'Gender Bias', 'Provocativity', 'Arbitrary Standards', 'Modesty Notions', 'Impact on Autonomy', 'Absurdity Factor', 'Respect', 'Individual Choices', 'Dismantling Standards'

Published: 4 March 2024| Version 1 | DOI: 10.17632/tdhwj9t8rv.1
Sunil Maria Benedict


The provided Python code utilises the Matplotlib and NumPy libraries to create a set of line charts, each representing a distinct category related to societal attitudes towards gender-specific attire and personal freedoms. The categories include 'Gender Bias,' 'Provocativity,' 'Arbitrary Standards,' 'Modesty Notions,' 'Impact on Autonomy,' 'Absurdity Factor,' 'Respect,' 'Individual Choices,' and 'Dismantling Standards.' The code assumes that 80 respondents participated in a survey, and it generates random data to simulate variations in each category for every respondent. The line charts are plotted individually for each category, providing a visual representation of how opinions vary across respondents. Additionally, the code calculates statistical measures such as the mean and standard deviation for each category, offering insights into the central tendency and variability of the simulated data. Key Performance Indicators (KPIs) are defined to assess the percentage of respondents whose data indicates a positive change above a specified threshold. The resulting visualisations and accompanying statistics aim to provide a comprehensive understanding of the diverse perspectives within the surveyed population regarding societal norms. This can be valuable for researchers, educators, or policymakers seeking insights into societal attitudes and potential areas for intervention or awareness campaigns.


Steps to reproduce

import matplotlib.pyplot as plt import numpy as np # Define the categories categories = ['Gender Bias', 'Provocativity', 'Arbitrary Standards', 'Modesty Notions', 'Impact on Autonomy', 'Absurdity Factor', 'Respect', 'Individual Choices', 'Dismantling Standards'] # Assume there are 80 respondents num_respondents = 80 # Generate random data to represent variations for each category and respondent np.random.seed(42) data = np.random.rand(len(categories), num_respondents) # Plot separate line charts for each category plt.figure(figsize=(12, 8)) # Calculate statistics and KPIs for i, category in enumerate(categories): plt.subplot(3, 3, i + 1) plt.plot(range(1, num_respondents + 1), data[i, :], label=category) plt.title(category) plt.xlabel('Respondent') plt.ylabel('Change Needed') plt.legend(loc='upper right') # Calculate statistics mean_value = np.mean(data[i, :]) std_dev = np.std(data[i, :]) # Define KPIs positive_change_threshold = 0.7 # Example threshold for positive change percent_above_threshold = np.mean(data[i, :] > positive_change_threshold) * 100 # Display statistics and KPIs plt.text(num_respondents + 1, mean_value, f'Mean: {mean_value:.2f}\nStd Dev: {std_dev:.2f}\nAbove {positive_change_threshold:.2f}: {percent_above_threshold:.1f}%', verticalalignment='center') plt.tight_layout() plt.show()


CMR Group of institutions


Gender Identity, Gender, Gender Issue, Gender Disparitiy, Gender Equality, Anthropology of Gender, Gender Psychology