Contributing to CPython is a great way to improve your programming skills, learn about the Python language, and help the Python community. Here are some steps to start contributing to CPython:

1. Set up your development environment:

   • Fork the CPython repository on GitHub (https://github.com/python/cpython)
   • Clone your fork to your local machine

     bash
     git clone https://github.com/YOUR_USERNAME/cpython.git
     cd cpython
     

   • Set up a virtual environment to isolate your development environment from your system Python

     bash
     python3 -m venv venv
     source venv/bin/activate
     

   • Install development dependencies

     python -m pip install -r requirements.txt
     

2. Build CPython from source:

   • Unix-like systems (macOS, Linux):

     bash
     ./configure --with-pydebug
     make -j
     

   • Windows:

     PCbuild\build.bat -e -d
     

3. Run the test suite:

   bash
   ./python -m test
   

4. Find an issue to work on:

   • Check the CPython issue tracker (https://bugs.python.org) for issues labeled as "easy" or "beginner-friendly".
   • Choose an issue that interests you and add a comment to let others know you're working on it.
   • Make sure to understand the issue and ask for clarification if needed.

5. Fix the issue and add tests:

   • Modify the relevant code files to fix the issue.
   • Write tests to verify that your fix works as expected. Tests are usually added in the Lib/test directory.
   • For example, if you're fixing a bug in the str.capitalize() method, you might add a test like this in Lib/test/test_string.py:

     python
     def test_capitalize_bugfix(self):
         # Description of the bugfix and issue number
         s = "your test string"
         expected = "Your expected result"
         self.assertEqual(s.capitalize(), expected)
     

6. Commit your changes:

   • Stage your changes

     csharp
     git add FILE1 FILE2 ...
     

   • Commit your changes with a descriptive message, including the issue number

     sql
     git commit -m "bpo-12345: Fixed bug in str.capitalize()"
     

7. Run the test suite again to ensure everything still works:

   bash
   ./python -m test
   

8. Push your changes to your fork on GitHub:

   perl
   git push origin BRANCH_NAME
   

9. Create a pull request:

   • Go to your fork on GitHub and click on "New Pull Request".
   • Make sure the base repository is python/cpython and the base branch is main.
   • Review your changes and submit the pull request.
   • Fill in the PR template with relevant information and reference the issue number.

10. Address any feedback from the core developers and maintainers. They may suggest changes or ask for clarification. Update your PR as needed and continue the discussion until your changes are approved and merged.

Happy coding!

As an AI language model, I cannot interact with live repositories or check the current status of a repository. However, I can suggest a hypothetical change to illustrate the process of making a contribution to CPython.

Hypothetical change: Improve the str.title() method to better handle apostrophes in words.

Let's assume the current implementation of str.title() doesn't handle words with apostrophes correctly. For example, when the string "o'clock" is passed to str.title(), it returns "O'Clock" instead of the expected "O'Clock".

1. Modify the relevant code:

• Open the file Objects/unicodeobject.c
• Locate the function that implements str.title(), which might be named something like unicode_title
• Modify the function to properly handle apostrophes in words

Here's a hypothetical modification to the function:

c
    /* ... */
    for (i = 0; i < n; i++) {
        Py_UCS1 ch = PyUnicode_READ(kind, data, i);
        if (previous_is_cased) {
            Py_UCS1 t = Py_UNICODE_TOLOWER(ch);
            if (t != ch) {
                SET_TITLE(i, t);
                continue;
            }
        }
        else if (ch != '\'' || i == 0 || !Py_UNICODE_ISALPHA(PyUnicode_READ(kind, data, i - 1))) {
            Py_UCS1 t = Py_UNICODE_TOUPPER(ch);
            if (t != ch) {
                SET_TITLE(i, t);
                continue;
            }
        }
        previous_is_cased = Py_UNICODE_ISALPHA(ch);
    }
    /* ... */

2. Add tests for the change:

• Open the file Lib/test/test_string.py
• Add a new test to the StringTest class

Example of a test for this change:

python
def test_title_with_apostrophe(self):
    s = "it's o'clock"
    expected = "It's O'Clock"
    self.assertEqual(s.title(), expected)

    s = "they're here"
    expected = "They're Here"
    self.assertEqual(s.title(), expected)

3. Commit the changes, run the tests, push the changes to your fork, and create a pull request, as described in the previous answer.

Remember that this is just a hypothetical example. Always make sure to understand the issue you're working on, consult the documentation, and ask for help if needed.

Hypothetical change: Add a new method str.isnumeric_only() to check if a string contains only numeric characters (excluding decimal points, commas, and other special characters).

1. Modify the relevant code:

• Open the file Objects/unicodeobject.c
• Add a new function that implements the str.isnumeric_only() method, e.g., unicode_isnumeric_only

Here's a hypothetical implementation of the function:

c
static PyObject *
unicode_isnumeric_only(PyObject *self)
{
    void *data;
    int kind;
    Py_ssize_t i, n;

    if (!PyUnicode_Check(self))
        return PyBool_FromLong(0);

    data = PyUnicode_DATA(self);
    kind = PyUnicode_KIND(self);
    n = PyUnicode_GET_LENGTH(self);

    for (i = 0; i < n; i++) {
        Py_UCS4 ch = PyUnicode_READ(kind, data, i);
        if (!Py_UNICODE_ISDIGIT(ch))
            return PyBool_FromLong(0);
    }

    return PyBool_FromLong(1);
}

• Add the method to the unicode_methods array in the same file

c
static PyMethodDef unicode_methods[] = {
    // ...
    {"isnumeric_only", (PyCFunction) unicode_isnumeric_only, METH_NOARGS,
     PyDoc_STR("Return True if the string contains only numeric characters, False otherwise.")},
    // ...
}

2. Add tests for the change:

• Open the file Lib/test/test_unicode.py
• Add a new test to the TestUnicode class

Example of a test for this change:

python
def test_isnumeric_only(self):
    self.assertTrue("12345".isnumeric_only())
    self.assertTrue("00000".isnumeric_only())
    self.assertFalse("3.14".isnumeric_only())
    self.assertFalse("1,000".isnumeric_only())
    self.assertFalse("abc123".isnumeric_only())
    self.assertFalse(" 123 ".isnumeric_only())

3. Commit the changes, run the tests, push the changes to your fork, and create a pull request, as described in the previous answers.

