Lets-Plot Cheatbook¶

Contents¶

Preparation
- Imports
- Functions
- Data
Basics
Features
Geoms
Stats
Scales
Coordinate Systems
Position Adjustments
Themes
Flavors
Faceting
Labels & Legends
Zooming

Preparation ¶

Imports ¶

In [1]:

from datetime import datetime
from io import BytesIO
import requests

import numpy as np
import pandas as pd
from PIL import Image
from scipy.stats import multivariate_normal

from lets_plot import *
from lets_plot.mapping import as_discrete
from lets_plot.bistro import *
from lets_plot.geo_data import *
LetsPlot.setup_html()

The geodata is provided by © OpenStreetMap contributors and is made available here under the Open Database License (ODbL).

Functions ¶

In [2]:

def plot_matrix(plots=[], width=400, height=300, columns=2):
    bunch = GGBunch()
    for i in range(len(plots)):
        row = int(i / columns)
        column = i % columns
        bunch.add_plot(plots[i], column * width, row * height, width, height)
    return bunch.show()

Data ¶

In [3]:

mpg_df = pd.read_csv('https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/mpg.csv')
mpg_df.head(3)

Out[3]:

	Unnamed: 0	manufacturer	model	displ	year	cyl	trans	drv	cty	hwy	fl	class
0	1	audi	a4	1.8	1999	4	auto(l5)	f	18	29	p	compact
1	2	audi	a4	1.8	1999	4	manual(m5)	f	21	29	p	compact
2	3	audi	a4	2.0	2008	4	manual(m6)	f	20	31	p	compact

In [4]:

class_df = mpg_df.groupby('class').hwy.agg(['min', 'median', 'max', 'count']).reset_index()
class_df.head(3)

Out[4]:

	class	min	median	max	count
0	2seater	23	25.0	26	5
1	compact	23	27.0	44	47
2	midsize	23	27.0	32	41

In [5]:

fl_df = mpg_df.groupby(['cty', 'hwy']).fl.agg(pd.Series.mode).to_frame('fl').reset_index()
fl_df.head(3)

Out[5]:

	cty	hwy	fl
0	9	12	e
1	11	14	[e, p]
2	11	15	r

In [6]:

economics_df = pd.read_csv('https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/economics.csv', \
                           parse_dates=['date'])
economics_df = economics_df[economics_df.date > datetime(2000, 1, 1)]
economics_df.head(3)

Out[6]:

	Unnamed: 0	date	pce	pop	psavert	uempmed	unemploy
391	392	2000-02-01	6619.7	281190.0	4.8	6.1	5858
392	393	2000-03-01	6685.8	281409.0	4.5	6.0	5733
393	394	2000-04-01	6671.1	281653.0	5.0	6.1	5481

In [7]:

midwest_df = pd.read_csv('https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/midwest.csv')
midwest_df.head()

Out[7]:

	Unnamed: 0	PID	county	state	area	poptotal	popdensity	popwhite	popblack	popamerindian	...	percollege	percprof	poppovertyknown	percpovertyknown	percbelowpoverty	percchildbelowpovert	percadultpoverty	percelderlypoverty	inmetro	category
0	1	561	ADAMS	IL	0.052	66090	1270.961540	63917	1702	98	...	19.631392	4.355859	63628	96.274777	13.151443	18.011717	11.009776	12.443812	0	AAR
1	2	562	ALEXANDER	IL	0.014	10626	759.000000	7054	3496	19	...	11.243308	2.870315	10529	99.087145	32.244278	45.826514	27.385647	25.228976	0	LHR
2	3	563	BOND	IL	0.022	14991	681.409091	14477	429	35	...	17.033819	4.488572	14235	94.956974	12.068844	14.036061	10.852090	12.697410	0	AAR
3	4	564	BOONE	IL	0.017	30806	1812.117650	29344	127	46	...	17.278954	4.197800	30337	98.477569	7.209019	11.179536	5.536013	6.217047	1	ALU
4	5	565	BROWN	IL	0.018	5836	324.222222	5264	547	14	...	14.475999	3.367680	4815	82.505140	13.520249	13.022889	11.143211	19.200000	0	AAR

5 rows × 29 columns

In [8]:

pop_df = midwest_df.groupby('state').poptotal.sum().to_frame('population').reset_index()
pop_df.head(3)

Out[8]:

	state	population
0	IL	11430602
1	IN	5544159
2	MI	9295297

In [9]:

states_df = geocode('state', pop_df.state, scope='US').get_boundaries(9)
states_df.head(3)

Out[9]:

	state	found name	geometry
0	IL	Illinois	MULTIPOLYGON (((-89.00848 37.22158, -89.00574 ...
1	IN	Indiana	MULTIPOLYGON (((-86.07513 37.99400, -86.07788 ...
2	MI	Michigan	MULTIPOLYGON (((-90.31860 46.54564, -90.32135 ...

In [10]:

def generate_random_data(size=50, mean=[0, 0], cov=[[1, .5], [.5, 1]], seed=42):
    np.random.seed(seed)
    x = np.linspace(-1, 1, size)
    y = np.linspace(-1, 1, size)
    X, Y = np.meshgrid(x, y)
    Z = multivariate_normal(mean, cov).pdf(np.dstack((X, Y)))
    return pd.DataFrame({'x': X.flatten(), 'y': Y.flatten(), 'z': Z.flatten()})

random_df = generate_random_data()
random_df.head(3)

Out[10]:

	x	y	z
0	-1.000000	-1.0	0.094354
1	-0.959184	-1.0	0.096849
2	-0.918367	-1.0	0.099189

In [11]:

response = requests.get('https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/source/examples/cookbook/images/fisher_boat.png')
img = Image.open(BytesIO(response.content))
img

Out[11]:

Basics ¶

In [12]:

ggplot(mpg_df, aes('cty', 'hwy')) + \
    geom_point(aes(color='cyl')) + \
    geom_smooth(method='lm') + \
    scale_color_brewer(type='div', palette='Spectral') + \
    theme_classic() + \
    ggtitle("Simple linear smoothing")

Out[12]:

Features ¶

Interactive Maps ¶

In [13]:

ggplot() + \
    geom_livemap() + \
    geom_map(aes(color='population', fill='population'), \
             data=pop_df, map=states_df, map_join='state', size=1, alpha=.3) + \
    scale_color_gradient(low='#1a9641', high='#d7191c') + \
    scale_fill_gradient(low='#1a9641', high='#d7191c')

Out[13]: