1570265165
I’ve already covered building a static dashboard with Cube.js and Chart.js in this tutorial. Now, I’m going to show you how to dynamically change the underlying chart’s data based on the user’s input. We’ll let the user pick a date range and, based on that, reload the chart. When a user picks a new set of dates, a new request will be sent to the Cube.js server. The Cube.js server will generate new SQL code, execute it against the database, and send the result back to the client. And finally, the client re-renders a chart with the new data.
Here is a Codesandbox demo of what we are going to build. You can click Open in Editor to check the source code.
We are going to use our sample e-commerce Postgres dataset. Use the following commands to download it and import it into the ecom
database.
$ curl http://cube.dev/downloads/ecom-dump.sql > ecom-dump.sql
$ createdb ecom
$ psql --dbname ecom -f ecom-dump.sql
Next, install Cube.js CLI if you don’t have it already and generate a new application.
$ npm install -g cubejs-cli
$ cubejs create chartjs-dynamic-data -d postgres
Cube.js uses environment variables for configuration. To configure the connection to our database, we need to specify the database type and name. In the Cube.js project folder, replace the contents of the .env file with the following:
CUBEJS_API_SECRET=SECRET
CUBEJS_DB_TYPE=postgres
CUBEJS_DB_NAME=ecom
Now, start the development server and open the localhost:4000 in your browser.
$ npm run dev
You can generate a schema for the tables in the ecom
database under the Schema tab. We’re going to use data from only one table — orders
. Once you generate this schema, you can play around with the data in the Explore section.
There are multiple ways to deploy Cube.js: Docker, serverless, and Heroku. You can read about all of them here. In this tutorial, we are going to use the Cube.js application deployed to Heroku here.
We’ll build our frontend on the Codesandox for simplicity. It will load the data from the Cube.js backend deployed on Heroku. You can check out the final source code and the demo app here. Feel free to fork it and play around.
We are using the Vanilla template from Codesanbox and are not going to add any framework, such as React or Vue, to keep things simple. The first step is to include the Cube.js client and Chart.js libraries. Insert the following code inside the <head>
tag.
<script src="https://unpkg.com/@cubejs-client/core@0.10.16/dist/cubejs-client-core.umd.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/Chart.js/2.7.2/Chart.bundle.min.js"></script>
To initialize the Cube.js client, you need to pass an API URL along with the Secret.
const cubejsApi = cubejs(
"eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.e30.K9PiJkjegbhnw4Ca5pPlkTmZihoOm42w8bja9Qs2qJg",
{ apiUrl: "https://cubejs-ecom.herokuapp.com/cubejs-api/v1" }
);
Once the client is initialized, we can request data from the backend and visualize it. The load
function accepts a query, which is a plain javascript object, and returns a promise. You can learn more about the query format here.
cubejsApi
.load({
measures: ["Orders.count"],
timeDimensions: [
{
dimension: "Orders.createdAt",
granularity: `day`,
dateRange: [`08/01/2019`,`09/01/2019`]
}
]
})
.then(resultSet => {
new Chart(document.getElementById("chart"), {
type: "line",
options: options,
data: chartJsData(resultSet)
});
});
We are loading Orders.count
, which is grouped by the created day to plot as a line chart. To make this code work, we need to make a couple of things. First, add the <canvas>
tag to the inside of your html <body>
.
<canvas id="chart-canvas"></canvas>
Next, we need to define a chartJsData
function, which should accept a resultSet returned from Cube.js and format it for Chart.js.
var chartJsData = function(resultSet) {
return {
datasets: [
{
label: "Orders Count",
data: resultSet.chartPivot().map(function(r) {
return r["Orders.count"];
}),
backgroundColor: "rgb(255, 99, 132)"
}
],
labels: resultSet.categories().map(function(c) {
return c.x;
})
};
};
Lastly, we are declaring some additional Chart.js options for nice axes formatting.
var options = {
scales: {
xAxes: [
{
type: "time",
time: {
displayFormats: {
hour: "MMM DD"
},
tooltipFormat: "MMM D"
}
}
],
yAxes: [
{
ticks: {
beginAtZero: true
}
}
]
}
};
That is all we need to load a static line chart. Next, we’re going to add a date range picker and load data dynamically based on the date range selected by the user.
