Package Methods (2.30.0)

Summary of entries of Methods for bigframes.

bigframes.bigquery.approx_top_count

approx_top_count(series, number)

Returns the approximate top elements of expression as an array of STRUCTs.

See more: bigframes.bigquery.approx_top_count

bigframes.bigquery.array_agg

array_agg(obj)

Group data and create arrays from selected columns, omitting NULLs to avoid BigQuery errors (NULLs not allowed in arrays).

See more: bigframes.bigquery.array_agg

bigframes.bigquery.array_length

array_length(series)

Compute the length of each array element in the Series.

See more: bigframes.bigquery.array_length

bigframes.bigquery.array_to_string

array_to_string(series, delimiter)

Converts array elements within a Series into delimited strings.

See more: bigframes.bigquery.array_to_string

bigframes.bigquery.create_vector_index

create_vector_index(
    table_id,
    column_name,
    *,
    replace=False,
    index_name=None,
    distance_type="cosine",
    stored_column_names=(),
    index_type="ivf",
    ivf_options=None,
    tree_ah_options=None,
    session=None
)

Creates a new vector index on a column of a table.

See more: bigframes.bigquery.create_vector_index

bigframes.bigquery.json_extract

json_extract(input, json_path)

Extracts a JSON value and converts it to a SQL JSON-formatted STRING or JSON value.

See more: bigframes.bigquery.json_extract

bigframes.bigquery.json_extract_array

json_extract_array(input, json_path="$")

Extracts a JSON array and converts it to a SQL array of JSON-formatted STRING or JSON values.

See more: bigframes.bigquery.json_extract_array

bigframes.bigquery.json_extract_string_array

json_extract_string_array(input, json_path="$", value_dtype=None)

Extracts a JSON array and converts it to a SQL array of STRING values.

See more: bigframes.bigquery.json_extract_string_array

bigframes.bigquery.json_keys

json_keys(input, max_depth=None)

Returns all keys in the root of a JSON object as an ARRAY of STRINGs.

See more: bigframes.bigquery.json_keys

bigframes.bigquery.json_query

json_query(input, json_path)

Extracts a JSON value and converts it to a SQL JSON-formatted STRING or JSON value.

See more: bigframes.bigquery.json_query

bigframes.bigquery.json_query_array

json_query_array(input, json_path="$")

Extracts a JSON array and converts it to a SQL array of JSON-formatted STRING or JSON values.

See more: bigframes.bigquery.json_query_array

bigframes.bigquery.json_set

json_set(input, json_path_value_pairs)

Produces a new JSON value within a Series by inserting or replacing values at specified paths.

See more: bigframes.bigquery.json_set

bigframes.bigquery.json_value

json_value(input, json_path="$")

Extracts a JSON scalar value and converts it to a SQL STRING value.

See more: bigframes.bigquery.json_value

bigframes.bigquery.json_value_array

json_value_array(input, json_path="$")

Extracts a JSON array of scalar values and converts it to a SQL ARRAY<STRING> value.

See more: bigframes.bigquery.json_value_array

bigframes.bigquery.parse_json

parse_json(input)

Converts a series with a JSON-formatted STRING value to a JSON value.

See more: bigframes.bigquery.parse_json

bigframes.bigquery.sql_scalar

sql_scalar(sql_template, columns)

Create a Series from a SQL template.

See more: bigframes.bigquery.sql_scalar

bigframes.bigquery.st_area

st_area(series)

Returns the area in square meters covered by the polygons in the input GEOGRAPHY.

See more: bigframes.bigquery.st_area

bigframes.bigquery.st_buffer

st_buffer(series, buffer_radius, num_seg_quarter_circle=8.0, use_spheroid=False)

Computes a GEOGRAPHY that represents all points whose distance from the input GEOGRAPHY is less than or equal to distance meters.

See more: bigframes.bigquery.st_buffer

bigframes.bigquery.st_centroid

st_centroid(series)

Computes the geometric centroid of a GEOGRAPHY type.

See more: bigframes.bigquery.st_centroid

bigframes.bigquery.st_convexhull

st_convexhull(series)

Computes the convex hull of a GEOGRAPHY type.

See more: bigframes.bigquery.st_convexhull

bigframes.bigquery.st_difference

st_difference(series, other)

Returns a GEOGRAPHY that represents the point set difference of geography_1 and geography_2.

See more: bigframes.bigquery.st_difference

bigframes.bigquery.st_distance

st_distance(series, other, *, use_spheroid=False)

Returns the shortest distance in meters between two non-empty GEOGRAPHY objects.

See more: bigframes.bigquery.st_distance

bigframes.bigquery.st_intersection

st_intersection(series, other)

Returns a GEOGRAPHY that represents the point set intersection of the two input GEOGRAPHYs.

See more: bigframes.bigquery.st_intersection

bigframes.bigquery.st_isclosed

st_isclosed(series)

Returns TRUE for a non-empty Geography, where each element in the Geography has an empty boundary.

See more: bigframes.bigquery.st_isclosed

bigframes.bigquery.st_length

st_length(series, *, use_spheroid=False)

Returns the total length in meters of the lines in the input GEOGRAPHY.

See more: bigframes.bigquery.st_length

bigframes.bigquery.st_regionstats

st_regionstats(geography, raster_id, band=None, include=None, options=None)

Returns statistics summarizing the pixel values of the raster image referenced by raster_id that intersect with geography.

See more: bigframes.bigquery.st_regionstats

bigframes.bigquery.st_simplify

st_simplify(geography, tolerance_meters)

Returns a simplified version of the input geography.

See more: bigframes.bigquery.st_simplify

bigframes.bigquery.struct

struct(value)

Takes a DataFrame and converts it into a Series of structs with each struct entry corresponding to a DataFrame row and each struct field corresponding to a DataFrame column.

See more: bigframes.bigquery.struct

bigframes.bigquery.to_json

to_json(input)

Converts a series with a JSON value to a JSON-formatted STRING value.

See more: bigframes.bigquery.to_json

bigframes.bigquery.to_json_string

to_json_string(input)

Converts a series to a JSON-formatted STRING value.

See more: bigframes.bigquery.to_json_string

bigframes.bigquery.unix_micros

unix_micros(input)

Converts a timestmap series to unix epoch microseconds.

See more: bigframes.bigquery.unix_micros

bigframes.bigquery.unix_millis

unix_millis(input)

Converts a timestmap series to unix epoch milliseconds.

See more: bigframes.bigquery.unix_millis

bigframes.bigquery.unix_seconds

unix_seconds(input)

Converts a timestmap series to unix epoch seconds.

See more: bigframes.bigquery.unix_seconds

bigframes.bigquery.vector_search

vector_search(
    base_table,
    column_to_search,
    query,
    *,
    query_column_to_search=None,
    top_k=None,
    distance_type=None,
    fraction_lists_to_search=None,
    use_brute_force=None,
    allow_large_results=None
)

Conduct vector search which searches embeddings to find semantically similar entities.

See more: bigframes.bigquery.vector_search

bigframes.bigquery.ai.classify

classify(input, categories, *, connection_id=None)

Classifies a given input into one of the specified categories.

See more: bigframes.bigquery.ai.classify

bigframes.bigquery.ai.forecast

forecast(
    df,
    *,
    data_col,
    timestamp_col,
    model="TimesFM 2.0",
    id_cols=None,
    horizon=10,
    confidence_level=0.95,
    context_window=None
)

Forecast time series at future horizon.

See more: bigframes.bigquery.ai.forecast

bigframes.bigquery.ai.generate

generate(
    prompt,
    *,
    connection_id=None,
    endpoint=None,
    request_type="unspecified",
    model_params=None,
    output_schema=None
)

Returns the AI analysis based on the prompt, which can be any combination of text and unstructured data.

See more: bigframes.bigquery.ai.generate

bigframes.bigquery.ai.generate_bool

generate_bool(
    prompt,
    *,
    connection_id=None,
    endpoint=None,
    request_type="unspecified",
    model_params=None
)

Returns the AI analysis based on the prompt, which can be any combination of text and unstructured data.

See more: bigframes.bigquery.ai.generate_bool

bigframes.bigquery.ai.generate_double

generate_double(
    prompt,
    *,
    connection_id=None,
    endpoint=None,
    request_type="unspecified",
    model_params=None
)

Returns the AI analysis based on the prompt, which can be any combination of text and unstructured data.

See more: bigframes.bigquery.ai.generate_double

bigframes.bigquery.ai.generate_int

generate_int(
    prompt,
    *,
    connection_id=None,
    endpoint=None,
    request_type="unspecified",
    model_params=None
)

Returns the AI analysis based on the prompt, which can be any combination of text and unstructured data.

See more: bigframes.bigquery.ai.generate_int

bigframes.bigquery.ai.if_

if_(prompt, *, connection_id=None)

Evaluates the prompt to True or False.

See more: bigframes.bigquery.ai.if_

bigframes.bigquery.ai.score

score(prompt, *, connection_id=None)

Computes a score based on rubrics described in natural language.

See more: bigframes.bigquery.ai.score

bigframes.exceptions.format_message

format_message(message, fill=True)

[Private] Formats a warning message.

See more: bigframes.exceptions.format_message

bigframes.ml.compose.cast

cast(typ, val)

Cast a value to a type.

See more: bigframes.ml.compose.cast

bigframes.ml.imported.cast

cast(typ, val)

Cast a value to a type.

See more: bigframes.ml.imported.cast

bigframes.ml.llm.cast

cast(typ, val)

Cast a value to a type.

See more: bigframes.ml.llm.cast

bigframes.ml.model_selection.cast

cast(typ, val)

Cast a value to a type.

See more: bigframes.ml.model_selection.cast

bigframes.ml.model_selection.cross_validate

cross_validate(estimator, X, y=None, *, cv=None)

Evaluate metric(s) by cross-validation and also record fit/score times.

See more: bigframes.ml.model_selection.cross_validate

bigframes.ml.model_selection.train_test_split

train_test_split(
    *arrays,
    test_size=None,
    train_size=None,
    random_state=None,
    stratify=None,
    shuffle=True
)

Splits dataframes or series into random train and test subsets.

See more: bigframes.ml.model_selection.train_test_split

bigframes.ml.preprocessing.cast

cast(typ, val)

Cast a value to a type.

See more: bigframes.ml.preprocessing.cast

bigframes.pandas.clean_up_by_session_id

clean_up_by_session_id(session_id, location=None, project=None)

Searches through BigQuery tables and routines and deletes the ones created during the session with the given session id.

See more: bigframes.pandas.clean_up_by_session_id

bigframes.pandas.close_session

close_session()

Start a fresh session the next time a function requires a session.

See more: bigframes.pandas.close_session

bigframes.pandas.concat

Concatenate BigQuery DataFrames objects along a particular axis.

See more: bigframes.pandas.concat

bigframes.pandas.crosstab

crosstab(
    index,
    columns,
    values=None,
    rownames=None,
    colnames=None,
    aggfunc=None,
    *,
    session=None
)

Compute a simple cross tabulation of two (or more) factors.

See more: bigframes.pandas.crosstab

bigframes.pandas.cut

cut(x, bins, *, right=True, labels=None, session=None)

Bin values into discrete intervals.

See more: bigframes.pandas.cut

bigframes.pandas.deploy_remote_function

deploy_remote_function(func, **kwargs)

Orchestrates the creation of a BigQuery remote function that deploys immediately.

See more: bigframes.pandas.deploy_remote_function

bigframes.pandas.deploy_udf

deploy_udf(func, **kwargs)

Orchestrates the creation of a BigQuery UDF that deploys immediately.

See more: bigframes.pandas.deploy_udf

bigframes.pandas.from_glob_path

from_glob_path(path, *, connection=None, name=None)

Create a BigFrames DataFrame that contains a BigFrames Blob column from a global wildcard path.

See more: bigframes.pandas.from_glob_path

bigframes.pandas.get_default_session_id

get_default_session_id()

Gets the session id that is used whenever a custom session has not been provided.

See more: bigframes.pandas.get_default_session_id

bigframes.pandas.get_dummies

get_dummies(
    data,
    prefix=None,
    prefix_sep="_",
    dummy_na=False,
    columns=None,
    drop_first=False,
    dtype=None,
)

Convert categorical variable into dummy/indicator variables.

See more: bigframes.pandas.get_dummies

bigframes.pandas.get_global_session

get_global_session()

Gets the global session.

See more: bigframes.pandas.get_global_session

bigframes.pandas.merge

merge(
    left,
    right,
    how="inner",
    on=None,
    *,
    left_on=None,
    right_on=None,
    left_index=False,
    right_index=False,
    sort=False,
    suffixes=("_x", "_y")
)

Merge DataFrame objects with a database-style join.

See more: bigframes.pandas.merge

bigframes.pandas.qcut

qcut(x, q, *, labels=None, duplicates="error")

Quantile-based discretization function.

See more: bigframes.pandas.qcut

bigframes.pandas.read_arrow

read_arrow(pa_table)

Load a PyArrow Table to a BigQuery DataFrames DataFrame.

See more: bigframes.pandas.read_arrow

bigframes.pandas.read_csv

read_csv(
    filepath_or_buffer,
    *,
    sep=",",
    header=0,
    names=None,
    index_col=None,
    usecols=None,
    dtype=None,
    engine=None,
    encoding=None,
    write_engine="default",
    **kwargs
)

Loads data from a comma-separated values (csv) file into a DataFrame.

See more: bigframes.pandas.read_csv

bigframes.pandas.read_gbq

Loads a DataFrame from BigQuery.

See more: bigframes.pandas.read_gbq

bigframes.pandas.read_gbq_function

read_gbq_function(function_name, is_row_processor=False)

Loads a BigQuery function from BigQuery.

See more: bigframes.pandas.read_gbq_function

bigframes.pandas.read_gbq_model

read_gbq_model(model_name)

Loads a BigQuery ML model from BigQuery.

See more: bigframes.pandas.read_gbq_model

bigframes.pandas.read_gbq_object_table

read_gbq_object_table(object_table, *, name=None)

Read an existing object table to create a BigFrames Blob DataFrame.

See more: bigframes.pandas.read_gbq_object_table

bigframes.pandas.read_gbq_query

Turn a SQL query into a DataFrame.

See more: bigframes.pandas.read_gbq_query

bigframes.pandas.read_gbq_table

Turn a BigQuery table into a DataFrame.

See more: bigframes.pandas.read_gbq_table

bigframes.pandas.read_json

read_json(
    path_or_buf,
    *,
    orient="columns",
    dtype=None,
    encoding=None,
    lines=False,
    engine="ujson",
    write_engine="default",
    **kwargs
)

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, *, engine="auto", write_engine="default")

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,
    compression="infer",
    storage_options=None,
    *,
    write_engine="default"
)

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

See more: bigframes.pandas.read_pickle

bigframes.pandas.remote_function

remote_function(
    input_types=None,
    output_type=None,
    dataset=None,
    *,
    bigquery_connection=None,
    reuse=True,
    name=None,
    packages=None,
    cloud_function_service_account,
    cloud_function_kms_key_name=None,
    cloud_function_docker_repository=None,
    max_batching_rows=1000,
    cloud_function_timeout=600,
    cloud_function_max_instances=None,
    cloud_function_vpc_connector=None,
    cloud_function_vpc_connector_egress_settings=None,
    cloud_function_memory_mib=1024,
    cloud_function_ingress_settings="internal-only",
    cloud_build_service_account=None
)

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

See more: bigframes.pandas.remote_function

bigframes.pandas.reset_session

reset_session()

Start a fresh session the next time a function requires a session.

See more: bigframes.pandas.reset_session

bigframes.pandas.to_datetime

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

See more: bigframes.pandas.to_datetime

bigframes.pandas.to_timedelta

to_timedelta(arg, unit=None, *, session=None)

Converts a scalar or Series to a timedelta object.

See more: bigframes.pandas.to_timedelta

bigframes.pandas.udf

udf(
    *,
    input_types=None,
    output_type=None,
    dataset,
    bigquery_connection=None,
    name,
    packages=None,
    max_batching_rows=None,
    container_cpu=None,
    container_memory=None
)

Decorator to turn a Python user defined function (udf) into a BigQuery managed user-defined function.

See more: bigframes.pandas.udf

bigframes.streaming.read_gbq_table

read_gbq_table(table)

Turn a BigQuery table into a StreamingDataFrame.

See more: bigframes.streaming.read_gbq_table

bigframes._config.BigQueryOptions

BigQueryOptions(
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    bq_connection: typing.Optional[str] = None,
    use_regional_endpoints: bool = False,
    application_name: typing.Optional[str] = None,
    kms_key_name: typing.Optional[str] = None,
    skip_bq_connection_check: bool = False,
    *,
    allow_large_results: bool = False,
    ordering_mode: typing.Literal["strict", "partial"] = "strict",
    client_endpoints_override: typing.Optional[dict] = None,
    requests_transport_adapters: typing.Sequence[
        typing.Tuple[str, requests.adapters.BaseAdapter]
    ] = (),
    enable_polars_execution: bool = False
)

Encapsulates configuration for working with a session.

See more: bigframes._config.BigQueryOptions

bigframes._config.BigQueryOptions.__init__

__init__(
    credentials=None,
    project=None,
    location=None,
    bq_connection=None,
    use_regional_endpoints=False,
    application_name=None,
    kms_key_name=None,
    skip_bq_connection_check=False,
    *,
    allow_large_results=False,
    ordering_mode="strict",
    client_endpoints_override=None,
    requests_transport_adapters=(),
    enable_polars_execution=False
)

Initialize self.

See more: bigframes.config.BigQueryOptions._init

bigframes._config.ComputeOptions

ComputeOptions(
    ai_ops_confirmation_threshold: typing.Optional[int] = 0,
    ai_ops_threshold_autofail: bool = False,
    allow_large_results: typing.Optional[bool] = None,
    enable_multi_query_execution: bool = False,
    maximum_bytes_billed: typing.Optional[int] = None,
    maximum_result_rows: typing.Optional[int] = None,
    semantic_ops_confirmation_threshold: typing.Optional[int] = 0,
)

Encapsulates the configuration for compute options.

See more: bigframes._config.ComputeOptions

bigframes._config.ComputeOptions.__init__

__init__(
    ai_ops_confirmation_threshold=0,
    ai_ops_threshold_autofail=False,
    allow_large_results=None,
    enable_multi_query_execution=False,
    maximum_bytes_billed=None,
    maximum_result_rows=None,
    semantic_ops_confirmation_threshold=0,
)

Initialize self.

See more: bigframes.config.ComputeOptions._init

bigframes._config.ComputeOptions.assign_extra_query_labels

assign_extra_query_labels(**kwargs)

Assigns additional custom labels for query configuration.

See more: bigframes._config.ComputeOptions.assign_extra_query_labels

bigframes._config.DisplayOptions

DisplayOptions(
    max_columns: int = 20,
    max_rows: int = 10,
    precision: int = 6,
    progress_bar: typing.Optional[str] = "auto",
    repr_mode: typing.Literal["head", "deferred", "anywidget"] = "head",
    max_colwidth: typing.Optional[int] = 50,
    max_info_columns: int = 100,
    max_info_rows: typing.Optional[int] = 200000,
    memory_usage: bool = True,
    blob_display: bool = True,
    blob_display_width: typing.Optional[int] = None,
    blob_display_height: typing.Optional[int] = None,
)

Encapsulates the configuration for displaying objects.

See more: bigframes._config.DisplayOptions

bigframes._config.DisplayOptions.__init__

__init__(
    max_columns=20,
    max_rows=10,
    precision=6,
    progress_bar="auto",
    repr_mode="head",
    max_colwidth=50,
    max_info_columns=100,
    max_info_rows=200000,
    memory_usage=True,
    blob_display=True,
    blob_display_width=None,
    blob_display_height=None,
)

Initialize self.

See more: bigframes.config.DisplayOptions._init

bigframes._config.ExperimentOptions

ExperimentOptions()

Encapsulates the configration for experiments.

See more: bigframes._config.ExperimentOptions

bigframes._config.ExperimentOptions.__init__

__init__()

API documentation for __init__ method.

See more: bigframes.config.ExperimentOptions._init

bigframes._config.Options

Options()

Global options affecting BigQuery DataFrames behavior.

See more: bigframes._config.Options

bigframes._config.Options.__init__

__init__()

API documentation for __init__ method.

See more: bigframes.config.Options._init

bigframes._config.Options.reset

reset()

Reset the option settings to defaults.

See more: bigframes._config.Options.reset

bigframes._config.SamplingOptions

SamplingOptions(
    max_download_size: typing.Optional[int] = 500,
    enable_downsampling: bool = False,
    sampling_method: typing.Literal["head", "uniform"] = "uniform",
    random_state: typing.Optional[int] = None,
)

Encapsulates the configuration for data sampling.

See more: bigframes._config.SamplingOptions

bigframes._config.SamplingOptions.__init__

__init__(
    max_download_size=500,
    enable_downsampling=False,
    sampling_method="uniform",
    random_state=None,
)

Initialize self.

See more: bigframes.config.SamplingOptions._init

bigframes._config.SamplingOptions.with_disabled

with_disabled()

Configures whether to disable downsampling .

See more: bigframes._config.SamplingOptions.with_disabled

bigframes._config.SamplingOptions.with_max_download_size

with_max_download_size(max_rows)

Configures the maximum download size for data sampling in MB .

See more: bigframes._config.SamplingOptions.with_max_download_size

bigframes._config.SamplingOptions.with_method

with_method(method)

Configures the downsampling algorithms to be chosen from .

See more: bigframes._config.SamplingOptions.with_method

bigframes._config.SamplingOptions.with_random_state

with_random_state(state)

Configures the seed for the uniform downsampling algorithm .

See more: bigframes._config.SamplingOptions.with_random_state

bigframes._config.option_context

option_context(*args)

Context manager to temporarily set thread-local options in the with statement context.

See more: bigframes._config.option_context

bigframes._config.option_context.__init__

__init__(*args)

API documentation for __init__ method.

See more: bigframes.config.option_context._init

bigframes.enums.DefaultIndexKind

DefaultIndexKind(value)

Sentinel values used to override default indexing behavior.

See more: bigframes.enums.DefaultIndexKind

bigframes.enums.OrderingMode

OrderingMode(value)

Values used to determine the ordering mode.

See more: bigframes.enums.OrderingMode

bigframes.geopandas.GeoSeries

GeoSeries(data=None, index=None, **kwargs)

A Series object designed to store geometry objects.

See more: bigframes.geopandas.GeoSeries

bigframes.geopandas.GeoSeries.__init__

__init__(data=None, index=None, **kwargs)

Initialize self.

See more: bigframes.geopandas.GeoSeries.init

bigframes.geopandas.GeoSeries.abs

abs()

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

See more: bigframes.geopandas.GeoSeries.abs

bigframes.geopandas.GeoSeries.add

add(other)

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

See more: bigframes.geopandas.GeoSeries.add

bigframes.geopandas.GeoSeries.add_prefix

add_prefix(prefix, axis=None)

Prefix labels with string prefix.

See more: bigframes.geopandas.GeoSeries.add_prefix

bigframes.geopandas.GeoSeries.add_suffix

add_suffix(suffix, axis=None)

Suffix labels with string suffix.

See more: bigframes.geopandas.GeoSeries.add_suffix

bigframes.geopandas.GeoSeries.agg

agg(func)

Aggregate using one or more operations over the specified axis.

See more: bigframes.geopandas.GeoSeries.agg

bigframes.geopandas.GeoSeries.aggregate

aggregate(func)

Aggregate using one or more operations over the specified axis.

See more: bigframes.geopandas.GeoSeries.aggregate

bigframes.geopandas.GeoSeries.all

all()

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

See more: bigframes.geopandas.GeoSeries.all

bigframes.geopandas.GeoSeries.any

any()

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

See more: bigframes.geopandas.GeoSeries.any

bigframes.geopandas.GeoSeries.apply

apply(func, by_row="compat", *, args=())

Invoke function on values of a Series.

See more: bigframes.geopandas.GeoSeries.apply

bigframes.geopandas.GeoSeries.argmax

argmax()

Return int position of the largest value in the series.

See more: bigframes.geopandas.GeoSeries.argmax

bigframes.geopandas.GeoSeries.argmin

argmin()

Return int position of the smallest value in the Series.

See more: bigframes.geopandas.GeoSeries.argmin

bigframes.geopandas.GeoSeries.astype

astype(dtype, *, errors="raise")

Cast a pandas object to a specified dtype dtype.

See more: bigframes.geopandas.GeoSeries.astype

bigframes.geopandas.GeoSeries.autocorr

autocorr(lag=1)

Compute the lag-N autocorrelation.

See more: bigframes.geopandas.GeoSeries.autocorr

bigframes.geopandas.GeoSeries.bar

bar(x=None, y=None, **kwargs)

Draw a vertical bar plot.

See more: bigframes.geopandas.GeoSeries.bar

bigframes.geopandas.GeoSeries.between

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

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

See more: bigframes.geopandas.GeoSeries.between

bigframes.geopandas.GeoSeries.bfill

bfill(*, limit=None)

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

See more: bigframes.geopandas.GeoSeries.bfill

bigframes.geopandas.GeoSeries.buffer

buffer(distance)

API documentation for buffer method.

See more: bigframes.geopandas.GeoSeries.buffer

bigframes.geopandas.GeoSeries.cache

cache()

Materializes the Series to a temporary table.

See more: bigframes.geopandas.GeoSeries.cache

bigframes.geopandas.GeoSeries.case_when

case_when(caselist)

Replace values where the conditions are True.

See more: bigframes.geopandas.GeoSeries.case_when

bigframes.geopandas.GeoSeries.clip

clip(lower=None, upper=None)

Trim values at input threshold(s).

See more: bigframes.geopandas.GeoSeries.clip

bigframes.geopandas.GeoSeries.combine

combine(other, func)

Combine the Series with a Series or scalar according to func.

See more: bigframes.geopandas.GeoSeries.combine

bigframes.geopandas.GeoSeries.combine_first

combine_first(other)

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

See more: bigframes.geopandas.GeoSeries.combine_first

bigframes.geopandas.GeoSeries.copy

copy()

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

See more: bigframes.geopandas.GeoSeries.copy

bigframes.geopandas.GeoSeries.corr

corr(other, method="pearson", min_periods=None)

Compute the correlation with the other Series.

See more: bigframes.geopandas.GeoSeries.corr

bigframes.geopandas.GeoSeries.count

count()

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

See more: bigframes.geopandas.GeoSeries.count

bigframes.geopandas.GeoSeries.cov

cov(other)

Compute covariance with Series, excluding missing values.

See more: bigframes.geopandas.GeoSeries.cov

bigframes.geopandas.GeoSeries.cummax

cummax()

Return cumulative maximum over a DataFrame or Series axis.

See more: bigframes.geopandas.GeoSeries.cummax

bigframes.geopandas.GeoSeries.cummin

cummin()

Return cumulative minimum over a DataFrame or Series axis.

See more: bigframes.geopandas.GeoSeries.cummin

bigframes.geopandas.GeoSeries.cumprod

cumprod()

Return cumulative product over a DataFrame or Series axis.

See more: bigframes.geopandas.GeoSeries.cumprod

bigframes.geopandas.GeoSeries.cumsum

cumsum()

Return cumulative sum over a DataFrame or Series axis.

See more: bigframes.geopandas.GeoSeries.cumsum

bigframes.geopandas.GeoSeries.describe

describe()

Generate descriptive statistics.

See more: bigframes.geopandas.GeoSeries.describe

bigframes.geopandas.GeoSeries.diff

diff(periods=1)

First discrete difference of element.

See more: bigframes.geopandas.GeoSeries.diff

bigframes.geopandas.GeoSeries.difference

difference(other)

Returns a GeoSeries of the points in each aligned geometry that are not in other.

See more: bigframes.geopandas.GeoSeries.difference

bigframes.geopandas.GeoSeries.distance

distance(other)

[Not Implemented] Use <xref uid="bigframes.bigquery.st_distance">bigframes.bigquery.st_distance</xref>(series, other) instead to return the shorted distance between two GEOGRAPHY objects in meters.

See more: bigframes.geopandas.GeoSeries.distance

bigframes.geopandas.GeoSeries.div

div(other)

Return floating division of Series and other, element-wise (binary operator truediv).

See more: bigframes.geopandas.GeoSeries.div

bigframes.geopandas.GeoSeries.divide

divide(other)

Return floating division of Series and other, element-wise (binary operator truediv).

See more: bigframes.geopandas.GeoSeries.divide

bigframes.geopandas.GeoSeries.divmod

divmod(other)

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

See more: bigframes.geopandas.GeoSeries.divmod

bigframes.geopandas.GeoSeries.dot

dot(other)

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

See more: bigframes.geopandas.GeoSeries.dot

bigframes.geopandas.GeoSeries.drop

drop(labels=None, *, axis=0, index=None, columns=None, level=None)

Return Series with specified index labels removed.

See more: bigframes.geopandas.GeoSeries.drop

bigframes.geopandas.GeoSeries.drop_duplicates

drop_duplicates(*, keep="first")

Return Series with duplicate values removed.

See more: bigframes.geopandas.GeoSeries.drop_duplicates

bigframes.geopandas.GeoSeries.droplevel

droplevel(level, axis=0)

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

See more: bigframes.geopandas.GeoSeries.droplevel

bigframes.geopandas.GeoSeries.dropna

dropna(*, axis=0, inplace=False, how=None, ignore_index=False)

Return a new Series with missing values removed.

See more: bigframes.geopandas.GeoSeries.dropna

bigframes.geopandas.GeoSeries.duplicated

duplicated(keep="first")

Indicate duplicate Series values.

See more: bigframes.geopandas.GeoSeries.duplicated

bigframes.geopandas.GeoSeries.eq

eq(other)

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

See more: bigframes.geopandas.GeoSeries.eq

bigframes.geopandas.GeoSeries.equals

equals(other)

Test whether two objects contain the same elements.

See more: bigframes.geopandas.GeoSeries.equals

bigframes.geopandas.GeoSeries.expanding

expanding(min_periods=1)

Provide expanding window calculations.

See more: bigframes.geopandas.GeoSeries.expanding

bigframes.geopandas.GeoSeries.explode

explode(*, ignore_index=False)

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

See more: bigframes.geopandas.GeoSeries.explode

bigframes.geopandas.GeoSeries.ffill

ffill(*, limit=None)

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

See more: bigframes.geopandas.GeoSeries.ffill

bigframes.geopandas.GeoSeries.fillna

fillna(value=None)

Fill NA (NULL in BigQuery) values using the specified method.

See more: bigframes.geopandas.GeoSeries.fillna

bigframes.geopandas.GeoSeries.filter

filter(items=None, like=None, regex=None, axis=None)

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

See more: bigframes.geopandas.GeoSeries.filter

bigframes.geopandas.GeoSeries.floordiv

floordiv(other)

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

See more: bigframes.geopandas.GeoSeries.floordiv

bigframes.geopandas.GeoSeries.from_wkt

from_wkt(data, index=None, *, session=None)

Alternate constructor to create a GeoSeries from a list or array of WKT objects.

See more: bigframes.geopandas.GeoSeries.from_wkt

bigframes.geopandas.GeoSeries.from_xy

from_xy(x, y, index=None, session=None, **kwargs)

Alternate constructor to create a GeoSeries of Point geometries from lists or arrays of x, y coordinates.

See more: bigframes.geopandas.GeoSeries.from_xy

bigframes.geopandas.GeoSeries.ge

ge(other)

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

See more: bigframes.geopandas.GeoSeries.ge

bigframes.geopandas.GeoSeries.get

get(key, default=None)

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

See more: bigframes.geopandas.GeoSeries.get

bigframes.geopandas.GeoSeries.groupby

groupby(by=None, axis=0, level=None, as_index=True, *, dropna=True)

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

See more: bigframes.geopandas.GeoSeries.groupby

bigframes.geopandas.GeoSeries.gt

gt(other)

Return Greater than of series and other, element-wise (binary operator gt).

See more: bigframes.geopandas.GeoSeries.gt

bigframes.geopandas.GeoSeries.head

head(n=5)

Return the first n rows.

See more: bigframes.geopandas.GeoSeries.head

bigframes.geopandas.GeoSeries.hist

hist(by=None, bins=10, **kwargs)

Draw one histogram of the DataFrame’s columns.

See more: bigframes.geopandas.GeoSeries.hist

bigframes.geopandas.GeoSeries.idxmax

idxmax()

Return the row label of the maximum value.

See more: bigframes.geopandas.GeoSeries.idxmax

bigframes.geopandas.GeoSeries.idxmin

idxmin()

Return the row label of the minimum value.

See more: bigframes.geopandas.GeoSeries.idxmin

bigframes.geopandas.GeoSeries.interpolate

interpolate(method="linear")

Fill NaN values using an interpolation method.

See more: bigframes.geopandas.GeoSeries.interpolate

bigframes.geopandas.GeoSeries.intersection

intersection(other)

Returns a GeoSeries of the intersection of points in each aligned geometry with other.

See more: bigframes.geopandas.GeoSeries.intersection

bigframes.geopandas.GeoSeries.isin

isin(values)

Whether elements in Series are contained in values.

See more: bigframes.geopandas.GeoSeries.isin

bigframes.geopandas.GeoSeries.isna

isna()

Detect missing (NULL) values.

See more: bigframes.geopandas.GeoSeries.isna

bigframes.geopandas.GeoSeries.isnull

isnull()

Detect missing (NULL) values.

See more: bigframes.geopandas.GeoSeries.isnull

bigframes.geopandas.GeoSeries.item

item()

Return the first element of the underlying data as a Python scalar.

See more: bigframes.geopandas.GeoSeries.item

bigframes.geopandas.GeoSeries.items

items()

Lazily iterate over (index, value) tuples.

See more: bigframes.geopandas.GeoSeries.items

bigframes.geopandas.GeoSeries.keys

keys()

Return alias for index.

See more: bigframes.geopandas.GeoSeries.keys

bigframes.geopandas.GeoSeries.kurt

kurt()

Return unbiased kurtosis over requested axis.

See more: bigframes.geopandas.GeoSeries.kurt

bigframes.geopandas.GeoSeries.kurtosis

kurtosis()

Return unbiased kurtosis over requested axis.

See more: bigframes.geopandas.GeoSeries.kurtosis

bigframes.geopandas.GeoSeries.le

le(other)

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

See more: bigframes.geopandas.GeoSeries.le

bigframes.geopandas.GeoSeries.line

line(x=None, y=None, **kwargs)

Plot Series or DataFrame as lines.

See more: bigframes.geopandas.GeoSeries.line

bigframes.geopandas.GeoSeries.lt

lt(other)

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

See more: bigframes.geopandas.GeoSeries.lt

bigframes.geopandas.GeoSeries.map

map(arg, na_action=None, *, verify_integrity=False)

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

See more: bigframes.geopandas.GeoSeries.map

bigframes.geopandas.GeoSeries.mask

mask(cond, other=None)

Replace values where the condition is True.

See more: bigframes.geopandas.GeoSeries.mask

bigframes.geopandas.GeoSeries.max

max()

Return the maximum of the values over the requested axis.

See more: bigframes.geopandas.GeoSeries.max

bigframes.geopandas.GeoSeries.mean

mean()

Return the mean of the values over the requested axis.

See more: bigframes.geopandas.GeoSeries.mean

bigframes.geopandas.GeoSeries.median

median(*, exact=True)

Return the median of the values over the requested axis.

See more: bigframes.geopandas.GeoSeries.median

bigframes.geopandas.GeoSeries.min

min()

Return the maximum of the values over the requested axis.

See more: bigframes.geopandas.GeoSeries.min

bigframes.geopandas.GeoSeries.mod

mod(other)

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

See more: bigframes.geopandas.GeoSeries.mod

bigframes.geopandas.GeoSeries.mode

mode()

Return the mode(s) of the Series.

See more: bigframes.geopandas.GeoSeries.mode

bigframes.geopandas.GeoSeries.mul

mul(other)

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

See more: bigframes.geopandas.GeoSeries.mul

bigframes.geopandas.GeoSeries.multiply

multiply(other)

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

See more: bigframes.geopandas.GeoSeries.multiply

bigframes.geopandas.GeoSeries.ne

ne(other)

Return not equal of Series and other, element-wise (binary operator ne).

See more: bigframes.geopandas.GeoSeries.ne

bigframes.geopandas.GeoSeries.nlargest

nlargest(n=5, keep="first")

Return the largest n elements.

See more: bigframes.geopandas.GeoSeries.nlargest

bigframes.geopandas.GeoSeries.notna

notna()

Detect existing (non-missing) values.

See more: bigframes.geopandas.GeoSeries.notna

bigframes.geopandas.GeoSeries.notnull

notnull()

Detect existing (non-missing) values.

See more: bigframes.geopandas.GeoSeries.notnull

bigframes.geopandas.GeoSeries.nsmallest

nsmallest(n=5, keep="first")

Return the smallest n elements.

See more: bigframes.geopandas.GeoSeries.nsmallest

bigframes.geopandas.GeoSeries.nunique

nunique()

Return number of unique elements in the object.

See more: bigframes.geopandas.GeoSeries.nunique

bigframes.geopandas.GeoSeries.pad

pad(*, limit=None)

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

See more: bigframes.geopandas.GeoSeries.pad

bigframes.geopandas.GeoSeries.pct_change

pct_change(periods=1)

Fractional change between the current and a prior element.

See more: bigframes.geopandas.GeoSeries.pct_change

bigframes.geopandas.GeoSeries.peek

peek(n=5, *, force=True, allow_large_results=None)

Preview n arbitrary elements from the series without guarantees about row selection or ordering.

See more: bigframes.geopandas.GeoSeries.peek

bigframes.geopandas.GeoSeries.pipe

pipe(func, *args, **kwargs)

Apply chainable functions that expect Series or DataFrames.

See more: bigframes.geopandas.GeoSeries.pipe

bigframes.geopandas.GeoSeries.pow

pow(other)

Return Exponential power of series and other, element-wise (binary operator pow).

See more: bigframes.geopandas.GeoSeries.pow

bigframes.geopandas.GeoSeries.prod

prod()

Return the product of the values over the requested axis.

See more: bigframes.geopandas.GeoSeries.prod

bigframes.geopandas.GeoSeries.product

product()

Return the product of the values over the requested axis.

See more: bigframes.geopandas.GeoSeries.product

bigframes.geopandas.GeoSeries.quantile

quantile(q=0.5)

Return value at the given quantile.

See more: bigframes.geopandas.GeoSeries.quantile

bigframes.geopandas.GeoSeries.radd

radd(other)

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

See more: bigframes.geopandas.GeoSeries.radd

bigframes.geopandas.GeoSeries.rank

rank(
    axis=0,
    method="average",
    numeric_only=False,
    na_option="keep",
    ascending=True,
    pct=False,
)

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

See more: bigframes.geopandas.GeoSeries.rank

bigframes.geopandas.GeoSeries.rdiv

rdiv(other)

Return floating division of Series and other, element-wise (binary operator rtruediv).

See more: bigframes.geopandas.GeoSeries.rdiv

bigframes.geopandas.GeoSeries.rdivmod

rdivmod(other)

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

See more: bigframes.geopandas.GeoSeries.rdivmod

bigframes.geopandas.GeoSeries.reindex

reindex(index=None, *, validate=None)

Conform Series to new index with optional filling logic.

