Class Series (0.24.0)

Series(*args, **kwargs)

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

Properties

T

Return the transpose, which is by definition self.

Examples:

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

>>> s = bpd.Series(['Ant', 'Bear', 'Cow'])
>>> s
0     Ant
1    Bear
2     Cow
dtype: string

>>> s.T
0     Ant
1    Bear
2     Cow
dtype: string

at

Access a single value for a row/column label pair.

dt

Accessor object for datetime-like properties of the Series values.

Returns
TypeDescription
bigframes.operations.datetimes.DatetimeMethodsAn accessor containing datetime methods.

dtype

Return the dtype object of the underlying data.

dtypes

Return the dtype object of the underlying data.

empty

Indicates whether Series/DataFrame is empty.

True if Series/DataFrame is entirely empty (no items), meaning any of the axes are of length 0.

Returns
TypeDescription
boolIf Series/DataFrame is empty, return True, if not return False.

iat

Access a single value for a row/column pair by integer position.

iloc

Purely integer-location based indexing for selection by position.

index

The index (axis labels) of the Series.

The index of a Series is used to label and identify each element of the underlying data. The index can be thought of as an immutable ordered set (technically a multi-set, as it may contain duplicate labels), and is used to index and align data.

Examples:

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

You can access the index of a Series via index property.

>>> df = bpd.DataFrame({'Name': ['Alice', 'Bob', 'Aritra'],
...                     'Age': [25, 30, 35],
...                     'Location': ['Seattle', 'New York', 'Kona']},
...                    index=([10, 20, 30]))
>>> s = df["Age"]
>>> s
10    25
20    30
30    35
Name: Age, dtype: Int64
>>> s.index # doctest: +ELLIPSIS
Index([10, 20, 30], dtype='Int64')
>>> s.index.values
array([10, 20, 30], dtype=object)

Let's try setting a multi-index case reflect via index property.

>>> df1 = df.set_index(["Name", "Location"])
>>> s1 = df1["Age"]
>>> s1
Name    Location
Alice   Seattle     25
Bob     New York    30
Aritra  Kona        35
Name: Age, dtype: Int64
>>> s1.index # doctest: +ELLIPSIS
MultiIndex([( 'Alice',  'Seattle'),
    (   'Bob', 'New York'),
    ('Aritra',     'Kona')],
   name='Name')
>>> s1.index.values
array([('Alice', 'Seattle'), ('Bob', 'New York'), ('Aritra', 'Kona')],
    dtype=object)
Returns
TypeDescription
IndexThe index object of the Series.

is_monotonic_decreasing

Return boolean if values in the object are monotonically decreasing.

Examples:

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

>>> s = bpd.Series([3, 2, 2, 1])
>>> s.is_monotonic_decreasing
True

>>> s = bpd.Series([1, 2, 3])
>>> s.is_monotonic_decreasing
False
Returns
TypeDescription
boolBoolean.

is_monotonic_increasing

Return boolean if values in the object are monotonically increasing.

Examples:

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

>>> s = bpd.Series([1, 2, 2])
>>> s.is_monotonic_increasing
True

>>> s = bpd.Series([3, 2, 1])
>>> s.is_monotonic_increasing
False
Returns
TypeDescription
boolBoolean.

loc

Access a group of rows and columns by label(s) or a boolean array.

.loc[] is primarily label based, but may also be used with a boolean array.

Allowed inputs are:

  • A single label, e.g. 5 or 'a', (note that 5 is interpreted as a label of the index, and never as an integer position along the index).
  • A list of labels, e.g. ['a', 'b', 'c'].
  • A boolean series of the same length as the axis being sliced, e.g. [True, False, True].
  • An alignable Index. The index of the returned selection will be the input.
  • Not supported yet An alignable boolean Series. The index of the key will be aligned before masking.
  • Not supported yet A slice object with labels, e.g. 'a':'f'. Note: contrary to usual python slices, both the start and the stop are included.
  • Not supported yet A callable function with one argument (the calling Series or DataFrame) that returns valid output for indexing (one of the above).

Exceptions
TypeDescription
NotImplementErrorif the inputs are not supported.

name

Return the name of the Series.

The name of a Series becomes its index or column name if it is used to form a DataFrame. It is also used whenever displaying the Series using the interpreter.

Examples:

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

For a Series:

>>> s = bpd.Series([1, 2, 3], dtype="Int64", name='Numbers')
>>> s
0    1
1    2
2    3
Name: Numbers, dtype: Int64
>>> s.name
'Numbers'

If the Series is part of a DataFrame:

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df
   col1  col2
0     1     3
1     2     4
<BLANKLINE>
[2 rows x 2 columns]
>>> s = df["col1"]
>>> s.name
'col1'
Returns
TypeDescription
hashable objectThe name of the Series, also the column name if part of a DataFrame.

ndim

Return an int representing the number of axes / array dimensions.

Returns
TypeDescription
intReturn 1 if Series. Otherwise return 2 if DataFrame.

plot

Make plots of Series.

Returns
TypeDescription
bigframes.operations.plotting.PlotAccessorAn accessor making plots.

query_job

BigQuery job metadata for the most recent query.

shape

Return a tuple of the shape of the underlying data.

Examples:

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

>>> s = bpd.Series([1, 4, 9, 16])
>>> s.shape
(4,)
>>> s = bpd.Series(['Alice', 'Bob', bpd.NA])
>>> s.shape
(3,)

size

Return the number of elements in the underlying data.

Examples:

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

For Series:

>>> s = bpd.Series({'a': 1, 'b': 2, 'c': 3})
>>> s.size
3

For Index:

>>> idx = bpd.Index(bpd.Series([1, 2, 3]))
>>> idx.size
3
Returns
TypeDescription
intReturn the number of elements in the underlying data.

str

Vectorized string functions for Series and Index.

NAs stay NA unless handled otherwise by a particular method. Patterned after Python’s string methods, with some inspiration from R’s stringr package.

Examples:

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

>>> s = bpd.Series(["A_Str_Series"])
>>> s
0    A_Str_Series
dtype: string

>>> s.str.lower()
0    a_str_series
dtype: string

>>> s.str.replace("_", "")
0    AStrSeries
dtype: string
Returns
TypeDescription
bigframes.operations.strings.StringMethodsAn accessor containing string methods.

struct

Accessor object for struct properties of the Series values.

Returns
TypeDescription
bigframes.operations.structs.StructAccessorAn accessor containing struct methods.

values

Return Series as ndarray or ndarray-like depending on the dtype.

Examples:

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

>>> bpd.Series([1, 2, 3]).values
array([1, 2, 3], dtype=object)

>>> bpd.Series(list('aabc')).values
array(['a', 'a', 'b', 'c'], dtype=object)
Returns
TypeDescription
numpy.ndarray or ndarray-likeValues in the Series.

Methods

__array_ufunc__

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

Used to support numpy ufuncs. See: https://numpy.org/doc/stable/reference/ufuncs.html

__rmatmul__

__rmatmul__(other)

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

abs

abs() -> bigframes.series.Series

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

This function only applies to elements that are all numeric.

add

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

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

Equivalent to series + other, but with support to substitute a fill_value for missing data in either one of the inputs.

Examples:

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

>>> a = bpd.Series([1, 2, 3, bpd.NA])
>>> a
0     1.0
1     2.0
2     3.0
3    <NA>
dtype: Float64

>>> b = bpd.Series([10, 20, 30, 40])
>>> b
0     10
1     20
2     30
3     40
dtype: Int64

>>> a.add(b)
0    11.0
1    22.0
2    33.0
3    <NA>
dtype: Float64

You can also use the mathematical operator +:

>>> a + b
0    11.0
1    22.0
2    33.0
3    <NA>
dtype: Float64

Adding two Series with explicit indexes:

>>> a = bpd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
>>> b = bpd.Series([10, 20, 30, 40], index=['a', 'b', 'd', 'e'])
>>> a.add(b)
a      11
b      22
c    <NA>
d      34
e    <NA>
dtype: Int64
Returns
TypeDescription
bigframes.series.SeriesThe result of the operation.

add_prefix

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

Prefix labels with string prefix.

For Series, the row labels are prefixed. For DataFrame, the column labels are prefixed.

Parameters
NameDescription
prefix str

The string to add before each label.

axis int or str or None, default None

{{0 or 'index', 1 or 'columns', None}}, default None. Axis to add prefix on.

add_suffix

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

Suffix labels with string suffix.

For Series, the row labels are suffixed. For DataFrame, the column labels are suffixed.

agg

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

Aggregate using one or more operations over the specified axis.

Examples:

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

>>> s = bpd.Series([1, 2, 3, 4])
>>> s
0    1
1    2
2    3
3    4
dtype: Int64

>>> s.agg('min')
1

>>> s.agg(['min', 'max'])
min    1.0
max    4.0
dtype: Float64
Parameter
NameDescription
func function

Function to use for aggregating the data. Accepted combinations are: string function name, list of function names, e.g. ['sum', 'mean'].

Returns
TypeDescription
scalar or SeriesAggregated results

aggregate

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

API documentation for aggregate method.

all

all() -> bool

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

Returns True unless there at least one element within a Series or along a DataFrame axis that is False or equivalent (e.g. zero or empty).

Returns
TypeDescription
scalar or SeriesIf level is specified, then, Series is returned; otherwise, scalar is returned.

any

any() -> bool

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

Returns False unless there is at least one element within a series or along a Dataframe axis that is True or equivalent (e.g. non-zero or non-empty).

Returns
TypeDescription
scalar or SeriesIf level is specified, then, Series is returned; otherwise, scalar is returned.

apply

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

Invoke function on values of a Series.

Can be ufunc (a NumPy function that applies to the entire Series) or a Python function that only works on single values. If it is an arbitrary python function then converting it into a remote_function is recommended.

Examples:

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

For applying arbitrary python function a remote_funciton is recommended. Let's use reuse=False flag to make sure a new remote_function is created every time we run the following code, but you can skip it to potentially reuse a previously deployed remote_function from the same user defined function.

