Top 10 Nice-To-Have Data Science Libraries

Cool finds to make your life easier

Ambar Kleinbort
Dec 3, 2019 · 3 min read

Make your project pop with these easy-to-use libraries! Note that these don’t have an essentials-of-data-science element to them, like pandas or scikit-learn, they’re just fun, useful finds.

Missingno: Visualizes missing data.

!pip install missingno
import missingno as msgn
#read in data heremsgn.matrix(data)

Plotly: Makes interactive plots, including maps and 3D graphs.

import plotly.offline as py
py.init_notebook_mode(connected=False)
import plotly_express as px
import cufflinks as cf
cf.set_config_file(offline=True)
#example line graph
data.iplot(kind='line', title='Title, xTitle='Epoch',yTitle='Loss')
Image from: https://towardsdatascience.com/the-next-level-of-data-visualization-in-python-dd6e99039d5e

More here: https://plot.ly/python/ipython-notebook-tutorial/

Selenium: Makes automatic mouse movements online (i.e. clicking, browsing, etc.).

!pip install selenium
from selenium import webdriver
browser = webdriver.Chrome(executable_path='/Users/User/chromedriver')
browser.get('https://xkcd.com/') # go to website
go_to_random_commic_button = browser.find_element_by_partial_link_text('Random')
browser.quit()

Geopandas + Geopy: These are good for making maps.

!pip install geopandas
!pip install geopy
#You can make all sorts of different things with these!

Py_translator: Translates.

!pip install py_translator
from py_translator import Translator

translator = Translator()
output = translator.translate('Hello World!', dest='fr')

output.text

Graphviz: Visualizes tree-based models.

!pip install graphviz
!brew install graphviz
from sklearn.tree import export_graphviz
#Make and fit modeltree_file = export_graphviz(model, out_file=None,feature_names=X.columns)

graphviz.Source(tree_file)

Jupyterlab_spellchecker: Spellchecks markdown text.

!jupyter labextension install @ijmbarr/jupyterlab_spellchecker

Nbextensions: This is not technically a library, its an extension. It will allow you to do a lot of nice things like code folding, automating a table of contents, and “prettifying” code.

!pip install jupyter_contrib_nbextensions
!jupyter contrib nbextension install --user
#enable the features you want from your jupyter homepage (an Nbextensions tab will appear as shown in the image)

More here: https://github.com/ipython-contrib/jupyter_contrib_nbextensions

Twitter scraper: Scrapes tweets based on date, location, words, etc. Make sure to include a time lag in your scrape to avoid being locked out of Twitter!

!pip install twitterscraper
from twitterscraper import query_tweets
list_of_tweets = query_tweets("'Hello' OR 'Goodbye' ",
limit = 50_000
enddate = datetime.date(2019, 9, 1),
begindate = datetime.date(2014, 1, 1),
poolsize = 1)

Imbalanced-learn: Includes several automated sampling methods to balance classes.

!pip install -U imbalanced-learn

Comment your coolest finds below!

Analytics Vidhya

Analytics Vidhya is a community of Analytics and Data Science professionals. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com

Ambar Kleinbort

Written by

Data Scientist and Neuroscientist — Based in NYC — https://www.linkedin.com/in/ambarkleinbort/

Analytics Vidhya

Analytics Vidhya is a community of Analytics and Data Science professionals. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com

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