matplotlib_charting module

matplotlib_charting.add_pad(list_in, pad=100)

Separate all elements in a monotonic list by a minimum pad value.

Used by plotting functions to prevent overlapping tick labels.

inputs

list_in (list)

a monotonic list of numbers

pad (integer)

the minimum separation required between list elements

If the function is unable to produce a list with the pad between all elements (excluding the last list spacing), the original list is returned. The function will permit the final list padding (between the last two elements) to be less than the pad value.

matplotlib_charting.age_kde_dist(df, color_list, p_dict, max_age, ds_dict=None, mnum=0, title_size=14, min_age=25, chart_style='darkgrid', xsize=12, ysize=10, image_dir=None, image_format='png')

From the seaborn website: Fit and plot a univariate or bivariate kernel density estimate.

inputs

df (dataframe)

dataset to examine, may be a dataframe variable or a string key from the ds_dict dictionary object

color_list (list)

list of colors for employee group plots

p_dict (dictionary)

eg to string dict for plot labels

max_age (float)

maximum age to plot (x axis limit)

ds_dict (dictionary)

output from load_datasets function

mnum (integer)

month number to analyze

title_size (integer or float)

text size of chart title

image_dir (string)

if not None, name of a directory in which to save an image of the chart output. If the directory does not exist, it will be created.

image_format (string)

file extension string for a saved chart image if the image_dir input is not None

Examples:

‘svg’, ‘png’

matplotlib_charting.age_vs_spcnt(df, eg_list, mnum, color_list, p_dict, ret_age, ds_dict=None, attr1=None, oper1='>=', val1=0, attr2=None, oper2='>=', val2=0, attr3=None, oper3='>=', val3=0, chart_style='darkgrid', size=20, alpha=0.8, suptitle_size=14, title_size=12, legend_size=12, xsize=10, ysize=8, image_dir=None, image_format='png')

scatter plot with age on x axis and list percentage on y axis. note: input df may be prefiltered to plot focus attributes, i.e. filter to include only employees at a certain job level, hired between certain dates, with a particular age range, etc.

inputs

df (string or dataframe)

text name of input proposal dataset, also will accept any dataframe variable (if a sliced dataframe subset is desired, for example) Example: input can be ‘proposal1’ (if that proposal exists, of course, or could be df[df.age > 50])

eg_list (list)

list of employee groups to include example: [1, 2]

mnum (int)

month number to study from dataset

color_list (list)

color codes for plotting each employee group

p_dict (dict)

dictionary, numerical eg code to string description

ret_age (integer or float)

chart xaxis limit for plotting

ds_dict (dict)

variable assigned to the output of the load_datasets function, reqired when string dictionary key is used as df input

attr(n) (string)

filter attribute or dataset column as string

oper(n) (string)

operator (i.e. <, >, ==, etc.) for attr(n) as string

val(n) (string, integer, float, date as string as appropriate)

attr(n) limiting value (combined with oper(n)) as string

chart_style (string)

any valid seaborn plotting style

size (integer)

size of scatter points

alpha (float)

scatter point alpha (0.0 to 1.0)

suptitle_size (integer or font)

text size of chart super title

title_size (integer or float)

text size of chart title

legend_size (integer or float)

text size of chart legend

xsize, ysize (integer or float)

plot size in inches

image_dir (string)

if not None, name of a directory in which to save an image of the chart output. If the directory does not exist, it will be created.

image_format (string)

file extension string for a saved chart image if the image_dir input is not None

Examples:

‘svg’, ‘png’

matplotlib_charting.build_subplotting_order(rows, cols)

build a list of integers to permit passing through subplots by columns note: only used when looping completes one vertical column before continuing to next column

inputs

rows, cols (integer)

number of rows and columns in multiple chart output

matplotlib_charting.cohort_differential(ds, base, sdict, cdict, adict, measure='ldate', compare_value='2010-12-31', mnum=None, ds_dict=None, single_eg_compare=None, sort_xax_by_measure=False, attr1=None, oper1='>=', val1=0, attr2=None, oper2='>=', val2=0, attr3=None, oper3='>=', val3=0, pos_color='g', neg_color='r', pos_alpha=0.25, neg_alpha=0.25, bg_color=None, zero_line_color='m', title_size=16, label_size=14, tick_size=12.5, legend_size=12.5, xsize=14, ysize=10, image_dir=None, image_format='png')

Compare proposed integrated list locations of employees from different groups who share a similar attribute value.

This function is best used with date-type attributes, such as longevity date or date of hire.

The comparative list locations are a continuous list of index locations determined by finding the last list position within an attribute column from another employee group which is less than or equal to a corresponding column from the base employee group. A variance or differential is calculated by comparing the base and comparative locations.

Attributes (measures) are sorted within each employee group prior to comparison. The x axis may be arranged to display proposed list ordering or the attribute value range (typically a date range).

Differences in list position are shown with a line above or below zero. One employee group (base) is compared to other group(s) in the proposed list within a selected month. When the line is above zero, it means that the base group cohort at a particular x axis position is on the list ahead of another group cohort by an amount equal to the y displacement of the line. The line colors correspond to the employee group color codes.

The default behavior is to compare the base group with all other groups at once, but single group comparison may be accomplished as well.

When the x axis is set to display list location (not attribute values), the user may designate a compare value. The list location of employees from each group who share the comparison attribute value will be marked on the chart with a color-coded vertical line.

inputs

ds (dataframe)

dataset for analysis

base (integer)

employee group number code

sdict (dictionary)

program settings dictionary

cdict (dictionary)

program color dictionary

adict (dictionary)

program attribute dictionary

measure (string)

attribute column for list location comparison, likely ‘ldate’ or ‘doh’

compare_value (type to match measure input dtype)

value to mark on chart if “sort_xax_by_measure” input is False. Likely a date string, such as “2001-01-31”

mnum (integer)

data model month number to study

ds_dict (dictionary)

dictionary of datasets, likely generated by the “load_datasets” function

single_eg_compare (integer)

if not None, compare base employee group to this group only

sort_xax_by_measure (boolean)

if True, use an x axis for the chart based on the selected measure. if False, use list location for the x axis

attr(n) (string)

filter attribute or dataset column as string

oper(n) (string)

operator (i.e. <, >, ==, etc.) for attr(n) as string

val(n) (integer, float, date as string, string (as appropriate))

attr(n) limiting value (combined with oper(n)) as string

pos_color, neg_color (color value string)

color used for the positive and negative area shading

pos_alpha, neg_alpha (integer or float)

transparency value assigned to the positive and negative color shading areas (0.0 to 1.0)

bg_color (color value string)

if not None, the color for the chart background

zero_line_color (color value string)

color for the zero line

title_size (integer or float)

text size for the chart title

label_size (integer or float)

text size for the chart axis labels

tick_size (integer or float)

text size for the chart tick labels

legend_size (integer or float)

text size for the chart legend

xsize, ysize (integer or float)

size of the chart in inches (width, height)

image_dir (string)

if not None, name of a directory in which to save an image of the chart output. If the directory does not exist, it will be created.

image_format (string)

file extension string for a saved chart image if the image_dir input is not None

Examples:

‘svg’, ‘png’

matplotlib_charting.cond_test(df, grp_sel, enhanced_jobs, job_colors, job_dict, basic_jobs=None, ds_dict=None, plot_all_jobs=False, min_mnum=None, max_mnum=None, limit_to_jobs=None, use_and=False, print_count_months=None, print_all_counts=False, plot_job_bands_chart=True, only_target_bands=False, legend_size=14, title_size=16, xsize=8, ysize=8, image_dir=None, image_format='png')

visualize selected job counts over time applicable to computed condition with optional printing of certain data.

Primary usage is validation of job assignment conditions by charting the count(s) of job(s) assigned by the program to particular employee groups over time.

The function may also be used to evaluate distribution of jobs with various proposals. Career progression of employees who enjoy special job rights may be understood particularily well by utilizing the print_all_counts option.

The output is 2 charts. The first chart is a line chart displaying selected job count information over time. The second is a stacked area chart displaying all job counts for the selected group(s) over time.

There are additional optional print outputs. The print_all_counts option will print a dataframe containing job count totals for each month. The print_count_months input is a list of months to print the only the plotted job counts, primarily for testing purposes.

inputs

df (dataframe)

dataset(dataframe) to examine

grp_sel (list)

integer input(s) representing the employee group code(s) to select for analysis. This argument also will accept the string ‘sg’ to select a special job rights group(s). Multiple inputs are normally handled as ‘or’ filters, meaning an input of [1, ‘sg’] would mean employee group 1 or any special job rights group, but can be modified to mean only group 1 and special job rights employees with the ‘use_and’ input.

enhanced_jobs (boolean)

if True, basic_jobs input job levels will be converted to enhanced job levels with reference to the job_dictionary input, otherwise basic_jobs input job levels will be used

job_colors (list)

list of color values to use for job plots

job_dict (dictionary)

dictionary containing basic to enhanced job level conversion data. This is likely the settings dictionary “jd” value.

basic_jobs (list)

basic job levels to plot. This list will be converted to the corresponding enhanced job list if the enhanced_jobs input is set to True. Defaults to [1] if not assigned.

ds_dict (dictionary)

dataset dictionary which allows df input to be a string description (proposal name)

plot_all_jobs (boolean)

option to plot all of the job counts within the input dataset vs only those selected with the basic_jobs input (or as converted to enhanced jobs if enhanced_jobs input is True). The jobs plotted may be filtered by the limit_to_jobs input.

min_mnum (integer)

integer input, only plot data including this month forward(mnum). Defaults to zero.

max_mnum (integer)

integer input, only plot data through selected month (mnum). Defaults to maximum mnum for input data

limit_to_jobs (list)

a list of jobs to plot, allowing focus on target jobs. Should be a subset of normal output, otherwise no filtering of normal output occurs

use_and (boolean)

when the grp_sel input has more than one element, require filtered dataframe for analysis to be part of all grp_sel input sets.

print_count_months (list)

list of month(s) for printing job counts

print_all_counts (boolean)

if True, print the entire job count dataframe.

plot_job_bands_chart (boolean)

if True, plot an area chart beneath the job count chart. The area chart will display all of the jobs available to the selected employee group(s) over time with job band areas

only_target_bands (boolean)

if True, plot area chart of jobs from job count chart only, vs the default of all job levels

legend_size (integer or float)

text size of legend labels

title_size (integer or float)

text size of chart title

xsize, ysize (integer or float)

size of chart display in inches (width and height)

image_dir (string)

if not None, name of a directory in which to save an image of the chart output. If the directory does not exist, it will be created.

image_format (string)

file extension string for a saved chart image if the image_dir input is not None

Examples:

‘svg’, ‘png’

matplotlib_charting.determine_dataset(ds_def, ds_dict=None, return_label=False)

this function permits either a dictionary key (string) or a dataframe variable to be used in functions as a dataframe object.

inputs

ds_def (dataframe or string)

A pandas dataframe or a string representing a key for a dictionary which contains dataframe(s) as values

ds_dict (dictionary)

A dictionary containing string to dataframes, used if ds_def input is not a dataframe

return_label (boolean)

