diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index 0e473a4..8910e5e 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -45,6 +45,7 @@ jobs:
           poetry run python -m pytest -n auto --disable-warnings --cov=edvart tests/
       - name: Lint
         run: |
+          poetry run mypy edvart/
           poetry run ruff check .
           poetry run ruff format --check .
 
diff --git a/edvart/export_utils.py b/edvart/export_utils.py
index 97a087f..d5283c5 100644
--- a/edvart/export_utils.py
+++ b/edvart/export_utils.py
@@ -29,6 +29,10 @@ def embed_image_base64(image_path: str, mime: str = "image/png") -> str:
     # Look up directory where currently executed template is located
     # Jinja's @environmentfilter or @contextfilter does not seem to provide
     # any information about the path of the template.
-    template_dir = os.path.dirname(inspect.getfile(inspect.currentframe().f_back))
+    current_frame = inspect.currentframe()
+    assert current_frame is not None
+    frame_back = current_frame.f_back
+    assert frame_back is not None
+    template_dir = os.path.dirname(inspect.getfile(frame_back))
     with open(os.path.join(template_dir, image_path), "rb") as img:
         return f"data:{mime};base64," + str(base64.b64encode(img.read()).decode("utf-8"))
diff --git a/edvart/plots.py b/edvart/plots.py
index 4bca094..2013968 100644
--- a/edvart/plots.py
+++ b/edvart/plots.py
@@ -107,7 +107,7 @@ def _scatter_plot_2d_noninteractive(
             color_categorical = pd.Categorical(df[color_col])
             color_codes = color_categorical.codes
         else:
-            color_codes = df[color_col]
+            color_codes = df[color_col].values.astype(np.signedinteger)
         scatter = ax.scatter(x, y, c=color_codes, alpha=opacity)
 
         if is_color_categorical:
diff --git a/edvart/report.py b/edvart/report.py
index c0f565f..081c245 100755
--- a/edvart/report.py
+++ b/edvart/report.py
@@ -5,9 +5,10 @@
 import sys
 import warnings
 from abc import ABC
+from collections.abc import Sized
 from copy import copy
 from enum import auto
-from typing import List, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import isort
 import nbconvert
@@ -83,7 +84,7 @@ def __init__(
         self.df = dataframe
         self.sections: list[Section] = []
         self.verbosity = Verbosity(verbosity)
-        self._table_of_contents = None
+        self._table_of_contents: Optional[TableOfContents] = None
 
     def _warn_if_empty(self) -> None:
         """Warns if the report contains no sections."""
@@ -132,7 +133,7 @@ def _export_data(
         return (
             code_dedent(
                 f"""
-                df_parquet = BytesIO(base64.b85decode({buffer}.decode()))
+                df_parquet = BytesIO(base64.b85decode({buffer!r}.decode()))
                 df = pd.read_parquet(df_parquet)"""
             ),
             ["import base64", "import pandas as pd", "from io import BytesIO"],
@@ -143,7 +144,9 @@ def export_notebook(
         notebook_filepath: Union[str, os.PathLike],
         dataset_name: str = "[INSERT DATASET NAME]",
         dataset_description: str = "[INSERT DATASET DESCRIPTION]",
-        export_data_mode: ExportDataMode = ExportDataMode.NONE,
+        # mypy assumes that the type of `ExportDataMode.NONE`` is `auto` instead of `ExportDataMode`
+        # since `auto()` is assigned to it in the enum
+        export_data_mode: ExportDataMode = ExportDataMode.NONE,  # type: ignore
     ) -> None:
         """Exports the report as an .ipynb file.
 
@@ -280,7 +283,7 @@ def _export_html(
             Maximum number of seconds to wait for a cell to finish execution.
         """
         # Execute notebook to produce output of cells
-        html_exp_kwargs = dict(
+        html_exp_kwargs: Dict[str, Any] = dict(
             preprocessors=[nbconvert.preprocessors.ExecutePreprocessor(timeout=timeout)]
         )
         if template_name is not None:
@@ -348,7 +351,7 @@ def export_html(
         # and unpickles the the whole report object from the decoded binary data
         unpickle_report = code_dedent(
             f"""
-                data = {buffer_base64}
+                data = {buffer_base64!r}
                 report = pickle.loads(base64.b85decode(data), fix_imports=False)
             """
         )
@@ -750,7 +753,7 @@ def __init__(
         columns_bivariate_analysis: Optional[List[str]] = None,
         columns_multivariate_analysis: Optional[List[str]] = None,
         columns_group_analysis: Optional[List[str]] = None,
-        groupby: Union[str, List[str]] = None,
+        groupby: Optional[Union[str, List[str]]] = None,
     ):
         super().__init__(dataframe, verbosity)
 
