🔍 Explore Dataset Insights
Dive deep into the rich analytics of our dataset. Uncover trends, distributions, and key metrics that will enhance your understanding and guide your research.
""", unsafe_allow_html=True)
# Centered button
col1, col2, col3 = st.columns([1,2,1])
with col2:
show_analysis = st.button("Reveal Dataset Analysis", use_container_width=True)
# Display dataset analysis if the button was clicked
if show_analysis:
# Load analysis results
@st.cache_data
def load_analysis_results():
with open('analysis_results.json', 'r') as f:
return json.load(f)
analysis_results = load_analysis_results()
st.subheader("Analysis of a Sample Reddit Dataset")
st.write("This analysis is based on a sample from one of the Reddit datasets.")
# Display Dataset Structure
st.subheader("Dataset Structure")
structure = analysis_results['structure']
col1, col2, col3, col4 = st.columns(4)
col1.metric("Total Partitions", structure['total_partitions'])
col2.metric("Total Rows", f"{structure['total_rows']:,}")
col3.metric("Number of Columns", len(structure['columns']))
col4.metric("Date Range", f"{structure['date_range'][0]} to {structure['date_range'][1]}")
with st.expander("Show Columns"):
st.write(", ".join(structure['columns']))
# Display Top Communities
st.subheader("Top Communities")
communities_df = pd.DataFrame(analysis_results['communities'])
fig = go.Figure(data=[go.Bar(
x=communities_df['communityName'],
y=communities_df['count'],
text=communities_df['percentage'].apply(lambda x: f'{x:.2%}'),
textposition='auto',
marker_color='#1e88e5'
)])
fig.update_layout(title_text='Top Communities Distribution')
fig.update_traces(marker_line_color='rgb(8,48,107)', marker_line_width=1.5, opacity=0.6)
st.plotly_chart(fig, use_container_width=True)
# Display Time Distribution
st.subheader("Time Distribution")
time_df = pd.DataFrame(analysis_results['time_distribution'])
time_df['date'] = pd.to_datetime(time_df['date'])
fig = go.Figure(data=[go.Scatter(x=time_df['date'], y=time_df['count'], mode='lines+markers')])
fig.update_layout(title_text='Posts Over Time')
st.plotly_chart(fig, use_container_width=True)
# Display Sentiment Distribution
st.subheader("Sentiment Distribution")
sentiment_df = pd.DataFrame(analysis_results['sentiment_distribution'])
fig = go.Figure(data=[go.Pie(labels=sentiment_df['sentiment'], values=sentiment_df['count'], textinfo='percent+label')])
fig.update_layout(title_text='Sentiment Distribution')
fig.update_traces(marker=dict(colors=['#4CAF50', '#FFC107', '#F44336']))
st.plotly_chart(fig, use_container_width=True)
# Display Data Type Distribution
st.subheader("Data Type Distribution")
data_type_df = pd.DataFrame(analysis_results['data_type_distribution'])
fig = go.Figure(data=[go.Pie(labels=data_type_df['dataType'], values=data_type_df['count'], textinfo='percent+label')])
fig.update_layout(title_text='Data Type Distribution')
fig.update_traces(marker=dict(colors=['#2196F3', '#FF9800']))
st.plotly_chart(fig, use_container_width=True)
# Display Top Topics
st.subheader("Top Topics")
topics_df = pd.DataFrame(analysis_results['top_topics'])
st.dataframe(topics_df, use_container_width=True)
# Display Average Text Length
st.metric("Average Text Length", f"{analysis_results['avg_text_length']:.2f} characters")
# Add instructions for using the datasets
st.subheader("How to Use These Datasets")
code = '''
from datasets import load_dataset
dataset = load_dataset("username/dataset_name")
'''
st.code(code, language='python')
st.markdown("""
1. Click on the dataset link to visit its Hugging Face page.
2. On the dataset page, you'll find information about the dataset's content, structure, and usage.
3. Use the code above to load a dataset, replacing `"username/dataset_name"` with the actual dataset identifier.
4. For these large datasets, consider using streaming or loading specific subsets to manage memory usage.
5. Always check the dataset's license and usage restrictions before incorporating it into your project.
""")
# Footer
st.markdown("---")
st.markdown("Created by Macrocosmos with ❤️")