feat: local config

introduce pydantic config for each ProfileReport object

Author: sbrugman

docsrc/source/conf.py

@@ -44,13 +44,13 @@ def _GetApiWrapperVersion():
 # ones.
 extensions = [
     "recommonmark",
-    # "sphinx_multiversion",
     "sphinx.ext.autodoc",
     "sphinx.ext.autosummary",
     "sphinx.ext.coverage",
     "sphinx.ext.napoleon",
     "sphinx_autodoc_typehints",
     "sphinx.ext.viewcode",
+    "sphinx-pydantic",
 ]
 
 # Add any paths that contain templates here, relative to this directory.

docsrc/source/pages/advanced_usage.rst

@@ -21,7 +21,7 @@ The configuration can be changed in the following ways:
   profile = df.profile_report(title="Pandas Profiling Report", pool_size=1)
 
   # Change the config after
-  profile.set_variable("html.minify_html", False)
+  profile.config.html.minify_html = False
 
   profile.to_file("output.html")
 
@@ -38,26 +38,24 @@ Variable summary settings
   :caption: Configuration example
 
   profile = df.profile_report(
-  	sort='ascending',
-  	vars={
-	    'num':{'low_categorical_threshold': 0},
-	    'cat':{
-	      'length':True,
-	      'characters':False,
-	      'words':False,
-	      'n_obs': 5,
-	    }
-  	}
-  )
-
-  profile.set_variable('variables.descriptions',
-      {
-        'files': 'Files in the filesystem',
-        'datec': 'Creation date',
-        'datem': 'Modification date',
+    sort='ascending',
+    vars={
+      'num':{'low_categorical_threshold': 0},
+      'cat':{
+        'length':True,
+        'characters':False,
+        'words':False,
+        'n_obs': 5,
       }
+    }
   )
 
+  profile.config.variables.descriptions = {
+    'files': 'Files in the filesystem',
+    'datec': 'Creation date',
+    'datem': 'Modification date',
+  }
+
   profile.to_file("report.html")
 
 
@@ -73,10 +71,10 @@ Missing data overview plots
   :caption: Configuration example: disable heatmap and dendrogram for large datasets
 
   profile = df.profile_report(
-  	missing_diagrams={
-	    'heatmap': False,
-	    'dendrogram': False,
-  	}
+    missing_diagrams={
+      'heatmap': False,
+      'dendrogram': False,
+    }
   )
   profile.to_file("report.html")
 
@@ -158,82 +156,56 @@ It's possible to disable certain groups of features through configuration shorth
     # Disable samples, correlations, missing diagrams and duplicates at once
     r = ProfileReport(samples=None, correlations=None, missing_diagrams=None, duplicates=None, interactions=None)
 
-    # Or use the .set_variable method
-    r = ProfileReport()
-    r.set_variable("samples", None)
-    r.set_variable("duplicates", None)
-    r.set_variable("correlations", None)
-    r.set_variable("missing_diagrams", None)
-    r.set_variable("interactions", None)
-
-
-
 
 Customise plots
 ---------------
-
-A way how to pass arguments to the underlying matplotlib is to use the ``plot`` argument. It is possible to change the default format of images to png (default svg) using the key-pair ``image_format: "png"`` and also the resolution of the image using ``dpi: 800``. 
-
+A way how to pass arguments to the underlying matplotlib is to use the ``plot`` argument. It is possible to change the default format of images to png (default svg) using the key-pair ``image_format: "png"`` and also the resolution of the image using ``dpi: 800``.
 An example would be:
-
 .. code-block:: python
 
-	profile = ProfileReport(planets, title='Pandas Profiling Report', explorative=True,
-			       plot={
-				   'dpi':200,
-				   'image_format': 'png'
-			       })
-
-
-Furthermore, it is possible to change the default values of histograms, the options for that are the following:
-
-    histogram:
-            x_axis_labels: True
-
-    # Number of bins (set to 0 to automatically detect the bin size)
-            bins: 50
-
-    # Maximum number of bins (when bins=0)
-            max_bins: 250
-
-
-
-
+    profile = ProfileReport(
+        planets,
+        title='Pandas Profiling Report',
+        explorative=True,
+        plot={
+            'dpi':200,
+            'image_format': 'png'
+        }
+    )
 
