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test_quantile.py
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test_quantile.py
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# -*- coding: utf-8 -*-
from __future__ import print_function
import pytest
import numpy as np
from pandas import (DataFrame, Series, Timestamp, _np_version_under1p11)
import pandas as pd
from pandas.util.testing import assert_series_equal, assert_frame_equal
import pandas.util.testing as tm
from pandas.tests.frame.common import TestData
class TestDataFrameQuantile(TestData):
def test_quantile(self):
from numpy import percentile
q = self.tsframe.quantile(0.1, axis=0)
assert q['A'] == percentile(self.tsframe['A'], 10)
tm.assert_index_equal(q.index, self.tsframe.columns)
q = self.tsframe.quantile(0.9, axis=1)
assert (q['2000-01-17'] ==
percentile(self.tsframe.loc['2000-01-17'], 90))
tm.assert_index_equal(q.index, self.tsframe.index)
# test degenerate case
q = DataFrame({'x': [], 'y': []}).quantile(0.1, axis=0)
assert(np.isnan(q['x']) and np.isnan(q['y']))
# non-numeric exclusion
df = DataFrame({'col1': ['A', 'A', 'B', 'B'], 'col2': [1, 2, 3, 4]})
rs = df.quantile(0.5)
xp = df.median().rename(0.5)
assert_series_equal(rs, xp)
# axis
df = DataFrame({"A": [1, 2, 3], "B": [2, 3, 4]}, index=[1, 2, 3])
result = df.quantile(.5, axis=1)
expected = Series([1.5, 2.5, 3.5], index=[1, 2, 3], name=0.5)
assert_series_equal(result, expected)
result = df.quantile([.5, .75], axis=1)
expected = DataFrame({1: [1.5, 1.75], 2: [2.5, 2.75],
3: [3.5, 3.75]}, index=[0.5, 0.75])
assert_frame_equal(result, expected, check_index_type=True)
# We may want to break API in the future to change this
# so that we exclude non-numeric along the same axis
# See GH #7312
df = DataFrame([[1, 2, 3],
['a', 'b', 4]])
result = df.quantile(.5, axis=1)
expected = Series([3., 4.], index=[0, 1], name=0.5)
assert_series_equal(result, expected)
def test_quantile_axis_mixed(self):
# mixed on axis=1
df = DataFrame({"A": [1, 2, 3],
"B": [2., 3., 4.],
"C": pd.date_range('20130101', periods=3),
"D": ['foo', 'bar', 'baz']})
result = df.quantile(.5, axis=1)
expected = Series([1.5, 2.5, 3.5], name=0.5)
assert_series_equal(result, expected)
# must raise
def f():
df.quantile(.5, axis=1, numeric_only=False)
pytest.raises(TypeError, f)
def test_quantile_axis_parameter(self):
# GH 9543/9544
df = DataFrame({"A": [1, 2, 3], "B": [2, 3, 4]}, index=[1, 2, 3])
result = df.quantile(.5, axis=0)
expected = Series([2., 3.], index=["A", "B"], name=0.5)
assert_series_equal(result, expected)
expected = df.quantile(.5, axis="index")
assert_series_equal(result, expected)
result = df.quantile(.5, axis=1)
expected = Series([1.5, 2.5, 3.5], index=[1, 2, 3], name=0.5)
assert_series_equal(result, expected)
result = df.quantile(.5, axis="columns")
assert_series_equal(result, expected)
pytest.raises(ValueError, df.quantile, 0.1, axis=-1)
pytest.raises(ValueError, df.quantile, 0.1, axis="column")
def test_quantile_interpolation(self):
