plot_table
has a cyclomatic complexity of 17 with "high" risk1111 return None
1112
1113
1114def plot_table(1115 tbl,
1116 columns=None,
1117 title="",
plot_rolling_beta
has a cyclomatic complexity of 28 with "very-high" risk 736 return None
737
738
739def plot_rolling_beta( 740 returns,
741 benchmark,
742 window1=126,
plot_rolling_stats
has a cyclomatic complexity of 25 with "high" risk 607 return None
608
609
610def plot_rolling_stats( 611 returns,
612 benchmark=None,
613 title="",
plot_histogram
has a cyclomatic complexity of 25 with "high" risk 403 return None
404
405
406def plot_histogram( 407 returns,
408 benchmark,
409 resample="M",
plot_timeseries
has a cyclomatic complexity of 39 with "very-high" risk 240 return None
241
242
243def plot_timeseries( 244 returns,
245 benchmark=None,
246 title="Returns",
plot_returns_bars
has a cyclomatic complexity of 31 with "very-high" risk 92 return colors, ls, alpha
93
94
95def plot_returns_bars( 96 returns,
97 benchmark=None,
98 returns_label="Strategy",
A function with high cyclomatic complexity can be hard to understand and maintain. Cyclomatic complexity is a software metric that measures the number of independent paths through a function. A higher cyclomatic complexity indicates that the function has more decision points and is more complex.
Functions with high cyclomatic complexity are more likely to have bugs and be harder to test. They may lead to reduced code maintainability and increased development time.
To reduce the cyclomatic complexity of a function, you can:
- Break the function into smaller, more manageable functions.
- Refactor complex logic into separate functions or classes.
- Avoid multiple return paths and deeply nested control expressions.
Bad practice
def number_to_name():
number = input()
if not number.isdigit():
print("Enter a valid number")
return
number = int(number)
if number >= 10:
print("Number is too big")
return
if number == 1:
print("one")
elif number == 2:
print("two")
elif number == 3:
print("three")
elif number == 4:
print("four")
elif number == 5:
print("five")
elif number == 6:
print("six")
elif number == 7:
print("seven")
elif number == 8:
print("eight")
elif number == 9:
print("nine")
Recommended
def number_to_name():
number = input()
if not number.isdigit():
print("Enter a valid number")
return
number = int(number)
if number >= 10:
print("Number is too big")
return
names = {
1: "one",
2: "two",
3: "three",
4: "four",
5: "five",
6: "six",
7: "seven",
8: "eight",
9: "nine",
}
print(names[number])
Issue configuration
Cyclomatic complexity threshold can be configured using the
cyclomatic_complexity_threshold
meta field in the
.deepsource.toml
config file.
Configuring this is optional. If you don't provide a value, the Analyzer will
raise issues for functions with complexity higher than the default threshold,
which is medium
for the Python Analyzer.
Here's the mapping of the risk category to the cyclomatic complexity score to help you configure this better:
Risk category | Cyclomatic complexity range | Recommended action |
---|---|---|
low | 1-5 | No action needed. |
medium | 6-15 | Review and monitor. |
high | 16-25 | Review and refactor. Recommended to add comments if the function is absolutely needed to be kept as it is. |
very-high | 26-50 | Refactor to reduce the complexity. |
critical | >50 | Must refactor this. This can make the code untestable and very difficult to understand. |