Add Jquery, Date Range Picker, and Moment.js libraries to your <head>
tag.
<script src="https://cdn.jsdelivr.net/jquery/latest/jquery.min.js"></script>
<script src="https://cdn.jsdelivr.net/momentjs/latest/moment.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/daterangepicker/daterangepicker.min.js"></script>
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/daterangepicker/daterangepicker.css"/>
Next, let’s wrap the code to render a chart into the drawChart
function, which is going to accept two arguments: start date and end date.
var drawChart = function(startDate, endDate) {
cubejsApi
.load({
measures: ["Orders.count"],
timeDimensions: [
{
dimension: "Orders.createdAt",
granularity: `day`,
dateRange: [startDate, endDate]
}
]
})
.then(resultSet => {
if (window.chart) {
window.chart.data = data;
window.chart.update();
} else {
window.chart = new Chart(document.getElementById("chart-canvas"),
{
type: "line",
options: options,
data: data
});
}
});
};
Besides making the dateRange
dynamic, we’re also saving the current chart into windows.chart
so we can update it later when we need to re-render the chart.
Finally, we can add an input to our html body and make it a date range picker:
<input name="dates" />
const START_DATE = "08/01/2019";
const END_DATE = "09/01/2019";
$('input[name="dates"]').daterangepicker(
{
startDate: START_DATE,
endDate: END_DATE
},
function(start, end) {
drawChart(start, end);
}
);
drawChart(START_DATE, END_DATE);
That is it! Now we have a fully working dynamic and interactive chart. You can select different dates from the date picker and see how the chart is changing.
If you have any questions about this tutorial or about Cube.js in general—feel free to ping me in the Slack Cube.js Community.
#database #web-development
1655630160
Install via pip:
$ pip install pytumblr
Install from source:
$ git clone https://github.com/tumblr/pytumblr.git
$ cd pytumblr
$ python setup.py install
A pytumblr.TumblrRestClient
is the object you'll make all of your calls to the Tumblr API through. Creating one is this easy:
client = pytumblr.TumblrRestClient(
'<consumer_key>',
'<consumer_secret>',
'<oauth_token>',
'<oauth_secret>',
)
client.info() # Grabs the current user information
Two easy ways to get your credentials to are:
interactive_console.py
tool (if you already have a consumer key & secret)client.info() # get information about the authenticating user
client.dashboard() # get the dashboard for the authenticating user
client.likes() # get the likes for the authenticating user
client.following() # get the blogs followed by the authenticating user
client.follow('codingjester.tumblr.com') # follow a blog
client.unfollow('codingjester.tumblr.com') # unfollow a blog
client.like(id, reblogkey) # like a post
client.unlike(id, reblogkey) # unlike a post
client.blog_info(blogName) # get information about a blog
client.posts(blogName, **params) # get posts for a blog
client.avatar(blogName) # get the avatar for a blog
client.blog_likes(blogName) # get the likes on a blog
client.followers(blogName) # get the followers of a blog
client.blog_following(blogName) # get the publicly exposed blogs that [blogName] follows
client.queue(blogName) # get the queue for a given blog
client.submission(blogName) # get the submissions for a given blog
Creating posts
PyTumblr lets you create all of the various types that Tumblr supports. When using these types there are a few defaults that are able to be used with any post type.
The default supported types are described below.
We'll show examples throughout of these default examples while showcasing all the specific post types.
