[Docs] Avocado Sales - Integrity Suite Quickstart (#1474)

* add data integrity quickstart

* align title

* fix to file and add build to gitignore

* fixing plot_quick_data_integrity.py

* update start

* Fixing integrity quickstart

Co-authored-by: Itay Gabbay <itay@deepchecks.com>

.gitignore

@@ -96,6 +96,9 @@ docs.error.log
 *MNIST*
 *ultralytics_yolov5_v6.1*
 
+# docs build files
+docs/source/_build
+
 # build folders of sphinx gallery
 docs/source/user-guide/general/customizations/examples/
 docs/source/user-guide/general/exporting_results/examples/

docs/source/user-guide/tabular/tutorials/plot_quick_data_integrity.py

@@ -0,0 +1,157 @@
+# -*- coding: utf-8 -*-
+"""
+Data Integrity Suite on Avocado Sales Data - Quickstart
+*******************************************************
+
+The deepchecks integrity suite is relevant any time you have data that you wish to validate:
+whether it's on a fresh batch of data, or right before splitting it or using it for training. 
+Here we'll use the avocado prices dataset, to demonstrate how you can run
+the suite with only a few simple lines of code, and see which kind of insights it can find.
+
+.. code-block:: bash
+
+    # Before we start, if you don't have deepchecks installed yet,
+    # make sure to run:
+    pip install deepchecks -U --quiet #--user
+"""
+
+#%%
+# Load and Prepare Data
+# ====================================================
+
+from deepchecks.tabular import datasets
+
+# load data
+data = datasets.regression.avocado.load_data(data_format='DataFrame', as_train_test=False)
+#%%
+
+# drop unused columns (remove after fix...)
+data = data.drop(columns=['Unnamed: 0'])
+
+#%%
+# Insert a few typcial problems to dataset for demonstration.
+
+import pandas as pd
+
+def add_dirty_data(df):
+    # change strings
+    df.loc[df[df['type'] == 'organic'].sample(frac=0.18).index,'type'] = 'Organic'
+    df.loc[df[df['type'] == 'organic'].sample(frac=0.01).index,'type'] = 'ORGANIC'
+    # add duplicates
+    df = pd.concat([df, df.sample(frac=0.156)], axis=0, ignore_index=True)
+    # add column with single value
+    df['Is Ripe'] = True
+    return df
+
+
+dirty_df = add_dirty_data(data)
+
+#%%
+# Run Deepchecks for Data Integrity
+# ====================================
+#
+# Define a Dataset Object
+# ------------------------
+#
+# Create a deepchecks Dataset, including the relevant metadata (label, date, index, etc.).
+# Check out :class:`deepchecks.tabular.Dataset` to see all of the columns that can be declared.
+
+from deepchecks.tabular import Dataset
+
+# We explicitly state the categorical features,
+# otherwise they will be automatically inferred, which may not work perfectly and is not recommended.
+# The label can be passed as a column name or a separate pd.Series / pd.DataFrame
+ds = Dataset(dirty_df, cat_features = ['type'], datetime_name='Date', label = 'AveragePrice')
+
+#%%
+# Run the Deepchecks Suite
+# --------------------------
+#
+# Validate your data with the :class:`deepchecks.tabular.suites.single_dataset_integrity` suite.
+# It runs on a single dataset, so you can run it on any batch of data (e.g. train data, test data, a new batch of data
+# that recently arrived)
+#
+# Check out the :doc:`when should you use </getting-started/when_should_you_use>`
+# deepchecks guide for some more info about the existing suites and when to use them.
+
+from deepchecks.tabular.suites import data_integrity
+
+# Run Suite:
+integ_suite = data_integrity()
+integ_suite.run(ds)
+
+#%%
+# We can inspect the suite outputs and see that there are a few problems we'd like to fix.
+# We'll now fix them and check that they're resolved by re-running those specific checks.
+
+
+#%%
+# Run a Single Check
+# -------------------
+# We can run a single check on a dataset, and see the results.
+
+from deepchecks.tabular.checks import IsSingleValue, DataDuplicates
+
+# first let's see how the check runs:
+IsSingleValue().run(ds)
+
+#%%
+
+# we can also add a condition:
+single_value_with_condition = IsSingleValue().add_condition_not_single_value()
+result = single_value_with_condition.run(ds)
+result
+
+#%%
+
+# We can also inspect and use the result's value:
+result.value
+
+#%%
+# Now let's remove the single value column and rerun (notice that we're using directly 
+# the ``data`` attribute that stores the dataframe inside the Dataset)
+
+ds.data.drop('Is Ripe', axis=1, inplace=True)
+result = single_value_with_condition.run(ds)
+result
+
+#%%
+
+# Alternatively we can fix the dataframe directly, and create a new dataset.
+# Let's fix also the duplicate values:
+dirty_df.drop_duplicates(inplace=True)
+dirty_df.drop('Is Ripe', axis=1, inplace=True)
+ds = Dataset(dirty_df, cat_features=['type'], datetime_name='Date', label='AveragePrice')
+result = DataDuplicates().add_condition_ratio_not_greater_than(0).run(ds)
+result
+
+#%%
+# Rerun Suite on the Fixed Dataset
+# ---------------------------------
+# Finally, we'll choose to keep the "organic" multiple spellings as they represent different sources.
+# So we'll customaize the suite by removing the condition from it (or delete check completely).
+# Alternatively - we can customize it by creating a new Suite with the desired checks and conditions.
+# See :doc:`/user-guide/general/customizations/examples/customizing-suites` for more info.
+
+# let's inspect the suite's structure
+integ_suite
+
+#%%
+
+# and remove the condition:
+integ_suite[3].clean_conditions()
+
+#%%
+# Now we can re-run the suite using:
+integ_suite.run(ds)
+
+#%%
+# and all of the conditions will pass.
+#
+# *Note: the check we manipulated will still run as part of the Suite, however
+# it won't appear in the Conditions Summary since it no longer has any
+# conditions defined on it. You can still see its display results in the 
+# Additional Outputs section*
+#
+# For more info about working with conditions, see the detailed
+# :doc:`/user-guide/general/customizations/examples/plot_configure_checks_conditions' guide.