Package Methods (1.1.0)

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Summary of entries of Methods for bigframes.

bigframes.ml.metrics.accuracy_score

accuracy_score(
    y_true: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y_pred: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    *,
    normalize=True
) -> float

Accuracy classification score.

See more: bigframes.ml.metrics.accuracy_score

bigframes.ml.metrics.auc

auc(
    x: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
) -> float

Compute Area Under the Curve (AUC) using the trapezoidal rule.

See more: bigframes.ml.metrics.auc

bigframes.ml.metrics.confusion_matrix

confusion_matrix(
    y_true: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y_pred: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
) -> pandas.core.frame.DataFrame

Compute confusion matrix to evaluate the accuracy of a classification.

See more: bigframes.ml.metrics.confusion_matrix

bigframes.ml.metrics.f1_score

f1_score(
    y_true: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y_pred: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    *,
    average: str = "binary"
) -> pandas.core.series.Series

Compute the F1 score, also known as balanced F-score or F-measure.

See more: bigframes.ml.metrics.f1_score

bigframes.ml.metrics.mean_squared_error

mean_squared_error(
    y_true: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y_pred: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
) -> float

Mean squared error regression loss.

See more: bigframes.ml.metrics.mean_squared_error

bigframes.ml.metrics.precision_score

precision_score(
    y_true: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y_pred: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    *,
    average: str = "binary"
) -> pandas.core.series.Series

Compute the precision.

See more: bigframes.ml.metrics.precision_score

bigframes.ml.metrics.r2_score

r2_score(
    y_true: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y_pred: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    *,
    force_finite=True
) -> float

:math:R^2 (coefficient of determination) regression score function.

See more: bigframes.ml.metrics.r2_score

bigframes.ml.metrics.recall_score

recall_score(
    y_true: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y_pred: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    *,
    average: str = "binary"
) -> pandas.core.series.Series

Compute the recall.

See more: bigframes.ml.metrics.recall_score

bigframes.ml.metrics.roc_auc_score

roc_auc_score(
    y_true: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y_score: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
) -> float

Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores.

See more: bigframes.ml.metrics.roc_auc_score

bigframes.ml.metrics.roc_curve

roc_curve(
    y_true: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y_score: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    *,
    drop_intermediate: bool = True
) -> typing.Tuple[
    bigframes.series.Series, bigframes.series.Series, bigframes.series.Series
]

Compute Receiver operating characteristic (ROC).

See more: bigframes.ml.metrics.roc_curve

bigframes.ml.metrics.pairwise.paired_cosine_distances

paired_cosine_distances(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    Y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
) -> bigframes.dataframe.DataFrame

Compute the paired cosine distances between X and Y.

See more: bigframes.ml.metrics.pairwise.paired_cosine_distances

bigframes.ml.metrics.pairwise.paired_euclidean_distances

paired_euclidean_distances(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    Y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
) -> bigframes.dataframe.DataFrame

Compute the paired euclidean distances between X and Y.

See more: bigframes.ml.metrics.pairwise.paired_euclidean_distances

bigframes.ml.metrics.pairwise.paired_manhattan_distance

paired_manhattan_distance(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    Y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
) -> bigframes.dataframe.DataFrame

Compute the L1 distances between the vectors in X and Y.

See more: bigframes.ml.metrics.pairwise.paired_manhattan_distance

bigframes.ml.model_selection.train_test_split

train_test_split(
    *arrays: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    test_size: typing.Optional[float] = None,
    train_size: typing.Optional[float] = None,
    random_state: typing.Optional[int] = None
) -> typing.List[typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]]

Splits dataframes or series into random train and test subsets.

See more: bigframes.ml.model_selection.train_test_split

bigframes.pandas.concat

Concatenate BigQuery DataFrames objects along a particular axis.

See more: bigframes.pandas.concat

bigframes.pandas.cut

cut(
    x: bigframes.series.Series, bins: int, *, labels: typing.Optional[bool] = None
) -> bigframes.series.Series

Bin values into discrete intervals.

See more: bigframes.pandas.cut

bigframes.pandas.get_dummies

get_dummies(
    data: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    prefix: typing.Optional[typing.Union[typing.List, dict, str]] = None,
    prefix_sep: typing.Optional[typing.Union[typing.List, dict, str]] = "_",
    dummy_na: bool = False,
    columns: typing.Optional[typing.List] = None,
    drop_first: bool = False,
    dtype: typing.Optional[typing.Any] = None,
) -> bigframes.dataframe.DataFrame

Convert categorical variable into dummy/indicator variables.

See more: bigframes.pandas.get_dummies

bigframes.pandas.merge

merge(
    left: bigframes.dataframe.DataFrame,
    right: bigframes.dataframe.DataFrame,
    how: typing.Literal["inner", "left", "outer", "right", "cross"] = "inner",
    on: typing.Optional[str] = None,
    *,
    left_on: typing.Optional[str] = None,
    right_on: typing.Optional[str] = None,
    sort: bool = False,
    suffixes: tuple[str, str] = ("_x", "_y")
) -> bigframes.dataframe.DataFrame

Merge DataFrame objects with a database-style join.

See more: bigframes.pandas.merge

bigframes.pandas.qcut

qcut(
    x: bigframes.series.Series,
    q: int,
    *,
    labels: typing.Optional[bool] = None,
    duplicates: typing.Literal["drop", "error"] = "error"
) -> bigframes.series.Series

Quantile-based discretization function.

See more: bigframes.pandas.qcut

bigframes.pandas.read_csv

read_csv(
    filepath_or_buffer: typing.Union[str, typing.IO[bytes]],
    *,
    sep: typing.Optional[str] = ",",
    header: typing.Optional[int] = 0,
    names: typing.Optional[
        typing.Union[
            typing.MutableSequence[typing.Any],
            numpy.ndarray[typing.Any, typing.Any],
            typing.Tuple[typing.Any, ...],
            range,
        ]
    ] = None,
    index_col: typing.Optional[
        typing.Union[
            int, str, typing.Sequence[typing.Union[str, int]], typing.Literal[False]
        ]
    ] = None,
    usecols: typing.Optional[
        typing.Union[
            typing.MutableSequence[str],
            typing.Tuple[str, ...],
            typing.Sequence[int],
            pandas.core.series.Series,
            pandas.core.indexes.base.Index,
            numpy.ndarray[typing.Any, typing.Any],
            typing.Callable[[typing.Any], bool],
        ]
    ] = None,
    dtype: typing.Optional[typing.Dict] = None,
    engine: typing.Optional[
        typing.Literal["c", "python", "pyarrow", "python-fwf", "bigquery"]
    ] = None,
    encoding: typing.Optional[str] = None,
    **kwargs
) -> bigframes.dataframe.DataFrame

Loads DataFrame from comma-separated values (csv) file locally or from Cloud Storage.

See more: bigframes.pandas.read_csv

bigframes.pandas.read_gbq

read_gbq(
    query_or_table: str,
    *,
    index_col: typing.Union[typing.Iterable[str], str] = (),
    columns: typing.Iterable[str] = (),
    configuration: typing.Optional[typing.Dict] = None,
    max_results: typing.Optional[int] = None,
    filters: typing.Union[
        typing.Iterable[
            typing.Tuple[
                str,
                typing.Literal[
                    "in", "not in", "<",><=", "="=" ,"="" "!=", ">=", ">", "LIKE"
                ],
                typing.Any,
            ]
        ],
        typing.Iterable[
            typing.Iterable[
                typing.Tuple[
                    str,
                    typing.Literal[
                        "in", "not in", "<",><=", "="=" ,"="" "!=", ">=", ">", "LIKE"
                    ],
                    typing.Any,
                ]
            ]
        ],
    ] = (),
    use_cache: typing.Optional[bool] = None,
    col_order: typing.Iterable[str] = ()
) -> bigframes.dataframe.DataFrame

Loads a DataFrame from BigQuery.

See more: bigframes.pandas.read_gbq

bigframes.pandas.read_gbq_function

read_gbq_function(function_name: str)

Loads a BigQuery function from BigQuery.

See more: bigframes.pandas.read_gbq_function

bigframes.pandas.read_gbq_model

read_gbq_model(model_name: str)

Loads a BigQuery ML model from BigQuery.

See more: bigframes.pandas.read_gbq_model

bigframes.pandas.read_gbq_query

read_gbq_query(
    query: str,
    *,
    index_col: typing.Union[typing.Iterable[str], str] = (),
    columns: typing.Iterable[str] = (),
    configuration: typing.Optional[typing.Dict] = None,
    max_results: typing.Optional[int] = None,
    use_cache: typing.Optional[bool] = None,
    col_order: typing.Iterable[str] = ()
) -> bigframes.dataframe.DataFrame

Turn a SQL query into a DataFrame.

See more: bigframes.pandas.read_gbq_query

bigframes.pandas.read_gbq_table

read_gbq_table(
    query: str,
    *,
    index_col: typing.Union[typing.Iterable[str], str] = (),
    columns: typing.Iterable[str] = (),
    max_results: typing.Optional[int] = None,
    filters: typing.Union[
        typing.Iterable[
            typing.Tuple[
                str,
                typing.Literal[
                    "in", "not in", "<",><=", "="=" ,"="" "!=", ">=", ">", "LIKE"
                ],
                typing.Any,
            ]
        ],
        typing.Iterable[
            typing.Iterable[
                typing.Tuple[
                    str,
                    typing.Literal[
                        "in", "not in", "<",><=", "="=" ,"="" "!=", ">=", ">", "LIKE"
                    ],
                    typing.Any,
                ]
            ]
        ],
    ] = (),
    use_cache: bool = True,
    col_order: typing.Iterable[str] = ()
) -> bigframes.dataframe.DataFrame

Turn a BigQuery table into a DataFrame.

See more: bigframes.pandas.read_gbq_table

bigframes.pandas.read_json

read_json(
    path_or_buf: typing.Union[str, typing.IO[bytes]],
    *,
    orient: typing.Literal[
        "split", "records", "index", "columns", "values", "table"
    ] = "columns",
    dtype: typing.Optional[typing.Dict] = None,
    encoding: typing.Optional[str] = None,
    lines: bool = False,
    engine: typing.Literal["ujson", "pyarrow", "bigquery"] = "ujson",
    **kwargs
) -> bigframes.dataframe.DataFrame

Convert a JSON string to DataFrame object.

See more: bigframes.pandas.read_json

bigframes.pandas.read_pandas

Loads DataFrame from a pandas DataFrame.

See more: bigframes.pandas.read_pandas

bigframes.pandas.read_parquet

read_parquet(
    path: typing.Union[str, typing.IO[bytes]], *, engine: str = "auto"
) -> bigframes.dataframe.DataFrame

Load a Parquet object from the file path (local or Cloud Storage), returning a DataFrame.

See more: bigframes.pandas.read_parquet

bigframes.pandas.read_pickle

read_pickle(
    filepath_or_buffer: FilePath | ReadPickleBuffer,
    compression: CompressionOptions = "infer",
    storage_options: StorageOptions = None,
)

Load pickled BigFrames object (or any object) from file.

See more: bigframes.pandas.read_pickle

bigframes.pandas.remote_function

remote_function(
    input_types: typing.List[type],
    output_type: type,
    dataset: typing.Optional[str] = None,
    bigquery_connection: typing.Optional[str] = None,
    reuse: bool = True,
    name: typing.Optional[str] = None,
    packages: typing.Optional[typing.Sequence[str]] = None,
    cloud_function_service_account: typing.Optional[str] = None,
    cloud_function_kms_key_name: typing.Optional[str] = None,
    cloud_function_docker_repository: typing.Optional[str] = None,
)

Decorator to turn a user defined function into a BigQuery remote function.

See more: bigframes.pandas.remote_function

bigframes.pandas.to_datetime

to_datetime(
    arg: typing.Union[
        int,
        float,
        str,
        datetime.datetime,
        typing.Iterable,
        pandas.core.series.Series,
        pandas.core.frame.DataFrame,
        typing.Mapping,
        bigframes.series.Series,
        bigframes.dataframe.DataFrame,
    ],
    *,
    utc: bool = False,
    format: typing.Optional[str] = None,
    unit: typing.Optional[str] = None
) -> typing.Union[
    pandas._libs.tslibs.timestamps.Timestamp, datetime.datetime, bigframes.series.Series
]

This function converts a scalar, array-like or Series to a datetime object.

See more: bigframes.pandas.to_datetime

bigframes.core.groupby.DataFrameGroupBy.agg

agg(func=None, **kwargs) -> bigframes.dataframe.DataFrame

Aggregate using one or more operations.

See more: bigframes.core.groupby.DataFrameGroupBy.agg

bigframes.core.groupby.DataFrameGroupBy.aggregate

aggregate(func=None, **kwargs) -> bigframes.dataframe.DataFrame

API documentation for aggregate method.

See more: bigframes.core.groupby.DataFrameGroupBy.aggregate

bigframes.core.groupby.DataFrameGroupBy.all

all() -> bigframes.dataframe.DataFrame

Return True if all values in the group are true, else False.

See more: bigframes.core.groupby.DataFrameGroupBy.all

bigframes.core.groupby.DataFrameGroupBy.any

any() -> bigframes.dataframe.DataFrame

Return True if any value in the group is true, else False.

See more: bigframes.core.groupby.DataFrameGroupBy.any

bigframes.core.groupby.DataFrameGroupBy.count

count() -> bigframes.dataframe.DataFrame

Compute count of group, excluding missing values.

See more: bigframes.core.groupby.DataFrameGroupBy.count

bigframes.core.groupby.DataFrameGroupBy.cumcount

cumcount(ascending: bool = True)

Number each item in each group from 0 to the length of that group - 1.

See more: bigframes.core.groupby.DataFrameGroupBy.cumcount

bigframes.core.groupby.DataFrameGroupBy.cummax

cummax(
    *args, numeric_only: bool = False, **kwargs
) -> bigframes.dataframe.DataFrame

Cumulative max for each group.

See more: bigframes.core.groupby.DataFrameGroupBy.cummax

bigframes.core.groupby.DataFrameGroupBy.cummin

cummin(
    *args, numeric_only: bool = False, **kwargs
) -> bigframes.dataframe.DataFrame

Cumulative min for each group.

See more: bigframes.core.groupby.DataFrameGroupBy.cummin

bigframes.core.groupby.DataFrameGroupBy.cumprod

cumprod(*args, **kwargs) -> bigframes.dataframe.DataFrame

Cumulative product for each group.

See more: bigframes.core.groupby.DataFrameGroupBy.cumprod

bigframes.core.groupby.DataFrameGroupBy.cumsum

cumsum(
    *args, numeric_only: bool = False, **kwargs
) -> bigframes.dataframe.DataFrame

Cumulative sum for each group.

See more: bigframes.core.groupby.DataFrameGroupBy.cumsum

bigframes.core.groupby.DataFrameGroupBy.diff

diff(periods=1) -> bigframes.series.Series

First discrete difference of element.

See more: bigframes.core.groupby.DataFrameGroupBy.diff

bigframes.core.groupby.DataFrameGroupBy.expanding

expanding(min_periods: int = 1) -> bigframes.core.window.Window

Provides expanding functionality.

See more: bigframes.core.groupby.DataFrameGroupBy.expanding

bigframes.core.groupby.DataFrameGroupBy.kurt

kurt(*, numeric_only: bool = False) -> bigframes.dataframe.DataFrame

Return unbiased kurtosis over requested axis.

See more: bigframes.core.groupby.DataFrameGroupBy.kurt

bigframes.core.groupby.DataFrameGroupBy.kurtosis

kurtosis(*, numeric_only: bool = False) -> bigframes.dataframe.DataFrame

API documentation for kurtosis method.

See more: bigframes.core.groupby.DataFrameGroupBy.kurtosis

bigframes.core.groupby.DataFrameGroupBy.max

max(numeric_only: bool = False, *args) -> bigframes.dataframe.DataFrame

Compute max of group values.

See more: bigframes.core.groupby.DataFrameGroupBy.max

bigframes.core.groupby.DataFrameGroupBy.mean

mean(numeric_only: bool = False, *args) -> bigframes.dataframe.DataFrame

Compute mean of groups, excluding missing values.

See more: bigframes.core.groupby.DataFrameGroupBy.mean

bigframes.core.groupby.DataFrameGroupBy.median

median(
    numeric_only: bool = False, *, exact: bool = False
) -> bigframes.dataframe.DataFrame

Compute median of groups, excluding missing values.

See more: bigframes.core.groupby.DataFrameGroupBy.median

bigframes.core.groupby.DataFrameGroupBy.min

min(numeric_only: bool = False, *args) -> bigframes.dataframe.DataFrame

Compute min of group values.

See more: bigframes.core.groupby.DataFrameGroupBy.min

bigframes.core.groupby.DataFrameGroupBy.nunique

nunique() -> bigframes.dataframe.DataFrame

Return DataFrame with counts of unique elements in each position.

See more: bigframes.core.groupby.DataFrameGroupBy.nunique

bigframes.core.groupby.DataFrameGroupBy.prod

prod(numeric_only: bool = False, min_count: int = 0)

Compute prod of group values.

See more: bigframes.core.groupby.DataFrameGroupBy.prod

bigframes.core.groupby.DataFrameGroupBy.rolling

rolling(window: int, min_periods=None) -> bigframes.core.window.Window

Returns a rolling grouper, providing rolling functionality per group.

See more: bigframes.core.groupby.DataFrameGroupBy.rolling

bigframes.core.groupby.DataFrameGroupBy.shift

shift(periods=1) -> bigframes.series.Series

Shift each group by periods observations.

