Cleaning up the app

.github/workflows/keep_alive.yml

@@ -16,21 +16,33 @@ jobs:
         with:
           fetch-depth: 0
 
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+
+      - name: Install pandas
+        run: pip install pandas
+
+      - name: Refresh FRED demo data
+        continue-on-error: true   # Don't break heartbeat if FRED is down
+        run: python scripts/download_fred_data.py
+
       - name: Generate heartbeat CSV
         run: |
           APP_LIVE_DATE="2026-02-11"
           TODAY=$(date -u +%Y-%m-%d)
           DAYS=$(( ( $(date -u -d "$TODAY" +%s) - $(date -u -d "$APP_LIVE_DATE" +%s) ) / 86400 ))
-          echo "date,days_since_app_went_live" > heartbeat.csv
-          echo "${TODAY},${DAYS}" >> heartbeat.csv
-          cat heartbeat.csv
+          echo "date,days_since_app_went_live" > data/heartbeat.csv
+          echo "${TODAY},${DAYS}" >> data/heartbeat.csv
+          cat data/heartbeat.csv
 
       - name: Commit and push to GitHub
         run: |
           git config user.name "github-actions[bot]"
           git config user.email "github-actions[bot]@users.noreply.github.com"
-          git add heartbeat.csv
-          git diff --staged --quiet || git commit -m "Daily heartbeat: $(date -u +%Y-%m-%d)"
+          git add data/
+          git diff --staged --quiet || git commit -m "Daily heartbeat + FRED refresh: $(date -u +%Y-%m-%d)"
           git push
 
       - name: Sync to HF Spaces

.gitignore

@@ -2,5 +2,7 @@
 .claude/
 __pycache__/
 requirements.md
+DEPLOY.md
 *.Rproj
-.Rproj.user/
+.Rproj.user/
+nul

app.py

@@ -33,6 +33,8 @@ from src.cleaning import (
     clean_dataframe,
     detect_frequency,
     add_time_features,
+    detect_long_format,
+    pivot_long_to_wide,
     CleaningReport,
     FrequencyInfo,
 )
@@ -74,9 +76,9 @@ from src.querychat_helpers import (
 # ---------------------------------------------------------------------------
 _DATA_DIR = Path(__file__).parent / "data"
 _DEMO_FILES = {
-    "Monthly Retail Sales (single)": _DATA_DIR / "demo_single.csv",
-    "Quarterly Revenue by Region (wide)": _DATA_DIR / "demo_multi_wide.csv",
-    "Daily Stock Prices – 20 Tickers (long)": _DATA_DIR / "demo_multi_long.csv",
+    "Ohio Unemployment Rate (single, monthly)": _DATA_DIR / "demo_ohio_unemployment.csv",
+    "Manufacturing Employment by State (wide, monthly)": _DATA_DIR / "demo_manufacturing_wide.csv",
+    "Manufacturing Employment by State (long, monthly)": _DATA_DIR / "demo_manufacturing_long.csv",
 }
 
 _CHART_TYPES = [
@@ -185,7 +187,7 @@ def _single_chart_fragment(working_df, date_col, y_cols, freq_info, style_dict):
         n_colors = max(12, len(y_cols))
         palette_colors = get_palette_colors(palette_name, n_colors)
         swatch_fig = render_palette_preview(palette_colors[:8])
-        st.pyplot(swatch_fig, use_container_width=True)
+        st.pyplot(swatch_fig, width="stretch")
 
         # Color-by control (for colored markers chart)
         color_by = None
@@ -323,7 +325,7 @@ def _single_chart_fragment(working_df, date_col, y_cols, freq_info, style_dict):
             st.error(f"Chart error: {exc}")
 
         if fig is not None:
-            st.pyplot(fig, use_container_width=True)
+            st.pyplot(fig, width="stretch")
 
     st.session_state["_single_df_plot"] = df_plot
     st.session_state["_single_fig"] = fig
@@ -393,7 +395,7 @@ def _panel_chart_fragment(working_df, date_col, y_cols, style_dict):
                 style_dict=style_dict,
                 palette_colors=palette_b,
             )
-            st.pyplot(fig_panel, use_container_width=True)
+            st.pyplot(fig_panel, width="stretch")
         except Exception as exc:
             st.error(f"Panel chart error: {exc}")
 
@@ -425,7 +427,7 @@ def _panel_insights_fragment(working_df, date_col, freq_info):
                 "trend_slope": "{:,.4f}",
                 "adf_pvalue": "{:.4f}",
             }),
-            use_container_width=True,
+            width="stretch",
         )
 
     # AI Interpretation
@@ -484,7 +486,7 @@ def _spaghetti_chart_fragment(working_df, date_col, y_cols, style_dict):
                 style_dict=style_dict,
                 palette_colors=palette_c,
             )
-            st.pyplot(fig_spag, use_container_width=True)
+            st.pyplot(fig_spag, width="stretch")
         except Exception as exc:
             st.error(f"Spaghetti chart error: {exc}")
 
@@ -515,7 +517,7 @@ def _spaghetti_insights_fragment(working_df, date_col, freq_info):
                 "trend_slope": "{:,.4f}",
                 "adf_pvalue": "{:.4f}",
             }),
-            use_container_width=True,
+            width="stretch",
         )
 
     # AI Interpretation
@@ -634,9 +636,10 @@ style_dict = get_miami_mpl_style()
 # Session state initialisation
 # ---------------------------------------------------------------------------
 for key in [
-    "raw_df", "cleaned_df", "cleaning_report", "freq_info",
+    "raw_df", "raw_df_original", "cleaned_df", "cleaning_report", "freq_info",
     "date_col", "y_cols", "qc", "qc_hash",
     "_upload_id", "_upload_delim", "_clean_key",
+    "_prev_data_format", "_prev_pivot_key",
 ]:
     if key not in st.session_state:
         st.session_state[key] = None
@@ -719,43 +722,141 @@ with st.sidebar:
     )
 
