# Seaborn Python Review

## Reviewing theses Plotting & Statistics Packs — #PySeries#Episode 20

**Seaborn** is a library that uses Matplotlib underneath to plot graphs. It will be used to visualize random distributions.

Answer these eight questions:)

# Visualization Exercise

# The Data

This classic dataset contains the prices and other attributes of almost 54,000 diamonds. It’s a great dataset for beginners learning to work with data analysis and visualization.

https://www.kaggle.com/shivam2503/diamonds

Google Drive:

Source: Diamond.csv

Content:price price in US dollars ($326 — $18,823)

carat weight of the diamond (0.2–5.01)

cut quality of the cut (Fair, Good, Very Good, Premium, Ideal)

color diamond colour, from J (worst) to D (best)

clarity a measurement of how clear the diamond is (I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best))

x length in mm (0–10.74)

y width in mm (0–58.9)

z depth in mm (0–31.8)

depth total depth percentage = z / mean(x, y) = 2 * z / (x + y) (43–79)

table width of top of diamond relative to widest point (43–95)

Follow the instructions to recreate the plots using this data:

01**#PyEx **— Python — MatlilPlot & Seaborn —Importing MatlibPlot**:**

**Import pandas, matplotlib.pyplot and seaborn**

02**#PyEx **— Python — MatlilPlot & Seaborn— Plotting Graphs**:**

**Read in the data set diamonds.csv**

03**#PyEx **— Python — MatlilPlot & Seaborn — Plotting Graphs **:**

**Create a scatterplot of ****price**** versus ****carat**** as shown below:**

04**#PyEx **— Python — MatlilPlot & Seaborn — Plotting Graphs**:**

**Make the previous plot larger with figure size (10, 8):**

05**#PyEx **— Python — MatlilPlot & Seaborn — Plotting Graphs **:**

```**Create a histogram of the ****price**** column with ****displot**** as shown below. Observe the x-axis limits. Also, set bins=50 and height=8:**

06**#PyEx **— Python — MatlilPlot & Seaborn —Plotting Graphs **:**

**Create a count plot of the instances per ****cut**** type as shown below:**

07**#PyEx **— Python — MatlilPlot & Seaborn —Plotting Graphs **:**

**7. Create a large box plot figure showing the ****price**** distribution per ****cut**** type as shown below. Set (10, 8) as figure size.**

08**#PyEx **— Python — MatlilPlot & Seaborn —Plotting Graphs **:**

**Figure out how to change the ordering of the box plot as shown below:**

If you find this post helpful, please consider to subscribe to the Jungletronics for more posts like this.

Until next time!

I wish you excellent day!

Be safe!

Cheers!

Colab Notebook Anwers link:)

Google Drive link:)

Google Colab Notebooks are here:

# Credits & References

INTRODUÇÃO A MACHINE LEARNING PARA CERTIFICAÇÃO HCIA-AI** by **crateus.ufc.br

# Posts Related:

00Episode#**PySeries **— Python — Jupiter Notebook Quick Start with VSCode — How to Set your Win10 Environment to use Jupiter Notebook

01Episode#**PySeries **— Python — Python 4 Engineers — Exercises! An overview of the Opportunities Offered by Python in Engineering!

02Episode#**PySeries **— Python — Geogebra Plus Linear Programming- We’ll Create a Geogebra program to help us with our linear programming

03Episode#**PySeries** — Python — Python 4 Engineers — More Exercises! — Another Round to Make Sure that Python is Really Amazing!

04Episode#**PySeries** — Python — Linear Regressions — The Basics — How to Understand Linear Regression Once and For All!

05Episode#**PySeries** — Python — NumPy Init & Python Review — A Crash Python Review & Initialization at NumPy lib.

06Episode#**PySeries** — Python — NumPy Arrays & Jupyter Notebook — Arithmetic Operations, Indexing & Slicing, and Conditional Selection w/ np arrays.

07Episode#**PySeries** — Python — Pandas — Intro & Series — What it is? How to use it?

08Episode#**PySeries** — Python — Pandas DataFrames — The primary Pandas data structure! It is a dict-like container for Series objects

09Episode#**PySeries** — Python — Python 4 Engineers — Even More Exercises! — More Practicing Coding Questions in Python!

10Episode#**PySeries** — Python — Pandas — Hierarchical Index & Cross-section — Open your Colab notebook and here are the follow-up exercises!

11Episode#**PySeries** — Python — Pandas — Missing Data — Let’s Continue the Python Exercises — Filling & Dropping Missing Data

12Episode#**PySeries** — Python — Pandas — Group By — Grouping large amounts of data and compute operations on these groups

13Episode#**PySeries** — Python — **Pandas **— Merging, Joining & Concatenations — Facilities For Easily Combining Together Series or DataFrame

14Episode#**PySeries** — Python — **Pandas **— Pandas Dataframe Examples: Column Operations

15Episode#**PySeries** — Python — **Python 4 Engineers **— Keeping It In The Short-Term Memory — **Test Yourself!** Coding in Python, Again!

16Episode#**PySeries** — NumPy — NumPy Review, Again;)** **— Python Review Free Exercises

17Episode#**PySeries** — Generators in Python** **— Python Review Free Hints

18Episode#**PySeries** — Pandas Review…Again;) — Python Review Free Exercise

19Episode#**PySeries** — MatlibPlot & Seaborn Python Libs — Reviewing theses Plotting & Statistics Packs

20Episode#**PySeries** —Seaborn Python Review — Reviewing theses Plotting & Statistics Packs (this one)