See more: bigframes.core.groupby.DataFrameGroupBy.shift

bigframes.core.groupby.DataFrameGroupBy.skew

skew(*, numeric_only: bool = False) -> bigframes.dataframe.DataFrame

Return unbiased skew within groups.

See more: bigframes.core.groupby.DataFrameGroupBy.skew

bigframes.core.groupby.DataFrameGroupBy.std

std(*, numeric_only: bool = False) -> bigframes.dataframe.DataFrame

Compute standard deviation of groups, excluding missing values.

See more: bigframes.core.groupby.DataFrameGroupBy.std

bigframes.core.groupby.DataFrameGroupBy.sum

sum(numeric_only: bool = False, *args) -> bigframes.dataframe.DataFrame

Compute sum of group values.

See more: bigframes.core.groupby.DataFrameGroupBy.sum

bigframes.core.groupby.DataFrameGroupBy.var

var(*, numeric_only: bool = False) -> bigframes.dataframe.DataFrame

Compute variance of groups, excluding missing values.

See more: bigframes.core.groupby.DataFrameGroupBy.var

bigframes.core.groupby.SeriesGroupBy.agg

agg(
    func=None,
) -> typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]

Aggregate using one or more operations.

See more: bigframes.core.groupby.SeriesGroupBy.agg

bigframes.core.groupby.SeriesGroupBy.aggregate

aggregate(
    func=None,
) -> typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]

API documentation for aggregate method.

See more: bigframes.core.groupby.SeriesGroupBy.aggregate

bigframes.core.groupby.SeriesGroupBy.all

all() -> bigframes.series.Series

Return True if all values in the group are true, else False.

See more: bigframes.core.groupby.SeriesGroupBy.all

bigframes.core.groupby.SeriesGroupBy.any

any() -> bigframes.series.Series

Return True if any value in the group is true, else False.

See more: bigframes.core.groupby.SeriesGroupBy.any

bigframes.core.groupby.SeriesGroupBy.count

count() -> bigframes.series.Series

Compute count of group, excluding missing values.

See more: bigframes.core.groupby.SeriesGroupBy.count

bigframes.core.groupby.SeriesGroupBy.cumcount

cumcount(*args, **kwargs) -> bigframes.series.Series

Number each item in each group from 0 to the length of that group - 1.

See more: bigframes.core.groupby.SeriesGroupBy.cumcount

bigframes.core.groupby.SeriesGroupBy.cummax

cummax(*args, **kwargs) -> bigframes.series.Series

Cumulative max for each group.

See more: bigframes.core.groupby.SeriesGroupBy.cummax

bigframes.core.groupby.SeriesGroupBy.cummin

cummin(*args, **kwargs) -> bigframes.series.Series

Cumulative min for each group.

See more: bigframes.core.groupby.SeriesGroupBy.cummin

bigframes.core.groupby.SeriesGroupBy.cumprod

cumprod(*args, **kwargs) -> bigframes.series.Series

Cumulative product for each group.

See more: bigframes.core.groupby.SeriesGroupBy.cumprod

bigframes.core.groupby.SeriesGroupBy.cumsum

cumsum(*args, **kwargs) -> bigframes.series.Series

Cumulative sum for each group.

See more: bigframes.core.groupby.SeriesGroupBy.cumsum

bigframes.core.groupby.SeriesGroupBy.diff

diff(periods=1) -> bigframes.series.Series

First discrete difference of element.

See more: bigframes.core.groupby.SeriesGroupBy.diff

bigframes.core.groupby.SeriesGroupBy.expanding

expanding(min_periods: int = 1) -> bigframes.core.window.Window

Provides expanding functionality.

See more: bigframes.core.groupby.SeriesGroupBy.expanding

bigframes.core.groupby.SeriesGroupBy.kurt

kurt(*args, **kwargs) -> bigframes.series.Series

Return unbiased kurtosis over requested axis.

See more: bigframes.core.groupby.SeriesGroupBy.kurt

bigframes.core.groupby.SeriesGroupBy.kurtosis

kurtosis(*args, **kwargs) -> bigframes.series.Series

API documentation for kurtosis method.

See more: bigframes.core.groupby.SeriesGroupBy.kurtosis

bigframes.core.groupby.SeriesGroupBy.max

max(*args) -> bigframes.series.Series

Compute max of group values.

See more: bigframes.core.groupby.SeriesGroupBy.max

bigframes.core.groupby.SeriesGroupBy.mean

mean(*args) -> bigframes.series.Series

Compute mean of groups, excluding missing values.

See more: bigframes.core.groupby.SeriesGroupBy.mean

bigframes.core.groupby.SeriesGroupBy.median

median(*args, **kwargs) -> bigframes.series.Series

Compute median of groups, excluding missing values.

See more: bigframes.core.groupby.SeriesGroupBy.median

bigframes.core.groupby.SeriesGroupBy.min

min(*args) -> bigframes.series.Series

Compute min of group values.

See more: bigframes.core.groupby.SeriesGroupBy.min

bigframes.core.groupby.SeriesGroupBy.nunique

nunique() -> bigframes.series.Series

Return number of unique elements in the group.

See more: bigframes.core.groupby.SeriesGroupBy.nunique

bigframes.core.groupby.SeriesGroupBy.prod

prod(*args) -> bigframes.series.Series

Compute prod of group values.

See more: bigframes.core.groupby.SeriesGroupBy.prod

bigframes.core.groupby.SeriesGroupBy.rolling

rolling(window: int, min_periods=None) -> bigframes.core.window.Window

Returns a rolling grouper, providing rolling functionality per group.

See more: bigframes.core.groupby.SeriesGroupBy.rolling

bigframes.core.groupby.SeriesGroupBy.shift

shift(periods=1) -> bigframes.series.Series

Shift index by desired number of periods.

See more: bigframes.core.groupby.SeriesGroupBy.shift

bigframes.core.groupby.SeriesGroupBy.skew

skew(*args, **kwargs) -> bigframes.series.Series

Return unbiased skew within groups.

See more: bigframes.core.groupby.SeriesGroupBy.skew

bigframes.core.groupby.SeriesGroupBy.std

std(*args, **kwargs) -> bigframes.series.Series

Compute standard deviation of groups, excluding missing values.

See more: bigframes.core.groupby.SeriesGroupBy.std

bigframes.core.groupby.SeriesGroupBy.sum

sum(*args) -> bigframes.series.Series

Compute sum of group values.

See more: bigframes.core.groupby.SeriesGroupBy.sum

bigframes.core.groupby.SeriesGroupBy.var

var(*args, **kwargs) -> bigframes.series.Series

Compute variance of groups, excluding missing values.

See more: bigframes.core.groupby.SeriesGroupBy.var

bigframes.core.indexers.ILocDataFrameIndexer.__getitem__

__getitem__(
    key,
) -> typing.Union[bigframes.dataframe.DataFrame, pandas.core.series.Series]

Index dataframe using integer offsets.

See more: bigframes.core.indexers.ILocDataFrameIndexer.getitem

bigframes.core.indexers.IlocSeriesIndexer.__getitem__

__getitem__(key) -> typing.Union[typing.Any, bigframes.series.Series]

Index series using integer offsets.

See more: bigframes.core.indexers.IlocSeriesIndexer.getitem

bigframes.core.indexes.base.Index.all

all() -> bool

Return whether all elements are Truthy.

See more: bigframes.core.indexes.base.Index.all

bigframes.core.indexes.base.Index.any

any() -> bool

Return whether any element is Truthy.

See more: bigframes.core.indexes.base.Index.any

bigframes.core.indexes.base.Index.argmax

argmax() -> int

Return int position of the largest value in the Series.

See more: bigframes.core.indexes.base.Index.argmax

bigframes.core.indexes.base.Index.argmin

argmin() -> int

Return int position of the smallest value in the Series.

See more: bigframes.core.indexes.base.Index.argmin

bigframes.core.indexes.base.Index.astype

astype(
    dtype: typing.Union[
        typing.Literal[
            "boolean",
            "Float64",
            "Int64",
            "int64[pyarrow]",
            "string",
            "string[pyarrow]",
            "timestamp[us, tz=UTC][pyarrow]",
            "timestamp[us][pyarrow]",
            "date32[day][pyarrow]",
            "time64[us][pyarrow]",
            "decimal128(38, 9)[pyarrow]",
            "decimal256(76, 38)[pyarrow]",
            "binary[pyarrow]",
        ],
        pandas.core.arrays.boolean.BooleanDtype,
        pandas.core.arrays.floating.Float64Dtype,
        pandas.core.arrays.integer.Int64Dtype,
        pandas.core.arrays.string_.StringDtype,
        pandas.core.dtypes.dtypes.ArrowDtype,
        geopandas.array.GeometryDtype,
    ]
) -> bigframes.core.indexes.base.Index

Create an Index with values cast to dtypes.

See more: bigframes.core.indexes.base.Index.astype

bigframes.core.indexes.base.Index.copy

copy(name: typing.Optional[typing.Hashable] = None)

Make a copy of this object.

See more: bigframes.core.indexes.base.Index.copy

bigframes.core.indexes.base.Index.drop

drop(labels: typing.Any) -> bigframes.core.indexes.base.Index

Make new Index with passed list of labels deleted.

See more: bigframes.core.indexes.base.Index.drop

bigframes.core.indexes.base.Index.drop_duplicates

drop_duplicates(*, keep: str = "first") -> bigframes.core.indexes.base.Index

Return Index with duplicate values removed.

See more: bigframes.core.indexes.base.Index.drop_duplicates

bigframes.core.indexes.base.Index.dropna

dropna(how: str = "any") -> bigframes.core.indexes.base.Index

Return Index without NA/NaN values.

See more: bigframes.core.indexes.base.Index.dropna

bigframes.core.indexes.base.Index.fillna

fillna(value=None) -> bigframes.core.indexes.base.Index

Fill NA/NaN values with the specified value.

See more: bigframes.core.indexes.base.Index.fillna

bigframes.core.indexes.base.Index.from_frame

from_frame(
    frame: typing.Union[bigframes.series.Series, bigframes.dataframe.DataFrame]
) -> bigframes.core.indexes.base.Index

API documentation for from_frame method.

See more: bigframes.core.indexes.base.Index.from_frame

bigframes.core.indexes.base.Index.get_level_values

get_level_values(level) -> bigframes.core.indexes.base.Index

Return an Index of values for requested level.

See more: bigframes.core.indexes.base.Index.get_level_values

bigframes.core.indexes.base.Index.isin

isin(values) -> bigframes.core.indexes.base.Index

Return a boolean array where the index values are in values.

See more: bigframes.core.indexes.base.Index.isin

bigframes.core.indexes.base.Index.max

max() -> typing.Any

Return the maximum value of the Index.

See more: bigframes.core.indexes.base.Index.max

bigframes.core.indexes.base.Index.min

min() -> typing.Any

Return the minimum value of the Index.

See more: bigframes.core.indexes.base.Index.min

bigframes.core.indexes.base.Index.nunique

nunique() -> int

Return number of unique elements in the object.

See more: bigframes.core.indexes.base.Index.nunique

bigframes.core.indexes.base.Index.rename

rename(
    name: typing.Union[str, typing.Sequence[str]]
) -> bigframes.core.indexes.base.Index

Alter Index or MultiIndex name.

See more: bigframes.core.indexes.base.Index.rename

bigframes.core.indexes.base.Index.sort_values

sort_values(*, ascending: bool = True, na_position: str = "last")

Return a sorted copy of the index.

See more: bigframes.core.indexes.base.Index.sort_values

bigframes.core.indexes.base.Index.to_numpy

to_numpy(dtype=None, **kwargs) -> numpy.ndarray

A NumPy ndarray representing the values in this Series or Index.

See more: bigframes.core.indexes.base.Index.to_numpy

bigframes.core.indexes.base.Index.to_pandas

to_pandas() -> pandas.core.indexes.base.Index

Gets the Index as a pandas Index.

See more: bigframes.core.indexes.base.Index.to_pandas

bigframes.core.indexes.base.Index.to_series

to_series(
    index: typing.Optional[bigframes.core.indexes.base.Index] = None,
    name: typing.Optional[typing.Hashable] = None,
) -> bigframes.series.Series

Create a Series with both index and values equal to the index keys.

See more: bigframes.core.indexes.base.Index.to_series

bigframes.core.indexes.base.Index.transpose

transpose() -> bigframes.core.indexes.base.Index

Return the transpose, which is by definition self.

See more: bigframes.core.indexes.base.Index.transpose

bigframes.core.indexes.base.Index.value_counts

value_counts(
    normalize: bool = False,
    sort: bool = True,
    ascending: bool = False,
    *,
    dropna: bool = True
)

Return a Series containing counts of unique values.

See more: bigframes.core.indexes.base.Index.value_counts

bigframes.core.window.Window.count

count()

Calculate the window count of non-NULL observations.

See more: bigframes.core.window.Window.count

bigframes.core.window.Window.max

max()

Calculate the weighted window maximum.

See more: bigframes.core.window.Window.max

bigframes.core.window.Window.mean

mean()

Calculate the weighted window mean.

See more: bigframes.core.window.Window.mean

bigframes.core.window.Window.min

min()

Calculate the weighted window minimum.

See more: bigframes.core.window.Window.min

bigframes.core.window.Window.std

std()

Calculate the weighted window standard deviation.

See more: bigframes.core.window.Window.std

bigframes.core.window.Window.sum

sum()

Calculate the weighted window sum.

See more: bigframes.core.window.Window.sum

bigframes.core.window.Window.var

var()

Calculate the weighted window variance.

See more: bigframes.core.window.Window.var

bigframes.dataframe.DataFrame.__array_ufunc__

__array_ufunc__(
    ufunc: numpy.ufunc, method: str, *inputs, **kwargs
) -> bigframes.dataframe.DataFrame

Used to support numpy ufuncs.

See more: bigframes.dataframe.DataFrame.array_ufunc

bigframes.dataframe.DataFrame.__getitem__

__getitem__(
    key: typing.Union[
        typing.Hashable,
        typing.Sequence[typing.Hashable],
        pandas.core.indexes.base.Index,
        bigframes.series.Series,
    ]
)

Gets the specified column(s) from the DataFrame.

See more: bigframes.dataframe.DataFrame.getitem

bigframes.dataframe.DataFrame.__repr__

__repr__() -> str

Converts a DataFrame to a string.

See more: bigframes.dataframe.DataFrame.repr

bigframes.dataframe.DataFrame.__setitem__

__setitem__(key: str, value: SingleItemValue)

Modify or insert a column into the DataFrame.

See more: bigframes.dataframe.DataFrame.setitem

bigframes.dataframe.DataFrame.abs

abs() -> bigframes.dataframe.DataFrame

Return a Series/DataFrame with absolute numeric value of each element.

See more: bigframes.dataframe.DataFrame.abs

bigframes.dataframe.DataFrame.add

add(
    other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

Get addition of DataFrame and other, element-wise (binary operator +).

See more: bigframes.dataframe.DataFrame.add

bigframes.dataframe.DataFrame.add_prefix

add_prefix(
    prefix: str, axis: int | str | None = None
) -> bigframes.dataframe.DataFrame

Prefix labels with string prefix.

See more: bigframes.dataframe.DataFrame.add_prefix

bigframes.dataframe.DataFrame.add_suffix

add_suffix(
    suffix: str, axis: int | str | None = None
) -> bigframes.dataframe.DataFrame

Suffix labels with string suffix.

See more: bigframes.dataframe.DataFrame.add_suffix

bigframes.dataframe.DataFrame.agg

agg(
    func: typing.Union[str, typing.Sequence[str]]
) -> bigframes.dataframe.DataFrame | bigframes.series.Series

Aggregate using one or more operations over columns.

See more: bigframes.dataframe.DataFrame.agg

bigframes.dataframe.DataFrame.aggregate

aggregate(
    func: typing.Union[str, typing.Sequence[str]]
) -> bigframes.dataframe.DataFrame | bigframes.series.Series

API documentation for aggregate method.

See more: bigframes.dataframe.DataFrame.aggregate

bigframes.dataframe.DataFrame.align

align(
    other: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    join: str = "outer",
    axis: typing.Optional[typing.Union[str, int]] = None,
) -> typing.Tuple[
    typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
]

Align two objects on their axes with the specified join method.

See more: bigframes.dataframe.DataFrame.align

bigframes.dataframe.DataFrame.all

all(
    axis: typing.Union[str, int] = 0, *, bool_only: bool = False
) -> bigframes.series.Series

Return whether all elements are True, potentially over an axis.

See more: bigframes.dataframe.DataFrame.all

bigframes.dataframe.DataFrame.any

any(
    *, axis: typing.Union[str, int] = 0, bool_only: bool = False
) -> bigframes.series.Series

Return whether any element is True, potentially over an axis.

See more: bigframes.dataframe.DataFrame.any

bigframes.dataframe.DataFrame.apply

apply(func, *, args: typing.Tuple = (), **kwargs)

Apply a function along an axis of the DataFrame.

See more: bigframes.dataframe.DataFrame.apply

bigframes.dataframe.DataFrame.applymap

applymap(
    func, na_action: typing.Optional[str] = None
) -> bigframes.dataframe.DataFrame

Apply a function to a Dataframe elementwise.

See more: bigframes.dataframe.DataFrame.applymap

bigframes.dataframe.DataFrame.assign

assign(**kwargs) -> bigframes.dataframe.DataFrame

Assign new columns to a DataFrame.

