There are many built-in Python methods that I would like to study the source code of to understand. How can I find their location on my computer? Is there some simple command that I could run either in a Python script or in my terminal on my Linux that I could use to locate a built-in method's source file?
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2 Answers
You can usually find the source files for core python modules in the python installation folder itself. For instance, on linux, I can find the source code for os module which is a quite popular python module in this location:
/usr/lib/python2.7/os.py
If you are on windows, this is generally C:\python27\lib, but you can verify it for yourself by running which python in case of linux and where python in case of windows.
3 Comments
Rohan
My example is the built-in string method isspace(). That does not require the importing of any additional core module. Where can I find the source for that?
Prahlad Yeri
The built-ins and other low-level functions are implemented in
C for the obvious reasons of performance, so they are only available in bit compiled form. However, you can still see the source code for these functions by visiting the python source repo. This other answer is for Reference.Prahlad Yeri
Particularly, in
/Objects/stringobject.c, here, you can find the function, string_isspace() - the one you are looking for.To get the file location of Python from the terminal:
$ which python
But you can see the source code of a function simply by appending it with ?? (note that some functions are C compiled and not written in Python).
For example:
# Example 1: Built in compiled function.
>>> open??
Docstring:
open(name[, mode[, buffering]]) -> file object
Open a file using the file() type, returns a file object. This is the
preferred way to open a file. See file.__doc__ for further information.
Type: builtin_function_or_method
# Example 2: Pandas function written in Python.
import pandas as pd
>>> pd.DataFrame??
Init signature: pd.DataFrame(self, data=None, index=None, columns=None, dtype=None, copy=False)
Source:
class DataFrame(NDFrame):
""" Two-dimensional size-mutable, potentially heterogeneous tabular data
structure with labeled axes (rows and columns). Arithmetic operations
align on both row and column labels. Can be thought of as a dict-like
container for Series objects. The primary pandas data structure
Parameters
----------
data : numpy ndarray (structured or homogeneous), dict, or DataFrame
Dict can contain Series, arrays, constants, or list-like objects
index : Index or array-like
Index to use for resulting frame. Will default to np.arange(n) if
no indexing information part of input data and no index provided
columns : Index or array-like
Column labels to use for resulting frame. Will default to
np.arange(n) if no column labels are provided
dtype : dtype, default None
Data type to force, otherwise infer
copy : boolean, default False
Copy data from inputs. Only affects DataFrame / 2d ndarray input
Examples
--------
>>> d = {'col1': ts1, 'col2': ts2}
>>> df = DataFrame(data=d, index=index)
>>> df2 = DataFrame(np.random.randn(10, 5))
>>> df3 = DataFrame(np.random.randn(10, 5),
... columns=['a', 'b', 'c', 'd', 'e'])
See also
--------
DataFrame.from_records : constructor from tuples, also record arrays
DataFrame.from_dict : from dicts of Series, arrays, or dicts
DataFrame.from_items : from sequence of (key, value) pairs
pandas.read_csv, pandas.read_table, pandas.read_clipboard
"""
@property
def _constructor(self):
return DataFrame
_constructor_sliced = Series
@property
def _constructor_expanddim(self):
from pandas.core.panel import Panel
return Panel
...