Creating a photo post
Creating a photo post supports a bunch of different options plus the described default options * caption - a string, the user supplied caption * link - a string, the "click-through" url for the photo * source - a string, the url for the photo you want to use (use this or the data parameter) * data - a list or string, a list of filepaths or a single file path for multipart file upload
#Creates a photo post using a source URL
client.create_photo(blogName, state="published", tags=["testing", "ok"],
source="https://68.media.tumblr.com/b965fbb2e501610a29d80ffb6fb3e1ad/tumblr_n55vdeTse11rn1906o1_500.jpg")
#Creates a photo post using a local filepath
client.create_photo(blogName, state="queue", tags=["testing", "ok"],
tweet="Woah this is an incredible sweet post [URL]",
data="/Users/johnb/path/to/my/image.jpg")
#Creates a photoset post using several local filepaths
client.create_photo(blogName, state="draft", tags=["jb is cool"], format="markdown",
data=["/Users/johnb/path/to/my/image.jpg", "/Users/johnb/Pictures/kittens.jpg"],
caption="## Mega sweet kittens")
Creating a text post
Creating a text post supports the same options as default and just a two other parameters * title - a string, the optional title for the post. Supports markdown or html * body - a string, the body of the of the post. Supports markdown or html
#Creating a text post
client.create_text(blogName, state="published", slug="testing-text-posts", title="Testing", body="testing1 2 3 4")
Creating a quote post
Creating a quote post supports the same options as default and two other parameter * quote - a string, the full text of the qote. Supports markdown or html * source - a string, the cited source. HTML supported
#Creating a quote post
client.create_quote(blogName, state="queue", quote="I am the Walrus", source="Ringo")
Creating a link post
#Create a link post
client.create_link(blogName, title="I like to search things, you should too.", url="https://duckduckgo.com",
description="Search is pretty cool when a duck does it.")
Creating a chat post
Creating a chat post supports the same options as default and two other parameters * title - a string, the title of the chat post * conversation - a string, the text of the conversation/chat, with diablog labels (no html)
#Create a chat post
chat = """John: Testing can be fun!
Renee: Testing is tedious and so are you.
John: Aw.
"""
client.create_chat(blogName, title="Renee just doesn't understand.", conversation=chat, tags=["renee", "testing"])
Creating an audio post
Creating an audio post allows for all default options and a has 3 other parameters. The only thing to keep in mind while dealing with audio posts is to make sure that you use the external_url parameter or data. You cannot use both at the same time. * caption - a string, the caption for your post * external_url - a string, the url of the site that hosts the audio file * data - a string, the filepath of the audio file you want to upload to Tumblr
#Creating an audio file
client.create_audio(blogName, caption="Rock out.", data="/Users/johnb/Music/my/new/sweet/album.mp3")
#lets use soundcloud!
client.create_audio(blogName, caption="Mega rock out.", external_url="https://soundcloud.com/skrillex/sets/recess")
Creating a video post
Creating a video post allows for all default options and has three other options. Like the other post types, it has some restrictions. You cannot use the embed and data parameters at the same time. * caption - a string, the caption for your post * embed - a string, the HTML embed code for the video * data - a string, the path of the file you want to upload
#Creating an upload from YouTube
client.create_video(blogName, caption="Jon Snow. Mega ridiculous sword.",
embed="http://www.youtube.com/watch?v=40pUYLacrj4")
#Creating a video post from local file
client.create_video(blogName, caption="testing", data="/Users/johnb/testing/ok/blah.mov")
Editing a post
Updating a post requires you knowing what type a post you're updating. You'll be able to supply to the post any of the options given above for updates.
client.edit_post(blogName, id=post_id, type="text", title="Updated")
client.edit_post(blogName, id=post_id, type="photo", data="/Users/johnb/mega/awesome.jpg")
Reblogging a Post
Reblogging a post just requires knowing the post id and the reblog key, which is supplied in the JSON of any post object.
client.reblog(blogName, id=125356, reblog_key="reblog_key")
Deleting a post
Deleting just requires that you own the post and have the post id
client.delete_post(blogName, 123456) # Deletes your post :(
A note on tags: When passing tags, as params, please pass them as a list (not a comma-separated string):
client.create_text(blogName, tags=['hello', 'world'], ...)
Getting notes for a post
In order to get the notes for a post, you need to have the post id and the blog that it is on.
data = client.notes(blogName, id='123456')
The results include a timestamp you can use to make future calls.
data = client.notes(blogName, id='123456', before_timestamp=data["_links"]["next"]["query_params"]["before_timestamp"])
# get posts with a given tag
client.tagged(tag, **params)
This client comes with a nice interactive console to run you through the OAuth process, grab your tokens (and store them for future use).
You'll need pyyaml
installed to run it, but then it's just:
$ python interactive-console.py
and away you go! Tokens are stored in ~/.tumblr
and are also shared by other Tumblr API clients like the Ruby client.