If True, return a descriptive dataframe label if the ds_dict was referenced, otherwise return a generic “Proposal” string

matplotlib_charting.diff_range(df_list, dfb, measure, eg_list, attr_dict, ds_dict=None, cm_name='Set1', attr1=None, oper1='>=', val1=0, attr2=None, oper2='>=', val2=0, attr3=None, oper3='>=', val3=0, year_clip=2042, show_range=False, range_alpha=0.25, show_mean=True, normalize_y=False, suptitle_size=16, title_size=16, tick_size=13, label_size=16, legend_size=14, chart_style='whitegrid', ysize=6, xsize=11, image_dir=None, image_format='png')

Plot a range of differential attributes or a differential average over time. Individual employee groups and proposals may be selected. Each chart indicates the results for one group with color bands or average lines indicating the results for that group under different proposals. This is different than the usual method of different groups being plotted on the same chart.

inputs

df_list (list)

list of datasets to compare, may be ds_dict (output of load_datasets function) string keys or dataframe variable(s) or mixture of each

dfb (dataframe, can be proposal string name)

baseline dataset, accepts same input types as df_list above

measure (string)

differential data to compare

eg_list (list)

list of integers for employee groups to be included in analysis. example: [1, 2, 3] A chart will be produced for each employee group number.

eg_colors (list)

list of colors to represent different proposal results

attr_dict (dictionary)

dataset column name description dictionary

ds_dict (dictionary)

output from load_datasets function

attr(n) (string)

filter attribute or dataset column as string

oper(n) (string)

operator (i.e. <, >, ==, etc.) for attr(n) as string

val(n) (integer, float, date as string, string (as appropriate))

attr(n) limiting value (combined with oper(n)) as string

year_clip (integer)

only plot data up to and including this year

show_range (boolean)

show a transparent background on the chart representing the range of values for each measure for each proposal

range_alpha (float)

transparancy level for range plotting (0.0 to 1.0)

show_mean (boolean)

plot a line representing the average of the measure values for the group under each proposal

normalize_y (boolean)

if measure is ‘spcnt’ or ‘lspcnt’, equalize the range of the y scale on all charts (-.5 to .5)

suptitle_size (integer or font)

text size of chart super title

title_size (integer or font)

text size of chart title

tick_size (integer or font)

text size of chart tick labels

label_size (integer or font)

text size of chart x and y axis labels

legend_size (integer or font)

text size of the legend labels

chart_style (string)

any valid seaborn plotting style (string)

xsize, ysize (integer or font)

size of chart in inches (width and height)

image_dir (string)

if not None, name of a directory in which to save an image of the chart output. If the directory does not exist, it will be created.

image_format (string)

file extension string for a saved chart image if the image_dir input is not None

Examples:

‘svg’, ‘png’

matplotlib_charting.differential_scatter(df_list, dfb, measure, eg_list, attr_dict, color_dict, p_dict, ds_dict=None, attr1=None, oper1='>=', val1=0, attr2=None, oper2='>=', val2=0, attr3=None, oper3='>=', val3=0, prop_order=True, show_scatter=True, show_lin_reg=True, show_mean=True, mean_len=50, dot_size=15, lin_reg_order=15, ylimit=False, ylim=5, suptitle_size=14, title_size=12, legend_size=14, tick_size=11, label_size=12, bright_bg=False, bright_bg_color='#faf6eb', chart_style='whitegrid', xsize=12, ysize=8, image_dir=None, image_format='png')

plot an attribute differential between datasets.

datasets may be filtered by other attributes if desired.

Example: plot the difference in cat_order (job rank number) between all integrated datasets vs. standalone for all employee groups, applicable to month 57. (optionally add a pre-filter(s), such as all employees hired prior to a certain date)

The chart may be set to use proposal order or native list percentage for the x axis.

The scatter markers are selectable on/off, as well as an average line and a linear regression line.

inputs

df_list (list)

list of datasets to compare, may be ds_dict (output of load_datasets function) string keys or dataframe variable(s) or mixture of each

dfb (string or variable)

baseline dataset, accepts same input types as df_list above

measure (string)

attribute to analyze

eg_list (list)

list of employee group codes

attr_dict (dictionary)

dataset column name description dictionary

color_dict (dictionary)

dictionary containing color list string titles to lists of color values generated by the build_program_files script

p_dict (dictionary)

employee group code number to description dictionary

ds_dict (dictionary)

output from load_datasets function

attr(n) (string)

filter attribute or dataset column as string

oper(n) (string)

operator (i.e. <, >, ==, etc.) for attr(n) as string

val(n) (string, integer, float, date as string as appropriate)

attr(n) limiting value (combined with oper(n)) as string

eg_list (list)

a list of employee groups to analyze

prop_order (boolean)

if True, organize x axis by proposal list order, otherwise use native list percent

show_scatter (boolean)

if True, draw the scatter chart markers

show_lin_reg (boolean)

if True, draw linear regression lines

show_mean (boolean)

if True, draw average lines

mean_len (integer)

moving average length for average lines

dot_size (integer or float)

scatter marker size

lin_reg_order (integer)

regression line is actually a polynomial regression lin_reg_order is the degree of the fitting polynomial

ylimit (boolean)

if True, set chart y axis limit to ylim (below)

ylim (integer or float)

y axis limit positive and negative if ylimit is True

suptitle_size (integer or float)

text size of chart super title

title_size (integer or float)

text size of chart title

legend_size (integer or float)

text size of chart legend labels

tick_size (integer or float)

text size of x and y tick labels

label_size (integer or float)

text size of x and y descriptive labels

bright_bg (boolean)

use a custom color chart background

bright_bg_color (color value)

chart background color if bright_bg input is set to True

chart_style (string)

style for chart, valid inputs are any seaborn chart style

xsize, ysize (integer or float)

size of chart (width, height)

image_dir (string)

if not None, name of a directory in which to save an image of the chart output. If the directory does not exist, it will be created.

image_format (string)

file extension string for a saved chart image if the image_dir input is not None

Examples:

‘svg’, ‘png’

matplotlib_charting.display_proposals()

print out a list of the proposal names which were generated and stored in the dill folder by the build_program_files script

no inputs

matplotlib_charting.eg_attributes(ds, xmeasure, ymeasure, sdict, adict, cdict, eg_list=None, mnum=None, ret_only=False, ds_dict=None, attr1=None, oper1='>=', val1=0, attr2=None, oper2='>=', val2=0, attr3=None, oper3='>=', val3=0, q_eglist_only=True, xquant_lines=True, x_quantiles=10, xl_alpha=1, xl_ls='dashed', xl_lw=1, xl_color='.7', x_bands=True, xb_fc='.3', xb_alpha=0.09, yquant_lines=True, y_quantiles=10, yl_alpha=1, yl_ls='dashed', yl_lw=1, yl_color='.7', y_bands=True, yb_fc='#66ffb3', yb_alpha=0.09, linestyle='', linewidth=0, markersize=5, marker_alpha=0.7, grid_alpha=0.25, chart_style='ticks', full_xpcnt=True, full_ypcnt=True, xax_rotate=70, label_size=13, qtick_size=12, tick_size=12, border_size=0.5, legend_size=14, title_size=18, y_title_pos=1.12, box_height=0.95, xsize=15, ysize=11, image_dir=None, image_format='png')

Plot selected employee group(s) attribute data.

Chart x and y axes may be any dataset attributes, including date attributes.

Quantile membership for the x and/or y attribute may also be displayed. Membership may be relative to the entire integrated population or only to the employee group(s) selected for display (q_eglist_only input).

inputs

ds (dataframe)

dataset to examine, may be a dataframe variable or a string key from the ds_dict dictionary object

xmeasure (string)

attribute to plot on x axis

ymeasure (string)

attribute to plot on y axis

sdict (dictionary)

program settings dictionary

adict (dictionary)

dataset column name description dictionary

cdict (dictionary)

program colors dictionary

eg_list (list)

list of employee groups to plot (integer codes)

mnum (integer)

month number for analysis

ret_only (boolean)

if True, mnum input is ignored and results are displayed for all employees at retirement

ds_dict (dictionary)

output of the load_datasets function, dictionary. This keyword argument must be set if a string key is used as the df input.

attr(n) (string)

filter attribute or dataset column as string

oper(n) (string)

operator (i.e. <, >, ==, etc.) for attr(n) as string

val(n) (string, integer, float, date as string as appropriate)

attr(n) limiting value (combined with oper(n)) as string

q_eglist_only (boolean)

if set to True:

if quantile bands are displayed, show membership based on selected employee groups (eg_list input).

if set to False:

if quantile bands are displayed, show membership based on the integrated group population (all groups).

xquant_lines (boolean)

if True, show quantile membership for x axis attribute

x_quantiles (integer)

number of quantiles to display if xquant_lines input is True

xl_alpha (float)

transparency value of x axis quantile lines (0.0 to 1.0)

xl_ls (string)

x axis quantile lines linestyle (‘dashed’, ‘dotted’, etc.)

xl_lw (integer or float)

x axis quantile lines line width

xl_color (string color value)

x axis quantile lines color

x_bands (boolean)

if True, show a background color within every other x axis quantile membership area

xb_fc (string color value)

x axis quantile bands background color

xb_alpha (float)

x axis quantile bands color transparency value (0.0 to 1.0)

yquant_lines (boolean)

if True, show quantile membership for y axis attribute

y_quantiles (integer)

number of quantiles to display if yquant_lines input is True

yl_alpha (float)

transparency value of y axis quantile lines (0.0 to 1.0)

yl_ls (string)

y axis quantile lines linestyle (‘dashed’, ‘dotted’, etc.)

yl_lw (integer or float)

y axis quantile lines line width

yl_color (string color value)

y axis quantile lines color

y_bands (boolean)

if True, show a background color within every other y axis quantile membership area

yb_fc (string color value)

y axis quantile bands background color

yb_alpha (float)

y axis quantile bands color transparency value (0.0 to 1.0)

markersize (integer or float)

size of chart scatter points

marker_alpha (integer or float)

transparency setting for plot lines or points (0.0 to 1.0)

grid_alpha (float)

transparency value for the chart grid corresponding to the x and y attribute values (not the quantile membership lines)

chart_style (string)

any valid seaborn chart style name

full_xpcnt (boolean)

if True, show full range percentage (0 to 100 percent) when a percentage attribute is displayed on the x axis

full_ypcnt (boolean)

if True, show full range percentage (0 to 100 percent) when a percentage attribute is displayed on the y axis

xax_rotate (integer)

rotation value (in degrees) for the x axis tick labels

qtick_size (integer or float)

text size of the quantile membership tick labels

tick_size (integer or float)

text size of the x and y attribute tick labels

label_size (integer or float)

text size of x and y axis labels

border_size (integer or float)

width of the chart border line (chart spines)

legend_size (integer or float)

text size of chart legend

title_size (integer or float)

text size of chart title

y_title_pos (float)

vertical position of the chart title when attribute filtering has been applied. (typical values are 1.1 to 1.2)

box_height (float)

chart height multiplier which slightly shrinks vertical chart area for proper printing (saving) purposes. This input does not affect the displayed values.

xsize, ysize (integer or float)

plot size in inches

image_dir (string)

if not None, name of a directory in which to save an image of the chart output. If the directory does not exist, it will be created.

image_format (string)

file extension string for a saved chart image if the image_dir input is not None

Examples:

