Python pandas.read_parquet方法代碼示例

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_parquet [as 別名]
def get_split(self, k):

        train_index_file = os.path.join(
            self._results_path, "folds", f"fold_{k}_train_indices.npy"
        )
        validation_index_file = os.path.join(
            self._results_path, "folds", f"fold_{k}_validation_indices.npy"
        )

        train_index = np.load(train_index_file)
        validation_index = np.load(validation_index_file)

        X = pd.read_parquet(self._X_train_path)
        y = pd.read_parquet(self._y_train_path)
        y = y["target"]

        return (
            {"X": X.loc[train_index], "y": y.loc[train_index]},
            {"X": X.loc[validation_index], "y": y.loc[validation_index]},
        ) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_parquet [as 別名]
def get_split(self, k=0):

        X = pd.read_parquet(self._X_train_path)
        y = pd.read_parquet(self._y_train_path)
        y = y["target"]

        stratify = None
        if self.stratify:
            stratify = y
        if self.shuffle == False:
            stratify = None

        X_train, X_validation, y_train, y_validation = train_test_split(
            X,
            y,
            train_size=self.train_ratio,
            test_size=1.0 - self.train_ratio,
            shuffle=self.shuffle,
            stratify=stratify,
            random_state=self.random_seed,
        )
        return {"X": X_train, "y": y_train}, {"X": X_validation, "y": y_validation} 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_parquet [as 別名]
def test_dataframe_parquet_materialization():
    check_parquet_support()

    @solid(output_defs=[OutputDefinition(DataFrame)])
    def return_df(_context):
        return pd.DataFrame({'num1': [1, 3], 'num2': [2, 4]})

    @pipeline
    def return_df_pipeline():
        return_df()

    with get_temp_file_name() as filename:
        result = execute_pipeline(
            return_df_pipeline,
            {'solids': {'return_df': {'outputs': [{'result': {'parquet': {'path': filename}}}]}}},
        )

        assert result.success

        df = pd.read_parquet(filename)
        assert df.to_dict('list') == {'num1': [1, 3], 'num2': [2, 4]} 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_parquet [as 別名]
def read_df_detect(path):
    """
    Read a Pandas data frame, auto-detecting the file format based on filename suffix.
    The following file types are supported:

    CSV
        File has suffix ``.csv``, read with :py:func:`pandas.read_csv`.
    Parquet
        File has suffix ``.parquet``, ``.parq``, or ``.pq``, read with
        :py:func:`pandas.read_parquet`.
    """
    import pandas as pd
    if not isinstance(path, pathlib.Path):
        path = pathlib.Path(path)

    if path.suffix == '.csv':
        return pd.read_csv(path)
    elif path.suffix in ('.parquet', '.parq', '.pq'):
        return pd.read_parquet(path) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_parquet [as 別名]
def test_parquet_write(self):
        with self.temp_dir() as tmp:
            pdf = self.test_pdf
            expected = ks.DataFrame(pdf)

            # Write out partitioned by one column
            expected.to_parquet(tmp, mode="overwrite", partition_cols="i32")
            # Reset column order, as once the data is written out, Spark rearranges partition
            # columns to appear first.
            actual = ks.read_parquet(tmp)[self.test_column_order]
            self.assert_eq(
                actual.sort_values(by="f").to_spark().toPandas(),
                expected.sort_values(by="f").to_spark().toPandas(),
            )

            # Write out partitioned by two columns
            expected.to_parquet(tmp, mode="overwrite", partition_cols=["i32", "bhello"])
            # Reset column order, as once the data is written out, Spark rearranges partition
            # columns to appear first.
            actual = ks.read_parquet(tmp)[self.test_column_order]
            self.assert_eq(
                actual.sort_values(by="f").to_spark().toPandas(),
                expected.sort_values(by="f").to_spark().toPandas(),
            ) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_parquet [as 別名]
def test_sort_parallel(self):
        # create `kde.parquet` file
        ParquetGenerator.gen_kde_pq()

        # TODO: better parallel sort test
        def test_impl():
            df = pd.read_parquet('kde.parquet')
            df['A'] = df.points.astype(np.float64)
            df.sort_values('points', inplace=True)
            res = df.A.values
            return res

        hpat_func = self.jit(locals={'res:return': 'distributed'})(test_impl)

        save_min_samples = sdc.hiframes.sort.MIN_SAMPLES
        try:
            sdc.hiframes.sort.MIN_SAMPLES = 10
            res = hpat_func()
            self.assertTrue((np.diff(res) >= 0).all())
        finally:
            # restore global val
            sdc.hiframes.sort.MIN_SAMPLES = save_min_samples 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_parquet [as 別名]
def make_subgen(self, chunk):
        filename = chunk
        subgen = single_sequence.SequenceForecastBatchGenerator(
            df=pd.read_parquet(filename).reset_index(),
            batch_size=self.batch_size,
            sequence_length=self.sequence_length,
            id_column=self.id_column,
            sequence_columns=self.sequence_columns,
            sequence_prefix=self.sequence_prefix,
            last_step_columns=self.last_step_columns,
            last_step_prefix=self.last_step_prefix,
            forecast_steps_min=self.forecast_steps_min,
            forecast_steps_max=self.forecast_steps_max,
            batch_offset=self.batch_offset,
            batch_offset_period=self.batch_offset_period,
            dt_column=self.dt_column,
            start_time=self.start_time,
        )
        return subgen 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_parquet [as 別名]
def read_parquet(cls, path, engine, columns, **kwargs):
        """Load a parquet object from the file path, returning a Modin DataFrame.
           Modin only supports pyarrow engine for now.

