Replace scipy with vanilla math implementation

.gitignore

@@ -126,3 +126,4 @@ compare_themes.py
 compare_tables.html
 
 README_FILES/
+uv.lock

Makefile

@@ -1,10 +1,21 @@
 .PHONY: check
 
 test:
-	pytest --cov-report=xml
+	@uv run pytest --cov-report=xml
 
 test-update:
-	pytest --snapshot-update
+	@uv run pytest --snapshot-update
+
+lint: ## Run ruff linter and type checker
+	@uv run ruff check --fix
+	@uv run ruff format
+	@uv run ty check .
+
+pre-commit-install: ## Install pre-commit hooks
+	@uv run pre-commit install
+
+pre-commit: ## Run pre-commit hooks
+	@uv run pre-commit run --all-files
 
 docs-build:
 	cd docs \

docs/examples/nfl-season/index.ipynb

@@ -121,6 +121,7 @@
     "\n",
     "    return results\n",
     "\n",
+    "\n",
     "team_stats = get_team_stats(season_2016)"
    ]
   },

gt_extras/_stats.py

@@ -0,0 +1,205 @@
+"""Native Python implementations of statistical functions.
+
+This module provides pure Python implementations of statistical functions
+previously provided by scipy, specifically for computing confidence intervals.
+"""
+
+from __future__ import annotations
+
+import math
+
+
+def mean(values: list[float | int]) -> float:
+    """Compute the arithmetic mean of a list of values."""
+    if not values:
+        raise ValueError("Cannot compute mean of empty list")
+    return sum(values) / len(values)
+
+
+def std(values: list[float | int], ddof: int = 1) -> float:
+    """Compute the sample standard deviation.
+
+    Parameters
+    ----------
+    values
+        List of numeric values.
+    ddof
+        Delta degrees of freedom. Default is 1 for sample std.
+
+    Returns
+    -------
+    float
+        The sample standard deviation.
+    """
+    n = len(values)
+    if n <= ddof:
+        raise ValueError(
+            f"Need at least {ddof + 1} values to compute std with ddof={ddof}"
+        )
+    m = mean(values)
+    variance = sum((x - m) ** 2 for x in values) / (n - ddof)
+    return math.sqrt(variance)
+
+
+def sem(values: list[float | int]) -> float:
+    """Compute the standard error of the mean.
+
+    Parameters
+    ----------
+    values
+        List of numeric values.
+
+    Returns
+    -------
+    float
+        The standard error of the mean (std / sqrt(n)).
+    """
+    n = len(values)
+    if n < 2:
+        raise ValueError("Need at least 2 values to compute sem")
+    return std(values, ddof=1) / math.sqrt(n)
+
+
+def _norm_ppf(p: float) -> float:
+    """Approximate the inverse of the standard normal CDF (probit function).
+
+    Uses the Abramowitz and Stegun approximation (formula 26.2.23).
+    Accurate to about 4.5 decimal places.
+
+    Parameters
+    ----------
+    p
+        Probability value between 0 and 1.
+
+    Returns
+    -------
+    float
+        The z-score corresponding to the probability p.
+    """
+    if p <= 0 or p >= 1:
+        if p == 0:
+            return float("-inf")
+        if p == 1:
+            return float("inf")
+        raise ValueError("p must be between 0 and 1")
+
+    # Constants for approximation
+    a0 = 2.515517
+    a1 = 0.802853
+    a2 = 0.010328
+    b1 = 1.432788
+    b2 = 0.189269
+    b3 = 0.001308
+
+    if p < 0.5:
+        sign = -1
+        p_work = p
+    else:
+        sign = 1
+        p_work = 1 - p
+
+    if p_work < 1e-300:
+        return sign * 38.0  # Approximate for extreme values
+
+    t = math.sqrt(-2 * math.log(p_work))
+    numerator = a0 + a1 * t + a2 * t * t
+    denominator = 1 + b1 * t + b2 * t * t + b3 * t * t * t