See more: bigframes.geopandas.GeoSeries.reindex

bigframes.geopandas.GeoSeries.reindex_like

reindex_like(other, *, validate=None)

Return an object with matching indices as other object.

See more: bigframes.geopandas.GeoSeries.reindex_like

bigframes.geopandas.GeoSeries.rename

rename(index=None, *, inplace=False, **kwargs)

Alter Series index labels or name.

See more: bigframes.geopandas.GeoSeries.rename

bigframes.geopandas.GeoSeries.rename_axis

rename_axis(mapper, *, inplace=False, **kwargs)

Set the name of the axis for the index or columns.

See more: bigframes.geopandas.GeoSeries.rename_axis

bigframes.geopandas.GeoSeries.reorder_levels

reorder_levels(order, axis=0)

Rearrange index levels using input order.

See more: bigframes.geopandas.GeoSeries.reorder_levels

bigframes.geopandas.GeoSeries.replace

replace(to_replace, value=None, *, regex=False)

Replace values given in to_replace with value.

See more: bigframes.geopandas.GeoSeries.replace

bigframes.geopandas.GeoSeries.resample

resample(rule, *, closed=None, label=None, level=None, origin="start_day")

Resample time-series data.

See more: bigframes.geopandas.GeoSeries.resample

bigframes.geopandas.GeoSeries.reset_index

reset_index(
    level=None, *, name=None, drop=False, inplace=False, allow_duplicates=None
)

Generate a new DataFrame or Series with the index reset.

See more: bigframes.geopandas.GeoSeries.reset_index

bigframes.geopandas.GeoSeries.rfloordiv

rfloordiv(other)

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

See more: bigframes.geopandas.GeoSeries.rfloordiv

bigframes.geopandas.GeoSeries.rmod

rmod(other)

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

See more: bigframes.geopandas.GeoSeries.rmod

bigframes.geopandas.GeoSeries.rmul

rmul(other)

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

See more: bigframes.geopandas.GeoSeries.rmul

bigframes.geopandas.GeoSeries.rolling

rolling(window, min_periods=None, closed="right")

Provide rolling window calculations.

See more: bigframes.geopandas.GeoSeries.rolling

bigframes.geopandas.GeoSeries.round

round(decimals=0)

Round each value in a Series to the given number of decimals.

See more: bigframes.geopandas.GeoSeries.round

bigframes.geopandas.GeoSeries.rpow

rpow(other)

Return Exponential power of series and other, element-wise (binary operator rpow).

See more: bigframes.geopandas.GeoSeries.rpow

bigframes.geopandas.GeoSeries.rsub

rsub(other)

Return subtraction of Series and other, element-wise (binary operator rsub).

See more: bigframes.geopandas.GeoSeries.rsub

bigframes.geopandas.GeoSeries.rtruediv

rtruediv(other)

Return floating division of Series and other, element-wise (binary operator rtruediv).

See more: bigframes.geopandas.GeoSeries.rtruediv

bigframes.geopandas.GeoSeries.sample

sample(n=None, frac=None, *, random_state=None, sort="random")

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

See more: bigframes.geopandas.GeoSeries.sample

bigframes.geopandas.GeoSeries.shift

shift(periods=1)

Shift index by desired number of periods.

See more: bigframes.geopandas.GeoSeries.shift

bigframes.geopandas.GeoSeries.simplify

simplify(tolerance, preserve_topology=True)

[Not Implemented] Use <xref uid="bigframes.bigquery.st_simplify">bigframes.bigquery.st_simplify</xref>(series, tolerance_meters), instead to set the tolerance in meters.

See more: bigframes.geopandas.GeoSeries.simplify

bigframes.geopandas.GeoSeries.skew

skew()

Return unbiased skew over requested axis.

See more: bigframes.geopandas.GeoSeries.skew

bigframes.geopandas.GeoSeries.sort_index

sort_index(*, axis=0, inplace=False, ascending=True, na_position="last")

Sort Series by index labels.

See more: bigframes.geopandas.GeoSeries.sort_index

bigframes.geopandas.GeoSeries.sort_values

sort_values(
    *, axis=0, inplace=False, ascending=True, kind="quicksort", na_position="last"
)

Sort by the values.

See more: bigframes.geopandas.GeoSeries.sort_values

bigframes.geopandas.GeoSeries.std

std()

Return sample standard deviation over requested axis.

See more: bigframes.geopandas.GeoSeries.std

bigframes.geopandas.GeoSeries.sub

sub(other)

Return subtraction of Series and other, element-wise (binary operator sub).

See more: bigframes.geopandas.GeoSeries.sub

bigframes.geopandas.GeoSeries.subtract

subtract(other)

Return subtraction of Series and other, element-wise (binary operator sub).

See more: bigframes.geopandas.GeoSeries.subtract

bigframes.geopandas.GeoSeries.sum

sum()

Return the sum of the values over the requested axis.

See more: bigframes.geopandas.GeoSeries.sum

bigframes.geopandas.GeoSeries.swaplevel

swaplevel(i=-2, j=-1)

Swap levels i and j in a MultiIndex.

See more: bigframes.geopandas.GeoSeries.swaplevel

bigframes.geopandas.GeoSeries.tail

tail(n=5)

Return the last n rows.

See more: bigframes.geopandas.GeoSeries.tail

bigframes.geopandas.GeoSeries.take

take(indices, axis=0, **kwargs)

Return the elements in the given positional indices along an axis.

See more: bigframes.geopandas.GeoSeries.take

bigframes.geopandas.GeoSeries.to_csv

to_csv(
    path_or_buf=None, sep=",", *, header=True, index=True, allow_large_results=None
)

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

See more: bigframes.geopandas.GeoSeries.to_csv

bigframes.geopandas.GeoSeries.to_dict

to_dict(into=dict, *, allow_large_results=None)

Convert Series to {label -> value} dict or dict-like object.

See more: bigframes.geopandas.GeoSeries.to_dict

bigframes.geopandas.GeoSeries.to_excel

to_excel(excel_writer, sheet_name="Sheet1", *, allow_large_results=None, **kwargs)

Write Series to an Excel sheet.

See more: bigframes.geopandas.GeoSeries.to_excel

bigframes.geopandas.GeoSeries.to_frame

to_frame(name=None)

Convert Series to DataFrame.

See more: bigframes.geopandas.GeoSeries.to_frame

bigframes.geopandas.GeoSeries.to_json

to_json(
    path_or_buf=None, orient=None, *, lines=False, index=True, allow_large_results=None
)

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

See more: bigframes.geopandas.GeoSeries.to_json

bigframes.geopandas.GeoSeries.to_latex

to_latex(
    buf=None,
    columns=None,
    header=True,
    index=True,
    *,
    allow_large_results=None,
    **kwargs
)

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

See more: bigframes.geopandas.GeoSeries.to_latex

bigframes.geopandas.GeoSeries.to_list

to_list(*, allow_large_results=None)

Return a list of the values.

See more: bigframes.geopandas.GeoSeries.to_list

bigframes.geopandas.GeoSeries.to_markdown

to_markdown(buf=None, mode="wt", index=True, *, allow_large_results=None, **kwargs)

Print Series in Markdown-friendly format.

See more: bigframes.geopandas.GeoSeries.to_markdown

bigframes.geopandas.GeoSeries.to_numpy

to_numpy(
    dtype=None,
    copy=False,
    na_value=_NoDefault.no_default,
    *,
    allow_large_results=None,
    **kwargs
)

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

See more: bigframes.geopandas.GeoSeries.to_numpy

bigframes.geopandas.GeoSeries.to_pandas

to_pandas(
    max_download_size=None,
    sampling_method=None,
    random_state=None,
    *,
    ordered=True,
    dry_run=False,
    allow_large_results=None
)

Writes Series to pandas Series.

See more: bigframes.geopandas.GeoSeries.to_pandas

bigframes.geopandas.GeoSeries.to_pandas_batches

to_pandas_batches(page_size=None, max_results=None, *, allow_large_results=None)

Stream Series results to an iterable of pandas Series.

See more: bigframes.geopandas.GeoSeries.to_pandas_batches

bigframes.geopandas.GeoSeries.to_pickle

to_pickle(path, *, allow_large_results=None, **kwargs)

Pickle (serialize) object to file.

See more: bigframes.geopandas.GeoSeries.to_pickle

bigframes.geopandas.GeoSeries.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,
    *,
    allow_large_results=None
)

Render a string representation of the Series.

See more: bigframes.geopandas.GeoSeries.to_string

bigframes.geopandas.GeoSeries.to_wkt

to_wkt()

Convert GeoSeries geometries to WKT.

See more: bigframes.geopandas.GeoSeries.to_wkt

bigframes.geopandas.GeoSeries.to_xarray

to_xarray(*, allow_large_results=None)

Return an xarray object from the pandas object.

See more: bigframes.geopandas.GeoSeries.to_xarray

bigframes.geopandas.GeoSeries.tolist

tolist(*, allow_large_results=None)

Return a list of the values.

See more: bigframes.geopandas.GeoSeries.tolist

bigframes.geopandas.GeoSeries.transpose

transpose()

Return the transpose, which is by definition self.

See more: bigframes.geopandas.GeoSeries.transpose

bigframes.geopandas.GeoSeries.truediv

truediv(other)

Return floating division of Series and other, element-wise (binary operator truediv).

See more: bigframes.geopandas.GeoSeries.truediv

bigframes.geopandas.GeoSeries.unique

unique(keep_order=True)

Return unique values of Series object.

See more: bigframes.geopandas.GeoSeries.unique

bigframes.geopandas.GeoSeries.unstack

unstack(level=-1)

Unstack, also known as pivot, Series with MultiIndex to produce DataFrame.

See more: bigframes.geopandas.GeoSeries.unstack

bigframes.geopandas.GeoSeries.update

update(other)

Modify Series in place using values from passed Series.

See more: bigframes.geopandas.GeoSeries.update

bigframes.geopandas.GeoSeries.value_counts

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

Return a Series containing counts of unique values.

See more: bigframes.geopandas.GeoSeries.value_counts

bigframes.geopandas.GeoSeries.var

var()

Return unbiased variance over requested axis.

See more: bigframes.geopandas.GeoSeries.var

bigframes.geopandas.GeoSeries.where

where(cond, other=None)

Replace values where the condition is False.

See more: bigframes.geopandas.GeoSeries.where

bigframes.ml.cluster.KMeans

KMeans(
    n_clusters: int = 8,
    *,
    init: typing.Literal["kmeans++", "random", "custom"] = "kmeans++",
    init_col: typing.Optional[str] = None,
    distance_type: typing.Literal["euclidean", "cosine"] = "euclidean",
    max_iter: int = 20,
    tol: float = 0.01,
    warm_start: bool = False
)

K-Means clustering.

See more: bigframes.ml.cluster.KMeans

bigframes.ml.cluster.KMeans.__init__

__init__(
    n_clusters=8,
    *,
    init="kmeans++",
    init_col=None,
    distance_type="euclidean",
    max_iter=20,
    tol=0.01,
    warm_start=False
)

API documentation for __init__ method.

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

bigframes.ml.cluster.KMeans.detect_anomalies

detect_anomalies(X, *, contamination=0.1)

Detect the anomaly data points of the input.

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

bigframes.ml.cluster.KMeans.fit

fit(X, y=None)

Compute k-means clustering.

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

bigframes.ml.cluster.KMeans.get_params

get_params(deep=True)

Get parameters for this estimator.

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

bigframes.ml.cluster.KMeans.predict

predict(X)

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=None)

Register the model to Vertex AI.

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

bigframes.ml.cluster.KMeans.score

score(X, y=None)

Calculate evaluation metrics of the model.

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

bigframes.ml.cluster.KMeans.to_gbq

to_gbq(model_name, replace=False)

Save the model to BigQuery.

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

bigframes.ml.compose.ColumnTransformer

ColumnTransformer(
    transformers: typing.Iterable[
        typing.Tuple[
            str,
            typing.Union[
                bigframes.ml.preprocessing.OneHotEncoder,
                bigframes.ml.preprocessing.StandardScaler,
                bigframes.ml.preprocessing.MaxAbsScaler,
                bigframes.ml.preprocessing.MinMaxScaler,
                bigframes.ml.preprocessing.KBinsDiscretizer,
                bigframes.ml.preprocessing.LabelEncoder,
                bigframes.ml.preprocessing.PolynomialFeatures,
                bigframes.ml.impute.SimpleImputer,
                bigframes.ml.compose.SQLScalarColumnTransformer,
            ],
            typing.Union[str, typing.Iterable[str]],
        ]
    ],
)

Applies transformers to columns of BigQuery DataFrames.

See more: bigframes.ml.compose.ColumnTransformer

bigframes.ml.compose.ColumnTransformer.__init__

__init__(transformers)

API documentation for __init__ method.

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

bigframes.ml.compose.ColumnTransformer.fit

fit(X, y=None)

Fit all transformers using X.

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

bigframes.ml.compose.ColumnTransformer.fit_transform

fit_transform(X, y=None)

API documentation for fit_transform method.

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

bigframes.ml.compose.ColumnTransformer.get_params

get_params(deep=True)

Get parameters for this estimator.

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

bigframes.ml.compose.ColumnTransformer.to_gbq

to_gbq(model_name, replace=False)

Save the transformer as a BigQuery model.

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

bigframes.ml.compose.ColumnTransformer.transform

transform(X)

Transform X separately by each transformer, concatenate results.

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

bigframes.ml.compose.SQLScalarColumnTransformer

SQLScalarColumnTransformer(sql: str, target_column: str = "transformed_{0}")

Wrapper for plain SQL code contained in a ColumnTransformer.

See more: bigframes.ml.compose.SQLScalarColumnTransformer

bigframes.ml.compose.SQLScalarColumnTransformer.__init__

__init__(sql, target_column="transformed_{0}")

Initialize self.

See more: bigframes.ml.compose.SQLScalarColumnTransformer.init

bigframes.ml.decomposition.MatrixFactorization

MatrixFactorization(
    *,
    feedback_type: typing.Literal["explicit", "implicit"] = "explicit",
    num_factors: int,
    user_col: str,
    item_col: str,
    rating_col: str = "rating",
    l2_reg: float = 1.0
)

Matrix Factorization (MF).

See more: bigframes.ml.decomposition.MatrixFactorization

bigframes.ml.decomposition.MatrixFactorization.__init__

__init__(
    *,
    feedback_type="explicit",
    num_factors,
    user_col,
    item_col,
    rating_col="rating",
    l2_reg=1.0
)

API documentation for __init__ method.

See more: bigframes.ml.decomposition.MatrixFactorization.init

bigframes.ml.decomposition.MatrixFactorization.fit

fit(X, y=None)

Fit the model according to the given training data.

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

bigframes.ml.decomposition.MatrixFactorization.get_params

get_params(deep=True)

Get parameters for this estimator.

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

bigframes.ml.decomposition.MatrixFactorization.predict

predict(X)

Generate a predicted rating for every user-item row combination for a matrix factorization model.

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

bigframes.ml.decomposition.MatrixFactorization.register

register(vertex_ai_model_id=None)

Register the model to Vertex AI.

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

bigframes.ml.decomposition.MatrixFactorization.score

score(X=None, y=None)

Calculate evaluation metrics of the model.

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

bigframes.ml.decomposition.MatrixFactorization.to_gbq

to_gbq(model_name, replace=False)

Save the model to BigQuery.

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

bigframes.ml.decomposition.PCA

PCA(
    n_components: typing.Optional[typing.Union[int, float]] = None,
    *,
    svd_solver: typing.Literal["full", "randomized", "auto"] = "auto"
)

Principal component analysis (PCA).

See more: bigframes.ml.decomposition.PCA

bigframes.ml.decomposition.PCA.__init__

__init__(n_components=None, *, svd_solver="auto")

API documentation for __init__ method.

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

bigframes.ml.decomposition.PCA.detect_anomalies

detect_anomalies(X, *, contamination=0.1)

Detect the anomaly data points of the input.

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

bigframes.ml.decomposition.PCA.fit

fit(X, y=None)

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=True)

Get parameters for this estimator.

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

bigframes.ml.decomposition.PCA.predict

predict(X)

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=None)

Register the model to Vertex AI.

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

bigframes.ml.decomposition.PCA.score

score(X=None, y=None)

Calculate evaluation metrics of the model.

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

bigframes.ml.decomposition.PCA.to_gbq

to_gbq(model_name, replace=False)

Save the model to BigQuery.

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

bigframes.ml.ensemble.RandomForestClassifier

RandomForestClassifier(
    n_estimators: int = 100,
    *,
    tree_method: typing.Literal["auto", "exact", "approx", "hist"] = "auto",
    min_tree_child_weight: int = 1,
    colsample_bytree: float = 1.0,
    colsample_bylevel: float = 1.0,
    colsample_bynode: float = 0.8,
    gamma: float = 0.0,
    max_depth: int = 15,
    subsample: float = 0.8,
    reg_alpha: float = 0.0,
    reg_lambda: float = 1.0,
    tol: float = 0.01,
    enable_global_explain: bool = False,
    xgboost_version: typing.Literal["0.9", "1.1"] = "0.9"
)

A random forest classifier.

See more: bigframes.ml.ensemble.RandomForestClassifier

bigframes.ml.ensemble.RandomForestClassifier.__init__

__init__(
    n_estimators=100,
    *,
    tree_method="auto",
    min_tree_child_weight=1,
    colsample_bytree=1.0,
    colsample_bylevel=1.0,
    colsample_bynode=0.8,
    gamma=0.0,
    max_depth=15,
    subsample=0.8,
    reg_alpha=0.0,
    reg_lambda=1.0,
    tol=0.01,
    enable_global_explain=False,
    xgboost_version="0.9"
)

API documentation for __init__ method.

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

bigframes.ml.ensemble.RandomForestClassifier.fit

fit(X, y, X_eval=None, y_eval=None)

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=True)

Get parameters for this estimator.

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

bigframes.ml.ensemble.RandomForestClassifier.predict

predict(X)

Predict regression target for X.

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

bigframes.ml.ensemble.RandomForestClassifier.register

register(vertex_ai_model_id=None)

Register the model to Vertex AI.

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

bigframes.ml.ensemble.RandomForestClassifier.score

score(X, y)

Calculate evaluation metrics of the model.

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

bigframes.ml.ensemble.RandomForestClassifier.to_gbq

to_gbq(model_name, replace=False)

Save the model to BigQuery.

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

bigframes.ml.ensemble.RandomForestRegressor

RandomForestRegressor(
    n_estimators: int = 100,
    *,
    tree_method: typing.Literal["auto", "exact", "approx", "hist"] = "auto",
    min_tree_child_weight: int = 1,
    colsample_bytree: float = 1.0,
    colsample_bylevel: float = 1.0,
    colsample_bynode: float = 0.8,
    gamma: float = 0.0,
    max_depth: int = 15,
    subsample: float = 0.8,
    reg_alpha: float = 0.0,
    reg_lambda: float = 1.0,
    tol: float = 0.01,
    enable_global_explain: bool = False,
    xgboost_version: typing.Literal["0.9", "1.1"] = "0.9"
)

A random forest regressor.

See more: bigframes.ml.ensemble.RandomForestRegressor

bigframes.ml.ensemble.RandomForestRegressor.__init__

__init__(
    n_estimators=100,
    *,
    tree_method="auto",
    min_tree_child_weight=1,
    colsample_bytree=1.0,
    colsample_bylevel=1.0,
    colsample_bynode=0.8,
    gamma=0.0,
    max_depth=15,
    subsample=0.8,
    reg_alpha=0.0,
    reg_lambda=1.0,
    tol=0.01,
    enable_global_explain=False,
    xgboost_version="0.9"
)

API documentation for __init__ method.

