8th day of python challenges 111-117
This commit is contained in:
abd.shallal
367
venv/lib/python3.6/site-packages/pandas/tests/window/test_api.py
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367
venv/lib/python3.6/site-packages/pandas/tests/window/test_api.py
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from collections import OrderedDict
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import warnings
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from warnings import catch_warnings
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import numpy as np
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import pytest
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import pandas.util._test_decorators as td
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import pandas as pd
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from pandas import DataFrame, Index, Series, Timestamp, concat
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from pandas.core.base import SpecificationError
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from pandas.tests.window.common import Base
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import pandas.util.testing as tm
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class TestApi(Base):
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def setup_method(self, method):
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self._create_data()
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def test_getitem(self):
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r = self.frame.rolling(window=5)
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tm.assert_index_equal(r._selected_obj.columns, self.frame.columns)
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r = self.frame.rolling(window=5)[1]
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assert r._selected_obj.name == self.frame.columns[1]
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# technically this is allowed
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r = self.frame.rolling(window=5)[1, 3]
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tm.assert_index_equal(r._selected_obj.columns, self.frame.columns[[1, 3]])
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r = self.frame.rolling(window=5)[[1, 3]]
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tm.assert_index_equal(r._selected_obj.columns, self.frame.columns[[1, 3]])
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def test_select_bad_cols(self):
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df = DataFrame([[1, 2]], columns=["A", "B"])
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g = df.rolling(window=5)
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with pytest.raises(KeyError, match="Columns not found: 'C'"):
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g[["C"]]
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with pytest.raises(KeyError, match="^[^A]+$"):
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# A should not be referenced as a bad column...
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# will have to rethink regex if you change message!
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g[["A", "C"]]
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def test_attribute_access(self):
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df = DataFrame([[1, 2]], columns=["A", "B"])
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r = df.rolling(window=5)
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tm.assert_series_equal(r.A.sum(), r["A"].sum())
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msg = "'Rolling' object has no attribute 'F'"
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with pytest.raises(AttributeError, match=msg):
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r.F
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def tests_skip_nuisance(self):
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df = DataFrame({"A": range(5), "B": range(5, 10), "C": "foo"})
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r = df.rolling(window=3)
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result = r[["A", "B"]].sum()
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expected = DataFrame(
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{"A": [np.nan, np.nan, 3, 6, 9], "B": [np.nan, np.nan, 18, 21, 24]},
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columns=list("AB"),
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)
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tm.assert_frame_equal(result, expected)
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def test_skip_sum_object_raises(self):
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df = DataFrame({"A": range(5), "B": range(5, 10), "C": "foo"})
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r = df.rolling(window=3)
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result = r.sum()
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expected = DataFrame(
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{"A": [np.nan, np.nan, 3, 6, 9], "B": [np.nan, np.nan, 18, 21, 24]},
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columns=list("AB"),
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)
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tm.assert_frame_equal(result, expected)
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def test_agg(self):
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df = DataFrame({"A": range(5), "B": range(0, 10, 2)})
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r = df.rolling(window=3)
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a_mean = r["A"].mean()
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a_std = r["A"].std()
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a_sum = r["A"].sum()
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b_mean = r["B"].mean()
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b_std = r["B"].std()
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b_sum = r["B"].sum()
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result = r.aggregate([np.mean, np.std])
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expected = concat([a_mean, a_std, b_mean, b_std], axis=1)
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expected.columns = pd.MultiIndex.from_product([["A", "B"], ["mean", "std"]])
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tm.assert_frame_equal(result, expected)
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result = r.aggregate({"A": np.mean, "B": np.std})
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expected = concat([a_mean, b_std], axis=1)
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tm.assert_frame_equal(result, expected, check_like=True)
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result = r.aggregate({"A": ["mean", "std"]})
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expected = concat([a_mean, a_std], axis=1)
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expected.columns = pd.MultiIndex.from_tuples([("A", "mean"), ("A", "std")])
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tm.assert_frame_equal(result, expected)
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result = r["A"].aggregate(["mean", "sum"])
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expected = concat([a_mean, a_sum], axis=1)
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expected.columns = ["mean", "sum"]
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tm.assert_frame_equal(result, expected)
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with catch_warnings(record=True):
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# using a dict with renaming
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warnings.simplefilter("ignore", FutureWarning)
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result = r.aggregate({"A": {"mean": "mean", "sum": "sum"}})
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expected = concat([a_mean, a_sum], axis=1)
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expected.columns = pd.MultiIndex.from_tuples([("A", "mean"), ("A", "sum")])
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tm.assert_frame_equal(result, expected, check_like=True)
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with catch_warnings(record=True):
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warnings.simplefilter("ignore", FutureWarning)
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result = r.aggregate(
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{
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"A": {"mean": "mean", "sum": "sum"},