>>> @bpd.remote_function([int], float, reuse=False)
... def minutes_to_hours(x):
...     return x/60

>>> minutes = bpd.Series([0, 30, 60, 90, 120])
>>> minutes
0      0
1     30
2     60
3     90
4    120
dtype: Int64

>>> hours = minutes.apply(minutes_to_hours)
>>> hours
0    0.0
1    0.5
2    1.0
3    1.5
4    2.0
dtype: Float64

To turn a user defined function with external package dependencies into a remote_function, you would provide the names of the packages via packages param.

>>> @bpd.remote_function(
...     [str],
...     str,
...     reuse=False,
...     packages=["cryptography"],
... )
... def get_hash(input):
...     from cryptography.fernet import Fernet
...
...     # handle missing value
...     if input is None:
...         input = ""
...
...     key = Fernet.generate_key()
...     f = Fernet(key)
...     return f.encrypt(input.encode()).decode()

>>> names = bpd.Series(["Alice", "Bob"])
>>> hashes = names.apply(get_hash)

Simple vectorized functions, lambdas or ufuncs can be applied directly with by_row=False.

>>> nums = bpd.Series([1, 2, 3, 4])
>>> nums
0    1
1    2
2    3
3    4
dtype: Int64
>>> nums.apply(lambda x: x*x + 2*x + 1, by_row=False)
0     4
1     9
2    16
3    25
dtype: Int64

>>> def is_odd(num):
...     return num % 2 == 1
>>> nums.apply(is_odd, by_row=False)
0     True
1    False
2     True
3    False
dtype: boolean

>>> nums.apply(np.log, by_row=False)
0         0.0
1    0.693147
2    1.098612
3    1.386294
dtype: Float64
Parameters
NameDescription
func function

BigFrames DataFrames remote_function to apply. The function should take a scalar and return a scalar. It will be applied to every element in the Series.

by_row False or "compat", default "compat"

If "compat" , func must be a remote function which will be passed each element of the Series, like Series.map. If False, the func will be passed the whole Series at once.

Returns
TypeDescription
bigframes.series.SeriesA new Series with values representing the return value of the func applied to each element of the original Series.

argmax

argmax() -> int

Return int position of the smallest value in the Series.

If the minimum is achieved in multiple locations, the first row position is returned.

Examples:

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

Consider dataset containing cereal calories.

>>> s = bpd.Series({'Corn Flakes': 100.0, 'Almond Delight': 110.0,
...                 'Cinnamon Toast Crunch': 120.0, 'Cocoa Puff': 110.0})
>>> s
Corn Flakes              100.0
Almond Delight           110.0
Cinnamon Toast Crunch    120.0
Cocoa Puff               110.0
dtype: Float64

>>> s.argmax()
2

>>> s.argmin()
0

The maximum cereal calories is the third element and the minimum cereal calories is the first element, since series is zero-indexed.

Returns
TypeDescription
SeriesRow position of the maximum value.

argmin

argmin() -> int

Return int position of the largest value in the Series.

If the maximum is achieved in multiple locations, the first row position is returned.

Examples:

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

Consider dataset containing cereal calories.

>>> s = bpd.Series({'Corn Flakes': 100.0, 'Almond Delight': 110.0,
...                 'Cinnamon Toast Crunch': 120.0, 'Cocoa Puff': 110.0})
>>> s
Corn Flakes              100.0
Almond Delight           110.0
Cinnamon Toast Crunch    120.0
Cocoa Puff               110.0
dtype: Float64

>>> s.argmax()
2

>>> s.argmin()
0

The maximum cereal calories is the third element and the minimum cereal calories is the first element, since series is zero-indexed.

Returns
TypeDescription
SeriesRow position of the minimum value.

astype

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

Cast a pandas object to a specified dtype dtype.

Examples:

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

Create a DataFrame:

>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df = bpd.DataFrame(data=d)
>>> df.dtypes
col1    Int64
col2    Int64
dtype: object

Cast all columns to Float64:

>>> df.astype('Float64').dtypes
col1    Float64
col2    Float64
dtype: object

Create a series of type Int64:

>>> ser = bpd.Series([1, 2], dtype='Int64')
>>> ser
0    1
1    2
dtype: Int64

Convert to Float64 type:

>>> ser.astype('Float64')
0    1.0
1    2.0
dtype: Float64
Parameter
NameDescription
dtype str or pandas.ExtensionDtype

A dtype supported by BigQuery DataFrame include 'boolean','Float64','Int64', 'string', 'string[pyarrow]','timestamp[us, tz=UTC][pyarrow]', 'timestampus][pyarrow]','date32day][pyarrow]','time64us][pyarrow]' A pandas.ExtensionDtype include pandas.BooleanDtype(), pandas.Float64Dtype(), pandas.Int64Dtype(), pandas.StringDtype(storage="pyarrow"), pd.ArrowDtype(pa.date32()), pd.ArrowDtype(pa.time64("us")), pd.ArrowDtype(pa.timestamp("us")), pd.ArrowDtype(pa.timestamp("us", tz="UTC")).

between

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

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

This function returns a boolean vector containing True wherever the corresponding Series element is between the boundary values left and right. NA values are treated as False.

Examples:

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

Boundary values are included by default:

>>> s = bpd.Series([2, 0, 4, 8, np.nan])
>>> s.between(1, 4)
0     True
1    False
2     True
3    False
4     <NA>
dtype: boolean

With inclusive set to "neither" boundary values are excluded:

>>> s.between(1, 4, inclusive="neither")
0     True
1    False
2    False
3    False
4     <NA>
dtype: boolean

left and right can be any scalar value:

>>> s = bpd.Series(['Alice', 'Bob', 'Carol', 'Eve'])
>>> s.between('Anna', 'Daniel')
0    False
1     True
2     True
3    False
dtype: boolean
Parameters
NameDescription
left scalar or list-like

Left boundary.

right scalar or list-like

Right boundary.

inclusive {"both", "neither", "left", "right"}

Include boundaries. Whether to set each bound as closed or open.

Returns
TypeDescription
SeriesSeries representing whether each element is between left and right (inclusive).

bfill

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

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

Returns
TypeDescription
Series/DataFrame or NoneObject with missing values filled.

clip

clip(lower, upper)

Trim values at input threshold(s).

Assigns values outside boundary to boundary values. Thresholds can be singular values or array like, and in the latter case the clipping is performed element-wise in the specified axis.

Parameters
NameDescription
lower float or array-like, default None

Minimum threshold value. All values below this threshold will be set to it. A missing threshold (e.g NA) will not clip the value.

upper float or array-like, default None

Maximum threshold value. All values above this threshold will be set to it. A missing threshold (e.g NA) will not clip the value.

Returns
TypeDescription
SeriesSeries.

copy

copy() -> bigframes.series.Series

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

A new object will be created with a copy of the calling object's data and indices. Modifications to the data or indices of the copy will not be reflected in the original object.

Examples:

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

Modification in the original Series will not affect the copy Series:

>>> s = bpd.Series([1, 2], index=["a", "b"])
>>> s
a    1
b    2
dtype: Int64

>>> s_copy = s.copy()
>>> s_copy
a    1
b    2
dtype: Int64

>>> s.loc['b'] = 22
>>> s
a     1
b    22
dtype: Int64
>>> s_copy
a    1
b    2
dtype: Int64

Modification in the original DataFrame will not affect the copy DataFrame:

>>> df = bpd.DataFrame({'a': [1, 3], 'b': [2, 4]})
>>> df
   a  b
0  1  2
1  3  4
<BLANKLINE>
[2 rows x 2 columns]

>>> df_copy = df.copy()
>>> df_copy
   a  b
0  1  2
1  3  4
<BLANKLINE>
[2 rows x 2 columns]

>>> df.loc[df["b"] == 2, "b"] = 22
>>> df
   a     b
0  1  22.0
1  3   4.0
<BLANKLINE>
[2 rows x 2 columns]
>>> df_copy
   a  b
0  1  2
1  3  4
<BLANKLINE>
[2 rows x 2 columns]

corr

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

Compute the correlation with the other Series. Non-number values are ignored in the computation.

Uses the "Pearson" method of correlation. Numbers are converted to float before calculation, so the result may be unstable.

Examples:

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

>>> s1 = bpd.Series([.2, .0, .6, .2])
>>> s2 = bpd.Series([.3, .6, .0, .1])
>>> s1.corr(s2)
-0.8510644963469901

>>> s1 = bpd.Series([1, 2, 3], index=[0, 1, 2])
>>> s2 = bpd.Series([1, 2, 3], index=[2, 1, 0])
>>> s1.corr(s2)
-1.0
Parameters
NameDescription
other Series

The series with which this is to be correlated.

method string, default "pearson"

Correlation method to use - currently only "pearson" is supported.

min_periods int, default None

The minimum number of observations needed to return a result. Non-default values are not yet supported, so a result will be returned for at least two observations.

Returns
TypeDescription
floatWill return NaN if there are fewer than two numeric pairs, either series has a variance or covariance of zero, or any input value is infinite.

count

count() -> int

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

Examples:

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

>>> s = bpd.Series([0.0, 1.0, bpd.NA])
>>> s
0     0.0
1     1.0
2    <NA>
dtype: Float64
>>> s.count()
2
Returns
TypeDescription
int or Series (if level specified)Number of non-null values in the Series.

cov

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

Compute covariance with Series, excluding missing values.

The two Series objects are not required to be the same length and will be aligned internally before the covariance is calculated.

Parameter
NameDescription
other Series

Series with which to compute the covariance.

Returns
TypeDescription
floatCovariance between Series and other normalized by N-1 (unbiased estimator).

cummax

cummax() -> bigframes.series.Series

Return cumulative maximum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative maximum.

Parameter
NameDescription
axis {{0 or 'index', 1 or 'columns'}}, default 0

The index or the name of the axis. 0 is equivalent to None or 'index'. For Series this parameter is unused and defaults to 0.

