Problem Statement: Consider that you have been given a list in Python. How will you count the number of unique values in the list?

Example: Let’s visualize the problem with the help of an example:

Given:  li = [‘a’, ‘a’, ‘b’, ‘c’, ‘b’, ‘d’, ‘d’, ‘a’] Output: The unique values in the given list are ‘a’, ‘b’, ‘c’, ‘d’. Thus the expected output is 4.

Now that you have a clear picture of what the question demands, let’s dive into the different ways of solving the problem.

Method 1: The Naive Approach

Approach:

• Create an empty list that will be used to store all the unique elements from the given list. Let’s say that the name of this list res.
• To store the unique elements in the new list that you created previously, simply traverse through all the elements of the given list with the help of a for loop and then check if each value from the given list is present in the list “res“.
  
  • If a particular value from the given list is not present in the newly created list then append it to the list res. This ensures that each unique value/item from the given list gets stored within res.
  • If it’s already present, then do not append the value.
• Finally, the list res represents a newly formed list that contains all unique values from the originally given list. All that remains to be done is to find the length of the list res which gives you the number of unique values present in the given list.

Code:

# Given list

li = ['a', 'a', 'b', 'c', 'b', 'd', 'd', 'a']

res = []

for ele in li: if ele not in res: res.append(ele)

print("The count of unique values in the list:", len(res)) # The count of unique values in the list: 4

Discussion: Since you have to create an extra list to store the unique values, this approach is not the most efficient way to find and count the unique values in a list as it takes a lot of time and space.

Method 2: Using set()

A more effective and pythonic approach to solve the given problem is to use the set() method. Set is a built-in data type that does not contain any duplicate elements.

Read more about sets here – “The Ultimate Guide to Python Sets“

Approach: Convert the given list into a set using the set() function. Since a set cannot contain duplicate values, only the unique values from the list will be stored within the set. Now that you have all the unique values at your disposal, you can simply count the number of unique values with the help of the len() function.

Code:

li = ['a', 'a', 'b', 'c', 'b', 'd', 'd', 'a']

s = set(li)

unique_values = len(s)

print("The count of unique values in the list:", unique_values) # The count of unique values in the list: 4

You can formulate the above solution in a single line of code by simply chaining both the functions (set() and len()) together, as shown below:

# Given list

li = ['a', 'a', 'b', 'c', 'b', 'd', 'd', 'a']

# One-liner

print("The count of unique values in the list:", len(set(li)))

Method 3: Using Dictionary fromkeys()

Python dictionaries have a method known as fromkeys() that is used to return a new dictionary from the given iterable ( such as list, set, string, tuple) as keys and with the specified value. If the value is not specified by default, it will be considered as None. 

Approach: Well! We all know that keys in a dictionary must be unique. Thus, we will pass the list to the fromkeys() method and then use only the key values of this dictionary to get the unique values from the list. Once we have stored all the unique values of the given list stored into another list, all that remains to be done is to find the length of the list containing the unique values which will return us the number of unique values.

Code:

# Given list

li = ['a', 'a', 'b', 'c', 'b', 'd', 'd', 'a']

# Using dictionary fromkeys()

# list elements get converted to dictionary keys. Keys are always unique!

x = dict.fromkeys(li)

# storing the keys of the dictionary in a list

l2 = list(x.keys())

print("Number of unique values in the list:", len(l2)) # Number of unique values in the list: 4

Method 4: Using Counter

Another way to solve the given problem is to use the Counter function from the collections module. The Counter function creates a dictionary where the dictionary’s keys represent the unique items of the list, and the corresponding values represent the count of a key (i.e. the number of occurrences of an item in the list). Once you have the dictionary all you need to do is to extract the keys of the dictionary and store them in a list and then find the length of this list.

from collections import Counter

# Given list

li = ['a', 'a', 'b', 'c', 'b', 'd', 'd', 'a']

# Creating a list containing the keys (the unique values)

key = Counter(li).keys()

# Calculating the length to get the count

res = len(key)

print("The count of unique values in the list:", res) # The count of unique values in the list: 4

Method 5: Using Numpy Module

We can also use Python’s Numpy module to get the count of unique values from the list. First, we must import the NumPy module into the code to use the numpy.unique() function that returns the unique values from the list.

Solution:

# Importing the numpy module

import numpy as np

# Given list

li = ['a', 'a', 'b', 'c', 'b', 'd', 'd', 'a']

res = []

# Using unique() function from numpy module

for ele in np.unique(li): res.append(ele)

# Calculating the length to get the count of unique elements

count = len(res)

print("The count of unique values in the list:", count) # The count of unique values in the list: 4

Another approach is to create an array using the array() function after importing the numpy module. Further, we will use the unique() function to remove the duplicate elements from the list. Finally, we will calculate the length of that array to get the count of the unique elements.

