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{ggplot} from Yhat

read more on our blog

from ggplot import *
ggplot(aes(x='date', y='beef'), data=meat) + \
geom_point(color='lightblue') + \
stat_smooth(span=.15, color='black', se=True) + \
ggtitle("Beef: It's What's for Dinner") + \
xlab("Date") + \
ylab("Head of Cattle Slaughtered")

What is it?

Yes, it's another port of ggplot2. One of the biggest reasons why I continue to reach for R instead of Python for data analysis is the lack of an easy to use, high level plotting package like ggplot2. I've tried other libraries like bokeh and d3py but what I really want is ggplot2.

ggplot is just that. It's an extremely un-pythonic package for doing exactly what ggplot2 does. The goal of the package is to mimic the ggplot2 API. This makes it super easy for people coming over from R to use, and prevents you from having to re-learn how to plot stuff.

Goals

  • same API as ggplot2 for R
  • never use matplotlib again
  • ability to use both American and British English spellings of aesthetics
  • tight integration with pandas
  • pip installable

Getting Started

Dependencies

I realize that these are not fun to install. My best luck has always been using brew if you're on a Mac or just using the binaries if you're on Windows. If you're using Linux then this should be relatively painless. You should be able to apt-get or yum all of these.

  • matplotlib
  • pandas
  • numpy
  • scipy
  • statsmodels
  • patsy

Installation

Ok the hard part is over. Installing ggplot is really easy. Just use pip!

$ pip install ggplot

Loading ggplot

# run an IPython shell (or don't)
$ ipython
In [1]: from ggplot import *

That's it! You're ready to go!

Examples

meat_lng = pd.melt(meat[['date', 'beef', 'pork', 'broilers']], id_vars='date')
ggplot(aes(x='date', y='value', colour='variable'), data=meat_lng) + \
geom_point() + \
stat_smooth(color='red')

####geom_point

from ggplot import *
ggplot(diamonds, aes('carat', 'price')) + \
geom_point(alpha=1/20.) + \
ylim(0, 20000)

####geom_histogram

p = ggplot(aes(x='carat'), data=diamonds)
p + geom_histogram() + ggtitle("Histogram of Diamond Carats") + labs("Carats", "Freq")

####geom_density

ggplot(diamonds, aes(x='price', color='cut')) + \
geom_density()


meat_lng = pd.melt(meat[['date', 'beef', 'broilers', 'pork']], id_vars=['date'])
p = ggplot(aes(x='value', colour='variable', fill=True, alpha=0.3), data=meat_lng)
p + geom_density()

####geom_bar

p = ggplot(mtcars, aes('factor(cyl)'))
p + geom_bar()

TODO

The list is long, but distinguished. We're looking for contributors! Email greg at yhathq.com for more info. For getting started with contributing, check out these docs

About

ggplot for python

Resources

Readme
BSD-2-Clause license

Contributing

Contributing
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

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Packages

Contributors

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
{ggplot} from Yhat
read more on our blog
from ggplot import *
ggplot(aes(x='date', y='beef'), data=meat) + \
geom_point(color='lightblue') + \
stat_smooth(span=.15, color='black', se=True) + \
ggtitle("Beef: It's What's for Dinner") + \
xlab("Date") + \
ylab("Head of Cattle Slaughtered")

What is it?
Yes, it's another port of ggplot2. One of the biggest reasons why I continue to reach for R instead of Python for data analysis is the lack of an easy to use, high level plotting package like ggplot2. I've tried other libraries like bokeh and d3py but what I really want is ggplot2.
ggplot is just that. It's an extremely un-pythonic package for doing exactly what ggplot2 does. The goal of the package is to mimic the ggplot2 API. This makes it super easy for people coming over from R to use, and prevents you from having to re-learn how to plot stuff.
Goals
  • same API as ggplot2 for R
  • never use matplotlib again
  • ability to use both American and British English spellings of aesthetics
  • tight integration with pandas
  • pip installable
Getting Started
Dependencies
I realize that these are not fun to install. My best luck has always been using brew if you're on a Mac
or just using the binaries if you're on Windows. If you're using Linux then this should be relatively
painless. You should be able to apt-get or yum all of these.
  • matplotlib
  • pandas
  • numpy
  • scipy
  • statsmodels
  • patsy
Installation
Ok the hard part is over. Installing ggplot is really easy. Just use pip!
$ pip install ggplot

Loading ggplot
# run an IPython shell (or don't)
$ ipython
In [1]: from ggplot import *