‘svg’, ‘png’

matplotlib_charting.eg_boxplot(df_list, eg_list, eg_colors, job_clip, attr_dict, measure='spcnt', ds_dict=None, attr1=None, oper1='>=', val1=0, attr2=None, oper2='>=', val2=0, attr3=None, oper3='>=', val3=0, year_clip=2035, exclude_fur=False, saturation=0.8, chart_style='dark', width=0.7, notch=True, show_whiskers=True, show_xgrid=True, show_ygrid=True, grid_alpha=0.4, grid_linestyle='solid', whisker=1.5, fliersize=1.0, linewidth=0.75, suptitle_size=14, title_size=12, tick_size=11, label_size=12, xsize=12, ysize=8, image_dir=None, image_format='png')

create a box plot chart displaying ACTUAL attribute values (vs. differential values) from a selected dataset(s) for selected employee group(s).

inputs

df_list (list)

list of datasets to compare, may be ds_dict (output of load_datasets function) string keys or dataframe variable(s) or mixture of each

eg_list (list)

list of integers for employee groups to be included in analysis example: [1, 2, 3]

measure (string)

attribute for analysis

eg_colors (list)

list of colors for plotting the employee groups

attr_dict (dictionary)

dataset column name description dictionary

ds_dict (dictionary)

output from load_datasets function

job_clip (float)

if measure is jnum or jobp, limit max y axis range to this value

attr(n) (string)

filter attribute or dataset column as string

oper(n) (string)

operator (i.e. <, >, ==, etc.) for attr(n) as string

val(n) (string, integer, float, date as string as appropriate)

attr(n) limiting value (combined with oper(n)) as string

year_clip (integer)

only present results through this year

exclude_fur (boolean)

remove all employees from analysis who are furloughed within the data model at any time (boolean)

chart_style (string)

chart styling (string), any valid seaborn chart style

width (float)

plotting width of boxplot or grouped boxplots for each year. a width of 1 leaves no gap between groups

notch (boolean)

If True, show boxplots with a notch at median point

show_xgrid (boolean)

include vertical grid lines on chart

show_ygrid (boolean)

include horizontal grid lines on chart

grid_alpha (float)

opacity value for grid lines

grid_linestyle (string)

examples: ‘solid’, ‘dotted’, ‘dashed’

suptitle_size (integer or float)

text size of chart super title

title_size (integer or float)

text size of chart title

tick_size (integer or float)

text size of x and y tick labels

label_size (integer or float)

text size of x and y descriptive labels

xsize, ysize (integer or float)

width and hieght of plot in inches

image_dir (string)

if not None, name of a directory in which to save an image of the chart output. If the directory does not exist, it will be created.

image_format (string)

file extension string for a saved chart image if the image_dir input is not None

Examples:

‘svg’, ‘png’

matplotlib_charting.eg_diff_boxplot(df_list, dfb, eg_list, eg_colors, job_levels, job_diff_clip, attr_dict, measure='spcnt', comparison='baseline', ds_dict=None, attr1=None, oper1='>=', val1=0, attr2=None, oper2='>=', val2=0, attr3=None, oper3='>=', val3=0, suptitle_size=14, title_size=12, tick_size=11, label_size=12, year_clip=None, exclude_fur=False, width=0.8, chart_style='dark', notch=True, linewidth=1.0, xsize=12, ysize=8, image_dir=None, image_format='png')

create a DIFFERENTIAL box plot chart comparing a selected measure from computed integrated dataset(s) vs. a baseline (likely standalone) dataset or with other integrated datasets.

inputs

df_list (list)

list of datasets to compare, may be ds_dict (output of load_datasets function) string keys or dataframe variable(s) or mixture of each

dfb (string or variable)

baseline dataset, accepts same input types as df_list above

eg_list (list)

list of integers for employee groups to be included in analysis example: [1, 2, 3]

eg_colors (list)

corresponding plot colors for eg_list input

job_levels (integer)

number of job levels in the data model (excluding furlough)

job_diff_clip (integer)

if measure is jnum or jobp, limit y axis range to +/- this value

attr_dict (dictionary)

dataset column name description dictionary

measure (string)

differential data to compare

comparison (string)

if ‘p2p’ (proposal to proposal), will compare proposals within the df_list to each other, otherwise will compare proposals to the baseline dataset (dfb)

ds_dict (dictionary)

output from load_datasets function

attr(n) (string)

filter attribute or dataset column as string

oper(n) (string)

operator (i.e. <, >, ==, etc.) for attr(n) as string

val(n) (string, integer, float, date as string as appropriate)

attr(n) limiting value (combined with oper(n)) as string

suptitle_size (integer or float)

text size of chart super title

title_size (integer or float)

text size of chart title

tick_size (integer or float)

text size of x and y tick labels

label_size (integer or float)

text size of x and y descriptive labels

year_clip (integer)

only present results through this year if not None

exclude_fur (boolean)

remove all employees from analysis who are furloughed within the data model at any time

use_eg_colors (boolean)

use case-specific employee group colors vs. default colors

width (float)

plotting width of boxplot or grouped boxplots for each year. a width of 1 leaves no gap between groups

chart_style (string)

chart styling (string), any valid seaborn chart style

notch (boolean)

If True, show boxplots with a notch at median point vs. only a line

xsize, ysize (integer or float)

plot size in inches

image_dir (string)

if not None, name of a directory in which to save an image of the chart output. If the directory does not exist, it will be created.

image_format (string)

file extension string for a saved chart image if the image_dir input is not None

Examples:

‘svg’, ‘png’

matplotlib_charting.eg_multiplot_with_cat_order(df, mnum, measure, xax, job_strs, job_level_colors, job_levels, settings_dict, attr_dict, color_dict, egs=[], ds_dict=None, fur_color=None, exclude_fur=False, plot_scatter=True, s=20, a=0.7, lw=0, job_bands_alpha=0.3, title_size=14, tick_size=12, label_pad=110, chart_style='whitegrid', remove_ax2_border=True, lgd_h_adj=None, xsize=13, ysize=10, image_dir=None, image_format='png')

plot any dataset attributes as x or y values for comparison

when “cat_order” is selected as measure, show job category bands

inputs

df (dataframe)

pandas dataframe input

mnum (integer)

month number for analysis

measure (string)

dataframe column name (attribute for analysis)

xax (string)

x axis attribute

job_strs (list)

list of job descriptions for labels (normally sdict[‘job_strs’])

job_level_colors (list)

list of colors for job level zones (normally cdict[‘job_colors’])

job_levels (integer)

number of job levels in model (sdict[‘num_of_job_levels’])

settings_dict (dictionary)

program job settings dictionary

attr_dict (dictionary)

program attribute name to attribute description dictionary

color_dict (dictionary)

color dictionary

egs (list)

list of employee groups for plotting

ds_dict (dictionary)

output from load_datasets function

fur_color (string color value)

if not None, color for furlough span color

exclude_fur (boolean)

if True, remove furloughed employees from input data

plot_scatter (boolean)

if True (default), plot a scatter chart, otherwise plot a line chart

s (integer or float)

size of scatter markers if a plot_scatter input is True

a (float)

transparency value for both line plots and scatter plots (0.0 to 1.0)

lw (integer or float)

width of maker edge lines with a scatter plot

job_bands_alpha (float)

transparency value for job level color spans

title_size (integer or float)

text size of chart title

tick_size (integer or float)

text size of chart tick labels

label_pad (integer)

minimum padding between job description labels that would otherwise overlap

chart_style (string)

any seaborn plotting style name

remove_ax2_border (boolean)

if True, remove axis 2 (ax2) chart spines

xsize, ysize (integer or float)

width and height of chart

lgd_h_adj (float)

set to a small float value (for example: .02, -.01) to adjust the horizontal position of the chart legend if required. Use negative values to move left, positive values to move right

image_dir (string)

if not None, name of a directory in which to save an image of the chart output. If the directory does not exist, it will be created.

image_format (string)

file extension string for a saved chart image if the image_dir input is not None

Examples:

‘svg’, ‘png’

matplotlib_charting.emp_quick_glance(empkey, df, ds_dict=None, title_size=14, tick_size=13, lw=4, chart_style='dark', xsize=8, ysize=48, image_dir=None, image_format='png')

view basic stats for selected employee and proposal

A separate chart is produced for each measure.

inputs

empkey (integer)

employee number (in data model)

df (dataframe)

dataset to study, will accept string proposal name

ds_dict (dictionary)

variable assigned to load_datasets function output

title_size (integer or float)

text size of chart title

tick_size (integer or font)

text size of chart tick labels

lw (integer or float)

line width of plot lines

chart_style (string)

any valid seaborn charting style

xsize, ysize (integer or float)

size of chart display

image_dir (string)

if not None, name of a directory in which to save an image of the chart output. If the directory does not exist, it will be created.

image_format (string)

file extension string for a saved chart image if the image_dir input is not None

Examples:

‘svg’, ‘png’

matplotlib_charting.filter_ds(ds, attr1=None, oper1=None, val1=None, attr2=None, oper2=None, val2=None, attr3=None, oper3=None, val3=None, return_title_string=True)

Filter a dataset (dataframe) by attribute(s).

Filter process is ignored if attr(n) input is None. All attr, oper, and val inputs must be strings. Up to 3 attribute filters may be combined.

Attr, oper, and val inputs are combined and then evaluated as expressions.

If return_title_string is set to True, returns tuple (ds, title_string), otherwise returns ds.

inputs

ds (dataframe)

the dataframe to filter

attr(n) (string)

an attribute (column) to filter. Example: ‘ldate’

oper(n) (string)

an operator to apply to the attr(n) input. Example: ‘<=’

val(n) (integer, float, date as string, string (as appropriate))

attr(n) limiting value (combined with oper(n)) as string

return_title_string (boolean)

If True, returns a string which dexcribes the filter(s) applied to the dataframe (ds)

matplotlib_charting.group_average_and_median(dfc, dfb, eg_list, eg_colors, measure, job_levels, settings_dict, attr_dict, ds_dict=None, attr1=None, oper1='>=', val1='0', attr2=None, oper2='>=', val2='0', attr3=None, oper3='>=', val3='0', plot_median=False, plot_average=True, compare_to_dfb=True, use_filtered_results=True, show_full_yscale=False, job_labels=True, max_date=None, chart_style='whitegrid', xsize=14, ysize=8, image_dir=None, image_format='png')

Plot group average and/or median for a selected attribute over time for compare and/or base datasets. Standalone data may be used as compare or baseline data.

Results may be further filtered/sliced by up to 3 constraints, such as age, longevity, or job level.