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

bigframes.ml.ensemble.RandomForestRegressor.fit

fit(X, y, X_eval=None, y_eval=None)

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=True)

Get parameters for this estimator.

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

bigframes.ml.ensemble.RandomForestRegressor.predict

predict(X)

Predict regression target for X.

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

bigframes.ml.ensemble.RandomForestRegressor.register

register(vertex_ai_model_id=None)

Register the model to Vertex AI.

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

bigframes.ml.ensemble.RandomForestRegressor.score

score(X, y)

Calculate evaluation metrics of the model.

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

bigframes.ml.ensemble.RandomForestRegressor.to_gbq

to_gbq(model_name, replace=False)

Save the model to BigQuery.

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

bigframes.ml.ensemble.XGBClassifier

XGBClassifier(
    n_estimators: int = 1,
    *,
    booster: typing.Literal["gbtree", "dart"] = "gbtree",
    dart_normalized_type: typing.Literal["tree", "forest"] = "tree",
    tree_method: typing.Literal["auto", "exact", "approx", "hist"] = "auto",
    min_tree_child_weight: int = 1,
    colsample_bytree: float = 1.0,
    colsample_bylevel: float = 1.0,
    colsample_bynode: float = 1.0,
    gamma: float = 0.0,
    max_depth: int = 6,
    subsample: float = 1.0,
    reg_alpha: float = 0.0,
    reg_lambda: float = 1.0,
    learning_rate: float = 0.3,
    max_iterations: int = 20,
    tol: float = 0.01,
    enable_global_explain: bool = False,
    xgboost_version: typing.Literal["0.9", "1.1"] = "0.9"
)

XGBoost classifier model.

See more: bigframes.ml.ensemble.XGBClassifier

bigframes.ml.ensemble.XGBClassifier.__init__

__init__(
    n_estimators=1,
    *,
    booster="gbtree",
    dart_normalized_type="tree",
    tree_method="auto",
    min_tree_child_weight=1,
    colsample_bytree=1.0,
    colsample_bylevel=1.0,
    colsample_bynode=1.0,
    gamma=0.0,
    max_depth=6,
    subsample=1.0,
    reg_alpha=0.0,
    reg_lambda=1.0,
    learning_rate=0.3,
    max_iterations=20,
    tol=0.01,
    enable_global_explain=False,
    xgboost_version="0.9"
)

API documentation for __init__ method.

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

bigframes.ml.ensemble.XGBClassifier.fit

fit(X, y, X_eval=None, y_eval=None)

Fit gradient boosting model.

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

bigframes.ml.ensemble.XGBClassifier.get_params

get_params(deep=True)

Get parameters for this estimator.

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

bigframes.ml.ensemble.XGBClassifier.predict

predict(X)

Predict using the XGB model.

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

bigframes.ml.ensemble.XGBClassifier.register

register(vertex_ai_model_id=None)

Register the model to Vertex AI.

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

bigframes.ml.ensemble.XGBClassifier.score

score(X, y)

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, replace=False)

Save the model to BigQuery.

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

bigframes.ml.ensemble.XGBRegressor

XGBRegressor(
    n_estimators: int = 1,
    *,
    booster: typing.Literal["gbtree", "dart"] = "gbtree",
    dart_normalized_type: typing.Literal["tree", "forest"] = "tree",
    tree_method: typing.Literal["auto", "exact", "approx", "hist"] = "auto",
    min_tree_child_weight: int = 1,
    colsample_bytree: float = 1.0,
    colsample_bylevel: float = 1.0,
    colsample_bynode: float = 1.0,
    gamma: float = 0.0,
    max_depth: int = 6,
    subsample: float = 1.0,
    reg_alpha: float = 0.0,
    reg_lambda: float = 1.0,
    learning_rate: float = 0.3,
    max_iterations: int = 20,
    tol: float = 0.01,
    enable_global_explain: bool = False,
    xgboost_version: typing.Literal["0.9", "1.1"] = "0.9"
)

XGBoost regression model.

See more: bigframes.ml.ensemble.XGBRegressor

bigframes.ml.ensemble.XGBRegressor.__init__

__init__(
    n_estimators=1,
    *,
    booster="gbtree",
    dart_normalized_type="tree",
    tree_method="auto",
    min_tree_child_weight=1,
    colsample_bytree=1.0,
    colsample_bylevel=1.0,
    colsample_bynode=1.0,
    gamma=0.0,
    max_depth=6,
    subsample=1.0,
    reg_alpha=0.0,
    reg_lambda=1.0,
    learning_rate=0.3,
    max_iterations=20,
    tol=0.01,
    enable_global_explain=False,
    xgboost_version="0.9"
)

API documentation for __init__ method.

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

bigframes.ml.ensemble.XGBRegressor.fit

fit(X, y, X_eval=None, y_eval=None)

Fit gradient boosting model.

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

bigframes.ml.ensemble.XGBRegressor.get_params

get_params(deep=True)

Get parameters for this estimator.

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

bigframes.ml.ensemble.XGBRegressor.predict

predict(X)

Predict using the XGB model.

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

bigframes.ml.ensemble.XGBRegressor.register

register(vertex_ai_model_id=None)

Register the model to Vertex AI.

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

bigframes.ml.ensemble.XGBRegressor.score

score(X, y)

Calculate evaluation metrics of the model.

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

bigframes.ml.ensemble.XGBRegressor.to_gbq

to_gbq(model_name, replace=False)

Save the model to BigQuery.

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

bigframes.ml.forecasting.ARIMAPlus

ARIMAPlus(
    *,
    horizon: int = 1000,
    auto_arima: bool = True,
    auto_arima_max_order: typing.Optional[int] = None,
    auto_arima_min_order: typing.Optional[int] = None,
    data_frequency: str = "auto_frequency",
    include_drift: bool = False,
    holiday_region: typing.Optional[str] = None,
    clean_spikes_and_dips: bool = True,
    adjust_step_changes: bool = True,
    forecast_limit_lower_bound: typing.Optional[float] = None,
    forecast_limit_upper_bound: typing.Optional[float] = None,
    time_series_length_fraction: typing.Optional[float] = None,
    min_time_series_length: typing.Optional[int] = None,
    max_time_series_length: typing.Optional[int] = None,
    trend_smoothing_window_size: typing.Optional[int] = None,
    decompose_time_series: bool = True
)

Time Series ARIMA Plus model.

See more: bigframes.ml.forecasting.ARIMAPlus

bigframes.ml.forecasting.ARIMAPlus.__init__

__init__(
    *,
    horizon=1000,
    auto_arima=True,
    auto_arima_max_order=None,
    auto_arima_min_order=None,
    data_frequency="auto_frequency",
    include_drift=False,
    holiday_region=None,
    clean_spikes_and_dips=True,
    adjust_step_changes=True,
    forecast_limit_lower_bound=None,
    forecast_limit_upper_bound=None,
    time_series_length_fraction=None,
    min_time_series_length=None,
    max_time_series_length=None,
    trend_smoothing_window_size=None,
    decompose_time_series=True
)

API documentation for __init__ method.

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

bigframes.ml.forecasting.ARIMAPlus.detect_anomalies

detect_anomalies(X, *, anomaly_prob_threshold=0.95)

Detect the anomaly data points of the input.

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

bigframes.ml.forecasting.ARIMAPlus.fit

fit(X, y, transforms=None, id_col=None)

API documentation for fit method.

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

bigframes.ml.forecasting.ARIMAPlus.get_params

get_params(deep=True)

Get parameters for this estimator.

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

bigframes.ml.forecasting.ARIMAPlus.predict

predict(X=None, *, horizon=3, confidence_level=0.95)

Forecast time series at future horizon.

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

bigframes.ml.forecasting.ARIMAPlus.predict_explain

predict_explain(X=None, *, horizon=3, confidence_level=0.95)

Explain Forecast time series at future horizon.

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

bigframes.ml.forecasting.ARIMAPlus.register

register(vertex_ai_model_id=None)

Register the model to Vertex AI.

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

bigframes.ml.forecasting.ARIMAPlus.score

score(X, y, id_col=None)

Calculate evaluation metrics of the model.

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

bigframes.ml.forecasting.ARIMAPlus.summary

summary(show_all_candidate_models=False)

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, replace=False)

Save the model to BigQuery.

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

bigframes.ml.imported.ONNXModel

ONNXModel(
    model_path: str, *, session: typing.Optional[bigframes.session.Session] = None
)

Imported Open Neural Network Exchange (ONNX) model.

See more: bigframes.ml.imported.ONNXModel

bigframes.ml.imported.ONNXModel.__init__

__init__(model_path, *, session=None)

API documentation for __init__ method.

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

bigframes.ml.imported.ONNXModel.get_params

get_params(deep=True)

Get parameters for this estimator.

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

bigframes.ml.imported.ONNXModel.predict

predict(X)

Predict the result from input DataFrame.

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

bigframes.ml.imported.ONNXModel.register

register(vertex_ai_model_id=None)

Register the model to Vertex AI.

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

bigframes.ml.imported.ONNXModel.to_gbq

to_gbq(model_name, replace=False)

Save the model to BigQuery.

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

bigframes.ml.imported.TensorFlowModel

TensorFlowModel(
    model_path: str, *, session: typing.Optional[bigframes.session.Session] = None
)

Imported TensorFlow model.

See more: bigframes.ml.imported.TensorFlowModel

bigframes.ml.imported.TensorFlowModel.__init__

__init__(model_path, *, session=None)

API documentation for __init__ method.

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

bigframes.ml.imported.TensorFlowModel.get_params

get_params(deep=True)

Get parameters for this estimator.

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

bigframes.ml.imported.TensorFlowModel.predict

predict(X)

Predict the result from input DataFrame.

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

bigframes.ml.imported.TensorFlowModel.register

register(vertex_ai_model_id=None)

Register the model to Vertex AI.

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

bigframes.ml.imported.TensorFlowModel.to_gbq

to_gbq(model_name, replace=False)

Save the model to BigQuery.

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

bigframes.ml.imported.XGBoostModel

XGBoostModel(
    model_path: str,
    *,
    input: typing.Optional[typing.Mapping[str, str]] = None,
    output: typing.Optional[typing.Mapping[str, str]] = None,
    session: typing.Optional[bigframes.session.Session] = None
)

Imported XGBoost model.

See more: bigframes.ml.imported.XGBoostModel

bigframes.ml.imported.XGBoostModel.__init__

__init__(model_path, *, input=None, output=None, session=None)

API documentation for __init__ method.

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

bigframes.ml.imported.XGBoostModel.get_params

get_params(deep=True)

Get parameters for this estimator.

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

bigframes.ml.imported.XGBoostModel.predict

predict(X)

Predict the result from input DataFrame.

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

bigframes.ml.imported.XGBoostModel.register

register(vertex_ai_model_id=None)

Register the model to Vertex AI.

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

bigframes.ml.imported.XGBoostModel.to_gbq

to_gbq(model_name, replace=False)

Save the model to BigQuery.

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

bigframes.ml.impute.SimpleImputer

SimpleImputer(strategy: typing.Literal["mean", "median", "most_frequent"] = "mean")

Univariate imputer for completing missing values with simple strategies.

See more: bigframes.ml.impute.SimpleImputer

bigframes.ml.impute.SimpleImputer.__init__

__init__(strategy="mean")

API documentation for __init__ method.

See more: bigframes.ml.impute.SimpleImputer.init

bigframes.ml.impute.SimpleImputer.fit

fit(X, y=None)

Fit the imputer on X.

See more: bigframes.ml.impute.SimpleImputer.fit

bigframes.ml.impute.SimpleImputer.fit_transform

fit_transform(X, y=None)

Fit to data, then transform it.

See more: bigframes.ml.impute.SimpleImputer.fit_transform

bigframes.ml.impute.SimpleImputer.get_params

get_params(deep=True)

Get parameters for this estimator.

See more: bigframes.ml.impute.SimpleImputer.get_params

bigframes.ml.impute.SimpleImputer.to_gbq

to_gbq(model_name, replace=False)

Save the transformer as a BigQuery model.

See more: bigframes.ml.impute.SimpleImputer.to_gbq

bigframes.ml.impute.SimpleImputer.transform

transform(X)

Impute all missing values in X.

See more: bigframes.ml.impute.SimpleImputer.transform

bigframes.ml.linear_model.LinearRegression

LinearRegression(
    *,
    optimize_strategy: typing.Literal[
        "auto_strategy", "batch_gradient_descent", "normal_equation"
    ] = "auto_strategy",
    fit_intercept: bool = True,
    l1_reg: typing.Optional[float] = None,
    l2_reg: float = 0.0,
    max_iterations: int = 20,
    warm_start: bool = False,
    learning_rate: typing.Optional[float] = None,
    learning_rate_strategy: typing.Literal["line_search", "constant"] = "line_search",
    tol: float = 0.01,
    ls_init_learning_rate: typing.Optional[float] = None,
    calculate_p_values: bool = False,
    enable_global_explain: bool = False
)

Ordinary least squares Linear Regression.

See more: bigframes.ml.linear_model.LinearRegression

bigframes.ml.linear_model.LinearRegression.__init__

__init__(
    *,
    optimize_strategy="auto_strategy",
    fit_intercept=True,
    l1_reg=None,
    l2_reg=0.0,
    max_iterations=20,
    warm_start=False,
    learning_rate=None,
    learning_rate_strategy="line_search",
    tol=0.01,
    ls_init_learning_rate=None,
    calculate_p_values=False,
    enable_global_explain=False
)

API documentation for __init__ method.

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

bigframes.ml.linear_model.LinearRegression.fit

fit(X, y, X_eval=None, y_eval=None)

Fit linear model.

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

bigframes.ml.linear_model.LinearRegression.get_params

get_params(deep=True)

Get parameters for this estimator.

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

bigframes.ml.linear_model.LinearRegression.global_explain

global_explain()

Provide explanations for an entire linear regression model.

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

bigframes.ml.linear_model.LinearRegression.predict

predict(X)

Predict using the linear model.

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

bigframes.ml.linear_model.LinearRegression.predict_explain

predict_explain(X, *, top_k_features=5)

Explain predictions for a linear regression model.

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

bigframes.ml.linear_model.LinearRegression.register

register(vertex_ai_model_id=None)

Register the model to Vertex AI.

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

bigframes.ml.linear_model.LinearRegression.score

score(X, y)

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, replace=False)

Save the model to BigQuery.

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

bigframes.ml.linear_model.LogisticRegression

LogisticRegression(
    *,
    optimize_strategy: typing.Literal[
        "auto_strategy", "batch_gradient_descent"
    ] = "auto_strategy",
    fit_intercept: bool = True,
    l1_reg: typing.Optional[float] = None,
    l2_reg: float = 0.0,
    max_iterations: int = 20,
    warm_start: bool = False,
    learning_rate: typing.Optional[float] = None,
    learning_rate_strategy: typing.Literal["line_search", "constant"] = "line_search",
    tol: float = 0.01,
    ls_init_learning_rate: typing.Optional[float] = None,
    calculate_p_values: bool = False,
    enable_global_explain: bool = False,
    class_weight: typing.Optional[
        typing.Union[typing.Literal["balanced"], typing.Dict[str, float]]
    ] = None
)

Logistic Regression (aka logit, MaxEnt) classifier.

See more: bigframes.ml.linear_model.LogisticRegression

bigframes.ml.linear_model.LogisticRegression.__init__

__init__(
    *,
    optimize_strategy="auto_strategy",
    fit_intercept=True,
    l1_reg=None,
    l2_reg=0.0,
    max_iterations=20,
    warm_start=False,
    learning_rate=None,
    learning_rate_strategy="line_search",
    tol=0.01,
    ls_init_learning_rate=None,
    calculate_p_values=False,
    enable_global_explain=False,
    class_weight=None
)

API documentation for __init__ method.

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

bigframes.ml.linear_model.LogisticRegression.fit

fit(X, y, X_eval=None, y_eval=None)

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=True)

Get parameters for this estimator.

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

bigframes.ml.linear_model.LogisticRegression.predict

predict(X)

Predict class labels for samples in X.

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

bigframes.ml.linear_model.LogisticRegression.predict_explain

predict_explain(X, *, top_k_features=5)

Explain predictions for a logistic regression model.

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

bigframes.ml.linear_model.LogisticRegression.register

register(vertex_ai_model_id=None)

Register the model to Vertex AI.

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

bigframes.ml.linear_model.LogisticRegression.score

score(X, y)

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, replace=False)

Save the model to BigQuery.

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

bigframes.ml.llm.Claude3TextGenerator

Claude3TextGenerator(
    *,
    model_name: typing.Optional[
        typing.Literal[
            "claude-3-sonnet", "claude-3-haiku", "claude-3-5-sonnet", "claude-3-opus"
        ]
    ] = None,
    session: typing.Optional[bigframes.session.Session] = None,
    connection_name: typing.Optional[str] = None
)

Claude3 text generator LLM model.

See more: bigframes.ml.llm.Claude3TextGenerator

bigframes.ml.llm.Claude3TextGenerator.__init__

__init__(*, model_name=None, session=None, connection_name=None)

API documentation for __init__ method.

See more: bigframes.ml.llm.Claude3TextGenerator.init

bigframes.ml.llm.Claude3TextGenerator.get_params

get_params(deep=True)

Get parameters for this estimator.

See more: bigframes.ml.llm.Claude3TextGenerator.get_params

bigframes.ml.llm.Claude3TextGenerator.predict

predict(X, *, max_output_tokens=128, top_k=40, top_p=0.95, max_retries=0)

Predict the result from input DataFrame.

See more: bigframes.ml.llm.Claude3TextGenerator.predict

bigframes.ml.llm.Claude3TextGenerator.to_gbq

to_gbq(model_name, replace=False)

Save the model to BigQuery.

See more: bigframes.ml.llm.Claude3TextGenerator.to_gbq

bigframes.ml.llm.GeminiTextGenerator

GeminiTextGenerator(
    *,
    model_name: typing.Optional[
        typing.Literal[
            "gemini-1.5-pro-preview-0514",
            "gemini-1.5-flash-preview-0514",
            "gemini-1.5-pro-001",
            "gemini-1.5-pro-002",
            "gemini-1.5-flash-001",
            "gemini-1.5-flash-002",
            "gemini-2.0-flash-exp",
            "gemini-2.0-flash-001",
            "gemini-2.0-flash-lite-001",
            "gemini-2.5-pro",
            "gemini-2.5-flash",
            "gemini-2.5-flash-lite",
        ]
    ] = None,
    session: typing.Optional[bigframes.session.Session] = None,
    connection_name: typing.Optional[str] = None,
    max_iterations: int = 300
)

Gemini text generator LLM model.

See more: bigframes.ml.llm.GeminiTextGenerator

bigframes.ml.llm.GeminiTextGenerator.__init__

__init__(
    *, model_name=None, session=None, connection_name=None, max_iterations=300
)

API documentation for __init__ method.

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

bigframes.ml.llm.GeminiTextGenerator.fit

fit(X, y)

Fine tune GeminiTextGenerator model.

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

bigframes.ml.llm.GeminiTextGenerator.get_params

get_params(deep=True)

Get parameters for this estimator.

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

bigframes.ml.llm.GeminiTextGenerator.predict

predict(
    X,
    *,
    temperature=0.9,
    max_output_tokens=8192,
    top_k=40,
    top_p=1.0,
    ground_with_google_search=False,
    max_retries=0,
    prompt=None,
    output_schema=None
)

Predict the result from input DataFrame.

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

bigframes.ml.llm.GeminiTextGenerator.score

score(X, y, task_type="text_generation")

Calculate evaluation metrics of the model.

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

bigframes.ml.llm.GeminiTextGenerator.to_gbq

to_gbq(model_name, replace=False)

Save the model to BigQuery.