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"B": {"mean2": "mean", "sum2": "sum"},
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}
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)
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expected = concat([a_mean, a_sum, b_mean, b_sum], axis=1)
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exp_cols = [("A", "mean"), ("A", "sum"), ("B", "mean2"), ("B", "sum2")]
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expected.columns = pd.MultiIndex.from_tuples(exp_cols)
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tm.assert_frame_equal(result, expected, check_like=True)
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result = r.aggregate({"A": ["mean", "std"], "B": ["mean", "std"]})
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expected = concat([a_mean, a_std, b_mean, b_std], axis=1)
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exp_cols = [("A", "mean"), ("A", "std"), ("B", "mean"), ("B", "std")]
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expected.columns = pd.MultiIndex.from_tuples(exp_cols)
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tm.assert_frame_equal(result, expected, check_like=True)
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def test_agg_apply(self, raw):
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# passed lambda
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df = DataFrame({"A": range(5), "B": range(0, 10, 2)})
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r = df.rolling(window=3)
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a_sum = r["A"].sum()
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result = r.agg({"A": np.sum, "B": lambda x: np.std(x, ddof=1)})
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rcustom = r["B"].apply(lambda x: np.std(x, ddof=1), raw=raw)
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expected = concat([a_sum, rcustom], axis=1)
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tm.assert_frame_equal(result, expected, check_like=True)
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def test_agg_consistency(self):
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df = DataFrame({"A": range(5), "B": range(0, 10, 2)})
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r = df.rolling(window=3)
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result = r.agg([np.sum, np.mean]).columns
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expected = pd.MultiIndex.from_product([list("AB"), ["sum", "mean"]])
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tm.assert_index_equal(result, expected)
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result = r["A"].agg([np.sum, np.mean]).columns
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expected = Index(["sum", "mean"])
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tm.assert_index_equal(result, expected)
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result = r.agg({"A": [np.sum, np.mean]}).columns
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expected = pd.MultiIndex.from_tuples([("A", "sum"), ("A", "mean")])
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tm.assert_index_equal(result, expected)
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def test_agg_nested_dicts(self):
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# API change for disallowing these types of nested dicts
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df = DataFrame({"A": range(5), "B": range(0, 10, 2)})
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r = df.rolling(window=3)
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msg = r"cannot perform renaming for (r1|r2) with a nested dictionary"
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with pytest.raises(SpecificationError, match=msg):
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r.aggregate({"r1": {"A": ["mean", "sum"]}, "r2": {"B": ["mean", "sum"]}})
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expected = concat(
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[r["A"].mean(), r["A"].std(), r["B"].mean(), r["B"].std()], axis=1
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)
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expected.columns = pd.MultiIndex.from_tuples(
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[("ra", "mean"), ("ra", "std"), ("rb", "mean"), ("rb", "std")]
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)
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with catch_warnings(record=True):
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warnings.simplefilter("ignore", FutureWarning)
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result = r[["A", "B"]].agg(
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{"A": {"ra": ["mean", "std"]}, "B": {"rb": ["mean", "std"]}}
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)
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tm.assert_frame_equal(result, expected, check_like=True)
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with catch_warnings(record=True):
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warnings.simplefilter("ignore", FutureWarning)
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result = r.agg({"A": {"ra": ["mean", "std"]}, "B": {"rb": ["mean", "std"]}})
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expected.columns = pd.MultiIndex.from_tuples(
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[
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("A", "ra", "mean"),
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("A", "ra", "std"),
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("B", "rb", "mean"),
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("B", "rb", "std"),
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]
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)
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tm.assert_frame_equal(result, expected, check_like=True)
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def test_count_nonnumeric_types(self):
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# GH12541
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cols = [
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"int",
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"float",
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"string",
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"datetime",
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"timedelta",
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"periods",
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"fl_inf",
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"fl_nan",
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"str_nan",
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"dt_nat",
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"periods_nat",
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]
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df = DataFrame(
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{
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"int": [1, 2, 3],
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"float": [4.0, 5.0, 6.0],
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"string": list("abc"),
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"datetime": pd.date_range("20170101", periods=3),
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"timedelta": pd.timedelta_range("1 s", periods=3, freq="s"),
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"periods": [
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pd.Period("2012-01"),
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pd.Period("2012-02"),
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pd.Period("2012-03"),
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],
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"fl_inf": [1.0, 2.0, np.Inf],
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"fl_nan": [1.0, 2.0, np.NaN],
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"str_nan": ["aa", "bb", np.NaN],
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"dt_nat": [
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Timestamp("20170101"),
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Timestamp("20170203"),
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Timestamp(None),
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],
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"periods_nat": [
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pd.Period("2012-01"),
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pd.Period("2012-02"),
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pd.Period(None),