The tests (and coverage reports) are run with nose, like this:
python setup.py test
Author: tumblr
Source Code: https://github.com/tumblr/pytumblr
License: Apache-2.0 license
1632537859
Not babashka. Node.js babashka!?
Ad-hoc CLJS scripting on Node.js.
Experimental. Please report issues here.
Nbb's main goal is to make it easy to get started with ad hoc CLJS scripting on Node.js.
Additional goals and features are:
Nbb requires Node.js v12 or newer.
CLJS code is evaluated through SCI, the same interpreter that powers babashka. Because SCI works with advanced compilation, the bundle size, especially when combined with other dependencies, is smaller than what you get with self-hosted CLJS. That makes startup faster. The trade-off is that execution is less performant and that only a subset of CLJS is available (e.g. no deftype, yet).
Install nbb
from NPM:
$ npm install nbb -g
Omit -g
for a local install.
Try out an expression:
$ nbb -e '(+ 1 2 3)'
6
And then install some other NPM libraries to use in the script. E.g.:
$ npm install csv-parse shelljs zx
Create a script which uses the NPM libraries:
(ns script
(:require ["csv-parse/lib/sync$default" :as csv-parse]
["fs" :as fs]
["path" :as path]
["shelljs$default" :as sh]
["term-size$default" :as term-size]
["zx$default" :as zx]
["zx$fs" :as zxfs]
[nbb.core :refer [*file*]]))
(prn (path/resolve "."))
(prn (term-size))
(println (count (str (fs/readFileSync *file*))))
(prn (sh/ls "."))
(prn (csv-parse "foo,bar"))
(prn (zxfs/existsSync *file*))
(zx/$ #js ["ls"])
Call the script:
$ nbb script.cljs
"/private/tmp/test-script"
#js {:columns 216, :rows 47}
510
#js ["node_modules" "package-lock.json" "package.json" "script.cljs"]
#js [#js ["foo" "bar"]]
true
$ ls
node_modules
package-lock.json
package.json
script.cljs
Nbb has first class support for macros: you can define them right inside your .cljs
file, like you are used to from JVM Clojure. Consider the plet
macro to make working with promises more palatable:
(defmacro plet
[bindings & body]
(let [binding-pairs (reverse (partition 2 bindings))
body (cons 'do body)]
(reduce (fn [body [sym expr]]
(let [expr (list '.resolve 'js/Promise expr)]
(list '.then expr (list 'clojure.core/fn (vector sym)
body))))
body
binding-pairs)))
Using this macro we can look async code more like sync code. Consider this puppeteer example:
(-> (.launch puppeteer)
(.then (fn [browser]
(-> (.newPage browser)
(.then (fn [page]
(-> (.goto page "https://clojure.org")
(.then #(.screenshot page #js{:path "screenshot.png"}))
(.catch #(js/console.log %))
(.then #(.close browser)))))))))
Using plet
this becomes:
(plet [browser (.launch puppeteer)
page (.newPage browser)
_ (.goto page "https://clojure.org")
_ (-> (.screenshot page #js{:path "screenshot.png"})
(.catch #(js/console.log %)))]
(.close browser))
See the puppeteer example for the full code.
Since v0.0.36, nbb includes promesa which is a library to deal with promises. The above plet
macro is similar to promesa.core/let
.
$ time nbb -e '(+ 1 2 3)'
6
nbb -e '(+ 1 2 3)' 0.17s user 0.02s system 109% cpu 0.168 total
The baseline startup time for a script is about 170ms seconds on my laptop. When invoked via npx
this adds another 300ms or so, so for faster startup, either use a globally installed nbb
or use $(npm bin)/nbb script.cljs
to bypass npx
.
Nbb does not depend on any NPM dependencies. All NPM libraries loaded by a script are resolved relative to that script. When using the Reagent module, React is resolved in the same way as any other NPM library.
To load .cljs
files from local paths or dependencies, you can use the --classpath
argument. The current dir is added to the classpath automatically. So if there is a file foo/bar.cljs
relative to your current dir, then you can load it via (:require [foo.bar :as fb])
. Note that nbb
uses the same naming conventions for namespaces and directories as other Clojure tools: foo-bar
in the namespace name becomes foo_bar
in the directory name.