See more: bigframes.dataframe.DataFrame.assign

bigframes.dataframe.DataFrame.astype

astype(
    dtype: typing.Union[
        typing.Literal[
            "boolean",
            "Float64",
            "Int64",
            "int64[pyarrow]",
            "string",
            "string[pyarrow]",
            "timestamp[us, tz=UTC][pyarrow]",
            "timestamp[us][pyarrow]",
            "date32[day][pyarrow]",
            "time64[us][pyarrow]",
            "decimal128(38, 9)[pyarrow]",
            "decimal256(76, 38)[pyarrow]",
            "binary[pyarrow]",
        ],
        pandas.core.arrays.boolean.BooleanDtype,
        pandas.core.arrays.floating.Float64Dtype,
        pandas.core.arrays.integer.Int64Dtype,
        pandas.core.arrays.string_.StringDtype,
        pandas.core.dtypes.dtypes.ArrowDtype,
        geopandas.array.GeometryDtype,
    ]
) -> bigframes.dataframe.DataFrame

Cast a pandas object to a specified dtype dtype.

See more: bigframes.dataframe.DataFrame.astype

bigframes.dataframe.DataFrame.bfill

bfill(*, limit: typing.Optional[int] = None) -> bigframes.dataframe.DataFrame

Fill NA/NaN values by using the next valid observation to fill the gap.

See more: bigframes.dataframe.DataFrame.bfill

bigframes.dataframe.DataFrame.combine

combine(
    other: bigframes.dataframe.DataFrame,
    func: typing.Callable[
        [bigframes.series.Series, bigframes.series.Series], bigframes.series.Series
    ],
    fill_value=None,
    overwrite: bool = True,
    *,
    how: str = "outer"
) -> bigframes.dataframe.DataFrame

Perform column-wise combine with another DataFrame.

See more: bigframes.dataframe.DataFrame.combine

bigframes.dataframe.DataFrame.combine_first

combine_first(other: bigframes.dataframe.DataFrame)

Update null elements with value in the same location in other.

See more: bigframes.dataframe.DataFrame.combine_first

bigframes.dataframe.DataFrame.copy

copy() -> bigframes.dataframe.DataFrame

Make a copy of this object's indices and data.

See more: bigframes.dataframe.DataFrame.copy

bigframes.dataframe.DataFrame.corr

corr(
    method="pearson", min_periods=None, numeric_only=False
) -> bigframes.dataframe.DataFrame

Compute pairwise correlation of columns, excluding NA/null values.

See more: bigframes.dataframe.DataFrame.corr

bigframes.dataframe.DataFrame.count

count(*, numeric_only: bool = False) -> bigframes.series.Series

Count non-NA cells for each column.

See more: bigframes.dataframe.DataFrame.count

bigframes.dataframe.DataFrame.cov

cov(*, numeric_only: bool = False) -> bigframes.dataframe.DataFrame

Compute pairwise covariance of columns, excluding NA/null values.

See more: bigframes.dataframe.DataFrame.cov

bigframes.dataframe.DataFrame.cummax

cummax() -> bigframes.dataframe.DataFrame

Return cumulative maximum over columns.

See more: bigframes.dataframe.DataFrame.cummax

bigframes.dataframe.DataFrame.cummin

cummin() -> bigframes.dataframe.DataFrame

Return cumulative minimum over columns.

See more: bigframes.dataframe.DataFrame.cummin

bigframes.dataframe.DataFrame.cumprod

cumprod() -> bigframes.dataframe.DataFrame

Return cumulative product over columns.

See more: bigframes.dataframe.DataFrame.cumprod

bigframes.dataframe.DataFrame.cumsum

cumsum()

Return cumulative sum over columns.

See more: bigframes.dataframe.DataFrame.cumsum

bigframes.dataframe.DataFrame.describe

describe() -> bigframes.dataframe.DataFrame

Generate descriptive statistics.

See more: bigframes.dataframe.DataFrame.describe

bigframes.dataframe.DataFrame.diff

diff(periods: int = 1) -> bigframes.dataframe.DataFrame

First discrete difference of element.

See more: bigframes.dataframe.DataFrame.diff

bigframes.dataframe.DataFrame.div

div(
    other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

API documentation for div method.

See more: bigframes.dataframe.DataFrame.div

bigframes.dataframe.DataFrame.divide

divide(
    other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

API documentation for divide method.

See more: bigframes.dataframe.DataFrame.divide

bigframes.dataframe.DataFrame.dot

dot(other: _DataFrameOrSeries) -> _DataFrameOrSeries

Compute the matrix multiplication between the DataFrame and other.

See more: bigframes.dataframe.DataFrame.dot

bigframes.dataframe.DataFrame.drop

drop(
    labels: typing.Any = None,
    *,
    axis: typing.Union[int, str] = 0,
    index: typing.Any = None,
    columns: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None,
    level: typing.Optional[typing.Hashable] = None
) -> bigframes.dataframe.DataFrame

Drop specified labels from columns.

See more: bigframes.dataframe.DataFrame.drop

bigframes.dataframe.DataFrame.drop_duplicates

drop_duplicates(
    subset: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None,
    *,
    keep: str = "first"
) -> bigframes.dataframe.DataFrame

Return DataFrame with duplicate rows removed.

See more: bigframes.dataframe.DataFrame.drop_duplicates

bigframes.dataframe.DataFrame.droplevel

droplevel(
    level: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]],
    axis: int | str = 0,
)

Return DataFrame with requested index / column level(s) removed.

See more: bigframes.dataframe.DataFrame.droplevel

bigframes.dataframe.DataFrame.dropna

dropna(
    *, axis: int | str = 0, inplace: bool = False, how: str = "any", ignore_index=False
) -> bigframes.dataframe.DataFrame

Remove missing values.

See more: bigframes.dataframe.DataFrame.dropna

bigframes.dataframe.DataFrame.duplicated

duplicated(subset=None, keep: str = "first") -> bigframes.series.Series

Return boolean Series denoting duplicate rows.

See more: bigframes.dataframe.DataFrame.duplicated

bigframes.dataframe.DataFrame.eq

eq(other: typing.Any, axis: str | int = "columns") -> bigframes.dataframe.DataFrame

Get equal to of DataFrame and other, element-wise (binary operator eq).

See more: bigframes.dataframe.DataFrame.eq

bigframes.dataframe.DataFrame.equals

equals(
    other: typing.Union[bigframes.series.Series, bigframes.dataframe.DataFrame]
) -> bool

Test whether two objects contain the same elements.

See more: bigframes.dataframe.DataFrame.equals

bigframes.dataframe.DataFrame.eval

eval(expr: str) -> bigframes.dataframe.DataFrame

Evaluate a string describing operations on DataFrame columns.

See more: bigframes.dataframe.DataFrame.eval

bigframes.dataframe.DataFrame.expanding

expanding(min_periods: int = 1) -> bigframes.core.window.Window

Provide expanding window calculations.

See more: bigframes.dataframe.DataFrame.expanding

bigframes.dataframe.DataFrame.explode

explode(
    column: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]],
    *,
    ignore_index: typing.Optional[bool] = False
) -> bigframes.dataframe.DataFrame

Transform each element of an array to a row, replicating index values.

See more: bigframes.dataframe.DataFrame.explode

bigframes.dataframe.DataFrame.ffill

ffill(*, limit: typing.Optional[int] = None) -> bigframes.dataframe.DataFrame

Fill NA/NaN values by propagating the last valid observation to next valid.

See more: bigframes.dataframe.DataFrame.ffill

bigframes.dataframe.DataFrame.fillna

fillna(value=None) -> bigframes.dataframe.DataFrame

Fill NA/NaN values using the specified method.

See more: bigframes.dataframe.DataFrame.fillna

bigframes.dataframe.DataFrame.filter

filter(
    items: typing.Optional[typing.Iterable] = None,
    like: typing.Optional[str] = None,
    regex: typing.Optional[str] = None,
    axis: int | str | None = None,
) -> bigframes.dataframe.DataFrame

Subset the dataframe rows or columns according to the specified index labels.

See more: bigframes.dataframe.DataFrame.filter

bigframes.dataframe.DataFrame.first_valid_index

first_valid_index()

API documentation for first_valid_index method.

See more: bigframes.dataframe.DataFrame.first_valid_index

bigframes.dataframe.DataFrame.floordiv

floordiv(
    other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

Get integer division of DataFrame and other, element-wise (binary operator //).

See more: bigframes.dataframe.DataFrame.floordiv

bigframes.dataframe.DataFrame.from_dict

from_dict(
    data: dict, orient: str = "columns", dtype=None, columns=None
) -> bigframes.dataframe.DataFrame

Construct DataFrame from dict of array-like or dicts.

See more: bigframes.dataframe.DataFrame.from_dict

bigframes.dataframe.DataFrame.from_records

from_records(
    data,
    index=None,
    exclude=None,
    columns=None,
    coerce_float: bool = False,
    nrows: typing.Optional[int] = None,
) -> bigframes.dataframe.DataFrame

Convert structured or record ndarray to DataFrame.

See more: bigframes.dataframe.DataFrame.from_records

bigframes.dataframe.DataFrame.ge

ge(other: typing.Any, axis: str | int = "columns") -> bigframes.dataframe.DataFrame

Get 'greater than or equal to' of DataFrame and other, element-wise (binary operator >=).

See more: bigframes.dataframe.DataFrame.ge

bigframes.dataframe.DataFrame.get

get(key, default=None)

Get item from object for given key (ex: DataFrame column).

See more: bigframes.dataframe.DataFrame.get

bigframes.dataframe.DataFrame.groupby

groupby(
    by: typing.Optional[
        typing.Union[
            typing.Hashable,
            bigframes.series.Series,
            typing.Sequence[typing.Union[typing.Hashable, bigframes.series.Series]],
        ]
    ] = None,
    *,
    level: typing.Optional[
        typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]]
    ] = None,
    as_index: bool = True,
    dropna: bool = True
) -> bigframes.core.groupby.DataFrameGroupBy

Group DataFrame by columns.

See more: bigframes.dataframe.DataFrame.groupby

bigframes.dataframe.DataFrame.gt

gt(other: typing.Any, axis: str | int = "columns") -> bigframes.dataframe.DataFrame

Get 'greater than' of DataFrame and other, element-wise (binary operator >).

See more: bigframes.dataframe.DataFrame.gt

bigframes.dataframe.DataFrame.head

head(n: int = 5) -> bigframes.dataframe.DataFrame

Return the first n rows.

See more: bigframes.dataframe.DataFrame.head

bigframes.dataframe.DataFrame.idxmax

idxmax() -> bigframes.series.Series

Return index of first occurrence of maximum over columns.

See more: bigframes.dataframe.DataFrame.idxmax

bigframes.dataframe.DataFrame.idxmin

idxmin() -> bigframes.series.Series

Return index of first occurrence of minimum over columns.

See more: bigframes.dataframe.DataFrame.idxmin

bigframes.dataframe.DataFrame.info

info(
    verbose: typing.Optional[bool] = None,
    buf=None,
    max_cols: typing.Optional[int] = None,
    memory_usage: typing.Optional[bool] = None,
    show_counts: typing.Optional[bool] = None,
)

Print a concise summary of a DataFrame.

See more: bigframes.dataframe.DataFrame.info

bigframes.dataframe.DataFrame.interpolate

interpolate(method: str = "linear") -> bigframes.dataframe.DataFrame

Fill NaN values using an interpolation method.

See more: bigframes.dataframe.DataFrame.interpolate

bigframes.dataframe.DataFrame.isin

isin(values) -> bigframes.dataframe.DataFrame

Whether each element in the DataFrame is contained in values.

See more: bigframes.dataframe.DataFrame.isin

bigframes.dataframe.DataFrame.isna

isna() -> bigframes.dataframe.DataFrame

Detect missing values.

See more: bigframes.dataframe.DataFrame.isna

bigframes.dataframe.DataFrame.isnull

isnull() -> bigframes.dataframe.DataFrame

Detect missing values.

See more: bigframes.dataframe.DataFrame.isnull

bigframes.dataframe.DataFrame.items

items()

Iterate over (column name, Series) pairs.

See more: bigframes.dataframe.DataFrame.items

bigframes.dataframe.DataFrame.iterrows

iterrows() -> typing.Iterable[tuple[typing.Any, pandas.core.series.Series]]

Iterate over DataFrame rows as (index, Series) pairs.

See more: bigframes.dataframe.DataFrame.iterrows

bigframes.dataframe.DataFrame.itertuples

itertuples(
    index: bool = True, name: typing.Optional[str] = "Pandas"
) -> typing.Iterable[tuple[typing.Any, ...]]

Iterate over DataFrame rows as namedtuples.

See more: bigframes.dataframe.DataFrame.itertuples

bigframes.dataframe.DataFrame.join

join(
    other: bigframes.dataframe.DataFrame,
    *,
    on: typing.Optional[str] = None,
    how: str = "left"
) -> bigframes.dataframe.DataFrame

Join columns of another DataFrame.

See more: bigframes.dataframe.DataFrame.join

bigframes.dataframe.DataFrame.keys

keys() -> pandas.core.indexes.base.Index

Get the 'info axis'.

See more: bigframes.dataframe.DataFrame.keys

bigframes.dataframe.DataFrame.kurt

kurt(*, numeric_only: bool = False)

Return unbiased kurtosis over columns.

See more: bigframes.dataframe.DataFrame.kurt

bigframes.dataframe.DataFrame.kurtosis

kurtosis(*, numeric_only: bool = False)

API documentation for kurtosis method.

See more: bigframes.dataframe.DataFrame.kurtosis

bigframes.dataframe.DataFrame.le

le(other: typing.Any, axis: str | int = "columns") -> bigframes.dataframe.DataFrame

Get 'less than or equal to' of dataframe and other, element-wise (binary operator <=).

See more: bigframes.dataframe.DataFrame.le

bigframes.dataframe.DataFrame.lt

lt(other: typing.Any, axis: str | int = "columns") -> bigframes.dataframe.DataFrame

Get 'less than' of DataFrame and other, element-wise (binary operator <).

See more: bigframes.dataframe.DataFrame.lt

bigframes.dataframe.DataFrame.map

map(func, na_action: typing.Optional[str] = None) -> bigframes.dataframe.DataFrame

Apply a function to a Dataframe elementwise.

See more: bigframes.dataframe.DataFrame.map

bigframes.dataframe.DataFrame.max

max(
    axis: typing.Union[str, int] = 0, *, numeric_only: bool = False
) -> bigframes.series.Series

Return the maximum of the values over the requested axis.

See more: bigframes.dataframe.DataFrame.max

bigframes.dataframe.DataFrame.mean

mean(
    axis: typing.Union[str, int] = 0, *, numeric_only: bool = False
) -> bigframes.series.Series

Return the mean of the values over the requested axis.

See more: bigframes.dataframe.DataFrame.mean

bigframes.dataframe.DataFrame.median

median(
    *, numeric_only: bool = False, exact: bool = False
) -> bigframes.series.Series

Return the median of the values over colunms.

See more: bigframes.dataframe.DataFrame.median

bigframes.dataframe.DataFrame.melt

melt(
    id_vars: typing.Optional[typing.Iterable[typing.Hashable]] = None,
    value_vars: typing.Optional[typing.Iterable[typing.Hashable]] = None,
    var_name: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None,
    value_name: typing.Hashable = "value",
)

Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.

See more: bigframes.dataframe.DataFrame.melt

bigframes.dataframe.DataFrame.memory_usage

memory_usage(index: bool = True)

Return the memory usage of each column in bytes.

See more: bigframes.dataframe.DataFrame.memory_usage

bigframes.dataframe.DataFrame.merge

merge(
    right: bigframes.dataframe.DataFrame,
    how: typing.Literal["inner", "left", "outer", "right", "cross"] = "inner",
    on: typing.Optional[
        typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]]
    ] = None,
    *,
    left_on: typing.Optional[
        typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]]
    ] = None,
    right_on: typing.Optional[
        typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]]
    ] = None,
    sort: bool = False,
    suffixes: tuple[str, str] = ("_x", "_y")
) -> bigframes.dataframe.DataFrame

Merge DataFrame objects with a database-style join.

See more: bigframes.dataframe.DataFrame.merge

bigframes.dataframe.DataFrame.min

min(
    axis: typing.Union[str, int] = 0, *, numeric_only: bool = False
) -> bigframes.series.Series

Return the minimum of the values over the requested axis.

See more: bigframes.dataframe.DataFrame.min

bigframes.dataframe.DataFrame.mod

mod(
    other: int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

Get modulo of DataFrame and other, element-wise (binary operator %).

See more: bigframes.dataframe.DataFrame.mod

bigframes.dataframe.DataFrame.mul

mul(
    other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

Get multiplication of DataFrame and other, element-wise (binary operator *).

See more: bigframes.dataframe.DataFrame.mul

bigframes.dataframe.DataFrame.multiply

multiply(
    other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

API documentation for multiply method.

See more: bigframes.dataframe.DataFrame.multiply

bigframes.dataframe.DataFrame.ne

ne(other: typing.Any, axis: str | int = "columns") -> bigframes.dataframe.DataFrame

Get not equal to of DataFrame and other, element-wise (binary operator ne).

See more: bigframes.dataframe.DataFrame.ne

bigframes.dataframe.DataFrame.nlargest

nlargest(
    n: int,
    columns: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]],
    keep: str = "first",
) -> bigframes.dataframe.DataFrame

Return the first n rows ordered by columns in descending order.