Returns
TypeDescription
bigframes.series.SeriesReturn cumulative maximum of scalar or Series.

cummin

cummin() -> bigframes.series.Series

Return cumulative minimum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative minimum.

Parameters
NameDescription
axis {0 or 'index', 1 or 'columns'}, default 0

The index or the name of the axis. 0 is equivalent to None or 'index'. For Series this parameter is unused and defaults to 0.

skipna bool, default True

Exclude NA/null values. If an entire row/column is NA, the result will be NA.

Returns
TypeDescription
bigframes.series.SeriesReturn cumulative minimum of scalar or Series.

cumprod

cumprod() -> bigframes.series.Series

Return cumulative product over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative product.

Examples:

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

>>> s = bpd.Series([2, np.nan, 5, -1, 0])
>>> s
0     2.0
1    <NA>
2     5.0
3    -1.0
4     0.0
dtype: Float64

By default, NA values are ignored.

>>> s.cumprod()
0     2.0
1    <NA>
2    10.0
3   -10.0
4     0.0
dtype: Float64
Returns
TypeDescription
bigframes.series.SeriesReturn cumulative sum of scalar or Series.

cumsum

cumsum() -> bigframes.series.Series

Return cumulative sum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative sum.

Parameter
NameDescription
axis {0 or 'index', 1 or 'columns'}, default 0

The index or the name of the axis. 0 is equivalent to None or 'index'. For Series this parameter is unused and defaults to 0.

Returns
TypeDescription
scalar or SeriesReturn cumulative sum of scalar or Series.

diff

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

First discrete difference of element.

Calculates the difference of a {klass} element compared with another element in the {klass} (default is element in previous row).

Parameter
NameDescription
periods int, default 1

Periods to shift for calculating difference, accepts negative values.

Returns
TypeDescription
SeriesFirst differences of the Series.

div

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

API documentation for div method.

divide

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

API documentation for divide method.

divmod

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

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

Equivalent to divmod(series, other).

Returns
TypeDescription
2-Tuple of SeriesThe result of the operation. The result is always consistent with (floordiv, mod) (though pandas may not).

dot

dot(other)

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

This method computes the dot product between the Series and another one, or the Series and each columns of a DataFrame, or the Series and each columns of an array.

It can also be called using self @ other in Python >= 3.5.

Examples:

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

>>> s = bpd.Series([0, 1, 2, 3])
>>> other = bpd.Series([-1, 2, -3, 4])
>>> s.dot(other)
8

You can also use the operator @ for the dot product:

>>> s @ other
8
Parameter
NameDescription
other Series

The other object to compute the dot product with its columns.

Returns
TypeDescription
scalar, Series or numpy.ndarrayReturn the dot product of the Series and other if other is a Series, the Series of the dot product of Series and each rows of other if other is a DataFrame or a numpy.ndarray between the Series and each columns of the numpy array.

drop

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

Return Series with specified index labels removed.

Remove elements of a Series based on specifying the index labels. When using a multi-index, labels on different levels can be removed by specifying the level.

Examples:

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

>>> s = bpd.Series(data=np.arange(3), index=['A', 'B', 'C'])
>>> s
A    0
B    1
C    2
dtype: Int64

Drop labels B and C:

>>> s.drop(labels=['B', 'C'])
A    0
dtype: Int64

Drop 2nd level label in MultiIndex Series:

>>> import pandas as pd
>>> midx = pd.MultiIndex(levels=[['llama', 'cow', 'falcon'],
...                              ['speed', 'weight', 'length']],
...                      codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
...                             [0, 1, 2, 0, 1, 2, 0, 1, 2]])

>>> s = bpd.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3],
...               index=midx)
>>> s
llama   speed      45.0
        weight    200.0
        length      1.2
cow     speed      30.0
        weight    250.0
        length      1.5
falcon  speed     320.0
        weight      1.0
        length      0.3
dtype: Float64

>>> s.drop(labels='weight', level=1)
llama   speed      45.0
        length      1.2
cow     speed      30.0
        length      1.5
falcon  speed     320.0
        length      0.3
dtype: Float64
Parameter
NameDescription
labels single label or list-like

Index labels to drop.

Exceptions
TypeDescription
KeyErrorIf none of the labels are found in the index.
Returns
TypeDescription
bigframes.series.SeriesSeries with specified index labels removed or None if inplace=True.

drop_duplicates

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

Return Series with duplicate values removed.

Parameter
NameDescription
keep {'first', 'last', False}, default 'first'

Method to handle dropping duplicates: 'first' : Drop duplicates except for the first occurrence. 'last' : Drop duplicates except for the last occurrence. False : Drop all duplicates.

Returns
TypeDescription
bigframes.series.SeriesSeries with duplicates dropped or None if inplace=True.

droplevel

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

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

Parameters
NameDescription
level int, str, or list-like

If a string is given, must be the name of a level If list-like, elements must be names or positional indexes of levels.

axis {0 or 'index', 1 or 'columns'}, default 0

For Series this parameter is unused and defaults to 0.

dropna

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

Return a new Series with missing values removed.

Examples:

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

Drop NA values from a Series:

>>> ser = bpd.Series([1., 2., np.nan])
>>> ser
0     1.0
1     2.0
2    <NA>
dtype: Float64

>>> ser.dropna()
0    1.0
1    2.0
dtype: Float64

Empty strings are not considered NA values. None is considered an NA value.

>>> ser = bpd.Series(['2', bpd.NA, '', None, 'I stay'], dtype='object')
>>> ser
0         2
1      <NA>
2
3      <NA>
4    I stay
dtype: string

>>> ser.dropna()
0         2
2
4    I stay
dtype: string
Parameters
NameDescription
axis 0 or 'index'

Unused. Parameter needed for compatibility with DataFrame.

inplace bool, default False

Unsupported, do not set.

how str, optional

Not in use. Kept for compatibility.

Returns
TypeDescription
SeriesSeries with NA entries dropped from it.

duplicated

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

Indicate duplicate Series values.

Duplicated values are indicated as True values in the resulting Series. Either all duplicates, all except the first or all except the last occurrence of duplicates can be indicated.

Parameter
NameDescription
keep {'first', 'last', False}, default 'first'

Method to handle dropping duplicates: 'first' : Mark duplicates as True except for the first occurrence. 'last' : Mark duplicates as True except for the last occurrence. False : Mark all duplicates as True.

Returns
TypeDescription
bigframes.series.SeriesSeries indicating whether each value has occurred in the preceding values.

eq

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

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

Equivalent to other == series, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
TypeDescription
SeriesThe result of the operation.

equals

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

API documentation for equals method.

expanding

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

Provide expanding window calculations.

Parameter
NameDescription
min_periods int, default 1

Minimum number of observations in window required to have a value; otherwise, result is np.nan.

Returns
TypeDescription
bigframes.core.window.WindowExpanding subclass.

ffill

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

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

Examples:

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

>>> df = bpd.DataFrame([[np.nan, 2, np.nan, 0],
...                     [3, 4, np.nan, 1],
...                     [np.nan, np.nan, np.nan, np.nan],
...                     [np.nan, 3, np.nan, 4]],
...                    columns=list("ABCD")).astype("Float64")
>>> df
      A     B     C     D
0  <NA>   2.0  <NA>   0.0
1   3.0   4.0  <NA>   1.0
2  <NA>  <NA>  <NA>  <NA>
3  <NA>   3.0  <NA>   4.0
<BLANKLINE>
[4 rows x 4 columns]

Fill NA/NaN values in DataFrames:

>>> df.ffill()
      A    B     C    D
0  <NA>  2.0  <NA>  0.0
1   3.0  4.0  <NA>  1.0
2   3.0  4.0  <NA>  1.0
3   3.0  3.0  <NA>  4.0
<BLANKLINE>
[4 rows x 4 columns]

Fill NA/NaN values in Series:

>>> series = bpd.Series([1, np.nan, 2, 3])
>>> series.ffill()
0    1.0
1    1.0
2    2.0
3    3.0
dtype: Float64
Returns
TypeDescription
Series/DataFrame or NoneObject with missing values filled.

fillna

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

Fill NA/NaN values using the specified method.

Examples:

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

>>> s = bpd.Series([np.nan, 2, np.nan, -1])
>>> s
0    <NA>
1     2.0
2    <NA>
3    -1.0
dtype: Float64

Replace all NA elements with 0s.

>>> s.fillna(0)
0    0.0
1    2.0
2    0.0
3   -1.0
dtype: Float64

You can use fill values from another Series:

>>> s_fill = bpd.Series([11, 22, 33])
>>> s.fillna(s_fill)
0    11.0
1     2.0
2    33.0
3    -1.0
dtype: Float64
Parameter
NameDescription
value scalar, dict, Series, or DataFrame, default None

Value to use to fill holes (e.g. 0).

Returns
TypeDescription
Series or NoneObject with missing values filled or None.

filter

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

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

Note that this routine does not filter a dataframe on its contents. The filter is applied to the labels of the index.

Parameters
NameDescription
items list-like

Keep labels from axis which are in items.

like str

Keep labels from axis for which "like in label == True".

regex str (regular expression)

Keep labels from axis for which re.search(regex, label) == True.

axis {0 or 'index', 1 or 'columns', None}, default None

The axis to filter on, expressed either as an index (int) or axis name (str). By default this is the info axis, 'columns' for DataFrame. For Series this parameter is unused and defaults to None.

floordiv

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

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

Equivalent to series // other, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
TypeDescription
bigframes.series.SeriesThe result of the operation.

ge

ge(other) -> bigframes.series.Series

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

Equivalent to series >= other, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
TypeDescription
bigframes.series.SeriesThe result of the operation.

get

get(key, default=None)

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

Returns default value if not found.

