IPython examples

Running Python code

Show Python version

[1]:
import sys


sys.version_info
[1]:
sys.version_info(major=3, minor=11, micro=4, releaselevel='final', serial=0)

Show versions of Python packages

Most Python packages provide a __version__ method for this:

[2]:
import pandas as pd


pd.__version__
[2]:
'2.0.3'

Alternatively, you can use version from importlib_metadata:

[3]:
from importlib_metadata import version


print(version("pandas"))
2.0.3

Information about the host operating system and the versions of installed Python packages

[4]:
pd.show_versions()
/Users/veit/.local/share/virtualenvs/python-311-6zxVKbDJ/lib/python3.11/site-packages/_distutils_hack/__init__.py:33: UserWarning: Setuptools is replacing distutils.
  warnings.warn("Setuptools is replacing distutils.")

INSTALLED VERSIONS
------------------
commit           : 0f437949513225922d851e9581723d82120684a6
python           : 3.11.4.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 22.5.0
Version          : Darwin Kernel Version 22.5.0: Thu Jun  8 22:22:23 PDT 2023; root:xnu-8796.121.3~7/RELEASE_ARM64_T6020
machine          : arm64
processor        : arm
byteorder        : little
LC_ALL           : None
LANG             : de_DE.UTF-8
LOCALE           : de_DE.UTF-8

pandas           : 2.0.3
numpy            : 1.23.5
pytz             : 2023.3
dateutil         : 2.8.2
setuptools       : 68.0.0
pip              : 23.1.2
Cython           : None
pytest           : 7.4.0
hypothesis       : 6.81.1
sphinx           : 7.0.1
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : 4.9.3
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 3.1.2
IPython          : 8.14.0
pandas_datareader: None
bs4              : 4.12.2
bottleneck       : None
brotli           : None
fastparquet      : 2023.7.0
fsspec           : 2023.6.0
gcsfs            : 2023.6.0
matplotlib       : 3.7.2
numba            : 0.57.1
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 12.0.1
pyreadstat       : None
pyxlsb           : None
s3fs             : 2023.6.0
scipy            : 1.11.1
snappy           : None
sqlalchemy       : None
tables           : None
tabulate         : 0.9.0
xarray           : 2023.6.0
xlrd             : None
zstandard        : None
tzdata           : 2023.3
qtpy             : None
pyqt5            : None

Only use Python versions ≥ 3.8

[5]:
import sys


assert sys.version_info[:2] >= (3, 8)

Shell commands

[6]:
!python3 -V
Python 3.11.4
[7]:
!python3 -m pip --version
pip 23.1.2 from /Users/veit/.local/share/virtualenvs/python-311-6zxVKbDJ/lib/python3.11/site-packages/pip (python 3.11)

Tab completion

… for objects with methods and attributes:

Tab completion for objects

… and also for modules:

Tab completion for modules

Note:

As you may have noticed in surprise, the __version__ method used above is not offered in the selection. IPython initially hides these private methods and attributes that begin with underscores. However, they can also be completed with a tabulator if you first enter an underscore. Alternatively, you can change this setting in the IPython configuration.

… for almost everything:

Tab completion for almost everything

Displaying information about an object

With a question mark (?) you can display information about an object if, for example, there is a method multiply with the following docstring:

[8]:
import numpy as np
[9]:
np.mean?
Signature:
np.mean(
    a,
    axis=None,
    dtype=None,
    out=None,
    keepdims=<no value>,
    *,
    where=<no value>,
)
Docstring:
Compute the arithmetic mean along the specified axis.

Returns the average of the array elements.  The average is taken over
the flattened array by default, otherwise over the specified axis.
`float64` intermediate and return values are used for integer inputs.

Parameters
----------
a : array_like
    Array containing numbers whose mean is desired. If `a` is not an
    array, a conversion is attempted.
axis : None or int or tuple of ints, optional
    Axis or axes along which the means are computed. The default is to
    compute the mean of the flattened array.

    .. versionadded:: 1.7.0

    If this is a tuple of ints, a mean is performed over multiple axes,
    instead of a single axis or all the axes as before.
dtype : data-type, optional
    Type to use in computing the mean.  For integer inputs, the default
    is `float64`; for floating point inputs, it is the same as the
    input dtype.
out : ndarray, optional
    Alternate output array in which to place the result.  The default
    is ``None``; if provided, it must have the same shape as the
    expected output, but the type will be cast if necessary.
    See :ref:`ufuncs-output-type` for more details.

keepdims : bool, optional
    If this is set to True, the axes which are reduced are left
    in the result as dimensions with size one. With this option,
    the result will broadcast correctly against the input array.

    If the default value is passed, then `keepdims` will not be
    passed through to the `mean` method of sub-classes of
    `ndarray`, however any non-default value will be.  If the
    sub-class' method does not implement `keepdims` any
    exceptions will be raised.

where : array_like of bool, optional
    Elements to include in the mean. See `~numpy.ufunc.reduce` for details.

    .. versionadded:: 1.20.0

Returns
-------
m : ndarray, see dtype parameter above
    If `out=None`, returns a new array containing the mean values,
    otherwise a reference to the output array is returned.

See Also
--------
average : Weighted average
std, var, nanmean, nanstd, nanvar

Notes
-----
The arithmetic mean is the sum of the elements along the axis divided
by the number of elements.

Note that for floating-point input, the mean is computed using the
same precision the input has.  Depending on the input data, this can
cause the results to be inaccurate, especially for `float32` (see
example below).  Specifying a higher-precision accumulator using the
`dtype` keyword can alleviate this issue.