# see gh-10174
from numpy import percentile
# interpolation = linear (default case)
q = self.tsframe.quantile(0.1, axis=0, interpolation='linear')
assert q['A'] == percentile(self.tsframe['A'], 10)
q = self.intframe.quantile(0.1)
assert q['A'] == percentile(self.intframe['A'], 10)
# test with and without interpolation keyword
q1 = self.intframe.quantile(0.1)
assert q1['A'] == np.percentile(self.intframe['A'], 10)
tm.assert_series_equal(q, q1)
# interpolation method other than default linear
df = DataFrame({"A": [1, 2, 3], "B": [2, 3, 4]}, index=[1, 2, 3])
result = df.quantile(.5, axis=1, interpolation='nearest')
expected = Series([1, 2, 3], index=[1, 2, 3], name=0.5)
tm.assert_series_equal(result, expected)
# cross-check interpolation=nearest results in original dtype
exp = np.percentile(np.array([[1, 2, 3], [2, 3, 4]]), .5,
axis=0, interpolation='nearest')
expected = Series(exp, index=[1, 2, 3], name=0.5, dtype='int64')
tm.assert_series_equal(result, expected)
# float
df = DataFrame({"A": [1., 2., 3.], "B": [2., 3., 4.]}, index=[1, 2, 3])
result = df.quantile(.5, axis=1, interpolation='nearest')
expected = Series([1., 2., 3.], index=[1, 2, 3], name=0.5)
tm.assert_series_equal(result, expected)
exp = np.percentile(np.array([[1., 2., 3.], [2., 3., 4.]]), .5,
axis=0, interpolation='nearest')
expected = Series(exp, index=[1, 2, 3], name=0.5, dtype='float64')
assert_series_equal(result, expected)
# axis
result = df.quantile([.5, .75], axis=1, interpolation='lower')
expected = DataFrame({1: [1., 1.], 2: [2., 2.],
3: [3., 3.]}, index=[0.5, 0.75])
assert_frame_equal(result, expected)
# test degenerate case
df = DataFrame({'x': [], 'y': []})
q = df.quantile(0.1, axis=0, interpolation='higher')
assert(np.isnan(q['x']) and np.isnan(q['y']))
# multi
df = DataFrame([[1, 1, 1], [2, 2, 2], [3, 3, 3]],
columns=['a', 'b', 'c'])
result = df.quantile([.25, .5], interpolation='midpoint')
# https://github.com/numpy/numpy/issues/7163
if _np_version_under1p11:
expected = DataFrame([[1.5, 1.5, 1.5], [2.5, 2.5, 2.5]],
index=[.25, .5], columns=['a', 'b', 'c'])
else:
expected = DataFrame([[1.5, 1.5, 1.5], [2.0, 2.0, 2.0]],
index=[.25, .5], columns=['a', 'b', 'c'])
assert_frame_equal(result, expected)
def test_quantile_multi(self):
df = DataFrame([[1, 1, 1], [2, 2, 2], [3, 3, 3]],
columns=['a', 'b', 'c'])
result = df.quantile([.25, .5])
expected = DataFrame([[1.5, 1.5, 1.5], [2., 2., 2.]],
index=[.25, .5], columns=['a', 'b', 'c'])
assert_frame_equal(result, expected)
# axis = 1
result = df.quantile([.25, .5], axis=1)
expected = DataFrame([[1.5, 1.5, 1.5], [2., 2., 2.]],
index=[.25, .5], columns=[0, 1, 2])
# empty
result = DataFrame({'x': [], 'y': []}).quantile([0.1, .9], axis=0)
expected = DataFrame({'x': [np.nan, np.nan], 'y': [np.nan, np.nan]},
index=[.1, .9])
assert_frame_equal(result, expected)
def test_quantile_datetime(self):
df = DataFrame({'a': pd.to_datetime(['2010', '2011']), 'b': [0, 5]})
# exclude datetime
result = df.quantile(.5)
expected = Series([2.5], index=['b'])
# datetime
result = df.quantile(.5, numeric_only=False)
