Python pandas.MultiIndex方法代碼示例

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import MultiIndex [as 別名]
def _link_index(self, df_a, df_b):
        """Build an index for linking two datasets.

        Parameters
        ----------
        df_a : (tuple of) pandas.Series
            The data of the left DataFrame to build the index with.
        df_b : (tuple of) pandas.Series
            The data of the right DataFrame to build the index with.

        Returns
        -------
        pandas.MultiIndex
            A pandas.MultiIndex with record pairs. Each record pair
            contains the index values of two records.

        """
        raise NotImplementedError(
            "Not possible to call index for the BaseEstimator"
        ) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import MultiIndex [as 別名]
def fit_predict(self, comparison_vectors, match_index=None):
        """Train the classifier.

        Parameters
        ----------
        comparison_vectors : pandas.DataFrame
            The comparison vectors.
        match_index : pandas.MultiIndex
            The true matches.
        return_type : str
            Deprecated. Use recordlinkage.options instead. Use the option
            `recordlinkage.set_option('classification.return_type', 'index')`
            instead.

        Returns
        -------
        pandas.Series
            A pandas Series with the labels 1 (for the matches) and 0 (for the
            non-matches).

        """
        self.fit(comparison_vectors, match_index)
        result = self.predict(comparison_vectors)

        return result 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import MultiIndex [as 別名]
def _febrl_links(df):
    """Get the links of a FEBRL dataset."""

    index = df.index.to_series()
    keys = index.str.extract(r'rec-(\d+)', expand=True)[0]

    index_int = numpy.arange(len(df))

    df_helper = pandas.DataFrame({
        'key': keys,
        'index': index_int
    })

    # merge the two frame and make MultiIndex.
    pairs_df = df_helper.merge(
        df_helper, on='key'
    )[['index_x', 'index_y']]
    pairs_df = pairs_df[pairs_df['index_x'] > pairs_df['index_y']]

    return pandas.MultiIndex(
        levels=[df.index.values, df.index.values],
        codes=[pairs_df['index_x'].values, pairs_df['index_y'].values],
        names=[None, None],
        verify_integrity=False
    ) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import MultiIndex [as 別名]
def true_positives(links_true, links_pred):
    """Count the number of True Positives.

    Returns the number of correctly predicted links, also called the number of
    True Positives (TP).

    Parameters
    ----------
    links_true: pandas.MultiIndex, pandas.DataFrame, pandas.Series
        The true (or actual) links.
    links_pred: pandas.MultiIndex, pandas.DataFrame, pandas.Series
        The predicted links.

    Returns
    -------
    int
        The number of correctly predicted links.
    """

    links_true = _get_multiindex(links_true)
    links_pred = _get_multiindex(links_pred)

    return len(links_true & links_pred) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import MultiIndex [as 別名]
def false_positives(links_true, links_pred):
    """Count the number of False Positives.

    Returns the number of incorrect predictions of true non-links. (true non-
    links, but predicted as links). This value is known as the number of False
    Positives (FP).

    Parameters
    ----------
    links_true: pandas.MultiIndex, pandas.DataFrame, pandas.Series
        The true (or actual) links.
    links_pred: pandas.MultiIndex, pandas.DataFrame, pandas.Series
        The predicted links.

    Returns
    -------
    int
        The number of false positives.

    """

    links_true = _get_multiindex(links_true)
    links_pred = _get_multiindex(links_pred)

    return len(links_pred.difference(links_true)) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import MultiIndex [as 別名]
def false_negatives(links_true, links_pred):
    """Count the number of False Negatives.

    Returns the number of incorrect predictions of true links. (true links,
    but predicted as non-links). This value is known as the number of False
    Negatives (FN).

    Parameters
    ----------
    links_true: pandas.MultiIndex, pandas.DataFrame, pandas.Series
        The true (or actual) links.
    links_pred: pandas.MultiIndex, pandas.DataFrame, pandas.Series
        The predicted links.

    Returns
    -------
    int
        The number of false negatives.

    """

    links_true = _get_multiindex(links_true)
    links_pred = _get_multiindex(links_pred)

    return len(links_true.difference(links_pred)) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import MultiIndex [as 別名]
def test_iterative(self):
        """Test the iterative behaviour."""

        # SINGLE STEP
        index_class = Full()
        pairs = index_class.index((self.a, self.b))
        pairs = pd.DataFrame(index=pairs).sort_index()

        # MULTI STEP
        index_class = Full()

        pairs1 = index_class.index((self.a[0:50], self.b))
        pairs2 = index_class.index((self.a[50:100], self.b))

        pairs_split = pairs1.append(pairs2)
        pairs_split = pd.DataFrame(index=pairs_split).sort_index()

        pdt.assert_frame_equal(pairs, pairs_split)
        # note possible to sort MultiIndex, so made a frame out of it. 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import MultiIndex [as 別名]
def test_index_names_pandas023(self, index_class):
        # Pandas changes the behaviour of MultiIndex names.
        # https://github.com/pandas-dev/pandas/pull/18882
        # https://github.com/J535D165/recordlinkage/issues/55
        # This test tests compatibility.

        # make an index for each dataframe with a new index name
        index_a = pd.Index(self.a.index, name='index')
        df_a = pd.DataFrame(self.a, index=index_a)

        index_b = pd.Index(self.b.index, name='index')
        df_b = pd.DataFrame(self.b, index=index_b)

        # make the index
        pairs_link = index_class._link_index(df_a, df_b)

        if pairs_link.names[0] is not None:
            assert pairs_link.names[0] != pairs_link.names[1]

        # make the index
        pairs_dedup = index_class._dedup_index(df_a)

        if pairs_link.names[0] is not None:
            assert pairs_dedup.names[0] != pairs_dedup.names[1] 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import MultiIndex [as 別名]
def test_lower_triangular(self, index_class):

        # make an index for each dataframe with a new index name
        index_a = pd.Index(self.a.index, name='index')
        df_a = pd.DataFrame(self.a, index=index_a)
        pairs = index_class.index(df_a)

        # expected
        levels = [df_a.index.values, df_a.index.values]
        codes = np.tril_indices(len(df_a.index), k=-1)

        full_pairs = pd.MultiIndex(levels=levels,
                                   codes=codes,
                                   verify_integrity=False)

        # all pairs are in the lower triangle of the matrix.
        assert len(pairs.difference(full_pairs)) == 0 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import MultiIndex [as 別名]
def test_krebs_dataset_download():

    # remove downloaded datasets
    clear_data_home()

    krebs_data, krebs_matches = load_krebsregister()

    for i in range(1, 11):
        assert Path(get_data_home(), "krebsregister",
                    "block_{}.zip".format(i)).is_file()

    # count the number of recordss
    assert type(krebs_data), pandas.DataFrame
    assert type(krebs_matches), pandas.MultiIndex
    assert len(krebs_data) == 5749132
    assert len(krebs_matches) == 20931 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import MultiIndex [as 別名]
def _infer_choices_with_experience(params, options):
    """Infer choices with experiences.

    Example
    -------
    >>> options = {"covariates": {"a": "exp_white_collar + exp_a", "b": "exp_b >= 2"}}
    >>> index = pd.MultiIndex.from_product([["category"], ["a", "b"]])
    >>> params = pd.Series(index=index, dtype="object")
    >>> _infer_choices_with_experience(params, options)
    ['a', 'b', 'white_collar']

    """
    covariates = options["covariates"]
    parameters = params.index.get_level_values(1)

    used_covariates = [cov for cov in covariates if cov in parameters]

    matches = []
    for param in parameters:
        matches += re.findall(r"\bexp_([A-Za-z_]+)\b", str(param))
    for cov in used_covariates:
        matches += re.findall(r"\bexp_([A-Za-z_]+)\b", covariates[cov])

    return sorted(set(matches)) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import MultiIndex [as 別名]
def multi_index_insert_row(df, index_row, values_row):
    """ Return a new dataframe with a row inserted for a multi-index dataframe.
        This will sort the rows according to the ordered multi-index levels.
    """
    if PD_VER < '0.24.0':
        row_index = pd.MultiIndex(levels=[[i] for i in index_row],
                                  labels=[[0] for i in index_row])
    else:
        row_index = pd.MultiIndex(levels=[[i] for i in index_row],
                                  codes=[[0] for i in index_row])
    row = pd.DataFrame(values_row, index=row_index, columns=df.columns)
    df = pd.concat((df, row))
    if df.index.lexsort_depth == len(index_row) and df.index[-2] < df.index[-1]:
        # We've just appended a row to an already-sorted dataframe
        return df
    # The df wasn't sorted or the row has to be put in the middle somewhere
    return df.sort_index() 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import MultiIndex [as 別名]
def test_data_info_cols(library):
    i = MultiIndex.from_tuples([(1, "ab"), (2, "bb"), (3, "cb")])
    s = DataFrame(data=[100, 200, 300], index=i)
    library.write('test_data', s)
    md = library.get_info('test_data')
    # {'dtype': [('level_0', '<i8'), ('level_1', 'S2'), ('0', '<i8')],
    #                  'col_names': {u'index': [u'level_0', u'level_1'], u'columns': [u'0'], 'index_tz': [None, None]},
    #                  'type': u'pandasdf',
    #                  'handler': 'PandasDataFrameStore',
    #                  'rows': 3,
    #                  'segment_count': 1,
    #                  'size': 50}
    assert 'size' in md
    assert md['segment_count'] == 1
    assert md['rows'] == 3
    assert md['handler'] == 'PandasDataFrameStore'
    assert md['type'] == 'pandasdf'
    assert md['col_names'] == {'index': ['level_0', u'level_1'], 'columns': [u'0'], 'index_tz': [None, None]}
    assert len(md['dtype']) == 3
    assert md['dtype'][0][0] == 'level_0'
    assert md['dtype'][1][0] == 'level_1'
    assert md['dtype'][2][0] == '0' 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import MultiIndex [as 別名]
def setup_method(self, method):
        self.indices = dict(unicodeIndex=tm.makeUnicodeIndex(100),
                            strIndex=tm.makeStringIndex(100),
                            dateIndex=tm.makeDateIndex(100),
                            periodIndex=tm.makePeriodIndex(100),
                            tdIndex=tm.makeTimedeltaIndex(100),
                            intIndex=tm.makeIntIndex(100),
                            uintIndex=tm.makeUIntIndex(100),
                            rangeIndex=tm.makeRangeIndex(100),
                            floatIndex=tm.makeFloatIndex(100),
                            boolIndex=Index([True, False]),
                            catIndex=tm.makeCategoricalIndex(100),
                            empty=Index([]),
                            tuples=MultiIndex.from_tuples(lzip(
                                ['foo', 'bar', 'baz'], [1, 2, 3])),
                            repeats=Index([0, 0, 1, 1, 2, 2]))
        self.setup_indices() 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import MultiIndex [as 別名]
def test_droplevel(self, indices):
        # GH 21115
        if isinstance(indices, MultiIndex):
            # Tested separately in test_multi.py
            return

        assert indices.droplevel([]).equals(indices)

        for level in indices.name, [indices.name]:
            if isinstance(indices.name, tuple) and level is indices.name:
                # GH 21121 : droplevel with tuple name
                continue
            with pytest.raises(ValueError):
                indices.droplevel(level)

        for level in 'wrong', ['wrong']:
            with pytest.raises(KeyError):
                indices.droplevel(level)