See more: bigframes.dataframe.DataFrame.nlargest

bigframes.dataframe.DataFrame.notna

notna() -> bigframes.dataframe.DataFrame

Detect existing (non-missing) values.

See more: bigframes.dataframe.DataFrame.notna

bigframes.dataframe.DataFrame.notnull

notnull() -> bigframes.dataframe.DataFrame

Detect existing (non-missing) values.

See more: bigframes.dataframe.DataFrame.notnull

bigframes.dataframe.DataFrame.nsmallest

nsmallest(
    n: int,
    columns: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]],
    keep: str = "first",
) -> bigframes.dataframe.DataFrame

Return the first n rows ordered by columns in ascending order.

See more: bigframes.dataframe.DataFrame.nsmallest

bigframes.dataframe.DataFrame.nunique

nunique() -> bigframes.series.Series

Count number of distinct elements in each column.

See more: bigframes.dataframe.DataFrame.nunique

bigframes.dataframe.DataFrame.pct_change

pct_change(periods: int = 1) -> bigframes.dataframe.DataFrame

Fractional change between the current and a prior element.

See more: bigframes.dataframe.DataFrame.pct_change

bigframes.dataframe.DataFrame.peek

peek(n: int = 5, *, force: bool = True) -> pandas.core.frame.DataFrame

Preview n arbitrary rows from the dataframe.

See more: bigframes.dataframe.DataFrame.peek

bigframes.dataframe.DataFrame.pipe

pipe(func: Callable[..., T] | tuple[Callable[..., T], str], *args, **kwargs) -> T

Apply chainable functions that expect Series or DataFrames.

See more: bigframes.dataframe.DataFrame.pipe

bigframes.dataframe.DataFrame.pivot

pivot(
    *,
    columns: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]],
    index: typing.Optional[
        typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]]
    ] = None,
    values: typing.Optional[
        typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]]
    ] = None
) -> bigframes.dataframe.DataFrame

Return reshaped DataFrame organized by given index / column values.

See more: bigframes.dataframe.DataFrame.pivot

bigframes.dataframe.DataFrame.pow

pow(
    other: int | bigframes.series.Series, axis: str | int = "columns"
) -> bigframes.dataframe.DataFrame

Get Exponential power of dataframe and other, element-wise (binary operator **).

See more: bigframes.dataframe.DataFrame.pow

bigframes.dataframe.DataFrame.prod

prod(
    axis: typing.Union[str, int] = 0, *, numeric_only: bool = False
) -> bigframes.series.Series

Return the product of the values over the requested axis.

See more: bigframes.dataframe.DataFrame.prod

bigframes.dataframe.DataFrame.product

product(
    axis: typing.Union[str, int] = 0, *, numeric_only: bool = False
) -> bigframes.series.Series

API documentation for product method.

See more: bigframes.dataframe.DataFrame.product

bigframes.dataframe.DataFrame.query

query(expr: str) -> bigframes.dataframe.DataFrame

Query the columns of a DataFrame with a boolean expression.

See more: bigframes.dataframe.DataFrame.query

bigframes.dataframe.DataFrame.radd

radd(
    other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

API documentation for radd method.

See more: bigframes.dataframe.DataFrame.radd

bigframes.dataframe.DataFrame.rank

rank(
    axis=0,
    method: str = "average",
    numeric_only=False,
    na_option: str = "keep",
    ascending=True,
) -> bigframes.dataframe.DataFrame

Compute numerical data ranks (1 through n) along axis.

See more: bigframes.dataframe.DataFrame.rank

bigframes.dataframe.DataFrame.rdiv

rdiv(
    other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

API documentation for rdiv method.

See more: bigframes.dataframe.DataFrame.rdiv

bigframes.dataframe.DataFrame.reindex

reindex(
    labels=None,
    *,
    index=None,
    columns=None,
    axis: typing.Optional[typing.Union[str, int]] = None,
    validate: typing.Optional[bool] = None
)

Conform DataFrame to new index with optional filling logic.

See more: bigframes.dataframe.DataFrame.reindex

bigframes.dataframe.DataFrame.reindex_like

reindex_like(
    other: bigframes.dataframe.DataFrame, *, validate: typing.Optional[bool] = None
)

Return an object with matching indices as other object.

See more: bigframes.dataframe.DataFrame.reindex_like

bigframes.dataframe.DataFrame.rename

rename(
    *, columns: typing.Mapping[typing.Hashable, typing.Hashable]
) -> bigframes.dataframe.DataFrame

Rename columns.

See more: bigframes.dataframe.DataFrame.rename

bigframes.dataframe.DataFrame.rename_axis

rename_axis(
    mapper: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]], **kwargs
) -> bigframes.dataframe.DataFrame

Set the name of the axis for the index.

See more: bigframes.dataframe.DataFrame.rename_axis

bigframes.dataframe.DataFrame.reorder_levels

reorder_levels(
    order: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]],
    axis: int | str = 0,
)

Rearrange index levels using input order.

See more: bigframes.dataframe.DataFrame.reorder_levels

bigframes.dataframe.DataFrame.replace

replace(to_replace: typing.Any, value: typing.Any = None, *, regex: bool = False)

Replace values given in to_replace with value.

See more: bigframes.dataframe.DataFrame.replace

bigframes.dataframe.DataFrame.reset_index

reset_index(*, drop: bool = False) -> bigframes.dataframe.DataFrame

Reset the index.

See more: bigframes.dataframe.DataFrame.reset_index

bigframes.dataframe.DataFrame.rfloordiv

rfloordiv(
    other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

Get integer division of DataFrame and other, element-wise (binary operator //).

See more: bigframes.dataframe.DataFrame.rfloordiv

bigframes.dataframe.DataFrame.rmod

rmod(
    other: int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

Get modulo of DataFrame and other, element-wise (binary operator %).

See more: bigframes.dataframe.DataFrame.rmod

bigframes.dataframe.DataFrame.rmul

rmul(
    other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

API documentation for rmul method.

See more: bigframes.dataframe.DataFrame.rmul

bigframes.dataframe.DataFrame.rolling

rolling(window: int, min_periods=None) -> bigframes.core.window.Window

Provide rolling window calculations.

See more: bigframes.dataframe.DataFrame.rolling

bigframes.dataframe.DataFrame.rpow

rpow(
    other: int | bigframes.series.Series, axis: str | int = "columns"
) -> bigframes.dataframe.DataFrame

Get Exponential power of dataframe and other, element-wise (binary operator rpow).

See more: bigframes.dataframe.DataFrame.rpow

bigframes.dataframe.DataFrame.rsub

rsub(
    other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

Get subtraction of DataFrame and other, element-wise (binary operator -).

See more: bigframes.dataframe.DataFrame.rsub

bigframes.dataframe.DataFrame.rtruediv

rtruediv(
    other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

Get floating division of DataFrame and other, element-wise (binary operator /).

See more: bigframes.dataframe.DataFrame.rtruediv

bigframes.dataframe.DataFrame.sample

sample(
    n: typing.Optional[int] = None,
    frac: typing.Optional[float] = None,
    *,
    random_state: typing.Optional[int] = None,
    sort: typing.Optional[typing.Union[bool, typing.Literal["random"]]] = "random"
) -> bigframes.dataframe.DataFrame

Return a random sample of items from an axis of object.

See more: bigframes.dataframe.DataFrame.sample

bigframes.dataframe.DataFrame.select_dtypes

select_dtypes(include=None, exclude=None) -> bigframes.dataframe.DataFrame

Return a subset of the DataFrame's columns based on the column dtypes.

See more: bigframes.dataframe.DataFrame.select_dtypes

bigframes.dataframe.DataFrame.set_index

set_index(
    keys: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]],
    append: bool = False,
    drop: bool = True,
) -> bigframes.dataframe.DataFrame

Set the DataFrame index using existing columns.

See more: bigframes.dataframe.DataFrame.set_index

bigframes.dataframe.DataFrame.shift

shift(periods: int = 1) -> bigframes.dataframe.DataFrame

Shift index by desired number of periods.

See more: bigframes.dataframe.DataFrame.shift

bigframes.dataframe.DataFrame.skew

skew(*, numeric_only: bool = False)

Return unbiased skew over columns.

See more: bigframes.dataframe.DataFrame.skew

bigframes.dataframe.DataFrame.sort_index

sort_index(
    ascending: bool = True, na_position: typing.Literal["first", "last"] = "last"
) -> bigframes.dataframe.DataFrame

Sort object by labels (along an axis).

See more: bigframes.dataframe.DataFrame.sort_index

bigframes.dataframe.DataFrame.sort_values

sort_values(
    by: typing.Union[str, typing.Sequence[str]],
    *,
    ascending: typing.Union[bool, typing.Sequence[bool]] = True,
    kind: str = "quicksort",
    na_position: typing.Literal["first", "last"] = "last"
) -> bigframes.dataframe.DataFrame

Sort by the values along row axis.

See more: bigframes.dataframe.DataFrame.sort_values

bigframes.dataframe.DataFrame.stack

stack(level: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = -1)

Stack the prescribed level(s) from columns to index.

See more: bigframes.dataframe.DataFrame.stack

bigframes.dataframe.DataFrame.std

std(
    axis: typing.Union[str, int] = 0, *, numeric_only: bool = False
) -> bigframes.series.Series

Return sample standard deviation over columns.

See more: bigframes.dataframe.DataFrame.std

bigframes.dataframe.DataFrame.sub

sub(
    other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

Get subtraction of DataFrame and other, element-wise (binary operator -).

See more: bigframes.dataframe.DataFrame.sub

bigframes.dataframe.DataFrame.subtract

subtract(
    other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

API documentation for subtract method.

See more: bigframes.dataframe.DataFrame.subtract

bigframes.dataframe.DataFrame.sum

sum(
    axis: typing.Union[str, int] = 0, *, numeric_only: bool = False
) -> bigframes.series.Series

Return the sum of the values over the requested axis.

See more: bigframes.dataframe.DataFrame.sum

bigframes.dataframe.DataFrame.swaplevel

swaplevel(i: int = -2, j: int = -1, axis: int | str = 0)

Swap levels i and j in a MultiIndex.

See more: bigframes.dataframe.DataFrame.swaplevel

bigframes.dataframe.DataFrame.tail

tail(n: int = 5) -> bigframes.dataframe.DataFrame

Return the last n rows.

See more: bigframes.dataframe.DataFrame.tail

bigframes.dataframe.DataFrame.to_csv

to_csv(
    path_or_buf: str, sep=",", *, header: bool = True, index: bool = True
) -> None

Write object to a comma-separated values (csv) file on Cloud Storage.

See more: bigframes.dataframe.DataFrame.to_csv

bigframes.dataframe.DataFrame.to_dict

to_dict(orient: typing.Literal['dict', 'list', 'series', 'split', 'tight', 'records', 'index'] = 'dict', into: type[dict] = 

Convert the DataFrame to a dictionary.

See more: bigframes.dataframe.DataFrame.to_dict

bigframes.dataframe.DataFrame.to_excel

to_excel(excel_writer, sheet_name: str = "Sheet1", **kwargs) -> None

Write DataFrame to an Excel sheet.

See more: bigframes.dataframe.DataFrame.to_excel

bigframes.dataframe.DataFrame.to_gbq

to_gbq(
    destination_table: typing.Optional[str] = None,
    *,
    if_exists: typing.Optional[typing.Literal["fail", "replace", "append"]] = None,
    index: bool = True,
    ordering_id: typing.Optional[str] = None,
    clustering_columns: typing.Union[
        pandas.core.indexes.base.Index, typing.Iterable[typing.Hashable]
    ] = ()
) -> str

Write a DataFrame to a BigQuery table.

See more: bigframes.dataframe.DataFrame.to_gbq

bigframes.dataframe.DataFrame.to_html

to_html(
    buf=None,
    columns: typing.Optional[typing.Sequence[str]] = None,
    col_space=None,
    header: bool = True,
    index: bool = True,
    na_rep: str = "NaN",
    formatters=None,
    float_format=None,
    sparsify: bool | None = None,
    index_names: bool = True,
    justify: str | None = None,
    max_rows: int | None = None,
    max_cols: int | None = None,
    show_dimensions: bool = False,
    decimal: str = ".",
    bold_rows: bool = True,
    classes: str | list | tuple | None = None,
    escape: bool = True,
    notebook: bool = False,
    border: int | None = None,
    table_id: str | None = None,
    render_links: bool = False,
    encoding: str | None = None,
) -> str

Render a DataFrame as an HTML table.

See more: bigframes.dataframe.DataFrame.to_html

bigframes.dataframe.DataFrame.to_json

to_json(
    path_or_buf: str,
    orient: typing.Literal[
        "split", "records", "index", "columns", "values", "table"
    ] = "columns",
    *,
    lines: bool = False,
    index: bool = True
) -> None

Convert the object to a JSON string, written to Cloud Storage.

See more: bigframes.dataframe.DataFrame.to_json

bigframes.dataframe.DataFrame.to_latex

to_latex(
    buf=None,
    columns: typing.Optional[typing.Sequence] = None,
    header: typing.Union[bool, typing.Sequence[str]] = True,
    index: bool = True,
    **kwargs
) -> str | None

Render object to a LaTeX tabular, longtable, or nested table.

See more: bigframes.dataframe.DataFrame.to_latex

bigframes.dataframe.DataFrame.to_markdown

to_markdown(buf=None, mode: str = "wt", index: bool = True, **kwargs) -> str | None

Print DataFrame in Markdown-friendly format.

See more: bigframes.dataframe.DataFrame.to_markdown

bigframes.dataframe.DataFrame.to_numpy

to_numpy(dtype=None, copy=False, na_value=None, **kwargs) -> numpy.ndarray

Convert the DataFrame to a NumPy array.

See more: bigframes.dataframe.DataFrame.to_numpy

bigframes.dataframe.DataFrame.to_orc

to_orc(path=None, **kwargs) -> bytes | None

Write a DataFrame to the ORC format.

See more: bigframes.dataframe.DataFrame.to_orc

bigframes.dataframe.DataFrame.to_pandas

to_pandas(
    max_download_size: typing.Optional[int] = None,
    sampling_method: typing.Optional[str] = None,
    random_state: typing.Optional[int] = None,
    *,
    ordered: bool = True
) -> pandas.core.frame.DataFrame

Write DataFrame to pandas DataFrame.

See more: bigframes.dataframe.DataFrame.to_pandas

bigframes.dataframe.DataFrame.to_pandas_batches

to_pandas_batches() -> typing.Iterable[pandas.core.frame.DataFrame]

Stream DataFrame results to an iterable of pandas DataFrame.

See more: bigframes.dataframe.DataFrame.to_pandas_batches

bigframes.dataframe.DataFrame.to_parquet

to_parquet(
    path: str,
    *,
    compression: typing.Optional[typing.Literal["snappy", "gzip"]] = "snappy",
    index: bool = True
) -> None

Write a DataFrame to the binary Parquet format.

See more: bigframes.dataframe.DataFrame.to_parquet

bigframes.dataframe.DataFrame.to_pickle

to_pickle(path, **kwargs) -> None

Pickle (serialize) object to file.

See more: bigframes.dataframe.DataFrame.to_pickle

bigframes.dataframe.DataFrame.to_records

to_records(
    index: bool = True, column_dtypes=None, index_dtypes=None
) -> numpy.recarray

Convert DataFrame to a NumPy record array.

See more: bigframes.dataframe.DataFrame.to_records

bigframes.dataframe.DataFrame.to_string

to_string(
    buf=None,
    columns: typing.Optional[typing.Sequence[str]] = None,
    col_space=None,
    header: typing.Union[bool, typing.Sequence[str]] = True,
    index: bool = True,
    na_rep: str = "NaN",
    formatters=None,
    float_format=None,
    sparsify: bool | None = None,
    index_names: bool = True,
    justify: str | None = None,
    max_rows: int | None = None,
    max_cols: int | None = None,
    show_dimensions: bool = False,
    decimal: str = ".",
    line_width: int | None = None,
    min_rows: int | None = None,
    max_colwidth: int | None = None,
    encoding: str | None = None,
) -> str | None

Render a DataFrame to a console-friendly tabular output.

See more: bigframes.dataframe.DataFrame.to_string

bigframes.dataframe.DataFrame.truediv

truediv(
    other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

Get floating division of DataFrame and other, element-wise (binary operator /).

See more: bigframes.dataframe.DataFrame.truediv

bigframes.dataframe.DataFrame.unstack

unstack(
    level: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = -1
)

Pivot a level of the (necessarily hierarchical) index labels.

See more: bigframes.dataframe.DataFrame.unstack

bigframes.dataframe.DataFrame.update

update(other, join: str = "left", overwrite=True, filter_func=None)

Modify in place using non-NA values from another DataFrame.

See more: bigframes.dataframe.DataFrame.update

bigframes.dataframe.DataFrame.value_counts

value_counts(
    subset: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None,
    normalize: bool = False,
    sort: bool = True,
    ascending: bool = False,
    dropna: bool = True,
)

Return a Series containing counts of unique rows in the DataFrame.

See more: bigframes.dataframe.DataFrame.value_counts

bigframes.dataframe.DataFrame.var

var(
    axis: typing.Union[str, int] = 0, *, numeric_only: bool = False
) -> bigframes.series.Series

Return unbiased variance over requested axis.

See more: bigframes.dataframe.DataFrame.var

bigframes.ml.cluster.KMeans.__repr__

__repr__()

Print the estimator's constructor with all non-default parameter values.

