diff --git a/README.rst b/README.rst
index b4c38eaa..cd35ea35 100644
--- a/README.rst
+++ b/README.rst
@@ -47,7 +47,7 @@ plothist
    :target: https://badge.fury.io/py/plothist
 .. |Code style: black| image:: https://img.shields.io/badge/code%20style-black-000000.svg
    :target: https://github.com/psf/black
-.. |Docs from latest| image:: https://img.shields.io/badge/docs-v0.9-blue.svg
+.. |Docs from latest| image:: https://img.shields.io/badge/docs-v1.0-blue.svg
    :target: https://plothist.readthedocs.io/en/latest/
 .. |Docs from main| image:: https://img.shields.io/badge/docs-main-blue.svg
    :target: https://plothist.readthedocs.io/en/main/
diff --git a/docs/conf.py b/docs/conf.py
index 96a19554..6898a5ab 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -36,9 +36,9 @@
 author = "Cyrille Praz, Tristan Fillinger"
 
 # The short X.Y version
-version = "0.9"
+version = "1.0"
 # The full version, including alpha/beta/rc tags
-release = "0.9"
+release = "1.0"
 
 
 # -- General configuration ---------------------------------------------------
diff --git a/docs/examples/model_ex/model_all_comparisons.py b/docs/examples/model_ex/model_all_comparisons.py
index d3b96ef0..897c2f4a 100644
--- a/docs/examples/model_ex/model_all_comparisons.py
+++ b/docs/examples/model_ex/model_all_comparisons.py
@@ -91,7 +91,7 @@
         h1_label="Data",
         h2_label="Pred.",
         ratio_uncertainty_type="split",
-        hist_1_uncertainty_type="asymmetrical",
+        h1_uncertainty_type="asymmetrical",
     )
     add_text(
         f'  $\mathbf{{→}}$ comparison = "{comparison}"', ax=ax_comparison, fontsize=13
diff --git a/docs/examples/model_ex/model_all_comparisons_no_model_unc.py b/docs/examples/model_ex/model_all_comparisons_no_model_unc.py
index d46f7cab..9097023c 100644
--- a/docs/examples/model_ex/model_all_comparisons_no_model_unc.py
+++ b/docs/examples/model_ex/model_all_comparisons_no_model_unc.py
@@ -99,7 +99,7 @@
         h1_label="Data",
         h2_label="Pred.",
         ratio_uncertainty_type="split",
-        hist_1_uncertainty_type="asymmetrical",
+        h1_uncertainty_type="asymmetrical",
     )
     if comparison == "pull":
         # Since the uncertainties of the model are neglected, the pull label is "(Data - Pred.)/sigma_Data"
diff --git a/docs/examples/model_ex/model_comparisons_ratio_options.py b/docs/examples/model_ex/model_comparisons_ratio_options.py
index b48dfdf2..3d1a211c 100644
--- a/docs/examples/model_ex/model_comparisons_ratio_options.py
+++ b/docs/examples/model_ex/model_comparisons_ratio_options.py
@@ -110,7 +110,7 @@
         h1_label="Data",
         h2_label="Pred.",
         ratio_uncertainty_type=ratio_uncertainty_type,
-        hist_1_uncertainty_type="asymmetrical",
+        h1_uncertainty_type="asymmetrical",
     )
     add_text(
         f'  $\mathbf{{→}}$ comparison = "ratio", \n  $\mathbf{{→}}$ ratio_uncertainty_type="{ratio_uncertainty_type}", model_uncertainty = {model_uncertainty}',
diff --git a/docs/examples/utility/matplotlib_vs_plothist_style.py b/docs/examples/utility/matplotlib_vs_plothist_style.py
index a0dcdbaa..5d01b1d3 100644
--- a/docs/examples/utility/matplotlib_vs_plothist_style.py
+++ b/docs/examples/utility/matplotlib_vs_plothist_style.py
@@ -33,14 +33,14 @@
     hist_0, bins, _ = ax1.hist(
         df["variable_0"], bins=20, histtype="step", linewidth=1.2, label="h1"
     )
-    hist_1 = ax1.hist(
+    h1 = ax1.hist(
         df["variable_1"], bins=bins, histtype="step", linewidth=1.2, label="h2"
     )
     ax1.set_ylabel("Entries")
     ax1.legend()
 
     # Calculate the ratio of histogram values and plot in the second subplot (ax2)
-    ratio = hist_0 / hist_1[0]  # Divide bin values of variable_0 by variable_1
+    ratio = hist_0 / h1[0]  # Divide bin values of variable_0 by variable_1
     bin_centers = 0.5 * (bins[:-1] + bins[1:])  # Calculate bin centers
 
     # Create fake error bars for the ratio
diff --git a/plothist/__init__.py b/plothist/__init__.py
index 7236d36f..b108a3d0 100644
--- a/plothist/__init__.py
+++ b/plothist/__init__.py
@@ -1,5 +1,5 @@
 """Plot histograms in a scalable way and a beautiful style."""
-__version__ = "0.9"
+__version__ = "1.0"
 
 from .plotters import (
     create_comparison_figure,
diff --git a/plothist/comparison.py b/plothist/comparison.py
index 62b611d7..af841d9c 100644
--- a/plothist/comparison.py
+++ b/plothist/comparison.py
@@ -100,15 +100,15 @@ def _check_binning_consistency(hist_list):
                 raise ValueError("The bins of the histograms must be equal.")
 
 
-def get_ratio_variances(hist_1, hist_2):
+def get_ratio_variances(h1, h2):
     """
-    Calculate the variances of the ratio of two histograms (hist_1/hist_2).
+    Calculate the variances of the ratio of two histograms (h1/h2).
 
     Parameters
     ----------
-    hist_1 : boost_histogram.Histogram
+    h1 : boost_histogram.Histogram
         The first histogram.
-    hist_2 : boost_histogram.Histogram
+    h2 : boost_histogram.Histogram
         The second histogram.
 
     Returns
@@ -121,15 +121,13 @@ def get_ratio_variances(hist_1, hist_2):
     ValueError
         If the bins of the histograms are not equal.
     """
-
-    _check_binning_consistency([hist_1, hist_2])
-    _check_binning_consistency([hist_1, hist_2])
+    _check_binning_consistency([h1, h2])
 
     np.seterr(divide="ignore", invalid="ignore")
     ratio_variances = np.where(
-        hist_2.values() != 0,
-        hist_1.variances() / hist_2.values() ** 2
-        + hist_2.variances() * hist_1.values() ** 2 / hist_2.values() ** 4,
+        h2.values() != 0,
+        h1.variances() / h2.values() ** 2
+        + h2.variances() * h1.values() ** 2 / h2.values() ** 4,
         np.nan,
     )
     np.seterr(divide="warn", invalid="warn")
@@ -137,18 +135,19 @@ def get_ratio_variances(hist_1, hist_2):
     return ratio_variances
 
 
-def get_pull(hist_1, hist_2, hist_1_uncertainty_type="symmetrical"):
+def get_pull(h1, h2, h1_uncertainty_type="symmetrical"):
     """
     Compute the pull between two histograms.
 
     Parameters
     ----------
-    hist_1 : boost_histogram.Histogram
+    h1 : boost_histogram.Histogram
         The first histogram.
-    hist_2 : boost_histogram.Histogram
+    h2 : boost_histogram.Histogram
         The second histogram.
-    hist_1_uncertainty_type : str, optional
-        What kind of bin uncertainty to use for hist_1: "symmetrical" for the Poisson standard deviation derived from the variance stored in the histogram object, "asymmetrical" for asymmetrical uncertainties based on a Poisson confidence interval. Default is "symmetrical".
+    h1_uncertainty_type : str, optional
+        What kind of bin uncertainty to use for h1: "symmetrical" for the Poisson standard deviation derived from the variance stored in the histogram object, "asymmetrical" for asymmetrical uncertainties based on a Poisson confidence interval. Default is "symmetrical".
+
     Returns
     -------
     comparison_values : numpy.ndarray
@@ -158,23 +157,23 @@ def get_pull(hist_1, hist_2, hist_1_uncertainty_type="symmetrical"):
     comparison_uncertainties_high : numpy.ndarray
         The upper uncertainties on the pull. Always ones.
     """
-    _check_uncertainty_type(hist_1_uncertainty_type)
-    _check_binning_consistency([hist_1, hist_2])
+    _check_uncertainty_type(h1_uncertainty_type)
+    _check_binning_consistency([h1, h2])
 
-    if hist_1_uncertainty_type == "asymmetrical":
-        uncertainties_low, uncertainties_high = get_asymmetrical_uncertainties(hist_1)
-        hist_1_variances = np.where(
-            hist_1.values() >= hist_2.values(),
+    if h1_uncertainty_type == "asymmetrical":
+        uncertainties_low, uncertainties_high = get_asymmetrical_uncertainties(h1)
+        h1_variances = np.where(
+            h1.values() >= h2.values(),
             uncertainties_low ** 2,
             uncertainties_high ** 2,
         )
-        hist_1 = hist_1.copy()
-        hist_1[:] = np.c_[hist_1.values(), hist_1_variances]
+        h1 = h1.copy()
+        h1[:] = np.c_[h1.values(), h1_variances]
 
     comparison_values = np.where(
-        hist_1.variances() + hist_2.variances() != 0,
-        (hist_1.values() - hist_2.values())
-        / np.sqrt(hist_1.variances() + hist_2.variances()),
+        h1.variances() + h2.variances() != 0,
+        (h1.values() - h2.values())
+        / np.sqrt(h1.variances() + h2.variances()),
         np.nan,
     )
     comparison_uncertainties_low = np.ones_like(comparison_values)
@@ -187,18 +186,19 @@ def get_pull(hist_1, hist_2, hist_1_uncertainty_type="symmetrical"):
     )
 
 
-def get_difference(hist_1, hist_2, hist_1_uncertainty_type="symmetrical"):
+def get_difference(h1, h2, h1_uncertainty_type="symmetrical"):
     """
     Compute the difference between two histograms.
 
     Parameters
     ----------
-    hist_1 : boost_histogram.Histogram
+    h1 : boost_histogram.Histogram
         The first histogram.
-    hist_2 : boost_histogram.Histogram
+    h2 : boost_histogram.Histogram
         The second histogram.
-    hist_1_uncertainty_type : str, optional
-        What kind of bin uncertainty to use for hist_1: "symmetrical" for the Poisson standard deviation derived from the variance stored in the histogram object, "asymmetrical" for asymmetrical uncertainties based on a Poisson confidence interval. Default is "symmetrical".
+    h1_uncertainty_type : str, optional
+        What kind of bin uncertainty to use for h1: "symmetrical" for the Poisson standard deviation derived from the variance stored in the histogram object, "asymmetrical" for asymmetrical uncertainties based on a Poisson confidence interval. Default is "symmetrical".
+
     Returns
     -------
     comparison_values : numpy.ndarray
@@ -208,22 +208,22 @@ def get_difference(hist_1, hist_2, hist_1_uncertainty_type="symmetrical"):
     comparison_uncertainties_high : numpy.ndarray
         The upper uncertainties on the difference.
     """
-    _check_uncertainty_type(hist_1_uncertainty_type)
-    _check_binning_consistency([hist_1, hist_2])
+    _check_uncertainty_type(h1_uncertainty_type)
+    _check_binning_consistency([h1, h2])
 
-    comparison_values = hist_1.values() - hist_2.values()
+    comparison_values = h1.values() - h2.values()
 
-    if hist_1_uncertainty_type == "asymmetrical":
-        uncertainties_low, uncertainties_high = get_asymmetrical_uncertainties(hist_1)
+    if h1_uncertainty_type == "asymmetrical":
+        uncertainties_low, uncertainties_high = get_asymmetrical_uncertainties(h1)
 
         comparison_uncertainties_low = np.sqrt(
-            uncertainties_low ** 2 + hist_2.variances()
+            uncertainties_low ** 2 + h2.variances()
         )
         comparison_uncertainties_high = np.sqrt(
-            uncertainties_high ** 2 + hist_2.variances()
+            uncertainties_high ** 2 + h2.variances()
         )
     else:
-        comparison_uncertainties_low = np.sqrt(hist_1.variances() + hist_2.variances())
+        comparison_uncertainties_low = np.sqrt(h1.variances() + h2.variances())
         comparison_uncertainties_high = comparison_uncertainties_low
 
     return (
@@ -233,17 +233,18 @@ def get_difference(hist_1, hist_2, hist_1_uncertainty_type="symmetrical"):
     )
 
 
-def get_asymmetry(hist_1, hist_2):
+def get_asymmetry(h1, h2):
     """
-    Get the asymmetry between two histograms hist_1 and hist_2, defined as (hist_1 - hist_2) / (hist_1 + hist_2).
+    Get the asymmetry between two histograms h1 and h2, defined as (h1 - h2) / (h1 + h2).
     Only symmetrical uncertainties are supported.
 
     Parameters
     ----------
-    hist_1 : boost_histogram.Histogram
+    h1 : boost_histogram.Histogram
         The first histogram.
-    hist_2 : boost_histogram.Histogram
+    h2 : boost_histogram.Histogram
         The second histogram.
+
     Returns
     -------
     comparison_values : numpy.ndarray
@@ -253,10 +254,10 @@ def get_asymmetry(hist_1, hist_2):
     comparison_uncertainties_high : numpy.ndarray
         The upper uncertainties on the asymmetry.
     """
-    _check_binning_consistency([hist_1, hist_2])
+    _check_binning_consistency([h1, h2])
 
-    hist_sum = hist_1 + hist_2
-    hist_diff = hist_1 + (-1 * hist_2)
+    hist_sum = h1 + h2
+    hist_diff = h1 + (-1 * h2)
     comparison_values = np.where(
         hist_sum.values() != 0, hist_diff.values() / hist_sum.values(), np.nan
     )
@@ -269,24 +270,25 @@ def get_asymmetry(hist_1, hist_2):
 
 
 def get_ratio(
-    hist_1,
-    hist_2,
+    h1,
+    h2,
+    h1_uncertainty_type="symmetrical",
     ratio_uncertainty_type="uncorrelated",
-    hist_1_uncertainty_type="symmetrical",
 ):
     """
     Compute the ratio between two histograms.
 
     Parameters
     ----------
-    hist_1 : boost_histogram.Histogram
+    h1 : boost_histogram.Histogram
         The numerator histogram.
-    hist_2 : boost_histogram.Histogram
+    h2 : boost_histogram.Histogram
         The denominator histogram.
+    h1_uncertainty_type : str, optional
+        What kind of bin uncertainty to use for h1: "symmetrical" for the Poisson standard deviation derived from the variance stored in the histogram object, "asymmetrical" for asymmetrical uncertainties based on a Poisson confidence interval. Default is "symmetrical".
     ratio_uncertainty_type : str, optional
-        How to treat the uncertainties of the histograms: "uncorrelated" for simple comparison, "split" for scaling and split hist_1 and hist_2 uncertainties. Default is "uncorrelated".
-    hist_1_uncertainty_type : str, optional
-        What kind of bin uncertainty to use for hist_1: "symmetrical" for the Poisson standard deviation derived from the variance stored in the histogram object, "asymmetrical" for asymmetrical uncertainties based on a Poisson confidence interval. Default is "symmetrical".
+        How to treat the uncertainties of the histograms: "uncorrelated" for simple comparison, "split" for scaling and split h1 and h2 uncertainties. Default is "uncorrelated".
+
     Returns
     -------
     comparison_values : numpy.ndarray
@@ -296,39 +298,39 @@ def get_ratio(
     comparison_uncertainties_high : numpy.ndarray
         The upper uncertainties on the ratio.
     """
-    _check_uncertainty_type(hist_1_uncertainty_type)
-    _check_binning_consistency([hist_1, hist_2])
+    _check_uncertainty_type(h1_uncertainty_type)
+    _check_binning_consistency([h1, h2])
 
     comparison_values = np.where(
-        hist_2.values() != 0, hist_1.values() / hist_2.values(), np.nan
+        h2.values() != 0, h1.values() / h2.values(), np.nan
     )
 
-    if hist_1_uncertainty_type == "asymmetrical":
-        uncertainties_low, uncertainties_high = get_asymmetrical_uncertainties(hist_1)
+    if h1_uncertainty_type == "asymmetrical":
+        uncertainties_low, uncertainties_high = get_asymmetrical_uncertainties(h1)
 
     if ratio_uncertainty_type == "uncorrelated":
-        if hist_1_uncertainty_type == "asymmetrical":
-            hist_1_high = hist_1.copy()
-            hist_1_high[:] = np.c_[hist_1_high.values(), uncertainties_high ** 2]
-            hist_1_low = hist_1.copy()
-            hist_1_low[:] = np.c_[hist_1_low.values(), uncertainties_low ** 2]
+        if h1_uncertainty_type == "asymmetrical":
+            h1_high = h1.copy()
+            h1_high[:] = np.c_[h1_high.values(), uncertainties_high ** 2]
+            h1_low = h1.copy()
+            h1_low[:] = np.c_[h1_low.values(), uncertainties_low ** 2]
             comparison_uncertainties_low = np.sqrt(
-                get_ratio_variances(hist_1_low, hist_2)
+                get_ratio_variances(h1_low, h2)
             )
             comparison_uncertainties_high = np.sqrt(
-                get_ratio_variances(hist_1_high, hist_2)
+                get_ratio_variances(h1_high, h2)
             )
         else:
-            comparison_uncertainties_low = np.sqrt(get_ratio_variances(hist_1, hist_2))
+            comparison_uncertainties_low = np.sqrt(get_ratio_variances(h1, h2))
             comparison_uncertainties_high = comparison_uncertainties_low
     elif ratio_uncertainty_type == "split":
-        if hist_1_uncertainty_type == "asymmetrical":
-            comparison_uncertainties_low = uncertainties_low / hist_2.values()
-            comparison_uncertainties_high = uncertainties_high / hist_2.values()
+        if h1_uncertainty_type == "asymmetrical":
+            comparison_uncertainties_low = uncertainties_low / h2.values()
+            comparison_uncertainties_high = uncertainties_high / h2.values()
         else:
             h1_scaled_uncertainties = np.where(
-                hist_2.values() != 0,
-                np.sqrt(hist_1.variances()) / hist_2.values(),
+                h2.values() != 0,
+                np.sqrt(h1.variances()) / h2.values(),
                 np.nan,
             )
             comparison_uncertainties_low = h1_scaled_uncertainties
@@ -344,27 +346,28 @@ def get_ratio(
 
 
 def get_comparison(
-    hist_1,
-    hist_2,
+    h1,
+    h2,
     comparison,
+    h1_uncertainty_type="symmetrical",
     ratio_uncertainty_type="uncorrelated",
-    hist_1_uncertainty_type="symmetrical",
 ):
     """
     Compute the comparison between two histograms.
 
     Parameters
     ----------
-    hist_1 : boost_histogram.Histogram
+    h1 : boost_histogram.Histogram
         The first histogram for comparison.
-    hist_2 : boost_histogram.Histogram
+    h2 : boost_histogram.Histogram
         The second histogram for comparison.
     comparison : str
         The type of comparison ("ratio", "pull", "difference", "relative_difference" or "asymmetry").
+    h1_uncertainty_type : str, optional
+        What kind of bin uncertainty to use for h1: "symmetrical" for the Poisson standard deviation derived from the variance stored in the histogram object, "asymmetrical" for asymmetrical uncertainties based on a Poisson confidence interval. Default is "symmetrical".
     ratio_uncertainty_type : str, optional
-        How to treat the uncertainties of the histograms when comparison is "ratio" or "relative_difference" ("uncorrelated" for simple comparison, "split" for scaling and split hist_1 and hist_2 uncertainties). This argument has no effect if comparison != "ratio" or "relative_difference". Default is "uncorrelated".
-    hist_1_uncertainty_type : str, optional
-        What kind of bin uncertainty to use for hist_1: "symmetrical" for the Poisson standard deviation derived from the variance stored in the histogram object, "asymmetrical" for asymmetrical uncertainties based on a Poisson confidence interval. Default is "symmetrical".
+        How to treat the uncertainties of the histograms when comparison is "ratio" or "relative_difference" ("uncorrelated" for simple comparison, "split" for scaling and split h1 and h2 uncertainties). This argument has no effect if comparison != "ratio" or "relative_difference". Default is "uncorrelated".
+
     Returns
     -------
     values : numpy.ndarray
@@ -374,34 +377,34 @@ def get_comparison(
     upper_uncertainties : numpy.ndarray
         The upper uncertainties on the comparison values.
     """
-    _check_uncertainty_type(hist_1_uncertainty_type)
-    _check_binning_consistency([hist_1, hist_2])
+    _check_uncertainty_type(h1_uncertainty_type)
+    _check_binning_consistency([h1, h2])
 
     np.seterr(divide="ignore", invalid="ignore")
 
     if comparison == "ratio":
         values, lower_uncertainties, upper_uncertainties = get_ratio(
-            hist_1, hist_2, ratio_uncertainty_type, hist_1_uncertainty_type
+            h1, h2, h1_uncertainty_type, ratio_uncertainty_type
         )
     elif comparison == "relative_difference":
         values, lower_uncertainties, upper_uncertainties = get_ratio(
-            hist_1, hist_2, ratio_uncertainty_type, hist_1_uncertainty_type
+            h1, h2, h1_uncertainty_type, ratio_uncertainty_type
         )
         values -= 1  # relative difference is ratio-1
     elif comparison == "pull":
         values, lower_uncertainties, upper_uncertainties = get_pull(
-            hist_1, hist_2, hist_1_uncertainty_type
+            h1, h2, h1_uncertainty_type
         )
     elif comparison == "difference":
         values, lower_uncertainties, upper_uncertainties = get_difference(
-            hist_1, hist_2, hist_1_uncertainty_type
+            h1, h2, h1_uncertainty_type
         )
     elif comparison == "asymmetry":
-        if hist_1_uncertainty_type == "asymmetrical":
+        if h1_uncertainty_type == "asymmetrical":
             raise ValueError(
                 "Asymmetrical uncertainties are not supported for the asymmetry comparison."
             )
-        values, lower_uncertainties, upper_uncertainties = get_asymmetry(hist_1, hist_2)
+        values, lower_uncertainties, upper_uncertainties = get_asymmetry(h1, h2)
     else:
         raise ValueError(
             f"{comparison} not available as a comparison ('ratio', 'pull', 'difference', 'relative_difference' or 'asymmetry')."
diff --git a/plothist/get_dummy_data.py b/plothist/get_dummy_data.py
index 35cd2db5..12bdb0c3 100644
--- a/plothist/get_dummy_data.py
+++ b/plothist/get_dummy_data.py
@@ -3,5 +3,13 @@
 
 
 def get_dummy_data():
+    """
+    Get dummy data for plotting examples.
+
+    Returns
+    -------
+    pandas.DataFrame
+        Dummy data.
+    """
     with resources_path("plothist", "dummy_data.csv") as dummy_data:
         return pd.read_csv(dummy_data)
diff --git a/plothist/plothist_style.py b/plothist/plothist_style.py
index 0f06980e..4ada86f3 100644
--- a/plothist/plothist_style.py
+++ b/plothist/plothist_style.py
@@ -10,12 +10,12 @@
 
 def set_style(style="default"):
     """
-    Set the matplotlib style.
+    Set the plothist style.
 
     Parameters
     ----------
     style : str, optional
-        Switch between different styles. Default is 'default'. More style might come in the futur.
+        Switch between different styles. Default is 'default'. More style might come in the future.
 
     Returns
     -------
diff --git a/plothist/plotters.py b/plothist/plotters.py
index 73f77507..e136d90b 100644
--- a/plothist/plotters.py
+++ b/plothist/plotters.py
@@ -13,7 +13,6 @@
     get_asymmetrical_uncertainties,
     _check_binning_consistency,
     _check_uncertainty_type,
-    _is_unweighted,
 )
 from plothist.histogramming import _make_hist_from_function
 from plothist.plothist_style import set_fitting_ylabel_fontsize
@@ -206,6 +205,9 @@ def plot_function(func, range, ax, stacked=False, npoints=1000, **kwargs):
                 **kwargs,
             )
     else:
+        if kwargs.get("labels", None) is None:
+            kwargs["labels"] = []
+
         if not isinstance(func, list):
             func = [func]
         n_collections_before = len(list(ax.collections))
@@ -415,8 +417,8 @@ def plot_hist_uncertainties(hist, ax, **kwargs):
 
 
 def plot_two_hist_comparison(
-    hist_1,
-    hist_2,
+    h1,
+    h2,
     xlabel=None,
     ylabel=None,
     h1_label="h1",
@@ -431,9 +433,9 @@ def plot_two_hist_comparison(
 
     Parameters
     ----------
-    hist_1 : boost_histogram.Histogram
+    h1 : boost_histogram.Histogram
         The first histogram to compare.
-    hist_2 : boost_histogram.Histogram
+    h2 : boost_histogram.Histogram
         The second histogram to compare.
     xlabel : str, optional
         The label for the x-axis. Default is None.
@@ -467,7 +469,7 @@ def plot_two_hist_comparison(
 
     """
 
-    _check_binning_consistency([hist_1, hist_2])
+    _check_binning_consistency([h1, h2])
 
     if fig is None and ax_main is None and ax_comparison is None:
         fig, (ax_main, ax_comparison) = create_comparison_figure()
@@ -476,18 +478,18 @@ def plot_two_hist_comparison(
             "Need to provid fig, ax_main and ax_comparison (or none of them)."
         )
 