@@ -773,7 +776,7 @@ def __init__(
         )
         if isinstance(groupby, str):
             color_col = groupby
-        elif hasattr(groupby, "__len__") and len(groupby) == 1:
+        elif isinstance(groupby, Sized) and len(groupby) == 1:
             color_col = groupby[0]
         else:
             color_col = None
@@ -814,7 +817,7 @@ def __init__(
         verbosity: Verbosity = Verbosity.LOW,
     ):
         super().__init__(dataframe, verbosity)
-        if not is_date(dataframe.index):
+        if not is_date(dataframe.index.to_series()):
             raise ValueError(
                 "Input dataframe needs to be indexed by time."
                 "Please reindex your data to be indexed by either a DatetimeIndex or a PeriodIndex."
diff --git a/edvart/report_sections/bivariate_analysis.py b/edvart/report_sections/bivariate_analysis.py
index a7e5651..dec6ed1 100644
--- a/edvart/report_sections/bivariate_analysis.py
+++ b/edvart/report_sections/bivariate_analysis.py
@@ -120,6 +120,8 @@ def __init__(
             raise ValueError("Either both or neither of columns_x, columns_y must be specified.")
         # For analyses which do not take columns_pairs, prepare columns_x and columns_y in case
         # columns_pairs is the only parameter specified
+        columns_x_no_pairs: Optional[List[str]]
+        columns_y_no_pairs: Optional[List[str]]
         if columns is None and columns_x is None and columns_pairs is not None:
             columns_x_no_pairs = [pair[0] for pair in columns_pairs]
             columns_y_no_pairs = [pair[1] for pair in columns_pairs]
@@ -456,6 +458,7 @@ def _get_columns_x_y(
         if columns is None:
             columns = list(df.columns)
         columns_x = columns_y = columns
+    assert columns_y is not None
     columns_x = [col for col in columns_x if is_numeric(df[col])]
     columns_y = [col for col in columns_y if is_numeric(df[col])]
 
@@ -722,6 +725,7 @@ def include_column(col: str) -> bool:
         columns_x = columns
         columns_y = columns
     if not allow_categorical:
+        assert columns_y is not None
         columns_x = list(filter(include_column, columns_x))
         columns_y = list(filter(include_column, columns_y))
     sns.pairplot(df, x_vars=columns_x, y_vars=columns_y, hue=color_col)
@@ -908,6 +912,8 @@ def include_column(col: str) -> bool:
         if columns_x is None:
             columns_pairs = list(itertools.combinations(columns, 2))
         else:
+            assert columns_x is not None
+            assert columns_y is not None
             columns_pairs = [
                 (col_x, col_y)
                 for (col_x, col_y) in itertools.product(columns_x, columns_y)
@@ -971,7 +977,7 @@ def contingency_table(
     annot = table.replace(0, "") if hide_zeros else table
 