     # Load data
+    def _on_new_data(df: pd.DataFrame) -> None:
+        """Store new dataset and clear stale format/pivot keys."""
+        st.session_state.raw_df_original = df
+        st.session_state.raw_df = df
+        # Clear format-related keys so auto-detection runs fresh
+        for _k in ("sidebar_data_format", "sidebar_group_col",
+                    "sidebar_value_col", "sidebar_y_cols",
+                    "_prev_data_format", "_prev_pivot_key"):
+            st.session_state.pop(_k, None)
+
     if uploaded is not None:
         file_id = (uploaded.name, uploaded.size)
         if st.session_state.get("_upload_id") != file_id:
             df_raw, delim = read_csv_upload(uploaded)
-            st.session_state.raw_df = df_raw
+            _on_new_data(df_raw)
             st.session_state._upload_delim = delim
             st.session_state._upload_id = file_id
         st.caption(f"Detected delimiter: `{repr(st.session_state._upload_delim)}`")
     elif demo_choice != "(none)":
         demo_key = ("demo", demo_choice)
         if st.session_state.get("_upload_id") != demo_key:
-            st.session_state.raw_df = _load_demo(_DEMO_FILES[demo_choice])
+            _on_new_data(_load_demo(_DEMO_FILES[demo_choice]))
             st.session_state._upload_id = demo_key
     # else: keep whatever was already in session state
 
+    raw_df_orig: pd.DataFrame | None = st.session_state.raw_df_original
     raw_df: pd.DataFrame | None = st.session_state.raw_df
 
-    if raw_df is not None:
+    if raw_df_orig is not None:
         st.divider()
         st.subheader("Column Selection")
 
-        # Auto-suggest
-        date_suggestions = suggest_date_columns(raw_df)
-        numeric_suggestions = suggest_numeric_columns(raw_df)
+        # Auto-suggest on the *original* (possibly long) DataFrame
+        date_suggestions = suggest_date_columns(raw_df_orig)
 
-        all_cols = list(raw_df.columns)
-        default_date_idx = all_cols.index(date_suggestions[0]) if date_suggestions else 0
+        all_cols = list(raw_df_orig.columns)
+        default_date_idx = (
+            all_cols.index(date_suggestions[0]) if date_suggestions else 0
+        )
 
         if "sidebar_date_col" not in st.session_state:
             st.session_state["sidebar_date_col"] = all_cols[default_date_idx]
         date_col = st.selectbox("Date column", all_cols, key="sidebar_date_col")
 
-        remaining = [c for c in all_cols if c != date_col]
-        default_y = [c for c in numeric_suggestions if c != date_col]
+        # ---- Auto-detect long vs wide format ---------------------------------
+        is_long, auto_group, auto_value = detect_long_format(raw_df_orig, date_col)
+
+        if "sidebar_data_format" not in st.session_state:
+            st.session_state["sidebar_data_format"] = "Long" if is_long else "Wide"
+
+        data_format = st.radio(
+            "Data format",
+            ["Wide", "Long"],
+            key="sidebar_data_format",
+            horizontal=True,
+        )
+
+        # When format radio changes, clear y_cols so the multiselect resets
+        if st.session_state.get("_prev_data_format") != data_format:
+            st.session_state.pop("sidebar_y_cols", None)
+            st.session_state["_prev_data_format"] = data_format
+
+        # ---- Build effective (wide) DataFrame --------------------------------
+        if data_format == "Long":
+            # Columns eligible for group (string/object) and value (numeric)
+            other_cols = [c for c in all_cols if c != date_col]
+            string_cols = [
+                c for c in other_cols
+                if raw_df_orig[c].dtype == object
+                or pd.api.types.is_string_dtype(raw_df_orig[c])
+            ]
+            numeric_cols = [
+                c for c in other_cols
+                if pd.api.types.is_numeric_dtype(raw_df_orig[c])
+            ]
+
+            if "sidebar_group_col" not in st.session_state:
+                st.session_state["sidebar_group_col"] = (
+                    auto_group if auto_group and auto_group in string_cols
+                    else (string_cols[0] if string_cols else None)
+                )
+            group_col = st.selectbox(
+                "Group column", string_cols, key="sidebar_group_col",
+            )
+
+            value_options = [c for c in numeric_cols if c != group_col]
+            if "sidebar_value_col" not in st.session_state:
+                st.session_state["sidebar_value_col"] = (
+                    auto_value if auto_value and auto_value in value_options
+                    else (value_options[0] if value_options else None)
+                )
+            value_col_sel = st.selectbox(
+                "Value column", value_options, key="sidebar_value_col",
+            )
+
+            # Clear y_cols when group/value changes
+            pivot_key = (group_col, value_col_sel)
+            if st.session_state.get("_prev_pivot_key") != pivot_key:
+                st.session_state.pop("sidebar_y_cols", None)
+                st.session_state["_prev_pivot_key"] = pivot_key
+
+            if group_col and value_col_sel:
+                effective_df = pivot_long_to_wide(
+                    raw_df_orig, date_col, group_col, value_col_sel,
+                )
+                n_groups = raw_df_orig[group_col].nunique()
+                st.caption(f"Pivoted **{n_groups}** groups from `{group_col}`")
+                available_y = [c for c in effective_df.columns if c != date_col]
+            else:
+                effective_df = raw_df_orig
+                available_y = []
+        else:
+            # Wide format — same as before
+            effective_df = raw_df_orig
+            numeric_suggestions = suggest_numeric_columns(raw_df_orig)
+            available_y = [c for c in numeric_suggestions if c != date_col]
+
+        # ---- Y-columns multiselect (shared by both paths) --------------------
+        # Filter any stale stored y_cols against current available options
+        if "sidebar_y_cols" in st.session_state:
+            st.session_state["sidebar_y_cols"] = [
+                c for c in st.session_state["sidebar_y_cols"]
+                if c in available_y
+            ]
         if "sidebar_y_cols" not in st.session_state:
-            st.session_state["sidebar_y_cols"] = default_y[:4] if default_y else []
-        y_cols = st.multiselect("Value column(s)", remaining, key="sidebar_y_cols")
+            st.session_state["sidebar_y_cols"] = (
+                available_y[:4] if available_y else []
+            )
+        y_cols = st.multiselect(
+            "Value column(s)", available_y, key="sidebar_y_cols",
+        )
+
+        # Push effective (wide) df into raw_df for the cleaning pipeline
+        st.session_state.raw_df = effective_df
+        raw_df = effective_df
 
         st.session_state.date_col = date_col
         st.session_state.y_cols = y_cols
@@ -913,9 +1014,10 @@ if cleaned_df is None or not y_cols:
             icon="\U0001f512",
         )
         st.info(
-            "**Demo Datasets** — Three built-in datasets are available in the sidebar: "
-            "monthly retail sales (single series), quarterly revenue by region (wide), "
-            "and daily stock prices with 20 tickers (long).",
+            "**Demo Datasets** — Three built-in FRED datasets are available in the sidebar: "
+            "Ohio Unemployment Rate (single series), Manufacturing Employment for five "
+            "states in wide format, and the same data in long/stacked format. "
+            "All sourced from the Federal Reserve Economic Data (FRED).",
             icon="\U0001f4ca",
         )
 

data/demo_manufacturing_long.csv

@@ -0,0 +1,2161 @@
+date,state,manufacturing_employment
+1990-01-01,California,1985.5
+1990-01-01,Indiana,605.6
+1990-01-01,Michigan,810.0
+1990-01-01,Ohio,1044.4
+1990-01-01,Texas,938.9
+1990-02-01,California,1985.4
+1990-02-01,Indiana,607.3
+1990-02-01,Michigan,836.0
+1990-02-01,Ohio,1061.6
+1990-02-01,Texas,939.4
+1990-03-01,California,1982.7
+1990-03-01,Indiana,609.6
+1990-03-01,Michigan,848.3
+1990-03-01,Ohio,1064.6
+1990-03-01,Texas,940.7
+1990-04-01,California,1983.1
+1990-04-01,Indiana,609.6
+1990-04-01,Michigan,843.3
+1990-04-01,Ohio,1068.6
+1990-04-01,Texas,942.1
+1990-05-01,California,1980.3
+1990-05-01,Indiana,609.7
+1990-05-01,Michigan,838.1
+1990-05-01,Ohio,1068.6
+1990-05-01,Texas,940.6
+1990-06-01,California,1974.6
+1990-06-01,Indiana,609.7
+1990-06-01,Michigan,841.8
+1990-06-01,Ohio,1069.4
+1990-06-01,Texas,942.0
+1990-07-01,California,1970.3
+1990-07-01,Indiana,612.3
+1990-07-01,Michigan,838.1
+1990-07-01,Ohio,1068.2
+1990-07-01,Texas,947.5
+1990-08-01,California,1964.0
+1990-08-01,Indiana,609.9
+1990-08-01,Michigan,832.2
+1990-08-01,Ohio,1066.3
+1990-08-01,Texas,950.2
+1990-09-01,California,1953.3
+1990-09-01,Indiana,608.0
+1990-09-01,Michigan,832.2
+1990-09-01,Ohio,1064.8
+1990-09-01,Texas,950.1
+1990-10-01,California,1951.5
+1990-10-01,Indiana,606.6
+1990-10-01,Michigan,830.7
+1990-10-01,Ohio,1059.3
+1990-10-01,Texas,951.0
+1990-11-01,California,1944.4
+1990-11-01,Indiana,593.9
+1990-11-01,Michigan,806.3
+1990-11-01,Ohio,1042.6
+1990-11-01,Texas,949.3
+1990-12-01,California,1939.2
+1990-12-01,Indiana,591.4
+1990-12-01,Michigan,798.7
+1990-12-01,Ohio,1038.9
+1990-12-01,Texas,944.2
+1991-01-01,California,1942.6
+1991-01-01,Indiana,589.9
+1991-01-01,Michigan,783.2
+1991-01-01,Ohio,1034.3
+1991-01-01,Texas,947.5
+1991-02-01,California,1928.9
+1991-02-01,Indiana,579.8
+1991-02-01,Michigan,785.4
+1991-02-01,Ohio,1020.0
+1991-02-01,Texas,946.9
+1991-03-01,California,1915.3
+1991-03-01,Indiana,573.3
+1991-03-01,Michigan,774.2
+1991-03-01,Ohio,1020.1
+1991-03-01,Texas,945.7
+1991-04-01,California,1914.9
+1991-04-01,Indiana,582.7
+1991-04-01,Michigan,774.2
+1991-04-01,Ohio,1018.9
+1991-04-01,Texas,936.9
+1991-05-01,California,1902.1
+1991-05-01,Indiana,584.5
+1991-05-01,Michigan,784.1
+1991-05-01,Ohio,1019.0
+1991-05-01,Texas,936.0
+1991-06-01,California,1891.7
+1991-06-01,Indiana,586.0
+1991-06-01,Michigan,782.3
+1991-06-01,Ohio,1021.0
+1991-06-01,Texas,936.1
+1991-07-01,California,1879.4
+1991-07-01,Indiana,588.6
+1991-07-01,Michigan,790.3
+1991-07-01,Ohio,1018.1
+1991-07-01,Texas,931.9
+1991-08-01,California,1880.4
+1991-08-01,Indiana,588.7
+1991-08-01,Michigan,784.8
+1991-08-01,Ohio,1019.2
+1991-08-01,Texas,930.4
+1991-09-01,California,1875.3
+1991-09-01,Indiana,589.3
+1991-09-01,Michigan,791.1
+1991-09-01,Ohio,1016.6
+1991-09-01,Texas,929.4
+1991-10-01,California,1876.4
+1991-10-01,Indiana,591.1
+1991-10-01,Michigan,792.4
+1991-10-01,Ohio,1014.0
