Lecture 20 - Plots

Viz Types, How to Make Them, and When to Use Them

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

penguins = sns.load_dataset("penguins")
# fmri = sns.load_dataset("fmri")
mpg = sns.load_dataset("mpg")

sns.get_dataset_names()

['anagrams',
 'anscombe',
 'attention',
 'brain_networks',
 'car_crashes',
 'diamonds',
 'dots',
 'exercise',
 'flights',
 'fmri',
 'gammas',
 'geyser',
 'iris',
 'mpg',
 'penguins',
 'planets',
 'taxis',
 'tips',
 'titanic']

Announcements:

Goals:

Know how to produce, interpret, and choose when to use several of the most commonly used types of data visualizations:

• Tables
• Dot and line plots
• Box and whisker plots
• Scatter plots
• Bar/column plots and (usually not) pie charts
• Histograms

penguins

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	Male
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	Female
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	Female
3	Adelie	Torgersen	NaN	NaN	NaN	NaN	NaN
4	Adelie	Torgersen	36.7	19.3	193.0	3450.0	Female
...	...	...	...	...	...	...	...
339	Gentoo	Biscoe	NaN	NaN	NaN	NaN	NaN
340	Gentoo	Biscoe	46.8	14.3	215.0	4850.0	Female
341	Gentoo	Biscoe	50.4	15.7	222.0	5750.0	Male
342	Gentoo	Biscoe	45.2	14.8	212.0	5200.0	Female
343	Gentoo	Biscoe	49.9	16.1	213.0	5400.0	Male

344 rows × 7 columns

fmri

mpg

	mpg	cylinders	displacement	horsepower	weight	acceleration	model_year	origin	name
0	18.0	8	307.0	130.0	3504	12.0	70	usa	chevrolet chevelle malibu
1	15.0	8	350.0	165.0	3693	11.5	70	usa	buick skylark 320
2	18.0	8	318.0	150.0	3436	11.0	70	usa	plymouth satellite
3	16.0	8	304.0	150.0	3433	12.0	70	usa	amc rebel sst
4	17.0	8	302.0	140.0	3449	10.5	70	usa	ford torino
...	...	...	...	...	...	...	...	...	...
393	27.0	4	140.0	86.0	2790	15.6	82	usa	ford mustang gl
394	44.0	4	97.0	52.0	2130	24.6	82	europe	vw pickup
395	32.0	4	135.0	84.0	2295	11.6	82	usa	dodge rampage
396	28.0	4	120.0	79.0	2625	18.6	82	usa	ford ranger
397	31.0	4	119.0	82.0	2720	19.4	82	usa	chevy s-10

398 rows × 9 columns

Matplotlib

colors = {"Adelie": "red", "Gentoo": "green", "Chinstrap": "blue"}
# size = lambda x: 10 if x > 40 else 1
plt.scatter("body_mass_g", "flipper_length_mm", data=penguins,
            c=penguins["species"].map(colors),
            s=(penguins["bill_length_mm"])/4)
plt.legend()
plt.xlabel("Body Mass (g)")
plt.ylabel("Flipper Length (mm)")

Text(0, 0.5, 'Flipper Length (mm)')

Seaborn

sns.relplot(x="body_mass_g", y="flipper_length_mm",
            hue="species", size="bill_depth_mm", data=penguins)
plt.xlabel("Body Mass (g)")
plt.ylabel("Flipper Length (mm)")

Text(33.37652777777779, 0.5, 'Flipper Length (mm)')

Key distinction: figure-level vs. axes-level: https://seaborn.pydata.org/tutorial/function_overview.html

Common Data Visualizations

Tables

Suppose you want to see the 5 biggest penguins.

penguins.sort_values("body_mass_g", ascending=False).iloc[:5,:]

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
237	Gentoo	Biscoe	49.2	15.2	221.0	6300.0	Male
253	Gentoo	Biscoe	59.6	17.0	230.0	6050.0	Male
297	Gentoo	Biscoe	51.1	16.3	220.0	6000.0	Male
337	Gentoo	Biscoe	48.8	16.2	222.0	6000.0	Male
331	Gentoo	Biscoe	49.8	15.9	229.0	5950.0	Male

Discuss: When should you use this? What are its strengths and weakenesses?

