Package pandas (1.28.0)

Version 1.28.0

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API documentation for pandas package.

Classes

ArrowDtype

An ExtensionDtype for PyArrow data types.

Warning: ArrowDtype is considered experimental. The implementation and parts of the API may change without warning.

BooleanDtype

Extension dtype for boolean data.

Warning: BooleanDtype is considered experimental. The implementation and parts of the API may change without warning.

.. method:: None :noindex:

.. rubric:: Examples

pd.BooleanDtype() BooleanDtype

DataFrame

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

Data structure also contains labeled axes (rows and columns). Arithmetic operations align on both row and column labels. Can be thought of as a dict-like container for Series objects. The primary pandas data structure.

Float64Dtype

An ExtensionDtype for float64 data.

This dtype uses pd.NA as missing value indicator.

For Float32Dtype:

ser = pd.Series([2.25, pd.NA], dtype=pd.Float32Dtype()) ser.dtype Float32Dtype()

For Float64Dtype:

ser = pd.Series([2.25, pd.NA], dtype=pd.Float64Dtype()) ser.dtype Float64Dtype()

Index

Immutable sequence used for indexing and alignment.

The basic object storing axis labels for all objects.

Int64Dtype

An ExtensionDtype for int64 integer data.

Uses pandas.NA as its missing value, rather than numpy.nan.

For Int8Dtype:

ser = pd.Series([2, pd.NA], dtype=pd.Int8Dtype()) ser.dtype Int8Dtype()

For Int16Dtype:

ser = pd.Series([2, pd.NA], dtype=pd.Int16Dtype()) ser.dtype Int16Dtype()

For Int32Dtype:

ser = pd.Series([2, pd.NA], dtype=pd.Int32Dtype()) ser.dtype Int32Dtype()

For Int64Dtype:

ser = pd.Series([2, pd.NA], dtype=pd.Int64Dtype()) ser.dtype Int64Dtype()

For UInt8Dtype:

ser = pd.Series([2, pd.NA], dtype=pd.UInt8Dtype()) ser.dtype UInt8Dtype()

For UInt16Dtype:

ser = pd.Series([2, pd.NA], dtype=pd.UInt16Dtype()) ser.dtype UInt16Dtype()

For UInt32Dtype:

ser = pd.Series([2, pd.NA], dtype=pd.UInt32Dtype()) ser.dtype UInt32Dtype()

For UInt64Dtype:

ser = pd.Series([2, pd.NA], dtype=pd.UInt64Dtype()) ser.dtype UInt64Dtype()

MultiIndex

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

NamedAgg

NamedAgg(column, aggfunc)

Series

N-dimensional analogue of DataFrame. Store multi-dimensional in a size-mutable, labeled data structure

StringDtype

Extension dtype for string data.

Warning: StringDtype is considered experimental. The implementation and parts of the API may change without warning.

option_context

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

You need to invoke as option_context(pat, val, [(pat, val), ...]).

Note: "bigquery" options can't be changed on a running session. Setting any of these options creates a new thread-local session that only lives for the lifetime of the context manager.

import bigframes

with bigframes.option_context('display.max_rows', 10, 'display.max_columns', 5): ... pass

Packages Functions

clean_up_by_session_id

clean_up_by_session_id(
    session_id: str,
    location: typing.Optional[str] = None,
    project: typing.Optional[str] = None,
) -> None

Searches through BigQuery tables and routines and deletes the ones created during the session with the given session id. The match is determined by having the session id present in the resource name or metadata. The cloud functions serving the cleaned up routines are also cleaned up.

This could be useful if the session object has been lost. Calling session.close() or <xref uid="bigframes.pandas.close_session">bigframes.pandas.close_session</xref>() is preferred in most cases.

Parameters
Name	Description
session_id	The session id to clean up. Can be found using session.session_id or get_default_session_id().
location	The location of the session to clean up. If given, used together with project kwarg to determine the dataset to search through for tables to clean up.
project	The project id associated with the session to clean up. If given, used together with location kwarg to determine the dataset to search through for tables to clean up.

close_session

close_session()

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

Closes the current session if it was already started, deleting any temporary tables that were created.

concat

concat(
    objs: typing.Iterable[
        typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
    ],
    *,
    axis: typing.Union[str, int] = 0,
    join: typing.Literal["inner", "outer"] = "outer",
    ignore_index: bool = False
) -> typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]

Concatenate BigQuery DataFrames objects along a particular axis.

Allows optional set logic along the other axes.

Can also add a layer of hierarchical indexing on the concatenation axis, which may be useful if the labels are the same (or overlapping) on the passed axis number.

Note: It is not recommended to build DataFrames by adding single rows in a for loop. Build a list of rows and make a DataFrame in a single concat.

Examples:

>>> import bigframes.pandas as pd
>>> pd.options.display.progress_bar = None

Combine two Series.

>>> s1 = pd.Series(['a', 'b'])
>>> s2 = pd.Series(['c', 'd'])
>>> pd.concat([s1, s2])
0    a
1    b
0    c
1    d
dtype: string

Clear the existing index and reset it in the result by setting the ignore_index option to True.

>>> pd.concat([s1, s2], ignore_index=True)
0    a
1    b
2    c
3    d
dtype: string

Combine two DataFrame objects with identical columns.