See more: bigframes.ml.cluster.KMeans.repr

bigframes.ml.cluster.KMeans.detect_anomalies

detect_anomalies(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    *,
    contamination: float = 0.1
) -> bigframes.dataframe.DataFrame

Detect the anomaly data points of the input.

See more: bigframes.ml.cluster.KMeans.detect_anomalies

bigframes.ml.cluster.KMeans.fit

fit(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Optional[
        typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
    ] = None,
) -> bigframes.ml.base._T

Compute k-means clustering.

See more: bigframes.ml.cluster.KMeans.fit

bigframes.ml.cluster.KMeans.get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

See more: bigframes.ml.cluster.KMeans.get_params

bigframes.ml.cluster.KMeans.predict

predict(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame

Predict the closest cluster each sample in X belongs to.

See more: bigframes.ml.cluster.KMeans.predict

bigframes.ml.cluster.KMeans.register

register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._T

Register the model to Vertex AI.

See more: bigframes.ml.cluster.KMeans.register

bigframes.ml.cluster.KMeans.score

score(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], y=None
) -> bigframes.dataframe.DataFrame

Calculate evaluation metrics of the model.

See more: bigframes.ml.cluster.KMeans.score

bigframes.ml.cluster.KMeans.to_gbq

to_gbq(model_name: str, replace: bool = False) -> bigframes.ml.cluster.KMeans

Save the model to BigQuery.

See more: bigframes.ml.cluster.KMeans.to_gbq

bigframes.ml.compose.ColumnTransformer.__repr__

__repr__()

Print the estimator's constructor with all non-default parameter values.

See more: bigframes.ml.compose.ColumnTransformer.repr

bigframes.ml.compose.ColumnTransformer.fit

fit(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], y=None
) -> bigframes.ml.compose.ColumnTransformer

Fit all transformers using X.

See more: bigframes.ml.compose.ColumnTransformer.fit

bigframes.ml.compose.ColumnTransformer.fit_transform

fit_transform(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Optional[
        typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
    ] = None,
) -> bigframes.dataframe.DataFrame

API documentation for fit_transform method.

See more: bigframes.ml.compose.ColumnTransformer.fit_transform

bigframes.ml.compose.ColumnTransformer.get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

See more: bigframes.ml.compose.ColumnTransformer.get_params

bigframes.ml.compose.ColumnTransformer.to_gbq

to_gbq(model_name: str, replace: bool = False) -> bigframes.ml.base._T

Save the transformer as a BigQuery model.

See more: bigframes.ml.compose.ColumnTransformer.to_gbq

bigframes.ml.compose.ColumnTransformer.transform

transform(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame

Transform X separately by each transformer, concatenate results.

See more: bigframes.ml.compose.ColumnTransformer.transform

bigframes.ml.decomposition.PCA.__repr__

__repr__()

Print the estimator's constructor with all non-default parameter values.

See more: bigframes.ml.decomposition.PCA.repr

bigframes.ml.decomposition.PCA.detect_anomalies

detect_anomalies(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    *,
    contamination: float = 0.1
) -> bigframes.dataframe.DataFrame

Detect the anomaly data points of the input.

See more: bigframes.ml.decomposition.PCA.detect_anomalies

bigframes.ml.decomposition.PCA.fit

fit(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Optional[
        typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
    ] = None,
) -> bigframes.ml.base._T

Fit the model according to the given training data.

See more: bigframes.ml.decomposition.PCA.fit

bigframes.ml.decomposition.PCA.get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

See more: bigframes.ml.decomposition.PCA.get_params

bigframes.ml.decomposition.PCA.predict

predict(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame

Predict the closest cluster for each sample in X.

See more: bigframes.ml.decomposition.PCA.predict

bigframes.ml.decomposition.PCA.register

register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._T

Register the model to Vertex AI.

See more: bigframes.ml.decomposition.PCA.register

bigframes.ml.decomposition.PCA.score

score(X=None, y=None) -> bigframes.dataframe.DataFrame

Calculate evaluation metrics of the model.

See more: bigframes.ml.decomposition.PCA.score

bigframes.ml.decomposition.PCA.to_gbq

to_gbq(model_name: str, replace: bool = False) -> bigframes.ml.decomposition.PCA

Save the model to BigQuery.

See more: bigframes.ml.decomposition.PCA.to_gbq

bigframes.ml.ensemble.RandomForestClassifier.__repr__

__repr__()

Print the estimator's constructor with all non-default parameter values.

See more: bigframes.ml.ensemble.RandomForestClassifier.repr

bigframes.ml.ensemble.RandomForestClassifier.fit

fit(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
) -> bigframes.ml.base._T

Build a forest of trees from the training set (X, y).

See more: bigframes.ml.ensemble.RandomForestClassifier.fit

bigframes.ml.ensemble.RandomForestClassifier.get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

See more: bigframes.ml.ensemble.RandomForestClassifier.get_params

bigframes.ml.ensemble.RandomForestClassifier.predict

predict(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame

Predict regression target for X.

See more: bigframes.ml.ensemble.RandomForestClassifier.predict

bigframes.ml.ensemble.RandomForestClassifier.register

register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._T

Register the model to Vertex AI.

See more: bigframes.ml.ensemble.RandomForestClassifier.register

bigframes.ml.ensemble.RandomForestClassifier.score

score(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
)

Calculate evaluation metrics of the model.

See more: bigframes.ml.ensemble.RandomForestClassifier.score

bigframes.ml.ensemble.RandomForestClassifier.to_gbq

to_gbq(
    model_name: str, replace: bool = False
) -> bigframes.ml.ensemble.RandomForestClassifier

Save the model to BigQuery.

See more: bigframes.ml.ensemble.RandomForestClassifier.to_gbq

bigframes.ml.ensemble.RandomForestRegressor.__repr__

__repr__()

Print the estimator's constructor with all non-default parameter values.

See more: bigframes.ml.ensemble.RandomForestRegressor.repr

bigframes.ml.ensemble.RandomForestRegressor.fit

fit(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
) -> bigframes.ml.base._T

Build a forest of trees from the training set (X, y).

See more: bigframes.ml.ensemble.RandomForestRegressor.fit

bigframes.ml.ensemble.RandomForestRegressor.get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

See more: bigframes.ml.ensemble.RandomForestRegressor.get_params

bigframes.ml.ensemble.RandomForestRegressor.predict

predict(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame

Predict regression target for X.

See more: bigframes.ml.ensemble.RandomForestRegressor.predict

bigframes.ml.ensemble.RandomForestRegressor.register

register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._T

Register the model to Vertex AI.

See more: bigframes.ml.ensemble.RandomForestRegressor.register

bigframes.ml.ensemble.RandomForestRegressor.score

score(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
)

Calculate evaluation metrics of the model.

See more: bigframes.ml.ensemble.RandomForestRegressor.score

bigframes.ml.ensemble.RandomForestRegressor.to_gbq

to_gbq(
    model_name: str, replace: bool = False
) -> bigframes.ml.ensemble.RandomForestRegressor

Save the model to BigQuery.

See more: bigframes.ml.ensemble.RandomForestRegressor.to_gbq

bigframes.ml.ensemble.XGBClassifier.__repr__

__repr__()

Print the estimator's constructor with all non-default parameter values.

See more: bigframes.ml.ensemble.XGBClassifier.repr

bigframes.ml.ensemble.XGBClassifier.fit

fit(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
) -> bigframes.ml.base._T

Fit gradient boosting model.

See more: bigframes.ml.ensemble.XGBClassifier.fit

bigframes.ml.ensemble.XGBClassifier.get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

See more: bigframes.ml.ensemble.XGBClassifier.get_params

bigframes.ml.ensemble.XGBClassifier.predict

predict(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame

Predict using the XGB model.

See more: bigframes.ml.ensemble.XGBClassifier.predict

bigframes.ml.ensemble.XGBClassifier.register

register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._T

Register the model to Vertex AI.

See more: bigframes.ml.ensemble.XGBClassifier.register

bigframes.ml.ensemble.XGBClassifier.score

score(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
)

Return the mean accuracy on the given test data and labels.

See more: bigframes.ml.ensemble.XGBClassifier.score

bigframes.ml.ensemble.XGBClassifier.to_gbq

to_gbq(
    model_name: str, replace: bool = False
) -> bigframes.ml.ensemble.XGBClassifier

Save the model to BigQuery.

See more: bigframes.ml.ensemble.XGBClassifier.to_gbq

bigframes.ml.ensemble.XGBRegressor.__repr__

__repr__()

Print the estimator's constructor with all non-default parameter values.

See more: bigframes.ml.ensemble.XGBRegressor.repr

bigframes.ml.ensemble.XGBRegressor.fit

fit(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
) -> bigframes.ml.base._T

Fit gradient boosting model.

See more: bigframes.ml.ensemble.XGBRegressor.fit

bigframes.ml.ensemble.XGBRegressor.get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

See more: bigframes.ml.ensemble.XGBRegressor.get_params

bigframes.ml.ensemble.XGBRegressor.predict

predict(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame

Predict using the XGB model.

See more: bigframes.ml.ensemble.XGBRegressor.predict

bigframes.ml.ensemble.XGBRegressor.register

register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._T

Register the model to Vertex AI.

See more: bigframes.ml.ensemble.XGBRegressor.register

bigframes.ml.ensemble.XGBRegressor.score

score(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
)

Calculate evaluation metrics of the model.

See more: bigframes.ml.ensemble.XGBRegressor.score

bigframes.ml.ensemble.XGBRegressor.to_gbq

to_gbq(
    model_name: str, replace: bool = False
) -> bigframes.ml.ensemble.XGBRegressor

Save the model to BigQuery.

See more: bigframes.ml.ensemble.XGBRegressor.to_gbq

bigframes.ml.forecasting.ARIMAPlus.__repr__

__repr__()

Print the estimator's constructor with all non-default parameter values.

See more: bigframes.ml.forecasting.ARIMAPlus.repr

bigframes.ml.forecasting.ARIMAPlus.detect_anomalies

detect_anomalies(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    *,
    anomaly_prob_threshold: float = 0.95
) -> bigframes.dataframe.DataFrame

Detect the anomaly data points of the input.

See more: bigframes.ml.forecasting.ARIMAPlus.detect_anomalies

bigframes.ml.forecasting.ARIMAPlus.fit

fit(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
) -> bigframes.ml.base._T

API documentation for fit method.

See more: bigframes.ml.forecasting.ARIMAPlus.fit

bigframes.ml.forecasting.ARIMAPlus.get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

See more: bigframes.ml.forecasting.ARIMAPlus.get_params

bigframes.ml.forecasting.ARIMAPlus.predict

predict(
    X=None, *, horizon: int = 3, confidence_level: float = 0.95
) -> bigframes.dataframe.DataFrame

Forecast time series at future horizon.

See more: bigframes.ml.forecasting.ARIMAPlus.predict

bigframes.ml.forecasting.ARIMAPlus.register

register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._T

Register the model to Vertex AI.

See more: bigframes.ml.forecasting.ARIMAPlus.register

bigframes.ml.forecasting.ARIMAPlus.score

score(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
) -> bigframes.dataframe.DataFrame

Calculate evaluation metrics of the model.

See more: bigframes.ml.forecasting.ARIMAPlus.score

bigframes.ml.forecasting.ARIMAPlus.summary

summary(show_all_candidate_models: bool = False) -> bigframes.dataframe.DataFrame

Summary of the evaluation metrics of the time series model.

See more: bigframes.ml.forecasting.ARIMAPlus.summary

bigframes.ml.forecasting.ARIMAPlus.to_gbq

to_gbq(
    model_name: str, replace: bool = False
) -> bigframes.ml.forecasting.ARIMAPlus

Save the model to BigQuery.

See more: bigframes.ml.forecasting.ARIMAPlus.to_gbq

bigframes.ml.imported.ONNXModel.__repr__

__repr__()

Print the estimator's constructor with all non-default parameter values.

See more: bigframes.ml.imported.ONNXModel.repr

bigframes.ml.imported.ONNXModel.get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

See more: bigframes.ml.imported.ONNXModel.get_params

bigframes.ml.imported.ONNXModel.predict

predict(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame

Predict the result from input DataFrame.

See more: bigframes.ml.imported.ONNXModel.predict

bigframes.ml.imported.ONNXModel.register

register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._T

Register the model to Vertex AI.

See more: bigframes.ml.imported.ONNXModel.register

bigframes.ml.imported.ONNXModel.to_gbq

to_gbq(model_name: str, replace: bool = False) -> bigframes.ml.imported.ONNXModel

Save the model to BigQuery.

See more: bigframes.ml.imported.ONNXModel.to_gbq

bigframes.ml.imported.TensorFlowModel.__repr__

__repr__()

Print the estimator's constructor with all non-default parameter values.

See more: bigframes.ml.imported.TensorFlowModel.repr

bigframes.ml.imported.TensorFlowModel.get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

See more: bigframes.ml.imported.TensorFlowModel.get_params

bigframes.ml.imported.TensorFlowModel.predict

predict(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame

Predict the result from input DataFrame.

See more: bigframes.ml.imported.TensorFlowModel.predict

bigframes.ml.imported.TensorFlowModel.register

register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._T

Register the model to Vertex AI.

See more: bigframes.ml.imported.TensorFlowModel.register

bigframes.ml.imported.TensorFlowModel.to_gbq

to_gbq(
    model_name: str, replace: bool = False
) -> bigframes.ml.imported.TensorFlowModel

Save the model to BigQuery.

See more: bigframes.ml.imported.TensorFlowModel.to_gbq

bigframes.ml.imported.XGBoostModel.__repr__

__repr__()

Print the estimator's constructor with all non-default parameter values.

See more: bigframes.ml.imported.XGBoostModel.repr

bigframes.ml.imported.XGBoostModel.get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

See more: bigframes.ml.imported.XGBoostModel.get_params

bigframes.ml.imported.XGBoostModel.predict

predict(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame

Predict the result from input DataFrame.

See more: bigframes.ml.imported.XGBoostModel.predict

bigframes.ml.imported.XGBoostModel.register

register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._T

Register the model to Vertex AI.

See more: bigframes.ml.imported.XGBoostModel.register

bigframes.ml.imported.XGBoostModel.to_gbq

to_gbq(
    model_name: str, replace: bool = False
) -> bigframes.ml.imported.XGBoostModel

Save the model to BigQuery.

See more: bigframes.ml.imported.XGBoostModel.to_gbq

bigframes.ml.linear_model.LinearRegression.__repr__

__repr__()

Print the estimator's constructor with all non-default parameter values.

See more: bigframes.ml.linear_model.LinearRegression.repr

bigframes.ml.linear_model.LinearRegression.fit

fit(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
) -> bigframes.ml.base._T

Fit linear model.

See more: bigframes.ml.linear_model.LinearRegression.fit

bigframes.ml.linear_model.LinearRegression.get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

See more: bigframes.ml.linear_model.LinearRegression.get_params

bigframes.ml.linear_model.LinearRegression.predict

predict(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame

Predict using the linear model.

See more: bigframes.ml.linear_model.LinearRegression.predict

bigframes.ml.linear_model.LinearRegression.register

register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._T

Register the model to Vertex AI.

See more: bigframes.ml.linear_model.LinearRegression.register

bigframes.ml.linear_model.LinearRegression.score

score(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
) -> bigframes.dataframe.DataFrame

Calculate evaluation metrics of the model.

See more: bigframes.ml.linear_model.LinearRegression.score

bigframes.ml.linear_model.LinearRegression.to_gbq

to_gbq(
    model_name: str, replace: bool = False
) -> bigframes.ml.linear_model.LinearRegression

Save the model to BigQuery.

See more: bigframes.ml.linear_model.LinearRegression.to_gbq

bigframes.ml.linear_model.LogisticRegression.__repr__

__repr__()

Print the estimator's constructor with all non-default parameter values.

See more: bigframes.ml.linear_model.LogisticRegression.repr

bigframes.ml.linear_model.LogisticRegression.fit

fit(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
) -> bigframes.ml.base._T

Fit the model according to the given training data.

See more: bigframes.ml.linear_model.LogisticRegression.fit

bigframes.ml.linear_model.LogisticRegression.get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

See more: bigframes.ml.linear_model.LogisticRegression.get_params

bigframes.ml.linear_model.LogisticRegression.predict

predict(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame

Predict class labels for samples in X.

See more: bigframes.ml.linear_model.LogisticRegression.predict

bigframes.ml.linear_model.LogisticRegression.register

register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._T

Register the model to Vertex AI.

See more: bigframes.ml.linear_model.LogisticRegression.register

bigframes.ml.linear_model.LogisticRegression.score

score(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
) -> bigframes.dataframe.DataFrame

Return the mean accuracy on the given test data and labels.

See more: bigframes.ml.linear_model.LogisticRegression.score

bigframes.ml.linear_model.LogisticRegression.to_gbq

to_gbq(
    model_name: str, replace: bool = False
) -> bigframes.ml.linear_model.LogisticRegression

Save the model to BigQuery.

See more: bigframes.ml.linear_model.LogisticRegression.to_gbq

bigframes.ml.llm.GeminiTextGenerator.__repr__

__repr__()

Print the estimator's constructor with all non-default parameter values.