Examples:

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

>>> df = bpd.DataFrame(
...     [
...         [24.3, 75.7, "high"],
...         [31, 87.8, "high"],
...         [22, 71.6, "medium"],
...         [35, 95, "medium"],
...     ],
...     columns=["temp_celsius", "temp_fahrenheit", "windspeed"],
...     index=["2014-02-12", "2014-02-13", "2014-02-14", "2014-02-15"],
... )
>>> df
            temp_celsius  temp_fahrenheit windspeed
2014-02-12          24.3             75.7      high
2014-02-13          31.0             87.8      high
2014-02-14          22.0             71.6    medium
2014-02-15          35.0             95.0    medium
<BLANKLINE>
[4 rows x 3 columns]

>>> df.get(["temp_celsius", "windspeed"])
            temp_celsius windspeed
2014-02-12          24.3      high
2014-02-13          31.0      high
2014-02-14          22.0    medium
2014-02-15          35.0    medium
<BLANKLINE>
[4 rows x 2 columns]

>>> ser = df['windspeed']
>>> ser
2014-02-12      high
2014-02-13      high
2014-02-14    medium
2014-02-15    medium
Name: windspeed, dtype: string
>>> ser.get('2014-02-13')
'high'

If the key is not found, the default value will be used.

>>> df.get(["temp_celsius", "temp_kelvin"])
>>> df.get(["temp_celsius", "temp_kelvin"], default="default_value")
'default_value'

groupby

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

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

A groupby operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups.

Examples:

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

You can group by a named index level.

>>> s = bpd.Series([380, 370., 24., 26.],
...                index=["Falcon", "Falcon", "Parrot", "Parrot"],
...                name="Max Speed")
>>> s.index.name="Animal"
>>> s
Animal
Falcon    380.0
Falcon    370.0
Parrot     24.0
Parrot     26.0
Name: Max Speed, dtype: Float64
>>> s.groupby("Animal").mean()
Animal
Falcon    375.0
Parrot     25.0
Name: Max Speed, dtype: Float64

You can also group by more than one index levels.

>>> import pandas as pd
>>> s = bpd.Series([380, 370., 24., 26.],
...                index=pd.MultiIndex.from_tuples(
...                    [("Falcon", "Clear"),
...                     ("Falcon", "Cloudy"),
...                     ("Parrot", "Clear"),
...                     ("Parrot", "Clear")],
...                    names=["Animal", "Sky"]),
...                name="Max Speed")
>>> s
Animal    Sky
Falcon  Clear     380.0
        Cloudy    370.0
Parrot  Clear      24.0
        Clear      26.0
Name: Max Speed, dtype: Float64

>>> s.groupby("Animal").mean()
Animal
Falcon    375.0
Parrot     25.0
Name: Max Speed, dtype: Float64

>>> s.groupby("Sky").mean()
Sky
Clear     143.333333
Cloudy         370.0
Name: Max Speed, dtype: Float64

>>> s.groupby(["Animal", "Sky"]).mean()
Animal  Sky
Falcon  Clear     380.0
        Cloudy    370.0
Parrot  Clear      25.0
Name: Max Speed, dtype: Float64

You can also group by values in a Series provided the index matches with the original series.

>>> df = bpd.DataFrame({'Animal': ['Falcon', 'Falcon', 'Parrot', 'Parrot'],
...                     'Max Speed': [380., 370., 24., 26.],
...                     'Age': [10., 20., 4., 6.]})
>>> df
Animal  Max Speed   Age
0  Falcon      380.0  10.0
1  Falcon      370.0  20.0
2  Parrot       24.0   4.0
3  Parrot       26.0   6.0
<BLANKLINE>
[4 rows x 3 columns]

>>> df['Max Speed'].groupby(df['Animal']).mean()
Animal
Falcon    375.0
Parrot     25.0
Name: Max Speed, dtype: Float64

>>> df['Age'].groupby(df['Animal']).max()
Animal
Falcon    20.0
Parrot     6.0
Name: Age, dtype: Float64
Parameters
NameDescription
by mapping, function, label, pd.Grouper or list of such, default None

Used to determine the groups for the groupby. If by is a function, it's called on each value of the object's index. If a dict or Series is passed, the Series or dict VALUES will be used to determine the groups (the Series' values are first aligned; see .align() method). If a list or ndarray of length equal to the selected axis is passed (see the groupby user guide https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#splitting-an-object-into-groups_), the values are used as-is to determine the groups. A label or list of labels may be passed to group by the columns in self. Notice that a tuple is interpreted as a (single) key.

axis {0 or 'index', 1 or 'columns'}, default 0

Split along rows (0) or columns (1). For Series this parameter is unused and defaults to 0.

level int, level name, or sequence of such, default None

If the axis is a MultiIndex (hierarchical), group by a particular level or levels. Do not specify both by and level.

as_index bool, default True

Return object with group labels as the index. Only relevant for DataFrame input. as_index=False is effectively "SQL-style" grouped output. This argument has no effect on filtrations (see the "filtrations in the user guide" https://pandas.pydata.org/docs/dev/user_guide/groupby.html#filtration), such as head(), tail(), nth() and in transformations (see the "transformations in the user guide" https://pandas.pydata.org/docs/dev/user_guide/groupby.html#transformation).

Returns
TypeDescription
bigframes.core.groupby.SeriesGroupByReturns a groupby object that contains information about the groups.

gt

gt(other) -> bigframes.series.Series

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

Equivalent to series <= other, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
TypeDescription
bigframes.series.SeriesThe result of the operation.

head

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

Return the first n rows.

This function returns the first n rows for the object based on position. It is useful for quickly testing if your object has the right type of data in it.

For negative values of n, this function returns all rows except the last |n| rows, equivalent to df[:n].

If n is larger than the number of rows, this function returns all rows.

Examples:

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

>>> df = bpd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',
...                     'monkey', 'parrot', 'shark', 'whale', 'zebra']})
>>> df
    animal
0  alligator
1        bee
2     falcon
3       lion
4     monkey
5     parrot
6      shark
7      whale
8      zebra
<BLANKLINE>
[9 rows x 1 columns]

Viewing the first 5 lines:

>>> df.head()
    animal
0  alligator
1        bee
2     falcon
3       lion
4     monkey
<BLANKLINE>
[5 rows x 1 columns]

Viewing the first n lines (three in this case):

>>> df.head(3)
    animal
0  alligator
1        bee
2     falcon
<BLANKLINE>
[3 rows x 1 columns]

For negative values of n:

>>> df.head(-3)
    animal
0  alligator
1        bee
2     falcon
3       lion
4     monkey
5     parrot
<BLANKLINE>
[6 rows x 1 columns]
Parameter
NameDescription
n int, default 5

Default 5. Number of rows to select.

Returns
TypeDescription
same type as callerThe first n rows of the caller object.

idxmax

idxmax() -> typing.Hashable

Return the row label of the maximum value.

If multiple values equal the maximum, the first row label with that value is returned.

Returns
TypeDescription
IndexLabel of the maximum value.

idxmin

idxmin() -> typing.Hashable

Return the row label of the minimum value.

If multiple values equal the minimum, the first row label with that value is returned.

Returns
TypeDescription
IndexLabel of the minimum value.

interpolate

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

Fill NaN values using an interpolation method.

Examples:

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

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3, None, None, 6],
...     'B': [None, 6, None, 2, None, 3],
...     }, index=[0, 0.1, 0.3, 0.7, 0.9, 1.0])
>>> df.interpolate()
       A     B
0.0  1.0  <NA>
0.1  2.0   6.0
0.3  3.0   4.0
0.7  4.0   2.0
0.9  5.0   2.5
1.0  6.0   3.0
<BLANKLINE>
[6 rows x 2 columns]
>>> df.interpolate(method="values")
            A         B
0.0       1.0      <NA>
0.1       2.0       6.0
0.3       3.0  4.666667
0.7  4.714286       2.0
0.9  5.571429  2.666667
1.0       6.0       3.0
<BLANKLINE>
[6 rows x 2 columns]
Parameter
NameDescription
method str, default 'linear'

Interpolation technique to use. Only 'linear' supported. 'linear': Ignore the index and treat the values as equally spaced. This is the only method supported on MultiIndexes. 'index', 'values': use the actual numerical values of the index. 'pad': Fill in NaNs using existing values. 'nearest', 'zero', 'slinear': Emulates scipy.interpolate.interp1d

Returns
TypeDescription
SeriesReturns the same object type as the caller, interpolated at some or all NaN values

isin

isin(values) -> "Series" | None

Whether elements in Series are contained in values.

Return a boolean Series showing whether each element in the Series matches an element in the passed sequence of values exactly.

Examples:

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

>>> s = bpd.Series(['llama', 'cow', 'llama', 'beetle', 'llama',
...                 'hippo'], name='animal')
>>> s
0     llama
1       cow
2     llama
3    beetle
4     llama
5     hippo
Name: animal, dtype: string

>>> s.isin(['cow', 'llama'])
0     True
1     True
2     True
3    False
4     True
5    False
Name: animal, dtype: boolean

Strings and integers are distinct and are therefore not comparable:

>>> bpd.Series([1]).isin(['1'])
0    False
dtype: boolean
>>> bpd.Series([1.1]).isin(['1.1'])
0    False
dtype: boolean
Parameter
NameDescription
values list-like

The sequence of values to test. Passing in a single string will raise a TypeError. Instead, turn a single string into a list of one element.

Exceptions
TypeDescription
TypeErrorIf input is not list-like.
Returns
TypeDescription
bigframes.series.SeriesSeries of booleans indicating if each element is in values.

isna

isna() -> bigframes.series.Series

Detect missing values.

Return a boolean same-sized object indicating if the values are NA. NA values get mapped to True values. Everything else gets mapped to False values. Characters such as empty strings '' or numpy.inf are not considered NA values.

