# --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See LICENSE.txt in the project root for license information.
# --------------------------------------------------------------------------------------------
"""
This module generates a count of the distinct persons in the data population.
Returns a bar plot of the counts by default, with an option to return a summary table.
"""
import pandas as pd
import matplotlib.pyplot as plt
from vivainsights.extract_date_range import extract_date_range
[docs]
def hrvar_count_calc(data: pd.DataFrame, hrvar: str):
"""Calculate the number of distinct persons in the data population, grouped by a selected HR variable."""
data = data.groupby([hrvar])
data = data['PersonId'].nunique().reset_index(name='n')
output = data.sort_values(by = 'n', ascending=False)
return output
[docs]
def hrvar_count_viz(data: pd.DataFrame, hrvar: str):
"""Visualise the number of distinct persons in the data population, grouped by a selected HR variable."""
sum_df = hrvar_count_calc(data = data, hrvar = hrvar)
cap_str = extract_date_range(data, return_type = 'text')
fig, ax = plt.subplots(figsize=(4, 6))
# Create grid
# Zorder tells it which layer to put it on. We are setting this to 1 and our data to 2 so the grid is behind the data.
ax.grid(which="major", axis='x', color='#758D99', alpha=0.6, zorder=1)
# Remove splines. Can be done one at a time or can slice with a list.
ax.spines[['top','right','bottom']].set_visible(False)
# Make left spine slightly thicker
ax.spines['left'].set_linewidth(1.1)
# Create bar plot
ax.barh(sum_df[hrvar], sum_df['n'], color='#1d627e', zorder=2)
# Shrink y-lim to make plot a bit tighter
# Using length of summary table to make it dynamic
ax.set_ylim(-0.5, len(sum_df)-0.5)
# Reformat x-axis tick labels
ax.xaxis.set_tick_params(
labeltop=True, # Put x-axis labels on top
labelbottom=False, # Set no x-axis labels on bottom
bottom=False, # Set no ticks on bottom
labelsize=9, # Set tick label size
pad=-1 # Lower tick labels a bit
)
# Reformat y-axis tick labels
ax.yaxis.set_tick_params(
pad=10, # Pad tick labels so they don't go over y-axis
labelsize=9, # Set label size
bottom=False # Set no ticks on bottom/left
)
# Reformat y-axis tick labels
ax.set_yticks(range(len(sum_df)))
ax.set_yticklabels(
sum_df[hrvar], # Set labels again
ha = 'right' # Set horizontal alignment to right
)
# Add in line and tag
ax.plot([-.35, .87], # Set width of line
[1.02, 1.02], # Set height of line
transform=fig.transFigure, # Set location relative to plot
clip_on=False,
color='#fe7f4f',
linewidth=.6)
ax.add_patch(plt.Rectangle((-.35,1.02), # Set location of rectangle by lower left corder
0.12, # Width of rectangle
-0.02, # Height of rectangle. Negative so it goes down.
facecolor='#fe7f4f',
transform=fig.transFigure,
clip_on=False,
linewidth = 0))
# Add in title, subtitle, and caption
ax.text(x=-.35, y=.96, s= f'People by {hrvar}', transform=fig.transFigure, ha='left', fontsize=13, weight='bold', alpha=.8)
# ax.text(x=-.35, y=.925, s= sub_title, transform=fig.transFigure, ha='left', fontsize=11, alpha=.8)
ax.text(x=-.35, y=.08, s=cap_str, transform=fig.transFigure, ha='left', fontsize=9, alpha=.7)
plt.bar_label(ax.containers[0], fmt = '%.0f', label_type='edge', padding = 3) # annotate
plt.margins(y=0.3) # pad the spacing between the number and the edge of the figure
# return the plot object
return fig
[docs]
def hrvar_count(data: pd.DataFrame, hrvar: str = 'Organization', return_type: str = "plot"):
"""
Name
----
hrvar_count
Description
-----------
This function generates a count of the distinct persons in the data population, grouped by a selected HR variable.
Parameters
---------
data : ppandas dataframe
person query data
hrvar : str
name of the organizational attribute to be used for grouping
return_type : str or optional
type of output to return. Defaults to "plot".
Example
-------
>>> hrvar_count(pq_data, hrvar = "LevelDesignation")
"""
if return_type == "plot":
out = hrvar_count_viz(data=data, hrvar=hrvar)
elif return_type == "table":
out = hrvar_count_calc(data=data, hrvar=hrvar)
else:
out = "Invalid input. Please check your inputs and try again."
return out