409 lines
15 KiB
Python
409 lines
15 KiB
Python
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import pytest
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import decimal
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import numpy as np
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import pandas as pd
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from pandas import to_numeric
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from pandas.util import testing as tm
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from numpy import iinfo
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class TestToNumeric(object):
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def test_empty(self):
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# see gh-16302
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s = pd.Series([], dtype=object)
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res = to_numeric(s)
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expected = pd.Series([], dtype=np.int64)
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tm.assert_series_equal(res, expected)
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# Original issue example
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res = to_numeric(s, errors='coerce', downcast='integer')
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expected = pd.Series([], dtype=np.int8)
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tm.assert_series_equal(res, expected)
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def test_series(self):
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s = pd.Series(['1', '-3.14', '7'])
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res = to_numeric(s)
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expected = pd.Series([1, -3.14, 7])
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tm.assert_series_equal(res, expected)
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s = pd.Series(['1', '-3.14', 7])
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res = to_numeric(s)
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tm.assert_series_equal(res, expected)
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def test_series_numeric(self):
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s = pd.Series([1, 3, 4, 5], index=list('ABCD'), name='XXX')
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res = to_numeric(s)
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tm.assert_series_equal(res, s)
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s = pd.Series([1., 3., 4., 5.], index=list('ABCD'), name='XXX')
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res = to_numeric(s)
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tm.assert_series_equal(res, s)
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# bool is regarded as numeric
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s = pd.Series([True, False, True, True],
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index=list('ABCD'), name='XXX')
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res = to_numeric(s)
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tm.assert_series_equal(res, s)
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def test_error(self):
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s = pd.Series([1, -3.14, 'apple'])
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msg = 'Unable to parse string "apple" at position 2'
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with tm.assert_raises_regex(ValueError, msg):
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to_numeric(s, errors='raise')
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res = to_numeric(s, errors='ignore')
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expected = pd.Series([1, -3.14, 'apple'])
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tm.assert_series_equal(res, expected)
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res = to_numeric(s, errors='coerce')
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expected = pd.Series([1, -3.14, np.nan])
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tm.assert_series_equal(res, expected)
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s = pd.Series(['orange', 1, -3.14, 'apple'])
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msg = 'Unable to parse string "orange" at position 0'
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with tm.assert_raises_regex(ValueError, msg):
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to_numeric(s, errors='raise')
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def test_error_seen_bool(self):
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s = pd.Series([True, False, 'apple'])
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msg = 'Unable to parse string "apple" at position 2'
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with tm.assert_raises_regex(ValueError, msg):
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to_numeric(s, errors='raise')
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res = to_numeric(s, errors='ignore')
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expected = pd.Series([True, False, 'apple'])
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tm.assert_series_equal(res, expected)
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# coerces to float
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res = to_numeric(s, errors='coerce')
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expected = pd.Series([1., 0., np.nan])
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tm.assert_series_equal(res, expected)
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def test_list(self):
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s = ['1', '-3.14', '7']
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res = to_numeric(s)
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expected = np.array([1, -3.14, 7])
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tm.assert_numpy_array_equal(res, expected)
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def test_list_numeric(self):
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s = [1, 3, 4, 5]
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res = to_numeric(s)
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tm.assert_numpy_array_equal(res, np.array(s, dtype=np.int64))
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s = [1., 3., 4., 5.]
