.ipynb .pdf

repository open issue suggest edit

Binder Colab

Contents

1.10. Libraries/Packages

1.10.1. Popular, nay, essential packages

As the semester proceeds, you will surely need to learn (to some degree) the following packages. For each, you might note the most common and useful functions, and copy common “cookbook” uses of the packages which you can paste into new programs. (E.g. how to open a csv file.)

Note: I do not personally, nor do many programmers, commit to memory many functions of many packages. We simply know what can be done and when needed, we search (tab completion/google/stack overflow) for the command/recipe for that function.

• Built-in packages:os sys itertools re datetime csv

• Datasci packages (Anaconda installs these for you!), note the aliases here aren’t strictly needed, but by convention, virtually everyone uses the shorter names

  • pandas as pd (pd is a short alias for pandas)

  • seaborn as sns

  • matplotlib as mpl

  • statsmodels.api as sm

  • matplotlib.pyplot as plt

  • numpy as np

  • sklearn

• Web crawling

  • requests, requests_html, urllib

  • time and tdqm

  • beautifulsoup4 as bs4

  • html5lib

  • selenium

1.10.2. Installing libraries

The Anaconda distribution we installed also installed most of the key data science Python libraries/packages we will use throughout the semester. In the event you need to install a new package to add functionality to Python, e.g. seaborn (which you already have!), you can

1. Open Anaconda Prompt (Windows) or Terminal (Mac) or a code cell in Jupyter Lab

2. conda install seaborn will install Seaborn

   • If conda install doesn’t work for a package, you can try to pip install it. E.g. pip install seaborn

Some packages can’t be pip installed, but hopefully you won’t need to deal with that this semester, so I’m going to skip discussion of such package installs.

1.9. Errors 1.11. Gitignore files