NumPy: a package for scientific computing with tools for algebra, random number generation, integrating with databases, and managing data.

Pandas: import data via a url and create a dataframe to easily handle data for analysis and graphing.

Some useful packages that we'll use in this tutorial include: You can reload all changed modules before executing a new line. IPython comes with automatic reloading magic. You may want to reload submodules if you've edited the code in one. When installing packages in Jupyter, you either need to install the package in your actual shell, or run the ! prefix, e.g.: !pip install packagename

Skip down to the for more information on using IRkernel with Jupyter notebooks and graphing examples. You can also use Jupyter notebooks to execute R code. %%! %%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 %%writefileĪutomagic is ON, % prefix IS NOT needed for line magics.The bulk of this tutorial discusses executing python code in Jupyter notebooks. %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 Machine Translation with Attention (Thushan) Machine Translation (Sequence-to-Sequence) Seqeunce Model with Attention for Addition Learning Sequence Model (many-to-one) with Attention Intutions for Types of Sequence-to-Sequence Models Generate Text Embeddings Using AutoEncoder

Sentiment Analysis with Traditional Machine Learning Machine Learning with Sklearn – Regression Chinese Natural Language Processing (spaCy)

Run r code in python jupyter notebook