arkas.plotter¶
arkas.plotter ¶
Contain data plotters.
arkas.plotter.BaseCachedPlotter ¶
Bases: BasePlotter
Define the base class to implement a plotter that caches the figures after the first generation.
arkas.plotter.BasePlotter ¶
Bases: ABC
Define the base class to implement a plotter.
Example usage:
>>> from arkas.plotter import Plotter
>>> plotter = Plotter()
>>> plotter
Plotter(count=0)
arkas.plotter.BasePlotter.compute
abstractmethod
¶
compute() -> BasePlotter
Compute the figures and return a new plotter.
Returns:
| Type | Description |
|---|---|
BasePlotter
|
A new plotter with the computed figures. |
Example usage:
>>> from arkas.plotter import Plotter
>>> plotter = Plotter({"fig": None})
>>> plotter
Plotter(count=1)
>>> plotter2 = plotter.compute()
>>> plotter2
Plotter(count=1)
arkas.plotter.BasePlotter.equal
abstractmethod
¶
equal(other: Any, equal_nan: bool = False) -> bool
Indicate if two plotters are equal or not.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
other
|
Any
|
The other plotter to compare. |
required |
equal_nan
|
bool
|
Whether to compare NaN's as equal. If |
False
|
Returns:
| Type | Description |
|---|---|
bool
|
|
Example usage:
>>> from arkas.plotter import Plotter
>>> plotter1 = Plotter()
>>> plotter2 = Plotter()
>>> plotter3 = Plotter({"fig": None})
>>> plotter1.equal(plotter2)
True
>>> plotter1.equal(plotter3)
False
arkas.plotter.BasePlotter.plot
abstractmethod
¶
plot(prefix: str = '', suffix: str = '') -> dict
Generate the figures.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
prefix
|
str
|
The key prefix in the returned dictionary. |
''
|
suffix
|
str
|
The key suffix in the returned dictionary. |
''
|
Returns:
| Type | Description |
|---|---|
dict
|
A dictionary with the generated figures. |
Example usage:
>>> from arkas.plotter import Plotter
>>> plotter = Plotter()
>>> plotter.plot()
{}
arkas.plotter.BaseStateCachedPlotter ¶
Bases: BaseCachedPlotter, Generic[T]
Define the base class to implement a plotter that caches the figures after the first generation, and computes the figures from a state object.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
state
|
T
|
The state with the data. |
required |
arkas.plotter.ColumnCooccurrencePlotter ¶
Bases: BaseStateCachedPlotter[ColumnCooccurrenceState]
Implement a pairwise column co-occurrence plotter.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
state
|
T
|
The state with the co-occurrence matrix. |
required |
Example usage:
>>> import numpy as np
>>> from arkas.plotter import ColumnCooccurrencePlotter
>>> from arkas.state import ColumnCooccurrenceState
>>> plotter = ColumnCooccurrencePlotter(
... ColumnCooccurrenceState(matrix=np.ones((3, 3)), columns=["a", "b", "c"])
... )
>>> plotter
ColumnCooccurrencePlotter(
(state): ColumnCooccurrenceState(matrix=(3, 3), figure_config=MatplotlibFigureConfig())
)
arkas.plotter.ContinuousSeriesPlotter ¶
Bases: BaseStateCachedPlotter[SeriesState]
Implement a plotter that analyzes a column with continuous values.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
state
|
T
|
The state containing the Series to analyze. |
required |
Example usage:
>>> import polars as pl
>>> from arkas.plotter import ContinuousSeriesPlotter
>>> from arkas.state import SeriesState
>>> plotter = ContinuousSeriesPlotter(SeriesState(pl.Series("col1", [1, 2, 3, 4, 5, 6, 7])))
>>> plotter
ContinuousSeriesPlotter(
(state): SeriesState(name='col1', values=(7,), figure_config=MatplotlibFigureConfig())
)
arkas.plotter.CorrelationPlotter ¶
Bases: BaseStateCachedPlotter[TwoColumnDataFrameState]
Implement a DataFrame column plotter.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
state
|
TwoColumnDataFrameState
|
