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DataFrame(
data=None,
index: vendored_pandas_typing.Axes | None = None,
columns: vendored_pandas_typing.Axes | None = None,
dtype: typing.Optional[
bigframes.dtypes.DtypeString | bigframes.dtypes.Dtype
] = None,
copy: typing.Optional[bool] = None,
*,
session: typing.Optional[bigframes.session.Session] = None
)
Two-dimensional, size-mutable, potentially heterogeneous tabular data.
Data structure also contains labeled axes (rows and columns). Arithmetic operations align on both row and column labels. Can be thought of as a dict-like container for Series objects. The primary pandas data structure.
Properties
axes
Return a list representing the axes of the DataFrame.
It has the row axis labels and column axis labels as the only members. They are returned in that order.
Examples
df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
df.axes
[RangeIndex(start=0, stop=2, step=1), Index(['col1', 'col2'],
dtype='object')]
columns
The column labels of the DataFrame.
dtypes
Return the dtypes in the DataFrame.
This returns a Series with the data type of each column. The result's index is the original DataFrame's columns. Columns with mixed types aren't supported yet in BigQuery DataFrames.
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 | |
---|---|
Type | Description |
bool | If 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 (row labels) of the DataFrame.
The index of a DataFrame is a series of labels that identify each row. The labels can be integers, strings, or any other hashable type. The index is used for label-based access and alignment, and can be accessed or modified using this attribute.
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 that5
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 | |
---|---|
Type | Description |
NotImplementError | if the inputs are not supported. |
ndim
Return an int representing the number of axes / array dimensions.
Returns | |
---|---|
Type | Description |
int | Return 1 if Series. Otherwise return 2 if DataFrame. |
query_job
BigQuery job metadata for the most recent query.
shape
Return a tuple representing the dimensionality of the DataFrame.
size
Return an int representing the number of elements in this object.
Returns | |
---|---|
Type | Description |
int | Return the number of rows if Series. Otherwise return the number of rows times number of columns if DataFrame. |
sql
Compiles this DataFrame's expression tree to SQL.
values
Return the values of DataFrame in the form of a NumPy array.
Methods
__array_ufunc__
__array_ufunc__(
ufunc: numpy.ufunc, method: str, *inputs, **kwargs
) -> bigframes.dataframe.DataFrame
Used to support numpy ufuncs. See: https://numpy.org/doc/stable/reference/ufuncs.html
__getitem__
__getitem__(
key: typing.Union[
typing.Hashable,
typing.Sequence[typing.Hashable],
pandas.core.indexes.base.Index,
bigframes.series.Series,
]
)
Gets the specified column(s) from the DataFrame.
__repr__
__repr__() -> str
Converts a DataFrame to a string. Calls to_pandas.
Only represents the first <xref uid="bigframes.options">bigframes.options</xref>.display.max_rows
.
__setitem__
__setitem__(
key: str, value: typing.Union[bigframes.series.Series, int, float, typing.Callable]
)
Modify or insert a column into the DataFrame.
Note: This does not modify the original table the DataFrame was derived from.
abs
abs() -> bigframes.dataframe.DataFrame
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 | DataFrame,
axis: str | int = "columns",
) -> DataFrame
Get addition of DataFrame and other, element-wise (binary operator +
).
Equivalent to dataframe + other
. With reverse version, radd
.
Among flexible wrappers (add
, sub
, mul
, div
, mod
, pow
) to
arithmetic operators: +
, -
, *
, /
, //
, %
, **
.
Parameters | |
---|---|
Name | Description |
other |
float, int, or Series
Any single or multiple element data structure, or list-like object. |
axis |
{0 or 'index', 1 or 'columns'}
Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on. |
Returns | |
---|---|
Type | Description |
DataFrame | DataFrame result of the arithmetic operation. |
add_prefix
add_prefix(prefix: str, axis: int | str | None = None) -> DataFrame
Prefix labels with string prefix
.
For Series, the row labels are prefixed. For DataFrame, the column labels are prefixed.
Parameters | |
---|---|
Name | Description |
prefix |
str
The string to add before each label. |
axis |
int or str or None, default None
|
add_suffix
add_suffix(suffix: str, axis: int | str | None = None) -> DataFrame
Suffix labels with string suffix
.
For Series, the row labels are suffixed. For DataFrame, the column labels are suffixed.
agg
agg(func: str | typing.Sequence[str]) -> DataFrame | bigframes.series.Series
Aggregate using one or more operations over the specified axis.
Parameter | |
---|---|
Name | Description |
func |
function
Function to use for aggregating the data. Accepted combinations are: string function name, list of function names, e.g. |
Returns | |
---|---|
Type | Description |
DataFrame or bigframes.series.Series | Aggregated results. |
aggregate
aggregate(func: str | typing.Sequence[str]) -> DataFrame | bigframes.series.Series
Aggregate using one or more operations over the specified axis.
Parameter | |
---|---|
Name | Description |
func |
function
Function to use for aggregating the data. Accepted combinations are: string function name, list of function names, e.g. |
Returns | |
---|---|
Type | Description |
DataFrame or bigframes.series.Series | Aggregated results. |
align
align(
other: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
join: str = "outer",
axis: typing.Optional[typing.Union[str, int]] = None,
) -> typing.Tuple[
typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
]
Align two objects on their axes with the specified join method.
Join method is specified for each axis Index.
Parameters | |
---|---|
Name | Description |
join |
{{'outer', 'inner', 'left', 'right'}}, default 'outer'
Type of alignment to be performed. left: use only keys from left frame, preserve key order. right: use only keys from right frame, preserve key order. outer: use union of keys from both frames, sort keys lexicographically. inner: use intersection of keys from both frames, preserve the order of the left keys. |
axis |
allowed axis of the other object, default None
Align on index (0), columns (1), or both (None). |
Returns | |
---|---|
Type | Description |
tuple of (DataFrame, type of other) | Aligned objects. |
all
all(
axis: typing.Union[str, int] = 0, *, bool_only: bool = False
) -> bigframes.series.Series
Return whether all elements are True, potentially over an axis.
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).
Parameters | |
---|---|
Name | Description |
axis |
{index (0), columns (1)}
Axis for the function to be applied on. For Series this parameter is unused and defaults to 0. |
bool_only |
bool. default False
Include only boolean columns. |
Returns | |
---|---|
Type | Description |
bigframes.series.Series | Series if all elements are True. |
any
any(
*, axis: typing.Union[str, int] = 0, bool_only: bool = False
) -> bigframes.series.Series
Return whether any element is True, potentially over an axis.
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).
Parameters | |
---|---|
Name | Description |
axis |
{index (0), columns (1)}
Axis for the function to be applied on. For Series this parameter is unused and defaults to 0. |
bool_only |
bool. default False
Include only boolean columns. |
apply
apply(func, *, args: typing.Tuple = (), **kwargs)
Apply a function along an axis of the DataFrame.
Objects passed to the function are Series objects whose index is
the DataFrame's index (axis=0
) the final return type
is inferred from the return type of the applied function.
Parameters | |
---|---|
Name | Description |
func |
function
Function to apply to each column or row. |
args |
tuple
Positional arguments to pass to |
Returns | |
---|---|
Type | Description |
pandas.Series or bigframes.DataFrame | Result of applying func along the given axis of the DataFrame. |
applymap
applymap(
func, na_action: typing.Optional[str] = None
) -> bigframes.dataframe.DataFrame
Apply a function to a Dataframe elementwise.
This method applies a function that accepts and returns a scalar to every element of a DataFrame.
Parameter | |
---|---|
Name | Description |
na_action |
Optional[str], default None
|
Returns | |
---|---|
Type | Description |
bigframes.dataframe.DataFrame | Transformed DataFrame. |
assign
assign(**kwargs) -> bigframes.dataframe.DataFrame
Assign new columns to a DataFrame.
Returns a new object with all original columns in addition to new ones. Existing columns that are re-assigned will be overwritten.
Returns | |
---|---|
Type | Description |
bigframes.dataframe.DataFrame | A new DataFrame with the new columns in addition to all the existing columns. |
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]",
],
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,
]
) -> bigframes.dataframe.DataFrame
Cast a pandas object to a specified dtype dtype
.
Parameter | |
---|---|
Name | Description |
dtype |
str or pandas.ExtensionDtype
A dtype supported by BigQuery DataFrame include 'boolean','Float64','Int64', 'string', 'tring[pyarrow]','timestamp[us, tz=UTC][pyarrow]', 'timestamp |
bfill
bfill(*, limit: typing.Optional[int] = None) -> bigframes.dataframe.DataFrame
Fill NA/NaN values by using the next valid observation to fill the gap.
