DS-Sprint-02-Work

[axrd]

No description provided.

[brennerhaverlock]

Good work Alejandro -- You have a good understanding of graphing new plots in seaborn and I hope you were able to explore more as it's a great library.

[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:

• I like your use of the describe method before using crosstab:

<df.Occupation.describe()>

• Great use of head method to take a look at data before continuing! :

<df.head()>

• Great use of indexing your head method statement after use of crosstab

<year_occ.head(17)>

What could be better:

• Instead of indexing the crosstab by 17 you could of just printed your table name instead

<year_occ = pd.crosstab(df.Year, df.Occupation, normalize = 'index')
year_occ>

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Part 2 — Recreate this explanatory visualization:

Code review

What went well:

• Great job on using matplotlib to plot and keeping it simple!

<year_occ.plot()>

What could be better:

• Before plottting you should of dropped the "Other" column:

<three_occupations = year_occ.drop(columns=['Other'])>

• Great job on labeling X and Y axes. Try to cluster your years together more.

<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:

• Great job graphing and labeling the X and Y axes!

<top_ten.plot.bar(x = 'Guest', y = 'Appearances');
plt.title('Top 10 Guest Appearances');
plt.ylabel('Number of Apperances (1999-2015)')
plt.ylim(5,20);>

What could be better:

• In Hindsight you could of done this in one line (two lines including labeling axes)

<df.Guest.value_counts().head(10).plot.bar();>

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Additional notes

Code review

If you want to take this further you could:

• Try graphing part 2 using Seaborn

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