Solution:

# Importing the numpy module

import numpy as np

# Given list

li = ['a', 'a', 'b', 'c', 'b', 'd', 'd', 'a']

array = np.array(li)

u = np.unique(array)

c = len(u)

print("The count of unique values in the list:", c) # The count of unique values in the list: 4

Method 6: Using List Comprehension

There’s yet another way of solving the given problem. You can use a list comprehension to get the count of each element in the list and then use the zip() function to create a zip object that creates pairs of each item along with the count of each item in the list. Store these paired items as key-value pairs in a dictionary by converting the zip object to a dictionary using the dict() function. Finally, return the dictionary’s keys’ calculated length (using the len() function).

Code:

# Given list

li = ['a', 'a', 'b', 'c', 'b', 'd', 'd', 'a']

# List comprehension using zip()

l2 = dict(zip(li, [li.count(i) for i in li]))

# Using len to get the count of unique elements

l = len(list(l2.keys()))

print("The count of the unique values in the list:", l) # The count of the unique values in the list: 4

Conclusion

In this article, we learned the different methods to count the unique values in a list in Python. We looked at how to do this using the counter, sets, numpy module, and list comprehensions. If you found this article helpful and want to receive more interesting solutions and discussions in the future, please subscribe and stay tuned!

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Wait is there every aspect of human life. Let’s get philosophical for a moment! For every good thing in life, you need to wait.

“There’s no such thing as failure – just waiting for success.” – John Osborne

Like in life, wait in programming is also unavoidable. It is a tool, that you will need some day on desperate situations. For example, a slider, a fading animation, a bouncing ball, you never know.

In this tutorial, we will learn about how to wait one second in JavaScript? One second is an example. It could be a “5 seconds” or any duration your code needs to sleep before continuing with operation.

Refer this linked article to learn about PHP sleep.

JavaScript wait 1 second

I have used the good old setTimeout JavaScript function to wait. It sleeps the processing for the milliseconds duration set. Then calls the callback function passed.

You should put the code to execute after wait inside this callback function. As for the wait duration 1000 millisecond is one second. If you want to wait 5 seconds, then pass 5000.

This code will be handy if you are creating a news ticker like scroll animation.

	var testWait = function(milliseconds) { console.log('Before wait'); setTimeout(function() { console.log('After wait'); }, milliseconds); } testWait(1000);

JavaScript wait 1 second for promise

If you are using a modern browser, then you can use the below code. Modern means, your browser should support ES6 JavaScript standard.

In summary, you need support for JavaScript Promise. Here we use the setTimeout function. It resolves the promise after the defined milliseconds wait.

// Promise is available with JavaScript ES6 standard

// Need latest browsers to run it

const wait = async (milliseconds) => { await new Promise(resolve => { return setTimeout(resolve, milliseconds) });

}; const testWait = async () => { console.log('Before wait.'); await wait(1000); console.log('After wait.');

} testWait();

JavaScript wait 1 second in loop

If you want to wait the processing inside a loop in JavaScript, then use the below code. It uses the above Promise function and setTimeout to achieve the wait.

If yours is an old browser then use the first code given above for the wait part. If you need to use this, then remember to read the last section of this tutorial. In particular, if you want to “wait” in a mission critical JavaScript application.

const wait = async (milliseconds) => { await new Promise(resolve => { return setTimeout(resolve, milliseconds) });

}; const waitInLoop = async () => { for (let i = 0; i < 10; i++) { console.log('Waiting ...'); await wait(1000); console.log(i); } console.log("The wait is over.");

} waitInLoop();

JavaScript wait 1 second in jQuery

This is for people out there who wishes to write everything in jQuery. It was one of the greatest frontend JavaScript libraries but nowadays losing popularity. React is the new kid in the block. Here in this wait scenario, there is no need to look for jQuery specific code even if you are in jQuery environment.

Because you will have support for JavaScript. You can use setTimeout without any jQuery specific constructs. I have wrapped setTimeout in a jQuery style code. Its old wine in a new bottle.

