Python 如何将pip命令的结果存储到Datafarme中
为了获取已安装库的列表，我在Jupyter笔记本中运行以下命令：Python 如何将pip命令的结果存储到Datafarme中,python,pandas,dataframe,pip,jupyter-notebook,Python,Pandas,Dataframe,Pip,Jupyter Notebook,为了获取已安装库的列表，我在Jupyter笔记本中运行以下命令： !pip list 我得到以下结果作为输出： Package Version ---------------------------------- --------- alabaster 0.7.12 altair 4.1.0 anaconda-client
!pip list
Package Version
---------------------------------- ---------
alabaster 0.7.12
altair 4.1.0
anaconda-client 1.7.2
anaconda-navigator 1.9.7
anaconda-project 0.8.3
asn1crypto 0.24.0
astroid 2.2.5
astropy 3.2.1
atomicwrites 1.3.0
attrs 20.3.0
alabaster 0.7.12
altair 4.1.0
anaconda-client 1.7.2
anaconda-navigator 1.9.7
anaconda-project 0.8.3
asn1crypto 0.24.0
astroid 2.2.5
astropy 3.2.1
atomicwrites 1.3.0
attrs 20.3.0
Automat 20.2.0
azureml 0.2.7
Babel 2.7.0
backcall 0.1.0
backports.functools-lru-cache 1.5
backports.os 0.1.1
backports.shutil-get-terminal-size 1.0.0
backports.tempfile 1.0
backports.weakref 1.0.post1
beautifulsoup4 4.7.1
bitarray 0.9.3
bkcharts 0.2
bleach 3.1.0
bokeh 1.2.0
boto 2.49.0
boto3 1.14.61
botocore 1.17.61
Bottleneck 1.2.1
branca 0.4.1
category-encoders 2.2.2
certifi 2019.6.16
cffi 1.12.3
chardet 3.0.4
Click 7.0
click-spinner 0.1.10
cloudpickle 1.2.1
clyent 1.2.2
colorama 0.4.1
colorlog 4.2.1
comtypes 1.1.7
conda 4.7.10
conda-build 3.18.8
conda-package-handling 1.3.11
conda-verify 3.4.2
configparser 5.0.1
constantly 15.1.0
contextlib2 0.5.5
coverage 5.3
coveralls 2.1.2
cryptography 2.7
cycler 0.10.0
Cython 0.29.14
cytoolz 0.10.0
dacite 1.5.1
dask 2.1.0
decorator 4.4.0
defusedxml 0.6.0
distributed 2.1.0
docopt 0.6.2
docutils 0.14
entrypoints 0.3
et-xmlfile 1.0.1
fastcache 1.1.0
feature-engine 0.6.1
filelock 3.0.12
Flask 1.1.1
flit-core 2.3.0
folium 0.11.0
furl 2.1.0
future 0.17.1
gensim 3.8.3
genson 1.2.2
gevent 1.4.0
glob2 0.7
greenlet 0.4.15
h5py 2.9.0
heapdict 1.0.0
html5lib 1.0.1
hyperlink 20.0.1
hypothesis 5.41.2
idna 2.8
imageio 2.5.0
imagesize 1.1.0
imbalanced-learn 0.7.0
importlib-metadata 0.17
incremental 17.5.0
ipykernel 5.1.1
ipython 7.6.1
ipython-genutils 0.2.0
ipywidgets 7.5.0
isort 4.3.21
itsdangerous 1.1.0
jdcal 1.4.1
jedi 0.13.3
Jinja2 2.11.2
jmespath 0.10.0
joblib 0.13.2
json5 0.8.4
jsonschema 3.2.0
jupyter 1.0.0
jupyter-client 5.3.1
jupyter-console 6.0.0
jupyter-core 4.5.0
jupyterlab 1.0.2
jupyterlab-server 1.0.0
keyring 18.0.0
kiwisolver 1.1.0
lazy-object-proxy 1.4.1
libarchive-c 2.8
llvmlite 0.29.0
locket 0.2.0
lxml 4.5.2
MarkupSafe 1.1.1
matplotlib 3.1.0
mccabe 0.6.1
menuinst 1.4.16
mistune 0.8.4
mkl-fft 1.0.12
mkl-random 1.0.2
mkl-service 2.0.2
mock 3.0.5
more-itertools 7.0.0
mpmath 1.1.0
msgpack 0.6.1
multipledispatch 0.6.0
munch 2.5.0
navigator-updater 0.2.1
nbconvert 5.6.1
nbformat 4.4.0
networkx 2.3
nibabel 3.1.1
nltk 3.4.4
nose 1.3.7
notebook 6.0.0
numba 0.44.1
numexpr 2.6.9
numpy 1.19.2
numpydoc 0.9.1
olefile 0.46
openpyxl 2.6.2
