Demo Notebook for the vivainsights Python package

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

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

[2]:

import warnings
warnings.filterwarnings('ignore')

[3]:

pq_data.head()

[3]:
PersonId MetricDate Collaboration_hours Copilot_actions_taken_in_Teams Meeting_and_call_hours Internal_network_size Email_hours Channel_message_posts Conflicting_meeting_hours Large_and_long_meeting_hours ... Summarise_chat_actions_taken_using_Copilot_in_Teams Summarise_email_thread_actions_taken_using_Copilot_in_Outlook Summarise_meeting_actions_taken_using_Copilot_in_Teams Summarise_presentation_actions_taken_using_Copilot_in_PowerPoint Summarise_Word_document_actions_taken_using_Copilot_in_Word FunctionType SupervisorIndicator Level Organization LevelDesignation
0 bf361ad4-fc29-432f-95f3-837e689f4ac4 2024-03-31 17.452987 4 11.767599 92 7.523189 0.753451 2.079210 0.635489 ... 2 0 0 0 0 Specialist Manager Level3 IT Senior IC
1 0500f22c-2910-4154-b6e2-66864898d848 2024-03-31 32.860820 6 26.743370 193 11.578396 0.000000 8.106997 1.402567 ... 2 0 4 1 0 Specialist Manager Level2 Legal Senior Manager
2 bb495ec9-8577-468a-8b48-e32677442f51 2024-03-31 21.502359 8 13.982031 113 9.073214 0.894786 3.001401 0.000192 ... 1 1 0 0 0 Manager Manager Level4 Legal Junior IC
3 f6d58aaf-a2b2-42ab-868f-d7ac2e99788d 2024-03-31 25.416502 4 16.895513 131 10.281204 0.528731 1.846423 1.441596 ... 0 0 0 0 0 Manager Manager Level1 HR Executive
4 c81cb49a-aa27-4cfc-8211-4087b733a3c6 2024-03-31 11.433377 4 6.957468 75 5.510535 2.288934 0.474048 0.269996 ... 0 0 1 0 0 Technician Manager Level1 Finance Executive

5 rows × 73 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

[4]:

plot_bar = vi.create_bar(data=pq_data, metric='Emails_sent', hrvar='Organization', mingroup=5)
_images/demo-vivainsights-py_6_0.png

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().

[5]:

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

  Organization     metric   n
1           HR  44.423377  33
5     Research  44.364859  52
4   Operations  44.345455  22
0      Finance  44.213445  68
2           IT  43.871867  68
6        Sales  43.723077  13
3        Legal  43.328571  44

[6]:

vi.export(tb)

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

[6]:

()

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

[7]:

plot_line = vi.create_line(data=pq_data, metric='Emails_sent', hrvar='Organization', mingroup=5, return_type='plot')
_images/demo-vivainsights-py_11_0.png 
[8]:

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

[8]:
MetricDate Organization metric n
0 2024-03-31 Finance 42.529412 68
1 2024-03-31 HR 41.090909 33
2 2024-03-31 IT 43.632353 68
3 2024-03-31 Legal 43.931818 44
4 2024-03-31 Operations 44.545455 22 
[9]:

plot_box = vi.create_boxplot(data=pq_data, metric='Emails_sent', hrvar='Organization', mingroup=5, return_type='plot')
_images/demo-vivainsights-py_13_0.png 
[10]:

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

[10]:
index group mean median sd min max n
0 0 Finance 44.213445 43.900000 2.446053 39.342857 50.000000 68
1 1 HR 44.423377 44.485714 2.140707 40.314286 49.428571 33
2 2 IT 43.871867 43.885714 2.565453 37.742857 48.542857 68
3 3 Legal 43.328571 43.585714 2.434427 38.314286 49.828571 44
4 4 Operations 44.345455 45.442857 3.152513 39.000000 50.085714 22
5 5 Research 44.364859 44.228571 2.276114 39.657143 48.971429 52
6 6 Sales 43.723077 43.971429 2.132463 40.000000 46.885714 13

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.

