Python pandas.util.testing.getSeriesData() Examples

The following are 30 code examples of pandas.util.testing.getSeriesData(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module pandas.util.testing , or try the search function .
Example #1
Source File: test_to_html.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def test_to_html_columns_arg():
    df = DataFrame(tm.getSeriesData())
    result = df.to_html(columns=['A'])
    assert '<th>B</th>' not in result
Example #2
Source File: conftest.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def seriesd():
    return tm.getSeriesData()
Example #3
Source File: test_concat.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def setup_method(self, method):
        self.frame = DataFrame(tm.getSeriesData())
        self.mixed_frame = self.frame.copy()
        self.mixed_frame['foo'] = 'bar'
Example #4
Source File: conftest.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def float_frame_with_na():
    """
    Fixture for DataFrame of floats with index of unique strings

    Columns are ['A', 'B', 'C', 'D']; some entries are missing
    """
    df = DataFrame(tm.getSeriesData())
    # set some NAs
    df.loc[5:10] = np.nan
    df.loc[15:20, -2:] = np.nan
    return df
Example #5
Source File: conftest.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def bool_frame_with_na():
    """
    Fixture for DataFrame of booleans with index of unique strings

    Columns are ['A', 'B', 'C', 'D']; some entries are missing
    """
    df = DataFrame(tm.getSeriesData()) > 0
    df = df.astype(object)
    # set some NAs
    df.loc[5:10] = np.nan
    df.loc[15:20, -2:] = np.nan
    return df
Example #6
Source File: conftest.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def int_frame():
    """
    Fixture for DataFrame of ints with index of unique strings

    Columns are ['A', 'B', 'C', 'D']
    """
    df = DataFrame({k: v.astype(int)
                   for k, v in compat.iteritems(tm.getSeriesData())})
    # force these all to int64 to avoid platform testing issues
    return DataFrame({c: s for c, s in compat.iteritems(df)}, dtype=np.int64)
Example #7
Source File: conftest.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def float_string_frame():
    """
    Fixture for DataFrame of floats and strings with index of unique strings

    Columns are ['A', 'B', 'C', 'D', 'foo'].
    """
    df = DataFrame(tm.getSeriesData())
    df['foo'] = 'bar'
    return df
Example #8
Source File: conftest.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def mixed_float_frame():
    """
    Fixture for DataFrame of different float types with index of unique strings

    Columns are ['A', 'B', 'C', 'D'].
    """
    df = DataFrame(tm.getSeriesData())
    df.A = df.A.astype('float32')
    df.B = df.B.astype('float32')
    df.C = df.C.astype('float16')
    df.D = df.D.astype('float64')
    return df
Example #9
Source File: conftest.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def mixed_float_frame2():
    """
    Fixture for DataFrame of different float types with index of unique strings

    Columns are ['A', 'B', 'C', 'D'].
    """
    df = DataFrame(tm.getSeriesData())
    df.D = df.D.astype('float32')
    df.C = df.C.astype('float32')
    df.B = df.B.astype('float16')
    df.D = df.D.astype('float64')
    return df
Example #10
Source File: test_operators.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def test_invert(self):
        _seriesd = tm.getSeriesData()
        df = pd.DataFrame(_seriesd)

        assert_frame_equal(-(df < 0), ~(df < 0))
Example #11
Source File: test_to_latex.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def frame():
    return DataFrame(tm.getSeriesData())
Example #12
Source File: conftest.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def float_frame():
    """
    Fixture for DataFrame of floats with index of unique strings