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

bigframes.ml.llm.MultimodalEmbeddingGenerator

MultimodalEmbeddingGenerator(
    *,
    model_name: typing.Optional[typing.Literal["multimodalembedding@001"]] = None,
    session: typing.Optional[bigframes.session.Session] = None,
    connection_name: typing.Optional[str] = None
)

Multimodal embedding generator LLM model.

See more: bigframes.ml.llm.MultimodalEmbeddingGenerator

bigframes.ml.llm.MultimodalEmbeddingGenerator.__init__

__init__(*, model_name=None, session=None, connection_name=None)

API documentation for __init__ method.

See more: bigframes.ml.llm.MultimodalEmbeddingGenerator.init

bigframes.ml.llm.MultimodalEmbeddingGenerator.get_params

get_params(deep=True)

Get parameters for this estimator.

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

bigframes.ml.llm.MultimodalEmbeddingGenerator.predict

predict(X, *, max_retries=0)

Predict the result from input DataFrame.

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

bigframes.ml.llm.MultimodalEmbeddingGenerator.to_gbq

to_gbq(model_name, replace=False)

Save the model to BigQuery.

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

bigframes.ml.llm.TextEmbeddingGenerator

TextEmbeddingGenerator(
    *,
    model_name: typing.Optional[
        typing.Literal[
            "text-embedding-005",
            "text-embedding-004",
            "text-multilingual-embedding-002",
        ]
    ] = None,
    session: typing.Optional[bigframes.session.Session] = None,
    connection_name: typing.Optional[str] = None
)

Text embedding generator LLM model.

See more: bigframes.ml.llm.TextEmbeddingGenerator

bigframes.ml.llm.TextEmbeddingGenerator.__init__

__init__(*, model_name=None, session=None, connection_name=None)

API documentation for __init__ method.

See more: bigframes.ml.llm.TextEmbeddingGenerator.init

bigframes.ml.llm.TextEmbeddingGenerator.get_params

get_params(deep=True)

Get parameters for this estimator.

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

bigframes.ml.llm.TextEmbeddingGenerator.predict

predict(X, *, max_retries=0)

Predict the result from input DataFrame.

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

bigframes.ml.llm.TextEmbeddingGenerator.to_gbq

to_gbq(model_name, replace=False)

Save the model to BigQuery.

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

bigframes.ml.model_selection.KFold

KFold(n_splits: int = 5, *, random_state: typing.Optional[int] = None)

K-Fold cross-validator.

See more: bigframes.ml.model_selection.KFold

bigframes.ml.model_selection.KFold.__init__

__init__(n_splits=5, *, random_state=None)

API documentation for __init__ method.

See more: bigframes.ml.model_selection.KFold.init

bigframes.ml.model_selection.KFold.get_n_splits

get_n_splits()

Returns the number of splitting iterations in the cross-validator.

See more: bigframes.ml.model_selection.KFold.get_n_splits

bigframes.ml.model_selection.KFold.split

split(X, y=None)

Generate indices to split data into training and test set.

See more: bigframes.ml.model_selection.KFold.split

bigframes.ml.model_selection.chain

chain(*iterables) --> chain object.

See more: bigframes.ml.model_selection.chain

bigframes.ml.model_selection.chain.__init__

__init__()

API documentation for __init__ method.

See more: bigframes.ml.model_selection.chain.init

bigframes.ml.model_selection.chain.from_iterable

from_iterable()

Alternative chain() constructor taking a single iterable argument that evaluates lazily.

See more: bigframes.ml.model_selection.chain.from_iterable

bigframes.ml.pipeline.Pipeline

Pipeline(steps: typing.List[typing.Tuple[str, bigframes.ml.base.BaseEstimator]])

Pipeline of transforms with a final estimator.

See more: bigframes.ml.pipeline.Pipeline

bigframes.ml.pipeline.Pipeline.__init__

__init__(steps)

API documentation for __init__ method.

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

bigframes.ml.pipeline.Pipeline.fit

fit(X, y=None)

Fit the model.

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

bigframes.ml.pipeline.Pipeline.get_params

get_params(deep=True)

Get parameters for this estimator.

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

bigframes.ml.pipeline.Pipeline.predict

predict(X)

API documentation for predict method.

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

bigframes.ml.pipeline.Pipeline.score

score(X, y=None)

API documentation for score method.

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

bigframes.ml.pipeline.Pipeline.to_gbq

to_gbq(model_name, replace=False)

Save the pipeline to BigQuery.

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

bigframes.ml.preprocessing.KBinsDiscretizer

KBinsDiscretizer(
    n_bins: int = 5, strategy: typing.Literal["uniform", "quantile"] = "quantile"
)

Bin continuous data into intervals.

See more: bigframes.ml.preprocessing.KBinsDiscretizer

bigframes.ml.preprocessing.KBinsDiscretizer.__init__

__init__(n_bins=5, strategy="quantile")

API documentation for __init__ method.

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

bigframes.ml.preprocessing.KBinsDiscretizer.fit

fit(X, y=None)

Fit the estimator.

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

bigframes.ml.preprocessing.KBinsDiscretizer.fit_transform

fit_transform(X, y=None)

Fit to data, then transform it.

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

bigframes.ml.preprocessing.KBinsDiscretizer.get_params

get_params(deep=True)

Get parameters for this estimator.

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

bigframes.ml.preprocessing.KBinsDiscretizer.to_gbq

to_gbq(model_name, replace=False)

Save the transformer as a BigQuery model.

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

bigframes.ml.preprocessing.KBinsDiscretizer.transform

transform(X)

Discretize the data.

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

bigframes.ml.preprocessing.LabelEncoder

LabelEncoder(
    min_frequency: typing.Optional[int] = None,
    max_categories: typing.Optional[int] = None,
)

Encode target labels with value between 0 and n_classes-1.

See more: bigframes.ml.preprocessing.LabelEncoder

bigframes.ml.preprocessing.LabelEncoder.__init__

__init__(min_frequency=None, max_categories=None)

API documentation for __init__ method.

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

bigframes.ml.preprocessing.LabelEncoder.fit

fit(y)

Fit label encoder.

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

bigframes.ml.preprocessing.LabelEncoder.fit_transform

fit_transform(y)

API documentation for fit_transform method.

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

bigframes.ml.preprocessing.LabelEncoder.get_params

get_params(deep=True)

Get parameters for this estimator.

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

bigframes.ml.preprocessing.LabelEncoder.to_gbq

to_gbq(model_name, replace=False)

Save the transformer as a BigQuery model.

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

bigframes.ml.preprocessing.LabelEncoder.transform

transform(y)

Transform y using label encoding.

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

bigframes.ml.preprocessing.MaxAbsScaler

MaxAbsScaler()

Scale each feature by its maximum absolute value.

See more: bigframes.ml.preprocessing.MaxAbsScaler

bigframes.ml.preprocessing.MaxAbsScaler.__init__

__init__()

API documentation for __init__ method.

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

bigframes.ml.preprocessing.MaxAbsScaler.fit

fit(X, y=None)

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, y=None)

Fit to data, then transform it.

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

bigframes.ml.preprocessing.MaxAbsScaler.get_params

get_params(deep=True)

Get parameters for this estimator.

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

bigframes.ml.preprocessing.MaxAbsScaler.to_gbq

to_gbq(model_name, replace=False)

Save the transformer as a BigQuery model.

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

bigframes.ml.preprocessing.MaxAbsScaler.transform

transform(X)

Scale the data.

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

bigframes.ml.preprocessing.MinMaxScaler

MinMaxScaler()

Transform features by scaling each feature to a given range.

See more: bigframes.ml.preprocessing.MinMaxScaler

bigframes.ml.preprocessing.MinMaxScaler.__init__

__init__()

API documentation for __init__ method.

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

bigframes.ml.preprocessing.MinMaxScaler.fit

fit(X, y=None)

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, y=None)

Fit to data, then transform it.

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

bigframes.ml.preprocessing.MinMaxScaler.get_params

get_params(deep=True)

Get parameters for this estimator.

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

bigframes.ml.preprocessing.MinMaxScaler.to_gbq

to_gbq(model_name, replace=False)

Save the transformer as a BigQuery model.

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

bigframes.ml.preprocessing.MinMaxScaler.transform

transform(X)

Scale the data.

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

bigframes.ml.preprocessing.OneHotEncoder

OneHotEncoder(
    drop: typing.Optional[typing.Literal["most_frequent"]] = None,
    min_frequency: typing.Optional[int] = None,
    max_categories: typing.Optional[int] = None,
)

Encode categorical features as a one-hot format.

See more: bigframes.ml.preprocessing.OneHotEncoder

bigframes.ml.preprocessing.OneHotEncoder.__init__

__init__(drop=None, min_frequency=None, max_categories=None)

API documentation for __init__ method.

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

bigframes.ml.preprocessing.OneHotEncoder.fit

fit(X, y=None)

Fit OneHotEncoder to X.

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

bigframes.ml.preprocessing.OneHotEncoder.fit_transform

fit_transform(X, y=None)

API documentation for fit_transform method.

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

bigframes.ml.preprocessing.OneHotEncoder.get_params

get_params(deep=True)

Get parameters for this estimator.

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

bigframes.ml.preprocessing.OneHotEncoder.to_gbq

to_gbq(model_name, replace=False)

Save the transformer as a BigQuery model.

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

bigframes.ml.preprocessing.OneHotEncoder.transform

transform(X)

Transform X using one-hot encoding.

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

bigframes.ml.preprocessing.PolynomialFeatures

PolynomialFeatures(degree: int = 2)

Generate polynomial and interaction features.

See more: bigframes.ml.preprocessing.PolynomialFeatures

bigframes.ml.preprocessing.PolynomialFeatures.__init__

__init__(degree=2)

API documentation for __init__ method.

See more: bigframes.ml.preprocessing.PolynomialFeatures.init

bigframes.ml.preprocessing.PolynomialFeatures.fit

fit(X, y=None)

Compute number of output features.

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

bigframes.ml.preprocessing.PolynomialFeatures.fit_transform

fit_transform(X, y=None)

Fit to data, then transform it.

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

bigframes.ml.preprocessing.PolynomialFeatures.get_params

get_params(deep=True)

Get parameters for this estimator.

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

bigframes.ml.preprocessing.PolynomialFeatures.to_gbq

to_gbq(model_name, replace=False)

Save the transformer as a BigQuery model.

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

bigframes.ml.preprocessing.PolynomialFeatures.transform

transform(X)

Transform data to polynomial features.

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

bigframes.ml.preprocessing.StandardScaler

StandardScaler()

Standardize features by removing the mean and scaling to unit variance.

See more: bigframes.ml.preprocessing.StandardScaler

bigframes.ml.preprocessing.StandardScaler.__init__

__init__()

API documentation for __init__ method.

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

bigframes.ml.preprocessing.StandardScaler.fit

fit(X, y=None)

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, y=None)

Fit to data, then transform it.

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

bigframes.ml.preprocessing.StandardScaler.get_params

get_params(deep=True)

Get parameters for this estimator.

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

bigframes.ml.preprocessing.StandardScaler.to_gbq

to_gbq(model_name, replace=False)

Save the transformer as a BigQuery model.

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

bigframes.ml.preprocessing.StandardScaler.transform

transform(X)

Perform standardization by centering and scaling.

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

bigframes.ml.remote.VertexAIModel

VertexAIModel(
    endpoint: str,
    input: typing.Mapping[str, str],
    output: typing.Mapping[str, str],
    *,
    session: typing.Optional[bigframes.session.Session] = None,
    connection_name: typing.Optional[str] = None
)

Remote model from a Vertex AI HTTPS endpoint.

See more: bigframes.ml.remote.VertexAIModel

bigframes.ml.remote.VertexAIModel.__init__

__init__(endpoint, input, output, *, session=None, connection_name=None)

API documentation for __init__ method.

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

bigframes.ml.remote.VertexAIModel.get_params

get_params(deep=True)

Get parameters for this estimator.

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

bigframes.ml.remote.VertexAIModel.predict

predict(X)

Predict the result from the input DataFrame.

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

bigframes.pandas.DataFrame

DataFrame(
    data=None,
    index: vendored_pandas_typing.Axes | None = None,
    columns: vendored_pandas_typing.Axes | None = None,
    dtype: typing.Optional[
        bigframes.dtypes.DtypeString | bigframes.dtypes.Dtype
    ] = None,
    copy: typing.Optional[bool] = None,
    *,
    session: typing.Optional[bigframes.session.Session] = None
)

Two-dimensional, size-mutable, potentially heterogeneous tabular data.

See more: bigframes.pandas.DataFrame

bigframes.pandas.DataFrame.__init__

__init__(
    data=None, index=None, columns=None, dtype=None, copy=None, *, session=None
)

API documentation for __init__ method.

See more: bigframes.pandas.DataFrame.init

bigframes.pandas.DataFrame.abs

abs()

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

See more: bigframes.pandas.DataFrame.abs

bigframes.pandas.DataFrame.add

add(other, axis="columns")

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

See more: bigframes.pandas.DataFrame.add

bigframes.pandas.DataFrame.add_prefix

add_prefix(prefix, axis=None)

Prefix labels with string prefix.

See more: bigframes.pandas.DataFrame.add_prefix

bigframes.pandas.DataFrame.add_suffix

add_suffix(suffix, axis=None)

Suffix labels with string suffix.

See more: bigframes.pandas.DataFrame.add_suffix

bigframes.pandas.DataFrame.agg

agg(func)

Aggregate using one or more operations over columns.

See more: bigframes.pandas.DataFrame.agg

bigframes.pandas.DataFrame.aggregate

aggregate(func)

Aggregate using one or more operations over columns.

See more: bigframes.pandas.DataFrame.aggregate

bigframes.pandas.DataFrame.align

align(other, join="outer", axis=None)

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

See more: bigframes.pandas.DataFrame.align

bigframes.pandas.DataFrame.all

all(axis=0, *, bool_only=False)

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

See more: bigframes.pandas.DataFrame.all

bigframes.pandas.DataFrame.any

any(*, axis=0, bool_only=False)

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

See more: bigframes.pandas.DataFrame.any

bigframes.pandas.DataFrame.apply

apply(func, *, axis=0, args=(), **kwargs)

Apply a function along an axis of the DataFrame.

See more: bigframes.pandas.DataFrame.apply

bigframes.pandas.DataFrame.applymap

applymap(func, na_action=None)

Apply a function to a Dataframe elementwise.

See more: bigframes.pandas.DataFrame.applymap

bigframes.pandas.DataFrame.area

area(x=None, y=None, stacked=True, **kwargs)

Draw a stacked area plot.

See more: bigframes.pandas.DataFrame.area

bigframes.pandas.DataFrame.assign

assign(**kwargs)

Assign new columns to a DataFrame.

See more: bigframes.pandas.DataFrame.assign

bigframes.pandas.DataFrame.astype

astype(dtype, *, errors="raise")

Cast a pandas object to a specified dtype dtype.

See more: bigframes.pandas.DataFrame.astype

bigframes.pandas.DataFrame.bar

bar(x=None, y=None, **kwargs)

Draw a vertical bar plot.

See more: bigframes.pandas.DataFrame.bar

bigframes.pandas.DataFrame.bfill

bfill(*, limit=None)

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

See more: bigframes.pandas.DataFrame.bfill

bigframes.pandas.DataFrame.cache

cache()

Materializes the DataFrame to a temporary table.

See more: bigframes.pandas.DataFrame.cache

bigframes.pandas.DataFrame.combine

combine(other, func, fill_value=None, overwrite=True, *, how="outer")

Perform column-wise combine with another DataFrame.

See more: bigframes.pandas.DataFrame.combine

bigframes.pandas.DataFrame.combine_first

combine_first(other)

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

See more: bigframes.pandas.DataFrame.combine_first

bigframes.pandas.DataFrame.copy

copy()

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

See more: bigframes.pandas.DataFrame.copy

bigframes.pandas.DataFrame.corr

corr(method="pearson", min_periods=None, numeric_only=False)

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

See more: bigframes.pandas.DataFrame.corr

bigframes.pandas.DataFrame.corrwith

corrwith(other, *, numeric_only=False)

Compute pairwise correlation.

See more: bigframes.pandas.DataFrame.corrwith

bigframes.pandas.DataFrame.count

count(*, numeric_only=False)

Count non-NA cells for each column.

See more: bigframes.pandas.DataFrame.count

bigframes.pandas.DataFrame.cov

cov(*, numeric_only=False)

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

See more: bigframes.pandas.DataFrame.cov

bigframes.pandas.DataFrame.cummax

cummax()

Return cumulative maximum over columns.

See more: bigframes.pandas.DataFrame.cummax

bigframes.pandas.DataFrame.cummin

cummin()

Return cumulative minimum over columns.

See more: bigframes.pandas.DataFrame.cummin

bigframes.pandas.DataFrame.cumprod

cumprod()

Return cumulative product over columns.

See more: bigframes.pandas.DataFrame.cumprod

bigframes.pandas.DataFrame.cumsum

cumsum()

Return cumulative sum over columns.

See more: bigframes.pandas.DataFrame.cumsum

bigframes.pandas.DataFrame.describe

describe(include=None)

Generate descriptive statistics.

See more: bigframes.pandas.DataFrame.describe

bigframes.pandas.DataFrame.diff

diff(periods=1)

First discrete difference of element.

See more: bigframes.pandas.DataFrame.diff

bigframes.pandas.DataFrame.div

div(other, axis="columns")

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

See more: bigframes.pandas.DataFrame.div

bigframes.pandas.DataFrame.divide

divide(other, axis="columns")

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

See more: bigframes.pandas.DataFrame.divide

bigframes.pandas.DataFrame.dot

dot(other)

Compute the matrix multiplication between the DataFrame and other.

See more: bigframes.pandas.DataFrame.dot

bigframes.pandas.DataFrame.drop

Drop specified labels from columns.

See more: bigframes.pandas.DataFrame.drop

bigframes.pandas.DataFrame.drop_duplicates

drop_duplicates(subset=None, *, keep="first")

Return DataFrame with duplicate rows removed.

See more: bigframes.pandas.DataFrame.drop_duplicates

bigframes.pandas.DataFrame.droplevel

droplevel(level, axis=0)

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

See more: bigframes.pandas.DataFrame.droplevel

bigframes.pandas.DataFrame.dropna

dropna(
    *, axis=0, how="any", thresh=None, subset=None, inplace=False, ignore_index=False
)

Remove missing values.

See more: bigframes.pandas.DataFrame.dropna

bigframes.pandas.DataFrame.duplicated

duplicated(subset=None, keep="first")

Return boolean Series denoting duplicate rows.

See more: bigframes.pandas.DataFrame.duplicated

bigframes.pandas.DataFrame.eq

eq(other, axis="columns")

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

See more: bigframes.pandas.DataFrame.eq

bigframes.pandas.DataFrame.equals

equals(other)

Test whether two objects contain the same elements.

See more: bigframes.pandas.DataFrame.equals

bigframes.pandas.DataFrame.eval

eval(expr)

Evaluate a string describing operations on DataFrame columns.

See more: bigframes.pandas.DataFrame.eval

bigframes.pandas.DataFrame.expanding

expanding(min_periods=1)

Provide expanding window calculations.

See more: bigframes.pandas.DataFrame.expanding

bigframes.pandas.DataFrame.explode

explode(column, *, ignore_index=False)

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

See more: bigframes.pandas.DataFrame.explode

bigframes.pandas.DataFrame.ffill

ffill(*, limit=None)

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

See more: bigframes.pandas.DataFrame.ffill

bigframes.pandas.DataFrame.fillna

fillna(value=None)

Fill NA (NULL in BigQuery) values using the specified method.