This function can plot basic data such as average list percentage or could, for example, plot the average job category rank for employees hired prior to a certain date who are over or under a certain age, for a selected integrated dataset and/or standalone data (or for two integrated datasets).

inputs

dfc (string or dataframe variable)

comparative dataset to examine, may be a dataframe variable or a string key from the ds_dict dictionary object

dfb (string or dataframe variable)

baseline dataset to plot (likely use standalone dataset here for comparison, but may plot and compare any dataset), may be a dataframe variable or a string key from the ds_dict dictionary object

eg_list (list)

list of integers representing the employee groups to analyze (i.e. [1, 2])

eg_colors (list)

list of colors for plotting the employee groups

measure (string)

attribute (column) to compare, such as ‘spcnt’ or ‘jobp’

job_levels (integer)

number of job levels in the data model

settings_dict (dictionary)

program settings dictionary generated by the build_program_files script

attr_dict (dictionary)

dataset column name description dictionary

ds_dict (dictionary)

dataset dictionary (variable assigned to the output of load_datasets function)

attr(n) (string)

filter attribute or dataset column as string

oper(n) (string)

operator (i.e. <, >, ==, etc.) for attr(n) as string

val(n) (integer, float, date as string, string (as appropriate))

attr(n) limiting value (combined with oper(n)) as string

plot_meadian (boolean)

plot the median of the measure for each employee group

plot_average (boolean)

plot the average(mean) of the measure for each employee group

compare_to_dfb (boolean)

plot average dfb[measure] data as dashed line. (likely show standalone data with dfb, or reverse and show standalone as primary and integrated as dfb) (dfb refers to baseline dataframe or dataset)

use_filtered_results (boolean)

if True, use the same employees from the filtered proposal list. For example, if the dfc list is filtered by age only, the dfb list could be filtered by the same age and return the same employees. However, if the dfc list is filtered by an attribute which diverges from the dfb measurements for the same attribute, a different set of employees could be returned. This option ensures that the same group of employees from both the dfc (filtered first) list and the dfb list are compared. (dfc refers to the comparison proposal, dfb refers to baseline)

show_full_yscale (boolean)

if measure input is one of these: ‘jnum’, ‘nbnf’, ‘jobp’, ‘fbff’, if True, show all job levels on chart. Otherwise, allow chart to autoscale with plotted data

job_labels (boolean)

if measure input is one of these: ‘jnum’, ‘nbnf’, ‘jobp’, ‘fbff’, use job text description labels vs. number labels on the y axis of the chart (boolean)

max_date (date string)

maximum chart date. If set to ‘None’, the maximum chart date will be the maximum date within the list data.

chart_style (string)

option to specify alternate seaborn chart style

xsize, ysize (integer or float)

x and y size of chart in inches

image_dir (string)

if not None, name of a directory in which to save an image of the chart output. If the directory does not exist, it will be created.

image_format (string)

file extension string for a saved chart image if the image_dir input is not None

Examples:

‘svg’, ‘png’

matplotlib_charting.job_count_bands(df_list, eg_list, job_colors, settings_dict, ds_dict=None, emp_list=None, attr1=None, oper1='>=', val1=0, attr2=None, oper2='>=', val2=0, attr3=None, oper3='>=', val3=0, fur_color=None, show_grid=True, max_date=None, plot_alpha=0.75, legend_alpha=0.9, legend_xadj=1.3, legend_yadj=1.0, legend_size=11, title_size=14, tick_size=12, label_size=13, chart_style='darkgrid', xsize=13, ysize=8, image_dir=None, image_format='png')

area chart representing count of jobs available over time

This chart displays the future job opportunities for each employee group with various list proposals.

This is not a comparative chart (for example, with standalone data), it is simply displaying job count outcome over time. However, the results for the employee groups may be compared and measured for equity.

Inputs

df_list (list)

list of datasets to compare, may be ds_dict (output of load_datasets function) string keys or dataframe variable(s) or mixture of each

eg_list (list)

list of integers for employee groups to be included in analysis example: [1, 2, 3]

job_colors (list)

list of colors to represent job levels

settings_dict (dictionary)

program settings dictionary generated by the build_program_files script

ds_dict (dictionary)

output from load_datasets function

emp_list (list)

optional list of employee number(s) to plot (empkey attribute)

attr(n) (string)

filter attribute or dataset column as string

oper(n) (string)

operator (i.e. <, >, ==, etc.) for attr(n) as string

val(n) (integer, float, date as string, string (as appropriate))

attr(n) limiting value (combined with oper(n)) as string

fur_color (color code in rgba, hex, or string style)

custom color to signify furloughed job level band (otherwise, last color in job_colors input will be used)

max_date (date string)

only include data up to this date example input: ‘1997-12-31’

plot_alpha (float, 0.0 to 1.0)

alpha value (opacity) for area plot (job level bands)

legend_alpha (float, 0.0 to 1.0)

alpha value (opacity) for legend markers

legend_xadj, legend_yadj (floats)

adjustment input for legend horizontal and vertical placement

legend_size (integer or float)

text size of legend labels

title_size (integer or float)

text size of chart title

tick_size (integer or float)

text size of x and y tick labels

label_size (integer or float)

text size of x and y descriptive labels

chart_style (string)

chart styling (string), any valid seaborn chart style

xsize, ysize (integer or float)

plot size in inches (width and height)

image_dir (string)

if not None, name of a directory in which to save an image of the chart output. If the directory does not exist, it will be created.

image_format (string)

file extension string for a saved chart image if the image_dir input is not None

Examples:

‘svg’, ‘png’

matplotlib_charting.job_count_charts(dfc, dfb, settings_dict, eg_colors, eg_list=None, ds_dict=None, attr1=None, oper1='>=', val1=0, attr2=None, oper2='>=', val2=0, attr3=None, oper3='>=', val3=0, plot_egs_sep=False, plot_total=True, xax='date', year_max=None, chart_style='darkgrid', base_ls='-', prop_ls=':', base_lw=1.6, prop_lw=2.5, suptitle_size=14, title_size=12, total_color='g', xsize=5, ysize=4, image_dir=None, image_format='png')

line-style charts displaying job category counts over time.

optionally display employee group results on separate charts or together

inputs

dfc (dataframe)

proposal (comparison) dataset to examine, may be a dataframe variable or a string key from the ds_dict dictionary object

dfb (dataframe)

baseline dataset; proposal dataset is compared to this dataset, may be a dataframe variable or a string key from the ds_dict dictionary object

settings_dict (dictionary)

program settings dictionary generated by the build_program_files script

eg_colors (list)

list of color values for plotting the employee groups, length is equal to the number of employee groups in the data model

eg_list (list)

list of employee group codes to plot Example: [1, 2]

ds_dict (dictionary)

variable assigned to load_datasets function output

attr(n) (string)

filter attribute or dataset column as string

oper(n) (string)

operator (i.e. <, >, ==, etc.) for attr(n) as string

val(n) (integer, float, date as string, string (as appropriate))

attr(n) limiting value (combined with oper(n)) as string

plot_egs_sep (boolean)

if True, plot each employee group job level counts separately

plot_total (boolean)

if True, include the combined job counts on chart(s)

xax (string)

x axis groupby attribute, options are ‘date’ or ‘mnum’, default is ‘date’

year_max (integer)

maximum year to include on chart Example: if input is 2030, chart would display data from beginning of data model through 2030 (integer)

base_ls (string)

line style for base job count line(s)

prop_ls (string)

line style for comparison (proposal) job count line(s)

base_lw (float)

line width for base job count line(s)

prop_lw (float)

line width for comparison (proposal) job count lines

suptitle_size (integer or float)

text size of chart super title

title_size (integer or float)

chart title(s) font size

total_color (color value)

color for combined job level count from all employee groups

xsize, ysize (integer or float)

size of chart display in inches (width and height)

image_dir (string)

if not None, name of a directory in which to save an image of the chart output. If the directory does not exist, it will be created.

image_format (string)

file extension string for a saved chart image if the image_dir input is not None

Examples:

‘svg’, ‘png’

matplotlib_charting.job_grouping_over_time(df, eg_list, jobs, job_colors, p_dict, plt_kind='bar', ds_dict=None, rets_only=True, attr1=None, oper1='>=', val1=0, attr2=None, oper2='>=', val2=0, attr3=None, oper3='>=', val3=0, time_group='A', display_yrs=40, legend_loc=4, chart_style='darkgrid', suptitle_size=14, title_size=12, legend_size=13, tick_size=11, label_size=13, xsize=12, ysize=10, image_dir=None, image_format='png')

Inverted bar chart display of job counts by group over time. Various filters may be applied to study slices of the datasets.

The ‘rets_only’ option will display the count of employees retiring from each year grouped by job level.

developer TODO: fix x axis scaling and labeling when quarterly (“Q”) or monthly (“M”) time group option selected.

inputs

df (dataframe)

dataset to examine, may be a dataframe variable or a string key from the ds_dict dictionary object

eg_list (list)

list of unique employee group numbers within the proposal Example: [1, 2]

jobs (list)

list of job label strings (for plot legend)

job_colors (list)

list of colors to be used for plotting

p_dict (dictionary)

employee group to string description dictionary

plt_kind (string)

‘bar’ or ‘area’ (bar recommended)

ds_dict (dictionary)

output from load_datasets function

rets_only (boolean)

calculate for employees at retirement age only

attr(n) (string)

filter attribute or dataset column as string

oper(n) (string)

operator (i.e. <, >, ==, etc.) for attr(n) as string

val(n) (string, integer, float, date as string as appropriate)

attr(n) limiting value (combined with oper(n)) as string

time_group (string)

group counts/percentages by year (‘A’), quarter (‘Q’), or month (‘M’)

display_years (integer)

when using the bar chart type display, evenly scale the x axis to include the number of years selected for all group charts

legend_loc (integer)

matplotlib legend location number code

2	9	1
6	10	7
3	8	4

suptitle_size (integer or float)

text size of chart super title

title_size (integer or float)

text size of chart title

legend_size (integer or float)

text size of chart legend labels

tick_size (integer or float)

text size of x and y tick labels

label_size (integer or float)

text size of x and y descriptive labels

xsize, ysize (integer or float)

size of each chart in inches (width, height)

image_dir (string)

if not None, name of a directory in which to save an image of the chart output. If the directory does not exist, it will be created.

image_format (string)

file extension string for a saved chart image if the image_dir input is not None

Examples:

‘svg’, ‘png’

matplotlib_charting.job_level_progression(df, emp_list, through_date, settings_dict, color_dict, eg_colors, band_colors, ds_dict=None, rank_metric='cat_order', chart_style='white', show_implementation_date=True, job_bands_alpha=0.1, max_plots_for_legend=5, xgrid_alpha=0.65, xgrid_linestyle='dotted', ygrid_alpha=0.5, ygrid_linestyle='dotted', tick_size=13, job_descr_size=12.5, job_descr_pad=115, label_size=15, title_size=18, xsize=12, ysize=10, image_dir=None, image_format='png')

show employee(s) career progression through job levels regardless of actual positioning within integrated seniority list.

This x axis of this chart represents rank within job category. There is an underlying stacked area chart representing job level bands, adjusted to reflect job count changes over time.

This chart reveals actual career path considering no bump no flush, special job assignment rights/restrictions, and furlough/recall events.