By default, `float16` results are computed using `float32` intermediates
for extra precision.

Examples
--------
>>> a = np.array([[1, 2], [3, 4]])
>>> np.mean(a)
2.5
>>> np.mean(a, axis=0)
array([2., 3.])
>>> np.mean(a, axis=1)
array([1.5, 3.5])

In single precision, `mean` can be inaccurate:

>>> a = np.zeros((2, 512*512), dtype=np.float32)
>>> a[0, :] = 1.0
>>> a[1, :] = 0.1
>>> np.mean(a)
0.54999924

Computing the mean in float64 is more accurate:

>>> np.mean(a, dtype=np.float64)
0.55000000074505806 # may vary

Specifying a where argument:
>>> a = np.array([[5, 9, 13], [14, 10, 12], [11, 15, 19]])
>>> np.mean(a)
12.0
>>> np.mean(a, where=[[True], [False], [False]])
9.0
File:      ~/spack/var/spack/environments/python-38/.spack-env/view/lib/python3.8/site-packages/numpy/core/fromnumeric.py
Type:      function

By using ?? the source code of the function is also displayed, if this is possible:

[10]:
np.mean??
Signature:
np.mean(
    a,
    axis=None,
    dtype=None,
    out=None,
    keepdims=<no value>,
    *,
    where=<no value>,
)
Source:
@array_function_dispatch(_mean_dispatcher)
def mean(a, axis=None, dtype=None, out=None, keepdims=np._NoValue, *,
         where=np._NoValue):
    """
    Compute the arithmetic mean along the specified axis.

    Returns the average of the array elements.  The average is taken over
    the flattened array by default, otherwise over the specified axis.
    `float64` intermediate and return values are used for integer inputs.

    Parameters
    ----------
    a : array_like
        Array containing numbers whose mean is desired. If `a` is not an
        array, a conversion is attempted.
    axis : None or int or tuple of ints, optional
        Axis or axes along which the means are computed. The default is to
        compute the mean of the flattened array.

        .. versionadded:: 1.7.0

        If this is a tuple of ints, a mean is performed over multiple axes,
        instead of a single axis or all the axes as before.
    dtype : data-type, optional
        Type to use in computing the mean.  For integer inputs, the default
        is `float64`; for floating point inputs, it is the same as the
        input dtype.
    out : ndarray, optional
        Alternate output array in which to place the result.  The default
        is ``None``; if provided, it must have the same shape as the
        expected output, but the type will be cast if necessary.
        See :ref:`ufuncs-output-type` for more details.

    keepdims : bool, optional
        If this is set to True, the axes which are reduced are left
        in the result as dimensions with size one. With this option,
        the result will broadcast correctly against the input array.

        If the default value is passed, then `keepdims` will not be
        passed through to the `mean` method of sub-classes of
        `ndarray`, however any non-default value will be.  If the
        sub-class' method does not implement `keepdims` any
        exceptions will be raised.

    where : array_like of bool, optional
        Elements to include in the mean. See `~numpy.ufunc.reduce` for details.

        .. versionadded:: 1.20.0

    Returns
    -------
    m : ndarray, see dtype parameter above
        If `out=None`, returns a new array containing the mean values,
        otherwise a reference to the output array is returned.

    See Also
    --------
    average : Weighted average
    std, var, nanmean, nanstd, nanvar

    Notes
    -----
    The arithmetic mean is the sum of the elements along the axis divided
    by the number of elements.

    Note that for floating-point input, the mean is computed using the
    same precision the input has.  Depending on the input data, this can
    cause the results to be inaccurate, especially for `float32` (see
    example below).  Specifying a higher-precision accumulator using the
    `dtype` keyword can alleviate this issue.

    By default, `float16` results are computed using `float32` intermediates
    for extra precision.

    Examples
    --------
    >>> a = np.array([[1, 2], [3, 4]])
    >>> np.mean(a)
    2.5
    >>> np.mean(a, axis=0)
    array([2., 3.])
    >>> np.mean(a, axis=1)
    array([1.5, 3.5])

    In single precision, `mean` can be inaccurate:

    >>> a = np.zeros((2, 512*512), dtype=np.float32)
    >>> a[0, :] = 1.0
    >>> a[1, :] = 0.1
    >>> np.mean(a)
    0.54999924

    Computing the mean in float64 is more accurate:

    >>> np.mean(a, dtype=np.float64)
    0.55000000074505806 # may vary

    Specifying a where argument:
    >>> a = np.array([[5, 9, 13], [14, 10, 12], [11, 15, 19]])
    >>> np.mean(a)
    12.0
    >>> np.mean(a, where=[[True], [False], [False]])
    9.0

    """
    kwargs = {}
    if keepdims is not np._NoValue:
        kwargs['keepdims'] = keepdims
    if where is not np._NoValue:
        kwargs['where'] = where
    if type(a) is not mu.ndarray:
        try:
            mean = a.mean
        except AttributeError:
            pass
        else:
            return mean(axis=axis, dtype=dtype, out=out, **kwargs)

    return _methods._mean(a, axis=axis, dtype=dtype,
                          out=out, **kwargs)
File:      ~/spack/var/spack/environments/python-38/.spack-env/view/lib/python3.8/site-packages/numpy/core/fromnumeric.py
Type:      function

? can also be used to search in the IPython namespace. In doing so, a series of characters can be represented with the wildcard (*). For example, to get a list of all functions in the top-level NumPy namespace that contain mean:

[11]:
np.*mean*?
np.mean
np.nanmean