See more: bigframes.ml.llm.GeminiTextGenerator.repr

bigframes.ml.llm.GeminiTextGenerator.get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

See more: bigframes.ml.llm.GeminiTextGenerator.get_params

bigframes.ml.llm.GeminiTextGenerator.predict

predict(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    *,
    temperature: float = 0.9,
    max_output_tokens: int = 8192,
    top_k: int = 40,
    top_p: float = 1.0
) -> bigframes.dataframe.DataFrame

Predict the result from input DataFrame.

See more: bigframes.ml.llm.GeminiTextGenerator.predict

bigframes.ml.llm.GeminiTextGenerator.to_gbq

to_gbq(
    model_name: str, replace: bool = False
) -> bigframes.ml.llm.GeminiTextGenerator

Save the model to BigQuery.

See more: bigframes.ml.llm.GeminiTextGenerator.to_gbq

bigframes.ml.llm.PaLM2TextEmbeddingGenerator.__repr__

__repr__()

Print the estimator's constructor with all non-default parameter values.

See more: bigframes.ml.llm.PaLM2TextEmbeddingGenerator.repr

bigframes.ml.llm.PaLM2TextEmbeddingGenerator.get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

See more: bigframes.ml.llm.PaLM2TextEmbeddingGenerator.get_params

bigframes.ml.llm.PaLM2TextEmbeddingGenerator.predict

predict(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame

Predict the result from input DataFrame.

See more: bigframes.ml.llm.PaLM2TextEmbeddingGenerator.predict

bigframes.ml.llm.PaLM2TextEmbeddingGenerator.to_gbq

to_gbq(
    model_name: str, replace: bool = False
) -> bigframes.ml.llm.PaLM2TextEmbeddingGenerator

Save the model to BigQuery.

See more: bigframes.ml.llm.PaLM2TextEmbeddingGenerator.to_gbq

bigframes.ml.llm.PaLM2TextGenerator.__repr__

__repr__()

Print the estimator's constructor with all non-default parameter values.

See more: bigframes.ml.llm.PaLM2TextGenerator.repr

bigframes.ml.llm.PaLM2TextGenerator.get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

See more: bigframes.ml.llm.PaLM2TextGenerator.get_params

bigframes.ml.llm.PaLM2TextGenerator.predict

predict(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    *,
    temperature: float = 0.0,
    max_output_tokens: int = 128,
    top_k: int = 40,
    top_p: float = 0.95
) -> bigframes.dataframe.DataFrame

Predict the result from input DataFrame.

See more: bigframes.ml.llm.PaLM2TextGenerator.predict

bigframes.ml.llm.PaLM2TextGenerator.to_gbq

to_gbq(
    model_name: str, replace: bool = False
) -> bigframes.ml.llm.PaLM2TextGenerator

Save the model to BigQuery.

See more: bigframes.ml.llm.PaLM2TextGenerator.to_gbq

bigframes.ml.pipeline.Pipeline.__repr__

__repr__()

Print the estimator's constructor with all non-default parameter values.

See more: bigframes.ml.pipeline.Pipeline.repr

bigframes.ml.pipeline.Pipeline.fit

fit(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Optional[
        typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
    ] = None,
) -> bigframes.ml.pipeline.Pipeline

Fit the model.

See more: bigframes.ml.pipeline.Pipeline.fit

bigframes.ml.pipeline.Pipeline.get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

See more: bigframes.ml.pipeline.Pipeline.get_params

bigframes.ml.pipeline.Pipeline.predict

predict(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame

API documentation for predict method.

See more: bigframes.ml.pipeline.Pipeline.predict

bigframes.ml.pipeline.Pipeline.score

score(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Optional[
        typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
    ] = None,
) -> bigframes.dataframe.DataFrame

API documentation for score method.

See more: bigframes.ml.pipeline.Pipeline.score

bigframes.ml.pipeline.Pipeline.to_gbq

to_gbq(model_name: str, replace: bool = False) -> bigframes.ml.pipeline.Pipeline

Save the pipeline to BigQuery.

See more: bigframes.ml.pipeline.Pipeline.to_gbq

bigframes.ml.preprocessing.KBinsDiscretizer.__repr__

__repr__()

Print the estimator's constructor with all non-default parameter values.

See more: bigframes.ml.preprocessing.KBinsDiscretizer.repr

bigframes.ml.preprocessing.KBinsDiscretizer.fit

fit(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], y=None
) -> bigframes.ml.preprocessing.KBinsDiscretizer

Fit the estimator.

See more: bigframes.ml.preprocessing.KBinsDiscretizer.fit

bigframes.ml.preprocessing.KBinsDiscretizer.fit_transform

fit_transform(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Optional[
        typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
    ] = None,
) -> bigframes.dataframe.DataFrame

Fit to data, then transform it.

See more: bigframes.ml.preprocessing.KBinsDiscretizer.fit_transform

bigframes.ml.preprocessing.KBinsDiscretizer.get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

See more: bigframes.ml.preprocessing.KBinsDiscretizer.get_params

bigframes.ml.preprocessing.KBinsDiscretizer.to_gbq

to_gbq(model_name: str, replace: bool = False) -> bigframes.ml.base._T

Save the transformer as a BigQuery model.

See more: bigframes.ml.preprocessing.KBinsDiscretizer.to_gbq

bigframes.ml.preprocessing.KBinsDiscretizer.transform

transform(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame

Discretize the data.

See more: bigframes.ml.preprocessing.KBinsDiscretizer.transform

bigframes.ml.preprocessing.LabelEncoder.__repr__

__repr__()

Print the estimator's constructor with all non-default parameter values.

See more: bigframes.ml.preprocessing.LabelEncoder.repr

bigframes.ml.preprocessing.LabelEncoder.fit

fit(
    y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.ml.preprocessing.LabelEncoder

Fit label encoder.

See more: bigframes.ml.preprocessing.LabelEncoder.fit

bigframes.ml.preprocessing.LabelEncoder.fit_transform

fit_transform(
    y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame

API documentation for fit_transform method.

See more: bigframes.ml.preprocessing.LabelEncoder.fit_transform

bigframes.ml.preprocessing.LabelEncoder.get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

See more: bigframes.ml.preprocessing.LabelEncoder.get_params

bigframes.ml.preprocessing.LabelEncoder.to_gbq

to_gbq(model_name: str, replace: bool = False) -> bigframes.ml.base._T

Save the transformer as a BigQuery model.

See more: bigframes.ml.preprocessing.LabelEncoder.to_gbq

bigframes.ml.preprocessing.LabelEncoder.transform

transform(
    y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame

Transform y using label encoding.

See more: bigframes.ml.preprocessing.LabelEncoder.transform

bigframes.ml.preprocessing.MaxAbsScaler.__repr__

__repr__()

Print the estimator's constructor with all non-default parameter values.

See more: bigframes.ml.preprocessing.MaxAbsScaler.repr

bigframes.ml.preprocessing.MaxAbsScaler.fit

fit(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], y=None
) -> bigframes.ml.preprocessing.MaxAbsScaler

Compute the maximum absolute value to be used for later scaling.

See more: bigframes.ml.preprocessing.MaxAbsScaler.fit

bigframes.ml.preprocessing.MaxAbsScaler.fit_transform

fit_transform(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Optional[
        typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
    ] = None,
) -> bigframes.dataframe.DataFrame

Fit to data, then transform it.

See more: bigframes.ml.preprocessing.MaxAbsScaler.fit_transform

bigframes.ml.preprocessing.MaxAbsScaler.get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

See more: bigframes.ml.preprocessing.MaxAbsScaler.get_params

bigframes.ml.preprocessing.MaxAbsScaler.to_gbq

to_gbq(model_name: str, replace: bool = False) -> bigframes.ml.base._T

Save the transformer as a BigQuery model.

See more: bigframes.ml.preprocessing.MaxAbsScaler.to_gbq

bigframes.ml.preprocessing.MaxAbsScaler.transform

transform(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame

Scale the data.

See more: bigframes.ml.preprocessing.MaxAbsScaler.transform

bigframes.ml.preprocessing.MinMaxScaler.__repr__

__repr__()

Print the estimator's constructor with all non-default parameter values.

See more: bigframes.ml.preprocessing.MinMaxScaler.repr

bigframes.ml.preprocessing.MinMaxScaler.fit

fit(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], y=None
) -> bigframes.ml.preprocessing.MinMaxScaler

Compute the minimum and maximum to be used for later scaling.

See more: bigframes.ml.preprocessing.MinMaxScaler.fit

bigframes.ml.preprocessing.MinMaxScaler.fit_transform

fit_transform(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Optional[
        typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
    ] = None,
) -> bigframes.dataframe.DataFrame

Fit to data, then transform it.

See more: bigframes.ml.preprocessing.MinMaxScaler.fit_transform

bigframes.ml.preprocessing.MinMaxScaler.get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

See more: bigframes.ml.preprocessing.MinMaxScaler.get_params

bigframes.ml.preprocessing.MinMaxScaler.to_gbq

to_gbq(model_name: str, replace: bool = False) -> bigframes.ml.base._T

Save the transformer as a BigQuery model.

See more: bigframes.ml.preprocessing.MinMaxScaler.to_gbq

bigframes.ml.preprocessing.MinMaxScaler.transform

transform(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame

Scale the data.

See more: bigframes.ml.preprocessing.MinMaxScaler.transform

bigframes.ml.preprocessing.OneHotEncoder.__repr__

__repr__()

Print the estimator's constructor with all non-default parameter values.

See more: bigframes.ml.preprocessing.OneHotEncoder.repr

bigframes.ml.preprocessing.OneHotEncoder.fit

fit(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], y=None
) -> bigframes.ml.preprocessing.OneHotEncoder

Fit OneHotEncoder to X.

See more: bigframes.ml.preprocessing.OneHotEncoder.fit

bigframes.ml.preprocessing.OneHotEncoder.fit_transform

fit_transform(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Optional[
        typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
    ] = None,
) -> bigframes.dataframe.DataFrame

API documentation for fit_transform method.

See more: bigframes.ml.preprocessing.OneHotEncoder.fit_transform

bigframes.ml.preprocessing.OneHotEncoder.get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

See more: bigframes.ml.preprocessing.OneHotEncoder.get_params

bigframes.ml.preprocessing.OneHotEncoder.to_gbq

to_gbq(model_name: str, replace: bool = False) -> bigframes.ml.base._T

Save the transformer as a BigQuery model.

See more: bigframes.ml.preprocessing.OneHotEncoder.to_gbq

bigframes.ml.preprocessing.OneHotEncoder.transform

transform(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame

Transform X using one-hot encoding.

See more: bigframes.ml.preprocessing.OneHotEncoder.transform

bigframes.ml.preprocessing.StandardScaler.__repr__

__repr__()

Print the estimator's constructor with all non-default parameter values.

See more: bigframes.ml.preprocessing.StandardScaler.repr

bigframes.ml.preprocessing.StandardScaler.fit

fit(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], y=None
) -> bigframes.ml.preprocessing.StandardScaler

Compute the mean and std to be used for later scaling.

See more: bigframes.ml.preprocessing.StandardScaler.fit

bigframes.ml.preprocessing.StandardScaler.fit_transform

fit_transform(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Optional[
        typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
    ] = None,
) -> bigframes.dataframe.DataFrame

Fit to data, then transform it.

See more: bigframes.ml.preprocessing.StandardScaler.fit_transform

bigframes.ml.preprocessing.StandardScaler.get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

See more: bigframes.ml.preprocessing.StandardScaler.get_params

bigframes.ml.preprocessing.StandardScaler.to_gbq

to_gbq(model_name: str, replace: bool = False) -> bigframes.ml.base._T

Save the transformer as a BigQuery model.

See more: bigframes.ml.preprocessing.StandardScaler.to_gbq

bigframes.ml.preprocessing.StandardScaler.transform

transform(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame

Perform standardization by centering and scaling.

See more: bigframes.ml.preprocessing.StandardScaler.transform

bigframes.ml.remote.VertexAIModel.__repr__

__repr__()

Print the estimator's constructor with all non-default parameter values.

See more: bigframes.ml.remote.VertexAIModel.repr

bigframes.ml.remote.VertexAIModel.get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

See more: bigframes.ml.remote.VertexAIModel.get_params

bigframes.ml.remote.VertexAIModel.predict

predict(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame

Predict the result from the input DataFrame.

See more: bigframes.ml.remote.VertexAIModel.predict

bigframes.operations.datetimes.DatetimeMethods.floor

floor(freq: str) -> bigframes.series.Series

Perform floor operation on the data to the specified freq.

See more: bigframes.operations.datetimes.DatetimeMethods.floor

bigframes.operations.datetimes.DatetimeMethods.normalize

normalize() -> bigframes.series.Series

Convert times to midnight.

See more: bigframes.operations.datetimes.DatetimeMethods.normalize

bigframes.operations.datetimes.DatetimeMethods.strftime

strftime(date_format: str) -> bigframes.series.Series

Convert to string Series using specified date_format.

See more: bigframes.operations.datetimes.DatetimeMethods.strftime

bigframes.operations.plotting.PlotAccessor.area

area(
    x: typing.Optional[typing.Hashable] = None,
    y: typing.Optional[typing.Hashable] = None,
    stacked: bool = True,
    **kwargs
)

Draw a stacked area plot.

See more: bigframes.operations.plotting.PlotAccessor.area

bigframes.operations.plotting.PlotAccessor.hist

hist(by: typing.Optional[typing.Sequence[str]] = None, bins: int = 10, **kwargs)

Draw one histogram of the DataFrame’s columns.

See more: bigframes.operations.plotting.PlotAccessor.hist

bigframes.operations.plotting.PlotAccessor.line

line(
    x: typing.Optional[typing.Hashable] = None,
    y: typing.Optional[typing.Hashable] = None,
    **kwargs
)

Plot Series or DataFrame as lines.

See more: bigframes.operations.plotting.PlotAccessor.line

bigframes.operations.plotting.PlotAccessor.scatter

scatter(
    x: typing.Optional[typing.Hashable] = None,
    y: typing.Optional[typing.Hashable] = None,
    s: typing.Optional[
        typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]]
    ] = None,
    c: typing.Optional[
        typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]]
    ] = None,
    **kwargs
)

Create a scatter plot with varying marker point size and color.

See more: bigframes.operations.plotting.PlotAccessor.scatter

bigframes.operations.plotting.PlotAccessor

PlotAccessor(data)

Make plots of Series or DataFrame with the matplotlib backend.

See more: bigframes.operations.plotting.PlotAccessor

bigframes.operations.strings.StringMethods.capitalize

capitalize() -> bigframes.series.Series

Convert strings in the Series/Index to be capitalized.

See more: bigframes.operations.strings.StringMethods.capitalize

bigframes.operations.strings.StringMethods.cat

cat(
    others: typing.Union[str, bigframes.series.Series],
    *,
    join: typing.Literal["outer", "left"] = "left"
) -> bigframes.series.Series

Concatenate strings in the Series/Index with given separator.

See more: bigframes.operations.strings.StringMethods.cat

bigframes.operations.strings.StringMethods.center

center(width: int, fillchar: str = " ") -> bigframes.series.Series

Pad left and right side of strings in the Series/Index.

See more: bigframes.operations.strings.StringMethods.center

bigframes.operations.strings.StringMethods.contains

contains(
    pat, case: bool = True, flags: int = 0, *, regex: bool = True
) -> bigframes.series.Series

Test if pattern or regex is contained within a string of a Series or Index.

See more: bigframes.operations.strings.StringMethods.contains

bigframes.operations.strings.StringMethods.endswith

endswith(pat: typing.Union[str, tuple[str, ...]]) -> bigframes.series.Series

Test if the end of each string element matches a pattern.

See more: bigframes.operations.strings.StringMethods.endswith

bigframes.operations.strings.StringMethods.extract

extract(pat: str, flags: int = 0) -> bigframes.dataframe.DataFrame

Extract capture groups in the regex pat as columns in a DataFrame.

See more: bigframes.operations.strings.StringMethods.extract

bigframes.operations.strings.StringMethods.find

find(
    sub: str, start: typing.Optional[int] = None, end: typing.Optional[int] = None
) -> bigframes.series.Series

Return lowest indexes in each strings in the Series/Index.

See more: bigframes.operations.strings.StringMethods.find

bigframes.operations.strings.StringMethods.fullmatch

fullmatch(pat, case=True, flags=0) -> bigframes.series.Series

Determine if each string entirely matches a regular expression.

See more: bigframes.operations.strings.StringMethods.fullmatch

bigframes.operations.strings.StringMethods.get

get(i: int) -> bigframes.series.Series

Extract element from each component at specified position or with specified key.

See more: bigframes.operations.strings.StringMethods.get

bigframes.operations.strings.StringMethods.isalnum

isalnum() -> bigframes.series.Series

Check whether all characters in each string are alphanumeric.

See more: bigframes.operations.strings.StringMethods.isalnum

bigframes.operations.strings.StringMethods.isalpha

isalpha() -> bigframes.series.Series

Check whether all characters in each string are alphabetic.

See more: bigframes.operations.strings.StringMethods.isalpha

bigframes.operations.strings.StringMethods.isdecimal

isdecimal() -> bigframes.series.Series

Check whether all characters in each string are decimal.

See more: bigframes.operations.strings.StringMethods.isdecimal

bigframes.operations.strings.StringMethods.isdigit

isdigit() -> bigframes.series.Series

Check whether all characters in each string are digits.

See more: bigframes.operations.strings.StringMethods.isdigit

bigframes.operations.strings.StringMethods.islower

islower() -> bigframes.series.Series

Check whether all characters in each string are lowercase.