>>> df1 = pd.DataFrame([['a', 1], ['b', 2]],
...                    columns=['letter', 'number'])
>>> df1
  letter  number
0      a       1
1      b       2
<BLANKLINE>
[2 rows x 2 columns]
>>> df2 = pd.DataFrame([['c', 3], ['d', 4]],
...                    columns=['letter', 'number'])
>>> df2
  letter  number
0      c       3
1      d       4
<BLANKLINE>
[2 rows x 2 columns]
>>> pd.concat([df1, df2])
  letter  number
0      a       1
1      b       2
0      c       3
1      d       4
<BLANKLINE>
[4 rows x 2 columns]

Combine DataFrame objects with overlapping columns and return everything. Columns outside the intersection will be filled with NaN values.

>>> df3 = pd.DataFrame([['c', 3, 'cat'], ['d', 4, 'dog']],
...                    columns=['letter', 'number', 'animal'])
>>> df3
  letter  number animal
0      c       3    cat
1      d       4    dog
<BLANKLINE>
[2 rows x 3 columns]
>>> pd.concat([df1, df3])
  letter  number animal
0      a       1   <NA>
1      b       2   <NA>
0      c       3    cat
1      d       4    dog
<BLANKLINE>
[4 rows x 3 columns]

Combine DataFrame objects with overlapping columns and return only those that are shared by passing inner to the join keyword argument.

>>> pd.concat([df1, df3], join="inner")
  letter  number
0      a       1
1      b       2
0      c       3
1      d       4
<BLANKLINE>
[4 rows x 2 columns]

Parameters
Name	Description
objs	Objects to concatenate. Any None objects will be dropped silently unless they are all None in which case a ValueError will be raised.
axis	The axis to concatenate along.
join	How to handle indexes on other axis (or axes).
ignore_index	If True, do not use the index values along the concatenation axis. The resulting axis will be labeled 0, ..., n - 1. This is useful if you are concatenating objects where the concatenation axis does not have meaningful indexing information. Note the index values on the other axes are still respected in the join.

cut

cut(
    x: bigframes.series.Series,
    bins: typing.Union[
        int, pandas.core.indexes.interval.IntervalIndex, typing.Iterable
    ],
    *,
    labels: typing.Optional[typing.Union[typing.Iterable[str], bool]] = None
) -> bigframes.series.Series

Bin values into discrete intervals.

Use cut when you need to segment and sort data values into bins. This function is also useful for going from a continuous variable to a categorical variable. For example, cut could convert ages to groups of age ranges. Supports binning into an equal number of bins, or a pre-specified array of bins.

labels=False implies you just want the bins back.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> s = bpd.Series([0, 1, 5, 10])
>>> s
0     0
1     1
2     5
3    10
dtype: Int64

Cut with an integer (equal-width bins):

>>> bpd.cut(s, bins=4)
    0    {'left_exclusive': -0.01, 'right_inclusive': 2.5}
    1    {'left_exclusive': -0.01, 'right_inclusive': 2.5}
    2      {'left_exclusive': 2.5, 'right_inclusive': 5.0}
    3     {'left_exclusive': 7.5, 'right_inclusive': 10.0}
    dtype: struct<left_exclusive: double, right_inclusive: double>[pyarrow]

Cut with an integer (equal-width bins) and labels=False:

>>> bpd.cut(s, bins=4, labels=False)
0    0
1    0
2    1
3    3
dtype: Int64

Cut with pd.IntervalIndex, requires importing pandas for IntervalIndex:

>>> import pandas as pd

>>> interval_index = pd.IntervalIndex.from_tuples([(0, 1), (1, 5), (5, 20)])
>>> bpd.cut(s, bins=interval_index)
0                                            <NA>
1     {'left_exclusive': 0, 'right_inclusive': 1}
2     {'left_exclusive': 1, 'right_inclusive': 5}
3    {'left_exclusive': 5, 'right_inclusive': 20}
dtype: struct<left_exclusive: int64, right_inclusive: int64>[pyarrow]

Cut with an iterable of tuples:

>>> bins_tuples = [(0, 1), (1, 4), (5, 20)]
>>> bpd.cut(s, bins=bins_tuples)
0                                            <NA>
1     {'left_exclusive': 0, 'right_inclusive': 1}
2                                            <NA>
3    {'left_exclusive': 5, 'right_inclusive': 20}
dtype: struct<left_exclusive: int64, right_inclusive: int64>[pyarrow]

Cut with an iterable of ints:

>>> bins_ints = [0, 1, 5, 20]
>>> bpd.cut(s, bins=bins_ints)
0                                            <NA>
1     {'left_exclusive': 0, 'right_inclusive': 1}
2     {'left_exclusive': 1, 'right_inclusive': 5}
3    {'left_exclusive': 5, 'right_inclusive': 20}
dtype: struct<left_exclusive: int64, right_inclusive: int64>[pyarrow]

Parameters
Name	Description
x	The input Series to be binned. Must be 1-dimensional.
bins	The criteria to bin by. int: Defines the number of equal-width bins in the range of x. The range of x is extended by .1% on each side to include the minimum and maximum values of x. pd.IntervalIndex or Iterable of tuples: Defines the exact bins to be used. It's important to ensure that these bins are non-overlapping. Iterable of numerics: Defines the exact bins by using the interval between each item and its following item. The items must be monotonically increasing.
labels	Specifies the labels for the returned bins. Must be the same length as the resulting bins. If False, returns only integer indicators of the bins. This affects the type of the output container.

get_default_session_id

get_default_session_id() -> str

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

It is the session id of the default global session. It is prefixed to the table id of all temporary tables created in the global session.

get_dummies

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

Convert categorical variable into dummy/indicator variables.