See more: bigframes.pandas.DataFrame.fillna

bigframes.pandas.DataFrame.filter

filter(items=None, like=None, regex=None, axis=None)

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

See more: bigframes.pandas.DataFrame.filter

bigframes.pandas.DataFrame.first_valid_index

first_valid_index()

API documentation for first_valid_index method.

See more: bigframes.pandas.DataFrame.first_valid_index

bigframes.pandas.DataFrame.floordiv

floordiv(other, axis="columns")

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

See more: bigframes.pandas.DataFrame.floordiv

bigframes.pandas.DataFrame.from_dict

from_dict(data, orient="columns", dtype=None, columns=None)

Construct DataFrame from dict of array-like or dicts.

See more: bigframes.pandas.DataFrame.from_dict

bigframes.pandas.DataFrame.from_records

from_records(
    data, index=None, exclude=None, columns=None, coerce_float=False, nrows=None
)

Convert structured or record ndarray to DataFrame.

See more: bigframes.pandas.DataFrame.from_records

bigframes.pandas.DataFrame.ge

ge(other, axis="columns")

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

See more: bigframes.pandas.DataFrame.ge

bigframes.pandas.DataFrame.get

get(key, default=None)

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

See more: bigframes.pandas.DataFrame.get

bigframes.pandas.DataFrame.groupby

groupby(by=None, *, level=None, as_index=True, dropna=True)

Group DataFrame by columns.

See more: bigframes.pandas.DataFrame.groupby

bigframes.pandas.DataFrame.gt

gt(other, axis="columns")

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

See more: bigframes.pandas.DataFrame.gt

bigframes.pandas.DataFrame.head

head(n=5)

Return the first n rows.

See more: bigframes.pandas.DataFrame.head

bigframes.pandas.DataFrame.hist

hist(by=None, bins=10, **kwargs)

Draw one histogram of the DataFrame’s columns.

See more: bigframes.pandas.DataFrame.hist

bigframes.pandas.DataFrame.idxmax

idxmax()

Return index of first occurrence of maximum over columns.

See more: bigframes.pandas.DataFrame.idxmax

bigframes.pandas.DataFrame.idxmin

idxmin()

Return index of first occurrence of minimum over columns.

See more: bigframes.pandas.DataFrame.idxmin

bigframes.pandas.DataFrame.info

info(verbose=None, buf=None, max_cols=None, memory_usage=None, show_counts=None)

Print a concise summary of a DataFrame.

See more: bigframes.pandas.DataFrame.info

bigframes.pandas.DataFrame.insert

insert(loc, column, value, allow_duplicates=False)

Insert column into DataFrame at specified location.

See more: bigframes.pandas.DataFrame.insert

bigframes.pandas.DataFrame.interpolate

interpolate(method="linear")

Fill NA (NULL in BigQuery) values using an interpolation method.

See more: bigframes.pandas.DataFrame.interpolate

bigframes.pandas.DataFrame.isin

isin(values)

Whether each element in the DataFrame is contained in values.

See more: bigframes.pandas.DataFrame.isin

bigframes.pandas.DataFrame.isna

isna()

Detect missing (NULL) values.

See more: bigframes.pandas.DataFrame.isna

bigframes.pandas.DataFrame.isnull

isnull()

Detect missing (NULL) values.

See more: bigframes.pandas.DataFrame.isnull

bigframes.pandas.DataFrame.items

items()

Iterate over (column name, Series) pairs.

See more: bigframes.pandas.DataFrame.items

bigframes.pandas.DataFrame.iterrows

iterrows()

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

See more: bigframes.pandas.DataFrame.iterrows

bigframes.pandas.DataFrame.itertuples

itertuples(index=True, name="Pandas")

Iterate over DataFrame rows as namedtuples.

See more: bigframes.pandas.DataFrame.itertuples

bigframes.pandas.DataFrame.join

join(other, on=None, how="left", lsuffix="", rsuffix="")

Join columns of another DataFrame.

See more: bigframes.pandas.DataFrame.join

bigframes.pandas.DataFrame.keys

keys()

Get the 'info axis'.

See more: bigframes.pandas.DataFrame.keys

bigframes.pandas.DataFrame.kurt

kurt(*, numeric_only=False)

Return unbiased kurtosis over columns.

See more: bigframes.pandas.DataFrame.kurt

bigframes.pandas.DataFrame.kurtosis

kurtosis(*, numeric_only=False)

Return unbiased kurtosis over columns.

See more: bigframes.pandas.DataFrame.kurtosis

bigframes.pandas.DataFrame.le

le(other, axis="columns")

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

See more: bigframes.pandas.DataFrame.le

bigframes.pandas.DataFrame.line

line(x=None, y=None, **kwargs)

Plot Series or DataFrame as lines.

See more: bigframes.pandas.DataFrame.line

bigframes.pandas.DataFrame.lt

lt(other, axis="columns")

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

See more: bigframes.pandas.DataFrame.lt

bigframes.pandas.DataFrame.map

map(func, na_action=None)

Apply a function to a Dataframe elementwise.

See more: bigframes.pandas.DataFrame.map

bigframes.pandas.DataFrame.mask

mask(cond, other=None)

Replace values where the condition is False.

See more: bigframes.pandas.DataFrame.mask

bigframes.pandas.DataFrame.max

max(axis=0, *, numeric_only=False)

Return the maximum of the values over the requested axis.

See more: bigframes.pandas.DataFrame.max

bigframes.pandas.DataFrame.mean

mean(axis=0, *, numeric_only=False)

Return the mean of the values over the requested axis.

See more: bigframes.pandas.DataFrame.mean

bigframes.pandas.DataFrame.median

median(*, numeric_only=False, exact=True)

Return the median of the values over colunms.

See more: bigframes.pandas.DataFrame.median

bigframes.pandas.DataFrame.melt

melt(id_vars=None, value_vars=None, var_name=None, value_name="value")

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

See more: bigframes.pandas.DataFrame.melt

bigframes.pandas.DataFrame.memory_usage

memory_usage(index=True)

Return the memory usage of each column in bytes.

See more: bigframes.pandas.DataFrame.memory_usage

bigframes.pandas.DataFrame.merge

merge(
    right,
    how="inner",
    on=None,
    *,
    left_on=None,
    right_on=None,
    left_index=False,
    right_index=False,
    sort=False,
    suffixes=("_x", "_y")
)

Merge DataFrame objects with a database-style join.

See more: bigframes.pandas.DataFrame.merge

bigframes.pandas.DataFrame.min

min(axis=0, *, numeric_only=False)

Return the minimum of the values over the requested axis.

See more: bigframes.pandas.DataFrame.min

bigframes.pandas.DataFrame.mod

mod(other, axis="columns")

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

See more: bigframes.pandas.DataFrame.mod

bigframes.pandas.DataFrame.mul

mul(other, axis="columns")

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

See more: bigframes.pandas.DataFrame.mul

bigframes.pandas.DataFrame.multiply

multiply(other, axis="columns")

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

See more: bigframes.pandas.DataFrame.multiply

bigframes.pandas.DataFrame.ne

ne(other, axis="columns")

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

See more: bigframes.pandas.DataFrame.ne

bigframes.pandas.DataFrame.nlargest

nlargest(n, columns, keep="first")

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

See more: bigframes.pandas.DataFrame.nlargest

bigframes.pandas.DataFrame.notna

notna()

Detect existing (non-missing) values.

See more: bigframes.pandas.DataFrame.notna

bigframes.pandas.DataFrame.notnull

notnull()

Detect existing (non-missing) values.

See more: bigframes.pandas.DataFrame.notnull

bigframes.pandas.DataFrame.nsmallest

nsmallest(n, columns, keep="first")

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

See more: bigframes.pandas.DataFrame.nsmallest

bigframes.pandas.DataFrame.nunique

nunique()

Count number of distinct elements in each column.

See more: bigframes.pandas.DataFrame.nunique

bigframes.pandas.DataFrame.pct_change

pct_change(periods=1)

Fractional change between the current and a prior element.

See more: bigframes.pandas.DataFrame.pct_change

bigframes.pandas.DataFrame.peek

peek(n=5, *, force=True, allow_large_results=None)

Preview n arbitrary rows from the dataframe.

See more: bigframes.pandas.DataFrame.peek

bigframes.pandas.DataFrame.pipe

pipe(func, *args, **kwargs)

Apply chainable functions that expect Series or DataFrames.

See more: bigframes.pandas.DataFrame.pipe

bigframes.pandas.DataFrame.pivot

pivot(*, columns, index=None, values=None)

Return reshaped DataFrame organized by given index / column values.

See more: bigframes.pandas.DataFrame.pivot

bigframes.pandas.DataFrame.pivot_table

pivot_table(
    values=None,
    index=None,
    columns=None,
    aggfunc="mean",
    fill_value=None,
    margins=False,
    dropna=True,
    margins_name="All",
    observed=False,
    sort=True,
)

Create a spreadsheet-style pivot table as a DataFrame.

See more: bigframes.pandas.DataFrame.pivot_table

bigframes.pandas.DataFrame.pow

pow(other, axis="columns")

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

See more: bigframes.pandas.DataFrame.pow

bigframes.pandas.DataFrame.prod

prod(axis=0, *, numeric_only=False)

Return the product of the values over the requested axis.

See more: bigframes.pandas.DataFrame.prod

bigframes.pandas.DataFrame.product

product(axis=0, *, numeric_only=False)

Return the product of the values over the requested axis.

See more: bigframes.pandas.DataFrame.product

bigframes.pandas.DataFrame.quantile

quantile(q=0.5, *, numeric_only=False)

Return values at the given quantile over requested axis.

See more: bigframes.pandas.DataFrame.quantile

bigframes.pandas.DataFrame.query

query(expr)

Query the columns of a DataFrame with a boolean expression.

See more: bigframes.pandas.DataFrame.query

bigframes.pandas.DataFrame.radd

radd(other, axis="columns")

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

See more: bigframes.pandas.DataFrame.radd

bigframes.pandas.DataFrame.rank

rank(
    axis=0,
    method="average",
    numeric_only=False,
    na_option="keep",
    ascending=True,
    pct=False,
)

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

See more: bigframes.pandas.DataFrame.rank

bigframes.pandas.DataFrame.rdiv

rdiv(other, axis="columns")

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

See more: bigframes.pandas.DataFrame.rdiv

bigframes.pandas.DataFrame.reindex

reindex(labels=None, *, index=None, columns=None, axis=None, validate=None)

Conform DataFrame to new index with optional filling logic.

See more: bigframes.pandas.DataFrame.reindex

bigframes.pandas.DataFrame.reindex_like

reindex_like(other, *, validate=None)

Return an object with matching indices as other object.

See more: bigframes.pandas.DataFrame.reindex_like

bigframes.pandas.DataFrame.rename

Rename columns.

See more: bigframes.pandas.DataFrame.rename

bigframes.pandas.DataFrame.rename_axis

Set the name of the axis for the index.

See more: bigframes.pandas.DataFrame.rename_axis

bigframes.pandas.DataFrame.reorder_levels

reorder_levels(order, axis=0)

Rearrange index levels using input order.

See more: bigframes.pandas.DataFrame.reorder_levels

bigframes.pandas.DataFrame.replace

replace(to_replace, value=None, *, regex=False)

Replace values given in to_replace with value.

See more: bigframes.pandas.DataFrame.replace

bigframes.pandas.DataFrame.resample

resample(rule, *, closed=None, label=None, on=None, level=None, origin="start_day")

Resample time-series data.

See more: bigframes.pandas.DataFrame.resample

bigframes.pandas.DataFrame.reset_index

Reset the index.

See more: bigframes.pandas.DataFrame.reset_index

bigframes.pandas.DataFrame.rfloordiv

rfloordiv(other, axis="columns")

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

See more: bigframes.pandas.DataFrame.rfloordiv

bigframes.pandas.DataFrame.rmod

rmod(other, axis="columns")

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

See more: bigframes.pandas.DataFrame.rmod

bigframes.pandas.DataFrame.rmul

rmul(other, axis="columns")

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

See more: bigframes.pandas.DataFrame.rmul

bigframes.pandas.DataFrame.rolling

rolling(window, min_periods=None, on=None, closed="right")

Provide rolling window calculations.

See more: bigframes.pandas.DataFrame.rolling

bigframes.pandas.DataFrame.round

round(decimals=0)

Round a DataFrame to a variable number of decimal places.

See more: bigframes.pandas.DataFrame.round

bigframes.pandas.DataFrame.rpow

rpow(other, axis="columns")

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

See more: bigframes.pandas.DataFrame.rpow

bigframes.pandas.DataFrame.rsub

rsub(other, axis="columns")

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

See more: bigframes.pandas.DataFrame.rsub

bigframes.pandas.DataFrame.rtruediv

rtruediv(other, axis="columns")

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

See more: bigframes.pandas.DataFrame.rtruediv

bigframes.pandas.DataFrame.sample

sample(n=None, frac=None, *, random_state=None, sort="random")

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

See more: bigframes.pandas.DataFrame.sample

bigframes.pandas.DataFrame.scatter

scatter(x=None, y=None, s=None, c=None, **kwargs)

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

See more: bigframes.pandas.DataFrame.scatter

bigframes.pandas.DataFrame.select_dtypes

select_dtypes(include=None, exclude=None)

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

See more: bigframes.pandas.DataFrame.select_dtypes

bigframes.pandas.DataFrame.set_index

set_index(keys, append=False, drop=True)

Set the DataFrame index using existing columns.

See more: bigframes.pandas.DataFrame.set_index

bigframes.pandas.DataFrame.shift

shift(periods=1)

Shift index by desired number of periods.

See more: bigframes.pandas.DataFrame.shift

bigframes.pandas.DataFrame.skew

skew(*, numeric_only=False)

Return unbiased skew over columns.

See more: bigframes.pandas.DataFrame.skew

bigframes.pandas.DataFrame.sort_index

Sort object by labels (along an axis).

See more: bigframes.pandas.DataFrame.sort_index

bigframes.pandas.DataFrame.sort_values

Sort by the values along row axis.

See more: bigframes.pandas.DataFrame.sort_values

bigframes.pandas.DataFrame.stack

stack(level=-1)

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

See more: bigframes.pandas.DataFrame.stack

bigframes.pandas.DataFrame.std

std(axis=0, *, numeric_only=False)

Return sample standard deviation over columns.

See more: bigframes.pandas.DataFrame.std

bigframes.pandas.DataFrame.sub

sub(other, axis="columns")

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

See more: bigframes.pandas.DataFrame.sub

bigframes.pandas.DataFrame.subtract

subtract(other, axis="columns")

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

See more: bigframes.pandas.DataFrame.subtract

bigframes.pandas.DataFrame.sum

sum(axis=0, *, numeric_only=False)

Return the sum of the values over the requested axis.

See more: bigframes.pandas.DataFrame.sum

bigframes.pandas.DataFrame.swaplevel

swaplevel(i=-2, j=-1, axis=0)

Swap levels i and j in a MultiIndex.

See more: bigframes.pandas.DataFrame.swaplevel

bigframes.pandas.DataFrame.tail

tail(n=5)

Return the last n rows.

See more: bigframes.pandas.DataFrame.tail

bigframes.pandas.DataFrame.take

take(indices, axis=0, **kwargs)

Return the elements in the given positional indices along an axis.

See more: bigframes.pandas.DataFrame.take

bigframes.pandas.DataFrame.to_arrow

to_arrow(*, ordered=True, allow_large_results=None)

Write DataFrame to an Arrow table / record batch.

See more: bigframes.pandas.DataFrame.to_arrow

bigframes.pandas.DataFrame.to_csv

to_csv(
    path_or_buf=None, sep=",", *, header=True, index=True, allow_large_results=None
)

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

See more: bigframes.pandas.DataFrame.to_csv

bigframes.pandas.DataFrame.to_dict

to_dict(orient="dict", into=dict, *, allow_large_results=None, **kwargs)

Convert the DataFrame to a dictionary.

See more: bigframes.pandas.DataFrame.to_dict

bigframes.pandas.DataFrame.to_excel

to_excel(excel_writer, sheet_name="Sheet1", *, allow_large_results=None, **kwargs)

Write DataFrame to an Excel sheet.

See more: bigframes.pandas.DataFrame.to_excel

bigframes.pandas.DataFrame.to_gbq

to_gbq(
    destination_table=None,
    *,
    if_exists=None,
    index=True,
    ordering_id=None,
    clustering_columns=(),
    labels={}
)

Write a DataFrame to a BigQuery table.

See more: bigframes.pandas.DataFrame.to_gbq

bigframes.pandas.DataFrame.to_html

to_html(
    buf=None,
    columns=None,
    col_space=None,
    header=True,
    index=True,
    na_rep="NaN",
    formatters=None,
    float_format=None,
    sparsify=None,
    index_names=True,
    justify=None,
    max_rows=None,
    max_cols=None,
    show_dimensions=False,
    decimal=".",
    bold_rows=True,
    classes=None,
    escape=True,
    notebook=False,
    border=None,
    table_id=None,
    render_links=False,
    encoding=None,
    *,
    allow_large_results=None
)

Render a DataFrame as an HTML table.

See more: bigframes.pandas.DataFrame.to_html

bigframes.pandas.DataFrame.to_json

to_json(
    path_or_buf=None, orient=None, *, lines=False, index=True, allow_large_results=None
)

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

See more: bigframes.pandas.DataFrame.to_json

bigframes.pandas.DataFrame.to_latex

to_latex(
    buf=None,
    columns=None,
    header=True,
    index=True,
    *,
    allow_large_results=None,
    **kwargs
)

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

See more: bigframes.pandas.DataFrame.to_latex

bigframes.pandas.DataFrame.to_markdown

to_markdown(buf=None, mode="wt", index=True, *, allow_large_results=None, **kwargs)

Print DataFrame in Markdown-friendly format.

See more: bigframes.pandas.DataFrame.to_markdown

bigframes.pandas.DataFrame.to_numpy

to_numpy(
    dtype=None,
    copy=False,
    na_value=_NoDefault.no_default,
    *,
    allow_large_results=None,
    **kwargs
)

Convert the DataFrame to a NumPy array.

See more: bigframes.pandas.DataFrame.to_numpy

bigframes.pandas.DataFrame.to_orc

to_orc(path=None, *, allow_large_results=None, **kwargs)

Write a DataFrame to the ORC format.

See more: bigframes.pandas.DataFrame.to_orc

bigframes.pandas.DataFrame.to_pandas

Write DataFrame to pandas DataFrame.

See more: bigframes.pandas.DataFrame.to_pandas

bigframes.pandas.DataFrame.to_pandas_batches

to_pandas_batches(page_size=None, max_results=None, *, allow_large_results=None)

Stream DataFrame results to an iterable of pandas DataFrame.

See more: bigframes.pandas.DataFrame.to_pandas_batches

bigframes.pandas.DataFrame.to_parquet

to_parquet(
    path=None, *, compression="snappy", index=True, allow_large_results=None
)

Write a DataFrame to the binary Parquet format.

See more: bigframes.pandas.DataFrame.to_parquet

bigframes.pandas.DataFrame.to_pickle

to_pickle(path, *, allow_large_results=None, **kwargs)

Pickle (serialize) object to file.

See more: bigframes.pandas.DataFrame.to_pickle

bigframes.pandas.DataFrame.to_records

to_records(
    index=True, column_dtypes=None, index_dtypes=None, *, allow_large_results=None
)

Convert DataFrame to a NumPy record array.

See more: bigframes.pandas.DataFrame.to_records

bigframes.pandas.DataFrame.to_string

to_string(
    buf=None,
    columns=None,
    col_space=None,
    header=True,
    index=True,
    na_rep="NaN",
    formatters=None,
    float_format=None,
    sparsify=None,
    index_names=True,
    justify=None,
    max_rows=None,
    max_cols=None,
    show_dimensions=False,
    decimal=".",
    line_width=None,
    min_rows=None,
    max_colwidth=None,
    encoding=None,
    *,
    allow_large_results=None
)

Render a DataFrame to a console-friendly tabular output.

