quinn_ds01_dougherty_sprint2

[quinn-dougherty]

No description provided.

[brennerhaverlock]

Sprint Challenge Code Review

Week 2 - Storytelling with Data

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Part 0 — Run this starter code

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Part 1 — What's the breakdown of guests’ occupations per year?

Code review

What went well:

-Great job keeping it short and sweet with crosstab!:

<print(year_by_occu.drop(['Media', 'Government and Politics'], axis=1))>

What could be better:

- You did great here, all I can say is make sure to not comment out your code next time for grading!

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Part 2 — Recreate this explanatory visualization:

Code review

What went well:

• Good job with graphing and applying yourself to the code!:

< ax.plot(acm, color=cs['acm'])
ax.plot(med, color=cs['med'])
ax.plot(gap, color=cs['gap'])>

```python ```

_What could be better:_

• Add a legend for a more visual and descriptive approach:

  <plt.legend(three_occupations.columns)>

• Great job with X and Y, however this could be shortented like so:

  ```

plt.text(x=1998, y=1.0, s='Occupation of guests, by year')>
```

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Part 3 — Who were the top 10 guests on The Daily Show?

Code review

What went well:

• Went above and beyond and used a tool we haven't gone over! (altair):

<chart = alt.Chart(top_ten).mark_bar().encode(
  y='Guest',
  x='Appearances',
  color='Occupation'
)
>

_What could be better:_

• Overall great job! I'd suggest getting comfortable with seaborn and/or matplotlib as well as the community is huge and these imports are the daily drivers of Data Scientist's:

-Seaborn Example

``` < import seaborn as sns assert sns.__version__ == '0.9.0'

sns.relplot(data=three_occupations, kind='line')
plt.text(x=1998, y=1.1, s="Who Got To Be On 'The Daily Show'?", fontweight='bold')
plt.text(x=1998, y=1.0, s='Occupation of guests, by year')
>