Actual jobs held may not be correlated to jobs normally associated with a certain list percentage for many years due to job assignment factors.

inputs

df (dataframe)

dataset to examine, may be a dataframe variable or a string key from the ds_dict dictionary object

emp_list (list)

list of empkeys to plot

through_date (date string)

string representation of y axis date limit, ex. ‘2025-12-31’

settings_dict (dictionary)

program settings dictionary generated by the build_program_files script

color_dict (dictionary)

dictionary containing color list string titles to lists of color values generated by the build_program_files script

eg_colors (list)

colors to be used for employee line plots corresponding to employee group membership

band_colors (list)

list of colors to be used for stacked area chart which represent job level bands

ds_dict (dictionary)

output from load_datasets function

rank_metric (string)

column name for y axis chart ranking. Currently only ‘cat_order’ is valid.

chart_style (string)

any valid seaborn plotting chart style name

show_implementation_date (boolean)

plot a vertical dashed line at the implementation date

job_bands_alpha (float)

opacity level of background job bands stacked area chart

max_plots_for_legend (integer)

if number of plots more than this number, reduce plot linewidth and remove legend

xgrid_alpha, ygrid_alpha (float)

transparency value for grid. x and y axis may be set independently

xgrid_linestyle, ygrid_linestyle (string)

matplotlib line style for grid, such as “dotted” or “dashed”. x and y axis may be set independently

job_descr_size (integer or float)

font size of job description text labels on right side of chart

job_descr_pad (integer)

padding to add between job description labels when they would otherwise overlap

tick_size (intger or float)

font size of tick labels

job_descr_size (integer or float)

font size of job description labels

label_size (integer or float)

font size of axis labels

title_size (integer or label)

font size of title

xsize, ysize (integer or float)

plot size in inches (width, height)

image_dir (string)

if not None, name of a directory in which to save an image of the chart output. If the directory does not exist, it will be created.

image_format (string)

file extension string for a saved chart image if the image_dir input is not None

Examples:

‘svg’, ‘png’

matplotlib_charting.job_time_change(ds_list, ds_base, eg_list, job_colors, job_strs_dict, job_levels, attr_dict, xax, ds_dict=None, attr1=None, oper1='>=', val1=0, attr2=None, oper2='>=', val2=0, attr3=None, oper3='>=', val3=0, marker='o', edgecolor='k', linewidth=0.05, size=25, alpha=0.95, bg_color='#ffffff', x_max=1.02, limit_yax=False, ylimit=40, zeroline_color='m', zeroline_width=1.5, pos_neg_face=True, pos_neg_face_alpha=0.03, legend_job_strings=True, legend_position=1.18, legend_marker_size=130, suptitle_size=16, title_size=14, tick_size=12, chart_style='whitegrid', label_size=13, xsize=12, ysize=10, image_dir=None, image_format='png', experimental=False)

Plots a scatter plot displaying monthly time in job differential, by proposal and employee group. X axis percentage reflects first month within each comparative dataset, which will be the same as standalone for all groups unless the data model implementation date occurs at month zero.

inputs

ds_list (list)

list of datasets to compare, may be ds_dict (output of load_datasets function) string keys or dataframe variable(s) or mixture of each

ds_base (string or variable)

baseline dataset, accepts same input types as ds_list above

eg_list (list)

list of integers for employee groups to be included in analysis example: [1, 2, 3]

job_levels (integer)

number of job levels in the data model

job_colors (list)

list of color values for job level plotting

job_strs_dict (dictionary)

dictionary of job code (integer) to job description label

attr_dict (dictionary)

dataset column name description dictionary

xax (string)

list percentage attrubute, i.e. spcnt or lspcnt

ds_dict (dictionary)

output from load_datasets function

attr(n) (string)

filter attribute or dataset column as string

oper(n) (string)

operator (i.e. <, >, ==, etc.) for attr(n) as string

val(n) (integer, float, date as string, string (as appropriate))

attr(n) limiting value (combined with oper(n)) as string

job_colors (list)

list of color values for the job level plotting

job_strs_dict (dictionary)

job number to job label dictionary

marker (string)

scatter chart matplotlib marker type

edgecolor (color value)

matplotlib marker edge color

linewidth (integer or float)

matplotlib marker edge line size

size (integer or float)

size of markers

alpha (float)

marker alpha (transparency) value

bg_color (color value)

background color of chart if not None

x_max (integer or float)

high limit of chart x axis

limit_yax (integer or float)

if True, restrict plot y scale to this value may be used to prevent outliers from exagerating chart scaling

ylimit (integer or float)

y axis limit if limit_yax is True

zeroline_color (color value)

color for zeroline on chart

zeroline_width (integer or float)

width of zeroline

pos_neg_face (boolean)

if True, apply a light green tint to the chart area above the zero line, and a light red tint below the line

legend_job_strings (boolean)

if True, use job description strings in legend vs. job numbers

legend_position (float)

controls the horizontal position of the legend

legend_marker_size (integer or float)

adjusts the size of the legend markers

suptitle_size (integer or float)

text size of chart super title

title_size (integer or float)

text size of chart title

tick_size (integer or float)

text size of chart tick labels

xsize, ysize (integer or float)

x and y size of each plot in inches

image_dir (string)

if not None, name of a directory in which to save an image of the chart output. If the directory does not exist, it will be created.

image_format (string)

file extension string for a saved chart image if the image_dir input is not None

Examples:

‘svg’, ‘png’

experimental (boolean)

show additional output under development consisting of a table, heatmap, and bar chart

matplotlib_charting.job_transfer(dfc, dfb, eg, job_colors, job_levels, job_strs, p_dict, ds_dict=None, gb_period='M', min_date=None, max_date=None, tgt_jobs_list=None, job_alpha=0.85, chart_style='whitegrid', fur_color=None, draw_face_color=False, draw_grid=True, grid_alpha=0.2, zero_line_color='m', ytick_interval=None, y_limit=None, title_size=14, legend_size=12, xsize=14, ysize=9, image_dir=None, image_format='png')

plot a differential stacked area chart displaying color-coded job transfer counts over time.

Output chart is actually 2 area charts (one for positive values and one for negative values) displayed on a shared axis.

inputs

dfc (dataframe)

proposal (comparison) dataset to examine, may be a dataframe variable or a string key from the ds_dict dictionary object

dfb (dataframe)

baseline dataset; proposal dataset is compared to this dataset, may be a dataframe variable or a string key from the ds_dict dictionary object

eg (integer)

integer code for employee group

job_colors (list)

list of colors for job levels, may be value from color dictionary

job_levels (integer)

number of job levels in data model

job_strs (list)

list of job descriptions (labels)

p_dict (dictionary)

dictionary of employee number codes to verbose string description, (normally “p_dict_verbose” from the settings dictionary)

Example:

{0: 'Standalone', 1: 'Acme', 2: 'Southern'}

ds_dict (dictionary)

output from load_datasets function

gb_period (string)

group_by period. default is ‘M’ for monthly, other options are ‘Q’ for quarterly and ‘A’ for annual

min_date (string date format)

if set, analyze job transfer data from this date forward

max_date (string date format)

if set, analyze job transfer data up to this date

tgt_jobs_list (list)

if not None, only plot job level(s) in this list

job_alpha (float)

chart alpha level for job transfer plotting (0.0 - 1.0)

chart_style (string)

seaborn plotting library style

fur_color (color code in rgba, hex, or string style)

custom color to signify furloughed employees (otherwise, last color in job_colors input will be used)

draw_face_color (boolean)

apply a transparent background to the chart, red below zero and green above zero

draw_grid (boolean)

show major tick label grid lines

grid_alpha (float)

opacity setting for grid lines (0.0 - 1.0)

zero_line_color (color value)

color of the horizontal line a zero

ytick_interval (integer)

optional manual ytick spacing setting (function has auto-spacing built in)

y_limit (integer)

optional manual y axis chart limit (enter positive value only). This input may be used to “lock” vertical scaling (shut off auto_scaling) for comparing gains and losses between proposals and employee groups.

title_size (integer or float)

chart title text size

legend_size (integer or float)

chart legend text size

xsize (integer or float)

horizontal size of chart

ysize (integer or float)

vertical size of chart

image_dir (string)

if not None, name of a directory in which to save an image of the chart output. If the directory does not exist, it will be created.

image_format (string)

file extension string for a saved chart image if the image_dir input is not None

Examples:

‘svg’, ‘png’

matplotlib_charting.make_color_list(num_of_colors=10, start=0.0, stop=1.0, exclude=None, reverse=False, cm_name_list=['Set1'], return_list=True, return_dict=False, print_all_names=False, palplot_cm_name=False, palplot_all=False)

Utility function to generate list(s) of colors (rgba format), any length and any from any section of any matplotlib colormap.

The function can return a list of colors, a dictionary of colormaps to color lists, plot result(s) as seaborn palplot(s), and print out the names of all of the colormaps available.

The end goal of this function is to provide customized color lists for plotting.

inputs

num_of_colors (integer)

number of colors to produce for the output color list(s), used within the cm_subsection data calculation

start (float)

the starting point within the selected colormap to begin the spectrum color selection (0.0 to 1.0), used within the cm_subsection data calculation

stop (float)

the ending point within the selected colormap to end the spectrum color selection (0.0 to 1.0), used within the cm_subsection data calculation

exclude (list)

list of 2 floats representing a section of the colormap(s) to remove before calculating the result list(s).

reverse (boolean)

reverse the color list order which reverses the color spectrum

cm_name_list (list)

any matplotlib colormap name(s)

return_list (boolean)

if True, return a list of rgba color codes for the cm_name_list colormap input only, or (if the return_dict input is set to True) a dictionary of all colormap names to all of the resultant corresponding calculated color lists using the cm_subsection data

return_dict (boolean)

if True (and return_list is True), return a dictionary of all colormap names to all of the resultant corresponding calculated color lists

print_all_names (boolean)

if True (and return_list is False), print all the names of available matplotlib colormaps

palplot_cm_name (boolean)

if True (and return_list is set to False), plot a seaborn palplot of the color list produced with the cm_name_list colormap input using the cm_subsection data

palplot_all (boolean)

if True (and return_list and palplot_cm_name are False), plot a seaborn palplot for all of the color lists produced from all available matplotlib colormaps using the cm_subsection data

matplotlib_charting.mark_quantiles(df, quantiles=10)

add a column to the input dataframe identifying quantile membership as integers (the column is named “quantile”). The quantile membership (category) is calculated for each employee group separately, based on the employee population in month zero.

The output dataframe permits attributes for employees within month zero quantile categories to be be analyzed throughout all the months of the data model.

The number of quantiles to create within each employee group is selected by the “quantiles” input.

The function utilizes numpy arrays and functions to compute the quantile assignments, and pandas index data alignment feature to assign month zero quantile membership to the long-form, multi-month output dataframe.