See more: bigframes.operations.strings.StringMethods.islower

bigframes.operations.strings.StringMethods.isnumeric

isnumeric() -> bigframes.series.Series

Check whether all characters in each string are numeric.

See more: bigframes.operations.strings.StringMethods.isnumeric

bigframes.operations.strings.StringMethods.isspace

isspace() -> bigframes.series.Series

Check whether all characters in each string are whitespace.

See more: bigframes.operations.strings.StringMethods.isspace

bigframes.operations.strings.StringMethods.isupper

isupper() -> bigframes.series.Series

Check whether all characters in each string are uppercase.

See more: bigframes.operations.strings.StringMethods.isupper

bigframes.operations.strings.StringMethods.len

len() -> bigframes.series.Series

Compute the length of each element in the Series/Index.

See more: bigframes.operations.strings.StringMethods.len

bigframes.operations.strings.StringMethods.ljust

ljust(width, fillchar=" ") -> bigframes.series.Series

Pad right side of strings in the Series/Index up to width.

See more: bigframes.operations.strings.StringMethods.ljust

bigframes.operations.strings.StringMethods.lower

lower() -> bigframes.series.Series

Convert strings in the Series/Index to lowercase.

See more: bigframes.operations.strings.StringMethods.lower

bigframes.operations.strings.StringMethods.lstrip

lstrip() -> bigframes.series.Series

Remove leading characters.

See more: bigframes.operations.strings.StringMethods.lstrip

bigframes.operations.strings.StringMethods.match

match(pat, case=True, flags=0) -> bigframes.series.Series

Determine if each string starts with a match of a regular expression.

See more: bigframes.operations.strings.StringMethods.match

bigframes.operations.strings.StringMethods.pad

pad(width, side="left", fillchar=" ") -> bigframes.series.Series

Pad strings in the Series/Index up to width.

See more: bigframes.operations.strings.StringMethods.pad

bigframes.operations.strings.StringMethods.repeat

repeat(repeats: int) -> bigframes.series.Series

Duplicate each string in the Series or Index.

See more: bigframes.operations.strings.StringMethods.repeat

bigframes.operations.strings.StringMethods.replace

replace(
    pat: typing.Union[str, re.Pattern],
    repl: str,
    *,
    case: typing.Optional[bool] = None,
    flags: int = 0,
    regex: bool = False
) -> bigframes.series.Series

Replace each occurrence of pattern/regex in the Series/Index.

See more: bigframes.operations.strings.StringMethods.replace

bigframes.operations.strings.StringMethods.reverse

reverse() -> bigframes.series.Series

Reverse strings in the Series.

See more: bigframes.operations.strings.StringMethods.reverse

bigframes.operations.strings.StringMethods.rjust

rjust(width, fillchar=" ") -> bigframes.series.Series

Pad left side of strings in the Series/Index up to width.

See more: bigframes.operations.strings.StringMethods.rjust

bigframes.operations.strings.StringMethods.rstrip

rstrip() -> bigframes.series.Series

Remove trailing characters.

See more: bigframes.operations.strings.StringMethods.rstrip

bigframes.operations.strings.StringMethods.slice

slice(
    start: typing.Optional[int] = None, stop: typing.Optional[int] = None
) -> bigframes.series.Series

Slice substrings from each element in the Series or Index.

See more: bigframes.operations.strings.StringMethods.slice

bigframes.operations.strings.StringMethods.startswith

startswith(pat: typing.Union[str, tuple[str, ...]]) -> bigframes.series.Series

Test if the start of each string element matches a pattern.

See more: bigframes.operations.strings.StringMethods.startswith

bigframes.operations.strings.StringMethods.strip

strip() -> bigframes.series.Series

Remove leading and trailing characters.

See more: bigframes.operations.strings.StringMethods.strip

bigframes.operations.strings.StringMethods.upper

upper() -> bigframes.series.Series

Convert strings in the Series/Index to uppercase.

See more: bigframes.operations.strings.StringMethods.upper

bigframes.operations.strings.StringMethods.zfill

zfill(width: int) -> bigframes.series.Series

Pad strings in the Series/Index by prepending '0' characters.

See more: bigframes.operations.strings.StringMethods.zfill

bigframes.operations.structs.StructAccessor.explode

explode() -> bigframes.dataframe.DataFrame

Extract all child fields of a struct as a DataFrame.

See more: bigframes.operations.structs.StructAccessor.explode

bigframes.operations.structs.StructAccessor.field

field(name_or_index: str | int) -> bigframes.series.Series

Extract a child field of a struct as a Series.

See more: bigframes.operations.structs.StructAccessor.field

bigframes.pandas.NamedAgg

NamedAgg(column, aggfunc)

Create new instance of NamedAgg(column, aggfunc).

See more: bigframes.pandas.NamedAgg

bigframes.series.Series.__array_ufunc__

__array_ufunc__(
    ufunc: numpy.ufunc, method: str, *inputs, **kwargs
) -> bigframes.series.Series

Used to support numpy ufuncs.

See more: bigframes.series.Series.array_ufunc

bigframes.series.Series.__rmatmul__

__rmatmul__(other)

Matrix multiplication using binary @ operator in Python>=3.5.

See more: bigframes.series.Series.rmatmul

bigframes.series.Series.abs

abs() -> bigframes.series.Series

Return a Series/DataFrame with absolute numeric value of each element.

See more: bigframes.series.Series.abs

bigframes.series.Series.add

add(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Return addition of Series and other, element-wise (binary operator add).

See more: bigframes.series.Series.add

bigframes.series.Series.add_prefix

add_prefix(prefix: str, axis: int | str | None = None) -> bigframes.series.Series

Prefix labels with string prefix.

See more: bigframes.series.Series.add_prefix

bigframes.series.Series.add_suffix

add_suffix(suffix: str, axis: int | str | None = None) -> bigframes.series.Series

Suffix labels with string suffix.

See more: bigframes.series.Series.add_suffix

bigframes.series.Series.agg

agg(
    func: typing.Union[str, typing.Sequence[str]]
) -> typing.Union[typing.Any, bigframes.series.Series]

Aggregate using one or more operations over the specified axis.

See more: bigframes.series.Series.agg

bigframes.series.Series.aggregate

aggregate(
    func: typing.Union[str, typing.Sequence[str]]
) -> typing.Union[typing.Any, bigframes.series.Series]

API documentation for aggregate method.

See more: bigframes.series.Series.aggregate

bigframes.series.Series.all

all() -> bool

Return whether all elements are True, potentially over an axis.

See more: bigframes.series.Series.all

bigframes.series.Series.any

any() -> bool

Return whether any element is True, potentially over an axis.

See more: bigframes.series.Series.any

bigframes.series.Series.apply

apply(
    func, by_row: typing.Union[typing.Literal["compat"], bool] = "compat"
) -> bigframes.series.Series

Invoke function on values of a Series.

See more: bigframes.series.Series.apply

bigframes.series.Series.argmax

argmax() -> int

Return int position of the smallest value in the Series.

See more: bigframes.series.Series.argmax

bigframes.series.Series.argmin

argmin() -> int

Return int position of the largest value in the Series.

See more: bigframes.series.Series.argmin

bigframes.series.Series.astype

astype(
    dtype: typing.Union[
        typing.Literal[
            "boolean",
            "Float64",
            "Int64",
            "int64[pyarrow]",
            "string",
            "string[pyarrow]",
            "timestamp[us, tz=UTC][pyarrow]",
            "timestamp[us][pyarrow]",
            "date32[day][pyarrow]",
            "time64[us][pyarrow]",
            "decimal128(38, 9)[pyarrow]",
            "decimal256(76, 38)[pyarrow]",
            "binary[pyarrow]",
        ],
        pandas.core.arrays.boolean.BooleanDtype,
        pandas.core.arrays.floating.Float64Dtype,
        pandas.core.arrays.integer.Int64Dtype,
        pandas.core.arrays.string_.StringDtype,
        pandas.core.dtypes.dtypes.ArrowDtype,
        geopandas.array.GeometryDtype,
    ]
) -> bigframes.series.Series

Cast a pandas object to a specified dtype dtype.

See more: bigframes.series.Series.astype

bigframes.series.Series.between

between(left, right, inclusive="both")

Return boolean Series equivalent to left <= series <= right.

See more: bigframes.series.Series.between

bigframes.series.Series.bfill

bfill(*, limit: typing.Optional[int] = None) -> bigframes.series.Series

Fill NA/NaN values by using the next valid observation to fill the gap.

See more: bigframes.series.Series.bfill

bigframes.series.Series.clip

clip(lower, upper)

Trim values at input threshold(s).

See more: bigframes.series.Series.clip

bigframes.series.Series.copy

copy() -> bigframes.series.Series

Make a copy of this object's indices and data.

See more: bigframes.series.Series.copy

bigframes.series.Series.corr

corr(other: bigframes.series.Series, method="pearson", min_periods=None) -> float

Compute the correlation with the other Series.

See more: bigframes.series.Series.corr

bigframes.series.Series.count

count() -> int

Return number of non-NA/null observations in the Series.

See more: bigframes.series.Series.count

bigframes.series.Series.cov

cov(other: bigframes.series.Series) -> float

Compute covariance with Series, excluding missing values.

See more: bigframes.series.Series.cov

bigframes.series.Series.cummax

cummax() -> bigframes.series.Series

Return cumulative maximum over a DataFrame or Series axis.

See more: bigframes.series.Series.cummax

bigframes.series.Series.cummin

cummin() -> bigframes.series.Series

Return cumulative minimum over a DataFrame or Series axis.

See more: bigframes.series.Series.cummin

bigframes.series.Series.cumprod

cumprod() -> bigframes.series.Series

Return cumulative product over a DataFrame or Series axis.

See more: bigframes.series.Series.cumprod

bigframes.series.Series.cumsum

cumsum() -> bigframes.series.Series

Return cumulative sum over a DataFrame or Series axis.

See more: bigframes.series.Series.cumsum

bigframes.series.Series.diff

diff(periods: int = 1) -> bigframes.series.Series

First discrete difference of element.

See more: bigframes.series.Series.diff

bigframes.series.Series.div

div(other: float | int | bigframes.series.Series) -> bigframes.series.Series

API documentation for div method.

See more: bigframes.series.Series.div

bigframes.series.Series.divide

divide(other: float | int | bigframes.series.Series) -> bigframes.series.Series

API documentation for divide method.

See more: bigframes.series.Series.divide

bigframes.series.Series.divmod

divmod(other) -> typing.Tuple[bigframes.series.Series, bigframes.series.Series]

Return integer division and modulo of Series and other, element-wise (binary operator divmod).

See more: bigframes.series.Series.divmod

bigframes.series.Series.dot

dot(other)

Compute the dot product between the Series and the columns of other.

See more: bigframes.series.Series.dot

bigframes.series.Series.drop

drop(
    labels: typing.Any = None,
    *,
    axis: typing.Union[int, str] = 0,
    index: typing.Any = None,
    columns: typing.Union[typing.Hashable, typing.Iterable[typing.Hashable]] = None,
    level: typing.Optional[typing.Union[str, int]] = None
) -> bigframes.series.Series

Return Series with specified index labels removed.

See more: bigframes.series.Series.drop

bigframes.series.Series.drop_duplicates

drop_duplicates(*, keep: str = "first") -> bigframes.series.Series

Return Series with duplicate values removed.

See more: bigframes.series.Series.drop_duplicates

bigframes.series.Series.droplevel

droplevel(
    level: typing.Union[str, int, typing.Sequence[typing.Union[str, int]]],
    axis: int | str = 0,
)

Return Series with requested index / column level(s) removed.

See more: bigframes.series.Series.droplevel

bigframes.series.Series.dropna

dropna(
    *,
    axis: int = 0,
    inplace: bool = False,
    how: typing.Optional[str] = None,
    ignore_index: bool = False
) -> bigframes.series.Series

Return a new Series with missing values removed.

See more: bigframes.series.Series.dropna

bigframes.series.Series.duplicated

duplicated(keep: str = "first") -> bigframes.series.Series

Indicate duplicate Series values.

See more: bigframes.series.Series.duplicated

bigframes.series.Series.eq

eq(other: object) -> bigframes.series.Series

Return equal of Series and other, element-wise (binary operator eq).

See more: bigframes.series.Series.eq

bigframes.series.Series.equals

equals(
    other: typing.Union[bigframes.series.Series, bigframes.dataframe.DataFrame]
) -> bool

API documentation for equals method.

See more: bigframes.series.Series.equals

bigframes.series.Series.expanding

expanding(min_periods: int = 1) -> bigframes.core.window.Window

Provide expanding window calculations.

See more: bigframes.series.Series.expanding

bigframes.series.Series.explode

explode(*, ignore_index: typing.Optional[bool] = False) -> bigframes.series.Series

Transform each element of a list-like to a row.

See more: bigframes.series.Series.explode

bigframes.series.Series.ffill

ffill(*, limit: typing.Optional[int] = None) -> bigframes.series.Series

Fill NA/NaN values by propagating the last valid observation to next valid.

See more: bigframes.series.Series.ffill

bigframes.series.Series.fillna

fillna(value=None) -> bigframes.series.Series

Fill NA/NaN values using the specified method.

See more: bigframes.series.Series.fillna

bigframes.series.Series.filter

filter(
    items: typing.Optional[typing.Iterable] = None,
    like: typing.Optional[str] = None,
    regex: typing.Optional[str] = None,
    axis: typing.Optional[typing.Union[str, int]] = None,
) -> bigframes.series.Series

Subset the dataframe rows or columns according to the specified index labels.

See more: bigframes.series.Series.filter

bigframes.series.Series.floordiv

floordiv(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Return integer division of Series and other, element-wise (binary operator floordiv).

See more: bigframes.series.Series.floordiv

bigframes.series.Series.ge

ge(other) -> bigframes.series.Series

Get 'greater than or equal to' of Series and other, element-wise (binary operator >=).

See more: bigframes.series.Series.ge

bigframes.series.Series.get

get(key, default=None)

Get item from object for given key (ex: DataFrame column).

See more: bigframes.series.Series.get

bigframes.series.Series.groupby

groupby(
    by: typing.Union[
        typing.Hashable,
        bigframes.series.Series,
        typing.Sequence[typing.Union[typing.Hashable, bigframes.series.Series]],
    ] = None,
    axis=0,
    level: typing.Optional[
        typing.Union[int, str, typing.Sequence[int], typing.Sequence[str]]
    ] = None,
    as_index: bool = True,
    *,
    dropna: bool = True
) -> bigframes.core.groupby.SeriesGroupBy

Group Series using a mapper or by a Series of columns.

See more: bigframes.series.Series.groupby

bigframes.series.Series.gt

gt(other) -> bigframes.series.Series

Get 'less than or equal to' of Series and other, element-wise (binary operator <=).

See more: bigframes.series.Series.gt

bigframes.series.Series.head

head(n: int = 5) -> bigframes.series.Series

Return the first n rows.

See more: bigframes.series.Series.head

bigframes.series.Series.idxmax

idxmax() -> typing.Hashable

Return the row label of the maximum value.

See more: bigframes.series.Series.idxmax

bigframes.series.Series.idxmin

idxmin() -> typing.Hashable

Return the row label of the minimum value.

See more: bigframes.series.Series.idxmin

bigframes.series.Series.interpolate

interpolate(method: str = "linear") -> bigframes.series.Series

Fill NaN values using an interpolation method.

See more: bigframes.series.Series.interpolate

bigframes.series.Series.isin

isin(values) -> "Series" | None

Whether elements in Series are contained in values.

See more: bigframes.series.Series.isin

bigframes.series.Series.isna

isna() -> bigframes.series.Series

Detect missing values.

See more: bigframes.series.Series.isna

bigframes.series.Series.isnull

isnull() -> bigframes.series.Series

Detect missing values.

See more: bigframes.series.Series.isnull

bigframes.series.Series.kurt

kurt()

Return unbiased kurtosis over requested axis.

See more: bigframes.series.Series.kurt

bigframes.series.Series.kurtosis

kurtosis()

API documentation for kurtosis method.

See more: bigframes.series.Series.kurtosis

bigframes.series.Series.le

le(other) -> bigframes.series.Series

Get 'less than or equal to' of Series and other, element-wise (binary operator <=).

See more: bigframes.series.Series.le

bigframes.series.Series.lt

lt(other) -> bigframes.series.Series

Get 'less than' of Series and other, element-wise (binary operator <).

See more: bigframes.series.Series.lt

bigframes.series.Series.map

map(
    arg: typing.Union[typing.Mapping, bigframes.series.Series],
    na_action: typing.Optional[str] = None,
    *,
    verify_integrity: bool = False
) -> bigframes.series.Series

Map values of Series according to an input mapping or function.

See more: bigframes.series.Series.map

bigframes.series.Series.mask

mask(cond, other=None) -> bigframes.series.Series

Replace values where the condition is True.

See more: bigframes.series.Series.mask

bigframes.series.Series.max

max() -> typing.Any

Return the maximum of the values over the requested axis.

See more: bigframes.series.Series.max

bigframes.series.Series.mean

mean() -> float

Return the mean of the values over the requested axis.

See more: bigframes.series.Series.mean

bigframes.series.Series.median

median(*, exact: bool = False) -> float

Return the median of the values over the requested axis.

See more: bigframes.series.Series.median

bigframes.series.Series.min

min() -> typing.Any

Return the maximum of the values over the requested axis.