// if for some strange reason you want to write // it in jQuery style // just wrapping the setTimout function in jQuery style $.wait = function(callback, milliseconds) { return window.setTimeout(callback, milliseconds); } $.wait(function() { $("#onDiv").slideUp() }, 1000);

Cancel before wait for function to finish

You may have to cancel the wait and re-initiate the setTimeout in special scenarios. In such a situation use the clearTimeout() function as below. Go through the next section to know about such a special wait scenario.

let timeoutId = setTimeout(() => { // do process }) // store the timeout id and call clearTimeout() function // to clear the already set timeout clearTimeout(timeoutId);

Is the wait real?

↑ Back to Top

Welcome to the bonus content of “The Book of Dash”.

Here you will find additional examples of Plotly Dash components, layouts and style. To learn more about making dashboards with Plotly Dash, and how to buy your copy of “The Book of Dash”, please see the reference section at the bottom of this article.

As you read the article, feel free to run the explainer video on the Card components from one of our coauthors’ “Charming Data” YT channel:

This article will focus on the Card components from the Dash Boostrap Component library. Using cards is a great way to create eye-catching content. We’ll show you how to make the card content interactive with callbacks, but first we’ll focus on the style and layout.

Plotly Dash App with a Bootstrap Card

We’ll start with the basics – a minimal Dash app to display a single card without any additional styling. Be sure to check out the complete reference for using Dash Bootstrap cards.

Next, we’ll show how to jazz it up to make it look better — and more importantly — so it conveys key information at a glance.

from dash import Dash, html

import dash_bootstrap_components as dbc app = Dash(__name__, external_stylesheets=[dbc.themes.SPACELAB, dbc.icons.BOOTSTRAP]) card = dbc.Card( dbc.CardBody( [ html.H1("Sales"), html.H3("$104.2M") ], ),

) app.layout=dbc.Container(card) if __name__ == "__main__": app.run_server(debug=True)

Styling a Dash Bootstrap Card

An easy way to style content is by using Boostrap utility classes. See all the utility classes at the Dash Bootstrap Cheatsheet app. This handy cheatsheet is made by a co-author of “The Book of Dash”.

In this card, we center the text and change the color with “text-center” and “text-success“. The Bootstrap themes have named colors and “success” is a shade of green.

Recommended Resource: For more information about styling your app with a Boostrap theme, see Dash Bootstrap Theme Explorer

card = dbc.Card( dbc.CardBody( [ html.H1("Sales"), html.H3("$104.2M", className="text-success") ], ), className="text-center"

)

Feel free to watch Adam’s explainer video on Bootstrap and styling your app if you need to get up to speed!

Dash Bootstrap Card with Icons

You can add Bootstrap and/or Font Awesome icons to your Dash Bootstrap components. In this example, we will add the bank icon as well as change the background color using the Bootstrap utility class bg-primary.

card = dbc.Card( dbc.CardBody( [ html.H1([html.I(className="bi bi-bank me-2"), "Profit"]), html.H3("$8.3M"), html.H4(html.I("10.3% vs LY", className="bi bi-caret-up-fill text-success")), ], ), className="text-center m-4 bg-primary text-white",

)

To learn more, see the Icons section of the dash-bootstrap-components documentation. You can also find more information about adding icons to dash components in the buttons article.

Recommended Tutorial: Plotly Dash Button Component – A Simple Illustrated Guide

Dash Bootstrap Cards Side-by-Side

In business intelligence dashboards, it’s common to highlight KPIs or Key Performance Indicators in a group of cards. You can find many examples in the Plotly App Gallery:

This app places three KPI cards side-by-side. We use the dbc.Row and dbc.Col components to create this responsive card layout. When you run this app, try changing the width of the browser window to see how the cards expand to fill the row based on the screen size.

This app also demonstrates the usage of Bootstrap border utility classes to add and style a border. Here we add a border on the left and change the color to highlight the results. Another trick is to use the “text-nowrap” class to keep the icon and the text together on the same line when the cards shrink to accommodate small screen sizes.

from dash import Dash, html

import dash_bootstrap_components as dbc app = Dash(__name__, external_stylesheets=[dbc.themes.SPACELAB, dbc.icons.BOOTSTRAP]) card_sales = dbc.Card( dbc.CardBody( [ html.H1([html.I(className="bi bi-currency-dollar me-2"), "Sales"], className="text-nowrap"), html.H3("$106.7M"), html.Div( [html.I("5.8%", className="bi bi-caret-up-fill text-success"), " vs LY",] ), ], className="border-start border-success border-5" ), className="text-center m-4"

) card_profit = dbc.Card( dbc.CardBody( [ html.H1([html.I(className="bi bi-bank me-2"), "Profit"], className="text-nowrap"), html.H3("$8.3M",), html.Div( [ html.I("12.3%", className="bi bi-caret-down-fill text-danger"), " vs LY", ] ), ], className="border-start border-danger border-5" ), className="text-center m-4",