This function is used within the quantile_groupby function.

inputs

df (dataframe)

Any pandas dataframe containing an “eg” (employee group) column

quantiles (integer)

The number of quantiles to create.

example:

If the input is 10, the output dataframe will be a column of integers 1 - 10. The count of each integer will be the same. The first quantile members will be marked with a 1, the second with 2, etc., through to the last quantile, 10.

matplotlib_charting.multiline_plot_by_emp(df, measure, xax, emp_list, job_levels, ret_age, color_list, job_str_list, sdict, attr_dict, ds_dict=None, plot_jobp=False, show_implementation_date=True, through_date=None, pcnt_ylimit=1.0, chart_style='ticks', linewidth=3, line_alpha=0.7, grid_linestyle='dotted', grid_alpha=0.75, legend_size=14, label_size=13, tick_size=13, title_size=18, xsize=12, ysize=9, image_dir=None, image_format='png')

select example individual employees and plot career measure from selected dataset attribute, i.e. list percentage, career earnings, job level, etc.

inputs

df (dataframe)

dataset to examine, may be a dataframe or a string key with the ds_dict dictionary object

measure (string)

dataset attribute to plot. Usually only one attribute to plot, but may be more than one, such as ‘jnum’ and ‘jobp’

xax (string)

dataset attribute for x axis

emp_list (list)

list of employee numbers or ids

job_levels (integer)

number of job levels in model

ret_age (float)

retirement age (example: 65.0)

color list (list)

list of colors for plotting

job_str_list (list)

list of string job descriptions corresponding to number of job levels

sdict (dictionary)

program settings dictionary

attr_dict (dictionary)

dataset column name description dictionary

ds_dict (dictionary)

output of the load_datasets function, dictionary. This keyword argument must be set if a string key is used as the df input.

plot_jobp (boolean)

if measure input is ‘jnum’, also plot ‘jobp’ if set to True

show_implementation_date (boolean)

if True and “xax” input is “date”, plot a vertical line at the implementation date

chart_style (string)

any seaborn plotting style name

linewidth (integer or float)

width of chart solid lines

line_alpha (float)

transparency value of the plotted lines (0.0 to 1.0)

grid_linestyle (string)

matplotlib line style for grid, such as “dotted” or “solid”

grid_alpha

transparency value for grid (0.0 to 1.0)

legend_size (integer or float)

text size of chart legend

label_size (integer or float)

font size of x and y axis labels

tick_size (integer or float)

font size of chart tick labels

title_size (integer or float)

font size of chart title

xsize, ysize (integer or float)

plot size in inches

image_dir (string)

if not None, name of a directory in which to save an image of the chart output. If the directory does not exist, it will be created.

image_format (string)

file extension string for a saved chart image if the image_dir input is not None

Examples:

‘svg’, ‘png’

matplotlib_charting.numeric_test(value)

determine if a variable is numeric

returns a boolean value

input

value

any variable

matplotlib_charting.parallel(df_list, dfb, eg_list, measure, month_list, job_levels, eg_colors, dict_settings, attr_dict, ds_dict=None, attr1=None, oper1='>=', val1=0, attr2=None, oper2='>=', val2=0, attr3=None, oper3='>=', val3=0, left=0, stride_list=None, chart_style='whitegrid', grid_color='.7', suptitle_size=14, title_size=12, facecolor='w', xsize=6, ysize=8, image_dir=None, image_format='png')

Compare positional or value differences for various proposals with a baseline position or value for selected months.

The vertical lines represent different proposed lists, in the order from the df_list list input.

inputs

df_list (list)

list of datasets to compare, may be ds_dict (output of load datasets function) string keys or dataframe variable(s) or mixture of each

dfb (string or variable)

baseline dataset, accepts same input types as df_list above. The order of the list is reflected in the chart x axis lables

eg_list (list)

list of employee group integer codes to compare example: [1, 2]

measure (string)

dataset attribute to compare

month_list (list)

list of month numbers for analysis. the function will plot comparative data from each month listed

job_levels (integer)

number of job levels in data model

eg_colors (list)

list of colors to represent the employee groups

dict_settings (dictionary)

program settings dictionary generated by the build_program_files script

attr_dict (dictionary)

dataset column name description dictionary

ds_dict (dictionary)

output from load_datasets function

attr(n) (string)

filter attribute or dataset column as string

oper(n) (string)

operator (i.e. <, >, ==, etc.) for attr(n) as string

val(n) (string, integer, float, date as string as appropriate)

attr(n) limiting value (combined with oper(n)) as string

left (integer)

integer representing the list comparison to plot on left side of the chart(s). zero (0) represents the standalone results and is the default. 1, 2, or 3 etc. represent the first, second, third, etc. dataset results in df_list input order

stride_list (list)

optional list of dataframe strides for plotting every other nth result (must be same length and correspond to eg_list)

grid_color (string)

string name for horizontal grid color

facecolor (color value)

chart background color

xsize, ysize (integer or float)

size of individual subplots (width, height)

image_dir (string)

if not None, name of a directory in which to save an image of the chart output. If the directory does not exist, it will be created.

image_format (string)

file extension string for a saved chart image if the image_dir input is not None

Examples:

‘svg’, ‘png’

matplotlib_charting.pct_format()

Apply “to_percent” custom format for chart tick labels

matplotlib_charting.percent_bins(eg, base, compare, measure='spcnt', by_year=True, quantiles=20, time_col='date', agg_method='median')

Return a tuple of two dataframes containing differential percentage bin counts, one containing positive counts and another containing negative counts.

This function first compares list percentage between two datasets on a grouped time period basis (annual or monthly), then counts the number of employees within specified percentage gain or loss quantiles.

The counts are returned in dataframes with indexes reflecting the quantiles and columns representing the grouped time period.

This function is used in the percent_diff_bins plotting function.

inputs

eg (integer)

employee group code

base (dataframe)

baseline dataframe (dataset) containing a list percentage column

compare (dataframe)

comparison dataframe (dataset) containing a list percentage column

measure (string)

dataset percentage attribute column (‘spcnt’ or ‘lspcnt’)

by_year (boolean)

if True, group employee percentage differentials by year, otherwise by time_col input

quantiles (integer)

number of quantiles to measure. An input of 20 would translate to quantiles of 5% each (100 / 20).

time_col (string)

if by_year is False, group percentage differentials by this time unit. Inputs may be “mnum” or “date”.

agg_method (string)

quantile bin aggregation method. Inputs may be “mean” or “median”

matplotlib_charting.percent_diff_bins(compare, base, eg, measure='spcnt', kind='bar', quantiles=40, num_display_colors=25, area_xax='date', ds_dict=None, attr1=None, oper1='>=', val1=0, attr2=None, oper2='>=', val2=0, attr3=None, oper3='>=', val3=0, man_plotlim=None, invert_barh=False, chart_style='ticks', cmap_pos='tab20c', cmap_neg='tab20c', zero_line_color='m', bright_bg=False, bg_color='#ffffe6', title_size=14, legend_size=12.5, xsize=16, ysize=10, image_dir=None, image_format='png')

Display employee group counts within differential list percentage bins over time.

Chart style options include bar, barh, and area.

Selectable inputs include the number of percentile bins, chart colors and the number of colors in the color cycle representing the bins.

The analysis groups may be targeted by up to three attribute value filters.

inputs

compare (dataframe)

comparison dataframe (dateset)

base (dataframe)

baseline dataframe (dataset)

eg (integer)

employee group code

measure (string)

list percentage attribute for comparison (‘spcnt’ or ‘lspcnt’)

kind (string)

chart style (‘bar’, ‘barh’, or ‘area’)

quantiles (integer)

the number of differential percentage bins. If the input is 40, each bin width will be 2.5% (100 / 40)

num_display_colors (integer)

the number of distinct colors to create from the cmap inputs. If the input is less than the number of bins found for display, the colors display will cycle or repeat as necessary.

area_xax (string)

attribute to use for the chart when the kind input is set to ‘area’. Inputs may be ‘mnum’ or ‘date’.

ds_dict (dictionary)

variable assigned to the output of the load_datasets function. This keyword variable must be set if string dictionary keys are used as inputs for the dfc and/or dfb inputs.

attr(n) (string)

filter attribute or dataset column as string

oper(n) (string)

operator (i.e. <, >, ==, etc.) for attr(n) as string

val(n) (integer, float, date as string, string (as appropriate))

attr(n) limiting value (combined with oper(n)) as string

man_plotlim (integer)

if not None, restrict chart differential axis to this value. Otherwise, limit is set by an algorithm.

invert_barh (boolean)

If ‘kind’ input is set to ‘barh’, if True, invert the chart y axis

chart_style (string)

any valid seaborn plotting style name

cmap_pos (string)

any matplotlib colormap name representing colors to be applied to positive chart values

cmap_neg (string)

any matplotlib colormap name representing colors to be applied to negative chart values

zero_line_color (color value)

color to be applied to the chart zero line

bright_bg (boolean)

if True, color the chart background with the ‘bg_color’ color value

bg_color (color value)

color to use for the chart background if ‘bright_bg’ is True

title_size (integer or float)

text size for the chart title

legend_size (integer or float)

text size for the chart legend

xsize, ysize (integers or floats)

Width and height of chart in inches

image_dir (string)

if not None, name of a directory in which to save an image of the chart output. If the directory does not exist, it will be created.

image_format (string)

file extension string for a saved chart image if the image_dir input is not None

Examples:

‘svg’, ‘png’

matplotlib_charting.pprint_dict(dct, marker1='#', marker2='', skip_line=True)

print the key-value pairs in a horizontal, organized fashion.

inputs

dct (dictionary)

the dictionary to print

marker1, marker2

prefix and suffix for the dictionary key headers

matplotlib_charting.quantile_bands_over_time(df, eg, measure, bins=20, ds_dict=None, year_clip=None, kind='area', quantile_ticks=False, cm_name='tab20c', chart_style='ticks', quantile_alpha=0.75, grid_alpha=0.4, custom_start=0.0, custom_finish=1.0, alt_bg_color=False, bg_color='#faf6eb', legend_size=13, label_size=13, xsize=14, ysize=8, image_dir=None, image_format='png')

Visualize quantile distribution for an employee group over time for a selected proposal.

This chart answers the question of where the different employee groups will be positioned within the seniority list for future months and years.

Note: this is not a comparative study. It is simply a presentation of resultant percentage positioning.

The chart contains a background grid for reference and may display quantiles as integers or percentages, using a bar or area type display, and includes several chart color options.

inputs

df (dataframe)

dataset to examine, may be a dataframe variable or a string key from the ds_dict dictionary object

eg (integer)

employee group number

measure (string)

a list percentage input, either ‘spcnt’ or ‘lspcnt’

bins (integer)

number of quantiles to calculate and display

ds_dict (dictionary)

output from load_datasets function

year_clip (integer)

maximum year to display on chart (requires ‘clip’ input to be True)

kind (string)

type of chart display, either ‘area’ or ‘bar’

quantile_ticks (boolean)

if True, display integers along y axis and in legend representing quantiles. Otherwise, present percentages.

cm_name (string)

colormap name (string), example: ‘Set1’

chart_style (string)

style for chart output, any valid seaborn plotting style name

quantile_alpha (float)

alpha (opacity setting) value for quantile plot

grid_alpha (float)

opacity setting for background grid

custom_start (float)

custom colormap start level (a section of a standard colormap may be used to create a custom color mapping)

custom_finish (float)

custom colormap finish level

alt_bg_color (boolean)

if True, set the background chart color to the bg_color input value

bg_color (color value)

color for chart background if ‘alt_bg_color’ is True (string)

legend_size (integer or float)

text size for chart legend

label_size (intger or float)

text size for chart x and y axis labels

xsize, ysize (integer or float)

chart size inputs in inches (width, height)

image_dir (string)

if not None, name of a directory in which to save an image of the chart output. If the directory does not exist, it will be created.

image_format (string)

file extension string for a saved chart image if the image_dir input is not None

Examples:

‘svg’, ‘png’

matplotlib_charting.quantile_groupby(dataset_list, eg_list, measure, quantiles, eg_colors, band_colors, settings_dict, attr_dict, job_dict, groupby_method='median', xax='date', ds_dict=None, num_cat_order_yticks=10, through_date=None, verbose_title=True, plot_total=True, show_job_bands=True, show_grid=True, plot_implementation_date=True, draw_reserve_levels=False, custom_color=False, cm_name='Set1', start=0.0, stop=1.0, exclude=None, reverse=False, chart_style='whitegrid', remove_ax2_border=True, line_width=1, use_dashed_line_compare=True, bg_color='.98', job_bands_alpha=0.15, line_alpha=0.7, grid_alpha=0.3, title_size=14, tick_size=12, label_size=13, label_pad=110, xsize=12, ysize=10, image_dir=None, image_format='png')

Plot representative values of a selected attribute measure for each employee group quantile over time.

