[Term Entry] Python - Pandas groupBy: prod() (#7711)

* Adding groupby.prod

* Adding groupby.prod

* minor fixes

* Update prod.md

* Update content/pandas/concepts/groupby/terms/prod/prod.md

* Update content/pandas/concepts/groupby/terms/prod/prod.md

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Author: SrikartikMateti

content/pandas/concepts/groupby/terms/prod/prod.md

@@ -0,0 +1,91 @@
+---
+Title: '.prod()'
+Description: 'Produces a new Series or DataFrame by computing the product of the values within the group.'
+Subjects:
+  - 'Computer Science'
+  - 'Data Science'
+Tags:
+  - 'Data Structures'
+  - 'Pandas'
+CatalogContent:
+  - 'learn-python-3'
+  - 'paths/data-science'
+---
+
+The **`.prod()`** method produces a new `Series` or [`DataFrame`](https://www.codecademy.com/resources/docs/pandas/dataframe) with the product of the values in a [`GroupBy`](https://www.codecademy.com/resources/docs/pandas/groupby) object.
+
+## Syntax
+
+```pseudo
+groupbyobject.prod(numeric_only=False, min_count=0)
+```
+
+**Parameters:**
+
+- `numeric_only`: If `True`, non-numeric columns are excluded. If `False`, attempts to include all columns (non-numeric columns are ignored in computation).
+- `min_count`: If the number of valid (non-NA) entries in a group is less than `min_count`, the result for that group is `NaN`.
+
+**Return value:**
+
+Returns a `DataFrame` (or `Series` if applied on a SeriesGroupBy object) containing the product of each numeric column for each group.
+
+## Example
+
+The following example produces a `GroupBy` object from a `DataFrame` and executes the `.prod()` method on it:
+
+```py
+import pandas as pd
+
+df = pd.DataFrame({
+    'Key' : ['A', 'A', 'B', 'B', 'C', 'C','D'],
+    'Value1' : [2, 3, 4, 5, 6, 9, 10],
+    'Value2' : [10, 5, 2, 3, 4, 2, 11]
+})
+print(df, end='\n\n')
+
+group_prod = df.groupby('Key').prod()
+
+print(group_prod)
+```
+
+This example produces the following output:
+
+```shell
+  Key  Value1  Value2
+0   A       2      10
+1   A       3       5
+2   B       4       2
+3   B       5       3
+4   C       6       4
+5   C       9       2
+6   D      10      11
+
+     Value1  Value2
+Key
+A          6      50
+B         20       6
+C         54       8
+D         10      11
+```
+
+## Codebyte Example
+
+This example computes the product of all prices and the product of all quantities within each category:
+
+```codebyte/python
+import pandas as pd
+
+# Sample sales data
+df = pd.DataFrame({
+    'Category': ['Electronics', 'Electronics', 'Clothing', 'Clothing', 'Books'],
+    'Price': [200, 150, 50, 30, 20],
+    'Quantity': [2, 3, 4, 5, 10]
+})
+
+print("Original DataFrame:\n", df, end='\n\n')
+
+# Group by Category and compute the product of numeric columns
+category_prod = df.groupby('Category').prod()
+
+print("Grouped product:\n", category_prod)
+```