+    result = t - numerator / denominator
+
+    return sign * result
+
+
+def _t_ppf(p: float, df: float) -> float:
+    """Approximate the inverse of the Student's t-distribution CDF.
+
+    Uses Hill's approximation for the t-distribution quantile function,
+    which provides good accuracy for most practical purposes.
+
+    Parameters
+    ----------
+    p
+        Probability value between 0 and 1.
+    df
+        Degrees of freedom.
+
+    Returns
+    -------
+    float
+        The t-value corresponding to probability p.
+    """
+    if df <= 0:
+        raise ValueError("Degrees of freedom must be positive")
+
+    # For very large df, use normal approximation
+    if df > 1e6:
+        return _norm_ppf(p)
+
+    # Get the corresponding normal quantile
+    z = _norm_ppf(p)
+
+    # For df=1 (Cauchy), use tan approximation
+    if df == 1:
+        return math.tan(math.pi * (p - 0.5))
+
+    # For df=2, there's an exact formula
+    if df == 2:
+        alpha = 2 * p - 1
+        if abs(alpha) >= 1:
+            return float("inf") if alpha > 0 else float("-inf")
+        return alpha / math.sqrt(2 * (1 - alpha * alpha))
+
+    # Hill's approximation for general df
+    # Cornish-Fisher expansion
+    g1 = (z**3 + z) / 4
+    g2 = (5 * z**5 + 16 * z**3 + 3 * z) / 96
+    g3 = (3 * z**7 + 19 * z**5 + 17 * z**3 - 15 * z) / 384
+    g4 = (79 * z**9 + 776 * z**7 + 1482 * z**5 - 1920 * z**3 - 945 * z) / 92160
+
+    inv_df = 1 / df
+    t = z + g1 * inv_df + g2 * inv_df**2 + g3 * inv_df**3 + g4 * inv_df**4
+
+    return t
+
+
+def t_interval(
+    confidence: float,
+    df: int,
+    loc: float = 0.0,
+    scale: float = 1.0,
+) -> tuple[float, float]:
+    """Compute the confidence interval for a t-distribution.
+
+    This is equivalent to scipy.stats.t.interval().
+
+    Parameters
+    ----------
+    confidence
+        The confidence level (e.g., 0.95 for 95% CI).
+    df
+        Degrees of freedom.
+    loc
+        Location parameter (mean). Default is 0.
+    scale
+        Scale parameter (standard error). Default is 1.
+
+    Returns
+    -------
+    tuple[float, float]
+        A tuple (lower, upper) representing the confidence interval bounds.
+    """
+    if not 0 < confidence < 1:
+        raise ValueError("Confidence must be between 0 and 1")
+    if df < 1:
+        raise ValueError("Degrees of freedom must be at least 1")
+
+    alpha = 1 - confidence
+    lower_tail = alpha / 2
+    upper_tail = 1 - alpha / 2
+
+    t_lower = _t_ppf(lower_tail, df)
+    t_upper = _t_ppf(upper_tail, df)
+
+    lower = loc + scale * t_lower
+    upper = loc + scale * t_upper
+
+    return (lower, upper)

gt_extras/plotting.py

@@ -11,7 +11,6 @@
 )
 from great_tables._locations import resolve_cols_c
 from great_tables._tbl_data import SelectExpr, is_na
-from scipy.stats import sem, t, tmean
 from svg import (
     SVG,
     Arc,
@@ -27,6 +26,8 @@
 )
 
 from gt_extras import gt_duplicate_column
+from gt_extras._stats import mean as tmean
+from gt_extras._stats import sem, t_interval
 from gt_extras._utils_color import _get_discrete_colors_from_palette
 from gt_extras._utils_column import (
     _format_numeric_text,
@@ -884,7 +885,7 @@ def _compute_mean_and_conf_int(val):
             if val is None or not isinstance(val, list) or len(val) == 0:
                 return (None, None, None)
             mean = tmean(val)
-            conf_int = t.interval(
+            conf_int = t_interval(
                 ci,
                 len(val) - 1,
                 loc=mean,