     ax = sns.heatmap(
-        scaling_func(table),
+        scaling_func(table.values),
         annot=annot,
         fmt="",
         cbar=False,
diff --git a/edvart/report_sections/dataset_overview.py b/edvart/report_sections/dataset_overview.py
index 3cdcd10..0d02f13 100644
--- a/edvart/report_sections/dataset_overview.py
+++ b/edvart/report_sections/dataset_overview.py
@@ -449,8 +449,11 @@ def data_types(df: pd.DataFrame, columns: Optional[List[str]] = None) -> None:
     """
     if columns is not None:
         df = df[columns]
-    dtypes = df.apply(
-        func=lambda x_: str(infer_data_type(x_)),
+
+    # Type ignored because the apply is not properly typed: the type hints for
+    # the parameter `func` do not cover the complete set of possible inputs.
+    dtypes: pd.Series[str] = df.apply(
+        func=lambda x_: str(infer_data_type(x_)),  # type: ignore
         axis=0,
         result_type="expand",
     )
@@ -652,7 +655,7 @@ def missing_values(
     bar_plot_title: str = "Missing Values Percentage of Each Column",
     bar_plot_ylim: float = 0,
     bar_plot_color: str = "#FFA07A",
-    **bar_plot_args: Dict[str, Any],
+    **bar_plot_args: Any,
 ) -> None:
     """Displays a table of missing values percentages for each column of df and a bar plot
     of the percentages.
@@ -675,7 +678,7 @@ def missing_values(
         Bar plot y axis bottom limit.
     bar_plot_color : str
         Color of bars in the bar plot in hex format.
-    bar_plot_args : Dict[str, Any]
+    bar_plot_args : Any
         Additional kwargs passed to pandas.Series.bar.
     """
     if columns is not None:
diff --git a/edvart/report_sections/group_analysis.py b/edvart/report_sections/group_analysis.py
index 7ad113e..e184e98 100644
--- a/edvart/report_sections/group_analysis.py
+++ b/edvart/report_sections/group_analysis.py
@@ -1,4 +1,5 @@
-from typing import Any, Callable, Dict, List, Optional, Union
+from collections.abc import Hashable
+from typing import Any, Callable, Dict, Iterable, List, Optional, Union
 
 import colorlover as cl
 import nbformat.v4 as nbfv4
@@ -102,7 +103,7 @@ def required_imports(self) -> List[str]:
             "import plotly.graph_objects as go",
             "from edvart.data_types import infer_data_type, DataType",
             "from edvart import utils",
-            "from typing import List, Dict, Optional, Callable",
+            "from typing import List, Dict, Optional, Callable, Iterable",
             "from plotly.subplots import make_subplots",
         ]
 
@@ -218,7 +219,7 @@ def add_cells(self, cells: List[Dict[str, Any]], df: pd.DataFrame) -> None:
                 )
             cells.append(nbfv4.new_code_cell(code))
 
-        columns = self.columns if self.columns is not None else df.columns
+        columns = self.columns if self.columns is not None else df.columns.to_list()
 
         if not self.show_statistics and not self.show_dist:
             return
@@ -362,7 +363,7 @@ def within_group_stats(
     df: pd.DataFrame,
     groupby: List[str],
     column: str,
-    stats: Dict[str, Callable[[pd.Series], float]] = None,
+    stats: Optional[Dict[str, Callable[[pd.Series], float]]] = None,
     round_decimals: int = 2,
 ) -> None:
     """Display withing group statistics for a column of df grouped by one or other more columns.
@@ -448,7 +449,9 @@ def group_missing_values(
     df_grouped = df.groupby(groupby)[columns]
 
     # Calculate number of samples in each group
-    sizes = df_grouped.size().rename("Group Size")
+    sizes = df_grouped.size()
+    assert isinstance(sizes, pd.Series)
+    sizes = sizes.rename("Group Size")
 
     # Calculate missing values percentage of each column for each group
     missing = df_grouped.apply(lambda g: g.isna().sum(axis=0))
@@ -490,7 +493,7 @@ def color_cell(value):
                 background-color: {bg_hex};
             """
 
-        render = final_table.style.applymap(
+        render = final_table.style.map(
             func=color_cell, subset=pd.IndexSlice[:, colored_columns]
         ).format(formatter="{0:.2f} %", subset=pd.IndexSlice[:, colored_columns])
     else:
@@ -553,7 +556,8 @@ def group_barplot(
 
     fig = go.Figure()
     for color_idx, (idx, row) in enumerate(pivot.iterrows()):
-        if hasattr(idx, "__len__") and not isinstance(idx, str):
+        group_name: Hashable
+        if isinstance(idx, Iterable) and not isinstance(idx, str):
             group_name = "_".join([str(i) for i in idx])
         else:
             group_name = idx
@@ -641,7 +645,8 @@ def overlaid_histograms(
     )
 