Each variable is converted in as many 0/1 variables as there are different values. Columns in the output are each named after a value; if the input is a DataFrame, the name of the original variable is prepended to the value.

Examples:

>>> import bigframes.pandas as pd
>>> pd.options.display.progress_bar = None
>>> s = pd.Series(list('abca'))
>>> pd.get_dummies(s)
       a      b      c
0   True  False  False
1  False   True  False
2  False  False   True
3   True  False  False
<BLANKLINE>
[4 rows x 3 columns]

>>> s1 = pd.Series(['a', 'b', None])
>>> pd.get_dummies(s1)
       a      b
0   True  False
1  False   True
2  False  False
<BLANKLINE>
[3 rows x 2 columns]

>>> pd.get_dummies(s1, dummy_na=True)
       a      b   <NA>
0   True  False  False
1  False   True  False
2  False  False   True
<BLANKLINE>
[3 rows x 3 columns]

>>> df = pd.DataFrame({'A': ['a', 'b', 'a'], 'B': ['b', 'a', 'c'], 'C': [1, 2, 3]})
>>> pd.get_dummies(df, prefix=['col1', 'col2'])
   C  col1_a  col1_b  col2_a  col2_b  col2_c
0  1    True   False   False    True   False
1  2   False    True    True   False   False
2  3    True   False   False   False    True
<BLANKLINE>
[3 rows x 6 columns]

>>> pd.get_dummies(pd.Series(list('abcaa')))
       a      b      c
0   True  False  False
1  False   True  False
2  False  False   True
3   True  False  False
4   True  False  False
<BLANKLINE>
[5 rows x 3 columns]

>>> pd.get_dummies(pd.Series(list('abcaa')), drop_first=True)
       b      c
0  False  False
1   True  False
2  False   True
3  False  False
4  False  False
<BLANKLINE>
[5 rows x 2 columns]

Parameters
Name	Description
data	Data of which to get dummy indicators.
prefix	String to append DataFrame column names. Pass a list with length equal to the number of columns when calling get_dummies on a DataFrame. Alternatively, prefix can be a dictionary mapping column names to prefixes.
prefix_sep	Separator/delimiter to use, appended to prefix. Or pass a list or dictionary as with prefix.
dummy_na	Add a column to indicate NaNs, if False NaNs are ignored.
columns	Column names in the DataFrame to be encoded. If columns is None then only the columns with string dtype will be converted.
drop_first	Whether to get k-1 dummies out of k categorical levels by removing the first level.
dtype	Data type for new columns. Only a single dtype is allowed.

get_global_session

get_global_session()

Gets the global session.

Creates the global session if it does not exist.

merge

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

Merge DataFrame objects with a database-style join.

The join is done on columns or indexes. If joining columns on columns, the DataFrame indexes will be ignored. Otherwise if joining indexes on indexes or indexes on a column or columns, the index will be passed on. When performing a cross merge, no column specifications to merge on are allowed.

Note: A named Series object is treated as a DataFrame with a single named column.

Warning: If both key columns contain rows where the key is a null value, those rows will be matched against each other. This is different from usual SQL join behaviour and can lead to unexpected results.

Parameters
Name	Description
on	Columns to join on. It must be found in both DataFrames. Either on or left_on + right_on must be passed in.
left_on	Columns to join on in the left DataFrame. Either on or left_on + right_on must be passed in.
right_on	Columns to join on in the right DataFrame. Either on or left_on + right_on must be passed in.

qcut

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

Quantile-based discretization function.

Discretize variable into equal-sized buckets based on rank or based on sample quantiles. For example 1000 values for 10 quantiles would produce a Categorical object indicating quantile membership for each data point.

Parameters
Name	Description
x	The input Series to be binned. Must be 1-dimensional.
q	Number of quantiles. 10 for deciles, 4 for quartiles, etc. Alternately array of quantiles, e.g. [0, .25, .5, .75, 1.] for quartiles.
labels	Used as labels for the resulting bins. Must be of the same length as the resulting bins. If False, return only integer indicators of the bins. If True, raises an error.
duplicates	If bin edges are not unique, raise ValueError or drop non-uniques.

read_csv

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

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

The CSV file data will be persisted as a temporary BigQuery table, which can be automatically recycled after the Session is closed.

Note: using engine="bigquery" will not guarantee the same ordering as the file. Instead, set a serialized index column as the index and sort by that in the resulting DataFrame. Only files stored on your local machine or in Google Cloud Storage are supported.

Note: For non-bigquery engine, data is inlined in the query SQL if it is small enough (roughly 5MB or less in memory). Larger size data is loaded to a BigQuery table instead.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> gcs_path = "gs://cloud-samples-data/bigquery/us-states/us-states.csv"
>>> df = bpd.read_csv(filepath_or_buffer=gcs_path)
>>> df.head(2)
      name post_abbr
0  Alabama        AL
1   Alaska        AK
<BLANKLINE>
[2 rows x 2 columns]

Parameters
Name	Description
filepath_or_buffer	A local or Google Cloud Storage (gs://) path with engine="bigquery" otherwise passed to pandas.read_csv.
sep	the separator for fields in a CSV file. For the BigQuery engine, the separator can be any ISO-8859-1 single-byte character. To use a character in the range 128-255, you must encode the character as UTF-8. Both engines support sep=" " to specify tab character as separator. Default engine supports having any number of spaces as separator by specifying sep="\s+". Separators longer than 1 character are interpreted as regular expressions by the default engine. BigQuery engine only supports single character separators.
header	row number to use as the column names. - None: Instructs autodetect that there are no headers and data should be read starting from the first row. - 0: If using engine="bigquery", Autodetect tries to detect headers in the first row. If they are not detected, the row is read as data. Otherwise data is read starting from the second row. When using default engine, pandas assumes the first row contains column names unless the names argument is specified. If names is provided, then the first row is ignored, second row is read as data, and column names are inferred from names. - N > 0: If using engine="bigquery", Autodetect skips N rows and tries to detect headers in row N+1. If headers are not detected, row N+1 is just skipped. Otherwise row N+1 is used to extract column names for the detected schema. When using default engine, pandas will skip N rows and assumes row N+1 contains column names unless the names argument is specified. If names is provided, row N+1 will be ignored, row N+2 will be read as data, and column names are inferred from names.
names	a list of column names to use. If the file contains a header row and you want to pass this parameter, then header=0 should be passed as well so the first (header) row is ignored. Only to be used with default engine.
index_col	column(s) to use as the row labels of the DataFrame, either given as string name or column index. index_col=False can be used with the default engine only to enforce that the first column is not used as the index. Using column index instead of column name is only supported with the default engine. The BigQuery engine only supports having a single column name as the index_col. Neither engine supports having a multi-column index.
usecols	List of column names to use): The BigQuery engine only supports having a list of string column names. Column indices and callable functions are only supported with the default engine. Using the default engine, the column names in usecols can be defined to correspond to column names provided with the names parameter (ignoring the document's header row of column names). The order of the column indices/names in usecols is ignored with the default engine. The order of the column names provided with the BigQuery engine will be consistent in the resulting dataframe. If using a callable function with the default engine, only column names that evaluate to True by the callable function will be in the resulting dataframe.
dtype	Data type for data or columns. Only to be used with default engine.
engine	Type of engine to use. If engine="bigquery" is specified, then BigQuery's load API will be used. Otherwise, the engine will be passed to pandas.read_csv.
encoding	encoding the character encoding of the data. The default encoding is UTF-8 for both engines. The default engine acceps a wide range of encodings. Refer to Python documentation for a comprehensive list, https://docs.python.org/3/library/codecs.html#standard-encodings The BigQuery engine only supports UTF-8 and ISO-8859-1.

read_gbq

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

Loads a DataFrame from BigQuery.

BigQuery tables are an unordered, unindexed data source. To add support pandas-compatibility, the following indexing options are supported via the index_col parameter:

• (Empty iterable, default) A default index. Behavior may change. Explicitly set index_col if your application makes use of specific index values.

  If a table has primary key(s), those are used as the index, otherwise a sequential index is generated.

• (<xref uid="bigframes.enums.DefaultIndexKind.SEQUENTIAL_INT64">bigframes.enums.DefaultIndexKind.SEQUENTIAL_INT64</xref>) Add an arbitrary sequential index and ordering. Warning This uses an analytic windowed operation that prevents filtering push down. Avoid using on large clustered or partitioned tables.
• (Recommended) Set the index_col argument to one or more columns. Unique values for the row labels are recommended. Duplicate labels are possible, but note that joins on a non-unique index can duplicate rows via pandas-like outer join behavior.

Note: By default, even SQL query inputs with an ORDER BY clause create a DataFrame with an arbitrary ordering. Use row_number() OVER (ORDER BY ...) AS rowindex in your SQL query and set index_col='rowindex' to preserve the desired ordering.

If your query doesn't have an ordering, select GENERATE_UUID() AS rowindex in your SQL and set index_col='rowindex' for the best performance.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

If the input is a table ID:

>>> df = bpd.read_gbq("bigquery-public-data.ml_datasets.penguins")

Read table path with wildcard suffix and filters:

df = bpd.read_gbq_table("bigquery-public-data.noaa_gsod.gsod19*", filters=[("_table_suffix", ">=", "30"), ("_table_suffix", "<=", "39")])

Preserve ordering in a query input.

>>> df = bpd.read_gbq('''
...    SELECT
...       -- Instead of an ORDER BY clause on the query, use
...       -- ROW_NUMBER() to create an ordered DataFrame.
...       ROW_NUMBER() OVER (ORDER BY AVG(pitchSpeed) DESC)
...         AS rowindex,
...
...       pitcherFirstName,
...       pitcherLastName,
...       AVG(pitchSpeed) AS averagePitchSpeed
...     FROM `bigquery-public-data.baseball.games_wide`
...     WHERE year = 2016
...     GROUP BY pitcherFirstName, pitcherLastName
... ''', index_col="rowindex")
>>> df.head(2)
         pitcherFirstName pitcherLastName  averagePitchSpeed
rowindex
1                Albertin         Chapman          96.514113
2                 Zachary         Britton          94.591039
<BLANKLINE>
[2 rows x 3 columns]

Reading data with columns and filters parameters:

>>> columns = ['pitcherFirstName', 'pitcherLastName', 'year', 'pitchSpeed']
>>> filters = [('year', '==', 2016), ('pitcherFirstName', 'in', ['John', 'Doe']), ('pitcherLastName', 'in', ['Gant']), ('pitchSpeed', '>', 94)]
>>> df = bpd.read_gbq(
...             "bigquery-public-data.baseball.games_wide",
...             columns=columns,
...             filters=filters,
...         )
>>> df.head(1)
  pitcherFirstName pitcherLastName  year  pitchSpeed
0             John            Gant  2016          95
<BLANKLINE>
[1 rows x 4 columns]

Parameters
Name	Description
query_or_table	A SQL string to be executed or a BigQuery table to be read. The table must be specified in the format of project.dataset.tablename or dataset.tablename. Can also take wildcard table name, such as project.dataset.table_prefix*. In tha case, will read all the matched table as one DataFrame.
index_col	Name of result column(s) to use for index in results DataFrame. If an empty iterable, such as (), a default index is generated. Do not depend on specific index values in this case. New in bigframes version 1.3.0: If index_cols is not set, the primary key(s) of the table are used as the index. New in bigframes version 1.4.0: Support bigframes.enums.DefaultIndexKind to override default index behavior.
columns	List of BigQuery column names in the desired order for results DataFrame.
configuration	Query config parameters for job processing. For example: configuration = {'query': {'useQueryCache': False}}. For more information see BigQuery REST API Reference https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs#configuration.query__.
max_results	If set, limit the maximum number of rows to fetch from the query results.
filters	To filter out data. Filter syntax: [[(column, op, val), …],…] where op is [==, >, >=, <, <=, !=, in, not in, LIKE]. The innermost tuples are transposed into a set of filters applied through an AND operation. The outer Iterable combines these sets of filters through an OR operation. A single Iterable of tuples can also be used, meaning that no OR operation between set of filters is to be conducted. If using wildcard table suffix in query_or_table, can specify '_table_suffix' pseudo column to filter the tables to be read into the DataFrame.
use_cache	Caches query results if set to True. When None, it behaves as True, but should not be combined with useQueryCache in configuration to avoid conflicts.
col_order	Alias for columns, retained for backwards compatibility.

read_gbq_function

read_gbq_function(function_name: str, is_row_processor: bool = False)

Loads a BigQuery function from BigQuery.

Then it can be applied to a DataFrame or Series.

Note: The return type of the function must be explicitly specified in the function's original definition even if not otherwise required.

BigQuery Utils provides many public functions under the bqutil project on Google Cloud Platform project (See: https://github.com/GoogleCloudPlatform/bigquery-utils/tree/master/udfs#using-the-udfs). You can checkout Community UDFs to use community-contributed functions. (See: https://github.com/GoogleCloudPlatform/bigquery-utils/tree/master/udfs/community#community-udfs).

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

Use the cw_lower_case_ascii_only function from Community UDFs.

>>> func = bpd.read_gbq_function("bqutil.fn.cw_lower_case_ascii_only")

You can run it on scalar input. Usually you would do so to verify that it works as expected before applying to all values in a Series.

>>> func('AURÉLIE')
'aurÉlie'

You can apply it to a BigQuery DataFrames Series.

>>> df = bpd.DataFrame({'id': [1, 2, 3], 'name': ['AURÉLIE', 'CÉLESTINE', 'DAPHNÉ']})
>>> df
   id       name
0   1    AURÉLIE
1   2  CÉLESTINE
2   3     DAPHNÉ
<BLANKLINE>
[3 rows x 2 columns]

>>> df1 = df.assign(new_name=df['name'].apply(func))
>>> df1
   id       name   new_name
0   1    AURÉLIE    aurÉlie
1   2  CÉLESTINE  cÉlestine
2   3     DAPHNÉ     daphnÉ
<BLANKLINE>
[3 rows x 3 columns]

You can even use a function with multiple inputs. For example, cw_regexp_replace_5 from Community UDFs.

>>> func = bpd.read_gbq_function("bqutil.fn.cw_regexp_replace_5")
>>> func('TestStr123456', 'Str', 'Cad$', 1, 1)
'TestCad$123456'

>>> df = bpd.DataFrame({
...     "haystack" : ["TestStr123456", "TestStr123456Str", "TestStr123456Str"],
...     "regexp" : ["Str", "Str", "Str"],
...     "replacement" : ["Cad$", "Cad$", "Cad$"],
...     "offset" : [1, 1, 1],
...     "occurrence" : [1, 2, 1]
... })
>>> df
           haystack regexp replacement  offset  occurrence
0     TestStr123456    Str        Cad$       1           1
1  TestStr123456Str    Str        Cad$       1           2
2  TestStr123456Str    Str        Cad$       1           1
<BLANKLINE>
[3 rows x 5 columns]
>>> df.apply(func, axis=1)
0       TestCad$123456
1    TestStr123456Cad$
2    TestCad$123456Str
dtype: string

Parameters
Name	Description
function_name	The function's name in BigQuery in the format project_id.dataset_id.function_name, or dataset_id.function_name to load from the default project, or function_name to load from the default project and the dataset associated with the current session.
is_row_processor	Whether the function is a row processor. This is set to True for a function which receives an entire row of a DataFrame as a pandas Series.

read_gbq_model

read_gbq_model(model_name: str)

Loads a BigQuery ML model from BigQuery.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

Read an existing BigQuery ML model.

>>> model_name = "bigframes-dev.bqml_tutorial.penguins_model"
>>> model = bpd.read_gbq_model(model_name)

Parameter
Name	Description
model_name	the model's name in BigQuery in the format project_id.dataset_id.model_id, or just dataset_id.model_id to load from the default project.

read_gbq_query

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

Turn a SQL query into a DataFrame.

Note: Because the results are written to a temporary table, ordering by ORDER BY is not preserved. A unique index_col is recommended. Use row_number() over () if there is no natural unique index or you want to preserve ordering.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

Simple query input:

>>> df = bpd.read_gbq_query('''
...    SELECT
...       pitcherFirstName,
...       pitcherLastName,
...       pitchSpeed,
...    FROM `bigquery-public-data.baseball.games_wide`
... ''')

Preserve ordering in a query input.

>>> df = bpd.read_gbq_query('''
...    SELECT
...       -- Instead of an ORDER BY clause on the query, use
...       -- ROW_NUMBER() to create an ordered DataFrame.
...       ROW_NUMBER() OVER (ORDER BY AVG(pitchSpeed) DESC)
...         AS rowindex,
...
...       pitcherFirstName,
...       pitcherLastName,
...       AVG(pitchSpeed) AS averagePitchSpeed
...     FROM `bigquery-public-data.baseball.games_wide`
...     WHERE year = 2016
...     GROUP BY pitcherFirstName, pitcherLastName
... ''', index_col="rowindex")
>>> df.head(2)
         pitcherFirstName pitcherLastName  averagePitchSpeed
rowindex
1                Albertin         Chapman          96.514113
2                 Zachary         Britton          94.591039
<BLANKLINE>
[2 rows x 3 columns]

See also: Session.read_gbq.

read_gbq_table

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

Turn a BigQuery table into a DataFrame.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

Read a whole table, with arbitrary ordering or ordering corresponding to the primary key(s).

>>> df = bpd.read_gbq_table("bigquery-public-data.ml_datasets.penguins")

See also: Session.read_gbq.

read_json

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

Convert a JSON string to DataFrame object.

Note: using engine="bigquery" will not guarantee the same ordering as the file. Instead, set a serialized index column as the index and sort by that in the resulting DataFrame.

Note: For non-bigquery engine, data is inlined in the query SQL if it is small enough (roughly 5MB or less in memory). Larger size data is loaded to a BigQuery table instead.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> gcs_path = "gs://bigframes-dev-testing/sample1.json"
>>> df = bpd.read_json(path_or_buf=gcs_path, lines=True, orient="records")
>>> df.head(2)
   id   name
0   1  Alice
1   2    Bob
<BLANKLINE>
[2 rows x 2 columns]

Parameters
Name	Description
path_or_buf	A local or Google Cloud Storage (gs://) path with engine="bigquery" otherwise passed to pandas.read_json.
orient	If engine="bigquery" orient only supports "records". Indication of expected JSON string format. Compatible JSON strings can be produced by to_json() with a corresponding orient value. The set of possible orients is: - 'split' : dict like {{index -> [index], columns -> [columns], data -> [values]}} - 'records' : list like [{{column -> value}}, ... , {{column -> value}}] - 'index' : dict like {{index -> {{column -> value}}}} - 'columns' : dict like {{column -> {{index -> value}}}} - 'values' : just the values array
dtype	If True, infer dtypes; if a dict of column to dtype, then use those; if False, then don't infer dtypes at all, applies only to the data. For all orient values except 'table', default is True.
encoding	The encoding to use to decode py3 bytes.
lines	Read the file as a json object per line. If using engine="bigquery" lines only supports True.
engine	Type of engine to use. If engine="bigquery" is specified, then BigQuery's load API will be used. Otherwise, the engine will be passed to pandas.read_json.

read_pandas

read_pandas(
    pandas_dataframe: typing.Union[
        pandas.core.frame.DataFrame,
        pandas.core.series.Series,
        pandas.core.indexes.base.Index,
    ]
)

Loads DataFrame from a pandas DataFrame.

The pandas DataFrame will be persisted as a temporary BigQuery table, which can be automatically recycled after the Session is closed.

Note: Data is inlined in the query SQL if it is small enough (roughly 5MB or less in memory). Larger size data is loaded to a BigQuery table instead.

Examples:

>>> import bigframes.pandas as bpd
>>> import pandas as pd
>>> bpd.options.display.progress_bar = None

>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> pandas_df = pd.DataFrame(data=d)
>>> df = bpd.read_pandas(pandas_df)
>>> df
   col1  col2
0     1     3
1     2     4
<BLANKLINE>
[2 rows x 2 columns]

Parameter
Name	Description
pandas_dataframe	a pandas DataFrame/Series/Index object to be loaded.

read_parquet

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

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

Note: This method will not guarantee the same ordering as the file. Instead, set a serialized index column as the index and sort by that in the resulting DataFrame.

Note: For non-"bigquery" engine, data is inlined in the query SQL if it is small enough (roughly 5MB or less in memory). Larger size data is loaded to a BigQuery table instead.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> gcs_path = "gs://cloud-samples-data/bigquery/us-states/us-states.parquet"
>>> df = bpd.read_parquet(path=gcs_path, engine="bigquery")

Parameters
Name	Description
path	Local or Cloud Storage path to Parquet file.
engine	One of 'auto', 'pyarrow', 'fastparquet', or 'bigquery'. Parquet library to parse the file. If set to 'bigquery', order is not preserved. Default, 'auto'.

read_pickle

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

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

Note: If the content of the pickle file is a Series and its name attribute is None, the name will be set to '0' by default.