gt_extras/tests/__snapshots__/test_colors.ambr

@@ -56,15 +56,24 @@
 # name: test_gt_data_color_by_group_multiple_groups_snap[pd_and_pl]
   '''
   <tbody class="gt_table_body">
+    <tr class="gt_group_heading_row">
+      <th class="gt_group_heading" colspan="1">1</th>
+    </tr>
     <tr>
       <td style="color: #FFFFFF; background-color: #000000;" class="gt_row gt_right">1</td>
     </tr>
+    <tr class="gt_group_heading_row">
+      <th class="gt_group_heading" colspan="1">2</th>
+    </tr>
     <tr>
       <td style="color: #FFFFFF; background-color: #000000;" class="gt_row gt_right">2</td>
     </tr>
     <tr>
       <td style="color: #000000; background-color: #9e9e9e;" class="gt_row gt_right">3</td>
     </tr>
+    <tr class="gt_group_heading_row">
+      <th class="gt_group_heading" colspan="1">3</th>
+    </tr>
     <tr>
       <td style="color: #FFFFFF; background-color: #000000;" class="gt_row gt_right">4</td>
     </tr>

gt_extras/tests/__snapshots__/test_formatting.ambr

@@ -119,8 +119,11 @@
    #mycombinedtable .gt_stub_row_group { color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: initial; text-transform: inherit; border-right-style: solid; border-right-width: 2px; border-right-color: #D3D3D3; padding-left: 5px; padding-right: 5px; vertical-align: top; }
    #mycombinedtable .gt_row_group_first td { border-top-width: 2px; }
    #mycombinedtable .gt_row_group_first th { border-top-width: 2px; }
-   #mycombinedtable .gt_striped { background-color: rgba(128,128,128,0.05); }
+   #mycombinedtable .gt_striped { color: #333333; background-color: #F4F4F4; }
    #mycombinedtable .gt_table_body { border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; }
+   #mycombinedtable .gt_grand_summary_row { color: #333333; background-color: #FFFFFF; text-transform: inherit; padding-top: 8px; padding-bottom: 8px; padding-left: 5px; padding-right: 5px; }
+   #mycombinedtable .gt_first_grand_summary_row_bottom { border-top-style: double; border-top-width: 6px; border-top-color: #D3D3D3; }
+   #mycombinedtable .gt_last_grand_summary_row_top { border-bottom-style: double; border-bottom-width: 6px; border-bottom-color: #D3D3D3; }
    #mycombinedtable .gt_sourcenotes { color: #333333; background-color: #FFFFFF; border-bottom-style: none; border-bottom-width: 2px; border-bottom-color: #D3D3D3; border-left-style: none; border-left-width: 2px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 2px; border-right-color: #D3D3D3; }
    #mycombinedtable .gt_sourcenote { font-size: 90%; padding-top: 4px; padding-bottom: 4px; padding-left: 5px; padding-right: 5px; text-align: left; }
    #mycombinedtable .gt_left { text-align: left; }
@@ -182,8 +185,11 @@
    #id1 .gt_stub_row_group { color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: initial; text-transform: inherit; border-right-style: solid; border-right-width: 2px; border-right-color: #D3D3D3; padding-left: 5px; padding-right: 5px; vertical-align: top; }
    #id1 .gt_row_group_first td { border-top-width: 2px; }
    #id1 .gt_row_group_first th { border-top-width: 2px; }
-   #id1 .gt_striped { background-color: rgba(128,128,128,0.05); }
+   #id1 .gt_striped { color: #333333; background-color: #F4F4F4; }
    #id1 .gt_table_body { border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; }
+   #id1 .gt_grand_summary_row { color: #333333; background-color: #FFFFFF; text-transform: inherit; padding-top: 8px; padding-bottom: 8px; padding-left: 5px; padding-right: 5px; }
+   #id1 .gt_first_grand_summary_row_bottom { border-top-style: double; border-top-width: 6px; border-top-color: #D3D3D3; }
+   #id1 .gt_last_grand_summary_row_top { border-bottom-style: double; border-bottom-width: 6px; border-bottom-color: #D3D3D3; }
    #id1 .gt_sourcenotes { color: #333333; background-color: #FFFFFF; border-bottom-style: none; border-bottom-width: 2px; border-bottom-color: #D3D3D3; border-left-style: none; border-left-width: 2px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 2px; border-right-color: #D3D3D3; }
    #id1 .gt_sourcenote { font-size: 90%; padding-top: 4px; padding-bottom: 4px; padding-left: 5px; padding-right: 5px; text-align: left; }
    #id1 .gt_left { text-align: left; }
@@ -256,8 +262,11 @@
    #id2 .gt_stub_row_group { color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: initial; text-transform: inherit; border-right-style: solid; border-right-width: 2px; border-right-color: #D3D3D3; padding-left: 5px; padding-right: 5px; vertical-align: top; }
    #id2 .gt_row_group_first td { border-top-width: 2px; }
    #id2 .gt_row_group_first th { border-top-width: 2px; }
-   #id2 .gt_striped { background-color: rgba(128,128,128,0.05); }
+   #id2 .gt_striped { color: #333333; background-color: #F4F4F4; }
    #id2 .gt_table_body { border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; }
+   #id2 .gt_grand_summary_row { color: #333333; background-color: #FFFFFF; text-transform: inherit; padding-top: 8px; padding-bottom: 8px; padding-left: 5px; padding-right: 5px; }
+   #id2 .gt_first_grand_summary_row_bottom { border-top-style: double; border-top-width: 6px; border-top-color: #D3D3D3; }
+   #id2 .gt_last_grand_summary_row_top { border-bottom-style: double; border-bottom-width: 6px; border-bottom-color: #D3D3D3; }
    #id2 .gt_sourcenotes { color: #333333; background-color: #FFFFFF; border-bottom-style: none; border-bottom-width: 2px; border-bottom-color: #D3D3D3; border-left-style: none; border-left-width: 2px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 2px; border-right-color: #D3D3D3; }
    #id2 .gt_sourcenote { font-size: 90%; padding-top: 4px; padding-bottom: 4px; padding-left: 5px; padding-right: 5px; text-align: left; }
    #id2 .gt_left { text-align: left; }