     for color_idx, (name, group) in enumerate(df.groupby(groupby)):
-        if hasattr(name, "__len__") and not isinstance(name, str):
+        group_name: Hashable
+        if isinstance(name, Iterable) and not isinstance(name, str):
             group_name = "_".join([str(i) for i in name])
         else:
             group_name = name
diff --git a/edvart/report_sections/multivariate_analysis.py b/edvart/report_sections/multivariate_analysis.py
index 63d194b..c94fbdc 100644
--- a/edvart/report_sections/multivariate_analysis.py
+++ b/edvart/report_sections/multivariate_analysis.py
@@ -1,5 +1,5 @@
 from enum import IntEnum
-from typing import Any, Dict, List, Optional, Tuple
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import matplotlib.pyplot as plt
 import nbformat.v4 as nbfv4
@@ -487,7 +487,7 @@ def pca_explained_variance(
     plt.ylabel("Explained variance ratio")
     plt.xticks(
         ticks=range(len(pca.explained_variance_ratio_)),
-        labels=range(1, (len(pca.explained_variance_ratio_) + 1)),
+        labels=[str(label) for label in range(1, (len(pca.explained_variance_ratio_) + 1))],
     )
     if show_grid:
         plt.grid()
@@ -630,13 +630,15 @@ def parallel_coordinates(
         columns = [col for col in columns if col not in hide_columns]
     if drop_na:
         df = df.dropna()
+
+    line: Optional[Dict[str, Any]] = None
     if color_col is not None:
         is_categorical_color = infer_data_type(df[color_col]) in (
             DataType.CATEGORICAL,
             DataType.UNIQUE,
             DataType.BOOLEAN,
         )
-
+        colorscale: Union[List[Tuple[float, str]], str]
         if is_categorical_color:
             categories = df[color_col].unique()
             colorscale = get_default_discrete_colorscale(n_colors=len(categories))
@@ -669,8 +671,6 @@ def parallel_coordinates(
                     "cmax": len(categories) - 0.5,
                 }
             )
-    else:
-        line = None
     # Add numeric columns to dimensions
     dimensions = [{"label": col_name, "values": df[col_name]} for col_name in numeric_columns]
     # Add categorical columns to dimensions
@@ -818,12 +818,15 @@ def parallel_categories(
         columns = [col for col in columns if col not in hide_columns]
     if drop_na:
         df = df.dropna()
+
+    line: Optional[Dict[str, Any]] = None
     if color_col is not None:
         categorical_color = infer_data_type(df[color_col]) in (
             DataType.CATEGORICAL,
             DataType.UNIQUE,
             DataType.BOOLEAN,
         )
+        colorscale: Union[List[Tuple[float, str]], str]
         if categorical_color:
             categories = df[color_col].unique()
             colorscale = get_default_discrete_colorscale(n_colors=len(categories))
@@ -833,14 +836,15 @@ def parallel_categories(
             color_series = df[color_col]
             colorscale = "Bluered_r"
 
+        colorbar: Dict[str, Any] = {"title": color_col}
         line = {
             "color": color_series,
             "colorscale": colorscale,
-            "colorbar": {"title": color_col},
+            "colorbar": colorbar,
         }
 
         if categorical_color:
-            line["colorbar"].update(
+            colorbar.update(
                 {
                     "tickvals": color_series.unique(),
                     "ticktext": categories,
@@ -855,8 +859,6 @@ def parallel_categories(
                     "cmax": len(categories) - 0.5,
                 }
             )
-    else:
-        line = None
 
     dimensions = [go.parcats.Dimension(values=df[col_name], label=col_name) for col_name in columns]
 
diff --git a/edvart/report_sections/table_of_contents.py b/edvart/report_sections/table_of_contents.py
index 198dcd5..b41117d 100644
--- a/edvart/report_sections/table_of_contents.py
+++ b/edvart/report_sections/table_of_contents.py
@@ -94,7 +94,7 @@ def show(self, sections: List[Section]) -> None:
         """
         display(Markdown(self._title))
 
-        lines = []
+        lines: List[str] = []
         for section in sections:
             self._add_section_lines(section, 1, lines, self._include_subsections)
         display(Markdown("\n".join(lines)))
diff --git a/edvart/report_sections/timeseries_analysis/boxplots_over_time.py b/edvart/report_sections/timeseries_analysis/boxplots_over_time.py
index 61f756d..6fe80ff 100644
--- a/edvart/report_sections/timeseries_analysis/boxplots_over_time.py
+++ b/edvart/report_sections/timeseries_analysis/boxplots_over_time.py
@@ -1,4 +1,3 @@
-from datetime import datetime
 from itertools import takewhile
 from typing import Any, Callable, Dict, List, Optional, Tuple
 
@@ -48,7 +47,7 @@ def __init__(
         self,
         verbosity: Verbosity = Verbosity.LOW,
         columns: Optional[List[str]] = None,
-        grouping_function: Callable[[Any], str] = None,
+        grouping_function: Optional[Callable[[Any], str]] = None,
         grouping_function_imports: Optional[List[str]] = None,
         grouping_name: Optional[str] = None,
         default_nunique_max: int = 80,
@@ -161,7 +160,7 @@ def show(self, df: pd.DataFrame) -> None:
         )
 
 
-def default_grouping_functions() -> Dict[str, Callable[[datetime], str]]:
+def default_grouping_functions() -> Dict[str, Callable[[pd.Timestamp], str]]:
     """Return a dictionary of function names and functions.
 