To load dependencies from the Clojure ecosystem, you can use the Clojure CLI or babashka to download them and produce a classpath:
$ classpath="$(clojure -A:nbb -Spath -Sdeps '{:aliases {:nbb {:replace-deps {com.github.seancorfield/honeysql {:git/tag "v2.0.0-rc5" :git/sha "01c3a55"}}}}}')"
and then feed it to the --classpath
argument:
$ nbb --classpath "$classpath" -e "(require '[honey.sql :as sql]) (sql/format {:select :foo :from :bar :where [:= :baz 2]})"
["SELECT foo FROM bar WHERE baz = ?" 2]
Currently nbb
only reads from directories, not jar files, so you are encouraged to use git libs. Support for .jar
files will be added later.
The name of the file that is currently being executed is available via nbb.core/*file*
or on the metadata of vars:
(ns foo
(:require [nbb.core :refer [*file*]]))
(prn *file*) ;; "/private/tmp/foo.cljs"
(defn f [])
(prn (:file (meta #'f))) ;; "/private/tmp/foo.cljs"
Nbb includes reagent.core
which will be lazily loaded when required. You can use this together with ink to create a TUI application:
$ npm install ink
ink-demo.cljs
:
(ns ink-demo
(:require ["ink" :refer [render Text]]
[reagent.core :as r]))
(defonce state (r/atom 0))
(doseq [n (range 1 11)]
(js/setTimeout #(swap! state inc) (* n 500)))
(defn hello []
[:> Text {:color "green"} "Hello, world! " @state])
(render (r/as-element [hello]))
Working with callbacks and promises can become tedious. Since nbb v0.0.36 the promesa.core
namespace is included with the let
and do!
macros. An example:
(ns prom
(:require [promesa.core :as p]))
(defn sleep [ms]
(js/Promise.
(fn [resolve _]
(js/setTimeout resolve ms))))
(defn do-stuff
[]
(p/do!
(println "Doing stuff which takes a while")
(sleep 1000)
1))
(p/let [a (do-stuff)
b (inc a)
c (do-stuff)
d (+ b c)]
(prn d))
$ nbb prom.cljs
Doing stuff which takes a while
Doing stuff which takes a while
3
Also see API docs.
Since nbb v0.0.75 applied-science/js-interop is available:
(ns example
(:require [applied-science.js-interop :as j]))
(def o (j/lit {:a 1 :b 2 :c {:d 1}}))
(prn (j/select-keys o [:a :b])) ;; #js {:a 1, :b 2}
(prn (j/get-in o [:c :d])) ;; 1
Most of this library is supported in nbb, except the following:
:syms
.-x
notation. In nbb, you must use keywords.See the example of what is currently supported.
See the examples directory for small examples.
Also check out these projects built with nbb:
See API documentation.
See this gist on how to convert an nbb script or project to shadow-cljs.
Prequisites:
To build:
bb release
Run bb tasks
for more project-related tasks.
Download Details:
Author: borkdude
Download Link: Download The Source Code
Official Website: https://github.com/borkdude/nbb
License: EPL-1.0
#node #javascript
1669003576
In this Python article, let's learn about Mutable and Immutable in Python.
Mutable is a fancy way of saying that the internal state of the object is changed/mutated. So, the simplest definition is: An object whose internal state can be changed is mutable. On the other hand, immutable doesn’t allow any change in the object once it has been created.
Both of these states are integral to Python data structure. If you want to become more knowledgeable in the entire Python Data Structure, take this free course which covers multiple data structures in Python including tuple data structure which is immutable. You will also receive a certificate on completion which is sure to add value to your portfolio.
Mutable is when something is changeable or has the ability to change. In Python, ‘mutable’ is the ability of objects to change their values. These are often the objects that store a collection of data.
Immutable is the when no change is possible over time. In Python, if the value of an object cannot be changed over time, then it is known as immutable. Once created, the value of these objects is permanent.
Objects of built-in type that are mutable are:
Objects of built-in type that are immutable are:
Object mutability is one of the characteristics that makes Python a dynamically typed language. Though Mutable and Immutable in Python is a very basic concept, it can at times be a little confusing due to the intransitive nature of immutability.