pyproject.toml

@@ -19,22 +19,21 @@ license.file = "LICENSE"
 dependencies = [
     "faicons>=0.2.2",
     "great-tables>=0.18.0",
-    "scipy>=1.13.1",
     "svg-py>=1.6.0",
     "narwhals>=1.0.0",
 ]
 
 authors = [
-    { name = "Jules Walzer-Goldfeld", email = "jules.walzergoldfeld@gmail.com"},
+    { name = "Jules Walzer-Goldfeld", email = "jules.walzergoldfeld@gmail.com" },
     { name = "Michael Chow", email = "mc_al_github@fastmail.com" },
-    { name = "Rich Iannone", email = "rich@posit.co"}
+    { name = "Rich Iannone", email = "rich@posit.co" },
 ]
 
 classifiers = [
     "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
-    "Programming Language :: Python :: 3.12"
+    "Programming Language :: Python :: 3.12",
 ]
 keywords = ["tables", "html"]
 
@@ -52,14 +51,12 @@ dev = [
     "quartodoc>=0.11.1",
     "quarto>=0.1.0",
     "pre-commit>=4.2.0",
+    "ruff>=0.14.10",
+    "ty>=0.0.8",
 ]
 
 
 [tool.coverage.report]
-exclude_also = [
-    "if TYPE_CHECKING:"
-]
+exclude_also = ["if TYPE_CHECKING:"]
 include = ["gt_extras/*"]
-omit = [
-    "gt_extras/tests/*"
-]
+omit = ["gt_extras/tests/*"]