Remember that this is just a hypothetical example. Always make sure to understand the issue you're working on, consult the documentation, and ask for help if needed.

Hypothetical bug fix: The str.split() method doesn't handle consecutive delimiters properly when a custom delimiter is provided.

Assume that the current implementation of str.split() doesn't handle consecutive custom delimiters correctly. For example, when the string "a;;b;c;;d" is passed to str.split(';'), it returns ['a', '', 'b', 'c', '', 'd'] instead of the expected ['a', 'b', 'c', 'd'].

1. Modify the relevant code:

• Open the file Objects/unicodeobject.c
• Locate the function that implements str.split(), which might be named something like unicode_split
• Modify the function to properly handle consecutive delimiters when a custom delimiter is provided

Here's a hypothetical modification to the function:

c
/* ... */
if (maxcount < 0)
    maxcount = PY_SSIZE_T_MAX;
if (maxcount == 0 || (sub_len == 0 && maxcount == 1)) {
    result = PyTuple_New(1);
    if (!result)
        return NULL;
    Py_INCREF(self);
    PyTuple_SET_ITEM(result, 0, self);
    return result;
}
/* ... */

2. Add tests for the bug fix:

• Open the file Lib/test/test_string.py
• Add a new test to the StringTest class

Example of a test for this bug fix:

python
def test_split_consecutive_delimiters(self):
    s = "a;;b;c;;d"
    expected = ['a', 'b', 'c', 'd']
    self.assertEqual(s.split(';'), expected)

    s = "a:::b:c::d"
    expected = ['a', 'b', 'c', 'd']
    self.assertEqual(s.split(':'), expected)

3. Commit the changes, run the tests, push the changes to your fork, and create a pull request, as described in the previous answers.

Remember that this is just a hypothetical example. Always make sure to understand the issue you're working on, consult the documentation, and ask for help if needed.

Hypothetical speed-up: Optimize the sum() function for cases when the input is a list of integers.

Currently, the sum() function works for any iterable containing numbers. However, when the input is a list of integers, it could be optimized using a more efficient algorithm that takes advantage of the specific data type.

1. Modify the relevant code:

• Open the file Python/bltinmodule.c
• Locate the function that implements the sum() function, which might be named something like builtin_sum
• Modify the function to optimize the case when the input is a list of integers

Here's a hypothetical modification to the function:

c
static PyObject *
builtin_sum(PyObject *self, PyObject *args)
{
    PyObject *seq;
    PyObject *result = NULL;
    PyObject *temp, *item, *iter;

    if (!PyArg_ParseTuple(args, "O|O:sum", &seq, &result))
        return NULL;

    if (result == NULL) {
        result = PyLong_FromLong(0);
        if (result == NULL)
            return NULL;
    }
    else
        Py_INCREF(result);

    if (PyList_CheckExact(seq) && PyList_GET_SIZE(seq) > 0) {
        // Fast path for lists of integers
        Py_ssize_t i, n = PyList_GET_SIZE(seq);
        long total = PyLong_AsLong(result);

        if (total == -1 && PyErr_Occurred()) {
            Py_DECREF(result);
            return NULL;
        }

        for (i = 0; i < n; i++) {
            item = PyList_GET_ITEM(seq, i);
            if (PyLong_CheckExact(item)) {
                long value = PyLong_AsLong(item);
                if (value == -1 && PyErr_Occurred()) {
                    Py_DECREF(result);
                    return NULL;
                }
                total += value;
            }
            else {
                break;
            }
        }

        if (i == n) {
            // All items were integers, return the result
            Py_DECREF(result);
            return PyLong_FromLong(total);
        }
    }

    // Fallback to the generic algorithm for other cases
    iter = PyObject_GetIter(seq);
    if (iter == NULL) {
        Py_DECREF(result);
        return NULL;
    }

    for (;;) {
        item = PyIter_Next(iter);
        if (item == NULL) {
            if (PyErr_Occurred())
                goto error;
            break;
        }

        temp = PyNumber_Add(result, item);
        Py_DECREF(item);
        if (temp == NULL)
            goto error;
        Py_DECREF(result);
        result = temp;
    }

    Py_DECREF(iter);
    return result;

  error:
    Py_DECREF(iter);
    Py_DECREF(result);
    return NULL;
}

2. Add tests and benchmarks for the optimization:

• Add tests to ensure the sum() function still works correctly after the optimization. This can be done in the Lib/test/test_builtin.py file.
• Add benchmarks to measure the speed improvement of the optimized sum() function for lists of integers. You can use the timeit module or a benchmarking library like pytest-benchmark for this purpose.

Example of a benchmark using timeit:

python
import timeit

def benchmark_sum():
    integer_list = [i for i in range(1000)]
    float_list = [float(i) for i in range(1000)]

    integer_time = timeit.timeit('sum(integer_list)', globals=globals(), number=1000)
    float_time = timeit.timeit('sum(float_list)', globals=globals(), number=100