NaT',
'1 min',
'NaT',
'3 min', ]))
expected = pd.Series(pd.to_timedelta(['NaT',
'2 min',
'NaT',
'1 min',
'NaT',
'1 min', ]))
result = s.cummin(skipna=True)
tm.assert_series_equal(expected, result)
expected = pd.Series(pd.to_timedelta(['NaT',
'2 min',
'2 min',
'1 min',
'1 min',
'1 min', ]))
result = s.cummin(skipna=False)
tm.assert_series_equal(expected, result)
def test_cummax_timedelta64(self):
s = pd.Series(pd.to_timedelta(['NaT',
'2 min',
'NaT',
'1 min',
'NaT',
'3 min', ]))
expected = pd.Series(pd.to_timedelta(['NaT',
'2 min',
'NaT',
'2 min',
'NaT',
'3 min', ]))
result = s.cummax(skipna=True)
tm.assert_series_equal(expected, result)
expected = pd.Series(pd.to_timedelta(['NaT',
'2 min',
'2 min',
'2 min',
'2 min',
'3 min', ]))
result = s.cummax(skipna=False)
tm.assert_series_equal(expected, result)
def test_npdiff(self):
pytest.skip("skipping due to Series no longer being an "
"ndarray")
# no longer works as the return type of np.diff is now nd.array
s = Series(np.arange(5))
r = np.diff(s)
assert_series_equal(Series([nan, 0, 0, 0, nan]), r)
def _check_stat_op(self, name, alternate, check_objects=False,
check_allna=False):
with pd.option_context('use_bottleneck', False):
f = getattr(Series, name)
# add some NaNs
self.series[5:15] = np.NaN
# idxmax, idxmin, min, and max are valid for dates
if name not in ['max', 'min']:
ds = Series(date_range('1/1/2001', periods=10))
pytest.raises(TypeError, f, ds)
# skipna or no
assert notna(f(self.series))
assert isna(f(self.series, skipna=False))
# check the result is correct
nona = self.series.dropna()
assert_almost_equal(f(nona), alternate(nona.values))
assert_almost_equal(f(self.series), alternate(nona.values))
allna = self.series * nan
if check_allna:
assert np.isnan(f(allna))
# dtype=object with None, it works!
s = Series([1, 2, 3, None, 5])
f(s)
# 2888
l = [0]
l.extend(lrange(2 ** 40, 2 ** 40 + 1000))
s = Series(l, dtype='int64')
assert_almost_equal(float(f(s)), float(alternate(s.values)))
# check date range
if check_objects:
s = Series(bdate_range('1/1/2000', periods=10))
res = f(s)
exp = alternate(s)
assert res == exp
# check on string data
if name not in ['sum', 'min', 'max']:
pytest.raises(TypeError, f, Series(list('abc')))
# Invalid axis.
pytest.raises(ValueError, f, self.series, axis=1)
# Unimplemented numeric_only parameter.
if 'numeric_only' in compat.signature(f).args:
tm.assert_raises_regex(NotImplementedError, name, f,
self.series, numeric_only=True)
def _check_accum_op(self, name, check_dtype=True):
func = getattr(np, name)
tm.assert_numpy_array_equal(func(self.ts).values,
func(np.array(self.ts)),
check_dtype=check_dtype)
# with missing values
ts = self.ts.copy()
ts[::2] = np.NaN
result = func(ts)[1::2]
expected = func(np.array(ts.valid()))
tm.assert_numpy_array_equal(result.values, expected,
check_dtype=False)
def test_compress(self):
cond = [True, False, True, False, False]
s = Series([1, -1, 5, 8, 7],
index=list('abcde'), name='foo')
expected = Series(s.values.compress(cond),
index=list('ac'), name='foo')
tm.assert_series_equal(s.compress(cond), expected)
def test_numpy_compress(self):
cond = [True, False, True, False, False]
s = Series([1, -1, 5, 8, 7],
index=list('abcde'), name='foo')
expected = Series(s.values.compress(cond),
index=list('ac'), name='foo')
tm.assert_series_equal(np.compress(cond, s), expected)
msg = "the 'axis' parameter is not supported"
tm.assert_raises_regex(ValueError, msg, np.compress,
cond, s, axis=1)
msg = "the 'out' parameter is not supported"
tm.assert_raises_regex(ValueError, msg, np.compress,
cond, s, out=s)
def test_round(self):
self.ts.index.name = "index_name"
result = self.ts.round(2)
expected = Series(np.round(self.ts.values, 2),
index=self.ts.index, name='ts')
assert_series_equal(result, expected)
assert result.name == self.ts.name
def test_numpy_round(self):
# See gh-12600
s = Series([1.53, 1.36, 0.06])
out = np.round(s, decimals=0)
expected = Series([2., 1., 0.])
assert_series_equal(out, expected)
msg = "the 'out' parameter is not supported"
with tm.assert_raises_regex(ValueError, msg):
np.round(s, decimals=0, out=s)
def test_built_in_round(self):
if not compat.PY3:
pytest.skip(
'build in round cannot be overriden prior to Python 3')
s = Series([1.123, 2.123, 3.123], index=lrange(3))
result = round(s)
expected_rounded0 = Series([1., 2., 3.], index=lrange(3))
tm.assert_series_equal(result, expected_rounded0)
decimals = 2
expected_rounded = Series([1.12, 2.12, 3.12], index=lrange(3))
result = round(s, decimals)
tm.assert_series_equal(result, expected_rounded)
def test_prod_numpy16_bug(self):
s = Series([1., 1., 1.], index=lrange(3))
result = s.prod()
assert not isinstance(result, Series)
def test_all_any(self):
ts = tm.makeTimeSeries()
bool_series = ts > 0
assert not bool_series.all()
assert bool_series.any()
# Alternative types, with implicit 'object' dtype.
s = Series(['abc', True])
assert 'abc' == s.any() # 'abc' || True => 'abc'
def test_all_any_params(self):