        Args:
            path: The filepath of the parquet file.
                  We only support local files for now.
            engine: Modin only supports pyarrow reader.
                    This argument doesn't do anything for now.
            kwargs: Pass into parquet's read_pandas function.

        Notes:
            ParquetFile API is used. Please refer to the documentation here
            https://arrow.apache.org/docs/python/parquet.html
        """
        ErrorMessage.default_to_pandas("`read_parquet`")
        return cls.from_pandas(pandas.read_parquet(path, engine, columns, **kwargs)) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_parquet [as 別名]
def parse(fname, **kwargs):
        num_splits = kwargs.pop("num_splits", None)
        columns = kwargs.get("columns", None)
        if num_splits is None:
            return pandas.read_parquet(fname, **kwargs)
        kwargs["use_pandas_metadata"] = True
        df = pandas.read_parquet(fname, **kwargs)
        if isinstance(df.index, pandas.RangeIndex):
            idx = len(df.index)
        else:
            idx = df.index
        columns = [c for c in columns if c not in df.index.names and c in df.columns]
        if columns is not None:
            df = df[columns]
        # Append the length of the index here to build it externally
        return _split_result_for_readers(0, num_splits, df) + [idx, df.dtypes] 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_parquet [as 別名]
def test_datetime_coercion_explicitly():
    """
    Sanity check that we're using a version of pyarrow that allows us to
    truncate timestamps
    """
    dt = _datetime.datetime(day=1, month=1, year=2017, hour=1, minute=1, second=1, microsecond=1)
    values = [(dt,)]
    df = _pd.DataFrame.from_records(values, columns=['testname'])
    assert df['testname'][0] == dt

    with _utils.AutoDeletingTempDir('test') as tmpdir:
        tmpfile = tmpdir.get_named_tempfile('repro.parquet')
        df.to_parquet(tmpfile, coerce_timestamps='ms', allow_truncated_timestamps=True)
        df2 = _pd.read_parquet(tmpfile)

    dt2 = _datetime.datetime(day=1, month=1, year=2017, hour=1, minute=1, second=1)
    assert df2['testname'][0] == dt2 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_parquet [as 別名]
def read_as_dataframe(input_path: str):
    if os.path.isfile(input_path):
        if input_path.endswith(".csv"):
            return pd.read_csv(input_path)
        elif input_path.endswith(".parquet"):
            return pd.read_parquet(input_path)
    else:
        dir_path = pathlib.Path(input_path)

        csv_files = list(dir_path.glob("**/*.csv"))
        if csv_files:
            df_from_csv_files = (pd.read_csv(f) for f in csv_files)
            return pd.concat(df_from_csv_files, ignore_index=True)

        parquet_files = list(dir_path.glob("**/*.parquet"))
        if parquet_files:
            df_from_parquet_files = (pd.read_parquet(f) for f in parquet_files)
            return pd.concat(df_from_parquet_files, ignore_index=True)

    raise ValueError(f"Failed to read path: {input_path}") 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_parquet [as 別名]
def load(self, file):
        # MEMO: read_parquet only supports a filepath as string (not a file handle)
        return pd.read_parquet(file.name) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_parquet [as 別名]
def add_ticker(data: pd.DataFrame, ticker: str) -> pd.DataFrame:
    """
    Add ticker as first layer of multi-index

    Args:
        data: raw data
        ticker: ticker

    Returns:
        pd.DataFrame

    Examples:
        >>> (
        ...     pd.read_parquet('xbbg/tests/data/sample_bdib.parq')
        ...     .pipe(add_ticker, ticker='SPY US Equity')
        ...     .pipe(get_series, col='close')
        ... )
                                   SPY US Equity
        2018-12-28 09:30:00-05:00         249.67
        2018-12-28 09:31:00-05:00         249.54
        2018-12-28 09:32:00-05:00         249.22
        2018-12-28 09:33:00-05:00         249.01
        2018-12-28 09:34:00-05:00         248.86
    """
    data.columns = pd.MultiIndex.from_product([
        [ticker], data.head().rename(columns={'numEvents': 'num_trds'}).columns
    ])
    return data 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_parquet [as 別名]
def get_stored_data(
        self, func: Callable, *args: Any, **kwargs: Any
    ) -> Union[pd.DataFrame, pathlib.Path, io.BytesIO]:

        argspec = inspect.getfullargspec(func)
        for arg_name, arg in zip(argspec.args, args):
            kwargs[arg_name] = arg

        WebvizStorage.complete_kwargs(func, kwargs)
        return_type = inspect.getfullargspec(func).annotations["return"]

        path = self._unique_path(func, WebvizStorage._dict_to_tuples(kwargs))

        try:
            if return_type == pd.DataFrame:
                return pd.read_parquet(f"{path}.parquet")
            if return_type == pathlib.Path:
                return pathlib.Path(glob.glob(f"{path}*")[0])
            if return_type == io.BytesIO:
                return io.BytesIO(pathlib.Path(path).read_bytes())
            raise ValueError(f"Unknown return type {return_type}")

        except OSError:
            raise OSError(
                f"Could not find file {path}, which should be the "
                "stored output of the function call "
                f"{WebvizStorage.string(func, kwargs)}."
            ) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_parquet [as 別名]
def read_df(self, f: BinaryIO) -> pd.DataFrame:
        columns = self.time_series_columns.columns
        datetime_column = self.time_series_columns.datetime_column
        df = pd.read_parquet(f, engine="pyarrow", columns=columns).set_index(
            datetime_column
        )
        df.index = pd.to_datetime(df.index, utc=True)
        return df