Examples:

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

>>> df = bpd.DataFrame(dict(
...         age=[5, 6, np.nan],
...         born=[bpd.NA, "1940-04-25", "1940-04-25"],
...         name=['Alfred', 'Batman', ''],
...         toy=[None, 'Batmobile', 'Joker'],
... ))
>>> df
    age        born    name        toy
0   5.0        <NA>  Alfred       <NA>
1   6.0  1940-04-25  Batman  Batmobile
2  <NA>  1940-04-25              Joker
<BLANKLINE>
[3 rows x 4 columns]

Show which entries in a DataFrame are NA:

>>> df.isna()
    age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False
<BLANKLINE>
[3 rows x 4 columns]

>>> df.isnull()
    age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False
<BLANKLINE>
[3 rows x 4 columns]

Show which entries in a Series are NA:

>>> ser = bpd.Series([5, None, 6, np.nan, bpd.NA])
>>> ser
0     5.0
1    <NA>
2     6.0
3    <NA>
4    <NA>
dtype: Float64

>>> ser.isna()
0    False
1     True
2    False
3     True
4     True
dtype: boolean

>>> ser.isnull()
0    False
1     True
2    False
3     True
4     True
dtype: boolean

isnull

isnull() -> bigframes.series.Series

Detect missing values.

Return a boolean same-sized object indicating if the values are NA. NA values get mapped to True values. Everything else gets mapped to False values. Characters such as empty strings '' or numpy.inf are not considered NA values.

Examples:

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

>>> df = bpd.DataFrame(dict(
...         age=[5, 6, np.nan],
...         born=[bpd.NA, "1940-04-25", "1940-04-25"],
...         name=['Alfred', 'Batman', ''],
...         toy=[None, 'Batmobile', 'Joker'],
... ))
>>> df
    age        born    name        toy
0   5.0        <NA>  Alfred       <NA>
1   6.0  1940-04-25  Batman  Batmobile
2  <NA>  1940-04-25              Joker
<BLANKLINE>
[3 rows x 4 columns]

Show which entries in a DataFrame are NA:

>>> df.isna()
    age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False
<BLANKLINE>
[3 rows x 4 columns]

>>> df.isnull()
    age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False
<BLANKLINE>
[3 rows x 4 columns]

Show which entries in a Series are NA:

>>> ser = bpd.Series([5, None, 6, np.nan, bpd.NA])
>>> ser
0     5.0
1    <NA>
2     6.0
3    <NA>
4    <NA>
dtype: Float64

>>> ser.isna()
0    False
1     True
2    False
3     True
4     True
dtype: boolean

>>> ser.isnull()
0    False
1     True
2    False
3     True
4     True
dtype: boolean

kurt

kurt()

Return unbiased kurtosis over requested axis.

Kurtosis obtained using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.

Returns
TypeDescription
scalar or scalarUnbiased kurtosis over requested axis.

kurtosis

kurtosis()

API documentation for kurtosis method.

le

le(other) -> bigframes.series.Series

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

Equivalent to series <= other, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
TypeDescription
bigframes.series.SeriesThe result of the comparison.

lt

lt(other) -> bigframes.series.Series

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

Equivalent to series < other, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
TypeDescription
bigframes.series.SeriesThe result of the operation.

map

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

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

Used for substituting each value in a Series with another value, that may be derived from a remote function, dict, or a Series.

If arg is a remote function, the overhead for remote functions applies. If mapping with a dict, fully deferred computation is possible. If mapping with a Series, fully deferred computation is only possible if verify_integrity=False.

Examples:

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

>>> s = bpd.Series(['cat', 'dog', bpd.NA, 'rabbit'])
>>> s
0       cat
1       dog
2      <NA>
3    rabbit
dtype: string

map can accepts a dict. Values that are not found in the dict are converted to NA:

>>> s.map({'cat': 'kitten', 'dog': 'puppy'})
0    kitten
1     puppy
2      <NA>
3      <NA>
dtype: string

It also accepts a remote function:

>>> @bpd.remote_function([str], str)
... def my_mapper(val):
...     vowels = ["a", "e", "i", "o", "u"]
...     if val:
...         return "".join([
...             ch.upper() if ch in vowels else ch for ch in val
...         ])
...     return "N/A"

>>> s.map(my_mapper)
0       cAt
1       dOg
2       N/A
3    rAbbIt
dtype: string
Parameter
NameDescription
arg function, Mapping, Series

remote function, collections.abc.Mapping subclass or Series Mapping correspondence.

Returns
TypeDescription
SeriesSame index as caller.

mask

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

Replace values where the condition is True.

Examples:

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

>>> s = bpd.Series([10, 11, 12, 13, 14])
>>> s
0    10
1    11
2    12
3    13
4    14
dtype: Int64

You can mask the values in the Series based on a condition. The values matching the condition would be masked. The condition can be provided in formm of a Series.

>>> s.mask(s % 2 == 0)
0    <NA>
1      11
2    <NA>
3      13
4    <NA>
dtype: Int64

You can specify a custom mask value.

>>> s.mask(s % 2 == 0, -1)
0    -1
1    11
2    -1
3    13
4    -1
dtype: Int64
>>> s.mask(s % 2 == 0, 100*s)
0    1000
1      11
2    1200
3      13
4    1400
dtype: Int64

You can also use a remote function to evaluate the mask condition. This is useful in situation such as the following, where the mask condition is evaluated based on a complicated business logic which cannot be expressed in form of a Series.

>>> @bpd.remote_function([str], bool, reuse=False)
... def should_mask(name):
...     hash = 0
...     for char_ in name:
...         hash += ord(char_)
...     return hash % 2 == 0

>>> s = bpd.Series(["Alice", "Bob", "Caroline"])
>>> s
0       Alice
1         Bob
2    Caroline
dtype: string
>>> s.mask(should_mask)
0        <NA>
1         Bob
2    Caroline
dtype: string
>>> s.mask(should_mask, "REDACTED")
0    REDACTED
1         Bob
2    Caroline
dtype: string

Simple vectorized (i.e. they only perform operations supported on a Series) lambdas or python functions can be used directly.

>>> nums = bpd.Series([1, 2, 3, 4], name="nums")
>>> nums
0    1
1    2
2    3
3    4
Name: nums, dtype: Int64
>>> nums.mask(lambda x: (x+1) % 2 == 1)
0        1
1     <NA>
2        3
3     <NA>
Name: nums, dtype: Int64

>>> def is_odd(num):
...     return num % 2 == 1
>>> nums.mask(is_odd)
0     <NA>
1        2
2     <NA>
3        4
Name: nums, dtype: Int64
Parameters
NameDescription
cond bool Series/DataFrame, array-like, or callable

Where cond is False, keep the original value. Where True, replace with corresponding value from other. If cond is callable, it is computed on the Series/DataFrame and should return boolean Series/DataFrame or array. The callable must not change input Series/DataFrame (though pandas doesn’t check it).

other scalar, Series/DataFrame, or callable

Entries where cond is True are replaced with corresponding value from other. If other is callable, it is computed on the Series/DataFrame and should return scalar or Series/DataFrame. The callable must not change input Series/DataFrame (though pandas doesn’t check it). If not specified, entries will be filled with the corresponding NULL value (np.nan for numpy dtypes, pd.NA for extension dtypes).

Returns
TypeDescription
bigframes.series.SeriesSeries after the replacement.

max

max() -> typing.Any

Return the maximum of the values over the requested axis.

If you want the index of the maximum, use idxmax. This is the equivalent of the numpy.ndarray method argmax.

Examples:

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

Calculating the max of a Series:

>>> s = bpd.Series([1, 3])
>>> s
0    1
1    3
dtype: Int64
>>> s.max()
3

Calculating the max of a Series containing NA values:

>>> s = bpd.Series([1, 3, bpd.NA])
>>> s
0     1.0
1     3.0
2    <NA>
dtype: Float64
>>> s.max()
3.0
Returns
TypeDescription
scalarScalar.

mean

mean() -> float

Return the mean of the values over the requested axis.

Examples:

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

Calculating the mean of a Series:

>>> s = bpd.Series([1, 3])
>>> s
0    1
1    3
dtype: Int64
>>> s.mean()
2.0

Calculating the mean of a Series containing NA values:

>>> s = bpd.Series([1, 3, bpd.NA])
>>> s
0     1.0
1     3.0
2    <NA>
dtype: Float64
>>> s.mean()
2.0
Returns
TypeDescription
scalarScalar.

median

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

Return the median of the values over the requested axis.

Parameter
NameDescription
exact bool. default False

Default False. Get the exact median instead of an approximate one. Note: exact=True not yet supported.

Returns
TypeDescription
scalarScalar.

min

min() -> typing.Any

Return the maximum of the values over the requested axis.

If you want the index of the minimum, use idxmin. This is the equivalent of the numpy.ndarray method argmin.

Examples:

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

Calculating the min of a Series:

>>> s = bpd.Series([1, 3])
>>> s
0    1
1    3
dtype: Int64
>>> s.min()
1

Calculating the min of a Series containing NA values:

>>> s = bpd.Series([1, 3, bpd.NA])
>>> s
0     1.0
1     3.0
2    <NA>
dtype: Float64
>>> s.min()
1.0
Returns
TypeDescription
scalarScalar.

mod

mod(other) -> bigframes.series.Series

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

Equivalent to series % other, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
TypeDescription
bigframes.series.SeriesThe result of the operation.

mode

mode() -> bigframes.series.Series

Return the mode(s) of the Series.

The mode is the value that appears most often. There can be multiple modes.

Always returns Series even if only one value is returned.

Returns
TypeDescription
bigframes.series.SeriesModes of the Series in sorted order.

mul

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

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

Equivalent to other * series, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
TypeDescription
bigframes.series.SeriesThe result of the operation.

multiply

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

API documentation for multiply method.

ne

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

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

Equivalent to other != series, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
TypeDescription
bigframes.series.SeriesThe result of the operation.

nlargest

nlargest(n: int = 5, keep: str = "first") -> bigframes.series.Series

Return the largest n elements.

Parameters
NameDescription
n int, default 5

Return this many descending sorted values.

keep {'first', 'last', 'all'}, default 'first'

When there are duplicate values that cannot all fit in a Series of n elements: first : return the first n occurrences in order of appearance. last : return the last n occurrences in reverse order of appearance. all : keep all occurrences. This can result in a Series of size larger than n.