Table Tips:

• Think about row and column ordering
• Label columns and rows well (clear but concise).
• Uniform precision, right-justified numbers.
• Sometimes: bold or emphasize max or min values in a column

p = penguins.rename(columns={"species": "Species", "island": "Island",
                 "bill_length_mm": "Bill Length (mm)","bill_depth_mm": "Bill Depth (mm)",
                 "flipper_length_mm": "Flipper Length (mm)", "body_mass_g": "Body Mass (g)",
                 "sex": "Sex"})
p = p[["Species", "Island", "Sex", "Body Mass (g)", "Bill Length (mm)", "Bill Depth (mm)", "Flipper Length (mm)"]]
p.sort_values("Body Mass (g)", ascending=False).iloc[:5,:]

	Species	Island	Sex	Body Mass (g)	Bill Length (mm)	Bill Depth (mm)	Flipper Length (mm)
237	Gentoo	Biscoe	Male	6300.0	49.2	15.2	221.0
253	Gentoo	Biscoe	Male	6050.0	59.6	17.0	230.0
297	Gentoo	Biscoe	Male	6000.0	51.1	16.3	220.0
337	Gentoo	Biscoe	Male	6000.0	48.8	16.2	222.0
331	Gentoo	Biscoe	Male	5950.0	49.8	15.9	229.0

Dot plots, Line Plots

Conceptually (but not technically) different from a scatter plot, in that $x$ values are assumed to be ordered.

mpg_year = mpg.groupby("model_year").mean()
mpg_year

	mpg	cylinders	displacement	horsepower	weight	acceleration
model_year
70	17.689655	6.758621	281.413793	147.827586	3372.793103	12.948276
71	21.250000	5.571429	209.750000	107.037037	2995.428571	15.142857
72	18.714286	5.821429	218.375000	120.178571	3237.714286	15.125000
73	17.100000	6.375000	256.875000	130.475000	3419.025000	14.312500
74	22.703704	5.259259	171.740741	94.230769	2877.925926	16.203704
75	20.266667	5.600000	205.533333	101.066667	3176.800000	16.050000
76	21.573529	5.647059	197.794118	101.117647	3078.735294	15.941176
77	23.375000	5.464286	191.392857	105.071429	2997.357143	15.435714
78	24.061111	5.361111	177.805556	99.694444	2861.805556	15.805556
79	25.093103	5.827586	206.689655	101.206897	3055.344828	15.813793
80	33.696552	4.137931	115.827586	77.481481	2436.655172	16.934483
81	30.334483	4.620690	135.310345	81.035714	2522.931034	16.306897
82	31.709677	4.193548	128.870968	81.466667	2453.548387	16.638710

No connected dots - technically the same as a scatter plot.

sns.relplot(x="model_year", y="mpg", kind="scatter", data=mpg_year)

<seaborn.axisgrid.FacetGrid at 0x16a224d60>

Connect the dots: now you have a line plot:

sns.relplot(x="model_year", y="mpg", kind="line", data=mpg_year)

<seaborn.axisgrid.FacetGrid at 0x16aa17250>

Seaborn does sensible things if you have multiple datapoints per $x$ value:

sns.relplot(x="model_year", y="mpg", kind="line", data=mpg)

<seaborn.axisgrid.FacetGrid at 0x16ad7fac0>

Discuss: When should you use this? What are its strengths and weakenesses?

Also: when should you connect the dots?

• continuous dataset (x axis), e.g., time
• relation between before and after - trends

Box and whisker plots

sns.boxplot(x="species", y="body_mass_g", data=penguins)

<AxesSubplot:xlabel='species', ylabel='body_mass_g'>

Discuss: When should you use this? What are its strengths and weakenesses?

• grades on canvas
• compare spread of different variables w/o multiple histograms
• kinda like the plotting equivalent of .describe()

Scatter plots

sns.relplot(data=penguins, x="flipper_length_mm", y="bill_length_mm", hue="species", s=2)

<seaborn.axisgrid.FacetGrid at 0x16af9f1c0>

Discuss: When should you use this? What are its strengths and weakenesses?

• show a lot of data
• outliers are obvious but might not skew whole picture
• 4 variables at a time (x, y, color, size)
• too many points overlap

Bar/column plots and (usually not) pie charts

sns.catplot(x="species", data=penguins, kind="count")

<seaborn.axisgrid.FacetGrid at 0x16adea460>

sns.catplot(x="species", data=penguins, kind="count", col="island")

<seaborn.axisgrid.FacetGrid at 0x16af42430>

sns.catplot(x="species", data=penguins, kind="count")

<seaborn.axisgrid.FacetGrid at 0x16b143940>

Discuss: When should you use this? What are its strengths and weakenesses?

• categorical data
• compare distribution of one variable
• low data ink?

Histograms

sns.displot(penguins, x="flipper_length_mm")

sns.displot(penguins, x="flipper_length_mm", stat='density')

sns.displot(penguins, x="flipper_length_mm", col='species')

sns.displot(penguins, x="flipper_length_mm", hue="species", stat="density")

sns.displot(penguins, x="flipper_length_mm", hue="species", col="island")

sns.displot(penguins, x="flipper_length_mm", hue="species", col="island", kde='True')

sns.jointplot(x="bill_length_mm", y="bill_depth_mm", data=penguins, kind='hex')

A helpful figure from the book: