diff --git a/06_dataframe/2_filters.py b/06_dataframe/2_filters.py
index 9a4fc4d..0c36798 100644
--- a/06_dataframe/2_filters.py
+++ b/06_dataframe/2_filters.py
@@ -1,5 +1,6 @@
 from datetime import datetime
 
+from chalk import online
 from chalk.features import DataFrame, features
 
 
@@ -18,30 +19,53 @@ class User:
     id: int
     txns: DataFrame[Transaction]
 
+    # Computed Fields
 
-# You can filter down the transactions by any of the
-# properties on the transaction
-credits = User.txns[Transaction.amount < 0]
+    num_valid_txns: int
+    num_rideshare_txns: int
 
-# Or works much like `and`:
-rideshare_income = User.txns[
-    Transaction.amount < 0
-    and (Transaction.merchant in ("uber", "lyft") or "uberpmts" == Transaction.memo)
-]
 
-# You can also check for set or list membership with `in`:
-rideshare_txns = User.txns[Transaction.merchant in ("uber", "lyft")]
+# You can filter down the transactions by any of the properties on the transaction
 
-# Filters separated by commas function as `and` filters:
-rideshare_credits = User.txns[
-    Transaction.amount < 0, Transaction.merchant in ("uber", "lyft")
-]
 
-# Equivalently, you can use the keyword `and` instead of separating by commas
-rideshare_credits = User.txns[
-    Transaction.amount < 0 and Transaction.merchant in ("uber", "lyft")
-]
+@online(tags=["v1"])
+def get_count_rideshare_transactions_v1(
+    txns: User.txns[Transaction.merchant in ("uber", "lyft")]
+) -> User.num_rideshare_txns:
+    # You can also check for set or list membership with `in`:
+    return len(txns)
 
 
-# Filters can also check for None the same way you check for None in Python
-valid_txns = User.txns[Transaction.canceled_at is not None]
+@online(tags=["v2"])
+def get_count_rideshare_transactions_v2(
+    txns: User.txns[Transaction.amount < 0, Transaction.merchant in ("uber", "lyft")]
+) -> User.num_rideshare_txns:
+    # Filters separated by commas function as `and` filters:
+    return len(txns)
+
+
+@online(tags=["v3"])
+def get_count_rideshare_transactions_v3(
+    txns: User.txns[Transaction.amount < 0 and Transaction.merchant in ("uber", "lyft")]
+) -> User.num_rideshare_txns:
+    # Equivalently, you can use the keyword `and` instead of separating by commas to apply multiple filters.
+    return len(txns)
+
+
+@online(tags=["v4"])
+def get_count_rideshare_transactions_v4(
+    txns: User.txns[
+        Transaction.amount < 0
+        and (Transaction.merchant in ("uber", "lyft") or "uberpmts" == Transaction.memo)
+    ]
+) -> User.num_rideshare_txns:
+    # or filters can also be applied through the "or" keyword
+    return len(txns)
+
+
+@online
+def get_count_valid_transactions(
+    valid_transactions: User.txns[Transaction.canceled_at is not None],
+) -> User.num_valid_transactions:
+    # Filters can also check for None the same way you check for None in Python
+    return len(valid_transactions)
diff --git a/06_dataframe/3_projections.py b/06_dataframe/3_projections.py
index af1ef6a..8e5c414 100644
--- a/06_dataframe/3_projections.py
+++ b/06_dataframe/3_projections.py
@@ -14,8 +14,13 @@ class Transaction:
 class User:
     id: int
     txns: DataFrame[Transaction]
+    txn_total: int
 
 
-# You can filter down the transactions by any of the
-# properties on the transaction
-credits = User.txns[Transaction.amount]
+def get_transaction_total(
+    txns: User.txns[Transaction.amount]
+) -> User.txn_total:
+    """we do not need the other fields in our transaction, so we can project, filtering out all columns except amount,
+    making the sum operation more efficient
+    """
+    return txns.sum()
diff --git a/06_dataframe/4_filters_and_projections.py b/06_dataframe/4_filters_and_projections.py
index dee296f..c33a583 100644
--- a/06_dataframe/4_filters_and_projections.py
+++ b/06_dataframe/4_filters_and_projections.py
@@ -1,4 +1,5 @@
 from chalk.features import DataFrame, _, features
+from chalk import online
 
 
 @features
@@ -14,11 +15,25 @@ class Transaction:
 class User:
     id: int
     txns: DataFrame[Transaction]
+    txn_total: int
 
 
-# You can filter down the transactions by any of the
-# properties on the transaction
-credits = User.txns[Transaction.amount < 0]
+# Filters and projections can be combined
+@online(tags=['v1'])
+def get_transaction_total_v1(
+    txns: User.txns[
+        Transaction.amount < 0, # filter
+        Transaction.amount      # projection
+    ]
+) -> User.txn_total:
+    return txns.sum()
 
-# You can also use the '_' as an alias for the current namespace
-credits = User.txns[_.amount < 0]
+
+@online(tags=['v2'])
+def get_transaction_total_v2(
+    txns: User.txns[
+        _.amount < 0,  # "_" is an alias for the current namespace
+        _.amount
+    ]
+) -> User.txn_total:
+    return txns.sum()
diff --git a/06_dataframe/5_aggregations.py b/06_dataframe/5_aggregations.py
index a84c78e..61d4473 100644
--- a/06_dataframe/5_aggregations.py
+++ b/06_dataframe/5_aggregations.py
@@ -1,4 +1,5 @@
 from chalk.features import DataFrame, features
+from chalk import online
 
 
 @features
@@ -20,5 +21,7 @@ class User:
 # You can filter down the transactions by any of the
 # properties on the transaction
 @online
-def get_num_credits(credits: User.txns[Transaction.amount < 0]) -> User.num_credits:
+def get_num_credits(
+    credits: User.txns[Transaction.amount < 0]
+) -> User.num_credits:
     return len(credits)