Returns
TypeDescription
bigframes.series.SeriesThe n largest values in the Series, sorted in decreasing order.

notna

notna() -> bigframes.series.Series

Detect existing (non-missing) values.

Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings '' or numpy.inf are not considered NA values. NA values get mapped to False values.

Returns
TypeDescription
NDFrameMask of bool values for each element that indicates whether an element is not an NA value.

notnull

notnull() -> bigframes.series.Series

Detect existing (non-missing) values.

Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings '' or numpy.inf are not considered NA values. NA values get mapped to False values.

Returns
TypeDescription
NDFrameMask of bool values for each element that indicates whether an element is not an NA value.

nsmallest

nsmallest(n: int = 5, keep: str = "first") -> bigframes.series.Series

Return the smallest n elements.

Parameters
NameDescription
n int, default 5

Return this many ascending sorted values.

keep {'first', 'last', 'all'}, default 'first'

When there are duplicate values that cannot all fit in a Series of n elements: first : return the first n occurrences in order of appearance. last : return the last n occurrences in reverse order of appearance. all : keep all occurrences. This can result in a Series of size larger than n.

Returns
TypeDescription
bigframes.series.SeriesThe n smallest values in the Series, sorted in increasing order.

nunique

nunique() -> int

Return number of unique elements in the object.

Excludes NA values by default.

Returns
TypeDescription
intnumber of unique elements in the object.

pad

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

API documentation for pad method.

pct_change

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

Fractional change between the current and a prior element.

Computes the fractional change from the immediately previous row by default. This is useful in comparing the fraction of change in a time series of elements.

Parameter
NameDescription
periods int, default 1

Periods to shift for forming percent change.

Returns
TypeDescription
Series or DataFrameThe same type as the calling object.

pow

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

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

Equivalent to series ** other, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
TypeDescription
bigframes.series.SeriesThe result of the operation.

prod

prod() -> float

Return the product of the values over the requested axis.

Returns
TypeDescription
scalarScalar.

product

product() -> float

API documentation for product method.

radd

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

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

Equivalent to other + series, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
TypeDescription
bigframes.series.SeriesThe result of the operation.

rank

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

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

By default, equal values are assigned a rank that is the average of the ranks of those values.

Parameters
NameDescription
method {'average', 'min', 'max', 'first', 'dense'}, default 'average'

How to rank the group of records that have the same value (i.e. ties): average: average rank of the group, min: lowest rank in the group max: highest rank in the group, first: ranks assigned in order they appear in the array, dense`: like 'min', but rank always increases by 1 between groups.

numeric_only bool, default False

For DataFrame objects, rank only numeric columns if set to True.

na_option {'keep', 'top', 'bottom'}, default 'keep'

How to rank NaN values: keep: assign NaN rank to NaN values, , top: assign lowest rank to NaN values, bottom: assign highest rank to NaN values.

ascending bool, default True

Whether or not the elements should be ranked in ascending order.

Returns
TypeDescription
same type as callerReturn a Series or DataFrame with data ranks as values.

rdiv

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

API documentation for rdiv method.

rdivmod

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

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

Equivalent to other divmod series.

Returns
TypeDescription
2-Tuple of SeriesThe result of the operation. The result is always consistent with (rfloordiv, rmod) (though pandas may not).

reindex

reindex(index=None, *, validate: typing.Optional[bool] = None)

Conform Series to new index with optional filling logic.

Places NA/NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and copy=False.

Parameter
NameDescription
index array-like, optional

New labels for the index. Preferably an Index object to avoid duplicating data.

Returns
TypeDescription
SeriesSeries with changed index.

reindex_like

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

Return an object with matching indices as other object.

Conform the object to the same index on all axes. Optional filling logic, placing Null in locations having no value in the previous index.

Parameter
NameDescription
other Object of the same data type

Its row and column indices are used to define the new indices of this object.

Returns
TypeDescription
Series or DataFrameSame type as caller, but with changed indices on each axis.

rename

rename(
    index: typing.Union[typing.Hashable, typing.Mapping[typing.Any, typing.Any]] = None,
    **kwargs
) -> bigframes.series.Series

Alter Series index labels or name.

Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left as-is. Extra labels listed don't throw an error.

Alternatively, change Series.name with a scalar value.

Examples:

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

>>> s = bpd.Series([1, 2, 3])
>>> s
0    1
1    2
2    3
dtype: Int64

You can changes the Series name by specifying a string scalar:

>>> s.rename("my_name")
0    1
1    2
2    3
Name: my_name, dtype: Int64

You can change the labels by specifying a mapping:

>>> s.rename({1: 3, 2: 5})
0    1
3    2
5    3
dtype: Int64
Parameter
NameDescription
index scalar, hashable sequence, dict-like or function optional

Functions or dict-like are transformations to apply to the index. Scalar or hashable sequence-like will alter the Series.name attribute.

Returns
TypeDescription
bigframes.series.SeriesSeries with index labels.

rename_axis

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

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

Parameter
NameDescription
mapper scalar, list-like, optional

Value to set the axis name attribute.

Returns
TypeDescription
bigframes.series.SeriesSeries with the name of the axis set.

reorder_levels

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

Rearrange index levels using input order.

May not drop or duplicate levels.

Parameters
NameDescription
order list of int representing new level order

Reference level by number or key.

axis {0 or 'index', 1 or 'columns'}, default 0

For Series this parameter is unused and defaults to 0.

replace

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

Replace values given in to_replace with value.

Values of the Series/DataFrame are replaced with other values dynamically. This differs from updating with .loc or .iloc, which require you to specify a location to update with some value.

Examples:

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

>>> s = bpd.Series([1, 2, 3, 4, 5])
>>> s
0    1
1    2
2    3
3    4
4    5
dtype: Int64

>>> s.replace(1, 5)
0    5
1    2
2    3
3    4
4    5
dtype: Int64

You can replace a list of values:

>>> s.replace([1, 3, 5], -1)
0    -1
1     2
2    -1
3     4
4    -1
dtype: Int64

You can use a replacement mapping:

>>> s.replace({1: 5, 3: 10})
0     5
1     2
2    10
3     4
4     5
dtype: Int64

With a string Series you can use a simple string replacement or a regex replacement:

>>> s = bpd.Series(["Hello", "Another Hello"])
>>> s.replace("Hello", "Hi")
0               Hi
1    Another Hello
dtype: string

>>> s.replace("Hello", "Hi", regex=True)
0            Hi
1    Another Hi
dtype: string

>>> s.replace("^Hello", "Hi", regex=True)
0               Hi
1    Another Hello
dtype: string

>>> s.replace("Hello$", "Hi", regex=True)
0            Hi
1    Another Hi
dtype: string

>>> s.replace("[Hh]e", "__", regex=True)
0            __llo
1    Anot__r __llo
dtype: string
Parameters
NameDescription
to_replace str, regex, list, int, float or None

How to find the values that will be replaced. * numeric, str or regex: - numeric: numeric values equal to to_replace will be replaced with value - str: string exactly matching to_replace will be replaced with value - regex: regexs matching to_replace will be replaced with value * list of str, regex, or numeric: - First, if to_replace and value are both lists, they must be the same length. - Second, if regex=True then all of the strings in both lists will be interpreted as regexs otherwise they will match directly. This doesn't matter much for value since there are only a few possible substitution regexes you can use. - str, regex and numeric rules apply as above.

value scalar, default None

Value to replace any values matching to_replace with. For a DataFrame a dict of values can be used to specify which value to use for each column (columns not in the dict will not be filled). Regular expressions, strings and lists or dicts of such objects are also allowed.

regex bool, default False

Whether to interpret to_replace and/or value as regular expressions. If this is True then to_replace must be a string.

Exceptions
TypeDescription
TypeError* If to_replace is not a scalar, array-like, dict, or None * If to_replace is a dict and value is not a list, dict, ndarray, or Series * If to_replace is None and regex is not compilable into a regular expression or is a list, dict, ndarray, or Series. * When replacing multiple bool or datetime64 objects and the arguments to to_replace does not match the type of the value being replaced
Returns
TypeDescription
Series/DataFrameObject after replacement.

reset_index

reset_index(
    *, name: typing.Optional[str] = None, drop: bool = False
) -> bigframes.dataframe.DataFrame | bigframes.series.Series

Generate a new DataFrame or Series with the index reset.

This is useful when the index needs to be treated as a column, or when the index is meaningless and needs to be reset to the default before another operation.

Examples:

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

>>> s = bpd.Series([1, 2, 3, 4], name='foo',
...                index=['a', 'b', 'c', 'd'])
>>> s.index.name = "idx"
>>> s
idx
a    1
b    2
c    3
d    4
Name: foo, dtype: Int64

Generate a DataFrame with default index.

>>> s.reset_index()
    idx  foo
0     a    1
1     b    2
2     c    3
3     d    4
<BLANKLINE>
[4 rows x 2 columns]

To specify the name of the new column use name param.

>>> s.reset_index(name="bar")
    idx   bar
0     a    1
1     b    2
2     c    3
3     d    4
<BLANKLINE>
[4 rows x 2 columns]

To generate a new Series with the default index set param drop=True.

>>> s.reset_index(drop=True)
0    1
1    2
2    3
3    4
Name: foo, dtype: Int64
Parameters
NameDescription
drop bool, default False

Just reset the index, without inserting it as a column in the new DataFrame.

name object, optional

The name to use for the column containing the original Series values. Uses self.name by default. This argument is ignored when drop is True.

rfloordiv

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

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

Equivalent to other // series, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
TypeDescription
bigframes.series.SeriesThe result of the operation.

rmod

rmod(other) -> bigframes.series.Series

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

Equivalent to series % other, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
TypeDescription
bigframes.series.SeriesThe result of the operation.

rmul

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

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

Equivalent to series * others, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
TypeDescription
SeriesThe result of the operation.

rolling

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

Provide rolling window calculations.