That's it! You're ready to go!
Examples
meat_lng = pd.melt(meat[['date', 'beef', 'pork', 'broilers']], id_vars='date')
ggplot(aes(x='date', y='value', colour='variable'), data=meat_lng) + \
geom_point() + \
stat_smooth(color='red')

####geom_point
from ggplot import *
ggplot(diamonds, aes('carat', 'price')) + \
geom_point(alpha=1/20.) + \
ylim(0, 20000)

####geom_histogram
p = ggplot(aes(x='carat'), data=diamonds)
p + geom_histogram() + ggtitle("Histogram of Diamond Carats") + labs("Carats", "Freq") 
####geom_density
ggplot(diamonds, aes(x='price', color='cut')) + \
geom_density()

meat_lng = pd.melt(meat[['date', 'beef', 'broilers', 'pork']], id_vars=['date'])
p = ggplot(aes(x='value', colour='variable', fill=True, alpha=0.3), data=meat_lng)
p + geom_density()

####geom_bar
p = ggplot(mtcars, aes('factor(cyl)'))
p + geom_bar()

TODO
The list is long, but distinguished.
We're looking for contributors! Email greg at yhathq.com for more info. For
getting started with contributing, check out these docs
About
ggplot for python
Resources
Readme
BSD-2-Clause license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
{ggplot} from Yhat
read more on our blog
from ggplot import *
ggplot(aes(x='date', y='beef'), data=meat) + \
geom_point(color='lightblue') + \
stat_smooth(span=.15, color='black', se=True) + \
ggtitle("Beef: It's What's for Dinner") + \
xlab("Date") + \
ylab("Head of Cattle Slaughtered")

What is it?
Yes, it's another port of ggplot2. One of the biggest reasons why I continue to reach for R instead of Python for data analysis is the lack of an easy to use, high level plotting package like ggplot2. I've tried other libraries like bokeh and d3py but what I really want is ggplot2.
ggplot is just that. It's an extremely un-pythonic package for doing exactly what ggplot2 does. The goal of the package is to mimic the ggplot2 API. This makes it super easy for people coming over from R to use, and prevents you from having to re-learn how to plot stuff.
Goals
  • same API as ggplot2 for R
  • never use matplotlib again
  • ability to use both American and British English spellings of aesthetics
  • tight integration with pandas
  • pip installable
Getting Started
Dependencies
I realize that these are not fun to install. My best luck has always been using brew if you're on a Mac
or just using the binaries if you're on Windows. If you're using Linux then this should be relatively
painless. You should be able to apt-get or yum all of these.
  • matplotlib
  • pandas
  • numpy
  • scipy
  • statsmodels
  • patsy
Installation
Ok the hard part is over. Installing ggplot is really easy. Just use pip!
$ pip install ggplot

Loading ggplot
# run an IPython shell (or don't)
$ ipython
In [1]: from ggplot import *

That's it! You're ready to go!
Examples
meat_lng = pd.melt(meat[['date', 'beef', 'pork', 'broilers']], id_vars='date')
ggplot(aes(x='date', y='value', colour='variable'), data=meat_lng) + \
geom_point() + \
stat_smooth(color='red')

####geom_point
from ggplot import *
ggplot(diamonds, aes('carat', 'price')) + \
geom_point(alpha=1/20.) + \
ylim(0, 20000)

####geom_histogram
p = ggplot(aes(x='carat'), data=diamonds)
p + geom_histogram() + ggtitle("Histogram of Diamond Carats") + labs("Carats", "Freq") 
####geom_density
ggplot(diamonds, aes(x='price', color='cut')) + \
geom_density()

meat_lng = pd.melt(meat[['date', 'beef', 'broilers', 'pork']], id_vars=['date'])
p = ggplot(aes(x='value', colour='variable', fill=True, alpha=0.3), data=meat_lng)
p + geom_density()

####geom_bar
p = ggplot(mtcars, aes('factor(cyl)'))
p + geom_bar()

TODO
The list is long, but distinguished.
We're looking for contributors! Email greg at yhathq.com for more info. For
getting started with contributing, check out these docs
About
ggplot for python
Resources
Readme
BSD-2-Clause license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
{ggplot} from Yhat
read more on our blog
from ggplot import *
ggplot(aes(x='date', y='beef'), data=meat) + \
geom_point(color='lightblue') + \
stat_smooth(span=.15, color='black', se=True) + \
ggtitle("Beef: It's What's for Dinner") + \
xlab("Date") + \
ylab("Head of Cattle Slaughtered")