In Python, everything is treated as an object. Every object has these three attributes:
While ID and Type cannot be changed once it’s created, values can be changed for Mutable objects.
Check out this free python certificate course to get started with Python.
I believe, rather than diving deep into the theory aspects of mutable and immutable in Python, a simple code would be the best way to depict what it means in Python. Hence, let us discuss the below code step-by-step:
#Creating a list which contains name of Indian cities
cities = [‘Delhi’, ‘Mumbai’, ‘Kolkata’]
# Printing the elements from the list cities, separated by a comma & space
for city in cities:
print(city, end=’, ’)
Output [1]: Delhi, Mumbai, Kolkata
#Printing the location of the object created in the memory address in hexadecimal format
print(hex(id(cities)))
Output [2]: 0x1691d7de8c8
#Adding a new city to the list cities
cities.append(‘Chennai’)
#Printing the elements from the list cities, separated by a comma & space
for city in cities:
print(city, end=’, ’)
Output [3]: Delhi, Mumbai, Kolkata, Chennai
#Printing the location of the object created in the memory address in hexadecimal format
print(hex(id(cities)))
Output [4]: 0x1691d7de8c8
The above example shows us that we were able to change the internal state of the object ‘cities’ by adding one more city ‘Chennai’ to it, yet, the memory address of the object did not change. This confirms that we did not create a new object, rather, the same object was changed or mutated. Hence, we can say that the object which is a type of list with reference variable name ‘cities’ is a MUTABLE OBJECT.
Let us now discuss the term IMMUTABLE. Considering that we understood what mutable stands for, it is obvious that the definition of immutable will have ‘NOT’ included in it. Here is the simplest definition of immutable– An object whose internal state can NOT be changed is IMMUTABLE.
Again, if you try and concentrate on different error messages, you have encountered, thrown by the respective IDE; you use you would be able to identify the immutable objects in Python. For instance, consider the below code & associated error message with it, while trying to change the value of a Tuple at index 0.
#Creating a Tuple with variable name ‘foo’
foo = (1, 2)
#Changing the index[0] value from 1 to 3
foo[0] = 3
TypeError: 'tuple' object does not support item assignment
Once again, a simple code would be the best way to depict what immutable stands for. Hence, let us discuss the below code step-by-step:
#Creating a Tuple which contains English name of weekdays
weekdays = ‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’
# Printing the elements of tuple weekdays
print(weekdays)
Output [1]: (‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’)
#Printing the location of the object created in the memory address in hexadecimal format
print(hex(id(weekdays)))
Output [2]: 0x1691cc35090
#tuples are immutable, so you cannot add new elements, hence, using merge of tuples with the # + operator to add a new imaginary day in the tuple ‘weekdays’
weekdays += ‘Pythonday’,
#Printing the elements of tuple weekdays
print(weekdays)
Output [3]: (‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’, ‘Pythonday’)
#Printing the location of the object created in the memory address in hexadecimal format
print(hex(id(weekdays)))
Output [4]: 0x1691cc8ad68
This above example shows that we were able to use the same variable name that is referencing an object which is a type of tuple with seven elements in it. However, the ID or the memory location of the old & new tuple is not the same. We were not able to change the internal state of the object ‘weekdays’. The Python program manager created a new object in the memory address and the variable name ‘weekdays’ started referencing the new object with eight elements in it. Hence, we can say that the object which is a type of tuple with reference variable name ‘weekdays’ is an IMMUTABLE OBJECT.
Also Read: Understanding the Exploratory Data Analysis (EDA) in Python
Where can you use mutable and immutable objects:
Mutable objects can be used where you want to allow for any updates. For example, you have a list of employee names in your organizations, and that needs to be updated every time a new member is hired. You can create a mutable list, and it can be updated easily.
Immutability offers a lot of useful applications to different sensitive tasks we do in a network centred environment where we allow for parallel processing. By creating immutable objects, you seal the values and ensure that no threads can invoke overwrite/update to your data. This is also useful in situations where you would like to write a piece of code that cannot be modified. For example, a debug code that attempts to find the value of an immutable object.