See more: bigframes.pandas.DataFrame.to_string

bigframes.pandas.DataFrame.transpose

transpose()

Transpose index and columns.

See more: bigframes.pandas.DataFrame.transpose

bigframes.pandas.DataFrame.truediv

truediv(other, axis="columns")

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

See more: bigframes.pandas.DataFrame.truediv

bigframes.pandas.DataFrame.unstack

unstack(level=-1)

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

See more: bigframes.pandas.DataFrame.unstack

bigframes.pandas.DataFrame.update

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

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

See more: bigframes.pandas.DataFrame.update

bigframes.pandas.DataFrame.value_counts

value_counts(subset=None, normalize=False, sort=True, ascending=False, dropna=True)

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

See more: bigframes.pandas.DataFrame.value_counts

bigframes.pandas.DataFrame.var

var(axis=0, *, numeric_only=False)

Return unbiased variance over requested axis.

See more: bigframes.pandas.DataFrame.var

bigframes.pandas.DataFrame.where

where(cond, other=None)

Replace values where the condition is False.

See more: bigframes.pandas.DataFrame.where

bigframes.pandas.DatetimeIndex

DatetimeIndex(data=None, dtype=None, *, name=None, session=None)

Immutable sequence used for indexing and alignment with datetime-like values.

See more: bigframes.pandas.DatetimeIndex

bigframes.pandas.DatetimeIndex.__init__

__init__()

API documentation for __init__ method.

See more: bigframes.pandas.DatetimeIndex.init

bigframes.pandas.DatetimeIndex.all

all()

Return whether all elements are Truthy.

See more: bigframes.pandas.DatetimeIndex.all

bigframes.pandas.DatetimeIndex.any

any()

Return whether any element is Truthy.

See more: bigframes.pandas.DatetimeIndex.any

bigframes.pandas.DatetimeIndex.argmax

argmax()

Return int position of the largest value in the Series.

See more: bigframes.pandas.DatetimeIndex.argmax

bigframes.pandas.DatetimeIndex.argmin

argmin()

Return int position of the smallest value in the series.

See more: bigframes.pandas.DatetimeIndex.argmin

bigframes.pandas.DatetimeIndex.astype

astype(dtype, *, errors="raise")

Create an Index with values cast to dtypes.

See more: bigframes.pandas.DatetimeIndex.astype

bigframes.pandas.DatetimeIndex.copy

copy(name=None)

Make a copy of this object.

See more: bigframes.pandas.DatetimeIndex.copy

bigframes.pandas.DatetimeIndex.drop

drop(labels)

Make new Index with passed list of labels deleted.

See more: bigframes.pandas.DatetimeIndex.drop

bigframes.pandas.DatetimeIndex.drop_duplicates

drop_duplicates(*, keep="first")

Return Index with duplicate values removed.

See more: bigframes.pandas.DatetimeIndex.drop_duplicates

bigframes.pandas.DatetimeIndex.dropna

dropna(how="any")

Return Index without NA/NaN values.

See more: bigframes.pandas.DatetimeIndex.dropna

bigframes.pandas.DatetimeIndex.fillna

fillna(value=None)

Fill NA (NULL in BigQuery) values using the specified method.

See more: bigframes.pandas.DatetimeIndex.fillna

bigframes.pandas.DatetimeIndex.from_frame

from_frame(frame)

Make a MultiIndex from a DataFrame.

See more: bigframes.pandas.DatetimeIndex.from_frame

bigframes.pandas.DatetimeIndex.get_level_values

get_level_values(level)

Return an Index of values for requested level.

See more: bigframes.pandas.DatetimeIndex.get_level_values

bigframes.pandas.DatetimeIndex.get_loc

get_loc(key)

Get integer location, slice or boolean mask for requested label.

See more: bigframes.pandas.DatetimeIndex.get_loc

bigframes.pandas.DatetimeIndex.isin

isin(values)

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

See more: bigframes.pandas.DatetimeIndex.isin

bigframes.pandas.DatetimeIndex.item

item()

Return the first element of the underlying data as a Python scalar.

See more: bigframes.pandas.DatetimeIndex.item

bigframes.pandas.DatetimeIndex.max

max()

Return the maximum value of the Index.

See more: bigframes.pandas.DatetimeIndex.max

bigframes.pandas.DatetimeIndex.min

min()

Return the minimum value of the Index.

See more: bigframes.pandas.DatetimeIndex.min

bigframes.pandas.DatetimeIndex.nunique

nunique()

Return number of unique elements in the object.

See more: bigframes.pandas.DatetimeIndex.nunique

bigframes.pandas.DatetimeIndex.rename

rename(name, *, inplace=False)

Alter Index or MultiIndex name.

See more: bigframes.pandas.DatetimeIndex.rename

bigframes.pandas.DatetimeIndex.sort_values

sort_values(*, inplace=False, ascending=True, na_position="last")

Return a sorted copy of the index.

See more: bigframes.pandas.DatetimeIndex.sort_values

bigframes.pandas.DatetimeIndex.to_list

to_list(*, allow_large_results=None)

API documentation for to_list method.

See more: bigframes.pandas.DatetimeIndex.to_list

bigframes.pandas.DatetimeIndex.to_numpy

to_numpy(dtype=None, *, allow_large_results=None, **kwargs)

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

See more: bigframes.pandas.DatetimeIndex.to_numpy

bigframes.pandas.DatetimeIndex.to_pandas

to_pandas(*, allow_large_results=None, dry_run=False)

Gets the Index as a pandas Index.

See more: bigframes.pandas.DatetimeIndex.to_pandas

bigframes.pandas.DatetimeIndex.to_series

to_series(index=None, name=None)

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

See more: bigframes.pandas.DatetimeIndex.to_series

bigframes.pandas.DatetimeIndex.transpose

transpose()

Return the transpose, which is by definition self.

See more: bigframes.pandas.DatetimeIndex.transpose

bigframes.pandas.DatetimeIndex.unique

unique(level=None)

Returns unique values in the index.

See more: bigframes.pandas.DatetimeIndex.unique

bigframes.pandas.DatetimeIndex.value_counts

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

Return a Series containing counts of unique values.

See more: bigframes.pandas.DatetimeIndex.value_counts

bigframes.pandas.Index

Index(data=None, dtype=None, *, name=None, session=None)

Immutable sequence used for indexing and alignment.

See more: bigframes.pandas.Index

bigframes.pandas.Index.__init__

__init__()

API documentation for __init__ method.

See more: bigframes.pandas.Index.init

bigframes.pandas.Index.all

all()

Return whether all elements are Truthy.

See more: bigframes.pandas.Index.all

bigframes.pandas.Index.any

any()

Return whether any element is Truthy.

See more: bigframes.pandas.Index.any

bigframes.pandas.Index.argmax

argmax()

Return int position of the largest value in the Series.

See more: bigframes.pandas.Index.argmax

bigframes.pandas.Index.argmin

argmin()

Return int position of the smallest value in the series.

See more: bigframes.pandas.Index.argmin

bigframes.pandas.Index.astype

astype(dtype, *, errors="raise")

Create an Index with values cast to dtypes.

See more: bigframes.pandas.Index.astype

bigframes.pandas.Index.copy

copy(name=None)

Make a copy of this object.

See more: bigframes.pandas.Index.copy

bigframes.pandas.Index.drop

drop(labels)

Make new Index with passed list of labels deleted.

See more: bigframes.pandas.Index.drop

bigframes.pandas.Index.drop_duplicates

drop_duplicates(*, keep="first")

Return Index with duplicate values removed.

See more: bigframes.pandas.Index.drop_duplicates

bigframes.pandas.Index.dropna

dropna(how="any")

Return Index without NA/NaN values.

See more: bigframes.pandas.Index.dropna

bigframes.pandas.Index.fillna

fillna(value=None)

Fill NA (NULL in BigQuery) values using the specified method.

See more: bigframes.pandas.Index.fillna

bigframes.pandas.Index.from_frame

from_frame(frame)

Make a MultiIndex from a DataFrame.

See more: bigframes.pandas.Index.from_frame

bigframes.pandas.Index.get_level_values

get_level_values(level)

Return an Index of values for requested level.

See more: bigframes.pandas.Index.get_level_values

bigframes.pandas.Index.get_loc

get_loc(key)

Get integer location, slice or boolean mask for requested label.

See more: bigframes.pandas.Index.get_loc

bigframes.pandas.Index.isin

isin(values)

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

See more: bigframes.pandas.Index.isin

bigframes.pandas.Index.item

item()

Return the first element of the underlying data as a Python scalar.

See more: bigframes.pandas.Index.item

bigframes.pandas.Index.max

max()

Return the maximum value of the Index.

See more: bigframes.pandas.Index.max

bigframes.pandas.Index.min

min()

Return the minimum value of the Index.

See more: bigframes.pandas.Index.min

bigframes.pandas.Index.nunique

nunique()

Return number of unique elements in the object.

See more: bigframes.pandas.Index.nunique

bigframes.pandas.Index.rename

Alter Index or MultiIndex name.

See more: bigframes.pandas.Index.rename

bigframes.pandas.Index.sort_values

sort_values(*, inplace=False, ascending=True, na_position="last")

Return a sorted copy of the index.

See more: bigframes.pandas.Index.sort_values

bigframes.pandas.Index.to_list

to_list(*, allow_large_results=None)

API documentation for to_list method.

See more: bigframes.pandas.Index.to_list

bigframes.pandas.Index.to_numpy

to_numpy(dtype=None, *, allow_large_results=None, **kwargs)

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

See more: bigframes.pandas.Index.to_numpy

bigframes.pandas.Index.to_pandas

Gets the Index as a pandas Index.

See more: bigframes.pandas.Index.to_pandas

bigframes.pandas.Index.to_series

to_series(index=None, name=None)

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

See more: bigframes.pandas.Index.to_series

bigframes.pandas.Index.transpose

transpose()

Return the transpose, which is by definition self.

See more: bigframes.pandas.Index.transpose

bigframes.pandas.Index.unique

unique(level=None)

Returns unique values in the index.

See more: bigframes.pandas.Index.unique

bigframes.pandas.Index.value_counts

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

Return a Series containing counts of unique values.

See more: bigframes.pandas.Index.value_counts

bigframes.pandas.MultiIndex

MultiIndex(data=None, dtype=None, *, name=None, session=None)

A multi-level, or hierarchical, index object for pandas objects.

See more: bigframes.pandas.MultiIndex

bigframes.pandas.MultiIndex.__init__

__init__()

API documentation for __init__ method.

See more: bigframes.pandas.MultiIndex.init

bigframes.pandas.MultiIndex.all

all()

Return whether all elements are Truthy.

See more: bigframes.pandas.MultiIndex.all

bigframes.pandas.MultiIndex.any

any()

Return whether any element is Truthy.

See more: bigframes.pandas.MultiIndex.any

bigframes.pandas.MultiIndex.argmax

argmax()

Return int position of the largest value in the Series.

See more: bigframes.pandas.MultiIndex.argmax

bigframes.pandas.MultiIndex.argmin

argmin()

Return int position of the smallest value in the series.

See more: bigframes.pandas.MultiIndex.argmin

bigframes.pandas.MultiIndex.astype

astype(dtype, *, errors="raise")

Create an Index with values cast to dtypes.

See more: bigframes.pandas.MultiIndex.astype

bigframes.pandas.MultiIndex.copy

copy(name=None)

Make a copy of this object.

See more: bigframes.pandas.MultiIndex.copy

bigframes.pandas.MultiIndex.drop

drop(labels)

Make new Index with passed list of labels deleted.

See more: bigframes.pandas.MultiIndex.drop

bigframes.pandas.MultiIndex.drop_duplicates

drop_duplicates(*, keep="first")

Return Index with duplicate values removed.

See more: bigframes.pandas.MultiIndex.drop_duplicates

bigframes.pandas.MultiIndex.dropna

dropna(how="any")

Return Index without NA/NaN values.

See more: bigframes.pandas.MultiIndex.dropna

bigframes.pandas.MultiIndex.fillna

fillna(value=None)

Fill NA (NULL in BigQuery) values using the specified method.

See more: bigframes.pandas.MultiIndex.fillna

bigframes.pandas.MultiIndex.from_arrays

from_arrays(arrays, sortorder=None, names=None, *, session=None)

Convert arrays to MultiIndex.

See more: bigframes.pandas.MultiIndex.from_arrays

bigframes.pandas.MultiIndex.from_frame

from_frame(frame)

Make a MultiIndex from a DataFrame.

See more: bigframes.pandas.MultiIndex.from_frame

bigframes.pandas.MultiIndex.from_tuples

from_tuples(tuples, sortorder=None, names=None, *, session=None)

Convert list of tuples to MultiIndex.

See more: bigframes.pandas.MultiIndex.from_tuples

bigframes.pandas.MultiIndex.get_level_values

get_level_values(level)

Return an Index of values for requested level.

See more: bigframes.pandas.MultiIndex.get_level_values

bigframes.pandas.MultiIndex.get_loc

get_loc(key)

Get integer location, slice or boolean mask for requested label.

See more: bigframes.pandas.MultiIndex.get_loc

bigframes.pandas.MultiIndex.isin

isin(values)

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

See more: bigframes.pandas.MultiIndex.isin

bigframes.pandas.MultiIndex.item

item()

Return the first element of the underlying data as a Python scalar.

See more: bigframes.pandas.MultiIndex.item

bigframes.pandas.MultiIndex.max

max()

Return the maximum value of the Index.

See more: bigframes.pandas.MultiIndex.max

bigframes.pandas.MultiIndex.min

min()

Return the minimum value of the Index.

See more: bigframes.pandas.MultiIndex.min

bigframes.pandas.MultiIndex.nunique

nunique()

Return number of unique elements in the object.

See more: bigframes.pandas.MultiIndex.nunique

bigframes.pandas.MultiIndex.rename

rename(name, *, inplace=False)

Alter Index or MultiIndex name.

See more: bigframes.pandas.MultiIndex.rename

bigframes.pandas.MultiIndex.sort_values

sort_values(*, inplace=False, ascending=True, na_position="last")

Return a sorted copy of the index.

See more: bigframes.pandas.MultiIndex.sort_values

bigframes.pandas.MultiIndex.to_list

to_list(*, allow_large_results=None)

API documentation for to_list method.

See more: bigframes.pandas.MultiIndex.to_list

bigframes.pandas.MultiIndex.to_numpy

to_numpy(dtype=None, *, allow_large_results=None, **kwargs)

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

See more: bigframes.pandas.MultiIndex.to_numpy

bigframes.pandas.MultiIndex.to_pandas

to_pandas(*, allow_large_results=None, dry_run=False)

Gets the Index as a pandas Index.

See more: bigframes.pandas.MultiIndex.to_pandas

bigframes.pandas.MultiIndex.to_series

to_series(index=None, name=None)

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

See more: bigframes.pandas.MultiIndex.to_series

bigframes.pandas.MultiIndex.transpose

transpose()

Return the transpose, which is by definition self.

See more: bigframes.pandas.MultiIndex.transpose

bigframes.pandas.MultiIndex.unique

unique(level=None)

Returns unique values in the index.

See more: bigframes.pandas.MultiIndex.unique

bigframes.pandas.MultiIndex.value_counts

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

Return a Series containing counts of unique values.

See more: bigframes.pandas.MultiIndex.value_counts

bigframes.pandas.NamedAgg

NamedAgg(column, aggfunc)

Create new instance of NamedAgg(column, aggfunc).

See more: bigframes.pandas.NamedAgg

bigframes.pandas.NamedAgg.__init__

__init__()

API documentation for __init__ method.

See more: bigframes.pandas.NamedAgg.init

bigframes.pandas.NamedAgg.count

count(value, /)

Return number of occurrences of value.

See more: bigframes.pandas.NamedAgg.count

bigframes.pandas.NamedAgg.index

index(value, start=0, stop=9223372036854775807, /)

Return first index of value.

See more: bigframes.pandas.NamedAgg.index

bigframes.pandas.Series

Series(
    data=None,
    index=None,
    dtype: typing.Optional[
        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]",
                "duration[us][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,
        ]
    ] = None,
    name: str | None = None,
    copy: typing.Optional[bool] = None,
    *,
    session: typing.Optional[bigframes.session.Session] = None
)

API documentation for Series method.

See more: bigframes.pandas.Series

bigframes.pandas.Series.__init__

__init__(data=None, index=None, dtype=None, name=None, copy=None, *, session=None)

API documentation for __init__ method.

See more: bigframes.pandas.Series.init

bigframes.pandas.Series.abs

abs()

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

See more: bigframes.pandas.Series.abs

bigframes.pandas.Series.add

add(other)

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

See more: bigframes.pandas.Series.add

bigframes.pandas.Series.add_prefix

add_prefix(prefix, axis=None)

Prefix labels with string prefix.

See more: bigframes.pandas.Series.add_prefix

bigframes.pandas.Series.add_suffix

add_suffix(suffix, axis=None)

Suffix labels with string suffix.

See more: bigframes.pandas.Series.add_suffix

bigframes.pandas.Series.agg

agg(func)

Aggregate using one or more operations over the specified axis.

See more: bigframes.pandas.Series.agg

bigframes.pandas.Series.aggregate

aggregate(func)

Aggregate using one or more operations over the specified axis.

See more: bigframes.pandas.Series.aggregate

bigframes.pandas.Series.all

all()

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

See more: bigframes.pandas.Series.all

bigframes.pandas.Series.any

any()

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

See more: bigframes.pandas.Series.any

bigframes.pandas.Series.apply

apply(func, by_row="compat", *, args=())

Invoke function on values of a Series.

See more: bigframes.pandas.Series.apply

bigframes.pandas.Series.area

area(x=None, y=None, stacked=True, **kwargs)

Draw a stacked area plot.

See more: bigframes.pandas.Series.area

bigframes.pandas.Series.argmax

argmax()

Return int position of the largest value in the series.

See more: bigframes.pandas.Series.argmax

bigframes.pandas.Series.argmin

argmin()

Return int position of the smallest value in the Series.

See more: bigframes.pandas.Series.argmin

bigframes.pandas.Series.astype

astype(dtype, *, errors="raise")

Cast a pandas object to a specified dtype dtype.

See more: bigframes.pandas.Series.astype

bigframes.pandas.Series.autocorr

autocorr(lag=1)

Compute the lag-N autocorrelation.

See more: bigframes.pandas.Series.autocorr

bigframes.pandas.Series.bar

bar(x=None, y=None, **kwargs)

Draw a vertical bar plot.

See more: bigframes.pandas.Series.bar

bigframes.pandas.Series.between

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

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

See more: bigframes.pandas.Series.between

bigframes.pandas.Series.bfill

bfill(*, limit=None)

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

See more: bigframes.pandas.Series.bfill

bigframes.pandas.Series.cache

cache()

Materializes the Series to a temporary table.

See more: bigframes.pandas.Series.cache

bigframes.pandas.Series.case_when

case_when(caselist)

Replace values where the conditions are True.

See more: bigframes.pandas.Series.case_when

bigframes.pandas.Series.clip

clip(lower=None, upper=None)

Trim values at input threshold(s).

See more: bigframes.pandas.Series.clip

bigframes.pandas.Series.combine

combine(other, func)

Combine the Series with a Series or scalar according to func.

See more: bigframes.pandas.Series.combine

bigframes.pandas.Series.combine_first

combine_first(other)

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

See more: bigframes.pandas.Series.combine_first

bigframes.pandas.Series.copy

copy()

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

See more: bigframes.pandas.Series.copy

bigframes.pandas.Series.corr

corr(other, method="pearson", min_periods=None)

Compute the correlation with the other Series.