# Check skipna, with implicit 'object' dtype.
s1 = Series([np.nan, True])
s2 = Series([np.nan, False])
assert s1.all(skipna=False) # nan && True => True
assert s1.all(skipna=True)
assert np.isnan(s2.any(skipna=False)) # nan || False => nan
assert not s2.any(skipna=True)
# Check level.
s = pd.Series([False, False, True, True, False, True],
index=[0, 0, 1, 1, 2, 2])
assert_series_equal(s.all(level=0), Series([False, True, False]))
assert_series_equal(s.any(level=0), Series([False, True, True]))
# bool_only is not implemented with level option.
pytest.raises(NotImplementedError, s.any, bool_only=True, level=0)
pytest.raises(NotImplementedError, s.all, bool_only=True, level=0)
# bool_only is not implemented alone.
pytest.raises(NotImplementedError, s.any, bool_only=True)
pytest.raises(NotImplementedError, s.all, bool_only=True)
def test_modulo(self):
with np.errstate(all='ignore'):
# GH3590, modulo as ints
p = DataFrame({'first': [3, 4, 5, 8], 'second': [0, 0, 0, 3]})
result = p['first'] % p['second']
expected = Series(p['first'].values % p['second'].values,
dtype='float64')
expected.iloc[0:3] = np.nan
assert_series_equal(result, expected)
result = p['first'] % 0
expected = Series(np.nan, index=p.index, name='first')
assert_series_equal(result, expected)
p = p.astype('float64')
result = p['first'] % p['second']
expected = Series(p['first'].values % p['second'].values)
assert_series_equal(result, expected)
p = p.astype('float64')
result = p['first'] % p['second']
result2 = p['second'] % p['first']
assert not np.array_equal(result, result2)
# GH 9144
s = Series([0, 1])
result = s % 0
expected = Series([nan, nan])
assert_series_equal(result, expected)
result = 0 % s
expected = Series([nan, 0.0])
assert_series_equal(result, expected)
def test_corr(self):
tm._skip_if_no_scipy()
import scipy.stats as stats
# full overlap
tm.assert_almost_equal(self.ts.corr(self.ts), 1)
# partial overlap
tm.assert_almost_equal(self.ts[:15].corr(self.ts[5:]), 1)
assert isna(self.ts[:15].corr(self.ts[5:], min_periods=12))
ts1 = self.ts[:15].reindex(self.ts.index)
ts2 = self.ts[5:].reindex(self.ts.index)
assert isna(ts1.corr(ts2, min_periods=12))
# No overlap
assert np.isnan(self.ts[::2].corr(self.ts[1::2]))
# all NA
cp = self.ts[:10].copy()
cp[:] = np.nan
assert isna(cp.corr(cp))
A = tm.makeTimeSeries()
B = tm.makeTimeSeries()
result = A.corr(B)
expected, _ = stats.pearsonr(A, B)
tm.assert_almost_equal(result, expected)
def test_corr_rank(self):
tm._skip_if_no_scipy()
import scipy
import scipy.stats as stats
# kendall and spearman
A = tm.makeTimeSeries()
B = tm.makeTimeSeries()
A[-5:] = A[:5]
result = A.corr(B, method='kendall')
expected = stats.kendalltau(A, B)[0]
tm.assert_almost_equal(result, expected)
result = A.corr(B, method='spearman')
expected = stats.spearmanr(A, B)[0]
tm.assert_almost_equal(result, expected)
# these methods got rewritten in 0.8
if scipy.__version__ < LooseVersion('0.9'):
pytest.skip("skipping corr rank because of scipy version "
"{0}".format(scipy.__version__))
# results from R
A = Series(
[-0.89926396, 0.94209606, -1.03289164, -0.95445587, 0.76910310, -
0.06430576, -2.09704447, 0.40660407, -0.89926396, 0.94209606])
B = Series(
[-1.01270225, -0.62210117, -1.56895827, 0.59592943, -0.01680292,
1.17258718, -1.06009347, -0.10222060, -0.89076239, 0.89372375])
kexp = 0.4319297
sexp = 0.5853767
tm.assert_almost_equal(A.corr(B, method='kendall'), kexp)
tm.assert_almost_equal(A.corr(B, method='spearman'), sexp)
def test_cov(self):
# full overlap
tm.assert_almost_equal(self.ts.cov(self.ts), self.ts.std() ** 2)
# partial overlap
tm.assert_almost_equal(self.ts[:15].cov(self.ts[5:]),
self.ts[5:15].std() ** 2)
# No overlap
assert np.isnan(self.ts[::2].cov(self.ts[1::2]))
# all NA
cp = self.ts[:10].copy()
cp[:] = np.nan
assert isna(cp.cov(cp))
# min_periods
assert isna(self.ts[:15].cov(self.ts[5:], min_periods=12))
ts1 = self.ts[:15].reindex(self.ts.index)
ts2 = self.ts[5:].reindex(self.ts.index)
assert isna(ts1.cov(ts2, min_periods=12))
def test_count(self):
assert self.ts.count() == len(self.ts)
self.ts[::2] = np.NaN
assert self.ts.count() == np.isfinite(self.ts).sum()
mi = MultiIndex.from_arrays([list('aabbcc'), [1, 2, 2, nan, 1, 2]])
ts = Series(np.arange(len(mi)), index=mi)
left = ts.count(level=1)
right = Series([2, 3, 1], index=[1, 2, nan])
assert_series_equal(left, right)
ts.iloc[[0, 3, 5]] = nan
assert_series_equal(ts.count(level=1), right - 1)
def test_dot(self):
a = Series(np.random.randn(4), index=['p', 'q', 'r', 's'])
b = DataFrame(np.random.randn(3, 4), index=['1', '2', '3'],
columns=['p', 'q', 'r', 's']).T
result = a.dot(b)
expected = Series(np.dot(a.values, b.values), index=['1', '2', '3'])
assert_series_equal(result, expected)
# Check index alignment
b2 = b.reindex(index=reversed(b.index))
result = a.dot(b)
assert_series_equal(result, expected)
# Check ndarray argument
result = a.dot(b.values)