Multiple employee groups may be plotted at the same time. Job bands may be plotted as a chart background to display job level progression when the measure input is set to “cat_order”.

Two data models may be plotted and compared on the same chart. Only the first employee group found within the eg_list input will be compared when plotting more than one dataset.

Example use case: plot the average job category rank of each employee quantile group, from the start date though the life of the data model.

The quantile group attribute may be analyzed with any of the following methods:

[mean, median, first, last, min, max]

If the eg_list input list contains a single employee group code and the custom_color input is set to “True”, the color of the plotted quantile result lines will be a spectrum of colors. The following inputs are related to the custom color generation:

[cm_name, start, stop, exclude, reverse]

The above inputs will be used by the make_color_list function located within this module to produce a list of colors with a length equal to the quantiles input. (Please see the docstring for the make_color_list function for further explaination). If the quantiles input is set to a relatively high value (100-200), the impact on the career profiles of the employee groups is easily discernible when using a qualitative color map.

inputs

dataset_list (dataframes)

A list of long-form dataframes, each of which contains “date” (and “mnum” if xax input is set to “mnum”) and “eg” columns and at least one attribute column for analysis. The normal input is a list of calculated datasets with many attribute columns. The list may only hold one or two datasets.

eg_list (list)

List of eg (employee group) codes for analysis. The order of the employee codes will determine the z-order of the plotted lines, last employee group plotted on top of the others.

measure (string)

Attribute column name

quantiles (integer)

The number of quantiles to create and plot for each employee group in the eg_list input.

eg_colors (list)

list of color values for plotting the employee groups

band_colors (list)

list of color values for plotting the background job level color bands when the using a measure of ‘cat_order’ with the ‘show_job_bands’ variable set to True

settings_dict (dictionary)

program settings dictionary generated by the build_program_files script

attr_dict (dictionary)

dataset column name description dictionary

job_dict (dictionary)

dictionary containing basic to enhanced job level conversion data. This is likely the settings dictionary “jd” value.

groupby_method (string)

The method applied to the attribute data within each quantile. The allowable methods are listed in the description above. Default is ‘median’.

xax (string)

The first groupby level and x axis value for the analysis. This value defaults to “date” which represents each month of the model. Alternatively, “mnum” may be used.

ds_dict (dictionary)

A dictionary containing string to dataframes, used if df input is not a dataframe but a string key (examples: ‘standalone’, ‘p1’)

num_cat_order_yticks (int)

approiximate number of y axis ticks to display on the lefthand side of the chart when “cat_order” is selected as the “measure” input. The actual number of ticks displayed will be adjusted to match an optimal numerical interval between tick values. This input does not have a linear relationship with the output and may require a significant input change to affect the chart display.

through_date (date string)

If set as a date string, such as ‘2020-12-31’, only show results up to and including this date.

verbose_title (boolean)

If True, chart title will use the long descriptions for each employee group from the settings.xlsx input file, proposal_dictionary worksheet. Otherwise, the eg number codes will be used in the title

plot_total (boolean)

If True, plot a dotted gray line representing the total count of active pilots over time (only when “measure” input is set to “cat_order” and “show_job_bands” input is True)

show_job_bands

If measure is set to “cat_order”, plot properly scaled job level color bands on chart background

show_grid (boolean)

If True, plot a grid on the chart

plot_implementation_date

If True and the xax argument is set to “date”, plot a dashed vertical line at the implementation date.

draw_reserve_levels (boolean)

If True and basic job levels have been selected via the settings.xlsx “scalars” worksheet, “enhanced jobs” setting, draw a horizontal red dashed line within each basic job category level representing the upper limit of reserve status

custom_color (boolean)

If set to True, will permit a custom color spectrum to be produced for plotting a single employee group “cat_order” result (color map is selected with the cm_name input)

cm_name (string)

The colormap name to be used for the custom color option

start (float)

The starting point of the colormap to begin a custom color list generation (0.0 to less than 1.0)

stop (float)

The ending point of the colormap to finish a custom color list generation (greater than 0.0 to 1.0)

exclude (list)

A list of 2 floats between 0.0 and 1.0 describing a section of the original colormap to exclude from a custom color list generation. (Example [.45, .55], the middle of the list excluded)

reverse (boolean)

If True, reverse the sequence of the custom color list

chart_style (string)

set the chart plot style for ax1 from the avialable seaborn plotting themes:

[“darkgrid”, “whitegrid”, “dark”, “white”, and “ticks”]

The default is “whitegrid”.

remove_ax2_border (boolean)

if “cat_order” is set as the measure input and the show_job_bands input is set True, a second axis is generated to be the container for the job level labels. The chart style for ax2 is “white” which avoids unwanted grid lines but includes a black solid chart border by default. This ax2 border may be removed if this input is set to True. (The border may be displayed if the chart_style input (for ax1) is set to “white” or “ticks”).

line_width (float)

The width of the plotted lines. Default is .75

use_dashed_line_compare (boolean)

If True, when comparing output from 2 datasets, plot the second dataset output with a dashed line, otherwise use a solid line

bg_color (color value)

The background color for the chart. May be a color name, color abreviation, hex value, or decimal between 0 and 1 (shades of black)

job_bands_alpha (float)

If show_job_bands input is set to True and measure is set to “cat_order”, this input controls the alpha or transparency of the background job level bands. (0.0 to 1.0)

line_alpha (float)

Transparency value of plotted lines (0.0 to 1.0)

grid_alpha (float)

Transparency value of grid lines (0.0 to 1.0)

title_size (integer or float)

Font size value for title

tick_size (integer or float)

Font size value for chart tick (value) labels

label_size (integer or float)

Font size value for x and y unit labels

xsize, ysize (integers or floats)

Width and height of chart in inches

image_dir (string)

if not None, name of a directory in which to save an image of the chart output. If the directory does not exist, it will be created.

image_format (string)

file extension string for a saved chart image if the image_dir input is not None

Examples:

‘svg’, ‘png’

matplotlib_charting.quantile_years_in_position(dfc, dfb, job_levels, num_bins, job_str_list, p_dict, color_list, style='bar', plot_differential=True, ds_dict=None, attr1=None, oper1='>=', val1=0, attr2=None, oper2='>=', val2=0, attr3=None, oper3='>=', val3=0, chart_style='darkgrid', grid_alpha=None, custom_color=False, cm_name='Dark2', start=0.0, stop=1.0, fur_color=None, flip_x=False, flip_y=False, rotate=False, gain_loss_bg=False, bg_alpha=0.05, normalize_yr_scale=False, year_clip=30, suptitle_size=14, title_size=12, xsize=12, ysize=12, image_dir=None, image_format='png')

stacked bar or area chart presenting the time spent in the various job levels for quantiles of a selected employee group.

inputs

dfc (string or dataframe variable)

text name of proposal (comparison) dataset to explore (ds_dict key) or dataframe

dfb (string or dataframe variable)

text name of baseline dataset to explore (ds_dict key) or dataframe

job_levels (integer)

the number of job levels in the model

num_bins (integer)

the total number of segments (divisions of the population) to calculate and display

job_str_list (list)

a list of strings which correspond with the job levels, used for the chart legend example: jobs = [‘Capt G4’, ‘Capt G3’, ‘Capt G2’, ….]

p_dict (dictionary)

dictionary used to convert employee group numbers to text, used with chart title text display

color_list (list)

a list of color codes for the job level color display

style (string)

option to select ‘area’ or ‘bar’ to determine the type of chart output. default is ‘bar’.

plot_differential (boolean)

if True, plot the difference between dfc and dfb values

ds_dict (dictionary)

variable assigned to the output of the load_datasets function. This keyword variable must be set if string dictionary keys are used as inputs for the dfc and/or dfb inputs.

attr(n) (string)

filter attribute or dataset column as string

oper(n) (string)

operator (i.e. <, >, ==, etc.) for attr(n) as string

val(n) (integer, float, date as string, string (as appropriate))

attr(n) limiting value (combined with oper(n)) as string

chart_style (string)

any valid seaborn plotting style name

custom_color, cm_name, start, stop (boolean, string, float, float)

if custom color is set to True, create a custom color map from the cm_name color map style. A portion of the color map may be selected for customization using the start and stop inputs.

fur_color (color code in rgba, hex, or string style)

custom color to signify furloughed employees (otherwise, last color in color_list input will be used)

flip_x (boolean)

‘flip’ the chart horizontally if True

flip_y (boolean)

‘flip’ the chart vertically if True

rotate (boolean)

transpose the chart output

gain_loss_bg (boolean)

if True, apply a green and red background to the chart in the gain and loss areas

bg_alpha (float)

the alpha of the gain_loss_bg (if selected)

normalize_yr_scale (boolean)

set all output charts to have the same x axis range

yr_clip (integer)

max x axis value (years) if normalize_yr_scale set True

suptitle_size (integer or float)

text size of chart super title

title_size (integer or float)

text size of chart title

xsize, ysize (integer or float)

size of chart display

image_dir (string)

if not None, name of a directory in which to save an image of the chart output. If the directory does not exist, it will be created.

image_format (string)

file extension string for a saved chart image if the image_dir input is not None

Examples: ‘svg’, ‘png’

matplotlib_charting.rows_of_color(df, mnum, measure_list, eg_colors, jnum_colors, dict_settings, ds_dict=None, attr1=None, oper1='>=', val1=0, attr2=None, oper2='>=', val2=0, attr3=None, oper3='>=', val3=0, cols=150, eg_list=None, job_only=False, jnum=1, shrink_to_fit=False, cell_border=True, eg_border_color='.2', job_border_color='.2', chart_style='whitegrid', fur_color=None, empty_color='#737373', suptitle_size=14, title_size=12, legend_size=14, xsize=15, ysize=9, image_dir=None, image_format='png')

plot a heatmap with the color of each rectangle representing an employee group, job level, or status.

This chart will show a position snapshot indicating the distribution of employees within the entire population, employees holding a certain job, or a combination of the two.

For example, all employees holding a certain job in month 36 may be plotted with original group delineated by color. Or, all employees from one group may be shown with the different jobs for that group displayed with different colors.

Also will display any other category such as a special group such as furloughed employees. Input dataframe must have a numerical representation of the selected measure, i.e. furloughed indicated by a 1, and others with a 0.

inputs

df (dataframe)

dataset to examine, may be a dataframe variable or a string key from the ds_dict dictionary object

mnum (integer)

month number of dataset to analyze

measure_list (list)

list form input, ‘categorical’ only such as employee group number or job number, such as [‘jnum’], or [‘eg’] [‘eg’, ‘fur’] is also valid when highlighting furloughees

eg_colors (list)

colors to use for plotting the employee groups. the first color in the list is used for the plot ‘background’ and is not an employee group color

jnum_colors (list)

job level plotting colors, list form

ds_dict (dictionary)

output from load_datasets function

attr(n) (string)

filter attribute or dataset column as string

oper(n) (string)

operator (i.e. <, >, ==, etc.) for attr(n) as string

val(n) (integer, float, date as string, string (as appropriate))

attr(n) limiting value (combined with oper(n)) as string

cols (integer)

number of columns to construct for the heatmap plot

eg_list (list)

employee group integer code list (if used), example: [1, 2]

job_only (boolean)

if True, plot only employees holding the job level identified with the jnum input

jnum (integer)

job level distribution to plot if job_only input is True

shrink_to_fit (boolean)

if True, adjust the size of the heatmap to match the size of the filtered monthly data. If False, maintain the number of cells in the heatmap to be equal to the starting size of the employee population

cell_border (boolean)

if True, show a border around the heatmap cells

eg_border_color (color value)

color of cell border if measure_list includes ‘eg’ (employee group)

job_border_color (color value)

color of cell border when plotting job information

chart_style (string)

underlying chart style, any valid seaborn chart style (string)

fur_color (color code in rgba, hex, or string style)

custom color to signify furloughed employees (otherwise, last color in jnum_colors input will be used)

empty_color (color value)

cell color for cells with no data

suptitle_size (integer or float)

text size of chart super title

title_size (integer or float)

text size of chart title

legend_size (integer or float)

text size of chart legend

xsize, ysize (integer or float)

size of chart in inches (width, height)

image_dir (string)

if not None, name of a directory in which to save an image of the chart output. If the directory does not exist, it will be created.

image_format (string)

file extension string for a saved chart image if the image_dir input is not None

Examples:

‘svg’, ‘png’

matplotlib_charting.single_emp_compare(emp, measure, df_list, xax, job_strs, eg_colors, p_dict, job_levels, attr_dict, ds_dict=None, chart_style='whitegrid', standalone_color='#ff00ff', title_size=14, tick_size=12, label_size=13, legend_size=14, xsize=12, ysize=8, image_dir=None, image_format='png')

Select a single employee and compare proposal outcome using various calculated measures.

inputs

emp (integer)

empkey for selected employee

measure (string)

calculated measure to compare examples: ‘jobp’ or ‘cpay’

df_list (list)

list of calculated datasets to compare

xax (string)

dataset column to set as x axis

job_strs (list)

string job description list

eg_colors (list)

list of colors to be assigned to line plots

p_dict (dictionary)

dictionary containing eg group integer to eg string descriptions

job_levels (integer)

number of jobs in the model

attr_dict (dictionary)

dataset column name description dictionary

ds_dict (dictionary)

output from load_datasets function

chart_style (string)

any valid seaborn plotting style

standalone_color (color value)

color of standalone plot (This function assumes one proposal from each group, any additional proposal is assumed to be standalone)

title_size (integer or float)

text size of chart title

tick_size (integer or float)

text size of chart tick labels

label_size (integer or float)

text size of x and y axis chart labels

legend_size (integer or float)

text size of chart legend

xsize, ysize (integer or float)

width and height of output chart in inches

image_dir (string)

if not None, name of a directory in which to save an image of the chart output. If the directory does not exist, it will be created.

image_format (string)

file extension string for a saved chart image if the image_dir input is not None

Examples:

‘svg’, ‘png’

matplotlib_charting.slice_ds_by_filtered_index(df, ds_dict=None, mnum=0, attr='age', attr_oper='>=', attr_val=50)

filter an entire dataframe by only selecting rows which match the filtered results from a target month. In other words, zero in on a slice of data from a particular month, such as employees holding a specific job in month 25. Then, using the index of those results, find only those employees within the entire dataset as an input for further analysis within the program.

The output may be used as an input to a plotting function or for other analysis. This function may also be used repeatedly with various filters, using output of one execution as input for another execution.

inputs

df (dataframe, can be proposal string name)

the dataframe (dataset) to be filtered

ds_dict (dictionary)

A dictionary containing string to dataframes, used if ds_def input is not a dataframe

mnum (integer)

month number of the data to filter

attr (string)

attribute (column) to use during filter

oper (string)

operator to use, such as ‘<=’ or ‘!=’

attr_val (integer, float, date as string, string (as appropriate))

attr1 limiting value (combined with oper) as string

Example filter:

jnum >= 7 (in mnum month)

matplotlib_charting.stripplot_dist_in_category(df, job_levels, full_time_pcnt, eg_colors, band_colors, job_strs, attr_dict, p_dict, ds_dict=None, rank_metric='cat_order', mnum=None, attr1=None, oper1='>=', val1='0', attr2=None, oper2='>=', val2='0', attr3=None, oper3='>=', val3='0', bg_alpha=0.12, fur_color=None, show_part_time_lvl=True, size=3, alpha=1, title_size=14, label_pad=110, label_size=13, tick_size=12, xsize=4, ysize=12, image_dir=None, image_format='png')

visually display employee group distribution concentration within accurately sized job bands for a selected month.

This chart reveals how evenly or unevenly the employee groups share the jobs available within each job category.

inputs

df (dataframe)

dataset to examine, may be a dataframe variable or a string key from the ds_dict dictionary object

job_levels (integer)

number of job levels in the data model

full_time_pcnt (float)

percentage of each job level which is full time

eg_colors (list)

list of colors for eg plots

band_colors (list)

list of colors for background job band colors

job_strs (list)

list of job strings for job description labels

attr_dict (dictionary)

dataset column name description dictionary

p_dict (dictionary)

eg to group string label

ds_dict (dictionary)

output from load_datasets function

rank_metric (string)

rank attribute (currently only accepts ‘cat_order’)

mnum (integer)

month number - if not None, analyze data from this month

attr(n) (string)

filter attribute or dataset column as string

oper(n) (string)

operator (i.e. <, >, ==, etc.) for attr(n) as string

val(n) (string, integer, float, date as string as appropriate)

attr(n) limiting value (combined with oper(n)) as string

bg_alpha (float)

color alpha for background job level color

fur_color (color code in rgba, hex, or string style)

custom color to signify furloughed job band area (otherwise, last color from band_colors list will be used)

show_part_time_lvl (boolean)

if True, draw a line within each job band representing the boundry between full and part-time jobs when using a basic jobs only data model (set this input to False when using an enhanced job data model)

size (integer or float)

size of density markers

alpha (float)

alpha of density markers (0.0 to 1.0)

title_size (integer or float)

text size of chart title

label_size (integer or float)

text size of x and y descriptive labels

tick_size (integer or float)

text size of x and y tick labels

xsize, ysize (integer or float)

width and height of chart in inches

image_dir (string)

if not None, name of a directory in which to save an image of the chart output. If the directory does not exist, it will be created.

image_format (string)

file extension string for a saved chart image if the image_dir input is not None

Examples:

‘svg’, ‘png’

matplotlib_charting.stripplot_eg_density(df, mnum, eg_colors, ds_dict=None, mnum_order=True, attr1=None, oper1='>=', val1=0, attr2=None, oper2='>=', val2=0, attr3=None, oper3='>=', val3=0, dot_size=3, chart_style='whitegrid', bg_color='white', title_size=12, suptitle_size=14, xsize=5, ysize=10, image_dir=None, image_format='png')

plot a stripplot showing density distribution for non-retired employees for each employee group separately at the selected month. The stripplot displays remaining employees positioned according to the selected month or initial month integrated list order (controlled by the “mnum_order” input).

Note: To analyze job category distribution density, use the “stripplot_dist_in_category” plotting function.

The input dataframe (df) may be a dictionary key (string) or a pandas dataframe.

The input dataframe may be filtered by attributes using the attr(x), oper(x), and val(x) inputs.

inputs

df (string or dataframe)

text name of input proposal dataset, also will accept any dataframe variable (if a sliced dataframe subset is desired, for example) Example: input can be ‘proposal1’ (if that proposal exists, of course, or could be df[df.age > 50])

mnum (integer)

view data for employees remaining (not yet retired) within this data model month number

eg_colors (list)

color codes for plotting each employee group

ds_dict (dictionary)

output from load_datasets function

mnum_order (boolean)

if True, plot list position in month selected with the “mnum” input, otherwise plot according to initial integrated list position

attr(n) (string)

filter attribute or dataset column as string

oper(n) (string)

operator (i.e. <, >, ==, etc.) for attr(n) as string

val(n) (integer, float, date as string, string (as appropriate))

attr(n) limiting value (combined with oper(n)) as string

dot_size (integer or float)

size of stripplot markers

bg_color (color value)

chart background color

title_size (integer or float)

chart title text size

suptitle_size (integer or float)

chart text size of suptitle

xsize, ysize (integer or float)

size of chart width and height in inches

image_dir (string)

if not None, name of a directory in which to save an image of the chart output. If the directory does not exist, it will be created.

image_format (string)

file extension string for a saved chart image if the image_dir input is not None

Examples:

‘svg’, ‘png’

matplotlib_charting.to_percent(decimal, position, precision=0)

Custom format for matplotlib axis as a percentage.

Ignores the passed in position variable. This has the effect of scaling the default tick locations.

inputs

decimal (axis values)

no user input

position

ignored

precision (integer)

number of decimals in output percentage labels

matplotlib_charting.violinplot_by_eg(df, measure, ret_age, cdict, attr_dict, ds_dict=None, mnum=0, linewidth=1.5, attr1=None, oper1='>=', val1='0', attr2=None, oper2='>=', val2='0', attr3=None, oper3='>=', val3='0', scale='count', saturation=1.0, title_size=12, chart_style='darkgrid', xsize=12, ysize=10, image_dir=None, image_format='png')

From the seaborn website: Draw a combination of boxplot and kernel density estimate.

A violin plot plays a similar role as a box and whisker plot. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. Unlike a box plot, in which all of the plot components correspond to actual datapoints, the violin plot features a kernel density estimation of the underlying distribution.

inputs

df (dataframe)

dataset to examine, may be a dataframe variable or a string key from the ds_dict dictionary object

measure (string)

attribute to plot

ret_age (float)

retirement age (example: 65.0)

cdict (dictionary)

color dictionary for plotting palatte

attr_dict (dictionary)

dataset column name description dictionary

ds_dict (dictionary)

output from load_datasets function

mnum (integer)

month number to analyze

linewidth (integer or float)

width of line surrounding each violin plot

attr(n) (string)

filter attribute or dataset column as string

oper(n) (string)

operator (i.e. <, >, ==, etc.) for attr(n) as string

val(n) (string, integer, float, date as string as appropriate)

attr(n) limiting value (combined with oper(n)) as string

scale (string)

From the seaborn website: The method used to scale the width of each violin. If ‘area’, each violin will have the same area. If ‘count’, the width of the violins will be scaled by the number of observations in that bin. If ‘width’, each violin will have the same width.

saturation (float)

Proportion of the original color saturation. Large patches often look better with slightly desaturated colors, but set this to 1.0 if you want the plot colors to perfectly match the input color spec.

title_size (integer or float)

text size of chart title

image_dir (string)

if not None, name of a directory in which to save an image of the chart output. If the directory does not exist, it will be created.

image_format (string)

file extension string for a saved chart image if the image_dir input is not None

Examples:

‘svg’, ‘png’