 Customise correlation matrix
 -----------------------------
+It's possible to directly access the correlation matrix as well. That is done with the ``plot`` argument and then with the `correlation` key. It is possible to customise the palette, one can use the following list used in seaborn or create `their own custom matplotlib palette <https://matplotlib.org/stable/gallery/color/custom_cmap.html>`_. Supported values are
 
-It's possible to directly access the correlation matrix as well. That is done with the ``plot`` argument and then with the `correlation` key. It is possible to customise the palett, one can use the following list used in seaborn or create [their own custom matplotlib palette](https://matplotlib.org/stable/gallery/color/custom_cmap.html). Supported values are 
-
-```
 'Accent', 'Accent_r', 'Blues', 'Blues_r', 'BrBG', 'BrBG_r', 'BuGn', 'BuGn_r', 'BuPu', 'BuPu_r', 'CMRmap', 'CMRmap_r', 'Dark2', 'Dark2_r', 'GnBu', 'GnBu_r', 'Greens', 'Greens_r', 'Greys', 'Greys_r', 'OrRd', 'OrRd_r', 'Oranges', 'Oranges_r', 'PRGn', 'PRGn_r', 'Paired', 'Paired_r', 'Pastel1', 'Pastel1_r', 'Pastel2', 'Pastel2_r', 'PiYG', 'PiYG_r', 'PuBu', 'PuBuGn', 'PuBuGn_r', 'PuBu_r', 'PuOr', 'PuOr_r', 'PuRd', 'PuRd_r', 'Purples', 'Purples_r', 'RdBu', 'RdBu_r', 'RdGy', 'RdGy_r', 'RdPu', 'RdPu_r', 'RdYlBu', 'RdYlBu_r', 'RdYlGn', 'RdYlGn_r', 'Reds', 'Reds_r', 'Set1', 'Set1_r', 'Set2', 'Set2_r', 'Set3', 'Set3_r', 'Spectral', 'Spectral_r', 'Wistia', 'Wistia_r', 'YlGn', 'YlGnBu', 'YlGnBu_r', 'YlGn_r', 'YlOrBr', 'YlOrBr_r', 'YlOrRd', 'YlOrRd_r', 'afmhot', 'afmhot_r', 'autumn', 'autumn_r', 'binary', 'binary_r', 'bone', 'bone_r', 'brg', 'brg_r', 'bwr', 'bwr_r', 'cividis', 'cividis_r', 'cool', 'cool_r', 'coolwarm', 'coolwarm_r', 'copper', 'copper_r', 'crest', 'crest_r', 'cubehelix', 'cubehelix_r', 'flag', 'flag_r', 'flare', 'flare_r', 'gist_earth', 'gist_earth_r', 'gist_gray', 'gist_gray_r', 'gist_heat', 'gist_heat_r', 'gist_ncar', 'gist_ncar_r', 'gist_rainbow', 'gist_rainbow_r', 'gist_stern', 'gist_stern_r', 'gist_yarg', 'gist_yarg_r', 'gnuplot', 'gnuplot2', 'gnuplot2_r', 'gnuplot_r', 'gray', 'gray_r', 'hot', 'hot_r', 'hsv', 'hsv_r', 'icefire', 'icefire_r', 'inferno', 'inferno_r', 'jet', 'jet_r', 'magma', 'magma_r', 'mako', 'mako_r', 'nipy_spectral', 'nipy_spectral_r', 'ocean', 'ocean_r', 'pink', 'pink_r', 'plasma', 'plasma_r', 'prism', 'prism_r', 'rainbow', 'rainbow_r', 'rocket', 'rocket_r', 'seismic', 'seismic_r', 'spring', 'spring_r', 'summer', 'summer_r', 'tab10', 'tab10_r', 'tab20', 'tab20_r', 'tab20b', 'tab20b_r', 'tab20c', 'tab20c_r', 'terrain', 'terrain_r', 'turbo', 'turbo_r', 'twilight', 'twilight_r', 'twilight_shifted', 'twilight_shifted_r', 'viridis', 'viridis_r', 'vlag', 'vlag_r', 'winter', 'winter_r'
-```
 