assert np.all(result == expected.values)
assert_almost_equal(a.dot(b['2'].values), expected['2'])
# Check series argument
assert_almost_equal(a.dot(b['1']), expected['1'])
assert_almost_equal(a.dot(b2['1']), expected['1'])
pytest.raises(Exception, a.dot, a.values[:3])
pytest.raises(ValueError, a.dot, b.T)
def test_value_counts_nunique(self):
# basics.rst doc example
series = Series(np.random.randn(500))
series[20:500] = np.nan
series[10:20] = 5000
result = series.nunique()
assert result == 11
# GH 18051
s = pd.Series(pd.Categorical([]))
assert s.nunique() == 0
s = pd.Series(pd.Categorical([np.nan]))
assert s.nunique() == 0
def test_unique(self):
# 714 also, dtype=float
s = Series([1.2345] * 100)
s[::2] = np.nan
result = s.unique()
assert len(result) == 2
s = Series([1.2345] * 100, dtype='f4')
s[::2] = np.nan
result = s.unique()
assert len(result) == 2
# NAs in object arrays #714
s = Series(['foo'] * 100, dtype='O')
s[::2] = np.nan
result = s.unique()
assert len(result) == 2
# decision about None
s = Series([1, 2, 3, None, None, None], dtype=object)
result = s.unique()
expected = np.array([1, 2, 3, None], dtype=object)
tm.assert_numpy_array_equal(result, expected)
def test_drop_duplicates(self):
# check both int and object
for s in [Series([1, 2, 3, 3]), Series(['1', '2', '3', '3'])]:
expected = Series([False, False, False, True])
assert_series_equal(s.duplicated(), expected)
assert_series_equal(s.drop_duplicates(), s[~expected])
sc = s.copy()
sc.drop_duplicates(inplace=True)
assert_series_equal(sc, s[~expected])
expected = Series([False, False, True, False])
assert_series_equal(s.duplicated(keep='last'), expected)
assert_series_equal(s.drop_duplicates(keep='last'), s[~expected])
sc = s.copy()
sc.drop_duplicates(keep='last', inplace=True)
assert_series_equal(sc, s[~expected])
expected = Series([False, False, True, True])
assert_series_equal(s.duplicated(keep=False), expected)
assert_series_equal(s.drop_duplicates(keep=False), s[~expected])
sc = s.copy()
sc.drop_duplicates(keep=False, inplace=True)
assert_series_equal(sc, s[~expected])
for s in [Series([1, 2, 3, 5, 3, 2, 4]),
Series(['1', '2', '3', '5', '3', '2', '4'])]:
expected = Series([False, False, False, False, True, True, False])
assert_series_equal(s.duplicated(), expected)
assert_series_equal(s.drop_duplicates(), s[~expected])
sc = s.copy()
sc.drop_duplicates(inplace=True)
assert_series_equal(sc, s[~expected])
expected = Series([False, True, True, False, False, False, False])
assert_series_equal(s.duplicated(keep='last'), expected)
assert_series_equal(s.drop_duplicates(keep='last'), s[~expected])
sc = s.copy()
sc.drop_duplicates(keep='last', inplace=True)
assert_series_equal(sc, s[~expected])
expected = Series([False, True, True, False, True, True, False])
assert_series_equal(s.duplicated(keep=False), expected)
assert_series_equal(s.drop_duplicates(keep=False), s[~expected])
sc = s.copy()
sc.drop_duplicates(keep=False, inplace=True)
assert_series_equal(sc, s[~expected])
# GH 18051
s = pd.Series(pd.Categorical([]))
tm.assert_categorical_equal(s.unique(), pd.Categorical([]),
check_dtype=False)
s = pd.Series(pd.Categorical([np.nan]))
tm.assert_categorical_equal(s.unique(), pd.Categorical([np.nan]),
check_dtype=False)
def test_clip(self):
val = self.ts.median()
assert self.ts.clip_lower(val).min() == val
assert self.ts.clip_upper(val).max() == val
assert self.ts.clip(lower=val).min() == val
assert self.ts.clip(upper=val).max() == val
result = self.ts.clip(-0.5, 0.5)
expected = np.clip(self.ts, -0.5, 0.5)
assert_series_equal(result, expected)
assert isinstance(expected, Series)
def test_clip_types_and_nulls(self):
sers = [Series([np.nan, 1.0, 2.0, 3.0]), Series([None, 'a', 'b', 'c']),
Series(pd.to_datetime(
[np.nan, 1, 2, 3], unit='D'))]
for s in sers:
thresh = s[2]
l = s.clip_lower(thresh)
u = s.clip_upper(thresh)
assert l[notna(l)].min() == thresh
assert u[notna(u)].max() == thresh
assert list(isna(s)) == list(isna(l))
assert list(isna(s)) == list(isna(u))
def test_clip_with_na_args(self):
"""Should process np.nan argument as None """
# GH # 17276
s = Series([1, 2, 3])
assert_series_equal(s.clip(np.nan), Series([1, 2, 3]))
assert_series_equal(s.clip(upper=[1, 1, np.nan]), Series([1, 2, 3]))
assert_series_equal(s.clip(lower=[1, np.nan, 1]), Series([1, 2, 3]))
assert_series_equal(s.clip(upper=np.nan, lower=np.nan),
Series([1, 2, 3]))
def test_clip_against_series(self):
# GH #6966
s = Series([1.0, 1.0, 4.0])
threshold = Series([1.0, 2.0, 3.0])
assert_series_equal(s.clip_lower(threshold), Series([1.0, 2.0, 4.0]))
assert_series_equal(s.clip_upper(threshold), Series([1.0, 1.0, 3.0]))
lower = Series([1.0, 2.0, 3.0])
upper = Series([1.5, 2.5, 3.5])
assert_series_equal(s.clip(lower, upper), Series([1.0, 2.0, 3.5]))
assert_series_equal(s.clip(1.5, upper), Series([1.5, 1.5, 3.5]))
