Datafier

Datafier is deprecated, use plot specific datafiers instead.

Datafier

Datafier is deprecated, use plot specific datafiers instead.

Contains data preparation modules, which includes interpolation, rank generation, color_generation. 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, should be in this format where time is set to index	required
`time_format`	`str`	Index datetime format	required
`ip_freq`	`str`	Interpolation frequency	required
`ip_frac`	`float`	Rank 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']`

    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`

`interpolate_even(data, freq, method='linear')`

Interpolates the given dataframe according to the frequency

Parameters:

Name	Type	Description	Default
`data`	`pd.DataFrame`	Dataframe contaning the data	required
`freq`	`str`	Interpolation frequency	required
`method`	`str`	Interpolation method, by default "linear"	`'linear'`

Returns:

Type	Description
`pd.DataFrame`	Interpolated dataframe

`get_prepared_data(data, ip_frac=0.5)`

Creates interpolated data and column ranks

Parameters:

Name	Type	Description	Default
`data`	`pd.DataFrame`	Dataframe containing the data	required
`ip_frac`	`float`	Interpolation fraction, by default 0.5	`0.5`

Returns:

Type	Description
`tuple[pd.DataFrame, pd.DataFrame]`	Tuple containing the following data `pd.DataFrame: Interpolated data values pd.DataFrame: Interpolated column ranks`

`get_top_cols()`

Selects columns where column_rank < n_bars in any timestamp

Returns:

Type	Description
`list[int]`	List of columns that will appear in the animation at least once

`get_bar_colors()`

Generates bar (column) colors based on the given color palettes

Returns:

Type	Description
`dict[str, str]`	dict containing column to color mapping