+1991-10-01,Texas,922.0
+1991-11-01,California,1862.5
+1991-11-01,Indiana,590.7
+1991-11-01,Michigan,791.9
+1991-11-01,Ohio,1012.6
+1991-11-01,Texas,922.9
+1991-12-01,California,1846.3
+1991-12-01,Indiana,591.3
+1991-12-01,Michigan,787.6
+1991-12-01,Ohio,1009.5
+1991-12-01,Texas,920.7
+1992-01-01,California,1841.8
+1992-01-01,Indiana,589.3
+1992-01-01,Michigan,771.7
+1992-01-01,Ohio,1004.4
+1992-01-01,Texas,922.7
+1992-02-01,California,1828.8
+1992-02-01,Indiana,590.6
+1992-02-01,Michigan,781.8
+1992-02-01,Ohio,1002.8
+1992-02-01,Texas,921.3
+1992-03-01,California,1830.6
+1992-03-01,Indiana,592.4
+1992-03-01,Michigan,784.9
+1992-03-01,Ohio,996.6
+1992-03-01,Texas,919.0
+1992-04-01,California,1814.2
+1992-04-01,Indiana,595.6
+1992-04-01,Michigan,792.4
+1992-04-01,Ohio,1000.8
+1992-04-01,Texas,923.2
+1992-05-01,California,1802.0
+1992-05-01,Indiana,597.8
+1992-05-01,Michigan,792.3
+1992-05-01,Ohio,998.9
+1992-05-01,Texas,926.3
+1992-06-01,California,1791.5
+1992-06-01,Indiana,598.5
+1992-06-01,Michigan,791.0
+1992-06-01,Ohio,994.6
+1992-06-01,Texas,926.7
+1992-07-01,California,1792.6
+1992-07-01,Indiana,597.9
+1992-07-01,Michigan,793.8
+1992-07-01,Ohio,993.9
+1992-07-01,Texas,928.2
+1992-08-01,California,1776.7
+1992-08-01,Indiana,598.5
+1992-08-01,Michigan,791.4
+1992-08-01,Ohio,987.8
+1992-08-01,Texas,928.0
+1992-09-01,California,1764.5
+1992-09-01,Indiana,598.6
+1992-09-01,Michigan,789.5
+1992-09-01,Ohio,984.4
+1992-09-01,Texas,926.8
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+1992-10-01,Indiana,598.4
+1992-10-01,Michigan,788.0
+1992-10-01,Ohio,986.7
+1992-10-01,Texas,930.9
+1992-11-01,California,1750.0
+1992-11-01,Indiana,597.7
+1992-11-01,Michigan,790.8
+1992-11-01,Ohio,985.5
+1992-11-01,Texas,932.4
+1992-12-01,California,1736.0
+1992-12-01,Indiana,600.6
+1992-12-01,Michigan,790.9
+1992-12-01,Ohio,983.9
+1992-12-01,Texas,933.9
+1993-01-01,California,1744.3
+1993-01-01,Indiana,604.6
+1993-01-01,Michigan,799.1
+1993-01-01,Ohio,983.9
+1993-01-01,Texas,932.5
+1993-02-01,California,1732.2
+1993-02-01,Indiana,603.5
+1993-02-01,Michigan,795.6
+1993-02-01,Ohio,982.8
+1993-02-01,Texas,937.2
+1993-03-01,California,1719.9
+1993-03-01,Indiana,604.2
+1993-03-01,Michigan,794.8
+1993-03-01,Ohio,980.2
+1993-03-01,Texas,939.8
+1993-04-01,California,1710.5
+1993-04-01,Indiana,605.5
+1993-04-01,Michigan,793.3
+1993-04-01,Ohio,978.4
+1993-04-01,Texas,934.9
+1993-05-01,California,1702.9
+1993-05-01,Indiana,606.0
+1993-05-01,Michigan,791.9
+1993-05-01,Ohio,979.1
+1993-05-01,Texas,937.3
+1993-06-01,California,1698.8
+1993-06-01,Indiana,606.6
+1993-06-01,Michigan,790.1
+1993-06-01,Ohio,979.4
+1993-06-01,Texas,939.3
+1993-07-01,California,1689.6
+1993-07-01,Indiana,608.9
+1993-07-01,Michigan,803.7
+1993-07-01,Ohio,983.2
+1993-07-01,Texas,940.2
+1993-08-01,California,1683.3
+1993-08-01,Indiana,610.2
+1993-08-01,Michigan,786.5
+1993-08-01,Ohio,983.1
+1993-08-01,Texas,941.7
+1993-09-01,California,1678.4
+1993-09-01,Indiana,612.7
+1993-09-01,Michigan,794.8
+1993-09-01,Ohio,986.7
+1993-09-01,Texas,944.4
+1993-10-01,California,1675.7
+1993-10-01,Indiana,614.8
+1993-10-01,Michigan,802.9
+1993-10-01,Ohio,975.6
+1993-10-01,Texas,945.7
+1993-11-01,California,1673.9
+1993-11-01,Indiana,618.1
+1993-11-01,Michigan,807.4
+1993-11-01,Ohio,978.9
+1993-11-01,Texas,946.8
+1993-12-01,California,1672.6
+1993-12-01,Indiana,619.8
+1993-12-01,Michigan,813.8
+1993-12-01,Ohio,982.5
+1993-12-01,Texas,949.0
+1994-01-01,California,1670.0
+1994-01-01,Indiana,615.0
+1994-01-01,Michigan,823.5
+1994-01-01,Ohio,990.5
+1994-01-01,Texas,949.5
+1994-02-01,California,1667.7
+1994-02-01,Indiana,616.5
+1994-02-01,Michigan,822.9
+1994-02-01,Ohio,991.9
+1994-02-01,Texas,949.0
+1994-03-01,California,1673.6
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data/demo_manufacturing_wide.csv