expected = Series([Timestamp('2010-07-02 12:00:00'), 2.5],
index=['a', 'b'],
name=0.5)
assert_series_equal(result, expected)
# datetime w/ multi
result = df.quantile([.5], numeric_only=False)
expected = DataFrame([[Timestamp('2010-07-02 12:00:00'), 2.5]],
index=[.5], columns=['a', 'b'])
assert_frame_equal(result, expected)
# axis = 1
df['c'] = pd.to_datetime(['2011', '2012'])
result = df[['a', 'c']].quantile(.5, axis=1, numeric_only=False)
expected = Series([Timestamp('2010-07-02 12:00:00'),
Timestamp('2011-07-02 12:00:00')],
index=[0, 1],
name=0.5)
assert_series_equal(result, expected)
result = df[['a', 'c']].quantile([.5], axis=1, numeric_only=False)
expected = DataFrame([[Timestamp('2010-07-02 12:00:00'),
Timestamp('2011-07-02 12:00:00')]],
index=[0.5], columns=[0, 1])
assert_frame_equal(result, expected)
# empty when numeric_only=True
# FIXME (gives empty frame in 0.18.1, broken in 0.19.0)
# result = df[['a', 'c']].quantile(.5)
# result = df[['a', 'c']].quantile([.5])
def test_quantile_invalid(self):
msg = 'percentiles should all be in the interval \\[0, 1\\]'
for invalid in [-1, 2, [0.5, -1], [0.5, 2]]:
with tm.assert_raises_regex(ValueError, msg):
self.tsframe.quantile(invalid)
def test_quantile_box(self):
df = DataFrame({'A': [pd.Timestamp('2011-01-01'),
pd.Timestamp('2011-01-02'),
pd.Timestamp('2011-01-03')],
'B': [pd.Timestamp('2011-01-01', tz='US/Eastern'),
pd.Timestamp('2011-01-02', tz='US/Eastern'),
pd.Timestamp('2011-01-03', tz='US/Eastern')],
'C': [pd.Timedelta('1 days'),
pd.Timedelta('2 days'),
pd.Timedelta('3 days')]})
res = df.quantile(0.5, numeric_only=False)
exp = pd.Series([pd.Timestamp('2011-01-02'),
pd.Timestamp('2011-01-02', tz='US/Eastern'),
pd.Timedelta('2 days')],
name=0.5, index=['A', 'B', 'C'])
tm.assert_series_equal(res, exp)
res = df.quantile([0.5], numeric_only=False)
exp = pd.DataFrame([[pd.Timestamp('2011-01-02'),
pd.Timestamp('2011-01-02', tz='US/Eastern'),
pd.Timedelta('2 days')]],
index=[0.5], columns=['A', 'B', 'C'])
tm.assert_frame_equal(res, exp)
# DatetimeBlock may be consolidated and contain NaT in different loc
df = DataFrame({'A': [pd.Timestamp('2011-01-01'),
pd.NaT,
pd.Timestamp('2011-01-02'),
pd.Timestamp('2011-01-03')],
'a': [pd.Timestamp('2011-01-01'),
pd.Timestamp('2011-01-02'),
pd.NaT,
pd.Timestamp('2011-01-03')],
'B': [pd.Timestamp('2011-01-01', tz='US/Eastern'),
pd.NaT,
pd.Timestamp('2011-01-02', tz='US/Eastern'),
pd.Timestamp('2011-01-03', tz='US/Eastern')],
'b': [pd.Timestamp('2011-01-01', tz='US/Eastern'),
pd.Timestamp('2011-01-02', tz='US/Eastern'),
pd.NaT,
pd.Timestamp('2011-01-03', tz='US/Eastern')],
'C': [pd.Timedelta('1 days'),
pd.Timedelta('2 days'),
pd.Timedelta('3 days'),
pd.NaT],
'c': [pd.NaT,
pd.Timedelta('1 days'),
pd.Timedelta('2 days'),
pd.Timedelta('3 days')]},
columns=list('AaBbCc'))
res = df.quantile(0.5, numeric_only=False)
exp = pd.Series([pd.Timestamp('2011-01-02'),
pd.Timestamp('2011-01-02'),
pd.Timestamp('2011-01-02', tz='US/Eastern'),
pd.Timestamp('2011-01-02', tz='US/Eastern'),