Returns | |
---|---|
Type | Description |
Series/DataFrame or None | Object with missing values filled. |
combine
combine(
other: bigframes.dataframe.DataFrame,
func: typing.Callable[
[bigframes.series.Series, bigframes.series.Series], bigframes.series.Series
],
fill_value=None,
overwrite: bool = True,
*,
how: str = "outer"
) -> bigframes.dataframe.DataFrame
Perform column-wise combine with another DataFrame.
Combines a DataFrame with other
DataFrame using func
to element-wise combine columns. The row and column indexes of the
resulting DataFrame will be the union of the two.
Parameters | |
---|---|
Name | Description |
other |
DataFrame
The DataFrame to merge column-wise. |
func |
function
Function that takes two series as inputs and return a Series or a scalar. Used to merge the two dataframes column by columns. |
fill_value |
scalar value, default None
The value to fill NaNs with prior to passing any column to the merge func. |
overwrite |
bool, default True
If True, columns in |
Returns | |
---|---|
Type | Description |
DataFrame | Combination of the provided DataFrames. |
combine_first
combine_first(other: bigframes.dataframe.DataFrame)
Update null elements with value in the same location in other
.
Combine two DataFrame objects by filling null values in one DataFrame with non-null values from other DataFrame. The row and column indexes of the resulting DataFrame will be the union of the two. The resulting dataframe contains the 'first' dataframe values and overrides the second one values where both first.loc[index, col] and second.loc[index, col] are not missing values, upon calling first.combine_first(second).
Parameter | |
---|---|
Name | Description |
other |
DataFrame
Provided DataFrame to use to fill null values. |
Returns | |
---|---|
Type | Description |
DataFrame | The result of combining the provided DataFrame with the other object. |
copy
copy() -> bigframes.dataframe.DataFrame
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.
count
count(*, numeric_only: bool = False) -> bigframes.series.Series
Count non-NA cells for each column or row.
The values None
, NaN
, NaT
, and optionally numpy.inf
(depending
on pandas.options.mode.use_inf_as_na
) are considered NA.
Parameter | |
---|---|
Name | Description |
numeric_only |
bool, default False
Include only |
Returns | |
---|---|
Type | Description |
bigframes.series.Series | For each column/row the number of non-NA/null entries. If level is specified returns a DataFrame . |
cummax
cummax() -> bigframes.dataframe.DataFrame
Return cumulative maximum over a DataFrame axis.
Returns a DataFrame of the same size containing the cumulative maximum.
Returns | |
---|---|
Type | Description |
bigframes.dataframe.DataFrame | Return cumulative maximum of DataFrame. |
cummin
cummin() -> bigframes.dataframe.DataFrame
Return cumulative minimum over a DataFrame axis.
Returns a DataFrame of the same size containing the cumulative minimum.
Returns | |
---|---|
Type | Description |
bigframes.dataframe.DataFrame | Return cumulative minimum of DataFrame. |
cumprod
cumprod() -> bigframes.dataframe.DataFrame
Return cumulative product over a DataFrame axis.
Returns a DataFrame of the same size containing the cumulative product.
Returns | |
---|---|
Type | Description |
bigframes.dataframe.DataFrame | Return cumulative product of DataFrame. |
cumsum
cumsum()
Return cumulative sum over a DataFrame axis.
Returns a DataFrame of the same size containing the cumulative sum.
Returns | |
---|---|
Type | Description |
bigframes.dataframe.DataFrame | Return cumulative sum of DataFrame. |
describe
describe() -> bigframes.dataframe.DataFrame
Generate descriptive statistics.
Descriptive statistics include those that summarize the central
tendency, dispersion and shape of a
dataset's distribution, excluding NaN
values.
Only supports numeric columns.
Returns | |
---|---|
Type | Description |
bigframes.dataframe.DataFrame | Summary statistics of the Series or Dataframe provided. |
diff
diff(periods: int = 1) -> bigframes.dataframe.DataFrame
First discrete difference of element.
Calculates the difference of a DataFrame element compared with another element in the DataFrame (default is element in previous row).
Parameter | |
---|---|
Name | Description |
periods |
int, default 1
Periods to shift for calculating difference, accepts negative values. |
Returns | |
---|---|
Type | Description |
bigframes.dataframe.DataFrame | First differences of the Series. |
div
div(
other: float | int | bigframes.series.Series | DataFrame,
axis: str | int = "columns",
) -> DataFrame
Get floating division of DataFrame and other, element-wise (binary operator /
).
Equivalent to dataframe / other
. With reverse version, rtruediv
.
Among flexible wrappers (add
, sub
, mul
, div
, mod
, pow
) to
arithmetic operators: +
, -
, *
, /
, //
, %
, **
.
Parameters | |
---|---|
Name | Description |
other |
float, int, or Series
Any single or multiple element data structure, or list-like object. |
axis |
{0 or 'index', 1 or 'columns'}
Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on. |
Returns | |
---|---|
Type | Description |
DataFrame | DataFrame result of the arithmetic operation. |
divide
divide(
other: float | int | bigframes.series.Series | DataFrame,
axis: str | int = "columns",
) -> DataFrame
Get floating division of DataFrame and other, element-wise (binary operator /
).
Equivalent to dataframe / other
. With reverse version, rtruediv
.
Among flexible wrappers (add
, sub
, mul
, div
, mod
, pow
) to
arithmetic operators: +
, -
, *
, /
, //
, %
, **
.
Parameters | |
---|---|
Name | Description |
other |
float, int, or Series
Any single or multiple element data structure, or list-like object. |
axis |
{0 or 'index', 1 or 'columns'}
Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on. |
Returns | |
---|---|
Type | Description |
DataFrame | DataFrame result of the arithmetic operation. |
drop
drop(
labels: typing.Optional[typing.Any] = None,
*,
axis: typing.Union[int, str] = 0,
index: typing.Optional[typing.Any] = None,
columns: typing.Optional[
typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]]
] = None,
level: typing.Optional[typing.Union[str, int]] = None
) -> bigframes.dataframe.DataFrame
Drop specified labels from columns.
Remove columns by directly specifying column names.
Exceptions | |
---|---|
Type | Description |
KeyError | If any of the labels is not found in the selected axis. |
Returns | |
---|---|
Type | Description |
bigframes.dataframe.DataFrame | DataFrame without the removed column labels. |
drop_duplicates
drop_duplicates(
subset: typing.Optional[
typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]]
] = None,
*,
keep: str = "first"
) -> bigframes.dataframe.DataFrame
Return DataFrame with duplicate rows removed.
Considering certain columns is optional. Indexes, including time indexes are ignored.
Parameters | |
---|---|
Name | Description |
subset |
column label or sequence of labels, optional
Only consider certain columns for identifying duplicates, by default use all of the columns. |
keep |
{'first', 'last',
Determines which duplicates (if any) to keep. - 'first' : Drop duplicates except for the first occurrence. - 'last' : Drop duplicates except for the last occurrence. - |
Returns | |
---|---|
Type | Description |
bigframes.dataframe.DataFrame | DataFrame with duplicates removed |
droplevel
droplevel(level: LevelsType, axis: int | str = 0)
Return DataFrame with requested index / column level(s) removed.
Parameters | |
---|---|
Name | Description |
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
Axis along which the level(s) is removed: * 0 or 'index': remove level(s) in column. * 1 or 'columns': remove level(s) in row. |
Returns | |
---|---|
Type | Description |
DataFrame | DataFrame with requested index / column level(s) removed. |
dropna
dropna(
*, axis: int | str = 0, inplace: bool = False, how: str = "any", ignore_index=False
) -> DataFrame
Remove missing values.
Parameters | |
---|---|
Name | Description |
axis |
{0 or 'index', 1 or 'columns'}, default 'columns'
Determine if rows or columns which contain missing values are removed. * 0, or 'index' : Drop rows which contain missing values. * 1, or 'columns' : Drop columns which contain missing value. |
how |
{'any', 'all'}, default 'any'
Determine if row or column is removed from DataFrame, when we have at least one NA or all NA. * 'any' : If any NA values are present, drop that row or column. * 'all' : If all values are NA, drop that row or column. |
ignore_index |
bool, default
If |
Returns | |
---|---|
Type | Description |
bigframes.dataframe.DataFrame | DataFrame with NA entries dropped from it. |
duplicated
duplicated(subset=None, keep: str = "first") -> bigframes.series.Series
Return boolean Series denoting duplicate rows.
Considering certain columns is optional.