) card_orders = dbc.Card( dbc.CardBody( [ html.H1([html.I(className="bi bi-cart me-2"), "Orders"], className="text-nowrap"), html.H3("91.4K"), html.Div( [ html.I("10.3%", className="bi bi-caret-up-fill text-success"), " vs LY", ] ), ], className="border-start border-success border-5" ), className="text-center m-4",

) app.layout = dbc.Container( dbc.Row( [dbc.Col(card_sales), dbc.Col(card_profit), dbc.Col(card_orders)], ), fluid=True,

) if __name__ == "__main__": app.run_server(debug=True)

Creating Dash Bootstrap Cards in a Loop

In the previous example, notice that a lot of the code for creating the card is the same. To reduce the amount of repetitive code, let’s create cards in a function.

In this app, we introduce the dbc.CardHeader component and the "shadow" class to style the card. We’ll show you how to add more style later in the app that displays crypto prices.

from dash import Dash, html

import dash_bootstrap_components as dbc app = Dash(__name__, external_stylesheets=[dbc.themes.SPACELAB]) summary = {"Sales": "$100K", "Profit": "$5K", "Orders": "6K", "Customers": "300"} def make_card(title, amount): return dbc.Card( [ dbc.CardHeader(html.H2(title)), dbc.CardBody(html.H3(amount, id=title)), ], className="text-center shadow", ) app.layout = dbc.Container( dbc.Row([dbc.Col(make_card(k, v)) for k, v in summary.items()], className="my-4"), fluid=True,

) if __name__ == "__main__": app.run_server(debug=True)

Dash Bootstrap Card with an Image

This card uses the dbc.CardImage component. This is a great format for the “who’s who” section of your app. It works well for displaying information about products too.

from dash import Dash, html

import dash_bootstrap_components as dbc app = Dash(__name__, external_stylesheets=[dbc.themes.SPACELAB]) count = "https://user-images.githubusercontent.com/72614349/194616425-107a62f9-06b3-4b84-ac89-2c42e04c00ac.png" card = dbc.Card([ dbc.CardImg(src=count, top=True), dbc.CardBody( [ html.H3("Count von Count", className="text-primary"), html.Div("Chief Financial Officer"), html.Div("Sesame Street, Inc.", className="small"), ] )], className="shadow my-2", style={"maxWidth": 350},

) app.layout=dbc.Container(card) if __name__ == "__main__": app.run_server(debug=True)

Dash Bootstrap Card with an Image and a Link

This app has a card with the dbc.CardLink component.

When you run this app, try clicking on either the logo or the title. You will see that both are links to the Plotly site displaying the current job openings.

We do this by including both the html.Img component with the Plotly logo and the html.Span with the title in the dbc.CardLink component.

from dash import Dash, html

import dash_bootstrap_components as dbc app = Dash(__name__, external_stylesheets=[dbc.themes.SPACELAB]) plotly_logo_dark = "https://user-images.githubusercontent.com/72614349/182967824-c73218d8-acbf-4aab-b1ad-7eb35669b781.png" card = dbc.Card( dbc.CardBody( [ dbc.CardLink( [ html.Img(src=plotly_logo_dark, height=65), html.Span("Plotly Job Openings", className="ms-2") ], className="text-decoration-none h2", href="https://plotly.com/careers/" ), html.Hr(), html.Div("Engineering", className="h3"), html.Div("Intermediate Backend Engineer", className="text-danger"), html.Div("Remote, Canada", className="small"), ] ), className="shadow my-2", style={"maxWidth": 450},

) app.layout=dbc.Container(card) if __name__ == "__main__": app.run_server(debug=True)

Dash Bootstrap Card with a Background Image

This app puts the image in the background and uses the dbc.CardImgOverlay component to place content on top of the image.

We also use dbc.Buttons to link to other sites for more information. See the buttons article for more information. Be sure to run the app and check out the links. The Webb Telescope app is pretty cool!

