直接使用
请打开Jupyter魔术命令使用技巧，并点击右上角 “ 在DSW中打开” 。
Jupyter 魔术命令使用技巧
Jupyter Notebook除了能够执行Python代码之外，还提供一些魔术命令(Magic Command)方便用户简洁地解决标准数据分析中的各种常见问题。 魔术命令有两种形式：
- line magic 由单个%前缀表示并在单行输入上运行
- cell magic 由双%%前缀表示并在多行输入上运行
魔术命令有很多种，我们可以通过%lsmagic命令查看所有可用的魔术命令:
%lsmagic
Available line magics: %alias %alias_magic %autoawait %autocall %automagic %autosave %bookmark %cat %cd %clear %colors %conda %config %connect_info %cp %debug %dhist %dirs %doctest_mode %ed %edit %env %gui %hist %history %killbgscripts %ldir %less %lf %lk %ll %load %load_ext %loadpy %logoff %logon %logstart %logstate %logstop %ls %lsmagic %lx %macro %magic %man %matplotlib %mkdir %more %mv %notebook %page %pastebin %pdb %pdef %pdoc %pfile %pinfo %pinfo2 %pip %popd %pprint %precision %prun %psearch %psource %pushd %pwd %pycat %pylab %qtconsole %quickref %recall %rehashx %reload_ext %rep %rerun %reset %reset_selective %rm %rmdir %run %save %sc %set_env %store %sx %system %tb %time %timeit %unalias %unload_ext %who %who_ls %whos %xdel %xmode Available cell magics: %%! %%HTML %%SVG %%bash %%capture %%debug %%file %%html %%javascript %%js %%latex %%markdown %%perl %%prun %%pypy %%python %%python2 %%python3 %%ruby %%script %%sh %%svg %%sx %%system %%time %%timeit %%writefile Automagic is ON, % prefix IS NOT needed for line magics.
本文会介绍几个常用魔术命令，其他的魔术命令您可以自己探索
使用魔术命令进行文件操作
您可以通过在代码之前添加%%writefile命令来导出单元格里的内容到文件中
%%writefile script.py import numpy as np def random_nparray(): print(np.random.rand(3,2)) random_nparray()
Writing script.py
用%pycat可以显示指定文件的内容
%pycat script.py
import numpy as np def random_nparray(): print(np.random.rand(3,2)) random_nparray()
在Jupyter Notebook中用%run魔术命令来执行python文件
%run script.py
[[0.40436084 0.71671436] [0.40652354 0.14977982] [0.40079174 0.74159821]]
变量管理
使用%who, %who_ls, %whos命令查看当前Jupyter Notebook中定义的变量及其信息，区别在于展示信息的详略程度
%who_ls
['np', 'random_nparray']
%who
np random_nparray
%whos
Variable Type Data/Info -------------------------------------- np module <module 'numpy' from '/ho<...>kages/numpy/__init__.py'> random_nparray function <function random_nparray at 0x7f9b183018c8>
可以使用%xdel删除指定的变量
%xdel np
%who
random_nparray
使用%reset可以清除当前环境中的所有变量
%reset
%who
Interactive namespace is empty.
Kernel环境包管理
使用%pip魔术命令可以管理当前运行的Kernel对应的Python虚拟环境中的包。
使用%pip install安装新的Python包之后，需要重启当前Kernel才会生效。
%pip install tdqm
Looking in indexes: https://mirrors.aliyun.com/pypi/simple/ Collecting tdqm Downloading https://mirrors.aliyun.com/pypi/packages/5a/38/58c9e22b95e98666fe29f35e217ab6126a03798dadf3f4f8f3e5f898510b/tdqm-0.0.1.tar.gz (1.4 kB) Preparing metadata (setup.py) ... ?25ldone ?25hRequirement already satisfied: tqdm in /home/pai/lib/python3.6/site-packages (from tdqm) (4.64.0) Requirement already satisfied: importlib-resources in /home/pai/lib/python3.6/site-packages (from tqdm->tdqm) (5.4.0) Requirement already satisfied: zipp>=3.1.0 in /home/pai/lib/python3.6/site-packages (from importlib-resources->tqdm->tdqm) (3.6.0) Building wheels for collected packages: tdqm Building wheel for tdqm (setup.py) ... ?25ldone ?25h Created wheel for tdqm: filename=tdqm-0.0.1-py3-none-any.whl size=1319 sha256=a08573330ae5b0310db3fda329c82cbbff6807652542c76933111066cf215eeb Stored in directory: /root/.cache/pip/wheels/64/bb/39/82c59ab975b19dc6cc2f99bb6034b69cbbae2f130ac112471d Successfully built tdqm Installing collected packages: tdqm Successfully installed tdqm-0.0.1 WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv Note: you may need to restart the kernel to use updated packages.
%pip show tdqm
Name: tdqm Version: 0.0.1 Summary: Alias for typos of tqdm Home-page: https://github.com/tqdm/tqdm Author: Author-email: License: MPLv2.0 Location: /home/pai/lib/python3.6/site-packages Requires: tqdm Required-by: Note: you may need to restart the kernel to use updated packages.
执行时长
使用%time命令可以记录函数的执行时长
import numpy as np %time np.random.normal(size=10)
CPU times: user 527 µs, sys: 135 µs, total: 662 µs Wall time: 634 µs
array([ 0.15190477, -0.13989631, -1.94123265, 0.76655039, -1.70196434, -0.21320566, -0.30997543, 1.34570534, -0.37875918, 0.30126323])
使用%timeit命令可以通过多次运行来评估代码的执行速度，并产生执行时间的平均值+标准差，我们举个例子:
import numpy as np %timeit np.random.normal(size=1000)
42.3 µs ± 919 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
当您想要确定代码执行和循环过程的稳定性时，这个命令非常有用。
%pinfo
在下面代码段中，我们可以初始化一个简单的对象，然后用％pinfo来获取它的详细信息。
val = 100 %pinfo val
Type: int String form: 100 Docstring: int(x=0) -> integer int(x, base=10) -> integer Convert a number or string to an integer, or return 0 if no arguments are given. If x is a number, return x.__int__(). For floating point numbers, this truncates towards zero. If x is not a number or if base is given, then x must be a string, bytes, or bytearray instance representing an integer literal in the given base. The literal can be preceded by '+' or '-' and be surrounded by whitespace. The base defaults to 10. Valid bases are 0 and 2-36. Base 0 means to interpret the base from the string as an integer literal. >>> int('0b100', base=0) 4