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res = to_numeric(s)
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tm.assert_numpy_array_equal(res, np.array(s))
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# bool is regarded as numeric
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s = [True, False, True, True]
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res = to_numeric(s)
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tm.assert_numpy_array_equal(res, np.array(s))
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def test_numeric(self):
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s = pd.Series([1, -3.14, 7], dtype='O')
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res = to_numeric(s)
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expected = pd.Series([1, -3.14, 7])
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tm.assert_series_equal(res, expected)
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s = pd.Series([1, -3.14, 7])
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res = to_numeric(s)
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tm.assert_series_equal(res, expected)
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# GH 14827
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df = pd.DataFrame(dict(
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a=[1.2, decimal.Decimal(3.14), decimal.Decimal("infinity"), '0.1'],
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b=[1.0, 2.0, 3.0, 4.0],
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))
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expected = pd.DataFrame(dict(
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a=[1.2, 3.14, np.inf, 0.1],
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b=[1.0, 2.0, 3.0, 4.0],
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))
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# Test to_numeric over one column
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df_copy = df.copy()
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df_copy['a'] = df_copy['a'].apply(to_numeric)
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tm.assert_frame_equal(df_copy, expected)
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# Test to_numeric over multiple columns
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df_copy = df.copy()
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df_copy[['a', 'b']] = df_copy[['a', 'b']].apply(to_numeric)
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tm.assert_frame_equal(df_copy, expected)
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def test_numeric_lists_and_arrays(self):
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# Test to_numeric with embedded lists and arrays
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df = pd.DataFrame(dict(
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a=[[decimal.Decimal(3.14), 1.0], decimal.Decimal(1.6), 0.1]
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))
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df['a'] = df['a'].apply(to_numeric)
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expected = pd.DataFrame(dict(
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a=[[3.14, 1.0], 1.6, 0.1],
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))
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tm.assert_frame_equal(df, expected)
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df = pd.DataFrame(dict(
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a=[np.array([decimal.Decimal(3.14), 1.0]), 0.1]
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))
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df['a'] = df['a'].apply(to_numeric)
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expected = pd.DataFrame(dict(
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a=[[3.14, 1.0], 0.1],
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))
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tm.assert_frame_equal(df, expected)
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def test_all_nan(self):
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s = pd.Series(['a', 'b', 'c'])
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res = to_numeric(s, errors='coerce')
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expected = pd.Series([np.nan, np.nan, np.nan])
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tm.assert_series_equal(res, expected)
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def test_type_check(self):
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# GH 11776
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df = pd.DataFrame({'a': [1, -3.14, 7], 'b': ['4', '5', '6']})
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with tm.assert_raises_regex(TypeError, "1-d array"):
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to_numeric(df)
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for errors in ['ignore', 'raise', 'coerce']:
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with tm.assert_raises_regex(TypeError, "1-d array"):
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to_numeric(df, errors=errors)
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def test_scalar(self):
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assert pd.to_numeric(1) == 1
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assert pd.to_numeric(1.1) == 1.1
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assert pd.to_numeric('1') == 1
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assert pd.to_numeric('1.1') == 1.1
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with pytest.raises(ValueError):
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to_numeric('XX', errors='raise')
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assert to_numeric('XX', errors='ignore') == 'XX'
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assert np.isnan(to_numeric('XX', errors='coerce'))
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def test_numeric_dtypes(self):
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idx = pd.Index([1, 2, 3], name='xxx')
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res = pd.to_numeric(idx)
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tm.assert_index_equal(res, idx)
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res = pd.to_numeric(pd.Series(idx, name='xxx'))
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tm.assert_series_equal(res, pd.Series(idx, name='xxx'))
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res = pd.to_numeric(idx.values)
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tm.assert_numpy_array_equal(res, idx.values)
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idx = pd.Index([1., np.nan, 3., np.nan], name='xxx')
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res = pd.to_numeric(idx)
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tm.assert_index_equal(res, idx)
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res = pd.to_numeric(pd.Series(idx, name='xxx'))
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tm.assert_series_equal(res, pd.Series(idx, name='xxx'))
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res = pd.to_numeric(idx.values)
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tm.assert_numpy_array_equal(res, idx.values)
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def test_str(self):
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idx = pd.Index(['1', '2', '3'], name='xxx')
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exp = np.array([1, 2, 3], dtype='int64')
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res = pd.to_numeric(idx)
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tm.assert_index_equal(res, pd.Index(exp, name='xxx'))
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res = pd.to_numeric(pd.Series(idx, name='xxx'))
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tm.assert_series_equal(res, pd.Series(exp, name='xxx'))
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res = pd.to_numeric(idx.values)
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tm.assert_numpy_array_equal(res, exp)
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idx = pd.Index(['1.5', '2.7', '3.4'], name='xxx')
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exp = np.array([1.5, 2.7, 3.4])
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res = pd.to_numeric(idx)
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tm.assert_index_equal(res, pd.Index(exp, name='xxx'))
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res = pd.to_numeric(pd.Series(idx, name='xxx'))
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tm.assert_series_equal(res, pd.Series(exp, name='xxx'))
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res = pd.to_numeric(idx.values)
