histograms#
API pages include interactive (HTML) plots that would possibly not render correctly on a mobile device.
- histograms(data: AnnData, keys: Sequence[str], *, group_by: str | None = None, groups: Sequence[str] | str | None = None, drop: Sequence[str] | str | None = None, mapping: FeatureSpec | None = None, axis: Literal[0, 1] | None = None, color: str | None = None, fill: str | None = None, bins: int | None = None, binwidth: float | None = None, add_keys: Sequence[str] | str | None = None, tooltips: Literal['none'] | Sequence[str] | FeatureSpec | None = None, geom_fill: str | None = None, geom_color: str | None = None, observations_name: str = 'Barcode', variables_name: str = 'Variable', interactive: bool = False, value_column: str = 'value', variable_column: str = 'variable', share_axis: bool = False, share_ticks: bool = False, layers: Sequence[FeatureSpec | LayerSpec] | FeatureSpec | LayerSpec | None = None, ncol: int | None = None, sharex: str | None = None, sharey: str | None = None, widths: list[float] | None = None, heights: list[float] | None = None, hspace: float | None = None, vspace: float | None = None, fit: bool | None = None, align: bool | None = None, guides: str = 'auto', **geom_kwargs) SupPlotsSpec#
Histograms.
- Parameters:
data (
AnnData) – The AnnData object of the single cell data.keys (
list[str] | tuple[str] | Sequence[str]) – The keys to get the values (numerical). e.g., [‘total_counts’, ‘pct_counts_in_top_50_genes’] or a list of gene names.group_by (
str | None, defaultNone) – Column to filter observations on. Only rows with a non-null value are kept.groups (
str | Sequence[str] | None, defaultNone) – Show only specific groups, keeping rows where group_by matches any of them. Categorical grouping columns only.drop (
str | Sequence[str] | None, defaultNone) – Drop specific groups, filtering out rows where group_by matches any of them. Categorical grouping columns only.mapping (
FeatureSpec | None, defaultNone) – Additional aesthetic mappings for the plot, the result of aes().axis (
{0,1}| None, defaultNone) – axis of the data, 0 for observations and 1 for variables.color (
str | None, defaultNone) – Color aesthetic to split the histogram (categorical). Shortcut for mapping=aes(color=…) e,g., ‘cell_type’ or ‘leiden’.fill (
str | None, defaultNone) – Fill aesthetic to split the histogram (categorical). Shortcut for mapping=aes(fill=…) e,g., ‘cell_type’ or ‘leiden’.bins (
int | None, defaultNone) – Number of bins. Overridden by binwidth if both are provided.binwidth (
float | None, defaultNone) – Width of each bin. Takes precedence over bins.add_keys (
Sequence[str] | str | None, defaultNone) – Additional keys to include in the dataframe.tooltips (
{'none'}| Sequence[str] | FeatureSpec | None, defaultNone) – Tooltips to show when hovering over the geom. Accepts Sequence[str] or result of layer_tooltips() for more complex tooltips. Use ‘none’ to disable tooltips.geom_fill (
str | None, defaultNone) – Fill color for all bars in the histogram.geom_color (
str | None, defaultNone) – Border color for all bars in the histogram.observations_name (
str, default'Barcode') – The name to give to barcode (or index) column in the dataframe.variables_name (
str, default'Variable') – The name to give to variable index column in the dataframe.interactive (
bool, defaultFalse) – Whether to make the plot interactive.variable_column (
str, default'variable') – The name of the variable column in the dataframe.value_column (
str, default'value') – The name of the value column in the dataframe.share_ticks (
bool, defaultFalse) – Whether to share the ticks across all plots. If True, only X tick texts on bottom row and Y tick text on left column are shown.share_axis (
bool, defaultFalse) – Whether to share the axis across all plots. If True, only X axis on bottom row and Y axis on left column is shown.layers (
Sequence[FeatureSpec | LayerSpec] | FeatureSpec | LayerSpec | None, defaultNone) – Additional layers to add to the plot.ncol (
int, defaultNone) – Number of columns in grid. If not specified, shows plots horizontally, in one row.sharex (
bool, defaultNone) – Controls sharing of axis limits between subplots in the grid. all/True - share limits between all subplots. none/False - do not share limits between subplots. row - share limits between subplots in the same row. col - share limits between subplots in the same column.sharey (
bool, defaultNone) – Controls sharing of axis limits between subplots in the grid. all/True - share limits between all subplots. none/False - do not share limits between subplots. row - share limits between subplots in the same row. col - share limits between subplots in the same column.widths (
list[float], defaultNone) – Relative width of each column of grid, left to right.heights (
list[float], defaultNone) – Relative height of each row of grid, top-down.hspace (
float | None, defaultNone) – Cell horizontal spacing in px.vspace (
float | None, defaultNone) – Cell vertical spacing in px.fit (
bool, defaultTrue) – Whether to stretch each plot to match the aspect ratio of its cell (fit=True), or to preserve the original aspect ratio of plots (fit=False).align (
bool, defaultFalse) – If True, align inner areas (i.e. “geom” bounds) of plots. However, cells containing other (sub)grids are not participating in the plot “inner areas” layouting.guides (
str, default'auto') –- Specifies how guides (legends and colorbars) should be treated in the layout.
’collect’ collect guides from all subplots, removing duplicates.
’keep’ keep guides in their original subplots; do not collect at this level.
’auto’ allow guides to be collected if an upper-level layout uses guides=’collect’;
otherwise, keep them in subplots.
For more information on gggrid parameters: https://lets-plot.org/python/pages/api/lets_plot.gggrid.html
**geom_kwargs – Additional parameters for the geom_histogram layer. For more information on geom_histogram parameters, see: https://lets-plot.org/python/pages/api/lets_plot.geom_histogram.html
- Returns:
SupPlotsSpec– Histograms.
Examples
Histograms.
import scanpy as sc from lets_plot import * import cellestial as cl data = cl.datasets.pbmc3k() cl.histograms( data, ["n_genes_by_counts", "log1p_total_counts_mt"], fill="cell_type_lvl1", bins=40, ncol=2, guides="collect", )