@pytest.mark.parametrize("inplace", [True, False])
@pytest.mark.parametrize("upper", [[1, 2, 3], np.asarray([1, 2, 3])])
def test_clip_against_list_like(self, inplace, upper):
# GH #15390
original = pd.Series([5, 6, 7])
result = original.clip(upper=upper, inplace=inplace)
expected = pd.Series([1, 2, 3])
if inplace:
result = original
tm.assert_series_equal(result, expected, check_exact=True)
def test_clip_with_datetimes(self):
# GH 11838
# naive and tz-aware datetimes
t = Timestamp('2015-12-01 09:30:30')
s = Series([Timestamp('2015-12-01 09:30:00'),
Timestamp('2015-12-01 09:31:00')])
result = s.clip(upper=t)
expected = Series([Timestamp('2015-12-01 09:30:00'),
Timestamp('2015-12-01 09:30:30')])
assert_series_equal(result, expected)
t = Timestamp('2015-12-01 09:30:30', tz='US/Eastern')
s = Series([Timestamp('2015-12-01 09:30:00', tz='US/Eastern'),
Timestamp('2015-12-01 09:31:00', tz='US/Eastern')])
result = s.clip(upper=t)
expected = Series([Timestamp('2015-12-01 09:30:00', tz='US/Eastern'),
Timestamp('2015-12-01 09:30:30', tz='US/Eastern')])
assert_series_equal(result, expected)
def test_cummethods_bool(self):
# GH 6270
# looks like a buggy np.maximum.accumulate for numpy 1.6.1, py 3.2
def cummin(x):
return np.minimum.accumulate(x)
def cummax(x):
return np.maximum.accumulate(x)
a = pd.Series([False, False, False, True, True, False, False])
b = ~a
c = pd.Series([False] * len(b))
d = ~c
methods = {'cumsum': np.cumsum,
'cumprod': np.cumprod,
'cummin': cummin,
'cummax': cummax}
args = product((a, b, c, d), methods)
for s, method in args:
expected = Series(methods[method](s.values))
result = getattr(s, method)()
assert_series_equal(result, expected)
e = pd.Series([False, True, nan, False])
cse = pd.Series([0, 1, nan, 1], dtype=object)
cpe = pd.Series([False, 0, nan, 0])
cmin = pd.Series([False, False, nan, False])
cmax = pd.Series([False, True, nan, True])
expecteds = {'cumsum': cse,
'cumprod': cpe,
'cummin': cmin,
'cummax': cmax}
for method in methods:
res = getattr(e, method)()
assert_series_equal(res, expecteds[method])
def test_isin(self):
s = Series(['A', 'B', 'C', 'a', 'B', 'B', 'A', 'C'])
result = s.isin(['A', 'C'])
expected = Series([True, False, True, False, False, False, True, True])
assert_series_equal(result, expected)
# GH: 16012
# This specific issue has to have a series over 1e6 in len, but the
# comparison array (in_list) must be large enough so that numpy doesn't
# do a manual masking trick that will avoid this issue altogether
s = Series(list('abcdefghijk' * 10 ** 5))
# If numpy doesn't do the manual comparison/mask, these
# unorderable mixed types are what cause the exception in numpy
in_list = [-1, 'a', 'b', 'G', 'Y', 'Z', 'E',
'K', 'E', 'S', 'I', 'R', 'R'] * 6
assert s.isin(in_list).sum() == 200000
def test_isin_with_string_scalar(self):
# GH4763
s = Series(['A', 'B', 'C', 'a', 'B', 'B', 'A', 'C'])
with pytest.raises(TypeError):
s.isin('a')
with pytest.raises(TypeError):
s = Series(['aaa', 'b', 'c'])
s.isin('aaa')
def test_isin_with_i8(self):
# GH 5021
expected = Series([True, True, False, False, False])
expected2 = Series([False, True, False, False, False])
# datetime64[ns]
s = Series(date_range('jan-01-2013', 'jan-05-2013'))
result = s.isin(s[0:2])
assert_series_equal(result, expected)
result = s.isin(s[0:2].values)
assert_series_equal(result, expected)
# fails on dtype conversion in the first place
result = s.isin(s[0:2].values.astype('datetime64[D]'))
assert_series_equal(result, expected)
result = s.isin([s[1]])
assert_series_equal(result, expected2)
result = s.isin([np.datetime64(s[1])])
assert_series_equal(result, expected2)
result = s.isin(set(s[0:2]))
assert_series_equal(result, expected)
# timedelta64[ns]
s = Series(pd.to_timedelta(lrange(5), unit='d'))
result = s.isin(s[0:2])
assert_series_equal(result, expected)
@pytest.mark.parametrize("empty", [[], Series(), np.array([])])
def test_isin_empty(self, empty):
# see gh-16991
s = Series(["a", "b"])
expected = Series([False, False])
result = s.isin(empty)
tm.assert_series_equal(expected, result)
def test_timedelta64_analytics(self):
from pandas import date_range
# index min/max
td = Series(date_range('2012-1-1', periods=3, freq='D')) - \
Timestamp('20120101')
result = td.idxmin()
assert result == 0
result = td.idxmax()
assert result == 2
# GH 2982
# with NaT
td[0] = np.nan
result = td.idxmin()
assert result == 1
result = td.idxmax()
assert result == 2
# abs
s1 = Series(date_range('20120101', periods=3))
s2 = Series(date_range('20120102', periods=3))
expected = Series(s2 - s1)
# this fails as numpy returns timedelta64[us]
# result = np.abs(s1-s2)
# assert_frame_equal(result,expected)
result = (s1 - s2).abs()
assert_series_equal(result, expected)
# max/min
result = td.max()
expected = Timedelta('2 days')
assert result == expected
result = td.min()
expected = Timedelta('1 days')
assert result == expected
def test_idxmin(self):