-An example can be:
 
+An example can be:
 .. code-block:: python
-
   from pandas_profiling import ProfileReport
-
-  profile = ProfileReport(df, title='Pandas Profiling Report', explorative=True,
-                       plot={
-                           'correlation':{
-                               'cmap': 'RdBu_r',
-                               'bad': '#000000'}}
-                       )
-
+  profile = ProfileReport(
+      df,
+      title='Pandas Profiling Report',
+      explorative=True,
+      plot={
+          'correlation':{
+             'cmap': 'RdBu_r',
+             'bad': '#000000'
+          }
+      }
+  )
 
 Similarly, one can change the palette for *Missing values* using the ``missing`` argument, eg:
-
 .. code-block:: python
-
   from pandas_profiling import ProfileReport
 
-  profile = ProfileReport(df, title='Pandas Profiling Report', explorative=True,
-                       plot={
-                           'missing':{
-                               'cmap': 'RdBu_r'}}
-                       )
-
-
-
+  profile = ProfileReport(
+      df,
+      title='Pandas Profiling Report',
+      explorative=True,
+      plot={
+          'missing':{
+              'cmap': 'RdBu_r'
+          }
+      }
+  )

docsrc/source/pages/api.rst

@@ -5,6 +5,7 @@ API
 .. toctree::
 
     api/profile_report
+    api/settings
     api/controller
     api/model
     api/report

docsrc/source/pages/api/settings.rst

@@ -0,0 +1,14 @@
+========
+Settings
+========
+
+.. currentmodule:: pandas_profiling.cfg
+.. toctree::
+
+.. autosummary::
+   :toctree: _autosummary
+
+   settings
+
+
+.. pydantic:: Settings

docsrc/source/pages/great_expectations_integration.rst

@@ -16,7 +16,7 @@ Great Expectations then uses this statement to validate whether the column ``pas
 
 Great Expectations renders Expectations to clean, human-readable documentation called *Data Docs*. These HTML docs contain both your Expectation Suites as well as your data validation results each time validation is run – think of it as a continuously updated data quality report.
 
-For more information about Great Expectations, check out the `Great Expectations documentation <https://docs.greatexpectations.io/en/latest/>`_ and join the `Great Expectations Slack channel <https://www.greatexpectations.io/slack>` for help.
+For more information about Great Expectations, check out the `Great Expectations documentation <https://docs.greatexpectations.io/en/latest/>`_ and join the `Great Expectations Slack channel <https://www.greatexpectations.io/slack>`_ for help.
 
 
 Creating Expectation Suites with Pandas Profiling

docsrc/source/pages/metadata.rst

@@ -72,8 +72,8 @@ By default, the descriptions are presented in the overview tab and next to each
 
         # We can disable showing the descriptions next to each variable
         report = df.profile_report(
-                variable=dict(descriptions=definitions),
-                show_variable_description=False
+            variable=dict(descriptions=definitions),
+            show_variable_description=False
         )
 
         report.to_file('report.html')

examples/census/census.py

@@ -5,6 +5,7 @@
 import pandas as pd
 
 from pandas_profiling import ProfileReport
+from pandas_profiling.config import Dataset
 from pandas_profiling.utils.cache import cache_file
 
 if __name__ == "__main__":
@@ -46,19 +47,16 @@
     with open("census_column_definition.json") as f:
         definitions = json.load(f)
 