orderedmultidict 1.0.1
packaging 20.4
pandas 1.1.3
pandocfilters 1.4.2
parso 0.5.0
partd 1.0.0
path.py 12.0.1
pathlib2 2.3.4
patsy 0.5.1
pep517 0.8.2
pep8 1.7.1
pickleshare 0.7.5
Pillow 6.1.0
pip 20.2.4
pkginfo 1.5.0.1
plotly 4.9.0
pluggy 0.12.0
ply 3.11
pmdarima 1.7.1
prometheus-client 0.7.1
prompt-toolkit 2.0.9
psutil 5.6.3
psycopg2 2.8.6
py 1.8.0
pycodestyle 2.5.0
pycosat 0.6.3
pycparser 2.19
pycrypto 2.6.1
pycurl 7.43.0.3
pyflakes 2.1.1
Pygments 2.4.2
PyHamcrest 2.0.2
pylint 2.3.1
pyodbc 4.0.26
pyOpenSSL 19.0.0
pyparsing 2.4.0
pyreadline 2.1
pyrsistent 0.14.11
PySocks 1.7.0
pytest 5.0.1
pytest-arraydiff 0.3
pytest-astropy 0.5.0
pytest-cov 2.10.1
pytest-doctestplus 0.3.0
pytest-openfiles 0.3.2
pytest-remotedata 0.3.1
python-dateutil 2.8.0
pytoml 0.1.21
pytz 2019.1
PyWavelets 1.0.3
pywin32 223
pywinpty 0.5.5
PyYAML 5.3.1
pyzmq 18.0.0
QtAwesome 0.5.7
qtconsole 4.5.1
QtPy 1.8.0
requests 2.22.0
requests-toolbelt 0.9.1
retrying 1.3.3
rope 0.14.0
ruamel-yaml 0.15.46
ruamel.yaml 0.16.12
ruamel.yaml.clib 0.2.2
s3transfer 0.3.3
scikit-image 0.15.0
scikit-learn 0.23.2
scipy 1.5.3
seaborn 0.10.1
Send2Trash 1.5.0
setuptools 41.0.1
simplegeneric 0.8.1
simplejson 3.17.2
singledispatch 3.4.0.3
six 1.12.0
sklearn 0.0
smart-open 2.1.1
snowballstemmer 1.9.0
sortedcollections 1.1.2
sortedcontainers 2.1.0
soupsieve 1.8
Sphinx 2.1.2
sphinxcontrib-applehelp 1.0.1
sphinxcontrib-devhelp 1.0.1
sphinxcontrib-htmlhelp 1.0.2
sphinxcontrib-jsmath 1.0.1
sphinxcontrib-qthelp 1.0.2
sphinxcontrib-serializinghtml 1.1.3
sphinxcontrib-websupport 1.1.2
spyder 3.3.6
spyder-kernels 0.5.1
SQLAlchemy 1.3.5
statsmodels 0.11.1
stumpy 1.5.0
sympy 1.4
tables 3.5.2
tabpy 2.3.1
tabulate 0.8.7
tblib 1.4.0
terminado 0.8.2
testpath 0.4.2
textblob 0.15.3
threadpoolctl 2.1.0
toml 0.10.1
toolz 0.10.0
tornado 6.0.3
tqdm 4.32.1
traitlets 4.3.2
Twisted 20.3.0
unicodecsv 0.14.1
urllib3 1.24.2
validators 0.16.0
wcwidth 0.1.7
webencodings 0.5.1
Werkzeug 0.15.4
wheel 0.33.4
widgetsnbextension 3.5.0
win-inet-pton 1.1.0
win-unicode-console 0.5
wincertstore 0.2
wrapt 1.11.2
xlrd 1.2.0
XlsxWriter 1.1.8
xlwings 0.15.8
xlwt 1.3.0
zict 1.0.0
zipp 0.5.1
zope.interface 5.2.0
注：我提到了，但没有任何帮助。我们可以使用
ospandas.read\u csv\S+导入操作系统
作为pd进口熊猫
#创建pip列表
操作系统（'pip list>pip_list.txt'）
#将内容读入文档
df=pd.read\u csv（'pip\u list.txt'，sep=r'\s+'，skiprows=[1]）
#清理
删除（'pip_list.txt'）
您可以使用
子流程import subprocess
from io import StringIO
command = "pip list"
process = subprocess.Popen(command.split(), stdout=subprocess.PIPE)
output, error = process.communicate()
pd.read_csv(StringIO(output.decode("utf-8")), sep=r"\s*", skiprows=[1])
酷！I/O流重定向也适用于windows吗？我想是的，我认为操作系统模块考虑了操作系统。对于具有
os.path.join的路径也一样。
import subprocess
from io import StringIO
command = "pip list"
process = subprocess.Popen(command.split(), stdout=subprocess.PIPE)
output, error = process.communicate()
pd.read_csv(StringIO(output.decode("utf-8")), sep=r"\s*", skiprows=[1])