# test idxmin
# _check_stat_op approach can not be used here because of isna check.
# add some NaNs
self.series[5:15] = np.NaN
# skipna or no
assert self.series[self.series.idxmin()] == self.series.min()
assert isna(self.series.idxmin(skipna=False))
# no NaNs
nona = self.series.dropna()
assert nona[nona.idxmin()] == nona.min()
assert (nona.index.values.tolist().index(nona.idxmin()) ==
nona.values.argmin())
# all NaNs
allna = self.series * nan
assert isna(allna.idxmin())
# datetime64[ns]
from pandas import date_range
s = Series(date_range('20130102', periods=6))
result = s.idxmin()
assert result == 0
s[0] = np.nan
result = s.idxmin()
assert result == 1
def test_numpy_argmin_deprecated(self):
# See gh-16830
data = np.arange(1, 11)
s = Series(data, index=data)
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
# The deprecation of Series.argmin also causes a deprecation
# warning when calling np.argmin. This behavior is temporary
# until the implemention of Series.argmin is corrected.
result = np.argmin(s)
assert result == 1
with tm.assert_produces_warning(FutureWarning):
# argmin is aliased to idxmin
result = s.argmin()
assert result == 1
if not _np_version_under1p10:
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
msg = "the 'out' parameter is not supported"
tm.assert_raises_regex(ValueError, msg, np.argmin,
s, out=data)
def test_idxmax(self):
# test idxmax
# _check_stat_op approach can not be used here because of isna check.
# add some NaNs
self.series[5:15] = np.NaN
# skipna or no
assert self.series[self.series.idxmax()] == self.series.max()
assert isna(self.series.idxmax(skipna=False))
# no NaNs
nona = self.series.dropna()
assert nona[nona.idxmax()] == nona.max()
assert (nona.index.values.tolist().index(nona.idxmax()) ==
nona.values.argmax())
# all NaNs
allna = self.series * nan
assert isna(allna.idxmax())
from pandas import date_range
s = Series(date_range('20130102', periods=6))
result = s.idxmax()
assert result == 5
s[5] = np.nan
result = s.idxmax()
assert result == 4
# Float64Index
# GH 5914
s = pd.Series([1, 2, 3], [1.1, 2.1, 3.1])
result = s.idxmax()
assert result == 3.1
result = s.idxmin()
assert result == 1.1
s = pd.Series(s.index, s.index)
result = s.idxmax()
assert result == 3.1
result = s.idxmin()
assert result == 1.1
def test_numpy_argmax_deprecated(self):
# See gh-16830
data = np.arange(1, 11)
s = Series(data, index=data)
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
# The deprecation of Series.argmax also causes a deprecation
# warning when calling np.argmax. This behavior is temporary
# until the implemention of Series.argmax is corrected.
result = np.argmax(s)
assert result == 10
with tm.assert_produces_warning(FutureWarning):
# argmax is aliased to idxmax
result = s.argmax()
assert result == 10
if not _np_version_under1p10:
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
msg = "the 'out' parameter is not supported"
tm.assert_raises_regex(ValueError, msg, np.argmax,
s, out=data)
def test_ptp(self):
N = 1000
arr = np.random.randn(N)
ser = Series(arr)
assert np.ptp(ser) == np.ptp(arr)
# GH11163
s = Series([3, 5, np.nan, -3, 10])
assert s.ptp() == 13
assert pd.isna(s.ptp(skipna=False))
mi = pd.MultiIndex.from_product([['a', 'b'], [1, 2, 3]])
s = pd.Series([1, np.nan, 7, 3, 5, np.nan], index=mi)
expected = pd.Series([6, 2], index=['a', 'b'], dtype=np.float64)
tm.assert_series_equal(s.ptp(level=0), expected)
expected = pd.Series([np.nan, np.nan], index=['a', 'b'])
tm.assert_series_equal(s.ptp(level=0, skipna=False), expected)
with pytest.raises(ValueError):
s.ptp(axis=1)
s = pd.Series(['a', 'b', 'c', 'd', 'e'])
with pytest.raises(TypeError):
s.ptp()
with pytest.raises(NotImplementedError):
s.ptp(numeric_only=True)
def test_empty_timeseries_redections_return_nat(self):
# covers #11245
for dtype in ('m8[ns]', 'm8[ns]', 'M8[ns]', 'M8[ns, UTC]'):
assert Series([], dtype=dtype).min() is pd.NaT
assert Series([], dtype=dtype).max() is pd.NaT
def test_unique_data_ownership(self):
# it works! #1807
Series(Series(["a", "c", "b"]).unique()).sort_values()
def test_repeat(self):
s = Series(np.random.randn(3), index=['a', 'b', 'c'])
reps = s.repeat(5)
exp = Series(s.values.repeat(5), index=s.index.values.repeat(5))
assert_series_equal(reps, exp)
with tm.assert_produces_warning(FutureWarning):
result = s.repeat(reps=5)
assert_series_equal(result, exp)
to_rep = [2, 3, 4]
reps = s.repeat(to_rep)
exp = Series(s.values.repeat(to_rep),
index=s.index.values.repeat(to_rep))
assert_series_equal(reps, exp)
def test_numpy_repeat(self):
s = Series(np.arange(3), name='x')
expected = Series(s.values.repeat(2), name='x',
index=s.index.values.repeat(2))
assert_series_equal(np.repeat(s, 2), expected)
msg = "the 'axis' parameter is not supported"