What is it?
Yes, it's another port of ggplot2. One of the biggest reasons why I continue to reach for R instead of Python for data analysis is the lack of an easy to use, high level plotting package like ggplot2. I've tried other libraries like bokeh and d3py but what I really want is ggplot2.
ggplot is just that. It's an extremely un-pythonic package for doing exactly what ggplot2 does. The goal of the package is to mimic the ggplot2 API. This makes it super easy for people coming over from R to use, and prevents you from having to re-learn how to plot stuff.
Goals
  • same API as ggplot2 for R
  • never use matplotlib again
  • ability to use both American and British English spellings of aesthetics
  • tight integration with pandas
  • pip installable
Getting Started
Dependencies
I realize that these are not fun to install. My best luck has always been using brew if you're on a Mac
or just using the binaries if you're on Windows. If you're using Linux then this should be relatively
painless. You should be able to apt-get or yum all of these.
  • matplotlib
  • pandas
  • numpy
  • scipy
  • statsmodels
  • patsy
Installation
Ok the hard part is over. Installing ggplot is really easy. Just use pip!
$ pip install ggplot

Loading ggplot
# run an IPython shell (or don't)
$ ipython
In [1]: from ggplot import *

That's it! You're ready to go!
Examples
meat_lng = pd.melt(meat[['date', 'beef', 'pork', 'broilers']], id_vars='date')
ggplot(aes(x='date', y='value', colour='variable'), data=meat_lng) + \
geom_point() + \
stat_smooth(color='red')

####geom_point
from ggplot import *
ggplot(diamonds, aes('carat', 'price')) + \
geom_point(alpha=1/20.) + \
ylim(0, 20000)

####geom_histogram
p = ggplot(aes(x='carat'), data=diamonds)
p + geom_histogram() + ggtitle("Histogram of Diamond Carats") + labs("Carats", "Freq") 
####geom_density
ggplot(diamonds, aes(x='price', color='cut')) + \
geom_density()

meat_lng = pd.melt(meat[['date', 'beef', 'broilers', 'pork']], id_vars=['date'])
p = ggplot(aes(x='value', colour='variable', fill=True, alpha=0.3), data=meat_lng)
p + geom_density()

####geom_bar
p = ggplot(mtcars, aes('factor(cyl)'))
p + geom_bar()

TODO
The list is long, but distinguished.
We're looking for contributors! Email greg at yhathq.com for more info. For
getting started with contributing, check out these docs
About
ggplot for python
Resources
Readme
BSD-2-Clause license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
{ggplot} from Yhat
read more on our blog
from ggplot import *
ggplot(aes(x='date', y='beef'), data=meat) + \
geom_point(color='lightblue') + \
stat_smooth(span=.15, color='black', se=True) + \
ggtitle("Beef: It's What's for Dinner") + \
xlab("Date") + \
ylab("Head of Cattle Slaughtered")

What is it?
Yes, it's another port of ggplot2. One of the biggest reasons why I continue to reach for R instead of Python for data analysis is the lack of an easy to use, high level plotting package like ggplot2. I've tried other libraries like bokeh and d3py but what I really want is ggplot2.
ggplot is just that. It's an extremely un-pythonic package for doing exactly what ggplot2 does. The goal of the package is to mimic the ggplot2 API. This makes it super easy for people coming over from R to use, and prevents you from having to re-learn how to plot stuff.
Goals
  • same API as ggplot2 for R
  • never use matplotlib again
  • ability to use both American and British English spellings of aesthetics
  • tight integration with pandas
  • pip installable
Getting Started
Dependencies
I realize that these are not fun to install. My best luck has always been using brew if you're on a Mac
or just using the binaries if you're on Windows. If you're using Linux then this should be relatively
painless. You should be able to apt-get or yum all of these.
  • matplotlib
  • pandas
  • numpy
  • scipy
  • statsmodels
  • patsy
Installation
Ok the hard part is over. Installing ggplot is really easy. Just use pip!
$ pip install ggplot

Loading ggplot
# run an IPython shell (or don't)
$ ipython
In [1]: from ggplot import *

That's it! You're ready to go!
Examples
meat_lng = pd.melt(meat[['date', 'beef', 'pork', 'broilers']], id_vars='date')
ggplot(aes(x='date', y='value', colour='variable'), data=meat_lng) + \
geom_point() + \
stat_smooth(color='red')

####geom_point
from ggplot import *
ggplot(diamonds, aes('carat', 'price')) + \
geom_point(alpha=1/20.) + \
ylim(0, 20000)