    Columns are ['A', 'B', 'C', 'D'].
    """
    return DataFrame(tm.getSeriesData())
Example #13
Source File: test_concat.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def setup_method(self, method):
        self.frame = DataFrame(tm.getSeriesData())
        self.mixed_frame = self.frame.copy()
        self.mixed_frame['foo'] = 'bar'
Example #14
Source File: test_aggregate.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def setup_method(self, method):
        self.ts = tm.makeTimeSeries()

        self.seriesd = tm.getSeriesData()
        self.tsd = tm.getTimeSeriesData()
        self.frame = DataFrame(self.seriesd)
        self.tsframe = DataFrame(self.tsd)

        self.df = DataFrame(
            {'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'],
             'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'],
             'C': np.random.randn(8),
             'D': np.random.randn(8)})

        self.df_mixed_floats = DataFrame(
            {'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'],
             'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'],
             'C': np.random.randn(8),
             'D': np.array(
                 np.random.randn(8), dtype='float32')})

        index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'], ['one', 'two',
                                                                  'three']],
                           labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
                                   [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
                           names=['first', 'second'])
        self.mframe = DataFrame(np.random.randn(10, 3), index=index,
                                columns=['A', 'B', 'C'])

        self.three_group = DataFrame(
            {'A': ['foo', 'foo', 'foo', 'foo', 'bar', 'bar', 'bar', 'bar',
                   'foo', 'foo', 'foo'],
             'B': ['one', 'one', 'one', 'two', 'one', 'one', 'one', 'two',
                   'two', 'two', 'one'],
             'C': ['dull', 'dull', 'shiny', 'dull', 'dull', 'shiny', 'shiny',
                   'dull', 'shiny', 'shiny', 'shiny'],
             'D': np.random.randn(11),
             'E': np.random.randn(11),
             'F': np.random.randn(11)})
Example #15
Source File: test_filters.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def setup_method(self, method):
        self.ts = tm.makeTimeSeries()

        self.seriesd = tm.getSeriesData()
        self.tsd = tm.getTimeSeriesData()
        self.frame = DataFrame(self.seriesd)
        self.tsframe = DataFrame(self.tsd)

        self.df = DataFrame(
            {'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'],
             'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'],
             'C': np.random.randn(8),
             'D': np.random.randn(8)})

        self.df_mixed_floats = DataFrame(
            {'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'],
             'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'],
             'C': np.random.randn(8),
             'D': np.array(
                 np.random.randn(8), dtype='float32')})

        index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'], ['one', 'two',
                                                                  'three']],
                           labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
                                   [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
                           names=['first', 'second'])
        self.mframe = DataFrame(np.random.randn(10, 3), index=index,
                                columns=['A', 'B', 'C'])

        self.three_group = DataFrame(
            {'A': ['foo', 'foo', 'foo', 'foo', 'bar', 'bar', 'bar', 'bar',
                   'foo', 'foo', 'foo'],
             'B': ['one', 'one', 'one', 'two', 'one', 'one', 'one', 'two',
                   'two', 'two', 'one'],
             'C': ['dull', 'dull', 'shiny', 'dull', 'dull', 'shiny', 'shiny',
                   'dull', 'shiny', 'shiny', 'shiny'],
             'D': np.random.randn(11),
             'E': np.random.randn(11),
             'F': np.random.randn(11)})
Example #16
Source File: common.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def setup_method(self, method):
        self.ts = tm.makeTimeSeries()

        self.seriesd = tm.getSeriesData()
        self.tsd = tm.getTimeSeriesData()
        self.frame = DataFrame(self.seriesd)
        self.tsframe = DataFrame(self.tsd)

        self.df = df()
        self.df_mixed_floats = DataFrame(
            {'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'],
             'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'],
             'C': np.random.randn(8),
             'D': np.array(
                 np.random.randn(8), dtype='float32')})

        self.mframe = mframe()

        self.three_group = DataFrame(
            {'A': ['foo', 'foo', 'foo', 'foo', 'bar', 'bar', 'bar', 'bar',
                   'foo', 'foo', 'foo'],
             'B': ['one', 'one', 'one', 'two', 'one', 'one', 'one', 'two',
                   'two', 'two', 'one'],
             'C': ['dull', 'dull', 'shiny', 'dull', 'dull', 'shiny', 'shiny',
                   'dull', 'shiny', 'shiny', 'shiny'],
             'D': np.random.randn(11),
             'E': np.random.randn(11),
             'F': np.random.randn(11)})
Example #17
Source File: test_to_html.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def test_to_html_columns_arg(self):
        frame = DataFrame(tm.getSeriesData())
        result = frame.to_html(columns=['A'])
        assert '<th>B</th>' not in result
Example #18
Source File: test_concat.py    From coffeegrindsize with MIT License 5 votes vote down vote up 
def setup_method(self, method):
        self.frame = DataFrame(tm.getSeriesData())
        self.mixed_frame = self.frame.copy()
        self.mixed_frame['foo'] = 'bar'
Example #19
Source File: test_concat.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def setup_method(self, method):
        self.frame = DataFrame(tm.getSeriesData())
        self.mixed_frame = self.frame.copy()
        self.mixed_frame['foo'] = 'bar'
Example #20
Source File: conftest.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def seriesd():
    return tm.getSeriesData()
Example #21
Source File: test_to_latex.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def frame():
    return DataFrame(tm.getSeriesData())
Example #22
Source File: test_to_html.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def test_to_html_columns_arg(self):
        frame = DataFrame(tm.getSeriesData())
        result = frame.to_html(columns=['A'])
        assert '<th>B</th>' not in result
Example #23
Source File: test_concat.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def setup_method(self, method):
        self.frame = DataFrame(tm.getSeriesData())
        self.mixed_frame = self.frame.copy()
        self.mixed_frame['foo'] = 'bar'
Example #24
Source File: conftest.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def seriesd():
    return tm.getSeriesData()
Example #25
Source File: conftest.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def float_frame():
    """
    Fixture for DataFrame of floats with index of unique strings

    Columns are ['A', 'B', 'C', 'D'].
    """
    return DataFrame(tm.getSeriesData())
Example #26
Source File: conftest.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def float_frame_with_na():
    """
    Fixture for DataFrame of floats with index of unique strings

    Columns are ['A', 'B', 'C', 'D']; some entries are missing
    """
    df = DataFrame(tm.getSeriesData())
    # set some NAs
    df.loc[5:10] = np.nan
    df.loc[15:20, -2:] = np.nan
    return df
Example #27
Source File: conftest.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def bool_frame_with_na():
    """
    Fixture for DataFrame of booleans with index of unique strings

    Columns are ['A', 'B', 'C', 'D']; some entries are missing
    """
    df = DataFrame(tm.getSeriesData()) > 0
    df = df.astype(object)
    # set some NAs
    df.loc[5:10] = np.nan
    df.loc[15:20, -2:] = np.nan
    return df
Example #28
Source File: conftest.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def int_frame():
    """
    Fixture for DataFrame of ints with index of unique strings

    Columns are ['A', 'B', 'C', 'D']
    """
    df = DataFrame({k: v.astype(int)
                   for k, v in compat.iteritems(tm.getSeriesData())})
    # force these all to int64 to avoid platform testing issues
    return DataFrame({c: s for c, s in compat.iteritems(df)}, dtype=np.int64)
Example #29
Source File: conftest.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def float_string_frame():
    """
    Fixture for DataFrame of floats and strings with index of unique strings

    Columns are ['A', 'B', 'C', 'D', 'foo'].
    """
    df = DataFrame(tm.getSeriesData())
    df['foo'] = 'bar'
    return df
Example #30
Source File: conftest.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def mixed_float_frame():
    """
    Fixture for DataFrame of different float types with index of unique strings

    Columns are ['A', 'B', 'C', 'D'].
    """
    df = DataFrame(tm.getSeriesData())
    df.A = df.A.astype('float32')
    df.B = df.B.astype('float32')
    df.C = df.C.astype('float16')
    df.D = df.D.astype('float64')
    return df