Recommended Tutorial: Before After Image in Plotly Dash

from dash import Dash, html

import dash_bootstrap_components as dbc app = Dash(__name__, external_stylesheets=[dbc.themes.SPACELAB, dbc.icons.BOOTSTRAP]) webb_deep_field = "https://user-images.githubusercontent.com/72614349/192781103-2ca62422-2204-41ab-9480-a730fc4e28d7.png"

card = dbc.Card( [ dbc.CardImg(src=webb_deep_field), dbc.CardImgOverlay([ html.H2("James Webb Space Telescope"), html.H3("First Images"), html.P( "Learn how to make an app to compare before and after images of Hubble vs Webb with ~40 lines of Python", style={"marginTop":175}, className="small", ), dbc.Button("See the App", href="https://jwt.pythonanywhere.com/"), dbc.Button( [html.I(className="bi bi-github me-2"), "source code"], className="ms-2 text-white", href="https://github.com/AnnMarieW/webb-compare", ) ]) ], style={"maxWidth": 500}, className="my-4 text-center text-white"

) app.layout=dbc.Container(card) if __name__ == "__main__": app.run_server(debug=True)

See this Plotly Dash app live: https://jwt.pythonanywhere.com/

Plotly Dash App with Live Updates

This app shows live updates of crypto prices. We use a dcc.Interval component to fetch the data from CoinGecko every 6 seconds.

The CoinGecko API is easy to use because you don’t need an API key, and it’s free if you keep the number of updates within the free tier limits. We pull the current price, 24 hour price change, and the coin logo from the data feed and display the data in a nicely styled card.

In this app we introduce callbacks to update the data, and show how to get the data from CoinGecko. All the other styling has been covered in previous examples.

Note that in this app, the color of the text and the up and down arrows are updated dynamically based on the data in the make_card function.

import dash

from dash import Dash, dcc, html, Input, Output

import dash_bootstrap_components as dbc

import requests app = Dash(__name__, external_stylesheets=[dbc.themes.SUPERHERO, dbc.icons.BOOTSTRAP]) coins = ["bitcoin", "ethereum", "binancecoin", "ripple"]

interval = 6000 # update frequency - adjust to keep within free tier

api_url = "https://api.coingecko.com/api/v3/coins/markets?vs_currency=usd" def get_data(): try: response = requests.get(api_url, timeout=1) return response.json() except requests.exceptions.RequestException as e: print(e) def make_card(coin): change = coin["price_change_percentage_24h"] price = coin["current_price"] color = "danger" if change < 0 else "success" icon = "bi bi-arrow-down" if change < 0 else "bi bi-arrow-up" return dbc.Card( html.Div( [ html.H4( [ html.Img(src=coin["image"], height=35, className="me-1"), coin["name"], ] ), html.H4(f"${price:,}"), html.H5( [f"{round(change, 2)}%", html.I(className=icon), " 24hr"], className=f"text-{color}", ), ], className=f"border-{color} border-start border-5", ), className="text-center text-nowrap my-2 p-2", ) mention = html.A( "Data from CoinGecko", href="https://www.coingecko.com/en/api", className="small"

)

interval = dcc.Interval(interval=interval)

cards = html.Div()

app.layout = dbc.Container([interval, cards, mention], className="my-5") @app.callback(Output(cards, "children"), Input(interval, "n_intervals"))

def update_cards(_): coin_data = get_data() if coin_data is None or type(coin_data) is dict: return dash.no_update # make a list of cards with updated prices coin_cards = [] updated = None for coin in coin_data: if coin["id"] in coins: updated = coin.get("last_updated") coin_cards.append(make_card(coin)) # make the card layout card_layout = [ dbc.Row([dbc.Col(card, md=3) for card in coin_cards]), dbc.Row(dbc.Col(f"Last Updated {updated}")), ] return card_layout if __name__ == "__main__": app.run_server(debug=True)

Plotly Dash App with a Sidebar

A common layout for Dash apps is to put inputs in a sidebar, and the output in the main section of the page. We can place both the sidebar and the output in Dash Boostrap Card components.

See the app and the code live at the Dash Example Index

Plotly Dash Example Index

See more examples of interactive apps in the Dash Example Index

Reference

Order Your Copy of “The Book of Dash” Today!

• Dash documentation – tutorial. Getting Started with Dash
• Dash Example Index. Sample apps with sliders
• Dash Bootstrap Components documentation – Themes
• Dash Bootstrap Components documentation – dbc.Card
• Dash Bootstrap Theme Explorer. A guide for styling Plotly Dash apps with a Bootstrap theme

The Book of Dash Authors

Feel free to learn more about the book’s coauthors here:

Ann Marie Ward:

• GitHub: https://github.com/AnnMarieW
• Dash Forum: https://community.plotly.com/u/annmariew/summary

Adam Schroeder:

• YouTube CharmingData: https://www.youtube.com/c/CharmingData

Chris Mayer:

• Python + Crypto Email Academy: https://blog.finxter.com/subscribe/

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