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],
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},
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columns=cols,
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)
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expected = DataFrame(
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{
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"int": [1.0, 2.0, 2.0],
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"float": [1.0, 2.0, 2.0],
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"string": [1.0, 2.0, 2.0],
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"datetime": [1.0, 2.0, 2.0],
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"timedelta": [1.0, 2.0, 2.0],
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"periods": [1.0, 2.0, 2.0],
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"fl_inf": [1.0, 2.0, 2.0],
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"fl_nan": [1.0, 2.0, 1.0],
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"str_nan": [1.0, 2.0, 1.0],
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"dt_nat": [1.0, 2.0, 1.0],
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"periods_nat": [1.0, 2.0, 1.0],
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},
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columns=cols,
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)
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result = df.rolling(window=2).count()
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tm.assert_frame_equal(result, expected)
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result = df.rolling(1).count()
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expected = df.notna().astype(float)
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tm.assert_frame_equal(result, expected)
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@td.skip_if_no_scipy
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@pytest.mark.filterwarnings("ignore:can't resolve:ImportWarning")
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def test_window_with_args(self):
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# make sure that we are aggregating window functions correctly with arg
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r = Series(np.random.randn(100)).rolling(
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window=10, min_periods=1, win_type="gaussian"
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)
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expected = concat([r.mean(std=10), r.mean(std=0.01)], axis=1)
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expected.columns = ["<lambda>", "<lambda>"]
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result = r.aggregate([lambda x: x.mean(std=10), lambda x: x.mean(std=0.01)])
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tm.assert_frame_equal(result, expected)
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def a(x):
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return x.mean(std=10)
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def b(x):
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return x.mean(std=0.01)
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expected = concat([r.mean(std=10), r.mean(std=0.01)], axis=1)
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expected.columns = ["a", "b"]
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result = r.aggregate([a, b])
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tm.assert_frame_equal(result, expected)
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def test_preserve_metadata(self):
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# GH 10565
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s = Series(np.arange(100), name="foo")
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s2 = s.rolling(30).sum()
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s3 = s.rolling(20).sum()
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assert s2.name == "foo"
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assert s3.name == "foo"
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@pytest.mark.parametrize(
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"func,window_size,expected_vals",
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[
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(
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"rolling",
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2,
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[
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[np.nan, np.nan, np.nan, np.nan],
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[15.0, 20.0, 25.0, 20.0],
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[25.0, 30.0, 35.0, 30.0],
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[np.nan, np.nan, np.nan, np.nan],
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[20.0, 30.0, 35.0, 30.0],
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[35.0, 40.0, 60.0, 40.0],
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[60.0, 80.0, 85.0, 80],
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],
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),
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(
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"expanding",
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None,
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[
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[10.0, 10.0, 20.0, 20.0],
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[15.0, 20.0, 25.0, 20.0],
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[20.0, 30.0, 30.0, 20.0],
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[10.0, 10.0, 30.0, 30.0],
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[20.0, 30.0, 35.0, 30.0],
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[26.666667, 40.0, 50.0, 30.0],
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[40.0, 80.0, 60.0, 30.0],
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],
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),
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],
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)
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def test_multiple_agg_funcs(self, func, window_size, expected_vals):
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# GH 15072
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df = pd.DataFrame(
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[
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["A", 10, 20],
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["A", 20, 30],
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["A", 30, 40],
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["B", 10, 30],
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["B", 30, 40],
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["B", 40, 80],
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["B", 80, 90],
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],
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columns=["stock", "low", "high"],
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)
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f = getattr(df.groupby("stock"), func)
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if window_size:
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window = f(window_size)
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else:
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window = f()
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index = pd.MultiIndex.from_tuples(
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[("A", 0), ("A", 1), ("A", 2), ("B", 3), ("B", 4), ("B", 5), ("B", 6)],
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names=["stock", None],
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)
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columns = pd.MultiIndex.from_tuples(
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[("low", "mean"), ("low", "max"), ("high", "mean"), ("high", "min")]
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)
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expected = pd.DataFrame(expected_vals, index=index, columns=columns)
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result = window.agg(
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OrderedDict((("low", ["mean", "max"]), ("high", ["mean", "min"])))
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)
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tm.assert_frame_equal(result, expected)
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