[11]:

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

[11]:
hrvar attributes metric n
4 FunctionType Technician 23.426417 292
0 LevelDesignation Executive 23.285180 37
0 FunctionType Advisor 23.252294 299
1 Organization HR 23.249373 33
4 Organization Operations 23.225234 22
5 Organization Research 23.187623 52
0 Organization Finance 23.100312 68
3 FunctionType Specialist 23.092292 300
2 LevelDesignation Senior IC 23.092287 87
0 SupervisorIndicator IC 23.065672 34
1 SupervisorIndicator Manager 23.052194 266
3 LevelDesignation Senior Manager 23.037783 40
2 FunctionType Manager 22.991247 300
6 Organization Sales 22.984326 13
2 Organization IT 22.980218 68
1 LevelDesignation Junior IC 22.970769 136
1 FunctionType Consultant 22.874061 298
3 Organization Legal 22.725075 44

This can be visualized as well:

[12]:

plot_rank = vi.create_rank(
    data=pq_data,
    metric='Collaboration_hours',
    hrvar = ['Organization', 'FunctionType', 'LevelDesignation', 'SupervisorIndicator'],
    mingroup=5,
    return_type = 'plot'
)
_images/demo-vivainsights-py_18_0.png

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.

[13]:

plot_hrcount = vi.hrvar_count(data=pq_data, hrvar='Organization', return_type='plot')
_images/demo-vivainsights-py_20_0.png 
[14]:

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

[14]:
Organization n
0 Finance 68
2 IT 68
5 Research 52
3 Legal 44
1 HR 33
4 Operations 22
6 Sales 13

Additional Examples

Below are additional examples using the demo dataset pq_data for some of the newer functions in vivainsights.

Bubble Plot: create_bubble()

The create_bubble() function visualizes the relationship between two metrics, with bubble size representing group size. This is useful for comparing two metrics across organizational groups.

[15]:

# Bubble plot: Collaboration_hours vs. Multitasking_hours by Organization
bubble_plot = vi.create_bubble(
    data=pq_data,
    metric_x="Collaboration_hours",
    metric_y="Multitasking_hours",
    hrvar="Organization",
    mingroup=5,
    return_type="plot"
)
_images/demo-vivainsights-py_24_0.png

Trend Plot: create_trend()

The create_trend() function provides a week-by-week heatmap view of a selected metric, grouped by an HR attribute. This helps identify trends and hotspots over time.

[16]:

# Trend plot: Collaboration_hours by LevelDesignation
trend_plot = vi.create_trend(
    data=pq_data,
    metric="Collaboration_hours",
    hrvar="LevelDesignation",
    mingroup=5,
    return_type="plot"
)
_images/demo-vivainsights-py_26_0.png

Key Metrics Scan: keymetrics_scan()

The keymetrics_scan() function summarizes multiple key metrics across a grouping variable, returning either a heatmap or a summary table. This is useful for a high-level scan of organizational health.

[17]:

# Key metrics scan: heatmap by Organization
keymetrics_plot = vi.keymetrics_scan(
    data=pq_data,
    hrvar="Organization",
    mingroup=5,
    return_type="plot"
)
_images/demo-vivainsights-py_28_0.png 
[18]:

# Key metrics scan: summary table by Organization
keymetrics_table = vi.keymetrics_scan(
    data=pq_data,
    hrvar="Organization",
    mingroup=5,
    return_type="table"
)
keymetrics_table.head()

[18]:
Organization Collaboration_hours After_hours_collaboration_hours Meetings Meeting_hours Emails_sent Email_hours Internal_network_size External_network_size Employee_Count
0 Finance 23.100312 6.917373 16.652941 19.172045 44.213445 8.818406 123.316807 32.385294 68
1 HR 23.249373 6.834921 16.757576 18.894323 44.423377 8.843827 124.337662 32.619913 33
2 IT 22.980218 7.079035 16.614569 18.561649 43.871867 8.743025 122.583193 32.331092 68
3 Legal 22.725075 7.239442 16.357143 18.121719 43.328571 8.627604 120.527922 31.561688 44
4 Operations 23.225234 7.540438 16.711688 20.339809 44.345455 8.858412 124.092208 32.531169 22