IPython-Beispiele¶
Ausführen von Python-Code¶
Python-Version anzeigen¶
[1]:
import sys
sys.version_info
[1]:
sys.version_info(major=3, minor=13, micro=0, releaselevel='final', serial=0)
Versionen von Python-Paketen anzeigen¶
Die meisten Python-Pakete bieten hierfür eine Methode __version__:
[2]:
import pandas as pd
pd.__version__
[2]:
'2.2.3'
Alternativ könnt ihr auch version aus importlib_metadata verwenden:
[3]:
from importlib.metadata import version
print(version("pandas"))
2.2.3
Informationen über das Host-Betriebssystem und die Versionen installierter Python-Pakete¶
[4]:
pd.show_versions()
INSTALLED VERSIONS
------------------
commit : 0691c5cf90477d3503834d983f69350f250a6ff7
python : 3.13.0
python-bits : 64
OS : Darwin
OS-release : 24.1.0
Version : Darwin Kernel Version 24.1.0: Thu Oct 10 21:03:11 PDT 2024; root:xnu-11215.41.3~2/RELEASE_ARM64_T6020
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : de_DE.UTF-8
LOCALE : de_DE.UTF-8
pandas : 2.2.3
numpy : 2.0.2
pytz : 2024.2
dateutil : 2.9.0.post0
pip : None
Cython : 3.0.11
sphinx : None
IPython : 8.29.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2024.10.0
html5lib : None
hypothesis : 6.116.0
gcsfs : None
jinja2 : 3.1.4
lxml.etree : 5.3.0
matplotlib : 3.9.2
numba : None
numexpr : None
odfpy : None
openpyxl : 3.1.5
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : 18.0.0
pyreadstat : None
pytest : 8.3.3
python-calamine : None
pyxlsb : None
s3fs : 2024.10.0
scipy : 1.14.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
tzdata : 2024.2
qtpy : None
pyqt5 : None
Nur Python-Versionen ≥ 3.9 verwenden¶
[5]:
import sys
assert sys.version_info[:2] >= (3, 9)
Shell-Kommandos¶
[6]:
!python3 -V
Python 3.13.0
[7]:
!python3 -m pip --version
/Users/veit/.cache/uv/archive-v0/XZHbu_8b1Dy5_kyKvyRMu/bin/python3: No module named pip
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:
[8]:
import numpy as np
[9]:
np.mean?
Signature:
np.mean(
a,
axis=None,
dtype=None,
out=None,
keepdims=<no value>,
*,
where=<no value>,
)
Call signature: np.mean(*args, **kwargs)
Type: _ArrayFunctionDispatcher
String form: <function mean at 0x1121440e0>
File: ~/cusy/trn/jupyter-tutorial/uvenvs/py313/.venv/lib/python3.13/site-packages/numpy/_core/fromnumeric.py
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.
…
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:
[10]:
np.mean??
Signature:
np.mean(
a,
axis=None,
dtype=None,
out=None,
keepdims=<no value>,
*,
where=<no value>,
)
Call signature: np.mean(*args, **kwargs)
Type: _ArrayFunctionDispatcher
String form: <function mean at 0x1121440e0>
File: ~/cusy/trn/jupyter-tutorial/uvenvs/py313/.venv/lib/python3.13/site-packages/numpy/_core/fromnumeric.py
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.
…
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:
[11]:
np.*mean*?
np.mean
np.nanmean
np.mean
np.nanmean