Parameters | |
---|---|
Name | Description |
subset |
column label or sequence of labels, optional
Only consider certain columns for identifying duplicates, by default use all of the columns. |
keep |
{'first', 'last', False}, default 'first'
Determines which duplicates (if any) to mark. - |
Returns | |
---|---|
Type | Description |
bigframes.series.Series | Boolean series for each duplicated rows. |
eq
eq(other: typing.Any, axis: str | int = "columns") -> DataFrame
Get equal to of DataFrame and other, element-wise (binary operator eq
).
Among flexible wrappers (eq
, ne
, le
, lt
, ge
, gt
) to comparison
operators.
Equivalent to ==
, !=
, <=
, <
, >=
, >
with support to choose axis
(rows or columns) and level for comparison.
Parameters | |
---|---|
Name | Description |
other |
scalar, sequence, Series, or DataFrame
Any single or multiple element data structure, or list-like object. |
axis |
{0 or 'index', 1 or 'columns'}, default 'columns'
Whether to compare by the index (0 or 'index') or columns (1 or 'columns'). |
equals
equals(
other: typing.Union[bigframes.series.Series, bigframes.dataframe.DataFrame]
) -> bool
Test whether two objects contain the same elements.
This function allows two Series or DataFrames to be compared against each other to see if they have the same shape and elements. NaNs in the same location are considered equal.
The row/column index do not need to have the same type, as long as the values are considered equal. Corresponding columns must be of the same dtype.
Parameter | |
---|---|
Name | Description |
other |
Series or DataFrame
The other Series or DataFrame to be compared with the first. |
Returns | |
---|---|
Type | Description |
bool | True if all elements are the same in both objects, False otherwise. |
expanding
expanding(min_periods: int = 1) -> bigframes.core.window.Window
Provide expanding window calculations.
Parameter | |
---|---|
Name | Description |
min_periods |
int, default 1
Minimum number of observations in window required to have a value; otherwise, result is |
Returns | |
---|---|
Type | Description |
bigframes.core.window.Window | Expanding subclass. |
ffill
ffill(*, limit: typing.Optional[int] = None) -> bigframes.dataframe.DataFrame
Fill NA/NaN values by propagating the last valid observation to next valid.
Returns | |
---|---|
Type | Description |
Series/DataFrame or None | Object with missing values filled. |
fillna
fillna(value=None) -> bigframes.dataframe.DataFrame
Fill NA/NaN values using the specified method.
Parameter | |
---|---|
Name | Description |
value |
scalar, Series
Value to use to fill holes (e.g. 0), alternately a Series of values specifying which value to use for each index (for a Series) or column (for a DataFrame). Values not in the Series will not be filled. This value cannot be a list. |
Returns | |
---|---|
Type | Description |
DataFrame | Object with missing values filled |
filter
filter(
items: typing.Optional[typing.Iterable] = None,
like: typing.Optional[str] = None,
regex: typing.Optional[str] = None,
axis: int | str | None = None,
) -> DataFrame
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 | |
---|---|
Name | Description |
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 |
first_valid_index
first_valid_index()
API documentation for first_valid_index
method.
floordiv
floordiv(
other: float | int | bigframes.series.Series | DataFrame,
axis: str | int = "columns",
) -> DataFrame
Get integer division of DataFrame and other, element-wise (binary operator //
).
Equivalent to dataframe // other
. With reverse version, rfloordiv
.
Among flexible wrappers (add
, sub
, mul
, div
, mod
, pow
) to
arithmetic operators: +
, -
, *
, /
, //
, %
, **
.
Parameters | |
---|---|
Name | Description |
other |
float, int, or Series
Any single or multiple element data structure, or list-like object. |
axis |
{0 or 'index', 1 or 'columns'}
Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on. |
Returns | |
---|---|
Type | Description |
DataFrame | DataFrame result of the arithmetic operation. |
ge
ge(other: typing.Any, axis: str | int = "columns") -> DataFrame
Get 'greater than or equal to' of DataFrame and other, element-wise (binary operator >=
).
Among flexible wrappers (eq
, ne
, le
, lt
, ge
, gt
) to comparison
operators.
Equivalent to ==
, !=
, <=
, <
, >=
, >
with support to choose axis
(rows or columns) and level for comparison.
Parameters | |
---|---|
Name | Description |
other |
scalar, sequence, Series, or DataFrame
Any single or multiple element data structure, or list-like object. |
axis |
{0 or 'index', 1 or 'columns'}, default 'columns'
Whether to compare by the index (0 or 'index') or columns (1 or 'columns'). |
Returns | |
---|---|
Type | Description |
DataFrame | DataFrame of bool. The result of the comparison. |
get
get(key, default=None)
Get item from object for given key (ex: DataFrame column).
Returns default value if not found.
groupby
groupby(
by: typing.Optional[
typing.Union[
typing.Hashable,
bigframes.series.Series,
typing.Sequence[typing.Union[typing.Hashable, bigframes.series.Series]],
]
] = None,
*,
level: typing.Optional[
typing.Union[str, int, typing.Sequence[typing.Union[str, int]]]
] = None,
as_index: bool = True,
dropna: bool = True
) -> bigframes.core.groupby.DataFrameGroupBy
Group DataFrame by 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.
Parameters | |
---|---|
Name | Description |
by |
str, Sequence[str]
A label or list of labels may be passed to group by the columns in |
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 |
as_index |
bool, default True
Default True. Return object with group labels as the index. Only relevant for DataFrame input. |
dropna |
bool, default True
Default True. If True, and if group keys contain NA values, NA values together with row/column will be dropped. If False, NA values will also be treated as the key in groups. |
Returns | |
---|---|
Type | Description |
bigframes.core.groupby.SeriesGroupBy | A groupby object that contains information about the groups. |
gt
gt(other: typing.Any, axis: str | int = "columns") -> DataFrame
Get 'greater than' of DataFrame and other, element-wise (binary operator >
).
Among flexible wrappers (eq
, ne
, le
, lt
, ge
, gt
) to comparison
operators.
Equivalent to ==
, !=
, <=
, <
, >=
, >
with support to choose axis
(rows or columns) and level for comparison.
Parameters | |
---|---|
Name | Description |
other |
scalar, sequence, Series, or DataFrame
Any single or multiple element data structure, or list-like object. |
axis |
{0 or 'index', 1 or 'columns'}, default 'columns'
Whether to compare by the index (0 or 'index') or columns (1 or 'columns'). |
Returns | |
---|---|
Type | Description |
DataFrame | DataFrame of bool: The result of the comparison. |
head
head(n: int = 5) -> bigframes.dataframe.DataFrame
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.
Not yet supported 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.
Parameter | |
---|---|
Name | Description |
n |
int, default 5
Default 5. Number of rows to select. |
idxmax
idxmax() -> bigframes.series.Series
Return index of first occurrence of maximum over requested axis.
NA/null values are excluded.
Returns | |
---|---|
Type | Description |
Series | Indexes of maxima along the specified axis. |
idxmin
idxmin() -> bigframes.series.Series
Return index of first occurrence of minimum over requested axis.
NA/null values are excluded.
Returns | |
---|---|
Type | Description |
Series | Indexes of minima along the specified axis. |
isin
isin(values) -> bigframes.dataframe.DataFrame
Whether each element in the DataFrame is contained in values.
Parameter | |
---|---|
Name | Description |
values |
iterable, or dict
The result will only be true at a location if all the labels match. If |
Returns | |
---|---|
Type | Description |
DataFrame | DataFrame of booleans showing whether each element in the DataFrame is contained in values. |
isna
isna() -> bigframes.dataframe.DataFrame
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.
isnull
isnull() -> bigframes.dataframe.DataFrame
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.
items
items()
Iterate over (column name, Series) pairs.
Iterates over the DataFrame columns, returning a tuple with the column name and the content as a Series.
Returns | |
---|---|
Type | Description |
Iterator | Iterator of label, Series for each column. |
join
join(
other: bigframes.dataframe.DataFrame,
*,
on: typing.Optional[str] = None,
how: str = "left"
) -> bigframes.dataframe.DataFrame
Join columns of another DataFrame.
Join columns with other
DataFrame on index
Parameter | |
---|---|
Name | Description |
how |
{'left', 'right', 'outer', 'inner'}, default 'left'`
How to handle the operation of the two objects. |
Returns | |
---|---|
Type | Description |
bigframes.dataframe.DataFrame | A dataframe containing columns from both the caller and other . |
kurt
kurt(*, numeric_only: bool = False)
Return unbiased kurtosis over requested axis.