Parameters
NameDescription
window int, timedelta, str, offset, or BaseIndexer subclass

Size of the moving window. If an integer, the fixed number of observations used for each window. If a timedelta, str, or offset, the time period of each window. Each window will be a variable sized based on the observations included in the time-period. This is only valid for datetime-like indexes. To learn more about the offsets & frequency strings, please see this link https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases__. If a BaseIndexer subclass, the window boundaries based on the defined get_window_bounds method. Additional rolling keyword arguments, namely min_periods, center, closed and step will be passed to get_window_bounds.

min_periods int, default None

Minimum number of observations in window required to have a value; otherwise, result is np.nan. For a window that is specified by an offset, min_periods will default to 1. For a window that is specified by an integer, min_periods will default to the size of the window.

Returns
TypeDescription
bigframes.core.window.WindowWindow subclass if a win_type is passed. Rolling subclass if win_type is not passed.

round

round(decimals=0) -> bigframes.series.Series

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

Examples:

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

>>> s = bpd.Series([0.1, 1.3, 2.7])
>>> s.round()
0    0.0
1    1.0
2    3.0
dtype: Float64

>>> s = bpd.Series([0.123, 1.345, 2.789])
>>> s.round(decimals=2)
0    0.12
1    1.34
2    2.79
dtype: Float64
Parameter
NameDescription
decimals int, default 0

Number of decimal places to round to. If decimals is negative, it specifies the number of positions to the left of the decimal point.

Returns
TypeDescription
bigframes.series.SeriesRounded values of the Series.

rpow

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

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

Equivalent to other ** series, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
TypeDescription
bigframes.series.SeriesThe result of the operation.

rsub

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

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

Equivalent to other - series, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
TypeDescription
bigframes.series.SeriesThe result of the operation.

rtruediv

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

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

Equivalent to other / series, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
TypeDescription
bigframes.series.SeriesThe result of the operation.

sample

sample(
    n: typing.Optional[int] = None,
    frac: typing.Optional[float] = None,
    *,
    random_state: typing.Optional[int] = None
) -> bigframes.series.Series

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

You can use random_state for reproducibility.

Examples:

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

>>> df = bpd.DataFrame({'num_legs': [2, 4, 8, 0],
...                     'num_wings': [2, 0, 0, 0],
...                     'num_specimen_seen': [10, 2, 1, 8]},
...                    index=['falcon', 'dog', 'spider', 'fish'])
>>> df
        num_legs  num_wings  num_specimen_seen
falcon         2          2                 10
dog            4          0                  2
spider         8          0                  1
fish           0          0                  8
<BLANKLINE>
[4 rows x 3 columns]

Fetch one random row from the DataFrame (Note that we use random_state to ensure reproducibility of the examples):

>>> df.sample(random_state=1)
     num_legs  num_wings  num_specimen_seen
dog         4          0                  2
<BLANKLINE>
[1 rows x 3 columns]

A random 50% sample of the DataFrame:

>>> df.sample(frac=0.5, random_state=1)
      num_legs  num_wings  num_specimen_seen
dog          4          0                  2
fish         0          0                  8
<BLANKLINE>
[2 rows x 3 columns]

Extract 3 random elements from the Series df['num_legs']:

>>> s = df['num_legs']
>>> s.sample(n=3, random_state=1)
dog       4
fish      0
spider    8
Name: num_legs, dtype: Int64
Parameters
NameDescription
n Optional[int], default None

Number of items from axis to return. Cannot be used with frac. Default = 1 if frac = None.

frac Optional[float], default None

Fraction of axis items to return. Cannot be used with n.

random_state Optional[int], default None

Seed for random number generator.

shift

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

Shift index by desired number of periods.

Shifts the index without realigning the data.

Returns
TypeDescription
NDFrameCopy of input object, shifted.

skew

skew()

Return unbiased skew over requested axis.

Normalized by N-1.

Returns
TypeDescription
scalarScalar.

sort_index

sort_index(
    *, axis=0, ascending=True, na_position="last"
) -> bigframes.series.Series

Sort Series by index labels.

Returns a new Series sorted by label if inplace argument is False, otherwise updates the original series and returns None.

Parameters
NameDescription
axis {0 or 'index'}

Unused. Parameter needed for compatibility with DataFrame.

ascending bool or list-like of bools, default True

Sort ascending vs. descending. When the index is a MultiIndex the sort direction can be controlled for each level individually.

na_position {'first', 'last'}, default 'last'

If 'first' puts NaNs at the beginning, 'last' puts NaNs at the end. Not implemented for MultiIndex.

Returns
TypeDescription
bigframes.series.SeriesThe original Series sorted by the labels or None if inplace=True.

sort_values

sort_values(
    *, axis=0, ascending=True, kind: str = "quicksort", na_position="last"
) -> bigframes.series.Series

Sort by the values.

Sort a Series in ascending or descending order by some criterion.

Examples:

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

>>> s = bpd.Series([np.nan, 1, 3, 10, 5])
>>> s
0    <NA>
1     1.0
2     3.0
3    10.0
4     5.0
dtype: Float64

Sort values ascending order (default behaviour):

>>> s.sort_values(ascending=True)
1     1.0
2     3.0
4     5.0
3    10.0
0    <NA>
dtype: Float64

Sort values descending order:

>>> s.sort_values(ascending=False)
3    10.0
4     5.0
2     3.0
1     1.0
0    <NA>
dtype: Float64

Sort values putting NAs first:

>>> s.sort_values(na_position='first')
0    <NA>
1     1.0
2     3.0
4     5.0
3    10.0
dtype: Float64

Sort a series of strings:

>>> s = bpd.Series(['z', 'b', 'd', 'a', 'c'])
>>> s
0    z
1    b
2    d
3    a
4    c
dtype: string

>>> s.sort_values()
3    a
1    b
4    c
2    d
0    z
dtype: string
Parameters
NameDescription
axis 0 or 'index'

Unused. Parameter needed for compatibility with DataFrame.

ascending bool or list of bools, default True

If True, sort values in ascending order, otherwise descending.

kind str, default to 'quicksort'

Choice of sorting algorithm. Accepts 'quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’. Ignored except when determining whether to sort stably. 'mergesort' or 'stable' will result in stable reorder

na_position {'first' or 'last'}, default 'last'

Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at the end.

Returns
TypeDescription
bigframes.series.SeriesSeries ordered by values or None if inplace=True.

std

std() -> float

Return sample standard deviation over requested axis.

Normalized by N-1 by default.

Examples:

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

>>> df = bpd.DataFrame({'person_id': [0, 1, 2, 3],
...                     'age': [21, 25, 62, 43],
...                     'height': [1.61, 1.87, 1.49, 2.01]}
...                   ).set_index('person_id')
>>> df
           age  height
person_id
0           21    1.61
1           25    1.87
2           62    1.49
3           43    2.01
<BLANKLINE>
[4 rows x 2 columns]

>>> df.std()
age       18.786076
height     0.237417
dtype: Float64

sub

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

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

Equivalent to series - other, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
TypeDescription
bigframes.series.SeriesThe result of the operation.

subtract

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

API documentation for subtract method.

sum

sum() -> float

Return the sum of the values over the requested axis.

This is equivalent to the method numpy.sum.

Examples:

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

Calculating the sum of a Series:

>>> s = bpd.Series([1, 3])
>>> s
0    1
1    3
dtype: Int64
>>> s.sum()
4

Calculating the sum of a Series containing NA values:

>>> s = bpd.Series([1, 3, bpd.NA])
>>> s
0     1.0
1     3.0
2    <NA>
dtype: Float64
>>> s.sum()
4.0
Returns
TypeDescription
scalarScalar.

swaplevel

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

Swap levels i and j in a MultiIndex.

Default is to swap the two innermost levels of the index.

Parameters
NameDescription
i int or str

Levels of the indices to be swapped. Can pass level name as string.

j int or str

Levels of the indices to be swapped. Can pass level name as string.

Returns
TypeDescription
SeriesSeries with levels swapped in MultiIndex

tail

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

Return the last n rows.

This function returns last n rows from the object based on position. It is useful for quickly verifying data, for example, after sorting or appending rows.

For negative values of n, this function returns all rows except the first |n| rows, equivalent to df[|n|:].

If n is larger than the number of rows, this function returns all rows.

Parameter
NameDescription
n int, default 5

Number of rows to select.

to_csv

to_csv(path_or_buf=None, **kwargs) -> typing.Optional[str]

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

Parameter
NameDescription
path_or_buf str, path object, file-like object, or None, default None

String, path object (implementing os.PathLike[str]), or file-like object implementing a write() function. If None, the result is returned as a string. If a non-binary file object is passed, it should be opened with newline='', disabling universal newlines. If a binary file object is passed, mode might need to contain a 'b'.

Returns
TypeDescription
None or strIf path_or_buf is None, returns the resulting csv format as a string. Otherwise returns None.

to_dict

to_dict(into: type[dict] = <class 'dict'>) -> typing.Mapping

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

Parameter
NameDescription
into class, default dict

The collections.abc.Mapping subclass to use as the return object. Can be the actual class or an empty instance of the mapping type you want. If you want a collections.defaultdict, you must pass it initialized.

Returns
TypeDescription
collections.abc.MappingKey-value representation of Series.

to_excel

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

Write Series to an Excel sheet.

To write a single Series to an Excel .xlsx file it is only necessary to specify a target file name. To write to multiple sheets it is necessary to create an ExcelWriter object with a target file name, and specify a sheet in the file to write to.

Multiple sheets may be written to by specifying unique sheet_name. With all data written to the file it is necessary to save the changes. Note that creating an ExcelWriter object with a file name that already exists will result in the contents of the existing file being erased.

Parameters
NameDescription
excel_writer path-like, file-like, or ExcelWriter object

File path or existing ExcelWriter.

sheet_name str, default 'Sheet1'

Name of sheet to contain Series.

to_frame

to_frame(name: typing.Hashable = None) -> bigframes.dataframe.DataFrame

Convert Series to DataFrame.

The column in the new dataframe will be named name (the keyword parameter) if the name parameter is provided and not None.