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tm.assert_numpy_array_equal(res, exp)
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def test_datetimelike(self):
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for tz in [None, 'US/Eastern', 'Asia/Tokyo']:
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idx = pd.date_range('20130101', periods=3, tz=tz, name='xxx')
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res = pd.to_numeric(idx)
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tm.assert_index_equal(res, pd.Index(idx.asi8, name='xxx'))
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res = pd.to_numeric(pd.Series(idx, name='xxx'))
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tm.assert_series_equal(res, pd.Series(idx.asi8, name='xxx'))
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res = pd.to_numeric(idx.values)
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tm.assert_numpy_array_equal(res, idx.asi8)
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def test_timedelta(self):
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idx = pd.timedelta_range('1 days', periods=3, freq='D', name='xxx')
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res = pd.to_numeric(idx)
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tm.assert_index_equal(res, pd.Index(idx.asi8, name='xxx'))
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res = pd.to_numeric(pd.Series(idx, name='xxx'))
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tm.assert_series_equal(res, pd.Series(idx.asi8, name='xxx'))
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res = pd.to_numeric(idx.values)
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tm.assert_numpy_array_equal(res, idx.asi8)
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def test_period(self):
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idx = pd.period_range('2011-01', periods=3, freq='M', name='xxx')
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res = pd.to_numeric(idx)
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tm.assert_index_equal(res, pd.Index(idx.asi8, name='xxx'))
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# ToDo: enable when we can support native PeriodDtype
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# res = pd.to_numeric(pd.Series(idx, name='xxx'))
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# tm.assert_series_equal(res, pd.Series(idx.asi8, name='xxx'))
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def test_non_hashable(self):
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# Test for Bug #13324
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s = pd.Series([[10.0, 2], 1.0, 'apple'])
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res = pd.to_numeric(s, errors='coerce')
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tm.assert_series_equal(res, pd.Series([np.nan, 1.0, np.nan]))
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res = pd.to_numeric(s, errors='ignore')
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tm.assert_series_equal(res, pd.Series([[10.0, 2], 1.0, 'apple']))
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with tm.assert_raises_regex(TypeError, "Invalid object type"):
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pd.to_numeric(s)
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def test_downcast(self):
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# see gh-13352
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mixed_data = ['1', 2, 3]
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int_data = [1, 2, 3]
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date_data = np.array(['1970-01-02', '1970-01-03',
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'1970-01-04'], dtype='datetime64[D]')
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invalid_downcast = 'unsigned-integer'
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msg = 'invalid downcasting method provided'
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smallest_int_dtype = np.dtype(np.typecodes['Integer'][0])
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smallest_uint_dtype = np.dtype(np.typecodes['UnsignedInteger'][0])
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# support below np.float32 is rare and far between
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float_32_char = np.dtype(np.float32).char
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smallest_float_dtype = float_32_char
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for data in (mixed_data, int_data, date_data):
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with tm.assert_raises_regex(ValueError, msg):
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pd.to_numeric(data, downcast=invalid_downcast)
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expected = np.array([1, 2, 3], dtype=np.int64)
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res = pd.to_numeric(data)
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tm.assert_numpy_array_equal(res, expected)
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res = pd.to_numeric(data, downcast=None)
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tm.assert_numpy_array_equal(res, expected)
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expected = np.array([1, 2, 3], dtype=smallest_int_dtype)
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for signed_downcast in ('integer', 'signed'):
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res = pd.to_numeric(data, downcast=signed_downcast)
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tm.assert_numpy_array_equal(res, expected)
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expected = np.array([1, 2, 3], dtype=smallest_uint_dtype)
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res = pd.to_numeric(data, downcast='unsigned')
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tm.assert_numpy_array_equal(res, expected)
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expected = np.array([1, 2, 3], dtype=smallest_float_dtype)
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res = pd.to_numeric(data, downcast='float')
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tm.assert_numpy_array_equal(res, expected)
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# if we can't successfully cast the given
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# data to a numeric dtype, do not bother
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# with the downcast parameter
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data = ['foo', 2, 3]
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expected = np.array(data, dtype=object)
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res = pd.to_numeric(data, errors='ignore',
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downcast='unsigned')
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tm.assert_numpy_array_equal(res, expected)
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# cannot cast to an unsigned integer because
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# we have a negative number
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data = ['-1', 2, 3]
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expected = np.array([-1, 2, 3], dtype=np.int64)
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res = pd.to_numeric(data, downcast='unsigned')
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tm.assert_numpy_array_equal(res, expected)
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# cannot cast to an integer (signed or unsigned)
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# because we have a float number
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data = (['1.1', 2, 3],
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[10000.0, 20000, 3000, 40000.36, 50000, 50000.00])
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expected = (np.array([1.1, 2, 3], dtype=np.float64),
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np.array([10000.0, 20000, 3000,
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40000.36, 50000, 50000.00], dtype=np.float64))
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for _data, _expected in zip(data, expected):
|
||
|
|
for downcast in ('integer', 'signed', 'unsigned'):
|
||
|
|
res = pd.to_numeric(_data, downcast=downcast)
|
||
|
|
tm.assert_numpy_array_equal(res, _expected)
|
||
|
|
|
||
|
|
# the smallest integer dtype need not be np.(u)int8
|
||
|
|
data = ['256', 257, 258]
|
||
|
|
|
||
|
|
for downcast, expected_dtype in zip(
|
||
|
|
['integer', 'signed', 'unsigned'],
|
||
|
|
[np.int16, np.int16, np.uint16]):
|
||
|
|
expected = np.array([256, 257, 258], dtype=expected_dtype)
|
||
|
|
res = pd.to_numeric(data, downcast=downcast)
|
||
|
|
tm.assert_numpy_array_equal(res, expected)
|
||
|
|
|
||
|
|
def test_downcast_limits(self):