tm.assert_raises_regex(ValueError, msg, np.repeat, s, 2, axis=0)
def test_searchsorted(self):
s = Series([1, 2, 3])
idx = s.searchsorted(1, side='left')
tm.assert_numpy_array_equal(idx, np.array([0], dtype=np.intp))
idx = s.searchsorted(1, side='right')
tm.assert_numpy_array_equal(idx, np.array([1], dtype=np.intp))
with tm.assert_produces_warning(FutureWarning):
idx = s.searchsorted(v=1, side='left')
tm.assert_numpy_array_equal(idx, np.array([0], dtype=np.intp))
def test_searchsorted_numeric_dtypes_scalar(self):
s = Series([1, 2, 90, 1000, 3e9])
r = s.searchsorted(30)
e = 2
assert r == e
r = s.searchsorted([30])
e = np.array([2], dtype=np.intp)
tm.assert_numpy_array_equal(r, e)
def test_searchsorted_numeric_dtypes_vector(self):
s = Series([1, 2, 90, 1000, 3e9])
r = s.searchsorted([91, 2e6])
e = np.array([3, 4], dtype=np.intp)
tm.assert_numpy_array_equal(r, e)
def test_search_sorted_datetime64_scalar(self):
s = Series(pd.date_range('20120101', periods=10, freq='2D'))
v = pd.Timestamp('20120102')
r = s.searchsorted(v)
e = 1
assert r == e
def test_search_sorted_datetime64_list(self):
s = Series(pd.date_range('20120101', periods=10, freq='2D'))
v = [pd.Timestamp('20120102'), pd.Timestamp('20120104')]
r = s.searchsorted(v)
e = np.array([1, 2], dtype=np.intp)
tm.assert_numpy_array_equal(r, e)
def test_searchsorted_sorter(self):
# GH8490
s = Series([3, 1, 2])
r = s.searchsorted([0, 3], sorter=np.argsort(s))
e = np.array([0, 2], dtype=np.intp)
tm.assert_numpy_array_equal(r, e)
def test_is_unique(self):
# GH11946
s = Series(np.random.randint(0, 10, size=1000))
assert not s.is_unique
s = Series(np.arange(1000))
assert s.is_unique
def test_is_monotonic(self):
s = Series(np.random.randint(0, 10, size=1000))
assert not s.is_monotonic
s = Series(np.arange(1000))
assert s.is_monotonic
assert s.is_monotonic_increasing
s = Series(np.arange(1000, 0, -1))
assert s.is_monotonic_decreasing
s = Series(pd.date_range('20130101', periods=10))
assert s.is_monotonic
assert s.is_monotonic_increasing
s = Series(list(reversed(s.tolist())))
assert not s.is_monotonic
assert s.is_monotonic_decreasing
def test_sort_index_level(self):
mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list('ABC'))
s = Series([1, 2], mi)
backwards = s.iloc[[1, 0]]
res = s.sort_index(level='A')
assert_series_equal(backwards, res)
res = s.sort_index(level=['A', 'B'])
assert_series_equal(backwards, res)
res = s.sort_index(level='A', sort_remaining=False)
assert_series_equal(s, res)
res = s.sort_index(level=['A', 'B'], sort_remaining=False)
assert_series_equal(s, res)
def test_apply_categorical(self):
values = pd.Categorical(list('ABBABCD'), categories=list('DCBA'),
ordered=True)
s = pd.Series(values, name='XX', index=list('abcdefg'))
result = s.apply(lambda x: x.lower())
# should be categorical dtype when the number of categories are
# the same
values = pd.Categorical(list('abbabcd'), categories=list('dcba'),
ordered=True)
exp = pd.Series(values, name='XX', index=list('abcdefg'))
tm.assert_series_equal(result, exp)
tm.assert_categorical_equal(result.values, exp.values)
result = s.apply(lambda x: 'A')
exp = pd.Series(['A'] * 7, name='XX', index=list('abcdefg'))
tm.assert_series_equal(result, exp)
assert result.dtype == np.object
def test_shift_int(self):
ts = self.ts.astype(int)
shifted = ts.shift(1)
expected = ts.astype(float).shift(1)
assert_series_equal(shifted, expected)
def test_shift_categorical(self):
# GH 9416
s = pd.Series(['a', 'b', 'c', 'd'], dtype='category')
assert_series_equal(s.iloc[:-1], s.shift(1).shift(-1).valid())
sp1 = s.shift(1)
assert_index_equal(s.index, sp1.index)
assert np.all(sp1.values.codes[:1] == -1)
assert np.all(s.values.codes[:-1] == sp1.values.codes[1:])
sn2 = s.shift(-2)
assert_index_equal(s.index, sn2.index)
assert np.all(sn2.values.codes[-2:] == -1)
assert np.all(s.values.codes[2:] == sn2.values.codes[:-2])
assert_index_equal(s.values.categories, sp1.values.categories)
assert_index_equal(s.values.categories, sn2.values.categories)
def test_reshape_deprecate(self):
x = Series(np.random.random(10), name='x')
tm.assert_produces_warning(FutureWarning, x.reshape, x.shape)
def test_reshape_non_2d(self):
# see gh-4554
with tm.assert_produces_warning(FutureWarning):
x = Series(np.random.random(201), name='x')
assert x.reshape(x.shape, ) is x
# see gh-2719
with tm.assert_produces_warning(FutureWarning):
a = Series([1, 2, 3, 4])
result = a.reshape(2, 2)
expected = a.values.reshape(2, 2)
tm.assert_numpy_array_equal(result, expected)
assert isinstance(result, type(expected))
def test_reshape_2d_return_array(self):
x = Series(np.random.random(201), name='x')
with tm.assert_produces_warning(FutureWarning):
result = x.reshape((-1, 1))
assert not isinstance(result, Series)