Kurtosis obtained using Fisher's definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.
Parameter | |
---|---|
Name | Description |
numeric_only |
bool, default False
Include only float, int, boolean columns. |
kurtosis
kurtosis(*, numeric_only: bool = False)
Return unbiased kurtosis over requested axis.
Kurtosis obtained using Fisher's definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.
Parameter | |
---|---|
Name | Description |
numeric_only |
bool, default False
Include only float, int, boolean columns. |
le
le(other: typing.Any, axis: str | int = "columns") -> DataFrame
Get 'less than or equal to' of dataframe and other, element-wise (binary operator <=
).
Among flexible wrappers (eq
, ne
, le
, lt
, ge
, gt
) to comparison
operators.
Equivalent to ==
, !=
, <=
, <
, >=
, >
with support to choose axis
(rows or columns) and level for comparison.
Parameters | |
---|---|
Name | Description |
other |
scalar, sequence, Series, or DataFrame
Any single or multiple element data structure, or list-like object. |
axis |
{0 or 'index', 1 or 'columns'}, default 'columns'
Whether to compare by the index (0 or 'index') or columns (1 or 'columns'). |
Returns | |
---|---|
Type | Description |
DataFrame | DataFrame of bool. The result of the comparison. |
lt
lt(other: typing.Any, axis: str | int = "columns") -> DataFrame
Get 'less than' of DataFrame and other, element-wise (binary operator <
).
Among flexible wrappers (eq
, ne
, le
, lt
, ge
, gt
) to comparison
operators.
Equivalent to ==
, !=
, <=
, <
, >=
, >
with support to choose axis
(rows or columns) and level for comparison.
Parameters | |
---|---|
Name | Description |
other |
scalar, sequence, Series, or DataFrame
Any single or multiple element data structure, or list-like object. |
axis |
{0 or 'index', 1 or 'columns'}, default 'columns'
Whether to compare by the index (0 or 'index') or columns (1 or 'columns'). |
Returns | |
---|---|
Type | Description |
DataFrame | DataFrame of bool. The result of the comparison. |
map
map(func, na_action: typing.Optional[str] = None) -> bigframes.dataframe.DataFrame
Apply a function to a Dataframe elementwise.
This method applies a function that accepts and returns a scalar to every element of a DataFrame.
Parameter | |
---|---|
Name | Description |
na_action |
Optional[str], default None
|
Returns | |
---|---|
Type | Description |
bigframes.dataframe.DataFrame | Transformed DataFrame. |
max
max(
axis: typing.Union[str, int] = 0, *, numeric_only: bool = False
) -> bigframes.series.Series
Return the maximum of the values over the requested axis.
If you want the index of the maximum, use idxmax
. This is
the equivalent of the numpy.ndarray
method argmax
.
Parameters | |
---|---|
Name | Description |
axis |
{index (0), columns (1)}
Axis for the function to be applied on. For Series this parameter is unused and defaults to 0. |
numeric_only |
bool. default False
Default False. Include only float, int, boolean columns. |
Returns | |
---|---|
Type | Description |
bigframes.series.Series | Series after the maximum of values. |
mean
mean(
axis: typing.Union[str, int] = 0, *, numeric_only: bool = False
) -> bigframes.series.Series
Return the mean of the values over the requested axis.
Parameters | |
---|---|
Name | Description |
axis |
{index (0), columns (1)}
Axis for the function to be applied on. For Series this parameter is unused and defaults to 0. |
numeric_only |
bool. default False
Default False. Include only float, int, boolean columns. |
Returns | |
---|---|
Type | Description |
bigframes.series.Series | Series with the mean of values. |
median
median(
*, numeric_only: bool = False, exact: bool = False
) -> bigframes.series.Series
Return the median of the values over the requested axis.
Parameters | |
---|---|
Name | Description |
numeric_only |
bool. default False
Default False. Include only float, int, boolean columns. |
exact |
bool. default False
Default False. Get the exact median instead of an approximate one. Note: |
Returns | |
---|---|
Type | Description |
bigframes.series.Series | Series with the median of values. |
merge
merge(
right: bigframes.dataframe.DataFrame,
how: typing.Literal["inner", "left", "outer", "right"] = "inner",
on: typing.Optional[
typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]]
] = None,
*,
left_on: typing.Optional[
typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]]
] = None,
right_on: typing.Optional[
typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]]
] = None,
sort: bool = False,
suffixes: tuple[str, str] = ("_x", "_y")
) -> bigframes.dataframe.DataFrame
Merge DataFrame objects with a database-style join.
The join is done on columns or indexes. If joining columns on columns, the DataFrame indexes will be ignored. Otherwise if joining indexes on indexes or indexes on a column or columns, the index will be passed on. When performing a cross merge, no column specifications to merge on are allowed.
Parameters | |
---|---|
Name | Description |
on |
label or list of labels
Columns to join on. It must be found in both DataFrames. Either on or left_on + right_on must be passed in. |
left_on |
label or list of labels
Columns to join on in the left DataFrame. Either on or left_on + right_on must be passed in. |
right_on |
label or list of labels
Columns to join on in the right DataFrame. Either on or left_on + right_on must be passed in. |
Returns | |
---|---|
Type | Description |
bigframes.dataframe.DataFrame | A DataFrame of the two merged objects. |
min
min(
axis: typing.Union[str, int] = 0, *, numeric_only: bool = False
) -> bigframes.series.Series
Return the minimum of the values over the requested axis.
If you want the index of the minimum, use idxmin
. This is the
equivalent of the numpy.ndarray
method argmin
.
Parameters | |
---|---|
Name | Description |
axis |
{index (0), columns (1)}
Axis for the function to be applied on. For Series this parameter is unused and defaults to 0. |
numeric_only |
bool, default False
Default False. Include only float, int, boolean columns. |
Returns | |
---|---|
Type | Description |
bigframes.series.Series | Series with the minimum of the values. |
mod
mod(
other: int | bigframes.series.Series | DataFrame, axis: str | int = "columns"
) -> DataFrame
Get modulo of DataFrame and other, element-wise (binary operator %
).
Equivalent to dataframe % other
. With reverse version, rmod
.
Among flexible wrappers (add
, sub
, mul
, div
, mod
, pow
) to
arithmetic operators: +
, -
, *
, /
, //
, %
, **
.
Parameter | |
---|---|
Name | Description |
axis |
{0 or 'index', 1 or 'columns'}
Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on. |
Returns | |
---|---|
Type | Description |
DataFrame | DataFrame result of the arithmetic operation. |
mul
mul(
other: float | int | bigframes.series.Series | DataFrame,
axis: str | int = "columns",
) -> DataFrame
Get multiplication of DataFrame and other, element-wise (binary operator *
).
Equivalent to dataframe * other
. With reverse version, rmul
.
Among flexible wrappers (add
, sub
, mul
, div
, mod
, pow
) to
arithmetic operators: +
, -
, *
, /
, //
, %
, **
.
Parameters | |
---|---|
Name | Description |
other |
float, int, or Series
Any single or multiple element data structure, or list-like object. |
axis |
{0 or 'index', 1 or 'columns'}
Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on. |
Returns | |
---|---|
Type | Description |
DataFrame | DataFrame result of the arithmetic operation. |
multiply
multiply(
other: float | int | bigframes.series.Series | DataFrame,
axis: str | int = "columns",
) -> DataFrame
Get multiplication of DataFrame and other, element-wise (binary operator *
).
Equivalent to dataframe * other
. With reverse version, rmul
.
Among flexible wrappers (add
, sub
, mul
, div
, mod
, pow
) to
arithmetic operators: +
, -
, *
, /
, //
, %
, **
.
Parameters | |
---|---|
Name | Description |
other |
float, int, or Series
Any single or multiple element data structure, or list-like object. |
axis |
{0 or 'index', 1 or 'columns'}
Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on. |
Returns | |
---|---|
Type | Description |
DataFrame | DataFrame result of the arithmetic operation. |
ne
ne(other: typing.Any, axis: str | int = "columns") -> DataFrame
Get not equal to of DataFrame and other, element-wise (binary operator ne
).
Among flexible wrappers (eq
, ne
, le
, lt
, ge
, gt
) to comparison
operators.
Equivalent to ==
, !=
, <=
, <
, >=
, >
with support to choose axis
(rows or columns) and level for comparison.
