<jupyter_start><jupyter_text>Lambda School Data Science - A First Look at Data  Lecture - let's explore Python DS libraries and examples!The Python Data Science ecosystem is huge. You've seen some of the big pieces - pandas, scikit-learn, matplotlib. What parts do you want to see more of?<jupyter_code># TODO - we'll be doing this live, taking requests
# and reproducing what it is to look up and learn things

# Definitely sciki-learn, it's one of the libraries I'm really interested in.<jupyter_output><empty_output><jupyter_text>Assignment - now it's your turnPick at least one Python DS library, and using documentation/examples reproduce in this notebook something cool. It's OK if you don't fully understand it or get it 100% working, but do put in effort and look things up.<jupyter_code>from bokeh.plotting import figure 
from bokeh.io import output_notebook, show
output_notebook()
from numpy import cos, linspace, sin
x = linspace(-6, 6, 100)
y = cos(x)

p = figure(width=300, height=300)
p.circle(x, y, size=7, color='red', alpha=0.5)
show(p)
from bokeh.plotting import figure 
from bokeh.io import output_notebook, show
output_notebook()
from numpy import cos, linspace, sin
w = linspace(-10, 10, 100)
z = cos(w)
q = figure(width=400, height=400)
q.circle(w, z, size=7, color='red', alpha=0.5)
show(q)
from bokeh.plotting import figure 
from bokeh.io import output_notebook, show
output_notebook()
from numpy import linspace, tan
w = linspace(-10, 10, 100)
z = tan(w)
q = figure(width=400, height=400)
q.circle(w, z, size=5, color='red', alpha=0.5)
show(q)<jupyter_output><empty_output><jupyter_text>Assignment questionsAfter you've worked on some code, answer the following questions in this text block:1.  Describe in a paragraph of text what you did and why, as if you were writing an email to somebody interested but nontechnical.*   We used the Bokeh library in Python to makes cool interactive graphs with whatever data or math functions we want. 2.  What was the most challenging part of what you did?* Having to install Python libraries. I am definitely used to the header system in C++ so I was confused for a bit on how to get the libraries working  3.  What was the most interesting thing you learned?* A lot of the python libraries are really intertwined. It was really intuitive just being able to just grab the code and added another library and having it work fairly quickly. 4.  What area would you like to explore with more time?* As of right now the output is just a set of hardcoded values. I was planning on having a dataset be used insted of hardcoded functions or values.  Stretch goals and resourcesFollowing are *optional* things for you to take a look at. Focus on the above assignment first, and make sure to commit and push your changes to GitHub (and since this is the first assignment of the sprint, open a PR as well).- [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/)- [scikit-learn documentation](http://scikit-learn.org/stable/documentation.html)- [matplotlib documentation](https://matplotlib.org/contents.html)- [Awesome Data Science](https://github.com/bulutyazilim/awesome-datascience) - a list of many types of DS resourcesStretch goals:- Find and read blogs, walkthroughs, and other examples of people working through cool things with data science - and share with your classmates!- Write a blog post (Medium is a popular place to publish) introducing yourself as somebody learning data science, and talking about what you've learned already and what you're excited to learn more about.<jupyter_code><jupyter_output><empty_output>
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