Fokko commented on code in PR #2423:
URL: https://github.com/apache/iceberg-python/pull/2423#discussion_r2324224411
##########
mkdocs/docs/recipe-count.md:
##########
@@ -0,0 +1,114 @@
+---
+title: Count Recipe - Efficiently Count Rows in Iceberg Tables
+---
+
+# Counting Rows in an Iceberg Table
+
+This recipe demonstrates how to use the `count()` function to efficiently
count rows in an Iceberg table using PyIceberg. The count operation is
optimized for performance by reading file metadata rather than scanning actual
data.
+
+## How Count Works
+
+The `count()` method leverages Iceberg's metadata architecture to provide fast
row counts by:
+
+1. **Reading file manifests**: Examines metadata about data files without
loading the actual data
+2. **Aggregating record counts**: Sums up record counts stored in Parquet file
footers
+3. **Applying filters at metadata level**: Pushes down predicates to skip
irrelevant files
+4. **Handling deletes**: Automatically accounts for delete files and tombstones
+
+## Basic Usage
+
+Count all rows in a table:
+
+```python
+from pyiceberg.catalog import load_catalog
+
+catalog = load_catalog("default")
+table = catalog.load_table("default.cities")
+
+# Get total row count
+row_count = table.scan().count()
+print(f"Total rows in table: {row_count}")
+```
+
+## Count with Filters
+
+Count rows matching specific conditions:
+
+```python
+from pyiceberg.expressions import GreaterThan, EqualTo, And
+
+# Count rows with population > 1,000,000
+large_cities = table.scan().filter(GreaterThan("population", 1000000)).count()
Review Comment:
I think using the SQL like expressions is easier to read:
```suggestion
# Count rows with population > 1,000,000
large_cities = table.scan().filter("population > 1000000").count()
```
##########
mkdocs/docs/recipe-count.md:
##########
@@ -0,0 +1,114 @@
+---
+title: Count Recipe - Efficiently Count Rows in Iceberg Tables
+---
+
+# Counting Rows in an Iceberg Table
+
+This recipe demonstrates how to use the `count()` function to efficiently
count rows in an Iceberg table using PyIceberg. The count operation is
optimized for performance by reading file metadata rather than scanning actual
data.
+
+## How Count Works
+
+The `count()` method leverages Iceberg's metadata architecture to provide fast
row counts by:
+
+1. **Reading file manifests**: Examines metadata about data files without
loading the actual data
+2. **Aggregating record counts**: Sums up record counts stored in Parquet file
footers
+3. **Applying filters at metadata level**: Pushes down predicates to skip
irrelevant files
+4. **Handling deletes**: Automatically accounts for delete files and tombstones
+
+## Basic Usage
+
+Count all rows in a table:
+
+```python
+from pyiceberg.catalog import load_catalog
+
+catalog = load_catalog("default")
+table = catalog.load_table("default.cities")
+
+# Get total row count
+row_count = table.scan().count()
+print(f"Total rows in table: {row_count}")
+```
+
+## Count with Filters
+
+Count rows matching specific conditions:
+
+```python
+from pyiceberg.expressions import GreaterThan, EqualTo, And
+
+# Count rows with population > 1,000,000
+large_cities = table.scan().filter(GreaterThan("population", 1000000)).count()
Review Comment:
I think using the SQL like expressions is easier to read:
```suggestion
large_cities = table.scan().filter("population > 1000000").count()
```
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