-    xlim = (hist_1.axes[0].edges[0], hist_1.axes[0].edges[-1])
+    xlim = (h1.axes[0].edges[0], h1.axes[0].edges[-1])
 
-    plot_hist(hist_1, ax=ax_main, label=h1_label, histtype="step")
-    plot_hist(hist_2, ax=ax_main, label=h2_label, histtype="step")
+    plot_hist(h1, ax=ax_main, label=h1_label, histtype="step")
+    plot_hist(h2, ax=ax_main, label=h2_label, histtype="step")
     ax_main.set_xlim(xlim)
     ax_main.set_ylabel(ylabel)
     ax_main.legend()
     _ = ax_main.xaxis.set_ticklabels([])
 
     plot_comparison(
-        hist_1,
-        hist_2,
+        h1,
+        h2,
         ax_comparison,
         xlabel=xlabel,
         h1_label=h1_label,
@@ -501,8 +503,8 @@ def plot_two_hist_comparison(
 
 
 def plot_comparison(
-    hist_1,
-    hist_2,
+    h1,
+    h2,
     ax,
     xlabel="",
     h1_label="h1",
@@ -510,8 +512,8 @@ def plot_comparison(
     comparison="ratio",
     comparison_ylabel=None,
     comparison_ylim=None,
+    h1_uncertainty_type="symmetrical",
     ratio_uncertainty_type="uncorrelated",
-    hist_1_uncertainty_type="symmetrical",
     **plot_hist_kwargs,
 ):
     """
@@ -519,9 +521,9 @@ def plot_comparison(
 
     Parameters
     ----------
-    hist_1 : boost_histogram.Histogram
+    h1 : boost_histogram.Histogram
         The first histogram for comparison.
-    hist_2 : boost_histogram.Histogram
+    h2 : boost_histogram.Histogram
         The second histogram for comparison.
     ax : matplotlib.axes.Axes
         The axes to plot the comparison.
@@ -537,10 +539,10 @@ def plot_comparison(
         The label for the y-axis. Default is the explicit formula used to compute the comparison plot.
     comparison_ylim : tuple or None, optional
         The y-axis limits for the comparison plot. Default is None. If None, standard y-axis limits are setup.
+    h1_uncertainty_type : str, optional
+        What kind of bin uncertainty to use for h1: "symmetrical" for the Poisson standard deviation derived from the variance stored in the histogram object, "asymmetrical" for asymmetrical uncertainties based on a Poisson confidence interval. Default is "symmetrical".
     ratio_uncertainty_type : str, optional
-        How to treat the uncertainties of the histograms when comparison is "ratio" or "relative_difference" ("uncorrelated" for simple comparison, "split" for scaling and split hist_1 and hist_2 uncertainties). This argument has no effect if comparison != "ratio" or "relative_difference". Default is "uncorrelated".
-    hist_1_uncertainty_type : str, optional
-        What kind of bin uncertainty to use for hist_1: "symmetrical" for the Poisson standard deviation derived from the variance stored in the histogram object, "asymmetrical" for asymmetrical uncertainties based on a Poisson confidence interval. Default is "symmetrical".
+        How to treat the uncertainties of the histograms when comparison is "ratio" or "relative_difference" ("uncorrelated" for simple comparison, "split" for scaling and split h1 and h2 uncertainties). This argument has no effect if comparison != "ratio" or "relative_difference". Default is "uncorrelated".
     **plot_hist_kwargs : optional
         Arguments to be passed to plot_hist() or plot_error_hist(), called in case the comparison is "pull" or "ratio", respectively. In case of pull, the default arguments are histtype="stepfilled" and color="darkgrey". In case of ratio, the default argument is color="black".
 