pd.Timedelta('2 days'),
pd.Timedelta('2 days')],
name=0.5, index=list('AaBbCc'))
tm.assert_series_equal(res, exp)
res = df.quantile([0.5], numeric_only=False)
exp = pd.DataFrame([[pd.Timestamp('2011-01-02'),
pd.Timestamp('2011-01-02'),
pd.Timestamp('2011-01-02', tz='US/Eastern'),
pd.Timestamp('2011-01-02', tz='US/Eastern'),
pd.Timedelta('2 days'),
pd.Timedelta('2 days')]],
index=[0.5], columns=list('AaBbCc'))
tm.assert_frame_equal(res, exp)
def test_quantile_nan(self):
# GH 14357 - float block where some cols have missing values
df = DataFrame({'a': np.arange(1, 6.0), 'b': np.arange(1, 6.0)})
df.iloc[-1, 1] = np.nan
res = df.quantile(0.5)
exp = Series([3.0, 2.5], index=['a', 'b'], name=0.5)
tm.assert_series_equal(res, exp)
res = df.quantile([0.5, 0.75])
exp = DataFrame({'a': [3.0, 4.0], 'b': [2.5, 3.25]}, index=[0.5, 0.75])
tm.assert_frame_equal(res, exp)
res = df.quantile(0.5, axis=1)
exp = Series(np.arange(1.0, 6.0), name=0.5)
tm.assert_series_equal(res, exp)
res = df.quantile([0.5, 0.75], axis=1)
exp = DataFrame([np.arange(1.0, 6.0)] * 2, index=[0.5, 0.75])
tm.assert_frame_equal(res, exp)
# full-nan column
df['b'] = np.nan
res = df.quantile(0.5)
exp = Series([3.0, np.nan], index=['a', 'b'], name=0.5)
tm.assert_series_equal(res, exp)
res = df.quantile([0.5, 0.75])
exp = DataFrame({'a': [3.0, 4.0], 'b': [np.nan, np.nan]},
index=[0.5, 0.75])
tm.assert_frame_equal(res, exp)
def test_quantile_nat(self):
# full NaT column
df = DataFrame({'a': [pd.NaT, pd.NaT, pd.NaT]})
res = df.quantile(0.5, numeric_only=False)
exp = Series([pd.NaT], index=['a'], name=0.5)
tm.assert_series_equal(res, exp)
res = df.quantile([0.5], numeric_only=False)
exp = DataFrame({'a': [pd.NaT]}, index=[0.5])
tm.assert_frame_equal(res, exp)
# mixed non-null / full null column
df = DataFrame({'a': [pd.Timestamp('2012-01-01'),
pd.Timestamp('2012-01-02'),
pd.Timestamp('2012-01-03')],
'b': [pd.NaT, pd.NaT, pd.NaT]})
res = df.quantile(0.5, numeric_only=False)
exp = Series([pd.Timestamp('2012-01-02'), pd.NaT], index=['a', 'b'],
name=0.5)
tm.assert_series_equal(res, exp)
res = df.quantile([0.5], numeric_only=False)
exp = DataFrame([[pd.Timestamp('2012-01-02'), pd.NaT]], index=[0.5],
columns=['a', 'b'])
tm.assert_frame_equal(res, exp)
def test_quantile_empty(self):
# floats
df = DataFrame(columns=['a', 'b'], dtype='float64')
res = df.quantile(0.5)
exp = Series([np.nan, np.nan], index=['a', 'b'], name=0.5)
tm.assert_series_equal(res, exp)
res = df.quantile([0.5])
exp = DataFrame([[np.nan, np.nan]], columns=['a', 'b'], index=[0.5])
tm.assert_frame_equal(res, exp)
# FIXME (gives empty frame in 0.18.1, broken in 0.19.0)
# res = df.quantile(0.5, axis=1)
# res = df.quantile([0.5], axis=1)
# ints
df = DataFrame(columns=['a', 'b'], dtype='int64')
# FIXME (gives empty frame in 0.18.1, broken in 0.19.0)
# res = df.quantile(0.5)
# datetimes
df = DataFrame(columns=['a', 'b'], dtype='datetime64[ns]')
# FIXME (gives NaNs instead of NaT in 0.18.1 or 0.19.0)
# res = df.quantile(0.5, numeric_only=False)