Update 3. Get Only What You Need, and Fast.md

Author: ortizfram

Introduction to MongoDB in Python/3. Get Only What You Need, and Fast.md

@@ -194,3 +194,56 @@ docs = db.prizes.find(
 for doc in docs:
   print(doc)
 ```
+## 🦍 High-share categories
+> Which of the following indexes is best suited to speeding up the operation 
+
+    db.prizes.distinct("category", {"laureates.share": {"$gt": "3"}})
+Possible Answers
+
+- [ ] [("category", 1)]
+- [ ] [("category", 1), ("laureates.share", 1)]
+- [ ] [("laureates.share", 1)]
+- [x] [("laureates.share", 1), ("category", 1)]
+
+## 🦍 Recently single?
+- [x] Specify an index model that indexes first on category (ascending) and second on year (descending).
+- [x] Save a string report for printing the last single-laureate year for each distinct category, one category per line. To do this, for each distinct prize category, find the latest-year prize (requiring a descending sort by year) of that category (so, find matches for that category) with a laureate share of "1".
+```py
+# Specify an index model for compound sorting
+index_model = [('category', 1), ('year', -1)]
+db.prizes.create_index(index_model)
+
+# Collect the last single-laureate year for each category
+report = ""
+for category in sorted(db.prizes.distinct("category")):
+    doc = db.prizes.find_one(
+        {'category': category, "laureates.share": "1"},
+        sort=[('year', -1)]
+    )
+    report += "{category}: {year}\n".format(**doc)
+
+print(report)
+```
+
+## 🦍 Born and affiliated
+- [x] Create an index on country of birth ("bornCountry") for db.laureates to ensure efficient gathering of distinct values and counting of documents
+- [x] Complete the skeleton dictionary comprehension to construct n_born_and_affiliated, the count of laureates as described above for each distinct country of birth. For each call to count_documents, ensure that you use the value of country to filter documents properly.
+```py
+from collections import Counter
+
+# Ensure an index on country of birth
+db.laureates.create_index([("bornCountry", 1)])
+
+# Collect a count of laureates for each country of birth
+n_born_and_affiliated = {
+    country: db.laureates.count_documents({
+        "bornCountry": country,
+        "prizes.affiliations.country": country
+    })
+    for country in db.laureates.distinct("bornCountry")
+}
+
+five_most_common = Counter(n_born_and_affiliated).most_common(5)
+print(five_most_common)
+```
+     [('USA', 241), ('United Kingdom', 56), ('France', 26), ('Germany', 19), ('Japan', 17)]