Watch outs: Non transitive nature of Immutability:
OK! Now we do understand what mutable & immutable objects in Python are. Let’s go ahead and discuss the combination of these two and explore the possibilities. Let’s discuss, as to how will it behave if you have an immutable object which contains the mutable object(s)? Or vice versa? Let us again use a code to understand this behaviour–
#creating a tuple (immutable object) which contains 2 lists(mutable) as it’s elements
#The elements (lists) contains the name, age & gender
person = (['Ayaan', 5, 'Male'], ['Aaradhya', 8, 'Female'])
#printing the tuple
print(person)
Output [1]: (['Ayaan', 5, 'Male'], ['Aaradhya', 8, 'Female'])
#printing the location of the object created in the memory address in hexadecimal format
print(hex(id(person)))
Output [2]: 0x1691ef47f88
#Changing the age for the 1st element. Selecting 1st element of tuple by using indexing [0] then 2nd element of the list by using indexing [1] and assigning a new value for age as 4
person[0][1] = 4
#printing the updated tuple
print(person)
Output [3]: (['Ayaan', 4, 'Male'], ['Aaradhya', 8, 'Female'])
#printing the location of the object created in the memory address in hexadecimal format
print(hex(id(person)))
Output [4]: 0x1691ef47f88
In the above code, you can see that the object ‘person’ is immutable since it is a type of tuple. However, it has two lists as it’s elements, and we can change the state of lists (lists being mutable). So, here we did not change the object reference inside the Tuple, but the referenced object was mutated.
Also Read: Real-Time Object Detection Using TensorFlow
Same way, let’s explore how it will behave if you have a mutable object which contains an immutable object? Let us again use a code to understand the behaviour–
#creating a list (mutable object) which contains tuples(immutable) as it’s elements
list1 = [(1, 2, 3), (4, 5, 6)]
#printing the list
print(list1)
Output [1]: [(1, 2, 3), (4, 5, 6)]
#printing the location of the object created in the memory address in hexadecimal format
print(hex(id(list1)))
Output [2]: 0x1691d5b13c8
#changing object reference at index 0
list1[0] = (7, 8, 9)
#printing the list
Output [3]: [(7, 8, 9), (4, 5, 6)]
#printing the location of the object created in the memory address in hexadecimal format
print(hex(id(list1)))
Output [4]: 0x1691d5b13c8
As an individual, it completely depends upon you and your requirements as to what kind of data structure you would like to create with a combination of mutable & immutable objects. I hope that this information will help you while deciding the type of object you would like to select going forward.
Before I end our discussion on IMMUTABILITY, allow me to use the word ‘CAVITE’ when we discuss the String and Integers. There is an exception, and you may see some surprising results while checking the truthiness for immutability. For instance:
#creating an object of integer type with value 10 and reference variable name ‘x’
x = 10
#printing the value of ‘x’
print(x)
Output [1]: 10
#Printing the location of the object created in the memory address in hexadecimal format
print(hex(id(x)))
Output [2]: 0x538fb560
#creating an object of integer type with value 10 and reference variable name ‘y’
y = 10
#printing the value of ‘y’
print(y)
Output [3]: 10
#Printing the location of the object created in the memory address in hexadecimal format
print(hex(id(y)))
Output [4]: 0x538fb560
As per our discussion and understanding, so far, the memory address for x & y should have been different, since, 10 is an instance of Integer class which is immutable. However, as shown in the above code, it has the same memory address. This is not something that we expected. It seems that what we have understood and discussed, has an exception as well.
Quick check – Python Data Structures
Tuples are immutable and hence cannot have any changes in them once they are created in Python. This is because they support the same sequence operations as strings. We all know that strings are immutable. The index operator will select an element from a tuple just like in a string. Hence, they are immutable.
Like all, there are exceptions in the immutability in python too. Not all immutable objects are really mutable. This will lead to a lot of doubts in your mind. Let us just take an example to understand this.
Consider a tuple ‘tup’.
Now, if we consider tuple tup = (‘GreatLearning’,[4,3,1,2]) ;
We see that the tuple has elements of different data types. The first element here is a string which as we all know is immutable in nature. The second element is a list which we all know is mutable. Now, we all know that the tuple itself is an immutable data type. It cannot change its contents. But, the list inside it can change its contents. So, the value of the Immutable objects cannot be changed but its constituent objects can. change its value.