     The function takes a pandas datetime and represents it as a rougher (in terms of time)
@@ -170,7 +169,7 @@ def default_grouping_functions() -> Dict[str, Callable[[datetime], str]]:
 
     Returns
     -------
-    Dict[str, Callable[[datetime], str]]
+    Dict[str, Callable[[pandas.Timestamp], str]]
         Dictionary from grouping function names to grouping functions.
     """
     return {
@@ -217,7 +216,7 @@ def get_default_grouping_func(df: pd.DataFrame, nunique_max: int = 80) -> Tuple[
 def show_boxplots_over_time(
     df: pd.DataFrame,
     columns: Optional[List[str]] = None,
-    grouping_function: Callable[[Any], str] = None,
+    grouping_function: Optional[Callable[[Any], str]] = None,
     grouping_name: Optional[str] = None,
     default_nunique_max: int = 80,
     figsize: Tuple[float, float] = (20, 7),
@@ -264,7 +263,7 @@ def show_boxplots_over_time(
         grouping_name, grouping_function = get_default_grouping_func(
             df, nunique_max=default_nunique_max
         )
-    elif default_grouping_funcs.get(grouping_name) is not None:
+    elif grouping_name is not None and default_grouping_funcs.get(grouping_name) is not None:
         grouping_function = default_grouping_funcs[grouping_name]
 
     if columns is None:
diff --git a/edvart/report_sections/timeseries_analysis/fourier_transform.py b/edvart/report_sections/timeseries_analysis/fourier_transform.py
index f28df76..f625ec7 100644
--- a/edvart/report_sections/timeseries_analysis/fourier_transform.py
+++ b/edvart/report_sections/timeseries_analysis/fourier_transform.py
@@ -145,7 +145,7 @@ def show_fourier_transform(
         for col in columns:
             if not is_numeric(df[col]):
                 raise ValueError(f"Cannot perform Fourier transform for non-numeric column `{col}`")
-    index_freq = pd.infer_freq(df.index) or ""
+    index_freq = pd.infer_freq(df.index.to_series()) or ""
     for col in columns:
         # FFT requires samples at regular intervals
         df_col = df[col].interpolate(method="time")
diff --git a/edvart/report_sections/timeseries_analysis/rolling_statistics.py b/edvart/report_sections/timeseries_analysis/rolling_statistics.py
index 32cb65a..752b5ab 100644
--- a/edvart/report_sections/timeseries_analysis/rolling_statistics.py
+++ b/edvart/report_sections/timeseries_analysis/rolling_statistics.py
@@ -3,6 +3,7 @@
 
 import nbformat.v4 as nbfv4
 import pandas as pd
+import plotly
 import plotly.graph_objects as go
 from IPython.display import Markdown, display
 
@@ -161,8 +162,7 @@ def show_rolling_statistics(
     index = df.index[window_size - 1 :]
 
