Python pandas.to_numeric方法代碼示例

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import to_numeric [as 別名]
def __init__(self, code, start=None, end=None):
        """

        :param code: str. 指數代碼，eg. SH000016
        :param start:
        :param end:
        """
        df = xu.get_daily("teb-" + code, start=start, end=end)
        df["e"] = pd.to_numeric(df["e"])
        df["b"] = pd.to_numeric(df["b"])
        df["lnb"] = df["b"].apply(lambda s: np.log(s))
        df["lne"] = df["e"].apply(lambda s: np.log(s))
        df["roe"] = df["e"] / df["b"] * 100
        df["date_count"] = (df["date"] - df["date"].iloc[0]).apply(
            lambda s: int(s.days)
        )
        self.df = df
        self.fit(verbose=False) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import to_numeric [as 別名]
def __init__(
        self, code, name=None, start=None, end=None, rate=0, col="close", **kws
    ):
        if not name:
            try:
                name = get_rt(code)["name"]
            except:
                name = code
        self.name = name
        self.code = code
        self.start = start  # None is one year ago
        self.end = end  # None is yesterday
        df = get_daily(code, start=start, end=end)
        df[col] = pd.to_numeric(df[col])  # in case the col is not float
        df["totvalue"] = df[col]
        df["netvalue"] = df[col] / df.iloc[0][col]
        self.price = df
        self.round_label = kws.get("round_label", 0)
        self.dividend_label = kws.get("dividend_label", 0)
        self.value_label = kws.get("value_label", 1)  # 默認按金額贖回
        self.specialdate = []
        self.fenhongdate = []
        self.zhesuandate = []
        self.rate = rate 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import to_numeric [as 別名]
def df_fx(self, currency, fx_provider):
        try:
            # First get the df from this currency
            if currency != 'USD':
                fx = PriceData(currency, fx_provider)
                fx.df = fx.df.rename(columns={'close': 'fx_close'})
                fx.df["fx_close"] = pd.to_numeric(fx.df.fx_close,
                                                  errors='coerce')
                # Merge the two dfs:
                merge_df = pd.merge(self.df, fx.df, on='date', how='inner')
                merge_df['close'] = merge_df['close'].astype(float)
                merge_df['close_converted'] = merge_df['close'] * merge_df[
                    'fx_close']
                return (merge_df)
            else:  # If currency is USD no conversion is needed - prices are all in USD
                self.df['fx_close'] = 1
                self.df['close_converted'] = self.df['close'].astype(float)
                return (self.df)
        except Exception as e:
            self.errors.append(e)
            return (None) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import to_numeric [as 別名]
def test_error(self):
        s = pd.Series([1, -3.14, 'apple'])
        msg = 'Unable to parse string "apple" at position 2'
        with pytest.raises(ValueError, match=msg):
            to_numeric(s, errors='raise')

        res = to_numeric(s, errors='ignore')
        expected = pd.Series([1, -3.14, 'apple'])
        tm.assert_series_equal(res, expected)

        res = to_numeric(s, errors='coerce')
        expected = pd.Series([1, -3.14, np.nan])
        tm.assert_series_equal(res, expected)

        s = pd.Series(['orange', 1, -3.14, 'apple'])
        msg = 'Unable to parse string "orange" at position 0'
        with pytest.raises(ValueError, match=msg):
            to_numeric(s, errors='raise') 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import to_numeric [as 別名]
def test_numeric_lists_and_arrays(self):
        # Test to_numeric with embedded lists and arrays
        df = pd.DataFrame(dict(
            a=[[decimal.Decimal(3.14), 1.0], decimal.Decimal(1.6), 0.1]
        ))
        df['a'] = df['a'].apply(to_numeric)
        expected = pd.DataFrame(dict(
            a=[[3.14, 1.0], 1.6, 0.1],
        ))
        tm.assert_frame_equal(df, expected)

