Barplot

Barplot is deprecated, use barhplot instead.

Barplot

Barplot is deprecated, use Barhplot instead

BarChartRace module that requires a valid time index.The data should be in this format where time is set to index

    Example:
    >>> time  col1 col2 col3 ...
    >>> 2012   1    0    2
    >>> 2013   2    3    1

Parameters:

Name	Type	Description	Default
data	pd.DataFrame	The data to be prepared	required
time_format	str	Index datetime format	required
ip_freq	str	Interpolation frequency	required
ip_frac	float	Interpolation fraction (check end of docstring), by default 0.5	0.5
n_bars	int	Number of bars to be visible on the plot, by default 10 or less	10
palettes	list[str]	List of color palettes to generate bar colors, by default ["viridis"]	['viridis']
post_update	Callable[[plt.Axes, int, Datafier, SimpleNamespace], None]	callback function for additional customization, by default None	None
annot_bars	bool	Sets bar annotations, by default True	True
fixed_xlim	bool	If False xlim will gradually change in every frame, by default True	True
xticks	bool	Sets xticks, by default True	True
yticks	bool	Sets yticks, by default True	True
grid	bool	Sets xgrid, by default True	True
rounded_edges	bool	Sets rounded bar edges, by default False	False

post_update args:

    plt.Axes: The matplotlib Axes used for the barplot
    int: Current animation frame or dataframe row
    Datafier: The underlying datafier instance
    SimpleNamespace: Contains the following attributes -
        bar_rank, bar_length, top_bars, bar_colors

example:

>>> def post_update(ax, i, datafier, bar_attr):
>>>     # sets log scale for x-axis
>>>     ax.set_xscale("log")

ip_frac description:

    ip_frac is the percentage of NaN values to be linearly
    interpolated for column ranks

    Consider this example
    >>>               a    b
    >>> date
    >>> 2021-11-13  1.0  4.0
    >>> 2021-11-14  NaN  NaN
    >>> 2021-11-15  NaN  NaN
    >>> 2021-11-16  NaN  NaN
    >>> 2021-11-17  NaN  NaN
    >>> 2021-11-18  2.0  6.0

    with ip_frac set to 0.5, 50% of NaN's will be linearly
    interpolated while the rest will back filled.

    >>>              a      b
    >>> 2021-11-13  1.00  4.00  << original value --------
    >>> 2021-11-14  1.33  4.67                            |
    >>> 2021-11-15  1.67  5.33                            |  50% linearly
    >>> 2021-11-16  2.00  6.00  <- linear interpolation   |  interpolated
    >>> 2021-11-17  2.00  6.00      upto here             |  rest are filled.
    >>> 2021-11-18  2.00  6.00  << original value---------

    This adds some stability in the barChartRace
    and reduces constantly shaking of bars.

add_var(row_var=None, col_var=None)

Adds additional variables to the data, both row and column wise.

Row wise data format: The index should be equal to that of the actual data

    time  leap_year col2   ...
    2012    yes      0
    2013    no       3

Column wise data format: The index should be equal to the columns of the actual data.

    index  continent   col2 ...
    ind    Asia         0
    usa    N America    3
    jap    Asia         2

Parameters:

Name	Type	Description	Default
row_var	pd.DataFrame	Dataframe containing variables related to time, by default None	None
col_var	pd.DataFrame	Dataframe containing variables related to columns, by default None	None

set_bar_color(colors)

If colors is a list, length of colors should be equal to no of datafier.bar_colors. If it is a dict, all columns of datafier.top_cols should be mapped to a color

Parameters:

Name	Type	Description	Default
colors	Union[list, dict[str, str]]	list of colors or dict of column to color mapping	required

getTopXY(i)

Prepares top n_bar columns and their respective attributes such as position, length, colors. Not meant to be used outside animation update

Parameters:

Name	Type	Description	Default
i	int	Animation frame index	required

Returns:

Type	Description
SimpleNamespace	Bar rank, length. Top columns and their respective colors

set_xylim(xlim=[], ylim=[])

Sets xlim and ylim

Parameters:

Name	Type	Description	Default
xlim	list[float]	x axis limits in this format [min, max], by default [min, max + 5]	[]
ylim	list[float]	y axis limits in this format [min, max], by default [0.5, n_bars + 0.6]	[]

set_title(title, x=0, y=1.01, size=13, color='#777777', **kwargs)

Sets the plot title and additional kwargs are passed to plt.text(**kwargs)

Parameters:

Name	Type	Description	Default
title	str	Title text	required
x	float	x coordinate of the text, by default 0	0
y	float	y coordinate, by default 1.01	1.01
size	float	text size, by default 13	13
color	str	text color, by default "#777777"	'#777777'

set_xlabel(text, x=0.43, y=-0.09, size=13, color='#777777', **kwargs)

Sets the plot xlabel and additional kwargs are passed to plt.text(**kwargs)

Parameters:

Name	Type	Description	Default
text	str	The xlabel text	required
x	float	X coordinate of the text, by default 0.43	0.43
y	float	Y coordinate, by default -0.09	-0.09
size	float	Text size, by default 13	13
color	str	Text color, by default "#777777"	'#777777'

set_time(callback=lambda i, datafier: datafier.data.index[i], x=0.97, y=0.27, size=46, weight=800, ha='right', color='#777777', **kwargs)