-    profile.set_variable(
-        "dataset",
-        {
-            "description": 'Predict whether income exceeds $50K/yr based on census data. Also known as "Census Income" dataset. Extraction was done by Barry Becker from the 1994 Census database. A set of reasonably clean records was extracted using the following conditions: ((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0)). Prediction task is to determine whether a person makes over 50K a year.',
-            "copyright_year": "1996",
-            "author": "Ronny Kohavi and Barry Becker",
-            "creator": "Barry Becker",
-            "url": "https://archive.ics.uci.edu/ml/datasets/adult",
-        },
+    profile.config.dataset = Dataset(
+        description='Predict whether income exceeds $50K/yr based on census data. Also known as "Census Income" dataset. Extraction was done by Barry Becker from the 1994 Census database. A set of reasonably clean records was extracted using the following conditions: ((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0)). Prediction task is to determine whether a person makes over 50K a year.',
+        copyright_year="1996",
+        author="Ronny Kohavi and Barry Becker",
+        creator="Barry Becker",
+        url="https://archive.ics.uci.edu/ml/datasets/adult",
     )
-    profile.set_variable("variables.descriptions", definitions)
+    profile.config.variables.descriptions = definitions
 
     # Only show the descriptions in the overview
-    profile.set_variable("show_variable_description", False)
+    profile.config.show_variable_description = False
 
     profile.to_file(Path("./census_report.html"))

examples/features/images_cats_and_dogs.py

@@ -40,23 +40,21 @@
 )
 
 # Give our variable a description
-profile.set_variable(
-    "variables.descriptions",
-    {
-        "files": "Paths linking to the cats and dogs found https://www.kaggle.com/tongpython/cat-and-dog."
-    },
-)
+profile.config.variables.descriptions = {
+    "files": "Paths linking to the cats and dogs found https://www.kaggle.com/tongpython/cat-and-dog."
+}
+
 # If the number of samples is above this threshold, the scatter plots are replaced with hexbin plots
 # We are just over the threshold of 10.000 samples, so let's increase the limit.
-profile.set_variable("plot.scatter_threshold", 25000)
+profile.config.plot.scatter_threshold = 25000
 
 # Enable files and images (off by default, as it uses relatively expensive computations when not interested)
-profile.set_variable("vars.path.active", True)
-profile.set_variable("vars.file.active", True)
-profile.set_variable("vars.image.active", True)
+profile.config.vars.path.active = True
+profile.config.vars.file.active = True
+profile.config.vars.image.active = True
 
 # No exif found, so turn off expensive computation
-profile.set_variable("vars.image.exif", False)
+profile.config.vars.image.exif = False
 
 # Save the report to a file
 profile.to_file("cats-and-dogs.html")

examples/features/images_exif.py

@@ -43,12 +43,9 @@
     explorative=True,
 )
 # We can also configure the report like this
-profile.set_variable(
-    "variables.descriptions",
-    {
-        "files": "The 5 Celebrity Faces Dataset found on Kaggle (dansbecker/5-celebrity-faces-dataset)."
-    },
-)
+profile.config.variables.descriptions = {
+    "files": "The 5 Celebrity Faces Dataset found on Kaggle (dansbecker/5-celebrity-faces-dataset)."
+}
 
 # Save the report
 profile.to_file("celebrity-faces.html")

requirements.txt

@@ -2,11 +2,11 @@ joblib
 scipy>=1.4.1
 pandas>=0.25.3,!=1.0.1,!=1.0.0,!=1.0.2,!=1.1.0
 matplotlib>=3.2.0
-confuse>=1.0.0
+pydantic>=1.8.1
+PyYAML>=5.0.0
 jinja2>=2.11.1
 visions[type_image_path]==0.7.1
 numpy>=1.16.0
-attrs>=19.3.0
 # Could be optional
 # Related to HTML report
 htmlmin>=0.1.12

src/pandas_profiling/__init__.py

@@ -3,18 +3,12 @@
 .. include:: ../../README.md
 """
 
-from pandas_profiling.config import Config, config
 from pandas_profiling.controller import pandas_decorator
 from pandas_profiling.profile_report import ProfileReport
 from pandas_profiling.version import __version__
 
-clear_config = ProfileReport.clear_config
-
 __all__ = [
-    "Config",
-    "config",
     "pandas_decorator",
     "ProfileReport",
     "__version__",
-    "clear_config",
 ]