See more: bigframes.pandas.Series.corr

bigframes.pandas.Series.count

count()

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

See more: bigframes.pandas.Series.count

bigframes.pandas.Series.cov

cov(other)

Compute covariance with Series, excluding missing values.

See more: bigframes.pandas.Series.cov

bigframes.pandas.Series.cummax

cummax()

Return cumulative maximum over a DataFrame or Series axis.

See more: bigframes.pandas.Series.cummax

bigframes.pandas.Series.cummin

cummin()

Return cumulative minimum over a DataFrame or Series axis.

See more: bigframes.pandas.Series.cummin

bigframes.pandas.Series.cumprod

cumprod()

Return cumulative product over a DataFrame or Series axis.

See more: bigframes.pandas.Series.cumprod

bigframes.pandas.Series.cumsum

cumsum()

Return cumulative sum over a DataFrame or Series axis.

See more: bigframes.pandas.Series.cumsum

bigframes.pandas.Series.describe

describe()

Generate descriptive statistics.

See more: bigframes.pandas.Series.describe

bigframes.pandas.Series.diff

diff(periods=1)

First discrete difference of element.

See more: bigframes.pandas.Series.diff

bigframes.pandas.Series.div

div(other)

Return floating division of Series and other, element-wise (binary operator truediv).

See more: bigframes.pandas.Series.div

bigframes.pandas.Series.divide

divide(other)

Return floating division of Series and other, element-wise (binary operator truediv).

See more: bigframes.pandas.Series.divide

bigframes.pandas.Series.divmod

divmod(other)

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

See more: bigframes.pandas.Series.divmod

bigframes.pandas.Series.dot

dot(other)

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

See more: bigframes.pandas.Series.dot

bigframes.pandas.Series.drop

drop(labels=None, *, axis=0, index=None, columns=None, level=None)

Return Series with specified index labels removed.

See more: bigframes.pandas.Series.drop

bigframes.pandas.Series.drop_duplicates

drop_duplicates(*, keep="first")

Return Series with duplicate values removed.

See more: bigframes.pandas.Series.drop_duplicates

bigframes.pandas.Series.droplevel

droplevel(level, axis=0)

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

See more: bigframes.pandas.Series.droplevel

bigframes.pandas.Series.dropna

dropna(*, axis=0, inplace=False, how=None, ignore_index=False)

Return a new Series with missing values removed.

See more: bigframes.pandas.Series.dropna

bigframes.pandas.Series.duplicated

duplicated(keep="first")

Indicate duplicate Series values.

See more: bigframes.pandas.Series.duplicated

bigframes.pandas.Series.eq

eq(other)

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

See more: bigframes.pandas.Series.eq

bigframes.pandas.Series.equals

equals(other)

Test whether two objects contain the same elements.

See more: bigframes.pandas.Series.equals

bigframes.pandas.Series.expanding

expanding(min_periods=1)

Provide expanding window calculations.

See more: bigframes.pandas.Series.expanding

bigframes.pandas.Series.explode

explode(*, ignore_index=False)

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

See more: bigframes.pandas.Series.explode

bigframes.pandas.Series.ffill

ffill(*, limit=None)

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

See more: bigframes.pandas.Series.ffill

bigframes.pandas.Series.fillna

fillna(value=None)

Fill NA (NULL in BigQuery) values using the specified method.

See more: bigframes.pandas.Series.fillna

bigframes.pandas.Series.filter

filter(items=None, like=None, regex=None, axis=None)

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

See more: bigframes.pandas.Series.filter

bigframes.pandas.Series.floordiv

floordiv(other)

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

See more: bigframes.pandas.Series.floordiv

bigframes.pandas.Series.ge

ge(other)

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

See more: bigframes.pandas.Series.ge

bigframes.pandas.Series.get

get(key, default=None)

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

See more: bigframes.pandas.Series.get

bigframes.pandas.Series.groupby

groupby(by=None, axis=0, level=None, as_index=True, *, dropna=True)

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

See more: bigframes.pandas.Series.groupby

bigframes.pandas.Series.gt

gt(other)

Return Greater than of series and other, element-wise (binary operator gt).

See more: bigframes.pandas.Series.gt

bigframes.pandas.Series.head

head(n=5)

Return the first n rows.

See more: bigframes.pandas.Series.head

bigframes.pandas.Series.hist

hist(by=None, bins=10, **kwargs)

Draw one histogram of the DataFrame’s columns.

See more: bigframes.pandas.Series.hist

bigframes.pandas.Series.idxmax

idxmax()

Return the row label of the maximum value.

See more: bigframes.pandas.Series.idxmax

bigframes.pandas.Series.idxmin

idxmin()

Return the row label of the minimum value.

See more: bigframes.pandas.Series.idxmin

bigframes.pandas.Series.interpolate

interpolate(method="linear")

Fill NaN values using an interpolation method.

See more: bigframes.pandas.Series.interpolate

bigframes.pandas.Series.isin

isin(values)

Whether elements in Series are contained in values.

See more: bigframes.pandas.Series.isin

bigframes.pandas.Series.isna

isna()

Detect missing (NULL) values.

See more: bigframes.pandas.Series.isna

bigframes.pandas.Series.isnull

isnull()

Detect missing (NULL) values.

See more: bigframes.pandas.Series.isnull

bigframes.pandas.Series.item

item()

Return the first element of the underlying data as a Python scalar.

See more: bigframes.pandas.Series.item

bigframes.pandas.Series.items

items()

Lazily iterate over (index, value) tuples.

See more: bigframes.pandas.Series.items

bigframes.pandas.Series.keys

keys()

Return alias for index.

See more: bigframes.pandas.Series.keys

bigframes.pandas.Series.kurt

kurt()

Return unbiased kurtosis over requested axis.

See more: bigframes.pandas.Series.kurt

bigframes.pandas.Series.kurtosis

kurtosis()

Return unbiased kurtosis over requested axis.

See more: bigframes.pandas.Series.kurtosis

bigframes.pandas.Series.le

le(other)

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

See more: bigframes.pandas.Series.le

bigframes.pandas.Series.line

line(x=None, y=None, **kwargs)

Plot Series or DataFrame as lines.

See more: bigframes.pandas.Series.line

bigframes.pandas.Series.lt

lt(other)

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

See more: bigframes.pandas.Series.lt

bigframes.pandas.Series.map

map(arg, na_action=None, *, verify_integrity=False)

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

See more: bigframes.pandas.Series.map

bigframes.pandas.Series.mask

mask(cond, other=None)

Replace values where the condition is True.

See more: bigframes.pandas.Series.mask

bigframes.pandas.Series.max

max()

Return the maximum of the values over the requested axis.

See more: bigframes.pandas.Series.max

bigframes.pandas.Series.mean

mean()

Return the mean of the values over the requested axis.

See more: bigframes.pandas.Series.mean

bigframes.pandas.Series.median

median(*, exact=True)

Return the median of the values over the requested axis.

See more: bigframes.pandas.Series.median

bigframes.pandas.Series.min

min()

Return the maximum of the values over the requested axis.

See more: bigframes.pandas.Series.min

bigframes.pandas.Series.mod

mod(other)

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

See more: bigframes.pandas.Series.mod

bigframes.pandas.Series.mode

mode()

Return the mode(s) of the Series.

See more: bigframes.pandas.Series.mode

bigframes.pandas.Series.mul

mul(other)

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

See more: bigframes.pandas.Series.mul

bigframes.pandas.Series.multiply

multiply(other)

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

See more: bigframes.pandas.Series.multiply

bigframes.pandas.Series.ne

ne(other)

Return not equal of Series and other, element-wise (binary operator ne).

See more: bigframes.pandas.Series.ne

bigframes.pandas.Series.nlargest

nlargest(n=5, keep="first")

Return the largest n elements.

See more: bigframes.pandas.Series.nlargest

bigframes.pandas.Series.notna

notna()

Detect existing (non-missing) values.

See more: bigframes.pandas.Series.notna

bigframes.pandas.Series.notnull

notnull()

Detect existing (non-missing) values.

See more: bigframes.pandas.Series.notnull

bigframes.pandas.Series.nsmallest

nsmallest(n=5, keep="first")

Return the smallest n elements.

See more: bigframes.pandas.Series.nsmallest

bigframes.pandas.Series.nunique

nunique()

Return number of unique elements in the object.

See more: bigframes.pandas.Series.nunique

bigframes.pandas.Series.pad

pad(*, limit=None)

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

See more: bigframes.pandas.Series.pad

bigframes.pandas.Series.pct_change

pct_change(periods=1)

Fractional change between the current and a prior element.

See more: bigframes.pandas.Series.pct_change

bigframes.pandas.Series.peek

peek(n=5, *, force=True, allow_large_results=None)

Preview n arbitrary elements from the series without guarantees about row selection or ordering.

See more: bigframes.pandas.Series.peek

bigframes.pandas.Series.pipe

pipe(func, *args, **kwargs)

Apply chainable functions that expect Series or DataFrames.

See more: bigframes.pandas.Series.pipe

bigframes.pandas.Series.pow

pow(other)

Return Exponential power of series and other, element-wise (binary operator pow).

See more: bigframes.pandas.Series.pow

bigframes.pandas.Series.prod

prod()

Return the product of the values over the requested axis.

See more: bigframes.pandas.Series.prod

bigframes.pandas.Series.product

product()

Return the product of the values over the requested axis.

See more: bigframes.pandas.Series.product

bigframes.pandas.Series.quantile

quantile(q=0.5)

Return value at the given quantile.

See more: bigframes.pandas.Series.quantile

bigframes.pandas.Series.radd

radd(other)

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

See more: bigframes.pandas.Series.radd

bigframes.pandas.Series.rank

rank(
    axis=0,
    method="average",
    numeric_only=False,
    na_option="keep",
    ascending=True,
    pct=False,
)

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

See more: bigframes.pandas.Series.rank

bigframes.pandas.Series.rdiv

rdiv(other)

Return floating division of Series and other, element-wise (binary operator rtruediv).

See more: bigframes.pandas.Series.rdiv

bigframes.pandas.Series.rdivmod

rdivmod(other)

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

See more: bigframes.pandas.Series.rdivmod

bigframes.pandas.Series.reindex

reindex(index=None, *, validate=None)

Conform Series to new index with optional filling logic.

See more: bigframes.pandas.Series.reindex

bigframes.pandas.Series.reindex_like

reindex_like(other, *, validate=None)

Return an object with matching indices as other object.

See more: bigframes.pandas.Series.reindex_like

bigframes.pandas.Series.rename

Alter Series index labels or name.

See more: bigframes.pandas.Series.rename

bigframes.pandas.Series.rename_axis

Set the name of the axis for the index or columns.

See more: bigframes.pandas.Series.rename_axis

bigframes.pandas.Series.reorder_levels

reorder_levels(order, axis=0)

Rearrange index levels using input order.

See more: bigframes.pandas.Series.reorder_levels

bigframes.pandas.Series.replace

replace(to_replace, value=None, *, regex=False)

Replace values given in to_replace with value.

See more: bigframes.pandas.Series.replace

bigframes.pandas.Series.resample

resample(rule, *, closed=None, label=None, level=None, origin="start_day")

Resample time-series data.

See more: bigframes.pandas.Series.resample

bigframes.pandas.Series.reset_index

Generate a new DataFrame or Series with the index reset.

See more: bigframes.pandas.Series.reset_index

bigframes.pandas.Series.rfloordiv

rfloordiv(other)

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

See more: bigframes.pandas.Series.rfloordiv

bigframes.pandas.Series.rmod

rmod(other)

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

See more: bigframes.pandas.Series.rmod

bigframes.pandas.Series.rmul

rmul(other)

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

See more: bigframes.pandas.Series.rmul

bigframes.pandas.Series.rolling

rolling(window, min_periods=None, closed="right")

Provide rolling window calculations.

See more: bigframes.pandas.Series.rolling

bigframes.pandas.Series.round

round(decimals=0)

Round each value in a Series to the given number of decimals.

See more: bigframes.pandas.Series.round

bigframes.pandas.Series.rpow

rpow(other)

Return Exponential power of series and other, element-wise (binary operator rpow).

See more: bigframes.pandas.Series.rpow

bigframes.pandas.Series.rsub

rsub(other)

Return subtraction of Series and other, element-wise (binary operator rsub).

See more: bigframes.pandas.Series.rsub

bigframes.pandas.Series.rtruediv

rtruediv(other)

Return floating division of Series and other, element-wise (binary operator rtruediv).

See more: bigframes.pandas.Series.rtruediv

bigframes.pandas.Series.sample

sample(n=None, frac=None, *, random_state=None, sort="random")

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

See more: bigframes.pandas.Series.sample

bigframes.pandas.Series.shift

shift(periods=1)

Shift index by desired number of periods.

See more: bigframes.pandas.Series.shift

bigframes.pandas.Series.skew

skew()

Return unbiased skew over requested axis.

See more: bigframes.pandas.Series.skew

bigframes.pandas.Series.sort_index

Sort Series by index labels.

See more: bigframes.pandas.Series.sort_index

bigframes.pandas.Series.sort_values

Sort by the values.

See more: bigframes.pandas.Series.sort_values

bigframes.pandas.Series.std

std()

Return sample standard deviation over requested axis.

See more: bigframes.pandas.Series.std

bigframes.pandas.Series.sub

sub(other)

Return subtraction of Series and other, element-wise (binary operator sub).

See more: bigframes.pandas.Series.sub

bigframes.pandas.Series.subtract

subtract(other)

Return subtraction of Series and other, element-wise (binary operator sub).

See more: bigframes.pandas.Series.subtract

bigframes.pandas.Series.sum

sum()

Return the sum of the values over the requested axis.

See more: bigframes.pandas.Series.sum

bigframes.pandas.Series.swaplevel

swaplevel(i=-2, j=-1)

Swap levels i and j in a MultiIndex.

See more: bigframes.pandas.Series.swaplevel

bigframes.pandas.Series.tail

tail(n=5)

Return the last n rows.

See more: bigframes.pandas.Series.tail

bigframes.pandas.Series.take

take(indices, axis=0, **kwargs)

Return the elements in the given positional indices along an axis.

See more: bigframes.pandas.Series.take

bigframes.pandas.Series.to_csv

to_csv(
    path_or_buf=None, sep=",", *, header=True, index=True, allow_large_results=None
)

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

See more: bigframes.pandas.Series.to_csv

bigframes.pandas.Series.to_dict

to_dict(into=dict, *, allow_large_results=None)

Convert Series to {label -> value} dict or dict-like object.

See more: bigframes.pandas.Series.to_dict

bigframes.pandas.Series.to_excel

to_excel(excel_writer, sheet_name="Sheet1", *, allow_large_results=None, **kwargs)

Write Series to an Excel sheet.

See more: bigframes.pandas.Series.to_excel

bigframes.pandas.Series.to_frame

to_frame(name=None)

Convert Series to DataFrame.

See more: bigframes.pandas.Series.to_frame

bigframes.pandas.Series.to_json

to_json(
    path_or_buf=None, orient=None, *, lines=False, index=True, allow_large_results=None
)

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

See more: bigframes.pandas.Series.to_json

bigframes.pandas.Series.to_latex

to_latex(
    buf=None,
    columns=None,
    header=True,
    index=True,
    *,
    allow_large_results=None,
    **kwargs
)

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

See more: bigframes.pandas.Series.to_latex

bigframes.pandas.Series.to_list

to_list(*, allow_large_results=None)

Return a list of the values.

See more: bigframes.pandas.Series.to_list

bigframes.pandas.Series.to_markdown

to_markdown(buf=None, mode="wt", index=True, *, allow_large_results=None, **kwargs)

Print Series in Markdown-friendly format.

See more: bigframes.pandas.Series.to_markdown

bigframes.pandas.Series.to_numpy

to_numpy(
    dtype=None,
    copy=False,
    na_value=_NoDefault.no_default,
    *,
    allow_large_results=None,
    **kwargs
)

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

See more: bigframes.pandas.Series.to_numpy

bigframes.pandas.Series.to_pandas

to_pandas(
    max_download_size=None,
    sampling_method=None,
    random_state=None,
    *,
    ordered=True,
    dry_run=False,
    allow_large_results=None
)

Writes Series to pandas Series.

See more: bigframes.pandas.Series.to_pandas

bigframes.pandas.Series.to_pandas_batches

to_pandas_batches(page_size=None, max_results=None, *, allow_large_results=None)

Stream Series results to an iterable of pandas Series.

See more: bigframes.pandas.Series.to_pandas_batches

bigframes.pandas.Series.to_pickle

to_pickle(path, *, allow_large_results=None, **kwargs)

Pickle (serialize) object to file.

See more: bigframes.pandas.Series.to_pickle

bigframes.pandas.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,
    *,
    allow_large_results=None
)

Render a string representation of the Series.

See more: bigframes.pandas.Series.to_string

bigframes.pandas.Series.to_xarray

to_xarray(*, allow_large_results=None)

Return an xarray object from the pandas object.

See more: bigframes.pandas.Series.to_xarray

bigframes.pandas.Series.tolist

tolist(*, allow_large_results=None)

Return a list of the values.

See more: bigframes.pandas.Series.tolist

bigframes.pandas.Series.transpose

transpose()

Return the transpose, which is by definition self.

See more: bigframes.pandas.Series.transpose

bigframes.pandas.Series.truediv

truediv(other)

Return floating division of Series and other, element-wise (binary operator truediv).

See more: bigframes.pandas.Series.truediv

bigframes.pandas.Series.unique

unique(keep_order=True)

Return unique values of Series object.

See more: bigframes.pandas.Series.unique

bigframes.pandas.Series.unstack

unstack(level=-1)

Unstack, also known as pivot, Series with MultiIndex to produce DataFrame.

See more: bigframes.pandas.Series.unstack

bigframes.pandas.Series.update

update(other)

Modify Series in place using values from passed Series.

See more: bigframes.pandas.Series.update

bigframes.pandas.Series.value_counts

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

Return a Series containing counts of unique values.

See more: bigframes.pandas.Series.value_counts

bigframes.pandas.Series.var

var()

Return unbiased variance over requested axis.

See more: bigframes.pandas.Series.var

bigframes.pandas.Series.where

where(cond, other=None)

Replace values where the condition is False.

See more: bigframes.pandas.Series.where

bigframes.streaming.StreamingDataFrame

StreamingDataFrame(df: bigframes.dataframe.DataFrame, *, create_key=0)

Two-dimensional, size-mutable, potentially heterogeneous tabular data.

See more: bigframes.streaming.StreamingDataFrame

bigframes.streaming.StreamingDataFrame.__init__

__init__(df, *, create_key=0)

API documentation for __init__ method.

See more: bigframes.streaming.StreamingDataFrame.init

bigframes.streaming.StreamingDataFrame.rename

rename(*args, **kwargs)

Rename columns.

See more: bigframes.streaming.StreamingDataFrame.rename

bigframes.streaming.StreamingDataFrame.to_bigtable

to_bigtable(
    *,
    instance,
    table,
    service_account_email=None,
    app_profile=None,
    truncate=False,
    overwrite=False,
    auto_create_column_families=False,
    bigtable_options=None,
    job_id=None,
    job_id_prefix=None,
    start_timestamp=None,
    end_timestamp=None
)

Export the StreamingDataFrame as a continue job and returns a QueryJob object for some management functionality.

See more: bigframes.streaming.StreamingDataFrame.to_bigtable

bigframes.streaming.StreamingDataFrame.to_pubsub

to_pubsub(
    *,
    topic,
    service_account_email,
    job_id=None,
    job_id_prefix=None,
    start_timestamp=None
)

Export the StreamingDataFrame as a continue job and returns a QueryJob object for some management functionality.

See more: bigframes.streaming.StreamingDataFrame.to_pubsub