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result2 = np.reshape(x, (-1, 1))
assert not isinstance(result2, Series)
with tm.assert_produces_warning(FutureWarning):
result = x[:, None]
expected = x.reshape((-1, 1))
tm.assert_almost_equal(result, expected)
def test_reshape_bad_kwarg(self):
a = Series([1, 2, 3, 4])
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
msg = "'foo' is an invalid keyword argument for this function"
tm.assert_raises_regex(
TypeError, msg, a.reshape, (2, 2), foo=2)
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
msg = r"reshape\(\) got an unexpected keyword argument 'foo'"
tm.assert_raises_regex(
TypeError, msg, a.reshape, a.shape, foo=2)
def test_numpy_reshape(self):
a = Series([1, 2, 3, 4])
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result = np.reshape(a, (2, 2))
expected = a.values.reshape(2, 2)
tm.assert_numpy_array_equal(result, expected)
assert isinstance(result, type(expected))
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result = np.reshape(a, a.shape)
tm.assert_series_equal(result, a)
def test_unstack(self):
from numpy import nan
index = MultiIndex(levels=[['bar', 'foo'], ['one', 'three', 'two']],
labels=[[1, 1, 0, 0], [0, 1, 0, 2]])
s = Series(np.arange(4.), index=index)
unstacked = s.unstack()
expected = DataFrame([[2., nan, 3.], [0., 1., nan]],
index=['bar', 'foo'],
columns=['one', 'three', 'two'])
assert_frame_equal(unstacked, expected)
unstacked = s.unstack(level=0)
assert_frame_equal(unstacked, expected.T)
index = MultiIndex(levels=[['bar'], ['one', 'two', 'three'], [0, 1]],
labels=[[0, 0, 0, 0, 0, 0], [0, 1, 2, 0, 1, 2],
[0, 1, 0, 1, 0, 1]])
s = Series(np.random.randn(6), index=index)
exp_index = MultiIndex(levels=[['one', 'two', 'three'], [0, 1]],
labels=[[0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]])
expected = DataFrame({'bar': s.values},
index=exp_index).sort_index(level=0)
unstacked = s.unstack(0).sort_index()
assert_frame_equal(unstacked, expected)
# GH5873
idx = pd.MultiIndex.from_arrays([[101, 102], [3.5, np.nan]])
ts = pd.Series([1, 2], index=idx)
left = ts.unstack()
right = DataFrame([[nan, 1], [2, nan]], index=[101, 102],
columns=[nan, 3.5])
assert_frame_equal(left, right)
idx = pd.MultiIndex.from_arrays([['cat', 'cat', 'cat', 'dog', 'dog'
], ['a', 'a', 'b', 'a', 'b'],
[1, 2, 1, 1, np.nan]])
ts = pd.Series([1.0, 1.1, 1.2, 1.3, 1.4], index=idx)
right = DataFrame([[1.0, 1.3], [1.1, nan], [nan, 1.4], [1.2, nan]],
columns=['cat', 'dog'])
tpls = [('a', 1), ('a', 2), ('b', nan), ('b', 1)]
right.index = pd.MultiIndex.from_tuples(tpls)
assert_frame_equal(ts.unstack(level=0), right)
def test_value_counts_datetime(self):
# most dtypes are tested in test_base.py
values = [pd.Timestamp('2011-01-01 09:00'),
pd.Timestamp('2011-01-01 10:00'),
pd.Timestamp('2011-01-01 11:00'),
pd.Timestamp('2011-01-01 09:00'),
pd.Timestamp('2011-01-01 09:00'),
pd.Timestamp('2011-01-01 11:00')]
exp_idx = pd.DatetimeIndex(['2011-01-01 09:00', '2011-01-01 11:00',
'2011-01-01 10:00'])
exp = pd.Series([3, 2, 1], index=exp_idx, name='xxx')
s = pd.Series(values, name='xxx')
tm.assert_series_equal(s.value_counts(), exp)
# check DatetimeIndex outputs the same result
idx = pd.DatetimeIndex(values, name='xxx')
tm.assert_series_equal(idx.value_counts(), exp)
# normalize
exp = pd.Series(np.array([3., 2., 1]) / 6.,
index=exp_idx, name='xxx')
tm.assert_series_equal(s.value_counts(normalize=True), exp)
tm.assert_series_equal(idx.value_counts(normalize=True), exp)
def test_value_counts_datetime_tz(self):
values = [pd.Timestamp('2011-01-01 09:00', tz='US/Eastern'),
pd.Timestamp('2011-01-01 10:00', tz='US/Eastern'),
pd.Timestamp('2011-01-01 11:00', tz='US/Eastern'),
pd.Timestamp('2011-01-01 09:00', tz='US/Eastern'),
pd.Timestamp('2011-01-01 09:00', tz='US/Eastern'),
pd.Timestamp('2011-01-01 11:00', tz='US/Eastern')]
exp_idx = pd.DatetimeIndex(['2011-01-01 09:00', '2011-01-01 11:00',
'2011-01-01 10:00'], tz='US/Eastern')
exp = pd.Series([3, 2, 1], index=exp_idx, name='xxx')
s = pd.Series(values, name='xxx')
tm.assert_series_equal(s.value_counts(), exp)
idx = pd.DatetimeIndex(values, name='xxx')
tm.assert_series_equal(idx.value_counts(), exp)
exp = pd.Series(np.array([3., 2., 1]) / 6.,
index=exp_idx, name='xxx')
tm.assert_series_equal(s.value_counts(normalize=True), exp)
tm.assert_series_equal(idx.value_counts(normalize=True), exp)
def test_value_counts_period(self):
values = [pd.Period('2011-01', freq='M'),
pd.Period('2011-02', freq='M'),
pd.Period('2011-03', freq='M'),
pd.Period('2011-01', freq='M'),
pd.Period('2011-01', freq='M'),
pd.Period('2011-03', freq='M')]
exp_idx = pd.PeriodIndex(['2011-01', '2011-03', '2011-02'], freq='M')
exp = pd.Series([3, 2, 1], index=exp_idx, name='xxx')
s = pd.Series(values, name='xxx')
