Build a “year in review” data visualizer

To build a “Year in Review” Data Visualizer, the tool should aggregate, analyze, and display key events, metrics, and highlights from a dataset spanning a full year. Here’s a breakdown of how to structure and implement this in a simple yet powerful way using Python with Streamlit for interactivity and Pandas + Plotly for data analysis and visualization.

Features of the Year-in-Review Visualizer

1. Upload Dataset (CSV/JSON)

2. Auto-detect Time Column

3. Monthly Summary Stats (views, revenue, etc.)

4. Top Events/Peaks Detection

5. Interactive Charts:

   • Time Series Trends

   • Heatmaps

   • Top Contributors

6. Word Cloud for Textual Insights

7. Export Summary Report

Code Implementation (Streamlit App)

python
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import streamlit as st
import pandas as pd
import plotly.express as px
import calendar
from wordcloud import WordCloud
import matplotlib.pyplot as plt

st.set_page_config(page_title="Year in Review", layout="wide")

st.title("📊 Year in Review - Data Visualizer")

# Upload Section
uploaded_file = st.file_uploader("Upload your yearly dataset (CSV or JSON)", type=["csv", "json"])

if uploaded_file:
    # Load Data
    if uploaded_file.name.endswith('.csv'):
        df = pd.read_csv(uploaded_file, parse_dates=True)
    else:
        df = pd.read_json(uploaded_file)

    # Try detecting datetime columns
    time_col = st.selectbox("Select a datetime column", options=df.columns[df.dtypes == 'object'].tolist() + df.columns[df.dtypes == 'datetime64[ns]'].tolist())
    
    try:
        df[time_col] = pd.to_datetime(df[time_col])
    except:
        st.error("Could not parse the selected column as datetime.")
        st.stop()
    
    df['Month'] = df[time_col].dt.month
    df['Month Name'] = df['Month'].apply(lambda x: calendar.month_abbr[x])
    df['Year'] = df[time_col].dt.year
    df['Date'] = df[time_col].dt.date

    numeric_cols = df.select_dtypes(include=['number']).columns.tolist()

    st.subheader("📅 Monthly Summary")
    selected_metric = st.selectbox("Choose a metric to review", options=numeric_cols)
    
    monthly_summary = df.groupby('Month Name')[selected_metric].sum().reindex(calendar.month_abbr[1:])
    fig = px.bar(monthly_summary, x=monthly_summary.index, y=monthly_summary.values, labels={'x': 'Month', 'y': selected_metric})
    st.plotly_chart(fig, use_container_width=True)

    st.subheader("📈 Trends Over Time")
    trend_fig = px.line(df.sort_values(time_col), x=time_col, y=selected_metric, title=f"{selected_metric} Over Time")
    st.plotly_chart(trend_fig, use_container_width=True)

    st.subheader("🔥 Peak Events")
    top_rows = df.sort_values(by=selected_metric, ascending=False).head(5)
    st.dataframe(top_rows[[time_col, selected_metric]])

    st.subheader("💬 Word Cloud from Text Columns")
    text_cols = df.select_dtypes(include='object').columns.tolist()
    text_col = st.selectbox("Select a column for word cloud (optional)", options=[""] + text_cols)
    if text_col:
        text_data = " ".join(df[text_col].dropna().astype(str).values)
        wordcloud = WordCloud(width=800, height=400, background_color='white').generate(text_data)
        plt.figure(figsize=(10, 5))
        plt.imshow(wordcloud, interpolation='bilinear')
        plt.axis('off')
        st.pyplot(plt)

    st.subheader("📥 Download Summary")
    summary_df = pd.DataFrame({
        "Month": monthly_summary.index,
        selected_metric: monthly_summary.values
    })
    st.download_button("Download Monthly Summary", data=summary_df.to_csv(index=False), file_name="year_summary.csv", mime="text/csv")

How to Use

1. Run the app with:

   bash
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   streamlit run year_in_review.py
   

2. Upload a dataset (e.g., sales data, user activity logs, social media insights).

3. Select the date column and the metric to analyze.

4. Explore auto-generated charts and summaries.

Ideal Use Cases

• Marketing Analytics: Review campaign performance month-by-month.

• E-commerce: Visualize revenue, orders, returns over the year.

• Content Creators: Summarize video views, likes, top posts.

• HR/Team Management: Annual productivity or engagement review.

Let me know if you want a version of this deployed on a web server or integrated into a specific platform (like Notion, Airtable, etc.).