Parameters | |
---|---|
Name | Description |
other |
scalar, sequence, Series, or DataFrame
Any single or multiple element data structure, or list-like object. |
axis |
{0 or 'index', 1 or 'columns'}, default 'columns'
Whether to compare by the index (0 or 'index') or columns (1 or 'columns'). |
Returns | |
---|---|
Type | Description |
DataFrame | Result of the comparison. |
nlargest
nlargest(
n: int,
columns: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]],
keep: str = "first",
) -> bigframes.dataframe.DataFrame
Return the first n
rows ordered by columns
in descending order.
Return the first n
rows with the largest values in columns
, in
descending order. The columns that are not specified are returned as
well, but not used for ordering.
This method is equivalent to
df.sort_values(columns, ascending=False).head(n)
, but more
performant.
Parameters | |
---|---|
Name | Description |
n |
int
Number of rows to return. |
columns |
label or list of labels
Column label(s) to order by. |
keep |
{'first', 'last', 'all'}, default 'first'
Where there are duplicate values: - |
Returns | |
---|---|
Type | Description |
DataFrame .. note:: This function cannot be used with all column types. For example, when specifying columns with | The first n rows ordered by the given columns in descending order. |
notna
notna() -> bigframes.dataframe.DataFrame
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 | |
---|---|
Type | Description |
NDFrame | Mask of bool values for each element that indicates whether an element is not an NA value. |
notnull
notnull() -> bigframes.dataframe.DataFrame
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 | |
---|---|
Type | Description |
NDFrame | Mask of bool values for each element that indicates whether an element is not an NA value. |
nsmallest
nsmallest(
n: int,
columns: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]],
keep: str = "first",
) -> bigframes.dataframe.DataFrame
Return the first n
rows ordered by columns
in ascending order.
Return the first n
rows with the smallest values in columns
, in
ascending order. The columns that are not specified are returned as
well, but not used for ordering.
This method is equivalent to
df.sort_values(columns, ascending=True).head(n)
, but more
performant.
Parameters | |
---|---|
Name | Description |
n |
int
Number of rows to return. |
columns |
label or list of labels
Column label(s) to order by. |
keep |
{'first', 'last', 'all'}, default 'first'
Where there are duplicate values: - |
Returns | |
---|---|
Type | Description |
DataFrame .. note:: This function cannot be used with all column types. For example, when specifying columns with | The first n rows ordered by the given columns in ascending order. |
nunique
nunique() -> bigframes.series.Series
Count number of distinct elements in specified axis.
Returns | |
---|---|
Type | Description |
bigframes.series.Series | Series with number of distinct elements. |
pct_change
pct_change(periods: int = 1) -> bigframes.dataframe.DataFrame
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 | |
---|---|
Name | Description |
periods |
int, default 1
Periods to shift for forming percent change. |
Returns | |
---|---|
Type | Description |
Series or DataFrame | The same type as the calling object. |
pivot
pivot(
*,
columns: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]],
index: typing.Optional[
typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]]
] = None,
values: typing.Optional[
typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]]
] = None
) -> bigframes.dataframe.DataFrame
Return reshaped DataFrame organized by given index / column values.
Reshape data (produce a "pivot" table) based on column values. Uses
unique values from specified index
/ columns
to form axes of the
resulting DataFrame. This function does not support data
aggregation, multiple values will result in a MultiIndex in the
columns.
Parameters | |
---|---|
Name | Description |
columns |
str or object or a list of str
Column to use to make new frame's columns. |
index |
str or object or a list of str, optional
Column to use to make new frame's index. If not given, uses existing index. |
values |
str, object or a list of the previous, optional
Column(s) to use for populating new frame's values. If not specified, all remaining columns will be used and the result will have hierarchically indexed columns. |
pow
pow(other: int | bigframes.series.Series, axis: str | int = "columns") -> DataFrame
Get Exponential power of dataframe and other, element-wise (binary operator pow
).
Equivalent to dataframe ** other
, but with support to substitute a fill_value
for missing data in one of the inputs. With reverse version, rpow
.
Among flexible wrappers (add
, sub
, mul
, div
, mod
, pow
) to
arithmetic operators: +
, -
, *
, /
, //
, %
, **
.
Parameters | |
---|---|
Name | Description |
other |
float, int, or Series
Any single or multiple element data structure, or list-like object. |
axis |
{0 or 'index', 1 or 'columns'}
Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on. |
Returns | |
---|---|
Type | Description |
DataFrame | DataFrame result of the arithmetic operation. |
prod
prod(
axis: typing.Union[str, int] = 0, *, numeric_only: bool = False
) -> bigframes.series.Series
Return the product of the values over the requested axis.
Parameters | |
---|---|
Name | Description |
aßxis |
{index (0), columns (1)}
Axis for the function to be applied on. For Series this parameter is unused and defaults to 0. |
numeric_only |
bool. default False
Include only float, int, boolean columns. |
Returns | |
---|---|
Type | Description |
bigframes.series.Series | Series with the product of the values. |
product
product(
axis: typing.Union[str, int] = 0, *, numeric_only: bool = False
) -> bigframes.series.Series
Return the product of the values over the requested axis.
Parameters | |
---|---|
Name | Description |
aßxis |
{index (0), columns (1)}
Axis for the function to be applied on. For Series this parameter is unused and defaults to 0. |
numeric_only |
bool. default False
Include only float, int, boolean columns. |
Returns | |
---|---|
Type | Description |
bigframes.series.Series | Series with the product of the values. |
radd
radd(
other: float | int | bigframes.series.Series | DataFrame,
axis: str | int = "columns",
) -> DataFrame
Get addition of DataFrame and other, element-wise (binary operator +
).
Equivalent to dataframe + other
. With reverse version, radd
.
Among flexible wrappers (add
, sub
, mul
, div
, mod
, pow
) to
arithmetic operators: +
, -
, *
, /
, //
, %
, **
.
Parameters | |
---|---|
Name | Description |
other |
float, int, or Series
Any single or multiple element data structure, or list-like object. |
axis |
{0 or 'index', 1 or 'columns'}
Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on. |
Returns | |
---|---|
Type | Description |
DataFrame | DataFrame result of the arithmetic operation. |
rank
rank(
axis=0,
method: str = "average",
numeric_only=False,
na_option: str = "keep",
ascending=True,
) -> bigframes.dataframe.DataFrame
Compute numerical data ranks (1 through n) along axis.
By default, equal values are assigned a rank that is the average of the ranks of those values.
Parameters | |
---|---|
Name | Description |
method |
{'average', 'min', 'max', 'first', 'dense'}, default 'average'
How to rank the group of records that have the same value (i.e. ties): |
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: |
ascending |
bool, default True
Whether or not the elements should be ranked in ascending order. |
Returns | |
---|---|
Type | Description |
same type as caller | Return a Series or DataFrame with data ranks as values. |
rdiv
rdiv(
other: float | int | bigframes.series.Series | DataFrame,
axis: str | int = "columns",
) -> DataFrame
Get floating division of DataFrame and other, element-wise (binary operator /
).
Equivalent to other / dataframe
. With reverse version, truediv
.
Among flexible wrappers (add
, sub
, mul
, div
, mod
, pow
) to
arithmetic operators: +
, -
, *
, /
, //
, %
, **
.
Parameters | |
---|---|
Name | Description |
other |
float, int, or Series
Any single or multiple element data structure, or list-like object. |
axis |
{0 or 'index', 1 or 'columns'}
Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on. |
reindex
reindex(
labels=None,
*,
index=None,
columns=None,
axis: typing.Optional[typing.Union[str, int]] = None,
validate: typing.Optional[bool] = None
)
Conform DataFrame to new index with optional filling logic.
Places NA in locations having no value in the previous index. A new object is produced.
Parameters | |
---|---|
Name | Description |
labels |
array-like, optional
New labels / index to conform the axis specified by 'axis' to. |
index |
array-like, optional
New labels for the index. Preferably an Index object to avoid duplicating data. |
columns |
array-like, optional
New labels for the columns. Preferably an Index object to avoid duplicating data. |
axis |
int or str, optional
Axis to target. Can be either the axis name ('index', 'columns') or number (0, 1). |
Returns | |
---|---|
Type | Description |
DataFrame | DataFrame with changed index. |
reindex_like
reindex_like(
other: bigframes.dataframe.DataFrame, *, 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 | |
---|---|
Name | Description |
other |
Object of the same data type
Its row and column indices are used to define the new indices of this object. |
Returns | |
---|---|
Type | Description |
Series or DataFrame | Same type as caller, but with changed indices on each axis. |
rename
rename(
*, columns: typing.Mapping[typing.Hashable, typing.Hashable]
) -> bigframes.dataframe.DataFrame
Rename columns.