Mutable Object | Immutable Object |
State of the object can be modified after it is created. | State of the object can’t be modified once it is created. |
They are not thread safe. | They are thread safe |
Mutable classes are not final. | It is important to make the class final before creating an immutable object. |
list, dictionary, set, user-defined classes.
int, float, decimal, bool, string, tuple, range.
Lists in Python are mutable data types as the elements of the list can be modified, individual elements can be replaced, and the order of elements can be changed even after the list has been created.
(Examples related to lists have been discussed earlier in this blog.)
Tuple and list data structures are very similar, but one big difference between the data types is that lists are mutable, whereas tuples are immutable. The reason for the tuple’s immutability is that once the elements are added to the tuple and the tuple has been created; it remains unchanged.
A programmer would always prefer building a code that can be reused instead of making the whole data object again. Still, even though tuples are immutable, like lists, they can contain any Python object, including mutable objects.
A set is an iterable unordered collection of data type which can be used to perform mathematical operations (like union, intersection, difference etc.). Every element in a set is unique and immutable, i.e. no duplicate values should be there, and the values can’t be changed. However, we can add or remove items from the set as the set itself is mutable.
Strings are not mutable in Python. Strings are a immutable data types which means that its value cannot be updated.
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Original article source at: https://www.mygreatlearning.com
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Hello Friends,
In this tutorial I will show you how to create dynamic pie chart in laravel 8, Pie charts are use to representing data in graphics view, for creation of dynamic pie chart example you need to create model, controller, route, blade file and database, So if you will follow my tutorial step by step then defiantly you will get output of dynamic pie chart example.
So, let’s start.
#how to create dynamic pie chart in laravel 8 #laravel #laravel8 #dynamic pie chart #pie chart #pie chart in laravel 8
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Data Science, Data Analytics, Big Data, these are the buzz words of today’s world. A huge amount of data is being generated and analyzed every day. So communicating the insights from that data becomes crucial. Charts help visualize the data and communicate the result of the analysis with charts, it becomes easy to understand the data.
There are a lot of libraries for angular that can be used to build charts. In this blog, we will look at one such library, NGX-Charts. We will see how to use it in angular and how to build data visualizations.
What we will cover:
Installing ngx-chart.
Building a vertical bar graph.
Building a pie chart.
Building an advanced pie chart.
NGX-Chart charting framework for angular2+. It’s open-source and maintained by Swimlane.
NGX-Charts does not merely wrap d3, nor any other chart engine for that matter. It is using Angular to render and animate the SVG elements with all of its binding and speed goodness and uses d3 for the excellent math functions, scales, axis and shape generators, etc. By having Angular do all of the renderings it opens us up to endless possibilities the Angular platform provides such as AoT, Universal, etc.
NGX-Charts supports various chart types like bar charts, line charts, area charts, pie charts, bubble charts, doughnut charts, gauge charts, heatmap, treemap, and number cards.
1. Install the ngx-chart package in your angular app.
npm install @swimlane/ngx-charts --save
2. At the time of installing or when you serve your application is you get an error:
ERROR in The target entry-point "@swimlane/ngx-charts" has missing dependencies: - @angular/cdk/portal
You also need to install angular/cdk
npm install @angular/cdk --save
3. Import NgxChartsModule from ‘ngx-charts’ in AppModule
4. NgxChartModule also requires BrowserAnimationModule. Import is inAppModule.
app.module.ts
import { BrowserModule } from '@angular/platform-browser';
import { NgModule } from '@angular/core';
import { AppComponent } from './app.component';
import { NgxChartsModule }from '@swimlane/ngx-charts';
import { BrowserAnimationsModule } from '@angular/platform-browser/animations';
@NgModule({
declarations: [
AppComponent
],
imports: [
BrowserModule,
BrowserAnimationsModule,
NgxChartsModule
],
providers: [],
bootstrap: [AppComponent]
})
export class AppModule { }
Amazing! Now we can start using ngx-chart component and build the graph we want.
In the AppComponent we will provide data that the chart will represent. It’s a sample data for vehicles on the road survey.
#angular #angular 6 #scala #angular #angular 9 #bar chart #charting #charts #d3 charts #data visualisation #ngx #ngx charts #pie