The state containing the DataFrame to analyze. The DataFrame must have only 2 columns, which are the two columns to analyze. |
required |
Example usage:
>>> import polars as pl
>>> from arkas.plotter import CorrelationPlotter
>>> from arkas.state import TwoColumnDataFrameState
>>> frame = pl.DataFrame(
... {
... "col1": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0],
... "col2": [2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0],
... },
... )
>>> plotter = CorrelationPlotter(
... TwoColumnDataFrameState(frame, column1="col1", column2="col2")
... )
>>> plotter
CorrelationPlotter(
(state): TwoColumnDataFrameState(dataframe=(7, 2), column1='col1', column2='col2', nan_policy='propagate', figure_config=MatplotlibFigureConfig())
)
arkas.plotter.HexbinColumnPlotter ¶
Bases: BaseStateCachedPlotter[ScatterDataFrameState]
Implement a DataFrame column plotter that makes a 2D hexagonal binning plot of points x, y.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
state
|
T
|
The state containing the DataFrame to analyze. |
required |
Example usage:
>>> import polars as pl
>>> from arkas.plotter import HexbinColumnPlotter
>>> from arkas.state import ScatterDataFrameState
>>> frame = pl.DataFrame(
... {
... "col1": [1.2, 4.2, 4.2, 2.2],
... "col2": [1, 1, 1, 1],
... "col3": [1, 2, 2, 2],
... },
... schema={"col1": pl.Float64, "col2": pl.Int64, "col3": pl.Int64},
... )
>>> plotter = HexbinColumnPlotter(
... ScatterDataFrameState(frame, x="col1", y="col2", color="col3")
... )
>>> plotter
HexbinColumnPlotter(
(state): ScatterDataFrameState(dataframe=(4, 3), x='col1', y='col2', color='col3', nan_policy='propagate', figure_config=MatplotlibFigureConfig())
)
arkas.plotter.NullValuePlotter ¶
Bases: BaseStateCachedPlotter[NullValueState]
Implement a plotter that plots the number of null values for each column.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
state
|
T
|
The state containing the number of null values per column. |
required |
Example usage:
>>> import numpy as np
>>> from arkas.plotter import NullValuePlotter
>>> from arkas.state import NullValueState
>>> plotter = NullValuePlotter(
... NullValueState(
... null_count=np.array([0, 1, 2]),
... total_count=np.array([5, 5, 5]),
... columns=["col1", "col2", "col3"],
... )
... )
>>> plotter
NullValuePlotter(
(state): NullValueState(num_columns=3, figure_config=MatplotlibFigureConfig())
)
arkas.plotter.PlotColumnPlotter ¶
Bases: BaseStateCachedPlotter[DataFrameState]
Implement a DataFrame column plotter.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
state
|
T
|
The state containing the DataFrame to analyze. |
required |
Example usage:
>>> import polars as pl
>>> from arkas.plotter import PlotColumnPlotter
>>> from arkas.state import DataFrameState
>>> frame = pl.DataFrame(
... {
... "col1": [1.2, 4.2, 4.2, 2.2],
... "col2": [1, 1, 1, 1],
... "col3": [1, 2, 2, 2],
... },
... schema={"col1": pl.Float64, "col2": pl.Int64, "col3": pl.Int64},
... )
>>> plotter = PlotColumnPlotter(DataFrameState(frame))
>>> plotter
PlotColumnPlotter(
(state): DataFrameState(dataframe=(4, 3), nan_policy='propagate', figure_config=MatplotlibFigureConfig())
)
arkas.plotter.Plotter ¶
Bases: BasePlotter
Implement a simple plotter.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
figures
|
dict | None
|
The dictionary of figures. |
None
|
Example usage:
>>> from arkas.plotter import Plotter
>>> plotter = Plotter()
>>> plotter
Plotter(count=0)
>>> plotter.plot()
{}
arkas.plotter.PlotterDict ¶
Bases: BasePlotter
Implement a plotter that generates figures from a mapping of plotters.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
plotters
|
Mapping[Hashable, BasePlotter]
|
The mapping of plotters. |
required |
Example usage:
>>> from arkas.plotter import PlotterDict, Plotter
