refactor: use mypy type checking

.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 .
 

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"))

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:

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 +144,7 @@ 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,
+        export_data_mode: ExportDataMode = ExportDataMode.NONE,  # type: ignore
     ) -> None:
         """Exports the report as an .ipynb file.
 
@@ -280,7 +281,7 @@ 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 +349,7 @@ 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 +751,7 @@ 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 +774,7 @@ 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 +815,7 @@ 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."

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,

edvart/report_sections/dataset_overview.py

@@ -449,7 +449,7 @@ def data_types(df: pd.DataFrame, columns: Optional[List[str]] = None) -> None:
     """
     if columns is not None:
         df = df[columns]
-    dtypes = df.apply(
+    dtypes = df.apply(  # type: ignore
         func=lambda x_: str(infer_data_type(x_)),
         axis=0,
         result_type="expand",
@@ -652,7 +652,7 @@ 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 +675,7 @@ 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:
@@ -717,7 +717,7 @@ def missing_values(
                 title=bar_plot_title,
                 ylim=bar_plot_ylim,
                 color=bar_plot_color,
-                **bar_plot_args,
+                **bar_plot_args,  # type: ignore
             )
             .set_ylabel("Missing Values [%]")
         )

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

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]
 

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)))