IPython-Beispiele

Ausführen von Python-Code

Python-Version anzeigen

import sys


sys.version_info

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

Versionen von Python-Paketen anzeigen

Die meisten Python-Pakete bieten hierfür eine Methode __version__:

import pandas as pd


pd.__version__

'2.0.3'

Alternativ könnt ihr auch version aus importlib_metadata verwenden:

from importlib_metadata import version


print(version("pandas"))

2.0.3

Informationen über das Host-Betriebssystem und die Versionen installierter Python-Pakete

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           : 3.0.0
pytest           : 7.4.0
hypothesis       : 6.82.0
sphinx           : 7.1.2
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            : None
matplotlib       : 3.7.2
numba            : 0.57.1
numexpr          : None
odfpy            : None
openpyxl         : 3.1.2
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         : None
xarray           : 2023.7.0
xlrd             : None
zstandard        : None
tzdata           : 2023.3
qtpy             : None
pyqt5            : None

Nur Python-Versionen ≥ 3.8 verwenden

import sys


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

Shell-Kommandos

!python3 -V

Python 3.11.4

!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-Vervollständigung

… für Objekte mit Methoden und Attributen:

… und auch für Module:

Bemerkung

Wie ihr jetzt vielleicht verwundert festgestellt habt, wird die oben verwendete Methode __version__ in der Auswahl nicht angeboten. IPython blendet diese privaten Methoden und Attribute, , die mit Unterstrichen beginnen, zunächst aus. Sie können jedoch auch mit einem Tabulator vervollständigt werden, wenn ihr zunächst einen Unterstrich eingebt. Alternativ könnt ihr diese Einstellung auch in der IPython-Konfiguration ändern.

… für fast alles:

Informationen über ein Objekt anzeigen

Mit einem Fragezeichen (?) könnt ihr euch Informationen über ein Objekt anzeigen lassen, wenn es z.B. eine Methode multiply mit folgendem Docstring gibt:

import numpy as np

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

Durch die Verwendung von ?? wird auch der Quellcode der Funktion angezeigt, sofern dies möglich ist:

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

? kann auch zur Suche im IPython-Namensraum verwendet werden. Dabei kann eine Reihe von Zeichen mit dem Platzhalter (*) dargestellt werden. Um z.B. eine Liste aller Funktionen im NumPy-Namensraum der obersten Ebene erhalten, die mean enthalten:

np.*mean*?

np.mean
np.nanmean