Note: Data is inlined in the query SQL if it is small enough (roughly 5MB or less in memory). Larger size data is loaded to a BigQuery table instead.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> gcs_path = "gs://bigframes-dev-testing/test_pickle.pkl"
>>> df = bpd.read_pickle(filepath_or_buffer=gcs_path)

Parameters
Name	Description
filepath_or_buffer	String, path object (implementing os.PathLike[str]), or file-like object implementing a binary readlines() function. Also accepts URL. URL is not limited to S3 and GCS.
compression	For on-the-fly decompression of on-disk data. If 'infer' and 'filepath_or_buffer' is path-like, then detect compression from the following extensions: '.gz', '.bz2', '.zip', '.xz', '.zst', '.tar', '.tar.gz', '.tar.xz' or '.tar.bz2' (otherwise no compression). If using 'zip' or 'tar', the ZIP file must contain only one data file to be read in. Set to None for no decompression. Can also be a dict with key 'method' set to one of {'zip', 'gzip', 'bz2', 'zstd', 'tar'} and other key-value pairs are forwarded to zipfile.ZipFile, gzip.GzipFile, bz2.BZ2File, zstandard.ZstdDecompressor or tarfile.TarFile, respectively. As an example, the following could be passed for Zstandard decompression using a custom compression dictionary compression={'method': 'zstd', 'dict_data': my_compression_dict}.
storage_options	Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here.

remote_function

remote_function(
    input_types: typing.Union[None, type, typing.Sequence[type]] = None,
    output_type: typing.Optional[type] = None,
    dataset: typing.Optional[str] = None,
    bigquery_connection: typing.Optional[str] = None,
    reuse: bool = True,
    name: typing.Optional[str] = None,
    packages: typing.Optional[typing.Sequence[str]] = None,
    cloud_function_service_account: typing.Optional[str] = None,
    cloud_function_kms_key_name: typing.Optional[str] = None,
    cloud_function_docker_repository: typing.Optional[str] = None,
    max_batching_rows: typing.Optional[int] = 1000,
    cloud_function_timeout: typing.Optional[int] = 600,
    cloud_function_max_instances: typing.Optional[int] = None,
    cloud_function_vpc_connector: typing.Optional[str] = None,
    cloud_function_memory_mib: typing.Optional[int] = 1024,
    cloud_function_ingress_settings: typing.Literal[
        "all", "internal-only", "internal-and-gclb"
    ] = "all",
)

Decorator to turn a user defined function into a BigQuery remote function. Check out the code samples at: https://cloud.google.com/bigquery/docs/remote-functions#bigquery-dataframes.

Note: input_types=Series scenario is in preview. It currently only supports dataframe with column types Int64/Float64/boolean/ string/binary[pyarrow].

Note: Please make sure following is setup before using this API:

1. Have the below APIs enabled for your project:

   • BigQuery Connection API
   • Cloud Functions API
   • Cloud Run API
   • Cloud Build API
   • Artifact Registry API
   • Cloud Resource Manager API

   This can be done from the cloud console (change PROJECT_ID to yours): https://console.cloud.google.com/apis/enableflow?apiid=bigqueryconnection.googleapis.com,cloudfunctions.googleapis.com,run.googleapis.com,cloudbuild.googleapis.com,artifactregistry.googleapis.com,cloudresourcemanager.googleapis.com&project=PROJECT_ID

   Or from the gcloud CLI:

   $ gcloud services enable bigqueryconnection.googleapis.com cloudfunctions.googleapis.com run.googleapis.com cloudbuild.googleapis.com artifactregistry.googleapis.com cloudresourcemanager.googleapis.com

2. Have following IAM roles enabled for you:

   • BigQuery Data Editor (roles/bigquery.dataEditor)
   • BigQuery Connection Admin (roles/bigquery.connectionAdmin)
   • Cloud Functions Developer (roles/cloudfunctions.developer)
   • Service Account User (roles/iam.serviceAccountUser) on the service account [e-mail]
   • Storage Object Viewer (roles/storage.objectViewer)
   • Project IAM Admin (roles/resourcemanager.projectIamAdmin) (Only required if the bigquery connection being used is not pre-created and is created dynamically with user credentials.)

3. Either the user has setIamPolicy privilege on the project, or a BigQuery connection is pre-created with necessary IAM role set:

   1. To create a connection, follow https://cloud.google.com/bigquery/docs/reference/standard-sql/remote-functions#create_a_connection

   2. To set up IAM, follow https://cloud.google.com/bigquery/docs/reference/standard-sql/remote-functions#grant_permission_on_function

      Alternatively, the IAM could also be setup via the gcloud CLI:

      $ gcloud projects add-iam-policy-binding PROJECT_ID --member="serviceAccount:CONNECTION_SERVICE_ACCOUNT_ID" --role="roles/run.invoker".