Dict values must be unique (1-to-1). Labels not contained in a dict will be left as-is. Extra labels listed don't throw an error.
Parameter | |
---|---|
Name | Description |
columns |
Mapping
Dict-like from old column labels to new column labels. |
Exceptions | |
---|---|
Type | Description |
KeyError | If any of the labels is not found. |
Returns | |
---|---|
Type | Description |
bigframes.dataframe.DataFrame | DataFrame with the renamed axis labels. |
rename_axis
rename_axis(
mapper: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]], **kwargs
) -> bigframes.dataframe.DataFrame
Set the name of the axis for the index.
Returns | |
---|---|
Type | Description |
bigframes.dataframe.DataFrame | DataFrame with the new index name |
reorder_levels
reorder_levels(order: LevelsType, axis: int | str = 0)
Rearrange index levels using input order. May not drop or duplicate levels.
Parameters | |
---|---|
Name | Description |
order |
list of int or list of str
List representing new level order. Reference level by number (position) or by key (label). |
axis |
{0 or 'index', 1 or 'columns'}, default 0
Where to reorder levels. |
Returns | |
---|---|
Type | Description |
DataFrame | DataFrame of rearranged index. |
reset_index
reset_index(*, drop: bool = False) -> bigframes.dataframe.DataFrame
Reset the index.
Reset the index of the DataFrame, and use the default one instead.
Parameter | |
---|---|
Name | Description |
drop |
bool, default False
Do not try to insert index into dataframe columns. This resets the index to the default integer index. |
Returns | |
---|---|
Type | Description |
bigframes.dataframe.DataFrame | DataFrame with the new index. |
rfloordiv
rfloordiv(
other: float | int | bigframes.series.Series | DataFrame,
axis: str | int = "columns",
) -> DataFrame
Get integer division of DataFrame and other, element-wise (binary operator //
).
Equivalent to other // dataframe
. With reverse version, rfloordiv
.
Among flexible wrappers (add
, sub
, mul
, div
, mod
, pow
) to
arithmetic operators: +
, -
, *
, /
, //
, %
, **
.
Parameters | |
---|---|
Name | Description |
other |
float, int, or Series
Any single or multiple element data structure, or list-like object. |
axis |
{0 or 'index', 1 or 'columns'}
Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on. |
Returns | |
---|---|
Type | Description |
DataFrame | DataFrame result of the arithmetic operation. |
rmod
rmod(
other: int | bigframes.series.Series | DataFrame, axis: str | int = "columns"
) -> DataFrame
Get modulo of DataFrame and other, element-wise (binary operator %
).
Equivalent to other % dataframe
. With reverse version, mod
.
Among flexible wrappers (add
, sub
, mul
, div
, mod
, pow
) to
arithmetic operators: +
, -
, *
, /
, //
, %
, **
.
Parameters | |
---|---|
Name | Description |
other |
float, int, or Series
Any single or multiple element data structure, or list-like object. |
axis |
{0 or 'index', 1 or 'columns'}
Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on. |
Returns | |
---|---|
Type | Description |
DataFrame | DataFrame result of the arithmetic operation. |
rmul
rmul(
other: float | int | bigframes.series.Series | DataFrame,
axis: str | int = "columns",
) -> DataFrame
Get multiplication of DataFrame and other, element-wise (binary operator *
).
Equivalent to dataframe * other
. With reverse version, rmul
.
Among flexible wrappers (add
, sub
, mul
, div
, mod
, pow
) to
arithmetic operators: +
, -
, *
, /
, //
, %
, **
.
Parameters | |
---|---|
Name | Description |
other |
float, int, or Series
Any single or multiple element data structure, or list-like object. |
axis |
{0 or 'index', 1 or 'columns'}
Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on. |
Returns | |
---|---|
Type | Description |
DataFrame | DataFrame result of the arithmetic operation. |
rolling
rolling(window: int, min_periods=None) -> bigframes.core.window.Window
Provide rolling window calculations.
Parameters | |
---|---|
Name | Description |
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 |
min_periods |
int, default None
Minimum number of observations in window required to have a value; otherwise, result is |
Returns | |
---|---|
Type | Description |
bigframes.core.window.Window | Window subclass if a win_type is passed. Rolling subclass if win_type is not passed. |
rpow
rpow(
other: int | bigframes.series.Series, axis: str | int = "columns"
) -> DataFrame
Get Exponential power of dataframe and other, element-wise (binary operator rpow
).
Equivalent to other ** dataframe
, but with support to substitute a fill_value
for missing data in one of the inputs. With reverse version, pow
.
Among flexible wrappers (add
, sub
, mul
, div
, mod
, pow
) to
arithmetic operators: +
, -
, *
, /
, //
, %
, **
.
Parameters | |
---|---|
Name | Description |
other |
float, int, or Series
Any single or multiple element data structure, or list-like object. |
axis |
{0 or 'index', 1 or 'columns'}
Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on. |
Returns | |
---|---|
Type | Description |
DataFrame | DataFrame result of the arithmetic operation. |
rsub
rsub(
other: float | int | bigframes.series.Series | DataFrame,
axis: str | int = "columns",
) -> DataFrame
Get subtraction of DataFrame and other, element-wise (binary operator -
).
Equivalent to other - dataframe
. With reverse version, sub
.
Among flexible wrappers (add
, sub
, mul
, div
, mod
, pow
) to
arithmetic operators: +
, -
, *
, /
, //
, %
, **
.
Parameters | |
---|---|
Name | Description |
other |
float, int, or Series
Any single or multiple element data structure, or list-like object. |
axis |
{0 or 'index', 1 or 'columns'}
Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on. |
Returns | |
---|---|
Type | Description |
DataFrame | DataFrame result of the arithmetic operation. |
rtruediv
rtruediv(
other: float | int | bigframes.series.Series | DataFrame,
axis: str | int = "columns",
) -> DataFrame
Get floating division of DataFrame and other, element-wise (binary operator /
).
Equivalent to other / dataframe
. With reverse version, truediv
.
Among flexible wrappers (add
, sub
, mul
, div
, mod
, pow
) to
arithmetic operators: +
, -
, *
, /
, //
, %
, **
.
Parameters | |
---|---|
Name | Description |
other |
float, int, or Series
Any single or multiple element data structure, or list-like object. |
axis |
{0 or 'index', 1 or 'columns'}
Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on. |
sample
sample(
n: typing.Optional[int] = None,
frac: typing.Optional[float] = None,
*,
random_state: typing.Optional[int] = None
) -> bigframes.dataframe.DataFrame
Return a random sample of items from an axis of object.
You can use random_state
for reproducibility.
Parameters | |
---|---|
Name | Description |
n |
Optional[int], default None
Number of items from axis to return. Cannot be used with |
frac |
Optional[float], default None
Fraction of axis items to return. Cannot be used with |
random_state |
Optional[int], default None
Seed for random number generator. |
set_index
set_index(
keys: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]],
append: bool = False,
drop: bool = True,
) -> bigframes.dataframe.DataFrame
Set the DataFrame index using existing columns.
Set the DataFrame index (row labels) using one existing column. The index can replace the existing index.
Returns | |
---|---|
Type | Description |
DataFrame | Changed row labels. |
shift
shift(periods: int = 1) -> bigframes.dataframe.DataFrame
Shift index by desired number of periods.
Shifts the index without realigning the data.
Returns | |
---|---|
Type | Description |
NDFrame | Copy of input object, shifted. |
skew
skew(*, numeric_only: bool = False)
Return unbiased skew over requested axis.
Normalized by N-1.
Parameter | |
---|---|
Name | Description |
numeric_only |
bool, default False
Include only float, int, boolean columns. |
sort_index
sort_index(
ascending: bool = True, na_position: typing.Literal["first", "last"] = "last"
) -> bigframes.dataframe.DataFrame
Sort object by labels (along an axis).
sort_values
sort_values(
by: str | typing.Sequence[str],
*,
ascending: bool | typing.Sequence[bool] = True,
kind: str = "quicksort",
na_position: typing.Literal["first", "last"] = "last"
) -> DataFrame
Sort by the values along row axis.
Parameters | |
---|---|
Name | Description |
by |
str or Sequence[str]
Name or list of names to sort by. |
ascending |
bool or Sequence[bool], default True
Sort ascending vs. descending. Specify list for multiple sort orders. If this is a list of bools, must match the length of the by. |
kind |
str, default
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', 'last'}, default
|
stack
stack(level: typing.Union[str, int, typing.Sequence[typing.Union[str, int]]] = -1)
Stack the prescribed level(s) from columns to index.