     layout = dict(xaxis_rangeslider_visible=True)
-
-    data = []
+    data: List[List[plotly.basedatatypes.BaseTraceType]] = []
     for col in columns:
         data.append([])
         if show_std_dev:
diff --git a/edvart/report_sections/timeseries_analysis/seasonal_decomposition.py b/edvart/report_sections/timeseries_analysis/seasonal_decomposition.py
index 40a25e8..991939f 100644
--- a/edvart/report_sections/timeseries_analysis/seasonal_decomposition.py
+++ b/edvart/report_sections/timeseries_analysis/seasonal_decomposition.py
@@ -149,7 +149,7 @@ def show_seasonal_decomposition(
         If the input data is not indexed by time in ascending order.
     """
     df = df.interpolate(method="time")
-    if pd.infer_freq(df.index) is None and period is None:
+    if pd.infer_freq(df.index.to_series()) is None and period is None:
         display(
             Markdown(
                 "<div class='alert alert-block alert-warning'>"
diff --git a/edvart/report_sections/timeseries_analysis/short_time_ft.py b/edvart/report_sections/timeseries_analysis/short_time_ft.py
index 6342a60..ed5d7e3 100644
--- a/edvart/report_sections/timeseries_analysis/short_time_ft.py
+++ b/edvart/report_sections/timeseries_analysis/short_time_ft.py
@@ -3,6 +3,7 @@
 import matplotlib.pyplot as plt
 import nbformat.v4 as nbfv4
 import numpy as np
+import numpy.typing as npt
 import pandas as pd
 from IPython.display import Markdown, display
 from scipy import signal
@@ -68,6 +69,7 @@ def required_imports(self) -> List[str]:
                 )
             ]
         return [
+            "import numpy.typing as npt",
             "from IPython.display import display, Markdown",
             "from edvart.data_types import is_numeric",
             "import matplotlib.pyplot as plt",
@@ -128,7 +130,7 @@ def show_short_time_ft(
     columns: Optional[List[str]] = None,
     overlap: Optional[int] = None,
     log: bool = True,
-    window: Union[str, Tuple, np.typing.ArrayLike] = "hamming",
+    window: Union[str, Tuple, npt.ArrayLike] = "hamming",
     scaling: str = "spectrum",
     figsize: Tuple[float, float] = (20, 7),
     colormap: Any = "viridis",
@@ -185,7 +187,7 @@ def show_short_time_ft(
         for col in columns:
             if not is_numeric(df[col]):
                 raise ValueError(f"Cannot perform STFT for non-numeric column {col}")
-    index_freq = pd.infer_freq(df.index) or ""
+    index_freq = pd.infer_freq(df.index.to_series()) or ""
     for col in columns:
         display(Markdown(f"---\n### {col}"))
         freqs, times, sx = signal.spectrogram(  # pylint: disable=invalid-name
diff --git a/edvart/report_sections/univariate_analysis.py b/edvart/report_sections/univariate_analysis.py
index 39cfe82..f3b01fd 100644
--- a/edvart/report_sections/univariate_analysis.py
+++ b/edvart/report_sections/univariate_analysis.py
@@ -1,8 +1,9 @@
-from typing import Any, Callable, Dict, List, Optional, Tuple
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 import matplotlib.pyplot as plt
 import nbformat.v4 as nbfv4
 import numpy as np
+import numpy.typing as npt
 import pandas as pd
 import seaborn as sns
 from IPython.display import HTML, Markdown, display
@@ -64,6 +65,7 @@ def required_imports(self) -> List[str]:
         # Verbosity.HIGH
         return [
             "import numpy as np",
+            "import numpy.typing as npt",
             "from edvart import utils",
             "from IPython.display import display",
             "from IPython.display import HTML",
@@ -243,7 +245,7 @@ def default_quantile_statistics():
 
 def histogram(
     series: pd.Series,
-    bins: Optional[int] = None,
+    bins: Optional[Union[int, str, npt.ArrayLike]] = None,
     density: bool = False,
     box_plot: bool = True,
     figsize: Tuple[float, float] = (20, 7),
@@ -256,8 +258,15 @@ def histogram(
     ----------
     series : pd.Series
         Numerical series.
-    bins : int, optional
-        Number of bins of the histogram. If None, the number of bins is inferred.
+    bins : int or str or array_like, optional
+        If bins is an int, it defines the number of equal-width bins in the range of the series.
+        If bins is a string, it defines the method used to calculate the optimal bin width.
+        If bins is an array, it defines the bin edges.
+
+        Can be any valid input for parameter `bins` of `numpy.histogram_bin_edges`.
+        https://numpy.org/doc/stable/reference/generated/numpy.histogram_bin_edges.html#numpy.histogram_bin_edges
+
+        By default, the number of bins is inferred based on the input data.
     density : bool (default = False)
         If True, the area of the histogram bars will sum up to 1.
     box_plot : bool (default = True)
@@ -285,7 +294,7 @@ def histogram(
             bins = "sturges"
         else:
             bins = bin_edges
-
+    assert bins is not None
     if box_plot:
         _fig, (ax_box, ax_hist) = plt.subplots(
             nrows=2,
@@ -295,7 +304,7 @@ def histogram(
         )
         sns.boxplot(x=series, ax=ax_box, **boxplot_kwargs)
         sns.histplot(
-            data=series,
+            data=series.to_frame(),
             bins=bins,
             stat="density" if density else "count",
             ax=ax_hist,
@@ -306,7 +315,7 @@ def histogram(
     else:
         plt.figure(figsize=figsize)
         sns.histplot(
-            data=series,
+            data=series.to_frame(),
             bins=bins,
             stat="density" if density else "count",
             kde=False,
@@ -320,7 +329,7 @@ def bar_plot(
     relative_count: bool = False,
     figsize: Tuple[float, float] = (20, 7),
     plotting_threshold: int = 50,
-    **bar_plot_args: Dict[str, Any],
+    **bar_plot_args: Any,
 ) -> None:
     """Plots a bar plot visualizing frequencies of series elements.
 
