Heartbeat survey analysis in python, inspired by the R packages "heaRtbeat" and "heartbeat". For more information on heartbeat analysis, check out the 2021 Wiley Award for Excellence in Survey Research
heartbeatpy is available on PyPI and can be installed using pip install heartbeatpy
heartbeatpy has one main function: calculate_votes() which takes in a dataframe of survey data, a list of the columns to perform heartbeat transformation on, and a standard deviation threshold.
import pandas as pd
import numpy as np
from heartbeatpy import create_sample_data, calculate_votes
df, _ = create_sample_data(ncols=2, nrows=10)
votes, stats = calculate_votes(data=df, columns=["Q1", "Q2"], threshold=.5)
The function calculate_votes() returns two objects, the first, votes, contains the data provided to the function with each specified column's likert data replaced with upvotes and downvotes:
votes.head()
ID Q1 Q2
0 1 1 -1
1 2 1 -1
2 3 -1 1
3 4 -1 1
4 5 1 -1
The second object returned by calcuate_votes() is stats, which contains the standard deviation for each indiviual (row) in the data.
stats.head()
individual_sd individual_mean
0 1.414214 3.0
1 2.121320 2.5
2 1.414214 2.0
3 0.707107 1.5
4 0.707107 1.5
After finding votes, the function calculate_summary() can be used to generate the percent of downvotes, neutral votes, and upvotes for each question. calculate_summary() also calculates controversy score for each question.
from heartbeatpy import calculate_summary
summary = calculate_summary(votes=votes, columns=cols)
summary.head()
Question Upvote Downvote Neutral Controversy_Score
0 Q1 0.3 0.3 0.4 1.5
1 Q2 0.4 0.4 0.2 4.0
Lastly, you can use the built-in visualize_heartbeat() function to generate an altair stacked bar chart visualization:
import altair as alt
from heartbeatpy import visualize_heartbeat
chart = visualize_heartbeat(summary)
display(chart)