> ## Documentation Index
> Fetch the complete documentation index at: https://docs.semgrep.dev/llms.txt
> Use this file to discover all available pages before exploring further.
# Symbolic propagation
> Symbolic propagation allows Semgrep to perform matching modulo variable assignments. Consider the following Python code:
```python theme={null}
import pandas
def test1():
# ruleid: test
pandas.DataFrame(x).index.set_value(a, b, c)
def test2():
df = pandas.DataFrame(x)
ix = df.index
# ruleid: test
ix.set_value(a, b, c)
```
If we tried to match the pattern `pandas.DataFrame(...).index.set_value(...)` against the above code, Semgrep would normally match `test1` but not `test2`. It does not match `test2` because there are intermediate assignments, and Semgrep does not know that `ix` is equals to `df.index` or that `df` is equals to `pandas.DataFrame(x)`. If we wanted Semgrep to match such code, we had to be explicit about it.
Symbolic propagation is a generalization of [constant propagation](/writing-rules/data-flow/constant-propagation) that addresses this limitation. It enables Semgrep to perform matching modulo variable assignments. Thus, Semgrep is then able to match both `test1` and `test2` with the same simple pattern. This feature needs to be enabled explicitly via rule `options:` by setting `symbolic_propagation: true`.
## Limitations of symbolic propagation
Currently, symbolic propagation does not cross branching boundaries, such as `if` clauses or loops. Consider the following Python code, adapted from the example shown above:
```python theme={null}
import pandas
def test1():
# ruleid: test
pandas.DataFrame(x).index.set_value(a, b, c)
def test2():
if (x < 5):
df = pandas.DataFrame(x)
pass
ix = df.index
# ruleid: test
ix.set_value(a, b, c)
```
In this case, even if `symbolic_propagation: true` is used, Semgrep does not match `test2`, because the assignment of `df` to `pandas.DataFrame(x)` is not propagated over the conditional to the final two lines.