Return a reshaped DataFrame or Series having a multi-level index with one or more new inner-most levels compared to the current DataFrame. The new inner-most levels are created by pivoting the columns of the current dataframe:
- if the columns have a single level, the output is a Series;
- if the columns have multiple levels, the new index level(s) is (are) taken from the prescribed level(s) and the output is a DataFrame.
Returns | |
---|---|
Type | Description |
DataFrame or Series | Stacked dataframe or series. |
std
std(
axis: typing.Union[str, int] = 0, *, numeric_only: bool = False
) -> bigframes.series.Series
Return sample standard deviation over requested axis.
Normalized by N-1 by default.
Parameter | |
---|---|
Name | Description |
numeric_only |
bool. default False
Default False. Include only float, int, boolean columns. |
Returns | |
---|---|
Type | Description |
bigframes.series.Series | Series with sample standard deviation. |
sub
sub(
other: float | int | bigframes.series.Series | DataFrame,
axis: str | int = "columns",
) -> DataFrame
Get subtraction of DataFrame and other, element-wise (binary operator -
).
Equivalent to dataframe - other
. With reverse version, rsub
.
Among flexible wrappers (add
, sub
, mul
, div
, mod
, pow
) to
arithmetic operators: +
, -
, *
, /
, //
, %
, **
.
Parameters | |
---|---|
Name | Description |
other |
float, int, or Series
Any single or multiple element data structure, or list-like object. |
axis |
{0 or 'index', 1 or 'columns'}
Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on. |
Returns | |
---|---|
Type | Description |
DataFrame | DataFrame result of the arithmetic operation. |
subtract
subtract(
other: float | int | bigframes.series.Series | DataFrame,
axis: str | int = "columns",
) -> DataFrame
Get subtraction of DataFrame and other, element-wise (binary operator -
).
Equivalent to dataframe - other
. With reverse version, rsub
.
Among flexible wrappers (add
, sub
, mul
, div
, mod
, pow
) to
arithmetic operators: +
, -
, *
, /
, //
, %
, **
.
Parameters | |
---|---|
Name | Description |
other |
float, int, or Series
Any single or multiple element data structure, or list-like object. |
axis |
{0 or 'index', 1 or 'columns'}
Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on. |
Returns | |
---|---|
Type | Description |
DataFrame | DataFrame result of the arithmetic operation. |
sum
sum(
axis: typing.Union[str, int] = 0, *, numeric_only: bool = False
) -> bigframes.series.Series
Return the sum of the values over the requested axis.
This is equivalent to the method numpy.sum
.
Parameters | |
---|---|
Name | Description |
axis |
{index (0), columns (1)}
Axis for the function to be applied on. For Series this parameter is unused and defaults to 0. |
numeric_only |
bool. default False
Default False. Include only float, int, boolean columns. |
Returns | |
---|---|
Type | Description |
bigframes.series.Series | Series with the sum of values. |
swaplevel
swaplevel(i: int = -2, j: int = -1, axis: int | str = 0)
Swap levels i and j in a MultiIndex
.
Default is to swap the two innermost levels of the index.
Parameters | |
---|---|
Name | Description |
i |
j: int or str
j: Levels of the indices to be swapped. Can pass level name as string. |
axis |
{0 or 'index', 1 or 'columns'}, default 0
The axis to swap levels on. 0 or 'index' for row-wise, 1 or 'columns' for column-wise. |
Returns | |
---|---|
Type | Description |
DataFrame | DataFrame with levels swapped in MultiIndex. |
tail
tail(n: int = 5) -> bigframes.dataframe.DataFrame
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 | |
---|---|
Name | Description |
n |
int, default 5
Number of rows to select. |
to_csv
to_csv(
path_or_buf: str, sep=",", *, header: bool = True, index: bool = True
) -> None
Write object to a comma-separated values (csv) file on Cloud Storage.
Parameters | |
---|---|
Name | Description |
path_or_buf |
str
A destination URI of Cloud Storage files(s) to store the extracted dataframe in format of |
index |
bool, default True
If True, write row names (index). |
Returns | |
---|---|
Type | Description |
None | String output not yet supported. |
to_dict
to_dict(orient: Literal['dict', 'list', 'series', 'split', 'tight', 'records', 'index'] = 'dict', into: type[dict] = <class 'dict'>, **kwargs) -> dict | list[dict]
Convert the DataFrame to a dictionary.
The type of the key-value pairs can be customized with the parameters (see below).
Parameters | |
---|---|
Name | Description |
orient |
str {'dict', 'list', 'series', 'split', 'tight', 'records', 'index'}
Determines the type of the values of the dictionary. 'dict' (default) : dict like {column -> {index -> value}}. 'list' : dict like {column -> [values]}. 'series' : dict like {column -> Series(values)}. split' : dict like {'index' -> [index], 'columns' -> [columns], 'data' -> [values]}. 'tight' : dict like {'index' -> [index], 'columns' -> [columns], 'data' -> [values], 'index_names' -> [index.names], 'column_names' -> [column.names]}. 'records' : list like [{column -> value}, ... , {column -> value}]. 'index' : dict like {index -> {column -> value}}. |
into |
class, default dict
The collections.abc.Mapping subclass used for all Mappings in the return value. 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. |
index |
bool, default True
Whether to include the index item (and index_names item if |
Returns | |
---|---|
Type | Description |
dict or list of dict | Return a collections.abc.Mapping object representing the DataFrame. The resulting transformation depends on the orient parameter. |
to_excel
to_excel(excel_writer, sheet_name: str = "Sheet1", **kwargs) -> None
Write DataFrame to an Excel sheet.
To write a single DataFrame 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 | |
---|---|
Name | Description |
excel_writer |
path-like, file-like, or ExcelWriter object
File path or existing ExcelWriter. |
sheet_name |
str, default 'Sheet1'
Name of sheet which will contain DataFrame. |
to_gbq
to_gbq(
destination_table: str,
*,
if_exists: typing.Optional[typing.Literal["fail", "replace", "append"]] = "fail",
index: bool = True,
ordering_id: typing.Optional[str] = None
) -> None
Write a DataFrame to a BigQuery table.
Parameters | |
---|---|
Name | Description |
destination_table |
str
Name of table to be written, in the form |
if_exists |
str, default 'fail'
Behavior when the destination table exists. Value can be one of: |
index |
bool. default True
whether write row names (index) or not. |
ordering_id |
Optional[str], default None
If set, write the ordering of the DataFrame as a column in the result table with this name. |
to_json
to_json(
path_or_buf: str,
orient: typing.Literal[
"split", "records", "index", "columns", "values", "table"
] = "columns",
*,
lines: bool = False,
index: bool = True
) -> None
Convert the object to a JSON string, written to Cloud Storage.
Note NaN's and None will be converted to null and datetime objects will be converted to UNIX timestamps.
Parameters | |
---|---|
Name | Description |
path_or_buf |
str
A destination URI of Cloud Storage files(s) to store the extracted dataframe in format of |
orient |
{
Indication of expected JSON string format. * Series: - default is 'index' - allowed values are: {{'split', 'records', 'index', 'table'}}. * DataFrame: - default is 'columns' - allowed values are: {{'split', 'records', 'index', 'columns', 'values', 'table'}}. * The format of the JSON string: - '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 |
index |
bool, default True
If True, write row names (index). |
lines |
bool, default False
If 'orient' is 'records' write out line-delimited json format. Will throw ValueError if incorrect 'orient' since others are not list-like. |
Returns | |
---|---|
Type | Description |
None | String output not yet supported. |
to_latex
to_latex(
buf=None,
columns: Sequence | None = None,
header: bool | Sequence[str] = True,
index: bool = True,
**kwargs
) -> str | None
Render object to a LaTeX tabular, longtable, or nested table.
Requires \usepackage{{booktabs}}
. The output can be copy/pasted
into a main LaTeX document or read from an external file
with \input{{table.tex}}
.
Parameters | |
---|---|
Name | Description |
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). |
to_markdown
to_markdown(buf=None, mode: str = "wt", index: bool = True, **kwargs) -> str | None
Print DataFrame in Markdown-friendly format.
Parameters | |
---|---|
Name | Description |
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. |
index |
bool, optional, default True
Add index (row) labels. |
to_numpy
to_numpy(dtype=None, copy=False, na_value=None, **kwargs) -> numpy.ndarray
Convert the DataFrame to a NumPy array.
