page_attitudes.py

import streamlit as st
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.font_manager import FontProperties
from fields.likert_fields import likert_fields
from fields.field_translation_mapping import field_translation_mapping
from fields.translation_mapping import translation_mapping


@st.cache_data
def show(df):
    st.title("Students Attitudes (Overall)")
    st.write("Students Attitudes across all Likert fields without clustering")

    # Chinese font
    chinese_font = FontProperties(fname='mingliu.ttf')

    if df is not None:

        # Rename the columns in the DataFrame for visualization
        df_translated = df.rename(columns={
            field: f"{field} ({field_translation_mapping[category][i]})"
            for category, fields in likert_fields.items()
            for i, field in enumerate(fields)
        })

        # Loop through each category in likert_fields to create visualizations
        for category, fields in likert_fields.items():
            st.markdown(
                f"<h2 style='text-align: center;'>{translation_mapping[category]}</h2>", unsafe_allow_html=True)

            # Calculate the number of rows needed for this category
            num_fields = len(fields)
            # Equivalent to ceil(num_fields / 2)
            num_rows = -(-num_fields // 2)

            # Create subplots with 2 columns for this category
            fig, axs = plt.subplots(num_rows, 2, figsize=(15, 5 * num_rows))
            axs = axs.flatten()  # Flatten the array of subplots

            # Add padding to fit in the Chinese titles
            plt.subplots_adjust(hspace=0.4)

            # Loop through each field in the category to create individual bar plots
            for i, field in enumerate(fields):
                # Create the bar plot
                sns.countplot(
                    x=f"{field} ({field_translation_mapping[category][i]})", data=df_translated, ax=axs[i], palette="coolwarm")

                # Add title and labels
                title_chinese = field
                title_english = field_translation_mapping[category][i]
                axs[i].set_title(
                    f"{title_chinese}\n{title_english}", fontproperties=chinese_font)
                axs[i].set_xlabel('Likert Scale')
                axs[i].set_ylabel('Frequency')

            # Remove any unused subplots
            for i in range(num_fields, num_rows * 2):
                fig.delaxes(axs[i])

            # Show the plot
            st.pyplot(fig)