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218 lines
7.6 KiB
218 lines
7.6 KiB
import numpy as np
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import pytest
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import pandas as pd
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from pandas import (
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DataFrame,
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Series,
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date_range,
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)
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import pandas._testing as tm
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class TestDataFrameRound:
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def test_round(self):
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# GH#2665
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# Test that rounding an empty DataFrame does nothing
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df = DataFrame()
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tm.assert_frame_equal(df, df.round())
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# Here's the test frame we'll be working with
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df = DataFrame({"col1": [1.123, 2.123, 3.123], "col2": [1.234, 2.234, 3.234]})
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# Default round to integer (i.e. decimals=0)
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expected_rounded = DataFrame({"col1": [1.0, 2.0, 3.0], "col2": [1.0, 2.0, 3.0]})
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tm.assert_frame_equal(df.round(), expected_rounded)
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# Round with an integer
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decimals = 2
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expected_rounded = DataFrame(
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{"col1": [1.12, 2.12, 3.12], "col2": [1.23, 2.23, 3.23]}
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)
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tm.assert_frame_equal(df.round(decimals), expected_rounded)
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# This should also work with np.round (since np.round dispatches to
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# df.round)
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tm.assert_frame_equal(np.round(df, decimals), expected_rounded)
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# Round with a list
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round_list = [1, 2]
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msg = "decimals must be an integer, a dict-like or a Series"
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with pytest.raises(TypeError, match=msg):
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df.round(round_list)
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# Round with a dictionary
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expected_rounded = DataFrame(
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{"col1": [1.1, 2.1, 3.1], "col2": [1.23, 2.23, 3.23]}
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)
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round_dict = {"col1": 1, "col2": 2}
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tm.assert_frame_equal(df.round(round_dict), expected_rounded)
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# Incomplete dict
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expected_partially_rounded = DataFrame(
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{"col1": [1.123, 2.123, 3.123], "col2": [1.2, 2.2, 3.2]}
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)
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partial_round_dict = {"col2": 1}
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tm.assert_frame_equal(df.round(partial_round_dict), expected_partially_rounded)
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# Dict with unknown elements
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wrong_round_dict = {"col3": 2, "col2": 1}
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tm.assert_frame_equal(df.round(wrong_round_dict), expected_partially_rounded)
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# float input to `decimals`
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non_int_round_dict = {"col1": 1, "col2": 0.5}
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msg = "Values in decimals must be integers"
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with pytest.raises(TypeError, match=msg):
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df.round(non_int_round_dict)
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# String input
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non_int_round_dict = {"col1": 1, "col2": "foo"}
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with pytest.raises(TypeError, match=msg):
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df.round(non_int_round_dict)
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non_int_round_Series = Series(non_int_round_dict)
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with pytest.raises(TypeError, match=msg):
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df.round(non_int_round_Series)
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# List input
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non_int_round_dict = {"col1": 1, "col2": [1, 2]}
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with pytest.raises(TypeError, match=msg):
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df.round(non_int_round_dict)
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non_int_round_Series = Series(non_int_round_dict)
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with pytest.raises(TypeError, match=msg):
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df.round(non_int_round_Series)
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# Non integer Series inputs
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non_int_round_Series = Series(non_int_round_dict)
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with pytest.raises(TypeError, match=msg):
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df.round(non_int_round_Series)
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non_int_round_Series = Series(non_int_round_dict)
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with pytest.raises(TypeError, match=msg):
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df.round(non_int_round_Series)
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# Negative numbers
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negative_round_dict = {"col1": -1, "col2": -2}
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big_df = df * 100
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expected_neg_rounded = DataFrame(
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{"col1": [110.0, 210, 310], "col2": [100.0, 200, 300]}
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)
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tm.assert_frame_equal(big_df.round(negative_round_dict), expected_neg_rounded)
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# nan in Series round
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nan_round_Series = Series({"col1": np.nan, "col2": 1})
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with pytest.raises(TypeError, match=msg):
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df.round(nan_round_Series)
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# Make sure this doesn't break existing Series.round
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tm.assert_series_equal(df["col1"].round(1), expected_rounded["col1"])