Parameters | |
---|---|
Name | Description |
dtype |
None
The dtype to pass to |
copy |
bool, default None
Whether to ensure that the returned value is not a view on another array. |
na_value |
Any, default None
The value to use for missing values. The default value depends on dtype and the dtypes of the DataFrame columns. |
Returns | |
---|---|
Type | Description |
numpy.ndarray | The converted NumPy array. |
to_orc
to_orc(path=None, **kwargs) -> bytes | None
Write a DataFrame to the ORC format.
Parameter | |
---|---|
Name | Description |
path |
str, file-like object or None, default None
If a string, it will be used as Root Directory path when writing a partitioned dataset. By file-like object, we refer to objects with a write() method, such as a file handle (e.g. via builtin open function). If path is None, a bytes object is returned. |
to_pandas
to_pandas(
max_download_size: typing.Optional[int] = None,
sampling_method: typing.Optional[str] = None,
random_state: typing.Optional[int] = None,
) -> pandas.core.frame.DataFrame
Write DataFrame to pandas DataFrame.
Parameters | |
---|---|
Name | Description |
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. |
Returns | |
---|---|
Type | Description |
pandas.DataFrame | A pandas DataFrame with all rows and columns of this DataFrame if the data_sampling_threshold_mb is not exceeded; otherwise, a pandas DataFrame with downsampled rows and all columns of this DataFrame. |
to_parquet
to_parquet(
path: str,
*,
compression: typing.Optional[typing.Literal["snappy", "gzip"]] = "snappy",
index: bool = True
) -> None
Write a DataFrame to the binary Parquet format.
This function writes the dataframe as a parquet file
<https://parquet.apache.org/>
_ to Cloud Storage.
Parameters | |
---|---|
Name | Description |
path |
str
Destination URI(s) of Cloud Storage files(s) to store the extracted dataframe in format of |
compression |
str, default 'snappy'
Name of the compression to use. Use |
index |
bool, default True
If |
to_pickle
to_pickle(path, **kwargs) -> None
Pickle (serialize) object to file.
Parameter | |
---|---|
Name | Description |
path |
str
File path where the pickled object will be stored. |
to_records
to_records(
index: bool = True, column_dtypes=None, index_dtypes=None
) -> numpy.recarray
Convert DataFrame to a NumPy record array.
Index will be included as the first field of the record array if requested.
Parameters | |
---|---|
Name | Description |
index |
bool, default True
Include index in resulting record array, stored in 'index' field or using the index label, if set. |
column_dtypes |
str, type, dict, default None
If a string or type, the data type to store all columns. If a dictionary, a mapping of column names and indices (zero-indexed) to specific data types. |
index_dtypes |
str, type, dict, default None
If a string or type, the data type to store all index levels. If a dictionary, a mapping of index level names and indices (zero-indexed) to specific data types. This mapping is applied only if |
Returns | |
---|---|
Type | Description |
np.recarray | NumPy ndarray with the DataFrame labels as fields and each row of the DataFrame as entries. |
to_string
to_string(
buf=None,
columns: Sequence[str] | None = None,
col_space=None,
header: bool | Sequence[str] = True,
index: bool = True,
na_rep: str = "NaN",
formatters=None,
float_format=None,
sparsify: bool | None = None,
index_names: bool = True,
justify: str | None = None,
max_rows: int | None = None,
max_cols: int | None = None,
show_dimensions: bool = False,
decimal: str = ".",
line_width: int | None = None,
min_rows: int | None = None,
max_colwidth: int | None = None,
encoding: str | None = None,
) -> str | None
Render a DataFrame to a console-friendly tabular output.
Parameters | |
---|---|
Name | Description |
buf |
str, Path or StringIO-like, optional, default None
Buffer to write to. If None, the output is returned as a string. |
columns |
sequence, optional, default None
The subset of columns to write. Writes all columns by default. |
col_space |
int, list or dict of int, optional
The minimum width of each column. |
header |
bool or sequence, optional
Write out the column names. If a list of strings is given, it is assumed to be aliases for the column names. |
index |
bool, optional, default True
Whether to print index (row) labels. |
na_rep |
str, optional, default 'NaN'
String representation of NAN to use. |
formatters |
list, tuple or dict of one-param. functions, optional
Formatter functions to apply to columns' elements by position or name. The result of each function must be a unicode string. List/tuple must be of length equal to the number of columns. |
float_format |
one-parameter function, optional, default None
Formatter function to apply to columns' elements if they are floats. The result of this function must be a unicode string. |
sparsify |
bool, optional, default True
Set to False for a DataFrame with a hierarchical index to print every multiindex key at each row. |
index_names |
bool, optional, default True
Prints the names of the indexes. |
justify |
str, default None
How to justify the column labels. If None uses the option from the print configuration (controlled by set_option), 'right' out of the box. Valid values are, 'left', 'right', 'center', 'justify', 'justify-all', 'start', 'end', 'inherit', 'match-parent', 'initial', 'unset'. |
max_rows |
int, optional
Maximum number of rows to display in the console. |
min_rows |
int, optional
The number of rows to display in the console in a truncated repr (when number of rows is above |
max_cols |
int, optional
Maximum number of columns to display in the console. |
show_dimensions |
bool, default False
Display DataFrame dimensions (number of rows by number of columns). |
decimal |
str, default '.'
Character recognized as decimal separator, e.g. ',' in Europe. |
line_width |
int, optional
Width to wrap a line in characters. |
max_colwidth |
int, optional
Max width to truncate each column in characters. By default, no limit. |
encoding |
str, default "utf-8"
Set character encoding. |
Returns | |
---|---|
Type | Description |
str or None | If buf is None, returns the result as a string. Otherwise returns None. |
truediv
truediv(
other: float | int | bigframes.series.Series | DataFrame,
axis: str | int = "columns",
) -> DataFrame
Get floating division of DataFrame and other, element-wise (binary operator /
).
Equivalent to dataframe / other
. With reverse version, rtruediv
.
Among flexible wrappers (add
, sub
, mul
, div
, mod
, pow
) to
arithmetic operators: +
, -
, *
, /
, //
, %
, **
.
Parameters | |
---|---|
Name | Description |
other |
float, int, or Series
Any single or multiple element data structure, or list-like object. |
axis |
{0 or 'index', 1 or 'columns'}
Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on. |
Returns | |
---|---|
Type | Description |
DataFrame | DataFrame result of the arithmetic operation. |
unstack
unstack()
Pivot a level of the (necessarily hierarchical) index labels.
Returns a DataFrame having a new level of column labels whose inner-most level consists of the pivoted index labels.
If the index is not a MultiIndex, the output will be a Series (the analogue of stack when the columns are not a MultiIndex).
update
update(other, join: str = "left", overwrite=True, filter_func=None)
Modify in place using non-NA values from another DataFrame.
Aligns on indices. There is no return value.
Parameters | |
---|---|
Name | Description |
other |
DataFrame, or object coercible into a DataFrame
Should have at least one matching index/column label with the original DataFrame. If a Series is passed, its name attribute must be set, and that will be used as the column name to align with the original DataFrame. |
join |
{'left'}, default 'left'
Only left join is implemented, keeping the index and columns of the original object. |
overwrite |
bool, default True
How to handle non-NA values for overlapping keys: True: overwrite original DataFrame's values with values from |
filter_func |
callable(1d-array) -> bool 1d-array, optional
Can choose to replace values other than NA. Return True for values that should be updated. |
Returns | |
---|---|
Type | Description |
None | This method directly changes calling object. |
value_counts
value_counts(
subset: typing.Optional[
typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]]
] = None,
normalize: bool = False,
sort: bool = True,
ascending: bool = False,
dropna: bool = True,
)
Return a Series containing counts of unique rows in the DataFrame.
Parameters | |
---|---|
Name | Description |
subset |
label or list of labels, optional
Columns to use when counting unique combinations. |
normalize |
bool, default False
Return proportions rather than frequencies. |
sort |
bool, default True
Sort by frequencies. |
ascending |
bool, default False
Sort in ascending order. |
dropna |
bool, default True
Don’t include counts of rows that contain NA values. |
Returns | |
---|---|
Type | Description |
Series | Series containing counts of unique rows in the DataFrame |
var
var(
axis: typing.Union[str, int] = 0, *, numeric_only: bool = False
) -> bigframes.series.Series
Return unbiased variance over requested axis.
Normalized by N-1 by default.
Parameters | |
---|---|
Name | Description |
axis |
{index (0), columns (1)}
Axis for the function to be applied on. For Series this parameter is unused and defaults to 0. |
numeric_only |
bool. default False
Default False. Include only float, int, boolean columns. |
Returns | |
---|---|
Type | Description |
bigframes.series.Series | Series with unbiased variance over requested axis. |