@@ -0,0 +1,433 @@
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data/demo_multi_wide.csv

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+2020-02-01,4.5
+2020-03-01,4.8
+2020-04-01,16.5
+2020-05-01,12.8
+2020-06-01,11.0
+2020-07-01,9.8
+2020-08-01,8.3
+2020-09-01,7.6
+2020-10-01,6.7
+2020-11-01,6.4
+2020-12-01,6.3
+2021-01-01,6.1
+2021-02-01,6.1
+2021-03-01,6.0
+2021-04-01,5.9
+2021-05-01,5.7
+2021-06-01,5.5
+2021-07-01,5.2
+2021-08-01,4.9
+2021-09-01,4.7
+2021-10-01,4.4
+2021-11-01,4.3
+2021-12-01,4.1
+2022-01-01,4.0
+2022-02-01,3.9
+2022-03-01,3.9
+2022-04-01,3.9
+2022-05-01,3.9
+2022-06-01,3.9
+2022-07-01,4.0
+2022-08-01,4.1
+2022-09-01,4.1
+2022-10-01,4.1
+2022-11-01,4.0
+2022-12-01,3.9
+2023-01-01,3.8
+2023-02-01,3.6
+2023-03-01,3.5
+2023-04-01,3.4
+2023-05-01,3.4
+2023-06-01,3.4
+2023-07-01,3.5
+2023-08-01,3.6
+2023-09-01,3.8
+2023-10-01,3.8
+2023-11-01,3.9
+2023-12-01,3.9
+2024-01-01,4.0
+2024-02-01,4.0
+2024-03-01,4.1
+2024-04-01,4.2
+2024-05-01,4.2
+2024-06-01,4.3
+2024-07-01,4.3
+2024-08-01,4.4
+2024-09-01,4.4
+2024-10-01,4.5
+2024-11-01,4.5
+2024-12-01,4.5
+2025-01-01,4.6
+2025-02-01,4.7
+2025-03-01,4.8
+2025-04-01,4.9
+2025-05-01,4.9
+2025-06-01,4.9
+2025-07-01,5.0
+2025-08-01,5.0
+2025-09-01,4.8
+2025-11-01,4.5
+2025-12-01,4.5

data/demo_single.csv

@@ -1,121 +0,0 @@
-date,sales
-2014-01-01,44065.23
-2014-02-01,45923.47
-2014-03-01,51695.38
-2014-04-01,57646.06
-2014-05-01,57259.9
-2014-06-01,58531.73
-2014-07-01,61286.63
-2014-08-01,56934.87
-2014-09-01,50661.05
-2014-10-01,48885.12
-2014-11-01,44144.96
-2014-12-01,43268.54
-2015-01-01,45955.72
-2015-02-01,44773.44
-2015-03-01,49350.16
-2015-04-01,55875.42
-2015-05-01,58102.54
-2015-06-01,62028.49
-2015-07-01,58712.16
-2015-08-01,54975.39
-2015-09-01,56931.3
-2015-10-01,49748.45
-2015-11-01,47606.85
-2015-12-01,43750.5
-2016-01-01,46783.03
-2016-02-01,51221.85
-2016-03-01,52898.01
-2016-04-01,60151.4
-2016-05-01,61326.93
-2016-06-01,63216.61
-2016-07-01,61724.79
-2016-08-01,63904.56
-2016-09-01,56373.01
-2016-10-01,50484.58
-2016-11-01,51516.89
-2016-12-01,46558.31
-2017-01-01,65689.52
-2017-02-01,49480.66
-2017-03-01,54943.63
-2017-04-01,62193.72
-2017-05-01,66405.14
-2017-06-01,66542.74
-2017-07-01,65096.91
-2017-08-01,61997.79
-2017-09-01,55842.96
-2017-10-01,53560.31
-2017-11-01,51350.52
-2017-12-01,53514.24
-2018-01-01,53359.03
-2018-02-01,52273.92
-2018-03-01,60648.17
-2018-04-01,63429.84
-2018-05-01,65974.36
-2018-06-01,69823.35
-2018-07-01,69790.2
-2018-08-01,66862.56
-2018-09-01,59521.56
-2018-10-01,56781.58
-2018-11-01,55334.32
-2018-12-01,55751.09
-2019-01-01,54113.45
-2019-02-01,57828.68
-2019-03-01,60187.33
-2019-04-01,64207.59
-2019-05-01,71353.25
-2019-06-01,73712.48
-2019-07-01,69984.18
-2019-08-01,69407.07
-2019-09-01,64323.27
-2019-10-01,58509.76
-2019-11-01,57794.59
-2019-12-01,59276.07
-2020-01-01,72400.14
-2020-02-01,63729.29
-2020-03-01,59560.51
-2020-04-01,70643.81
-2020-05-01,72302.3
-2020-06-01,72801.99
-2020-07-01,72711.72
-2020-08-01,65824.86
-2020-09-01,65560.66
-2020-10-01,62914.23
-2020-11-01,62427.58
-2020-12-01,57563.46
-2021-01-01,58254.81
-2021-02-01,61996.49
-2021-03-01,69030.8
-2021-04-01,72057.5
-2021-05-01,73468.68
-2021-06-01,76826.53
-2021-07-01,75122.36
-2021-08-01,74137.29
-2021-09-01,66995.89
-2021-10-01,63944.68
-2021-11-01,61087.58
-2021-12-01,58072.97
-2022-01-01,62864.04
-2022-02-01,65922.11
-2022-03-01,69610.23
-2022-04-01,73330.83
-2022-05-01,89097.46
-2022-06-01,77358.71
-2022-07-01,76642.77
-2022-08-01,72995.45
-2022-09-01,70477.43
-2022-10-01,67808.1
-2022-11-01,68044.17
-2022-12-01,63749.16
-2023-01-01,65186.9
-2023-02-01,67651.11
-2023-03-01,68162.46
-2023-04-01,76146.97
-2023-05-01,79448.66
-2023-06-01,85526.48
-2023-07-01,79343.48
-2023-08-01,77603.09
-2023-09-01,73130.58
-2023-10-01,67062.64
-2023-11-01,68957.44
-2023-12-01,67303.87

scripts/download_fred_data.py

@@ -0,0 +1,128 @@
+"""Download real FRED datasets for the time-series visualization app.
+
+Uses only ``urllib.request`` (stdlib) — no extra dependencies.
+
+Series
+------
+* OHUR  — Ohio Unemployment Rate (%, Monthly, 1976–present)
+* OHMFG — Ohio Manufacturing Employment (1000s, SA, Monthly)
+* INMFG — Indiana Manufacturing Employment
+* MIMFG — Michigan Manufacturing Employment
+* TXMFG — Texas Manufacturing Employment
+* CAMFG — California Manufacturing Employment
+
+Run once locally, then commit the resulting CSVs::
+
+    python scripts/download_fred_data.py
+"""
+
+from __future__ import annotations
+
+import io
+import urllib.request
+from pathlib import Path
+
+import pandas as pd
+
+# Resolve paths relative to the project root (parent of scripts/)
+PROJECT_ROOT = Path(__file__).resolve().parent.parent
+DATA_DIR = PROJECT_ROOT / "data"
+DATA_DIR.mkdir(parents=True, exist_ok=True)
+
+_FRED_CSV_URL = "https://fred.stlouisfed.org/graph/fredgraph.csv?id={series_id}"
+
+
+def _fetch_fred(series_id: str) -> pd.DataFrame:
+    """Download a single FRED series and return a two-column DataFrame."""
+    url = _FRED_CSV_URL.format(series_id=series_id)
+    print(f"  Downloading {series_id} …")
+    with urllib.request.urlopen(url, timeout=30) as resp:  # noqa: S310
+        raw = resp.read().decode("utf-8")
+    df = pd.read_csv(io.StringIO(raw))
+    # FRED uses "." for missing values — coerce to NaN
+    df.columns = ["date", series_id]
+    df[series_id] = pd.to_numeric(df[series_id], errors="coerce")
+    df["date"] = pd.to_datetime(df["date"])
+    df = df.dropna()
+    return df
+
+
+# ---------------------------------------------------------------------------
+# 1. Single series: Ohio Unemployment Rate
+# ---------------------------------------------------------------------------
+def build_ohio_unemployment() -> pd.DataFrame:
+    df = _fetch_fred("OHUR")
+    df = df.rename(columns={"OHUR": "unemployment_rate"})
+    return df
+
+
+# ---------------------------------------------------------------------------
+# 2. Multi-series wide: Manufacturing Employment by State
+# ---------------------------------------------------------------------------
+_MFG_SERIES = {
+    "OHMFG": "Ohio",
+    "INMFG": "Indiana",
+    "MIMFG": "Michigan",
+    "TXMFG": "Texas",
+    "CAMFG": "California",
+}
+
+
+def build_manufacturing_wide() -> pd.DataFrame:
+    frames = []
+    for sid, state_name in _MFG_SERIES.items():
+        df = _fetch_fred(sid)
+        df = df.rename(columns={sid: state_name})
+        frames.append(df)
+
+    # Inner-join on date so all states share the same date range
+    wide = frames[0]
+    for f in frames[1:]:
+        wide = wide.merge(f, on="date", how="inner")
+
+    wide = wide.sort_values("date").reset_index(drop=True)
+    return wide
+
+
+# ---------------------------------------------------------------------------
+# 3. Multi-series long: same data melted
+# ---------------------------------------------------------------------------
+def build_manufacturing_long(wide: pd.DataFrame) -> pd.DataFrame:
+    long = wide.melt(
+        id_vars="date",
+        var_name="state",
+        value_name="manufacturing_employment",
+    )
+    long = long.sort_values(["date", "state"]).reset_index(drop=True)
+    return long
+
+
+# ---------------------------------------------------------------------------
+# Main
+# ---------------------------------------------------------------------------
+def main() -> None:
+    print("=== Downloading FRED data ===\n")
+
+    # 1. Ohio Unemployment
+    single = build_ohio_unemployment()
+    out = DATA_DIR / "demo_ohio_unemployment.csv"
+    single.to_csv(out, index=False)
+    print(f"  -> {len(single)} rows  =>  {out}\n")
+
+    # 2. Manufacturing wide
+    wide = build_manufacturing_wide()
+    out = DATA_DIR / "demo_manufacturing_wide.csv"
+    wide.to_csv(out, index=False)
+    print(f"  -> {len(wide)} rows  =>  {out}\n")
+
+    # 3. Manufacturing long
+    long = build_manufacturing_long(wide)
+    out = DATA_DIR / "demo_manufacturing_long.csv"
+    long.to_csv(out, index=False)
+    print(f"  -> {len(long)} rows  =>  {out}\n")
+
+    print("Done.")
+
+
+if __name__ == "__main__":
+    main()