|
||
|
|
# Test the limits of each downcast. Bug: #14401.
|
||
|
|
|
||
|
|
i = 'integer'
|
||
|
|
u = 'unsigned'
|
||
|
|
dtype_downcast_min_max = [
|
||
|
|
('int8', i, [iinfo(np.int8).min, iinfo(np.int8).max]),
|
||
|
|
('int16', i, [iinfo(np.int16).min, iinfo(np.int16).max]),
|
||
|
|
('int32', i, [iinfo(np.int32).min, iinfo(np.int32).max]),
|
||
|
|
('int64', i, [iinfo(np.int64).min, iinfo(np.int64).max]),
|
||
|
|
('uint8', u, [iinfo(np.uint8).min, iinfo(np.uint8).max]),
|
||
|
|
('uint16', u, [iinfo(np.uint16).min, iinfo(np.uint16).max]),
|
||
|
|
('uint32', u, [iinfo(np.uint32).min, iinfo(np.uint32).max]),
|
||
|
|
('uint64', u, [iinfo(np.uint64).min, iinfo(np.uint64).max]),
|
||
|
|
('int16', i, [iinfo(np.int8).min, iinfo(np.int8).max + 1]),
|
||
|
|
('int32', i, [iinfo(np.int16).min, iinfo(np.int16).max + 1]),
|
||
|
|
('int64', i, [iinfo(np.int32).min, iinfo(np.int32).max + 1]),
|
||
|
|
('int16', i, [iinfo(np.int8).min - 1, iinfo(np.int16).max]),
|
||
|
|
('int32', i, [iinfo(np.int16).min - 1, iinfo(np.int32).max]),
|
||
|
|
('int64', i, [iinfo(np.int32).min - 1, iinfo(np.int64).max]),
|
||
|
|
('uint16', u, [iinfo(np.uint8).min, iinfo(np.uint8).max + 1]),
|
||
|
|
('uint32', u, [iinfo(np.uint16).min, iinfo(np.uint16).max + 1]),
|
||
|
|
('uint64', u, [iinfo(np.uint32).min, iinfo(np.uint32).max + 1])
|
||
|
|
]
|
||
|
|
|
||
|
|
for dtype, downcast, min_max in dtype_downcast_min_max:
|
||
|
|
series = pd.to_numeric(pd.Series(min_max), downcast=downcast)
|
||
|
|
assert series.dtype == dtype
|
||
|
|
|
||
|
|
def test_coerce_uint64_conflict(self):
|
||
|
|
# see gh-17007 and gh-17125
|
||
|
|
#
|
||
|
|
# Still returns float despite the uint64-nan conflict,
|
||
|
|
# which would normally force the casting to object.
|
||
|
|
df = pd.DataFrame({"a": [200, 300, "", "NaN", 30000000000000000000]})
|
||
|
|
expected = pd.Series([200, 300, np.nan, np.nan,
|
||
|
|
30000000000000000000], dtype=float, name="a")
|
||
|
|
result = to_numeric(df["a"], errors="coerce")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
s = pd.Series(["12345678901234567890", "1234567890", "ITEM"])
|
||
|
|
expected = pd.Series([12345678901234567890,
|
||
|
|
1234567890, np.nan], dtype=float)
|
||
|
|
result = to_numeric(s, errors="coerce")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
# For completeness, check against "ignore" and "raise"
|
||
|
|
result = to_numeric(s, errors="ignore")
|
||
|
|
tm.assert_series_equal(result, s)
|
||
|
|
|
||
|
|
msg = "Unable to parse string"
|
||
|
|
with tm.assert_raises_regex(ValueError, msg):
|
||
|
|
to_numeric(s, errors="raise")
|