@@ -335,7 +344,7 @@ def bar_plot(
     plotting_threshold : int
         If the number of unique values in the series is greater than this, no plot is created
         instead a warning is issued.
-    bar_plot_args : Dict[str, Any]
+    bar_plot_args : Any
         Additional kwargs passed to pandas.Series.bar.
     """
     if series.nunique() > plotting_threshold:
@@ -406,7 +415,7 @@ def top_most_frequent(series: pd.Series, n_top: int = 5) -> None:
     n_top : int
         The number of most frequent values to include in the table.
     """
-    frequent_values = dict_to_html(utils.top_frequent_values(series, n_top=n_top))
+    frequent_values = dict_to_html(dict(utils.top_frequent_values(series, n_top=n_top)))
     frequent_values_html = add_html_heading(frequent_values, "Most frequent values", 3)
     display(HTML(frequent_values_html))
 
diff --git a/edvart/utils.py b/edvart/utils.py
index 63fdab8..11fd9b2 100755
--- a/edvart/utils.py
+++ b/edvart/utils.py
@@ -1,6 +1,6 @@
 import os
 from contextlib import contextmanager
-from typing import Any, Dict, Iterable, Iterator, List, Literal, Optional, Tuple, Union
+from typing import Any, Dict, Iterable, Iterator, List, Literal, Mapping, Optional, Tuple, Union
 
 import pandas as pd
 import plotly
@@ -10,7 +10,7 @@
 from edvart.data_types import is_numeric
 
 
-def top_frequent_values(series: pd.Series, n_top: int = 10) -> Dict[Any, float]:
+def top_frequent_values(series: pd.Series, n_top: int = 10) -> Mapping[str, Any]:
     """
     Counts top n most frequent values in series along with other value counts and NULL value counts.
 
@@ -30,7 +30,7 @@ def top_frequent_values(series: pd.Series, n_top: int = 10) -> Dict[Any, float]:
     # Calculate frequencies
     counts = series.value_counts()
     nan_count = series.isna().sum()
-    result_dict = {
+    result_dict: Dict[str, Any] = {
         **(counts[:n_top].to_dict()),
         "Other values count": counts[n_top:].sum(),
         "Null": nan_count,
@@ -446,7 +446,7 @@ def mode(series: pd.Series) -> float:
     most_frequent = series.mode(dropna=True)
     if len(most_frequent) == 0:
         return float("nan")
-    return most_frequent[0]
+    return float(most_frequent[0])
 
 
 def std(series: pd.Series) -> float:
@@ -622,4 +622,5 @@ def env_var(name: str, value: str) -> Iterator[None]:
     try:
         yield
     finally:
-        os.environ = original_env
+        os.environ.clear()
+        os.environ.update(original_env)
diff --git a/pyproject.toml b/pyproject.toml
index 6a5a6e3..fbce2c4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -51,12 +51,19 @@ toml = "^0.10.0"
 jupyter = "*"
 sphinx-copybutton = "^0.5.2"
 pytest-xdist = "^3.3.1"
+pandas-stubs = "^2.2.0"
+mypy = "^1.8.0"
 ruff = "^0.3.0"
 
 [build-system]
 requires = ["poetry_core>=1.0.0"]
 build-backend = "poetry.core.masonry.api"
 
+[tool.mypy]
+ignore_missing_imports = true
+python_version = "3.9"
+plugins = "numpy.typing.mypy_plugin"
+
 [tool.ruff]
 line-length = 100
 target-version = "py310"