tm.assert_series_equal(s.value_counts(), exp)
# check DatetimeIndex outputs the same result
idx = pd.PeriodIndex(values, name='xxx')
tm.assert_series_equal(idx.value_counts(), exp)
# normalize
exp = pd.Series(np.array([3., 2., 1]) / 6.,
index=exp_idx, name='xxx')
tm.assert_series_equal(s.value_counts(normalize=True), exp)
tm.assert_series_equal(idx.value_counts(normalize=True), exp)
def test_value_counts_categorical_ordered(self):
# most dtypes are tested in test_base.py
values = pd.Categorical([1, 2, 3, 1, 1, 3], ordered=True)
exp_idx = pd.CategoricalIndex([1, 3, 2], categories=[1, 2, 3],
ordered=True)
exp = pd.Series([3, 2, 1], index=exp_idx, name='xxx')
s = pd.Series(values, name='xxx')
tm.assert_series_equal(s.value_counts(), exp)
# check CategoricalIndex outputs the same result
idx = pd.CategoricalIndex(values, name='xxx')
tm.assert_series_equal(idx.value_counts(), exp)
# normalize
exp = pd.Series(np.array([3., 2., 1]) / 6.,
index=exp_idx, name='xxx')
tm.assert_series_equal(s.value_counts(normalize=True), exp)
tm.assert_series_equal(idx.value_counts(normalize=True), exp)
def test_value_counts_categorical_not_ordered(self):
values = pd.Categorical([1, 2, 3, 1, 1, 3], ordered=False)
exp_idx = pd.CategoricalIndex([1, 3, 2], categories=[1, 2, 3],
ordered=False)
exp = pd.Series([3, 2, 1], index=exp_idx, name='xxx')
s = pd.Series(values, name='xxx')
tm.assert_series_equal(s.value_counts(), exp)
# check CategoricalIndex outputs the same result
idx = pd.CategoricalIndex(values, name='xxx')
tm.assert_series_equal(idx.value_counts(), exp)
# normalize
exp = pd.Series(np.array([3., 2., 1]) / 6.,
index=exp_idx, name='xxx')
tm.assert_series_equal(s.value_counts(normalize=True), exp)
tm.assert_series_equal(idx.value_counts(normalize=True), exp)
@pytest.fixture
def s_main_dtypes():
df = pd.DataFrame(
{'datetime': pd.to_datetime(['2003', '2002',
'2001', '2002',
'2005']),
'datetimetz': pd.to_datetime(
['2003', '2002',
'2001', '2002',
'2005']).tz_localize('US/Eastern'),
'timedelta': pd.to_timedelta(['3d', '2d', '1d',
'2d', '5d'])})
for dtype in ['int8', 'int16', 'int32', 'int64',
'float32', 'float64',
'uint8', 'uint16', 'uint32', 'uint64']:
df[dtype] = Series([3, 2, 1, 2, 5], dtype=dtype)
return df
class TestNLargestNSmallest(object):
@pytest.mark.parametrize(
"r", [Series([3., 2, 1, 2, '5'], dtype='object'),
Series([3., 2, 1, 2, 5], dtype='object'),
# not supported on some archs
# Series([3., 2, 1, 2, 5], dtype='complex256'),
Series([3., 2, 1, 2, 5], dtype='complex128'),
Series(list('abcde')),
Series(list('abcde'), dtype='category')])
def test_error(self, r):
dt = r.dtype
msg = ("Cannot use method 'n(larg|small)est' with "
"dtype {dt}".format(dt=dt))
args = 2, len(r), 0, -1
methods = r.nlargest, r.nsmallest
for method, arg in product(methods, args):
with tm.assert_raises_regex(TypeError, msg):
method(arg)
@pytest.mark.parametrize(
"s",
[v for k, v in s_main_dtypes().iteritems()])
def test_nsmallest_nlargest(self, s):
# float, int, datetime64 (use i8), timedelts64 (same),
# object that are numbers, object that are strings
assert_series_equal(s.nsmallest(2), s.iloc[[2, 1]])
assert_series_equal(s.nsmallest(2, keep='last'), s.iloc[[2, 3]])
empty = s.iloc[0:0]
assert_series_equal(s.nsmallest(0), empty)
assert_series_equal(s.nsmallest(-1), empty)
assert_series_equal(s.nlargest(0), empty)
assert_series_equal(s.nlargest(-1), empty)
assert_series_equal(s.nsmallest(len(s)), s.sort_values())
assert_series_equal(s.nsmallest(len(s) + 1), s.sort_values())
assert_series_equal(s.nlargest(len(s)), s.iloc[[4, 0, 1, 3, 2]])
assert_series_equal(s.nlargest(len(s) + 1),
s.iloc[[4, 0, 1, 3, 2]])
def test_misc(self):
s = Series([3., np.nan, 1, 2, 5])
assert_series_equal(s.nlargest(), s.iloc[[4, 0, 3, 2]])
assert_series_equal(s.nsmallest(), s.iloc[[2, 3, 0, 4]])
msg = 'keep must be either "first", "last"'
with tm.assert_raises_regex(ValueError, msg):
s.nsmallest(keep='invalid')
with tm.assert_raises_regex(ValueError, msg):
s.nlargest(keep='invalid')
# GH 15297
s = Series([1] * 5, index=[1, 2, 3, 4, 5])
expected_first = Series([1] * 3, index=[1, 2, 3])
expected_last = Series([1] * 3, index=[5, 4, 3])
result = s.nsmallest(3)
assert_series_equal(result, expected_first)
result = s.nsmallest(3, keep='last')
assert_series_equal(result, expected_last)
result = s.nlargest(3)
assert_series_equal(result, expected_first)
result = s.nlargest(3, keep='last')
assert_series_equal(result, expected_last)
@pytest.mark.parametrize('n', range(1, 5))
def test_n(self, n):
# GH 13412
s = Series([1, 4, 3, 2], index=[0, 0, 1, 1])
result = s.nlargest(n)
expected = s.sort_values(ascending=False).head(n)
assert_series_equal(result, expected)
result = s.nsmallest(n)
expected = s.sort_values().head(n)
assert_series_equal(result, expected)