apache / arrow-datafusion-pythonConditional Complexity

Intro

Conditional complexity (also called cyclomatic complexity) is a term used to measure the complexity of software. The term refers to the number of possible paths through a program function. A higher value ofter means higher maintenance and testing costs (infosecinstitute.com).
Conditional complexity is calculated by counting all conditions in the program that can affect the execution path (e.g. if statement, loops, switches, and/or operators, try and catch blocks...).
Conditional complexity is measured at the unit level (methods, functions...).
Units are classified in four categories based on the measured McCabe index: 1-5 (simple units), 6-10 (medium complex units), 11-25 (complex units), 26+ (very complex units).

Learn more...

Conditional Complexity Overall

There are 610 units with 3,453 lines of code in units (37.9% of code).

0 very complex units (0 lines of code)
0 complex units (0 lines of code)
2 medium complex units (76 lines of code)
6 simple units (172 lines of code)
602 very simple units (3,205 lines of code)

Legend:

51+

26-50

11-25

6-10

1-5

Alternative Visuals

3D view of all units

Conditional Complexity per Extension

51+

26-50

11-25

6-10

1-5

Conditional Complexity per Logical Component

primary logical decomposition

51+

26-50

11-25

6-10

1-5

Most Complex Units

Top 50 most complex units

Unit	# lines	McCabe index	# params
def generate_changelog() in dev/release/generate-changelog.py	42	20	4
def to_polars_df() in datafusion/polars.py	34	11	2
def bench() in benchmarks/tpch/tpch.py	48	9	2
fn new() in src/store.rs	50	7	10
def to_pandas_df() in datafusion/pandas.py	12	6	2
def to_cudf_df() in datafusion/cudf.py	12	6	2
fn py_obj_to_scalar_value() in src/config.rs	15	6	2
fn new() in src/store.rs	35	6	7
fn __getitem__() in src/dataframe.rs	18	4	2
fn read_csv() in src/context.rs	37	4	9
def to_pandas_expr() in datafusion/pandas.py	6	3	2
def udf() in datafusion/__init__.py	12	3	5
def udaf() in datafusion/__init__.py	15	3	6
def to_cudf_expr() in datafusion/cudf.py	6	3	2
def to_polars_expr() in datafusion/polars.py	6	3	2
def ans_shape() in benchmarks/db-benchmark/join-datafusion.py	9	3	1
def ans_shape() in benchmarks/db-benchmark/groupby-datafusion.py	9	3	1
def print_pulls() in dev/release/generate-changelog.py	14	3	3
fn case() in src/functions.rs	5	3	1
fn supports_filter_pushdown() in src/common/schema.rs	13	3	2
fn new() in src/context.rs	9	3	1
fn new() in src/context.rs	16	3	2
fn register_csv() in src/context.rs	30	3	9
fn read_json() in src/context.rs	26	3	7
def __init__() in datafusion/pandas.py	9	2	2
def literal() in datafusion/__init__.py	4	2	1
def __init__() in datafusion/cudf.py	9	2	3
def cli() in dev/release/generate-changelog.py	14	2	1
fn union() in src/dataframe.rs	11	2	3
fn new() in src/store.rs	14	2	1
fn new() in src/store.rs	14	2	2
fn window() in src/functions.rs	30	2	4
fn explain_string() in src/expr/explain.rs	7	2	1
fn from() in src/expr/case.rs	3	2	1
fn from() in src/expr/case.rs	3	2	1
fn when_then_expr() in src/expr/case.rs	7	2	1
fn table() in src/catalog.rs	7	2	3
fn fmt_as() in src/dataset_exec.rs	32	2	3
fn register_object_store() in src/context.rs	29	2	4
fn read_avro() in src/context.rs	21	2	6
fn inputs() in src/sql/logical.rs	7	2	1
def __getattr__() in datafusion/functions.py	2	1	1
def create_schema() in datafusion/pandas.py	4	1	3
def update_schema() in datafusion/pandas.py	2	1	4
def drop_schema() in datafusion/pandas.py	2	1	3
def show_schemas() in datafusion/pandas.py	2	1	2
def register_table() in datafusion/pandas.py	3	1	4
def explain() in datafusion/pandas.py	2	1	2
def sql() in datafusion/pandas.py	4	1	2
def is_correct_input() in datafusion/input/location.py	2	1	4