>>> plotter = PlotterDict(
... {
... "one": Plotter(),
... "two": Plotter({"fig": None}),
... }
... )
>>> plotter
PlotterDict(
(one): Plotter(count=0)
(two): Plotter(count=1)
)
>>> figures = plotter.plot()
>>> figures
{'one': {}, 'two': {'fig': None}}
arkas.plotter.ScatterColumnPlotter ¶
Bases: BaseStateCachedPlotter[ScatterDataFrameState]
Implement a DataFrame column plotter.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
state
|
T
|
The state containing the DataFrame to analyze. |
required |
Example usage:
>>> import polars as pl
>>> from arkas.plotter import ScatterColumnPlotter
>>> from arkas.state import ScatterDataFrameState
>>> frame = pl.DataFrame(
... {
... "col1": [1.2, 4.2, 4.2, 2.2],
... "col2": [1, 1, 1, 1],
... "col3": [1, 2, 2, 2],
... },
... schema={"col1": pl.Float64, "col2": pl.Int64, "col3": pl.Int64},
... )
>>> plotter = ScatterColumnPlotter(
... ScatterDataFrameState(frame, x="col1", y="col2", color="col3")
... )
>>> plotter
ScatterColumnPlotter(
(state): ScatterDataFrameState(dataframe=(4, 3), x='col1', y='col2', color='col3', nan_policy='propagate', figure_config=MatplotlibFigureConfig())
)
arkas.plotter.TemporalNullValuePlotter ¶
Bases: BaseStateCachedPlotter[TemporalDataFrameState]
Implement a DataFrame column plotter.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
state
|
T
|
The state containing the DataFrame to analyze. |
required |
Example usage:
>>> from datetime import datetime, timezone
>>> import polars as pl
>>> from arkas.plotter import TemporalNullValuePlotter
>>> from arkas.state import TemporalDataFrameState
>>> frame = pl.DataFrame(
... {
... "col1": [0, 1, 1, 0],
... "col2": [0, 1, 0, 1],
... "col3": [1, 0, 0, 0],
... "datetime": [
... datetime(year=2020, month=1, day=3, tzinfo=timezone.utc),
... datetime(year=2020, month=2, day=3, tzinfo=timezone.utc),
... datetime(year=2020, month=3, day=3, tzinfo=timezone.utc),
... datetime(year=2020, month=4, day=3, tzinfo=timezone.utc),
... ],
... },
... schema={
... "col1": pl.Int64,
... "col2": pl.Int64,
... "col3": pl.Int64,
... "datetime": pl.Datetime(time_unit="us", time_zone="UTC"),
... },
... )
>>> plotter = TemporalNullValuePlotter(
... TemporalDataFrameState(frame, temporal_column="datetime", period="1d")
... )
>>> plotter
TemporalNullValuePlotter(
(state): TemporalDataFrameState(dataframe=(4, 4), temporal_column='datetime', period='1d', nan_policy='propagate', figure_config=MatplotlibFigureConfig())
)
arkas.plotter.TemporalPlotColumnPlotter ¶
Bases: BaseStateCachedPlotter[TemporalDataFrameState]
Implement a DataFrame column plotter.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
state
|
T
|
The state containing the DataFrame to analyze. |
required |
Example usage:
>>> from datetime import datetime, timezone
>>> import polars as pl
>>> from arkas.plotter import TemporalPlotColumnPlotter
>>> from arkas.state import TemporalDataFrameState
>>> frame = pl.DataFrame(
... {
... "col1": [0, 1, 1, 0],
... "col2": [0, 1, 0, 1],
... "col3": [1, 0, 0, 0],
... "datetime": [
... datetime(year=2020, month=1, day=3, tzinfo=timezone.utc),
... datetime(year=2020, month=2, day=3, tzinfo=timezone.utc),
... datetime(year=2020, month=3, day=3, tzinfo=timezone.utc),
... datetime(year=2020, month=4, day=3, tzinfo=timezone.utc),
... ],
... },
... schema={
... "col1": pl.Int64,
... "col2": pl.Int64,
... "col3": pl.Int64,
... "datetime": pl.Datetime(time_unit="us", time_zone="UTC"),
... },
... )
>>> plotter = TemporalPlotColumnPlotter(
... TemporalDataFrameState(frame, temporal_column="datetime")
... )
>>> plotter
TemporalPlotColumnPlotter(
(state): TemporalDataFrameState(dataframe=(4, 4), temporal_column='datetime', period=None, nan_policy='propagate', figure_config=MatplotlibFigureConfig())
)