Merge pull request #166 from Swish78/dynamic-figsize-adjustment

Fix issue #30

CHANGELOG.md

@@ -1,5 +1,8 @@
 # Change Log
 
+## 0.7.8
+* `nominal.associations` now attempts to set the figure-size automatically based on output (issue [#30](https://github.com/shakedzy/dython/issues/30), by **[@Swish78](https://github.com/Swish78)**)
+
 ## 0.7.7
 * _Drop support for Python 3.8 as it reaches its end-of-life date_
 * Fix issue [#160](https://github.com/shakedzy/dython/issues/160)

VERSION

@@ -1 +1 @@
-0.7.7
+0.7.8

dev_requirements.txt

@@ -1,4 +1,4 @@
-pytest[testing]>=8.3.2
+pytest>=8.3.2
 hypothesis>=6.111.0
 black>=24.8.0
 pre-commit>=3.8.0

docs/modules/nominal.md

@@ -161,11 +161,12 @@ continuous features using:
 
      Matplotlib Axis on which the heat-map will be plotted
 
-- **`figsize`** : `(int,int)` or `None`
+- **`figsize`** : `(float, float)` or `None`
 
      _Default: None_
 
-     A Matplotlib figure-size tuple. If `None`, falls back to Matplotlib's default. Only used if `ax=None`.
+     A Matplotlib figure-size tuple. If `None`, will attempt to set the size automatically.
+     Only used if `ax=None`.
 
 - **`annot`** : `Boolean`
 

dython/nominal.py

@@ -396,7 +396,7 @@ def associations(
     nan_strategy: str = _REPLACE,
     nan_replace_value: Any = _DEFAULT_REPLACE_VALUE,
     ax: Optional[plt.Axes] = None,
-    figsize: Optional[Tuple[int, int]] = None,
+    figsize: Optional[Tuple[float, float]] = None,
     annot: bool = True,
     fmt: str = ".2f",
     cmap: Optional[Colormap] = None,
@@ -439,16 +439,16 @@ def associations(
         '(con)' based on their type (nominal or continuous), as provided
         by nominal_columns
     nom_nom_assoc : callable / string, default = 'cramer'
-        If callable, a function which recieves two `pd.Series` and returns a single number.
+        If callable, a function which receives two `pd.Series` and returns a single number.
         If string, name of nominal-nominal (categorical-categorical) association to use.
         Options are 'cramer' for Cramer's V or `theil` for Theil's U. If 'theil',
         heat-map columns are the provided information (U = U(row|col)).
     num_num_assoc : callable / string, default = 'pearson'
-        If callable, a function which recieves two `pd.Series` and returns a single number.
+        If callable, a function which receives two `pd.Series` and returns a single number.
         If string, name of numerical-numerical association to use. Options are 'pearson'
         for Pearson's R, 'spearman' for Spearman's R, 'kendall' for Kendall's Tau.
     nom_num_assoc : callable / string, default = 'correlation_ratio'
-        If callable, a function which recieves two `pd.Series` and returns a single number.
+        If callable, a function which receives two `pd.Series` and returns a single number.
         If string, name of nominal-numerical association to use. Options are 'correlation_ratio'
         for correlation ratio.
     symmetric_nom_nom : Boolean, default = True
@@ -458,19 +458,19 @@ def associations(
         Relevant only if `num_num_assoc` is a callable. Declare whether the function is symmetric (f(x,y) = f(y,x)).
         If False, heat-map values should be interpreted as f(row,col)
     display_rows : list / string, default = 'all'
-        Choose which of the dataset's features will be displyed in the output's
+        Choose which of the dataset's features will be displayed in the output's
         correlations table rows. If string, can either be a single feature's name or 'all'.
         Only used if `hide_rows` is `None`.
     display_columns : list / string, default = 'all'
-        Choose which of the dataset's features will be displyed in the output's
+        Choose which of the dataset's features will be displayed in the output's
         correlations table columns. If string, can either be a single feature's name or 'all'.
         Only used if `hide_columns` is `None`.
     hide_rows : list / string, default = None
-        Choose which of the dataset's features will not be displyed in the output's
+        Choose which of the dataset's features will not be displayed in the output's
         correlations table rows. If string, must be a single feature's name. If `None`,
         `display_rows` is used.
     hide_columns : list / string, default = None
-        Choose which of the dataset's features will not be displyed in the output's
+        Choose which of the dataset's features will not be displayed in the output's
         correlations table columns. If string, must be a single feature's name. If `None`,
         `display_columns` is used.
     cramers_v_bias_correction : Boolean, default = True
@@ -488,9 +488,9 @@ def associations(
         nan_strategy is set to 'replace'
     ax : matplotlib ax, default = None
         Matplotlib Axis on which the heat-map will be plotted
-    figsize : (int,int) or None, default = None
-        A Matplotlib figure-size tuple. If `None`, falls back to Matplotlib's
-        default. Only used if `ax=None`.
+    figsize : (float, float) or None, default = None
+        A Matplotlib figure-size tuple. If `None`, will attempt to set the size automatically.
+        Only used if `ax=None`.
     annot : Boolean, default = True
         Plot number annotations on the heat-map
     fmt : string, default = '.2f'
@@ -564,7 +564,7 @@ def associations(
         pass  # will be handled pair-by-pair during calculations
     else:
         raise ValueError(
-            "Argument nan_stragety [{:s}] is not a valid choice.".format(
+            "Argument nan_strategy [{:s}] is not a valid choice.".format(
                 nan_strategy
             )
         )
@@ -614,6 +614,12 @@ def associations(
         )
     displayed_features_set = set.union(set(display_rows), set(display_columns))
 
+    #  Adjusting figsize based on the number of features
+    if figsize is None:
+        BASE_SIZE = 1.5  # Size multiplier per feature
+        num_features = len(displayed_features_set)
+        figsize = (BASE_SIZE * num_features, BASE_SIZE * num_features)
+
     # convert timestamp columns to numerical columns, so correlation can be performed
     datetime_dtypes = [
         str(x) for x in df.dtypes if str(x).startswith("datetime64")