added note about size() groupby method

swricci · swricci · commit 579530dc8acd · 2023-10-12T12:38:43.000-04:00
diff --git a/Open_Lab_4_exploratory_analysis_with_pandas/Open_Lab_4_exploratory_analysis_with_pandas.ipynb b/Open_Lab_4_exploratory_analysis_with_pandas/Open_Lab_4_exploratory_analysis_with_pandas.ipynb
@@ -271,7 +271,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can also use `groupby()` to group data by multiple variables. We will create a hierarchical grouping of `OilPainting` and then `Gender` to see the counts of oil paintings by different genders. We can use `.mean()` to find the average for each of those subcategories, or we can use `.size()` to find counts of each category."
+    "You can also use `groupby()` to group data by multiple variables. We will create a hierarchical grouping of `OilPainting` and then `Gender` to see the counts of oil paintings by different genders. We can use `.mean()` to find the average for each of those subcategories, or we can use `.size()` to find counts of each category.\n",
+    "\n",
+    "Note: the `.size()` method used in this example is a function for groupby objects. This method will return the number of items in each group. The documentation for this method can be found [here](https://pandas.pydata.org/docs/reference/api/pandas.core.groupby.DataFrameGroupBy.size.html). The pandas DataFrame `.size` property (documentation [here](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.size.html)) returns the number of elements."
    ]
   },
   {
diff --git a/Open_Lab_4_exploratory_analysis_with_pandas/filled_Open_Lab_4_exploratory_analysis_with_pandas.ipynb b/Open_Lab_4_exploratory_analysis_with_pandas/filled_Open_Lab_4_exploratory_analysis_with_pandas.ipynb
@@ -2994,7 +2994,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can also use `groupby()` to group data by multiple variables. We will create a hierarchical grouping of `OilPainting` and then `Gender` to see the counts of oil paintings by different genders. We can use `.mean()` to find the average for each of those subcategories, or we can use `.size()` to find counts of each category."
+    "You can also use `groupby()` to group data by multiple variables. We will create a hierarchical grouping of `OilPainting` and then `Gender` to see the counts of oil paintings by different genders. We can use `.mean()` to find the average for each of those subcategories, or we can use `.size()` to find counts of each category.\n",
+    "\n",
+    "Note: the `.size()` method used in this example is a function for groupby objects. This method will return the number of items in each group. The documentation for this method can be found [here](https://pandas.pydata.org/docs/reference/api/pandas.core.groupby.DataFrameGroupBy.size.html). The pandas DataFrame `.size` property (documentation [here](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.size.html)) returns the number of elements."
    ]
   },
   {
@@ -3021,7 +3023,7 @@
    "source": [
     "# Group the data by OilPainting and then Gender \n",
     "# find the counts of subcategories with .size()\n",
-    "paintings.groupby(['OilPainting', 'Gender']).size()"
+    "paintings.groupby(['OilPainting', 'Gender']).value_counts()"
    ]
   },
   {

Original file line number	Diff line number	Diff line change
`@@ -271,7 +271,9 @@`
`271`	`271`	`"cell_type": "markdown",`
`272`	`272`	`"metadata": {},`
`273`	`273`	`"source": [`
`274`		- "You can also use `groupby()` to group data by multiple variables. We will create a hierarchical grouping of `OilPainting` and then `Gender` to see the counts of oil paintings by different genders. We can use `.mean()` to find the average for each of those subcategories, or we can use `.size()` to find counts of each category."
	`274`	+ "You can also use `groupby()` to group data by multiple variables. We will create a hierarchical grouping of `OilPainting` and then `Gender` to see the counts of oil paintings by different genders. We can use `.mean()` to find the average for each of those subcategories, or we can use `.size()` to find counts of each category.\n",
	`275`	`+ "\n",`
	`276`	+ "Note: the `.size()` method used in this example is a function for groupby objects. This method will return the number of items in each group. The documentation for this method can be found [here](https://pandas.pydata.org/docs/reference/api/pandas.core.groupby.DataFrameGroupBy.size.html). The pandas DataFrame `.size` property (documentation [here](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.size.html)) returns the number of elements."
`275`	`277`	`]`
`276`	`278`	`},`
`277`	`279`	`{`
Original file line number	Diff line number	Diff line change
`@@ -2994,7 +2994,9 @@`
`2994`	`2994`	`"cell_type": "markdown",`
`2995`	`2995`	`"metadata": {},`
`2996`	`2996`	`"source": [`
`2997`		- "You can also use `groupby()` to group data by multiple variables. We will create a hierarchical grouping of `OilPainting` and then `Gender` to see the counts of oil paintings by different genders. We can use `.mean()` to find the average for each of those subcategories, or we can use `.size()` to find counts of each category."
	`2997`	+ "You can also use `groupby()` to group data by multiple variables. We will create a hierarchical grouping of `OilPainting` and then `Gender` to see the counts of oil paintings by different genders. We can use `.mean()` to find the average for each of those subcategories, or we can use `.size()` to find counts of each category.\n",
	`2998`	`+ "\n",`
	`2999`	+ "Note: the `.size()` method used in this example is a function for groupby objects. This method will return the number of items in each group. The documentation for this method can be found [here](https://pandas.pydata.org/docs/reference/api/pandas.core.groupby.DataFrameGroupBy.size.html). The pandas DataFrame `.size` property (documentation [here](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.size.html)) returns the number of elements."
`2998`	`3000`	`]`
`2999`	`3001`	`},`
`3000`	`3002`	`{`
`@@ -3021,7 +3023,7 @@`
`3021`	`3023`	`"source": [`
`3022`	`3024`	`"# Group the data by OilPainting and then Gender \n",`
`3023`	`3025`	`"# find the counts of subcategories with .size()\n",`
`3024`		`- "paintings.groupby(['OilPainting', 'Gender']).size()"`
	`3026`	`+ "paintings.groupby(['OilPainting', 'Gender']).value_counts()"`
`3025`	`3027`	`]`
`3026`	`3028`	`},`
`3027`	`3029`	`{`