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Machine Learning
avec Scikit-Learn
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Classe « Axis »
Signature de la méthode set
def set(self, *, agg_filter=<UNSET>, alpha=<UNSET>, animated=<UNSET>, clip_box=<UNSET>, clip_on=<UNSET>, clip_path=<UNSET>, converter=<UNSET>, data_interval=<UNSET>, gid=<UNSET>, in_layout=<UNSET>, inverted=<UNSET>, label=<UNSET>, label_coords=<UNSET>, label_position=<UNSET>, label_text=<UNSET>, major_formatter=<UNSET>, major_locator=<UNSET>, minor_formatter=<UNSET>, minor_locator=<UNSET>, mouseover=<UNSET>, path_effects=<UNSET>, picker=<UNSET>, pickradius=<UNSET>, rasterized=<UNSET>, remove_overlapping_locs=<UNSET>, sketch_params=<UNSET>, snap=<UNSET>, tick_params=<UNSET>, ticklabels=<UNSET>, ticks=<UNSET>, transform=<UNSET>, units=<UNSET>, url=<UNSET>, view_interval=<UNSET>, visible=<UNSET>, zorder=<UNSET>)
Description
help(Axis.set)
Set multiple properties at once.
Supported properties are
Properties:
agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array and two offsets from the bottom left corner of the image
alpha: scalar or None
animated: bool
clip_box: `~matplotlib.transforms.BboxBase` or None
clip_on: bool
clip_path: unknown
converter: `~matplotlib.units.ConversionInterface`
data_interval: unknown
figure: `~matplotlib.figure.Figure` or `~matplotlib.figure.SubFigure`
gid: str
in_layout: bool
inverted: unknown
label: unknown
label_coords: unknown
label_position: {'top', 'bottom'}
label_text: str
major_formatter: `~matplotlib.ticker.Formatter`, ``str``, or function
major_locator: `~matplotlib.ticker.Locator`
minor_formatter: `~matplotlib.ticker.Formatter`, ``str``, or function
minor_locator: `~matplotlib.ticker.Locator`
mouseover: bool
path_effects: list of `.AbstractPathEffect`
picker: None or bool or float or callable
pickradius: float
rasterized: bool
remove_overlapping_locs: unknown
sketch_params: (scale: float, length: float, randomness: float)
snap: bool or None
tick_params: unknown
ticklabels: sequence of str or of `.Text`\s
ticks: 1D array-like
transform: `~matplotlib.transforms.Transform`
units: units tag
url: str
view_interval: unknown
visible: bool
zorder: float
Vous êtes un professionnel et vous avez besoin d'une formation ?
Programmation Python
Les compléments
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