Spaces:
Sleeping
Sleeping
| import streamlit as st | |
| import pandas as pd | |
| from PIL import Image | |
| import plotly.express as px | |
| def run(): | |
| ''' | |
| Function for EDA page | |
| ''' | |
| st.title('Exploration Data Analysis Section') | |
| # ============================= Simple Analysis ======================== | |
| eda=pd.read_csv('eda.csv') | |
| # basic summary analysis | |
| emotion_counts = eda['Emotion'].value_counts() | |
| eda['Comment Length'] = eda['Comment'].apply(len) | |
| eda['Word Count'] = eda['Comment'].apply(lambda x: len(x.split())) | |
| # emotion distribution | |
| fig_emotions = px.bar(emotion_counts, | |
| x=emotion_counts.index, | |
| y=emotion_counts.values, | |
| labels={'x': 'Emotion', 'y': 'Count'}, | |
| title='Distribution of Emotions') | |
| fig_emotions.update_traces(marker_line_width=1, marker_line_color='black') | |
| fig_emotions.update_layout(xaxis_title='Emotions', yaxis_title='Count', width=1000) | |
| # comment distribution | |
| fig_comment_length = px.histogram(eda, | |
| x='Comment Length', | |
| nbins=30, | |
| marginal='box', | |
| title='Distribution of Comment Length') | |
| fig_comment_length.update_traces(marker_line_width=1, marker_line_color='black') | |
| fig_comment_length.update_layout(xaxis_title='Length of Comment', yaxis_title='Count', width=1000, bargap=0) | |
| # word count distribution | |
| fig_word_count = px.histogram(eda, | |
| x='Word Count', | |
| nbins=30, | |
| marginal='box', | |
| title='Distribution of Word Count') | |
| fig_word_count.update_traces(marker_line_width=1, marker_line_color='black') | |
| fig_word_count.update_layout(xaxis_title='Word Count', yaxis_title='Count', width=1000) | |
| # Display the figures in Streamlit | |
| st.plotly_chart(fig_emotions) | |
| st.plotly_chart(fig_comment_length) | |
| st.plotly_chart(fig_word_count) | |