DOC: make shared include for filtering for comparison pages

Author: afeld

doc/source/getting_started/comparison/comparison_with_sas.rst

@@ -223,12 +223,7 @@ or more columns.
           DATA step begins and can also be used in PROC statements */
    run;
 
-DataFrames can be filtered in multiple ways; the most intuitive of which is using
-:ref:`boolean indexing <indexing.boolean>`
-
-.. ipython:: python
-
-   tips[tips["total_bill"] > 10].head()
+.. include:: filtering.rst
 
 If/then logic
 ~~~~~~~~~~~~~

doc/source/getting_started/comparison/comparison_with_sql.rst

@@ -73,24 +73,9 @@ Filtering in SQL is done via a WHERE clause.
 
     SELECT *
     FROM tips
-    WHERE time = 'Dinner'
-    LIMIT 5;
-
-DataFrames can be filtered in multiple ways; the most intuitive of which is using
-:ref:`boolean indexing <indexing.boolean>`
-
-.. ipython:: python
-
-    tips[tips["time"] == "Dinner"].head(5)
-
-The above statement is simply passing a ``Series`` of True/False objects to the DataFrame,
-returning all rows with True.
-
-.. ipython:: python
+    WHERE time = 'Dinner';
 
-    is_dinner = tips["time"] == "Dinner"
-    is_dinner.value_counts()
-    tips[is_dinner].head(5)
+.. include:: filtering.rst
 
 Just like SQL's OR and AND, multiple conditions can be passed to a DataFrame using | (OR) and &
 (AND).

doc/source/getting_started/comparison/comparison_with_stata.rst

@@ -217,12 +217,7 @@ Filtering in Stata is done with an ``if`` clause on one or more columns.
 
    list if total_bill > 10
 
-DataFrames can be filtered in multiple ways; the most intuitive of which is using
-:ref:`boolean indexing <indexing.boolean>`.
-
-.. ipython:: python
-
-   tips[tips["total_bill"] > 10].head()
+.. include:: filtering.rst
 
 If/then logic
 ~~~~~~~~~~~~~

doc/source/getting_started/comparison/filtering.rst

@@ -0,0 +1,18 @@
+:orphan:
+
+DataFrames can be filtered in multiple ways; the most intuitive of which is using
+:ref:`boolean indexing <indexing.boolean>`
+
+.. ipython:: python
+
+   tips[tips["total_bill"] > 10]
+
+The above statement is simply passing a ``Series`` of ``True``/``False`` objects to the DataFrame,
+returning all rows with ``True``.
+
+.. ipython:: python
+
+    is_dinner = tips["time"] == "Dinner"
+    is_dinner
+    is_dinner.value_counts()
+    tips[is_dinner]