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