Annotates the time in the plot and additional kwargs are passed to plt.text(**kwargs)

Parameters:

Name	Type	Description	Default
callback	Callable[[int, pd.DataFrame], str]	Callback function to customize the time text, by default lambda i, datafier: datafier.data.index[i]	lambda i, datafier: datafier.data.index[i]
x	float	x coordinate of the text, by default 0.97	0.97
y	float	y coordinate of the text, by default 0.27	0.27
size	float	text size, by default 46	46
weight	float	text weight, by default 800	800
ha	str	horizontal alignment, by default "right"	'right'
color	str	text color, by default "#777777"	'#777777'

callback args:

    i: Animation frame / data row index
    datafier: The datafier instance,
        access the data using datafier.data

set_text(key, text=None, callback=None, x=0, y=0, size=13, color='#777777', **kwargs)

General function to add custom texts in the plot. Either text or callback should be passd but not both.

Parameters:

Name	Type	Description	Default
key	str	Unique identifier for each texts, note: These keys, title, xlabel, time, are reserved. overwrite them if you wish to use callbacks instead of texts in title or xlabel	required
text	str	The text to be added in the plot, by default None	None
callback	Callable[[int, pd.DataFrame], str]	Callback function to customize the text, by default None	None
x	float	X coordinate of the text, by default 0	0
y	str	Y coordinate of the text, by default 0	0
size	float	Text size, by default 13	13
color	str	Text color, by default "#777777"	'#777777'

Callback args:

    args:
    i: Animation frame / data row index
    datafier: The datafier instance

    Example:
    >>> lambda i, datafier: datafier.data.index[i]

remove_text(keys)

Removes texts by key

Parameters:

Name	Type	Description	Default
keys	list[str]	List of keys to be removed	required

set_bar_border_props(edge_color='k', radius=0.5, pad=-0.004, mutation_aspect=0.2, **kwargs)

Sets bar border properties. Additional kwargs are passed to FancyBboxPatch. See https://matplotlib.org/3.1.0/api/_as_gen/matplotlib.patches.FancyBboxPatch.html

Parameters:

Name	Type	Description	Default
edge_color	str	Bar edge color, by default "k"	'k'
radius	float	Bar border radius, by default 0.5	0.5
pad	float	See above link, by default -0.0040	-0.004
mutation_aspect	float	See above link, by default 0.2	0.2

set_barh(bar_height=0.86, **kwargs)

Sets barh properties, addition kwargs are passed to ax.barh(**kwargs)

Parameters:

Name	Type	Description	Default
bar_height	float	Height of the bars (Note this is horizontal barplot), by default 0.86	0.86

set_xticks(axis='x', colors='#777777', labelsize=12, **kwargs)

Sets xtick properties, additional kwargs are passed to ax.tick_params(**.kwargs)

Parameters:

Name	Type	Description	Default
axis	str	Defines tick axis, by default "x"	'x'
colors	str	Sets tick color, by default "#777777"	'#777777'
labelsize	float	Sets tick size, by default 12	12

set_yticks(axis='y', colors='#777777', labelsize=10, **kwargs)

Sets ytick properties, additional kwargs are passed to ax.tick_params(**kwargs)

Parameters:

Name	Type	Description	Default
axis	str	Defines tick axis, by default "y"	'y'
colors	str	Sets tick color, by default "#777777"	'#777777'
labelsize	float	Sets tick size, by default 10	10

set_grid(which='major', axis='x', linestyle='-', grid_behind=True, **kwargs)

Sets the plots grid, additional kwargs are passed to ax.grid(**kwargs)

Parameters:

Name	Type	Description	Default
which	str	The grid lines to apply the changes on, by default "major"	'major'
axis	str	Sets the axis of the grid, by default "x"	'x'
linestyle	str	Grids line style, by default "-"	'-'
grid_behind	bool	Sets the grid behind the bars, by default True	True

set_bar_annots(text_callback=lambda val: np.round(val, 2), xoffset=0.1, yoffset=-0.1, ha='left', **kwargs)

Sets bar annotation properties, additional kwargs are passed to ax.text(**kwargs). (Note these annotations are the texts near the bars)

Parameters:

Name	Type	Description	Default
text_callback	Callable[[float], Union[str, float]]	Callback function for customizing the text, by default lambda val:np.round(val, 2)	lambda val: np.round(val, 2)
xoffset	float	X offset relative to bar length, by default 0.1	0.1
yoffset	float	Y offset relative to bar height, by default -0.1	-0.1
ha	str	Horizontal alignment, by default "left"	'left'

add_extras(key, callback)

Adds extra callback functions for additional customizations

Parameters:

Name	Type	Description	Default
key	str	Unique identifier for each callback function	required
callback	list[Callable[[plt.Axes, int, pd.DataFrame, pd.DataFrame], None]]	Callback function for additional customization	required

Callback args:

    plt.Axes: The matplotlib Axes used for the barplot
    int: Current animation frame / dataframe row
    Datafier: The underlying datafier instance
    SimpleNamespace: Contains the following attributes -
    bar_rank, bar_length, top_bars, bar_colors

    Example:
    >>> lambda ax, *args: ax.set_xcale("log)