####geom_histogram
p = ggplot(aes(x='carat'), data=diamonds)
p + geom_histogram() + ggtitle("Histogram of Diamond Carats") + labs("Carats", "Freq") 
####geom_density
ggplot(diamonds, aes(x='price', color='cut')) + \
geom_density()

meat_lng = pd.melt(meat[['date', 'beef', 'broilers', 'pork']], id_vars=['date'])
p = ggplot(aes(x='value', colour='variable', fill=True, alpha=0.3), data=meat_lng)
p + geom_density()

####geom_bar
p = ggplot(mtcars, aes('factor(cyl)'))
p + geom_bar()

TODO
The list is long, but distinguished.
We're looking for contributors! Email greg at yhathq.com for more info. For
getting started with contributing, check out these docs
About
ggplot for python
Resources
Readme
BSD-2-Clause license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
{ggplot} from Yhat
read more on our blog
from ggplot import *
ggplot(aes(x='date', y='beef'), data=meat) + \
geom_point(color='lightblue') + \
stat_smooth(span=.15, color='black', se=True) + \
ggtitle("Beef: It's What's for Dinner") + \
xlab("Date") + \
ylab("Head of Cattle Slaughtered")

What is it?
Yes, it's another port of ggplot2. One of the biggest reasons why I continue to reach for R instead of Python for data analysis is the lack of an easy to use, high level plotting package like ggplot2. I've tried other libraries like bokeh and d3py but what I really want is ggplot2.
ggplot is just that. It's an extremely un-pythonic package for doing exactly what ggplot2 does. The goal of the package is to mimic the ggplot2 API. This makes it super easy for people coming over from R to use, and prevents you from having to re-learn how to plot stuff.
Goals
  • same API as ggplot2 for R
  • never use matplotlib again
  • ability to use both American and British English spellings of aesthetics
  • tight integration with pandas
  • pip installable
Getting Started
Dependencies
I realize that these are not fun to install. My best luck has always been using brew if you're on a Mac
or just using the binaries if you're on Windows. If you're using Linux then this should be relatively
painless. You should be able to apt-get or yum all of these.
  • matplotlib
  • pandas
  • numpy
  • scipy
  • statsmodels
  • patsy
Installation
Ok the hard part is over. Installing ggplot is really easy. Just use pip!
$ pip install ggplot

Loading ggplot
# run an IPython shell (or don't)
$ ipython
In [1]: from ggplot import *

That's it! You're ready to go!
Examples
meat_lng = pd.melt(meat[['date', 'beef', 'pork', 'broilers']], id_vars='date')
ggplot(aes(x='date', y='value', colour='variable'), data=meat_lng) + \
geom_point() + \
stat_smooth(color='red')

####geom_point
from ggplot import *
ggplot(diamonds, aes('carat', 'price')) + \
geom_point(alpha=1/20.) + \
ylim(0, 20000)

####geom_histogram
p = ggplot(aes(x='carat'), data=diamonds)
p + geom_histogram() + ggtitle("Histogram of Diamond Carats") + labs("Carats", "Freq") 
####geom_density
ggplot(diamonds, aes(x='price', color='cut')) + \
geom_density()

meat_lng = pd.melt(meat[['date', 'beef', 'broilers', 'pork']], id_vars=['date'])
p = ggplot(aes(x='value', colour='variable', fill=True, alpha=0.3), data=meat_lng)
p + geom_density()

####geom_bar
p = ggplot(mtcars, aes('factor(cyl)'))
p + geom_bar()

TODO
The list is long, but distinguished.
We're looking for contributors! Email greg at yhathq.com for more info. For
getting started with contributing, check out these docs
About
ggplot for python
Resources
Readme
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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{ggplot} from Yhat
read more on our blog
from ggplot import *
ggplot(aes(x='date', y='beef'), data=meat) + \
geom_point(color='lightblue') + \
stat_smooth(span=.15, color='black', se=True) + \
ggtitle("Beef: It's What's for Dinner") + \
xlab("Date") + \
ylab("Head of Cattle Slaughtered")

What is it?
Yes, it's another port of ggplot2. One of the biggest reasons why I continue to reach for R instead of Python for data analysis is the lack of an easy to use, high level plotting package like ggplot2. I've tried other libraries like bokeh and d3py but what I really want is ggplot2.
ggplot is just that. It's an extremely un-pythonic package for doing exactly what ggplot2 does. The goal of the package is to mimic the ggplot2 API. This makes it super easy for people coming over from R to use, and prevents you from having to re-learn how to plot stuff.
Goals
  • same API as ggplot2 for R
  • never use matplotlib again
  • ability to use both American and British English spellings of aesthetics
  • tight integration with pandas
  • pip installable
Getting Started
Dependencies
I realize that these are not fun to install. My best luck has always been using brew if you're on a Mac
or just using the binaries if you're on Windows. If you're using Linux then this should be relatively
painless. You should be able to apt-get or yum all of these.
  • matplotlib
  • pandas
  • numpy
  • scipy
  • statsmodels
  • patsy
Installation
Ok the hard part is over. Installing ggplot is really easy. Just use pip!
$ pip install ggplot