        df = pd.DataFrame(dict(
            a=[np.array([decimal.Decimal(3.14), 1.0]), 0.1]
        ))
        df['a'] = df['a'].apply(to_numeric)
        expected = pd.DataFrame(dict(
            a=[[3.14, 1.0], 0.1],
        ))
        tm.assert_frame_equal(df, expected) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import to_numeric [as 別名]
def test_numeric_dtypes(self):
        idx = pd.Index([1, 2, 3], name='xxx')
        res = pd.to_numeric(idx)
        tm.assert_index_equal(res, idx)

        res = pd.to_numeric(pd.Series(idx, name='xxx'))
        tm.assert_series_equal(res, pd.Series(idx, name='xxx'))

        res = pd.to_numeric(idx.values)
        tm.assert_numpy_array_equal(res, idx.values)

        idx = pd.Index([1., np.nan, 3., np.nan], name='xxx')
        res = pd.to_numeric(idx)
        tm.assert_index_equal(res, idx)

        res = pd.to_numeric(pd.Series(idx, name='xxx'))
        tm.assert_series_equal(res, pd.Series(idx, name='xxx'))

        res = pd.to_numeric(idx.values)
        tm.assert_numpy_array_equal(res, idx.values) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import to_numeric [as 別名]
def test_str(self):
        idx = pd.Index(['1', '2', '3'], name='xxx')
        exp = np.array([1, 2, 3], dtype='int64')
        res = pd.to_numeric(idx)
        tm.assert_index_equal(res, pd.Index(exp, name='xxx'))

        res = pd.to_numeric(pd.Series(idx, name='xxx'))
        tm.assert_series_equal(res, pd.Series(exp, name='xxx'))

        res = pd.to_numeric(idx.values)
        tm.assert_numpy_array_equal(res, exp)

        idx = pd.Index(['1.5', '2.7', '3.4'], name='xxx')
        exp = np.array([1.5, 2.7, 3.4])
        res = pd.to_numeric(idx)
        tm.assert_index_equal(res, pd.Index(exp, name='xxx'))

        res = pd.to_numeric(pd.Series(idx, name='xxx'))
        tm.assert_series_equal(res, pd.Series(exp, name='xxx'))

        res = pd.to_numeric(idx.values)
        tm.assert_numpy_array_equal(res, exp) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import to_numeric [as 別名]
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 pytest.raises(ValueError, match=msg):
            to_numeric(s, errors="raise") 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import to_numeric [as 別名]
def test_metadata_no_nan(self, mock_etl2, mock_open):
        edges = triangleEdges.copy()
        edges['testNone'] = triangleNodes.a1.map(lambda x: numpy.nan)
        edges['testNone'] = pandas.to_numeric(edges.testNone, errors='ignore')
        edges['testInt'] = triangleNodes.a1.map(lambda x: numpy.nan if x%2 == 1 else 0)
        edges['testFloat'] = triangleNodes.a1.map(lambda x: numpy.nan if x%2 == 1 else 0.5)
        edges['testString'] = triangleNodes.a1.map(lambda x: numpy.nan if x%2 == 1 else 'foo')
        edges['testBool'] = triangleNodes.a1.map(lambda x: numpy.nan if x%2 == 1 else True)
        graphistry.bind(source='src', destination='dst', node='id').plot(edges)
        dataset = mock_etl2.call_args[0][0]

        #for attrib in ['testInt', 'testFloat', 'testString', 'testBool', 'testNone']:
        #    for entry in list(dataset['attributes']['edges'][attrib]['aggregations'].values()):
        #        if entry is None or isinstance(entry, str):
        #            pass
        #        else:
        #            self.assertFalse(numpy.isnan(entry)) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import to_numeric [as 別名]
def _sanitize_dataset(self, edges, nodes, nodeid):
        self._check_bound_attribs(edges, ['source', 'destination'], 'Edge')
        elist = edges.reset_index(drop=True) \
                     .dropna(subset=[self._source, self._destination])

        obj_df = elist.select_dtypes(include=[numpy.object_])
        elist[obj_df.columns] = obj_df.apply(pandas.to_numeric, errors='ignore')

        if nodes is None:
            nodes = pandas.DataFrame()
            nodes[nodeid] = pandas.concat([edges[self._source], edges[self._destination]],
                                           ignore_index=True).drop_duplicates()
        else:
            self._check_bound_attribs(nodes, ['node'], 'Vertex')

        nlist = nodes.reset_index(drop=True) \
                     .dropna(subset=[nodeid]) \
                     .drop_duplicates(subset=[nodeid])

        obj_df = nlist.select_dtypes(include=[numpy.object_])
        nlist[obj_df.columns] = obj_df.apply(pandas.to_numeric, errors='ignore')

        return (elist, nlist) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import to_numeric [as 別名]
def get_user_specified_features(df, featurizer, verbose=True):
  """Extract and merge user specified features. 

  Merge features included in dataset provided by user
  into final features dataframe

  Three types of featurization here:

    1) Molecule featurization
      -) Smiles string featurization
      -) Rdkit MOL featurization
    2) Complex featurization
      -) PDB files for interacting molecules.
    3) User specified featurizations.

  """
  time1 = time.time()
  df[featurizer.feature_fields] = df[featurizer.feature_fields].apply(
      pd.to_numeric)
  X_shard = df.as_matrix(columns=featurizer.feature_fields)
  time2 = time.time()
  log("TIMING: user specified processing took %0.3f s" % (time2 - time1),
      verbose)
  return X_shard 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import to_numeric [as 別名]
def test_error(self):
        s = pd.Series([1, -3.14, 'apple'])
        msg = 'Unable to parse string "apple" at position 2'
        with tm.assert_raises_regex(ValueError, msg):
            to_numeric(s, errors='raise')

        res = to_numeric(s, errors='ignore')
        expected = pd.Series([1, -3.14, 'apple'])
        tm.assert_series_equal(res, expected)

        res = to_numeric(s, errors='coerce')
        expected = pd.Series([1, -3.14, np.nan])
        tm.assert_series_equal(res, expected)

        s = pd.Series(['orange', 1, -3.14, 'apple'])
        msg = 'Unable to parse string "orange" at position 0'
        with tm.assert_raises_regex(ValueError, msg):
            to_numeric(s, errors='raise') 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import to_numeric [as 別名]
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") 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import to_numeric [as 別名]
def load_metrics_from_db(db_path, tx_mode, aln_mode):
    """
    Loads the alignment metrics for the mRNA/CDS alignments of transMap/AugustusTM/TMR
    """
    session = tools.sqlInterface.start_session(db_path)
    metrics_table = tools.sqlInterface.tables[aln_mode][tx_mode]['metrics']
    metrics_df = tools.sqlInterface.load_metrics(metrics_table, session)
    # unstack flattens the long-form data structure
    metrics_df = metrics_df.set_index(['AlignmentId', 'classifier']).unstack('classifier')
    metrics_df.columns = [col[1] for col in metrics_df.columns]
    metrics_df = metrics_df.reset_index()
    cols = ['AlnCoverage', 'AlnGoodness', 'AlnIdentity', 'PercentUnknownBases']
    metrics_df[cols] = metrics_df[cols].apply(pd.to_numeric)
    metrics_df['OriginalIntrons'] = metrics_df['OriginalIntrons'].fillna('')
    metrics_df['OriginalIntrons'] = [list(map(int, x)) if len(x[0]) > 0 else [] for x in
                                     metrics_df['OriginalIntrons'].str.split(',').tolist()]
    metrics_df['OriginalIntronsPercent'] = metrics_df['OriginalIntrons'].apply(calculate_vector_support, resolve_nan=1)
    session.close()
    return metrics_df 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import to_numeric [as 別名]
def _format_series_data(data_series):
    """
    The purpose of this function is to convert the series data into a rasterizeable
    format if possible.

    Parameters
    ----------
    data_series: :obj:`geopandas.GeoSeries`
        The series to be converted.

    Returns
    -------
    :obj:`geopandas.GeoSeries`: The series that was converted if possible.

    """
    if "datetime" in str(data_series.dtype):
        data_series = pandas.to_numeric(data_series).astype(numpy.float64)
        get_logger().warning(
            f"The series '{data_series.name}' was converted from a date to a number to "
            "rasterize the data. To load the data back in as a date, "
            "use 'pandas.to_datetime()'."
        )
    elif str(data_series.dtype) == "category":
        data_series = data_series.cat.codes
    return data_series