Returns
TypeDescription
bigframes.dataframe.DataFrameDataFrame representation of Series.

to_json

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

Convert the object to a JSON string.

Note NaN's and None will be converted to null and datetime objects will be converted to UNIX timestamps.

Parameters
NameDescription
path_or_buf str, path object, file-like object, or None, default None

String, path object (implementing os.PathLike[str]), or file-like object implementing a write() function. If None, the result is returned as a string.

orient {"split", "records", "index", "columns", "values", "table"}, default "columns"

Indication of expected JSON string format. '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 'table' : dict like {{'schema': {{schema}}, 'data': {{data}}}} Describing the data, where data component is like orient='records'.

Returns
TypeDescription
None or strIf path_or_buf is None, returns the resulting json format as a string. Otherwise returns None.

to_latex

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

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

Parameters
NameDescription
buf str, Path or StringIO-like, optional, default None

Buffer to write to. If None, the output is returned as a string.

columns list of label, optional

The subset of columns to write. Writes all columns by default.

header bool or list of str, default True

Write out the column names. If a list of strings is given, it is assumed to be aliases for the column names.

index bool, default True

Write row names (index).

Returns
TypeDescription
str or NoneIf buf is None, returns the result as a string. Otherwise returns None.

to_list

to_list() -> list

Return a list of the values.

These are each a scalar type, which is a Python scalar (for str, int, float) or a pandas scalar (for Timestamp/Timedelta/Interval/Period).

Examples:

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

>>> s = bpd.Series([1, 2, 3])
>>> s
0    1
1    2
2    3
dtype: Int64

>>> s.to_list()
[1, 2, 3]
Returns
TypeDescription
listlist of the values

to_markdown

to_markdown(
    buf: typing.Optional[typing.IO[str]] = None,
    mode: str = "wt",
    index: bool = True,
    **kwargs
) -> typing.Optional[str]

Print {klass} in Markdown-friendly format.

Parameters
NameDescription
buf str, Path or StringIO-like, optional, default None

Buffer to write to. If None, the output is returned as a string.

mode str, optional

Mode in which file is opened, "wt" by default.

index bool, optional, default True

Add index (row) labels.

Returns
TypeDescription
str{klass} in Markdown-friendly format.

to_numpy

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

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

Parameters
NameDescription
dtype str or numpy.dtype, optional

The dtype to pass to numpy.asarray.

copy bool, default False

Whether to ensure that the returned value is not a view on another array. Note that copy=False does not ensure that to_numpy() is no-copy. Rather, copy=True ensure that a copy is made, even if not strictly necessary.

na_value Any, optional

The value to use for missing values. The default value depends on dtype and the type of the array.

Returns
TypeDescription
numpy.ndarrayA NumPy ndarray representing the values in this Series or Index.

to_pandas

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

Writes Series to pandas Series.

Parameters
NameDescription
max_download_size int, default None

Download size threshold in MB. If max_download_size is exceeded when downloading data (e.g., to_pandas()), the data will be downsampled if bigframes.options.sampling.enable_downsampling is True, otherwise, an error will be raised. If set to a value other than None, this will supersede the global config.

sampling_method str, default None

Downsampling algorithms to be chosen from, the choices are: "head": This algorithm returns a portion of the data from the beginning. It is fast and requires minimal computations to perform the downsampling; "uniform": This algorithm returns uniform random samples of the data. If set to a value other than None, this will supersede the global config.

random_state int, default None

The seed for the uniform downsampling algorithm. If provided, the uniform method may take longer to execute and require more computation. If set to a value other than None, this will supersede the global config.

ordered bool, default True

Determines whether the resulting pandas series will be deterministically ordered. In some cases, unordered may result in a faster-executing query.

Returns
TypeDescription
pandas.SeriesA pandas Series with all rows of this Series if the data_sampling_threshold_mb is not exceeded; otherwise, a pandas Series with downsampled rows of the DataFrame.

to_pickle

to_pickle(path, **kwargs) -> None

Pickle (serialize) object to file.

Parameter
NameDescription
path str, path object, or file-like object

String, path object (implementing os.PathLike[str]), or file-like object implementing a binary write() function. File path where the pickled object will be stored.

to_string

to_string(
    buf=None,
    na_rep="NaN",
    float_format=None,
    header=True,
    index=True,
    length=False,
    dtype=False,
    name=False,
    max_rows=None,
    min_rows=None,
) -> typing.Optional[str]

Render a string representation of the Series.

Parameters
NameDescription
buf StringIO-like, optional

Buffer to write to.

na_rep str, optional

String representation of NaN to use, default 'NaN'.

float_format one-parameter function, optional

Formatter function to apply to columns' elements if they are floats, default None.

header bool, default True

Add the Series header (index name).

index bool, optional

Add index (row) labels, default True.

length bool, default False

Add the Series length.

dtype bool, default False

Add the Series dtype.

name bool, default False

Add the Series name if not None.

max_rows int, optional

Maximum number of rows to show before truncating. If None, show all.

min_rows int, optional

The number of rows to display in a truncated repr (when number of rows is above max_rows).

Returns
TypeDescription
str or NoneString representation of Series if buf=None, otherwise None.

to_xarray

to_xarray()

Return an xarray object from the pandas object.

Returns
TypeDescription
xarray.DataArray or xarray.DatasetData in the pandas structure converted to Dataset if the object is a DataFrame, or a DataArray if the object is a Series.

tolist

tolist() -> list

Return a list of the values.

These are each a scalar type, which is a Python scalar (for str, int, float) or a pandas scalar (for Timestamp/Timedelta/Interval/Period).

Examples:

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

>>> s = bpd.Series([1, 2, 3])
>>> s
0    1
1    2
2    3
dtype: Int64

>>> s.to_list()
[1, 2, 3]
Returns
TypeDescription
listlist of the values

transpose

transpose() -> bigframes.series.Series

Return the transpose, which is by definition self.

Examples:

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

>>> s = bpd.Series(['Ant', 'Bear', 'Cow'])
>>> s
0     Ant
1    Bear
2     Cow
dtype: string

>>> s.transpose()
0     Ant
1    Bear
2     Cow
dtype: string
Returns
TypeDescription
SeriesSeries.

truediv

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

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

Equivalent to series / other, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
TypeDescription
bigframes.series.SeriesThe result of the operation.

unique

unique() -> bigframes.series.Series

Return unique values of Series object.

Uniques are returned in order of appearance. Hash table-based unique, therefore does NOT sort.

Examples:

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

>>> s = bpd.Series([2, 1, 3, 3], name='A')
>>> s
0    2
1    1
2    3
3    3
Name: A, dtype: Int64
>>> s.unique()
0    2
1    1
2    3
Name: A, dtype: Int64
Returns
TypeDescription
SeriesThe unique values returned as a Series.

unstack

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

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

Parameter
NameDescription
level int, str, or list of these, default last level

Level(s) to unstack, can pass level name.

Returns
TypeDescription
DataFrameUnstacked Series.

value_counts

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

Return a Series containing counts of unique values.

The resulting object will be in descending order so that the first element is the most frequently-occurring element. Excludes NA values by default.

Examples:

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

>>> s = bpd.Series([3, 1, 2, 3, 4, bpd.NA], dtype="Int64")

>>> s
0       3
1       1
2       2
3       3
4       4
5    <NA>
dtype: Int64

value_counts sorts the result by counts in a descending order by default:

>>> s.value_counts()
3      2
1      1
2      1
4      1
Name: count, dtype: Int64

You can normalize the counts to return relative frequencies by setting normalize=True:

>>> s.value_counts(normalize=True)
3    0.4
1    0.2
2    0.2
4    0.2
Name: proportion, dtype: Float64

You can get the values in the ascending order of the counts by setting ascending=True:

>>> s.value_counts(ascending=True)
1    1
2    1
4    1
3    2
Name: count, dtype: Int64

You can include the counts of the NA values by setting dropna=False:

>>> s.value_counts(dropna=False)
3       2
1       1
2       1
4       1
<NA>    1
Name: count, dtype: Int64
Parameters
NameDescription
normalize bool, default False

If True then the object returned will contain the relative frequencies of the unique values.

sort bool, default True

Sort by frequencies.

ascending bool, default False

Sort in ascending order.

dropna bool, default True

Don't include counts of NaN.

Returns
TypeDescription
SeriesSeries containing counts of unique values.

var

var() -> float

Return unbiased variance over requested axis.

Normalized by N-1 by default.

Returns
TypeDescription
scalar or Series (if level specified)Variance.

where

where(cond, other=None)

Replace values where the condition is False.

Examples:

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

>>> s = bpd.Series([10, 11, 12, 13, 14])
>>> s
0    10
1    11
2    12
3    13
4    14
dtype: Int64

You can filter the values in the Series based on a condition. The values matching the condition would be kept, and not matching would be replaced. The default replacement value is NA.

>>> s.where(s % 2 == 0)
0      10
1    <NA>
2      12
3    <NA>
4      14
dtype: Int64

You can specify a custom replacement value for non-matching values.

>>> s.where(s % 2 == 0, -1)
0    10
1    -1
2    12
3    -1
4    14
dtype: Int64
>>> s.where(s % 2 == 0, 100*s)
0      10
1    1100
2      12
3    1300
4      14
dtype: Int64
Parameters
NameDescription
cond bool Series/DataFrame, array-like, or callable

Where cond is True, keep the original value. Where False, replace with corresponding value from other. If cond is callable, it is computed on the Series/DataFrame and returns boolean Series/DataFrame or array. The callable must not change input Series/DataFrame (though pandas doesn’t check it).

other scalar, Series/DataFrame, or callable

Entries where cond is False are replaced with corresponding value from other. If other is callable, it is computed on the Series/DataFrame and returns scalar or Series/DataFrame. The callable must not change input Series/DataFrame (though pandas doesn’t check it). If not specified, entries will be filled with the corresponding NULL value (np.nan for numpy dtypes, pd.NA for extension dtypes).

Returns
TypeDescription
bigframes.series.SeriesSeries after the replacement.