Loading ggplot
# run an IPython shell (or don't)
$ ipython
In [1]: from ggplot import *

That's it! You're ready to go!
Examples
meat_lng = pd.melt(meat[['date', 'beef', 'pork', 'broilers']], id_vars='date')
ggplot(aes(x='date', y='value', colour='variable'), data=meat_lng) + \
geom_point() + \
stat_smooth(color='red')

####geom_point
from ggplot import *
ggplot(diamonds, aes('carat', 'price')) + \
geom_point(alpha=1/20.) + \
ylim(0, 20000)

####geom_histogram
p = ggplot(aes(x='carat'), data=diamonds)
p + geom_histogram() + ggtitle("Histogram of Diamond Carats") + labs("Carats", "Freq") 
####geom_density
ggplot(diamonds, aes(x='price', color='cut')) + \
geom_density()

meat_lng = pd.melt(meat[['date', 'beef', 'broilers', 'pork']], id_vars=['date'])
p = ggplot(aes(x='value', colour='variable', fill=True, alpha=0.3), data=meat_lng)
p + geom_density()

####geom_bar
p = ggplot(mtcars, aes('factor(cyl)'))
p + geom_bar()

TODO
The list is long, but distinguished.
We're looking for contributors! Email greg at yhathq.com for more info. For
getting started with contributing, check out these docs
About
ggplot for python
Resources
Readme
BSD-2-Clause license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
{ggplot} from Yhat
read more on our blog
from ggplot import *
ggplot(aes(x='date', y='beef'), data=meat) + \
geom_point(color='lightblue') + \
stat_smooth(span=.15, color='black', se=True) + \
ggtitle("Beef: It's What's for Dinner") + \
xlab("Date") + \
ylab("Head of Cattle Slaughtered")

What is it?
Yes, it's another port of ggplot2. One of the biggest reasons why I continue to reach for R instead of Python for data analysis is the lack of an easy to use, high level plotting package like ggplot2. I've tried other libraries like bokeh and d3py but what I really want is ggplot2.
ggplot is just that. It's an extremely un-pythonic package for doing exactly what ggplot2 does. The goal of the package is to mimic the ggplot2 API. This makes it super easy for people coming over from R to use, and prevents you from having to re-learn how to plot stuff.
Goals
  • same API as ggplot2 for R
  • never use matplotlib again
  • ability to use both American and British English spellings of aesthetics
  • tight integration with pandas
  • pip installable
Getting Started
Dependencies
I realize that these are not fun to install. My best luck has always been using brew if you're on a Mac
or just using the binaries if you're on Windows. If you're using Linux then this should be relatively
painless. You should be able to apt-get or yum all of these.
  • matplotlib
  • pandas
  • numpy
  • scipy
  • statsmodels
  • patsy
Installation
Ok the hard part is over. Installing ggplot is really easy. Just use pip!
$ pip install ggplot

Loading ggplot
# run an IPython shell (or don't)
$ ipython
In [1]: from ggplot import *

That's it! You're ready to go!
Examples
meat_lng = pd.melt(meat[['date', 'beef', 'pork', 'broilers']], id_vars='date')
ggplot(aes(x='date', y='value', colour='variable'), data=meat_lng) + \
geom_point() + \
stat_smooth(color='red')

####geom_point
from ggplot import *
ggplot(diamonds, aes('carat', 'price')) + \
geom_point(alpha=1/20.) + \
ylim(0, 20000)

####geom_histogram
p = ggplot(aes(x='carat'), data=diamonds)
p + geom_histogram() + ggtitle("Histogram of Diamond Carats") + labs("Carats", "Freq") 
####geom_density
ggplot(diamonds, aes(x='price', color='cut')) + \
geom_density()

meat_lng = pd.melt(meat[['date', 'beef', 'broilers', 'pork']], id_vars=['date'])
p = ggplot(aes(x='value', colour='variable', fill=True, alpha=0.3), data=meat_lng)
p + geom_density()

####geom_bar
p = ggplot(mtcars, aes('factor(cyl)'))
p + geom_bar()

TODO
The list is long, but distinguished.
We're looking for contributors! Email greg at yhathq.com for more info. For
getting started with contributing, check out these docs
About
ggplot for python
Resources
Readme
BSD-2-Clause license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages