Demo Notebook for the vivainsights Python package

This notebook provides a demo for the vivainsights package. For more information about the package, please see: - Documentation - GitHub Page

Loading the library and dataset

The following code loads the library ‘vivainsights’, and the function load_pq_data() loads a sample Person Query data to the environment. The first five rows of the data looks like the following:

import vivainsights as vi

# load in-built datasets
pq_data = vi.load_pq_data() # load and assign in-built person query

import warnings
warnings.filterwarnings('ignore')

pq_data.head()

	Unnamed: 0	PersonId	MetricDate	After_hours_call_hours	After_hours_chat_hours	After_hours_collaboration_hours	After_hours_email_hours	After_hours_meeting_hours	After_hours_scheduled_call_hours	After_hours_unscheduled_call_hours	...	Working_hours_meeting_hours	Working_hours_scheduled_call_hours	Working_hours_unscheduled_call_hours	LevelDesignation	Layer	SupervisorIndicator	Organization	FunctionType	WeekendDays	IsActive
0	1	a6afe34c-8524-32d3-a368-1517b29b68cd	2022-05-01	0.0	0.0	18.675938	0.722722	18.25	0.0	0	...	19.50	0	0	Manager	3	Manager	Sales and Marketing	G_and_A	[SUNDAY, SATURDAY]	True
1	2	d6368140-9312-380b-bbc9-9a32bcef4b83	2022-05-01	0.0	0.0	4.827803	0.925556	4.00	0.0	0	...	8.75	0	0	Support	3	Individual Contributor	Finance	Sales	[SUNDAY, SATURDAY]	True
2	3	60bf99b0-65fd-3c3f-94fb-8ceb451d59e7	2022-05-01	0.0	0.0	1.497806	0.812806	0.75	0.0	0	...	12.50	0	0	Support	3	Individual Contributor	Product	IT	[SUNDAY, SATURDAY]	True
3	4	93fddd74-3667-392b-ba5a-92d855772cb0	2022-05-01	0.0	0.0	59.265892	2.283668	59.00	0.0	0	...	28.50	0	0	Director	2	Manager+	Sales and Marketing	Analytics	[SUNDAY, SATURDAY]	True
4	5	53183116-2cb2-32ee-9042-d62eb7061407	2022-05-01	0.0	0.0	2.146806	0.520167	1.75	0.0	0	...	7.50	0	0	Support	3	Individual Contributor	Sales and Marketing	IT	[SUNDAY, SATURDAY]	True

5 rows × 155 columns

Run visuals

There are currently several visuals that you can run with the Person Query dataset. The first of these that you can run is a bar plot, which produces a person-average (first averaging by person, then averaging by group) for the groups in question. The parameters that you can provide to create_bar() are:

• metric - metric to analyze

• hrvar - grouping HR variable

• mingroup - minimum group size to display

plot_bar = vi.create_bar(data=pq_data, metric='Emails_sent', hrvar='Organization', mingroup=5)

You can also ask the function to return a summary table by specifying the parameter return_type. This summary table can be copied to a clipboard with export().

tb = vi.create_bar(data=pq_data, metric='Emails_sent', hrvar='Organization', mingroup=5, return_type='table')
print(tb)

          Organization     metric   n
0              Finance  46.566667  27
3  Sales and Marketing  45.625806  31
1                   HR  38.971429  21
2              Product  35.433333  21

vi.export(tb)

Data frame copied to clipboard.
You may paste the contents directly to Excel.

()

Here are some other visual outputs, and their accompanying summary table outputs:

plot_line = vi.create_line(data=pq_data, metric='Emails_sent', hrvar='Organization', mingroup=5, return_type='plot')

vi.create_line(data=pq_data, metric='Emails_sent', hrvar='Organization', mingroup=5, return_type='table').head()

	MetricDate	Organization	metric	n
0	2022-05-01	Finance	52.370370	27
1	2022-05-01	HR	43.238095	21
2	2022-05-01	Product	38.428571	21
3	2022-05-01	Sales and Marketing	49.193548	31
4	2022-05-08	Finance	50.000000	27

plot_box = vi.create_boxplot(data=pq_data, metric='Emails_sent', hrvar='Organization', mingroup=5, return_type='plot')

vi.create_boxplot(data=pq_data, metric='Emails_sent', hrvar='Organization', mingroup=5, return_type='table')

	index	group	mean	median	sd	min	max	n
0	0	Finance	46.566667	39.6	21.739330	28.5	134.5	27
1	1	HR	38.971429	36.0	16.793366	22.1	98.9	21
2	2	Product	35.433333	33.4	6.608505	29.6	59.5	21
3	3	Sales and Marketing	45.625806	38.4	20.387577	24.4	119.0	31

Exploratory data analysis

Some functions are designed to perform a rapid exploratory analysis of the dataset, and one of these functions is create_rank(). With create_rank(), you can explore all the groupings within your population and rank them with respect to a metric you choose.

vi.create_rank(
    data=pq_data,
    metric='Collaboration_hours',
    hrvar = ['Organization', 'FunctionType', 'LevelDesignation', 'SupervisorIndicator'],
    mingroup=5,
    return_type = 'table'
)

	hrvar	attributes	metric	n
2	SupervisorIndicator	Manager+	72.043614	6
0	LevelDesignation	Director	72.043614	6
5	FunctionType	Marketing	28.992283	12
1	SupervisorIndicator	Manager	26.884630	11
2	LevelDesignation	Manager	26.884630	11
3	Organization	Sales and Marketing	25.765574	31
1	LevelDesignation	Junior IC	21.447389	10
7	FunctionType	Sales	21.294210	11
2	FunctionType	Engineering	19.436244	20
0	FunctionType	Analytics	18.348926	12
1	Organization	HR	17.775103	21
4	FunctionType	IT	17.698799	11
3	FunctionType	G_and_A	17.478766	6
0	Organization	Finance	16.694477	27
1	FunctionType	Customer_Service	15.080618	12
0	SupervisorIndicator	Individual Contributor	13.752266	83
4	LevelDesignation	Support	12.937993	53
6	FunctionType	R_and_D	12.365513	16
3	LevelDesignation	Senior IC	12.062528	20
2	Organization	Product	11.746185	21

This can be visualized as well:

plot_rank = vi.create_rank(
    data=pq_data,
    metric='Collaboration_hours',
    hrvar = ['Organization', 'FunctionType', 'LevelDesignation', 'SupervisorIndicator'],
    mingroup=5,
    return_type = 'plot'
)

Validating / exploring the data

Since HR variables or organizational attributes are a key part of the analysis process, it is also possible to perform some exploration or validation before we begin the analysis.

plot_hrcount = vi.hrvar_count(data=pq_data, hrvar='Organization', return_type='plot')

vi.hrvar_count(data=pq_data, hrvar='Organization', return_type='table')

	Organization	n
3	Sales and Marketing	31
0	Finance	27
1	HR	21
2	Product	21