Update 4. Aggregation Pipelines: Let the Server Do It For You.md

Author: ortizfram

Introduction to MongoDB in Python/4. Aggregation Pipelines: Let the Server Do It For You.md

@@ -136,4 +136,58 @@ Possible Answers
 - [ ] {"$expr": {"$in": ["$bornCountry", db.laureates.distinct("bornCountry")]}}
 - [ ] {"$expr": {"$eq": [{"$type": "$bornCountry"}, "string"]}}
 - [ ] {"bornCountry": {"$type": "string"}}
-- [ ] All of the above
+- [x] All of the above
+## 🦍 Here and elsewhere
+- [x] Use $unwind stages to ensure a single prize affiliation country per pipeline document.
+- [x] Filter out prize-affiliation-country values that are "empty" (null, not present, etc.) -- ensure values are "$in" the list of known values.
+- [x] Produce a count of documents for each value of "affilCountrySameAsBorn" (a field we've projected for you using the $indexOfBytes operator) by adding 1 to the running sum.
+```py
+key_ac = "prizes.affiliations.country"
+key_bc = "bornCountry"
+pipeline = [
+    {"$project": {key_bc: 1, key_ac: 1}},
+
+    # Ensure a single prize affiliation country per pipeline document
+    {"$unwind": "$prizes"},
+    {"$unwind": "$prizes.affiliations"},
+
+    # Ensure values in the list of distinct values (so not empty)
+    {"$match": {key_ac: {"$in": db.laureates.distinct(key_ac)}}},
+    {"$project": {"affilCountrySameAsBorn": {
+        "$gte": [{"$indexOfBytes": ["$"+key_ac, "$"+key_bc]}, 0]}}},
+
+    # Count by "$affilCountrySameAsBorn" value (True or False)
+    {"$group": {"_id": "$affilCountrySameAsBorn",
+                "count": {"$sum": 1 }}},
+]
+for doc in db.laureates.aggregate(pipeline): print(doc)
+```
+## 🦍 Countries of birth by prize category
+- [x] $unwind the laureates array field to output one pipeline document for each array element.
+- [x] After pulling in laureate bios with a $lookup stage, unwind the new laureate_bios array field (each laureate has only a single biography document).
+- [x] Collect the set of bornCountries associated with each prize category.
+- [x] Project out the size of each category's set of bornCountries.
+```py
+pipeline = [
+    # Unwind the laureates array
+    {"$unwind": "$laureates"},
+    {"$lookup": {
+        "from": "laureates", "foreignField": "id",
+        "localField": "laureates.id", "as": "laureate_bios"}},
+
+    # Unwind the new laureate_bios array
+    {"$unwind": "$laureate_bios"},
+    {"$project": {"category": 1,
+                  "bornCountry": "$laureate_bios.bornCountry"}},
+
+    # Collect bornCountry values associated with each prize category
+    {"$group": {"_id": "$category",
+                "bornCountries": {"$addToSet": "$bornCountry"}}},
+
+    # Project out the size of each category's (set of) bornCountries
+    {"$project": {"category": 1,
+                  "nBornCountries": {"$size": "$bornCountries"}}},
+    {"$sort": {"nBornCountries": -1}},
+]
+for doc in db.prizes.aggregate(pipeline): print(doc)
+```