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# named columns
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# GH#11986
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decimals = 2
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expected_rounded = DataFrame(
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{"col1": [1.12, 2.12, 3.12], "col2": [1.23, 2.23, 3.23]}
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)
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df.columns.name = "cols"
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expected_rounded.columns.name = "cols"
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tm.assert_frame_equal(df.round(decimals), expected_rounded)
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# interaction of named columns & series
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tm.assert_series_equal(df["col1"].round(decimals), expected_rounded["col1"])
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tm.assert_series_equal(df.round(decimals)["col1"], expected_rounded["col1"])
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def test_round_numpy(self):
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# GH#12600
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df = DataFrame([[1.53, 1.36], [0.06, 7.01]])
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out = np.round(df, decimals=0)
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expected = DataFrame([[2.0, 1.0], [0.0, 7.0]])
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tm.assert_frame_equal(out, expected)
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msg = "the 'out' parameter is not supported"
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with pytest.raises(ValueError, match=msg):
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np.round(df, decimals=0, out=df)
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def test_round_numpy_with_nan(self):
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# See GH#14197
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df = Series([1.53, np.nan, 0.06]).to_frame()
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with tm.assert_produces_warning(None):
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result = df.round()
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expected = Series([2.0, np.nan, 0.0]).to_frame()
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tm.assert_frame_equal(result, expected)
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def test_round_mixed_type(self):
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# GH#11885
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df = DataFrame(
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{
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"col1": [1.1, 2.2, 3.3, 4.4],
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"col2": ["1", "a", "c", "f"],
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"col3": date_range("20111111", periods=4),
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}
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)
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round_0 = DataFrame(
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{
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"col1": [1.0, 2.0, 3.0, 4.0],
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"col2": ["1", "a", "c", "f"],
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"col3": date_range("20111111", periods=4),
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}
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)
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tm.assert_frame_equal(df.round(), round_0)
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tm.assert_frame_equal(df.round(1), df)
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tm.assert_frame_equal(df.round({"col1": 1}), df)
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tm.assert_frame_equal(df.round({"col1": 0}), round_0)
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tm.assert_frame_equal(df.round({"col1": 0, "col2": 1}), round_0)
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tm.assert_frame_equal(df.round({"col3": 1}), df)
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def test_round_with_duplicate_columns(self):
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# GH#11611
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df = DataFrame(
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np.random.random([3, 3]),
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columns=["A", "B", "C"],
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index=["first", "second", "third"],
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)
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dfs = pd.concat((df, df), axis=1)
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rounded = dfs.round()
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tm.assert_index_equal(rounded.index, dfs.index)
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decimals = Series([1, 0, 2], index=["A", "B", "A"])
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msg = "Index of decimals must be unique"
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with pytest.raises(ValueError, match=msg):
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df.round(decimals)
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def test_round_builtin(self):
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# GH#11763
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# Here's the test frame we'll be working with
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df = DataFrame({"col1": [1.123, 2.123, 3.123], "col2": [1.234, 2.234, 3.234]})
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# Default round to integer (i.e. decimals=0)
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expected_rounded = DataFrame({"col1": [1.0, 2.0, 3.0], "col2": [1.0, 2.0, 3.0]})
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tm.assert_frame_equal(round(df), expected_rounded)
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def test_round_nonunique_categorical(self):
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# See GH#21809
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idx = pd.CategoricalIndex(["low"] * 3 + ["hi"] * 3)
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df = DataFrame(np.random.rand(6, 3), columns=list("abc"))
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expected = df.round(3)
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expected.index = idx
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df_categorical = df.copy().set_index(idx)
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assert df_categorical.shape == (6, 3)
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result = df_categorical.round(3)
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assert result.shape == (6, 3)
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tm.assert_frame_equal(result, expected)
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def test_round_interval_category_columns(self):
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# GH#30063
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columns = pd.CategoricalIndex(pd.interval_range(0, 2))
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df = DataFrame([[0.66, 1.1], [0.3, 0.25]], columns=columns)
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result = df.round()
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expected = DataFrame([[1.0, 1.0], [0.0, 0.0]], columns=columns)
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tm.assert_frame_equal(result, expected)
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