@@ -558,18 +560,18 @@ def plot_comparison(
     h1_label = _get_math_text(h1_label)
     h2_label = _get_math_text(h2_label)
 
-    _check_binning_consistency([hist_1, hist_2])
+    _check_binning_consistency([h1, h2])
 
     comparison_values, lower_uncertainties, upper_uncertainties = get_comparison(
-        hist_1, hist_2, comparison, ratio_uncertainty_type, hist_1_uncertainty_type
+        h1, h2, comparison, h1_uncertainty_type, ratio_uncertainty_type
     )
 
     if np.allclose(lower_uncertainties, upper_uncertainties, equal_nan=True):
-        hist_comparison = bh.Histogram(hist_2.axes[0], storage=bh.storage.Weight())
+        hist_comparison = bh.Histogram(h2.axes[0], storage=bh.storage.Weight())
         hist_comparison[:] = np.c_[comparison_values, lower_uncertainties**2]
     else:
         plot_hist_kwargs.setdefault("yerr", [lower_uncertainties, upper_uncertainties])
-        hist_comparison = bh.Histogram(hist_2.axes[0], storage=bh.storage.Weight())
+        hist_comparison = bh.Histogram(h2.axes[0], storage=bh.storage.Weight())
         hist_comparison[:] = np.c_[comparison_values, np.zeros_like(comparison_values)]
 
     if comparison == "pull":
@@ -601,13 +603,13 @@ def plot_comparison(
         if ratio_uncertainty_type == "split":
             np.seterr(divide="ignore", invalid="ignore")
             h2_scaled_uncertainties = np.where(
-                hist_2.values() != 0,
-                np.sqrt(hist_2.variances()) / hist_2.values(),
+                h2.values() != 0,
+                np.sqrt(h2.variances()) / h2.values(),
                 np.nan,
             )
             np.seterr(divide="warn", invalid="warn")
             ax.bar(
-                x=hist_2.axes[0].centers,
+                x=h2.axes[0].centers,
                 bottom=np.nan_to_num(
                     bottom_shift - h2_scaled_uncertainties, nan=comparison_ylim[0]
                 ),
@@ -615,7 +617,7 @@ def plot_comparison(
                     2 * h2_scaled_uncertainties,
                     nan=comparison_ylim[-1] - comparison_ylim[0],
                 ),
-                width=hist_2.axes[0].widths,
+                width=h2.axes[0].widths,
                 edgecolor="dimgrey",
                 hatch="////",
                 fill=False,
@@ -640,7 +642,7 @@ def plot_comparison(
         ax.axhline(0, ls="--", lw=1.0, color="black")
         ax.set_ylabel(rf"$\frac{{{h1_label} - {h2_label}}}{{{h1_label} + {h2_label}}}$")
 
-    xlim = (hist_1.axes[0].edges[0], hist_1.axes[0].edges[-1])
+    xlim = (h1.axes[0].edges[0], h1.axes[0].edges[-1])
     ax.set_xlim(xlim)
     ax.set_xlabel(xlabel)
     if comparison_ylim is not None:
@@ -1117,7 +1119,7 @@ def plot_data_model_comparison(
         model_hist,
         ax=ax_comparison,
         xlabel=xlabel,
-        hist_1_uncertainty_type=data_uncertainty_type,
+        h1_uncertainty_type=data_uncertainty_type,
         **comparison_kwargs,
     )
 
diff --git a/plothist/variable_registry.py b/plothist/variable_registry.py
index 547f331c..6e1088a9 100644
--- a/plothist/variable_registry.py
+++ b/plothist/variable_registry.py
@@ -134,14 +134,14 @@ def create_variable_registry(
     _save_variable_registry(variable_registry, path=path)
 
 
-def get_variable_from_registry(variable, path="./variable_registry.yaml"):
+def get_variable_from_registry(variable_key, path="./variable_registry.yaml"):
     """
     This function retrieves the parameter information for a variable from the variable registry file specified by the 'path' parameter.
     It loads the variable registry file and returns the dictionary entry corresponding to the specified variable name.
 
     Parameters
     ----------
-    variable : str
+    variable_key : str
         The name of the variable for which to retrieve parameter information.
     path : str, optional
         The path to the variable registry file (default is "./variable_registry.yaml").
@@ -160,7 +160,7 @@ def get_variable_from_registry(variable, path="./variable_registry.yaml"):
 
     with open(path, "r") as f:
         variable_registry = yaml.safe_load(f)
-        return variable_registry[variable]
+        return variable_registry[variable_key]
 
 
 def update_variable_registry(
@@ -250,8 +250,8 @@ def update_variable_registry_ranges(
 
     Parameters
     ----------
-    data : dict
-        A dictionary containing the data for the variables.
+    data : dict or pandas.DataFrame
+        A dataset containing the data for the variables.
     variable_keys : list
         A list of variable keys for which to update the range parameters in the registry. The variable needs to have a bin and range properties in the registry. Default is None: all variables in the registry are updated.
     path : str, optional