scripts/generate_demo_data.py

@@ -1,134 +0,0 @@
-"""Generate demo CSV datasets for the time-series visualization app."""
-
-import os
-from pathlib import Path
-
-import numpy as np
-import pandas as pd
-
-# Reproducibility
-np.random.seed(42)
-
-# Resolve paths relative to the project root (parent of scripts/)
-PROJECT_ROOT = Path(__file__).resolve().parent.parent
-DATA_DIR = PROJECT_ROOT / "data"
-DATA_DIR.mkdir(parents=True, exist_ok=True)
-
-
-# ---------------------------------------------------------------------------
-# 1. data/demo_single.csv  --  Monthly retail sales (Jan 2014 - Dec 2023)
-# ---------------------------------------------------------------------------
-def generate_single_series() -> pd.DataFrame:
-    n = 120  # 10 years * 12 months
-    dates = pd.date_range(start="2014-01-01", periods=n, freq="MS")
-
-    months = np.arange(n)
-
-    # Upward trend: start ~50 000, grow ~200 per month
-    trend = 50_000 + 200 * months
-
-    # Seasonal component: sin wave peaking in December (month index 11)
-    # sin peaks at pi/2; December is month 11 (0-indexed within each year).
-    # Shift so that sin(...) = 1 when month-of-year == 11 (December).
-    month_of_year = months % 12
-    seasonal = 8_000 * np.sin(2 * np.pi * (month_of_year - 2) / 12)
-
-    # Random noise
-    noise = np.random.normal(0, 2_000, size=n)
-
-    sales = trend + seasonal + noise
-
-    # Inject 2-3 anomaly spikes
-    for idx in [36, 72, 100]:
-        sales[idx] += 15_000
-
-    df = pd.DataFrame({"date": dates, "sales": np.round(sales, 2)})
-    return df
-
-
-# ---------------------------------------------------------------------------
-# 2. data/demo_multi_wide.csv  --  Quarterly revenue by region (Q1 2017 - Q4 2023)
-# ---------------------------------------------------------------------------
-def generate_multi_wide() -> pd.DataFrame:
-    n = 28  # 7 years * 4 quarters
-    dates = pd.date_range(start="2017-01-01", periods=n, freq="QS")
-
-    quarters = np.arange(n)
-    quarter_of_year = quarters % 4  # 0=Q1 .. 3=Q4
-
-    regions = {
-        "North": 100_000,
-        "South": 80_000,
-        "East": 120_000,
-        "West": 90_000,
-    }
-
-    data: dict[str, object] = {"date": dates}
-
-    for name, base in regions.items():
-        trend = base + 800 * quarters
-        seasonal = 5_000 * np.sin(2 * np.pi * quarter_of_year / 4)
-        noise = np.random.normal(0, 3_000, size=n)
-        data[name] = np.round(trend + seasonal + noise, 2)
-
-    return pd.DataFrame(data)
-
-
-# ---------------------------------------------------------------------------
-# 3. data/demo_multi_long.csv  --  Daily stock prices for 20 tickers
-#    (2022-01-03 to 2023-12-29, business days only)
-# ---------------------------------------------------------------------------
-def generate_multi_long() -> pd.DataFrame:
-    trading_days = pd.bdate_range(start="2022-01-03", end="2023-12-29")
-
-    # 20 simple four-letter tickers: AAAA, BBBB, ..., TTTT
-    tickers = [chr(ord("A") + i) * 4 for i in range(20)]
-
-    daily_drift = 0.0002
-    daily_vol = 0.02
-
-    frames: list[pd.DataFrame] = []
-
-    for ticker in tickers:
-        start_price = np.random.uniform(50, 500)
-        n_days = len(trading_days)
-
-        # Geometric Brownian Motion: S_t = S_0 * exp(cumsum(log returns))
-        log_returns = np.random.normal(
-            daily_drift - 0.5 * daily_vol**2, daily_vol, size=n_days
-        )
-        log_returns[0] = 0  # first day: price = start_price
-        prices = start_price * np.exp(np.cumsum(log_returns))
-
-        frames.append(
-            pd.DataFrame(
-                {
-                    "date": trading_days,
-                    "ticker": ticker,
-                    "price": np.round(prices, 2),
-                }
-            )
-        )
-
-    return pd.concat(frames, ignore_index=True)
-
-
-# ---------------------------------------------------------------------------
-# Main
-# ---------------------------------------------------------------------------
-def main() -> None:
-    single = generate_single_series()
-    single.to_csv(DATA_DIR / "demo_single.csv", index=False)
-    print(f"Wrote {len(single)} rows -> {DATA_DIR / 'demo_single.csv'}")
-
-    wide = generate_multi_wide()
-    wide.to_csv(DATA_DIR / "demo_multi_wide.csv", index=False)
-    print(f"Wrote {len(wide)} rows -> {DATA_DIR / 'demo_multi_wide.csv'}")
-
-    long = generate_multi_long()
-    long.to_csv(DATA_DIR / "demo_multi_long.csv", index=False)
-    print(f"Wrote {len(long)} rows -> {DATA_DIR / 'demo_multi_long.csv'}")
-
-
-if __name__ == "__main__":
-    main()

src/cleaning.py

@@ -10,6 +10,7 @@ calendar-feature extraction.
 import csv
 import io
 import re
+import warnings
 from dataclasses import dataclass, field
 from datetime import timedelta
 
@@ -109,7 +110,9 @@ def suggest_date_columns(df: pd.DataFrame) -> list[str]:
         sample = df[col].dropna().head(5)
         if not sample.empty:
             try:
-                pd.to_datetime(sample)
+                with warnings.catch_warnings():
+                    warnings.simplefilter("ignore", UserWarning)
+                    pd.to_datetime(sample)
                 if col not in candidates:
                     candidates.append(col)
                 continue
@@ -311,6 +314,94 @@ def detect_frequency(df: pd.DataFrame, date_col: str) -> FrequencyInfo:
     return FrequencyInfo(label=label, median_delta=median_delta, is_regular=is_regular)
 