See more: bigframes.series.Series.min

bigframes.series.Series.mod

mod(other) -> bigframes.series.Series

Return modulo of Series and other, element-wise (binary operator mod).

See more: bigframes.series.Series.mod

bigframes.series.Series.mode

mode() -> bigframes.series.Series

Return the mode(s) of the Series.

See more: bigframes.series.Series.mode

bigframes.series.Series.mul

mul(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Return multiplication of Series and other, element-wise (binary operator mul).

See more: bigframes.series.Series.mul

bigframes.series.Series.multiply

multiply(other: float | int | bigframes.series.Series) -> bigframes.series.Series

API documentation for multiply method.

See more: bigframes.series.Series.multiply

bigframes.series.Series.ne

ne(other: object) -> bigframes.series.Series

Return not equal of Series and other, element-wise (binary operator ne).

See more: bigframes.series.Series.ne

bigframes.series.Series.nlargest

nlargest(n: int = 5, keep: str = "first") -> bigframes.series.Series

Return the largest n elements.

See more: bigframes.series.Series.nlargest

bigframes.series.Series.notna

notna() -> bigframes.series.Series

Detect existing (non-missing) values.

See more: bigframes.series.Series.notna

bigframes.series.Series.notnull

notnull() -> bigframes.series.Series

Detect existing (non-missing) values.

See more: bigframes.series.Series.notnull

bigframes.series.Series.nsmallest

nsmallest(n: int = 5, keep: str = "first") -> bigframes.series.Series

Return the smallest n elements.

See more: bigframes.series.Series.nsmallest

bigframes.series.Series.nunique

nunique() -> int

Return number of unique elements in the object.

See more: bigframes.series.Series.nunique

bigframes.series.Series.pad

pad(*, limit: typing.Optional[int] = None) -> bigframes.series.Series

API documentation for pad method.

See more: bigframes.series.Series.pad

bigframes.series.Series.pct_change

pct_change(periods: int = 1) -> bigframes.series.Series

Fractional change between the current and a prior element.

See more: bigframes.series.Series.pct_change

bigframes.series.Series.pipe

pipe(func: Callable[..., T] | tuple[Callable[..., T], str], *args, **kwargs) -> T

Apply chainable functions that expect Series or DataFrames.

See more: bigframes.series.Series.pipe

bigframes.series.Series.pow

pow(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Return Exponential power of series and other, element-wise (binary operator pow).

See more: bigframes.series.Series.pow

bigframes.series.Series.prod

prod() -> float

Return the product of the values over the requested axis.

See more: bigframes.series.Series.prod

bigframes.series.Series.product

product() -> float

API documentation for product method.

See more: bigframes.series.Series.product

bigframes.series.Series.radd

radd(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Return addition of Series and other, element-wise (binary operator radd).

See more: bigframes.series.Series.radd

bigframes.series.Series.rank

rank(
    axis=0,
    method: str = "average",
    numeric_only=False,
    na_option: str = "keep",
    ascending: bool = True,
) -> bigframes.series.Series

Compute numerical data ranks (1 through n) along axis.

See more: bigframes.series.Series.rank

bigframes.series.Series.rdiv

rdiv(other: float | int | bigframes.series.Series) -> bigframes.series.Series

API documentation for rdiv method.

See more: bigframes.series.Series.rdiv

bigframes.series.Series.rdivmod

rdivmod(other) -> typing.Tuple[bigframes.series.Series, bigframes.series.Series]

Return integer division and modulo of Series and other, element-wise (binary operator rdivmod).

See more: bigframes.series.Series.rdivmod

bigframes.series.Series.reindex

reindex(index=None, *, validate: typing.Optional[bool] = None)

Conform Series to new index with optional filling logic.

See more: bigframes.series.Series.reindex

bigframes.series.Series.reindex_like

reindex_like(
    other: bigframes.series.Series, *, validate: typing.Optional[bool] = None
)

Return an object with matching indices as other object.

See more: bigframes.series.Series.reindex_like

bigframes.series.Series.rename

rename(
    index: typing.Union[typing.Hashable, typing.Mapping[typing.Any, typing.Any]] = None,
    **kwargs
) -> bigframes.series.Series

Alter Series index labels or name.

See more: bigframes.series.Series.rename

bigframes.series.Series.rename_axis

rename_axis(
    mapper: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]], **kwargs
) -> bigframes.series.Series

Set the name of the axis for the index or columns.

See more: bigframes.series.Series.rename_axis

bigframes.series.Series.reorder_levels

reorder_levels(
    order: typing.Union[str, int, typing.Sequence[typing.Union[str, int]]],
    axis: int | str = 0,
)

Rearrange index levels using input order.

See more: bigframes.series.Series.reorder_levels

bigframes.series.Series.replace

replace(to_replace: typing.Any, value: typing.Any = None, *, regex: bool = False)

Replace values given in to_replace with value.

See more: bigframes.series.Series.replace

bigframes.series.Series.reset_index

reset_index(
    *, name: typing.Optional[str] = None, drop: bool = False
) -> bigframes.dataframe.DataFrame | bigframes.series.Series

Generate a new DataFrame or Series with the index reset.

See more: bigframes.series.Series.reset_index

bigframes.series.Series.rfloordiv

rfloordiv(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Return integer division of Series and other, element-wise (binary operator rfloordiv).

See more: bigframes.series.Series.rfloordiv

bigframes.series.Series.rmod

rmod(other) -> bigframes.series.Series

Return modulo of Series and other, element-wise (binary operator mod).

See more: bigframes.series.Series.rmod

bigframes.series.Series.rmul

rmul(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Return multiplication of Series and other, element-wise (binary operator mul).

See more: bigframes.series.Series.rmul

bigframes.series.Series.rolling

rolling(window: int, min_periods=None) -> bigframes.core.window.Window

Provide rolling window calculations.

See more: bigframes.series.Series.rolling

bigframes.series.Series.round

round(decimals=0) -> bigframes.series.Series

Round each value in a Series to the given number of decimals.

See more: bigframes.series.Series.round

bigframes.series.Series.rpow

rpow(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Return Exponential power of series and other, element-wise (binary operator rpow).

See more: bigframes.series.Series.rpow

bigframes.series.Series.rsub

rsub(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Return subtraction of Series and other, element-wise (binary operator rsub).

See more: bigframes.series.Series.rsub

bigframes.series.Series.rtruediv

rtruediv(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Return floating division of Series and other, element-wise (binary operator rtruediv).

See more: bigframes.series.Series.rtruediv

bigframes.series.Series.sample

sample(
    n: typing.Optional[int] = None,
    frac: typing.Optional[float] = None,
    *,
    random_state: typing.Optional[int] = None,
    sort: typing.Optional[typing.Union[bool, typing.Literal["random"]]] = "random"
) -> bigframes.series.Series

Return a random sample of items from an axis of object.

See more: bigframes.series.Series.sample

bigframes.series.Series.shift

shift(periods: int = 1) -> bigframes.series.Series

Shift index by desired number of periods.

See more: bigframes.series.Series.shift

bigframes.series.Series.skew

skew()

Return unbiased skew over requested axis.

See more: bigframes.series.Series.skew

bigframes.series.Series.sort_index

sort_index(
    *, axis=0, ascending=True, na_position="last"
) -> bigframes.series.Series

Sort Series by index labels.

See more: bigframes.series.Series.sort_index

bigframes.series.Series.sort_values

sort_values(
    *, axis=0, ascending=True, kind: str = "quicksort", na_position="last"
) -> bigframes.series.Series

Sort by the values.

See more: bigframes.series.Series.sort_values

bigframes.series.Series.std

std() -> float

Return sample standard deviation over requested axis.

See more: bigframes.series.Series.std

bigframes.series.Series.sub

sub(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Return subtraction of Series and other, element-wise (binary operator sub).

See more: bigframes.series.Series.sub

bigframes.series.Series.subtract

subtract(other: float | int | bigframes.series.Series) -> bigframes.series.Series

API documentation for subtract method.

See more: bigframes.series.Series.subtract

bigframes.series.Series.sum

sum() -> float

Return the sum of the values over the requested axis.

See more: bigframes.series.Series.sum

bigframes.series.Series.swaplevel

swaplevel(i: int = -2, j: int = -1)

Swap levels i and j in a MultiIndex.

See more: bigframes.series.Series.swaplevel

bigframes.series.Series.tail

tail(n: int = 5) -> bigframes.series.Series

Return the last n rows.

See more: bigframes.series.Series.tail

bigframes.series.Series.to_csv

to_csv(
    path_or_buf: str, sep=",", *, header: bool = True, index: bool = True
) -> None

Write object to a comma-separated values (csv) file on Cloud Storage.

See more: bigframes.series.Series.to_csv

bigframes.series.Series.to_dict

to_dict(into: type[dict] = 

Convert Series to {label -> value} dict or dict-like object.

See more: bigframes.series.Series.to_dict

bigframes.series.Series.to_excel

to_excel(excel_writer, sheet_name="Sheet1", **kwargs) -> None

Write Series to an Excel sheet.

See more: bigframes.series.Series.to_excel

bigframes.series.Series.to_frame

to_frame(name: typing.Hashable = None) -> bigframes.dataframe.DataFrame

Convert Series to DataFrame.

See more: bigframes.series.Series.to_frame

bigframes.series.Series.to_json

to_json(
    path_or_buf: str,
    orient: typing.Literal[
        "split", "records", "index", "columns", "values", "table"
    ] = "columns",
    *,
    lines: bool = False,
    index: bool = True
) -> None

Convert the object to a JSON string, written to Cloud Storage.

See more: bigframes.series.Series.to_json

bigframes.series.Series.to_latex

to_latex(
    buf=None, columns=None, header=True, index=True, **kwargs
) -> typing.Optional[str]

Render object to a LaTeX tabular, longtable, or nested table.

See more: bigframes.series.Series.to_latex

bigframes.series.Series.to_list

to_list() -> list

Return a list of the values.

See more: bigframes.series.Series.to_list

bigframes.series.Series.to_markdown

to_markdown(
    buf: typing.Optional[typing.IO[str]] = None,
    mode: str = "wt",
    index: bool = True,
    **kwargs
) -> typing.Optional[str]

Print {klass} in Markdown-friendly format.

See more: bigframes.series.Series.to_markdown

bigframes.series.Series.to_numpy

to_numpy(dtype=None, copy=False, na_value=None, **kwargs) -> numpy.ndarray

A NumPy ndarray representing the values in this Series or Index.

See more: bigframes.series.Series.to_numpy

bigframes.series.Series.to_pandas

to_pandas(
    max_download_size: typing.Optional[int] = None,
    sampling_method: typing.Optional[str] = None,
    random_state: typing.Optional[int] = None,
    *,
    ordered: bool = True
) -> pandas.core.series.Series

Writes Series to pandas Series.

See more: bigframes.series.Series.to_pandas

bigframes.series.Series.to_pickle

to_pickle(path, **kwargs) -> None

Pickle (serialize) object to file.

See more: bigframes.series.Series.to_pickle

bigframes.series.Series.to_string

to_string(
    buf=None,
    na_rep="NaN",
    float_format=None,
    header=True,
    index=True,
    length=False,
    dtype=False,
    name=False,
    max_rows=None,
    min_rows=None,
) -> typing.Optional[str]

Render a string representation of the Series.

See more: bigframes.series.Series.to_string

bigframes.series.Series.to_xarray

to_xarray()

Return an xarray object from the pandas object.

See more: bigframes.series.Series.to_xarray

bigframes.series.Series.tolist

tolist() -> list

Return a list of the values.

See more: bigframes.series.Series.tolist

bigframes.series.Series.transpose

transpose() -> bigframes.series.Series

Return the transpose, which is by definition self.

See more: bigframes.series.Series.transpose

bigframes.series.Series.truediv

truediv(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Return floating division of Series and other, element-wise (binary operator truediv).

See more: bigframes.series.Series.truediv

bigframes.series.Series.unique

unique() -> bigframes.series.Series

Return unique values of Series object.

See more: bigframes.series.Series.unique

bigframes.series.Series.unstack

unstack(
    level: typing.Union[str, int, typing.Sequence[typing.Union[str, int]]] = -1
)

Unstack, also known as pivot, Series with MultiIndex to produce DataFrame.

See more: bigframes.series.Series.unstack

bigframes.series.Series.value_counts

value_counts(
    normalize: bool = False,
    sort: bool = True,
    ascending: bool = False,
    *,
    dropna: bool = True
)

Return a Series containing counts of unique values.

See more: bigframes.series.Series.value_counts

bigframes.series.Series.var

var() -> float

Return unbiased variance over requested axis.

See more: bigframes.series.Series.var

bigframes.series.Series.where

where(cond, other=None)

Replace values where the condition is False.

See more: bigframes.series.Series.where

bigframes.session.Session.close

close()

No-op.

See more: bigframes.session.Session.close

bigframes.session.Session.read_csv

read_csv(
    filepath_or_buffer: str | IO["bytes"],
    *,
    sep: Optional[str] = ",",
    header: Optional[int] = 0,
    names: Optional[
        Union[MutableSequence[Any], np.ndarray[Any, Any], Tuple[Any, ...], range]
    ] = None,
    index_col: Optional[
        Union[int, str, Sequence[Union[str, int]], Literal[False]]
    ] = None,
    usecols: Optional[
        Union[
            MutableSequence[str],
            Tuple[str, ...],
            Sequence[int],
            pandas.Series,
            pandas.Index,
            np.ndarray[Any, Any],
            Callable[[Any], bool],
        ]
    ] = None,
    dtype: Optional[Dict] = None,
    engine: Optional[
        Literal["c", "python", "pyarrow", "python-fwf", "bigquery"]
    ] = None,
    encoding: Optional[str] = None,
    **kwargs
) -> dataframe.DataFrame

Loads DataFrame from comma-separated values (csv) file locally or from Cloud Storage.

See more: bigframes.session.Session.read_csv

bigframes.session.Session.read_gbq

read_gbq(
    query_or_table: str,
    *,
    index_col: Iterable[str] | str = (),
    columns: Iterable[str] = (),
    configuration: Optional[Dict] = None,
    max_results: Optional[int] = None,
    filters: third_party_pandas_gbq.FiltersType = (),
    use_cache: Optional[bool] = None,
    col_order: Iterable[str] = ()
) -> dataframe.DataFrame

Loads a DataFrame from BigQuery.

See more: bigframes.session.Session.read_gbq

bigframes.session.Session.read_gbq_function

read_gbq_function(function_name: str)

Loads a BigQuery function from BigQuery.

See more: bigframes.session.Session.read_gbq_function

bigframes.session.Session.read_gbq_model

read_gbq_model(model_name: str)

Loads a BigQuery ML model from BigQuery.

See more: bigframes.session.Session.read_gbq_model

bigframes.session.Session.read_gbq_query

read_gbq_query(
    query: str,
    *,
    index_col: Iterable[str] | str = (),
    columns: Iterable[str] = (),
    configuration: Optional[Dict] = None,
    max_results: Optional[int] = None,
    use_cache: Optional[bool] = None,
    col_order: Iterable[str] = ()
) -> dataframe.DataFrame

Turn a SQL query into a DataFrame.

See more: bigframes.session.Session.read_gbq_query

bigframes.session.Session.read_gbq_table

read_gbq_table(
    query: str,
    *,
    index_col: Iterable[str] | str = (),
    columns: Iterable[str] = (),
    max_results: Optional[int] = None,
    filters: third_party_pandas_gbq.FiltersType = (),
    use_cache: bool = True,
    col_order: Iterable[str] = ()
) -> dataframe.DataFrame

Turn a BigQuery table into a DataFrame.

See more: bigframes.session.Session.read_gbq_table

bigframes.session.Session.read_json

read_json(
    path_or_buf: str | IO["bytes"],
    *,
    orient: Literal[
        "split", "records", "index", "columns", "values", "table"
    ] = "columns",
    dtype: Optional[Dict] = None,
    encoding: Optional[str] = None,
    lines: bool = False,
    engine: Literal["ujson", "pyarrow", "bigquery"] = "ujson",
    **kwargs
) -> dataframe.DataFrame

Convert a JSON string to DataFrame object.

See more: bigframes.session.Session.read_json

bigframes.session.Session.read_pandas

Loads DataFrame from a pandas DataFrame.

See more: bigframes.session.Session.read_pandas

bigframes.session.Session.read_parquet

read_parquet(
    path: str | IO["bytes"], *, engine: str = "auto"
) -> dataframe.DataFrame

Load a Parquet object from the file path (local or Cloud Storage), returning a DataFrame.

See more: bigframes.session.Session.read_parquet

bigframes.session.Session.read_pickle

read_pickle(
    filepath_or_buffer: FilePath | ReadPickleBuffer,
    compression: CompressionOptions = "infer",
    storage_options: StorageOptions = None,
)

Load pickled BigFrames object (or any object) from file.

See more: bigframes.session.Session.read_pickle

bigframes.session.Session.remote_function

remote_function(
    input_types: typing.List[type],
    output_type: type,
    dataset: typing.Optional[str] = None,
    bigquery_connection: typing.Optional[str] = None,
    reuse: bool = True,
    name: typing.Optional[str] = None,
    packages: typing.Optional[typing.Sequence[str]] = None,
    cloud_function_service_account: typing.Optional[str] = None,
    cloud_function_kms_key_name: typing.Optional[str] = None,
    cloud_function_docker_repository: typing.Optional[str] = None,
)

Decorator to turn a user defined function into a BigQuery remote function.

See more: bigframes.session.Session.remote_function