FM / methods

/usr/local/lib64/python3.6/site-packages/pandas/tests/frame/methods/

/usr/local/lib64/python3.6/site-packages/pandas/tests/frame/methods

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
test_align.py	10339	0644	edit dl rm
test_append.py	7399	0644	edit dl rm
test_asfreq.py	1970	0644	edit dl rm
test_asof.py	5767	0644	edit dl rm
test_assign.py	2982	0644	edit dl rm
test_astype.py	21027	0644	edit dl rm
test_at_time.py	3205	0644	edit dl rm
test_between_time.py	3675	0644	edit dl rm
test_clip.py	6028	0644	edit dl rm
test_combine.py	1359	0644	edit dl rm
test_combine_first.py	12748	0644	edit dl rm
test_compare.py	6158	0644	edit dl rm
test_count.py	1068	0644	edit dl rm
test_cov_corr.py	11888	0644	edit dl rm
test_describe.py	12492	0644	edit dl rm
test_diff.py	7374	0644	edit dl rm
test_drop.py	15662	0644	edit dl rm
test_droplevel.py	855	0644	edit dl rm
test_drop_duplicates.py	13387	0644	edit dl rm
test_duplicated.py	3191	0644	edit dl rm
test_explode.py	5294	0644	edit dl rm
test_filter.py	4933	0644	edit dl rm
test_first_and_last.py	1803	0644	edit dl rm
test_head_tail.py	1194	0644	edit dl rm
test_interpolate.py	11816	0644	edit dl rm
test_isin.py	7324	0644	edit dl rm
test_nlargest.py	6731	0644	edit dl rm
test_pct_change.py	3436	0644	edit dl rm
test_pop.py	1226	0644	edit dl rm
test_quantile.py	18028	0644	edit dl rm
test_rank.py	11377	0644	edit dl rm
test_reindex_like.py	1187	0644	edit dl rm
test_rename.py	12543	0644	edit dl rm
test_rename_axis.py	4074	0644	edit dl rm
test_replace.py	59230	0644	edit dl rm
test_reset_index.py	12506	0644	edit dl rm
test_round.py	7916	0644	edit dl rm
test_select_dtypes.py	12104	0644	edit dl rm
test_set_index.py	19571	0644	edit dl rm
test_shift.py	10324	0644	edit dl rm
test_sort_index.py	25678	0644	edit dl rm
test_sort_values.py	25358	0644	edit dl rm
test_to_dict.py	9989	0644	edit dl rm
test_to_period.py	984	0644	edit dl rm
test_to_records.py	13981	0644	edit dl rm
test_to_timestamp.py	4020	0644	edit dl rm
test_transpose.py	2426	0644	edit dl rm
test_truncate.py	4180	0644	edit dl rm
test_tz_convert.py	3415	0644	edit dl rm
test_tz_localize.py	666	0644	edit dl rm
test_update.py	4259	0644	edit dl rm
test_value_counts.py	2631	0644	edit dl rm
__init__.py	229	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/pandas/tests/frame/methods/test_round.py (7916B)

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 = "integer argument expected, got float"
        with pytest.raises(TypeError, match=msg):
            df.round(non_int_round_dict)

# String input
        non_int_round_dict = {"col1": 1, "col2": "foo"}
        msg = r"an integer is required \(got type str\)"
        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]}
        msg = r"an integer is required \(got type list\)"
        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})

msg = "integer argument expected, got float"
        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 = pd.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 = pd.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 = pd.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)

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 = "integer argument expected, got float" with pytest.raises(TypeError, match=msg): df.round(non_int_round_dict) # String input non_int_round_dict = {"col1": 1, "col2": "foo"} msg = r"an integer is required \(got type str\)" 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]} msg = r"an integer is required \(got type list\)" 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}) msg = "integer argument expected, got float" 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 = pd.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 = pd.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 = pd.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)