Parameters
Name	Description
input_types	For scalar user defined function it should be the input type or sequence of input types. For row processing user defined function, type Series should be specified.
output_type	Data type of the output in the user defined function.
dataset	Dataset in which to create a BigQuery remote function. It should be in <project_id>.<dataset_name> or <dataset_name> format. If this parameter is not provided then session dataset id is used.
bigquery_connection	Name of the BigQuery connection. You should either have the connection already created in the location you have chosen, or you should have the Project IAM Admin role to enable the service to create the connection for you if you need it. If this parameter is not provided then the BigQuery connection from the session is used.
reuse	Reuse the remote function if already exists. True by default, which will result in reusing an existing remote function and corresponding cloud function that was previously created (if any) for the same udf. Please note that for an unnamed (i.e. created without an explicit name argument) remote function, the BigQuery DataFrames session id is attached in the cloud artifacts names. So for the effective reuse across the sessions it is recommended to create the remote function with an explicit name. Setting it to False would force creating a unique remote function. If the required remote function does not exist then it would be created irrespective of this param.
name	Explicit name of the persisted BigQuery remote function. Use it with caution, because more than one users working in the same project and dataset could overwrite each other's remote functions if they use the same persistent name. When an explicit name is provided, any session specific clean up ( bigframes.session.Session.close/ bigframes.pandas.close_session/ bigframes.pandas.reset_session/ bigframes.pandas.clean_up_by_session_id) does not clean up the function, and leaves it for the user to manage the function and the associated cloud function directly.
packages	Explicit name of the external package dependencies. Each dependency is added to the requirements.txt as is, and can be of the form supported in https://pip.pypa.io/en/stable/reference/requirements-file-format/.
cloud_function_service_account	Service account to use for the cloud functions. If not provided then the default service account would be used. See https://cloud.google.com/functions/docs/securing/function-identity for more details. Please make sure the service account has the necessary IAM permissions configured as described in https://cloud.google.com/functions/docs/reference/iam/roles#additional-configuration.
cloud_function_kms_key_name	Customer managed encryption key to protect cloud functions and related data at rest. This is of the format projects/PROJECT_ID/locations/LOCATION/keyRings/KEYRING/cryptoKeys/KEY. Read https://cloud.google.com/functions/docs/securing/cmek for more details including granting necessary service accounts access to the key.
cloud_function_docker_repository	Docker repository created with the same encryption key as cloud_function_kms_key_name to store encrypted artifacts created to support the cloud function. This is of the format projects/PROJECT_ID/locations/LOCATION/repositories/REPOSITORY_NAME. For more details see https://cloud.google.com/functions/docs/securing/cmek#before_you_begin.
max_batching_rows	The maximum number of rows to be batched for processing in the BQ remote function. Default value is 1000. A lower number can be passed to avoid timeouts in case the user code is too complex to process large number of rows fast enough. A higher number can be used to increase throughput in case the user code is fast enough. None can be passed to let BQ remote functions service apply default batching. See for more details https://cloud.google.com/bigquery/docs/remote-functions#limiting_number_of_rows_in_a_batch_request.
cloud_function_timeout	The maximum amount of time (in seconds) BigQuery should wait for the cloud function to return a response. See for more details https://cloud.google.com/functions/docs/configuring/timeout. Please note that even though the cloud function (2nd gen) itself allows seeting up to 60 minutes of timeout, BigQuery remote function can wait only up to 20 minutes, see for more details https://cloud.google.com/bigquery/quotas#remote_function_limits. By default BigQuery DataFrames uses a 10 minute timeout. None can be passed to let the cloud functions default timeout take effect.
cloud_function_max_instances	The maximumm instance count for the cloud function created. This can be used to control how many cloud function instances can be active at max at any given point of time. Lower setting can help control the spike in the billing. Higher setting can help support processing larger scale data. When not specified, cloud function's default setting applies. For more details see https://cloud.google.com/functions/docs/configuring/max-instances.
cloud_function_vpc_connector	The VPC connector you would like to configure for your cloud function. This is useful if your code needs access to data or service(s) that are on a VPC network. See for more details https://cloud.google.com/functions/docs/networking/connecting-vpc.
cloud_function_memory_mib	The amounts of memory (in mebibytes) to allocate for the cloud function (2nd gen) created. This also dictates a corresponding amount of allocated CPU for the function. By default a memory of 1024 MiB is set for the cloud functions created to support BigQuery DataFrames remote function. If you want to let the default memory of cloud functions be allocated, pass None. See for more details https://cloud.google.com/functions/docs/configuring/memory.
cloud_function_ingress_settings	Ingress settings controls dictating what traffic can reach the function. By default all will be used. It must be one of: all, internal-only, internal-and-gclb. See for more details https://cloud.google.com/functions/docs/networking/network-settings#ingress_settings.

reset_session

reset_session()

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

Closes the current session if it was already started, deleting any temporary tables that were created.

to_datetime

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

Note: BigQuery only supports precision up to microseconds (us). Therefore, when working with timestamps that have a finer granularity than microseconds, be aware that the additional precision will not be represented in BigQuery.

Note: The format strings for specifying datetime representations in BigQuery and pandas are not completely identical. Ensure that the format string provided is compatible with BigQuery (https://cloud.google.com/bigquery/docs/reference/standard-sql/format-elements#format_elements_date_time).

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

Converting a Scalar to datetime:

>>> scalar = 123456.789
>>> bpd.to_datetime(scalar, unit = 's')
Timestamp('1970-01-02 10:17:36.789000')

Converting a List of Strings without Timezone Information:

>>> list_str = ["01-31-2021 14:30", "02-28-2021 15:45"]
>>> bpd.to_datetime(list_str, format="%m-%d-%Y %H:%M", utc=True)
0    2021-01-31 14:30:00+00:00
1    2021-02-28 15:45:00+00:00
dtype: timestamp[us, tz=UTC][pyarrow]

Converting a Series of Strings with Timezone Information:

>>> series_str = bpd.Series(["01-31-2021 14:30+08:00", "02-28-2021 15:45+00:00"])
>>> bpd.to_datetime(series_str, format="%m-%d-%Y %H:%M%Z", utc=True)
0    2021-01-31 06:30:00+00:00
1    2021-02-28 15:45:00+00:00
dtype: timestamp[us, tz=UTC][pyarrow]

Parameters
Name	Description
arg	The object to convert to a datetime.
utc	Control timezone-related parsing, localization and conversion. If True, the function always returns a timezone-aware UTC-localized timestamp or series. If False (default), inputs will not be coerced to UTC.
format	The strftime to parse time, e.g. "%d/%m/%Y".
unit	The unit of the arg (D,s,ms,us,ns) denote the unit, which is an integer or float number.