 
+# ---------------------------------------------------------------------------
+# Calendar feature extraction
+# ---------------------------------------------------------------------------
+
+def detect_long_format(
+    df: pd.DataFrame,
+    date_col: str,
+) -> tuple[bool, str | None, str | None]:
+    """Heuristic: detect whether *df* is in long (stacked) format.
+
+    Returns ``(is_long, group_col, value_col)``.
+
+    A DataFrame is flagged as *long* when the date column contains
+    duplicate values **and** there is at least one string/object column
+    among the remaining columns (the likely group identifier).
+    """
+    if date_col not in df.columns:
+        return False, None, None
+
+    dates = df[date_col]
+    if dates.nunique() >= len(dates):
+        # Every date is unique → wide format
+        return False, None, None
+
+    remaining = [c for c in df.columns if c != date_col]
+
+    # Find first string/object column → candidate group column
+    group_col: str | None = None
+    for c in remaining:
+        if df[c].dtype == object or pd.api.types.is_string_dtype(df[c]):
+            group_col = c
+            break
+
+    if group_col is None:
+        return False, None, None
+
+    # Find first numeric column (excluding the group column) → candidate value
+    value_col: str | None = None
+    for c in remaining:
+        if c == group_col:
+            continue
+        if pd.api.types.is_numeric_dtype(df[c]):
+            value_col = c
+            break
+
+    if value_col is None:
+        return False, None, None
+
+    return True, group_col, value_col
+
+
+def pivot_long_to_wide(
+    df: pd.DataFrame,
+    date_col: str,
+    group_col: str,
+    value_col: str,
+) -> pd.DataFrame:
+    """Pivot a long-format DataFrame to wide format.
+
+    Parameters
+    ----------
+    df:
+        Long-format dataframe.
+    date_col:
+        Column with date values (becomes the index/row key).
+    group_col:
+        Column whose unique values become the new column headers.
+    value_col:
+        Column with the numeric values to spread.
+
+    Returns
+    -------
+    pd.DataFrame
+        Wide dataframe with *date_col* as a regular column and one
+        column per unique value in *group_col*.
+    """
+    wide = df.pivot_table(
+        index=date_col,
+        columns=group_col,
+        values=value_col,
+        aggfunc="first",
+    )
+    # Flatten MultiIndex column names to plain strings
+    wide.columns = [str(c) for c in wide.columns]
+    wide = wide.reset_index()
+    return wide
+
+
 # ---------------------------------------------------------------------------
 # Calendar feature extraction
 # ---------------------------------------------------------------------------

src/plotting.py

@@ -572,12 +572,22 @@ def plot_panel(
         )
         flat_axes = axes.flatten()
 
+        # Compute a sensible bar width from the median date spacing
+        bar_width = None
+        if chart_type == "bar":
+            dates_sorted = df[date_col].dropna().sort_values()
+            if len(dates_sorted) >= 2:
+                median_gap = dates_sorted.diff().dropna().median()
+                bar_width = median_gap * 0.9
+            else:
+                bar_width = 1
+
         for i, col in enumerate(y_cols):
             ax = flat_axes[i]
             color = _default_color(palette_colors, i)
 
             if chart_type == "bar":
-                ax.bar(df[date_col], df[col], color=color, width=2, edgecolor="white", linewidth=0.3)
+                ax.bar(df[date_col], df[col], width=bar_width, color=color, alpha=0.85, edgecolor="white", linewidth=0.3)
             else:
                 ax.plot(df[date_col], df[col], linewidth=1.3, color=color)
 

src/querychat_helpers.py

@@ -95,11 +95,18 @@ def create_querychat(
         f"{freq_part}"
     )
 
+    # Build example bullets that reference actual column names
+    if y_cols:
+        first_y = y_cols[0]
+        filter_example = f'- "Filter where {first_y} > median"'
+    else:
+        filter_example = '- "Filter where value > 100"'
+
     greeting = (
         f"Hi! I can help you filter and explore the **{name}** dataset.  "
         "Try asking me something like:\n"
-        '- "Show only 2023 data"\n'
-        '- "Filter where sales > 60000"\n'
+        '- "Show only the last 5 years"\n'
+        f"{filter_example}\n"
         '- "Show rows from January to March"'
     )
 

src/ui_theme.py

@@ -93,14 +93,16 @@ def apply_miami_theme() -> None:
             background-color: {_LIGHT_GRAY};
             box-shadow: inset 4px 0 0 0 {MIAMI_RED};
             border-radius: 6px;
+            padding: 0.6rem 0.75rem 0.6rem 1rem;
         }}
         div[data-testid="stMetric"] label {{
             color: {_BLACK};
-            font-size: 0.85rem;
+            font-size: 0.78rem;
         }}
         div[data-testid="stMetric"] div[data-testid="stMetricValue"] {{
             color: {_BLACK};
             font-weight: 700;
+            font-size: 0.95rem;
         }}
 
         /* ---- Sidebar developer card ---- */
@@ -136,15 +138,17 @@ def apply_miami_theme() -> None:
             flex-wrap: wrap;
             margin-top: 0.35rem;
         }}
-        .dev-link {{
+        .dev-link,
+        .dev-link:visited,
+        .dev-link:link {{
             display: inline-flex;
             align-items: center;
             gap: 0.2rem;
             padding: 0.15rem 0.4rem;
-            border: 1px solid #dee2e6;
+            border: 1px solid {MIAMI_RED};
             border-radius: 4px;
             font-size: 0.65rem;
-            color: {_BLACK};
+            color: {MIAMI_RED} !important;
             text-decoration: none;
             background: {_WHITE};
             line-height: 1.4;
@@ -153,11 +157,14 @@ def apply_miami_theme() -> None:
         .dev-link svg {{
             width: 11px;
             height: 11px;
-            fill: currentColor;
+            fill: {MIAMI_RED};
         }}
         .dev-link:hover {{
-            border-color: {MIAMI_RED};
-            color: {MIAMI_RED};
+            background-color: {MIAMI_RED};
+            color: {_WHITE} !important;
+        }}
+        .dev-link:hover svg {{
+            fill: {_WHITE};
         }}
     </style>
     """