Trending February 2024 # How To Generate A New Sources List For Ubuntu # Suggested March 2024 # Top 6 Popular

You are reading the article How To Generate A New Sources List For Ubuntu updated in February 2024 on the website Minhminhbmm.com. We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested March 2024 How To Generate A New Sources List For Ubuntu

If you have ever peeked into the “sources.list” file located at the “/etc/apt/” folder, you will know that it contains the repository of all the packages available to your machine. Additionally, if you want to add PPA manually, you have to open this file and add the PPA to the end of the list. What if, on a fresh install of Ubuntu, you discover that your “sources.list” is empty? Or you need to change the whole repository to one that is specific to your country? How can you generate a new sources list without any technical skill?

The Ubuntu Sources List Generator is one great tool that you can use to generate source list for your Ubuntu.

1. Go to the Ubuntu Sources List Generator site.

2. Select the Country where you want to download the repository from.

3. Select your Ubuntu release.

4. Scroll down the list and select the components that you want in your repository. The standard ones are “Main”, “Restricted”, “Universe”, “Multiverse”, “Security” and “Updates”. You can also include “Partner” and “Extra” to include additional software that are not provided by Ubuntu.

5. In addition to the main sources, the Generator also include popular PPAs like Cairo Composite Manager, Cortina Wallpaper changer, GIMP, Google Chrome, Virtualbox, Steam, Spotify etc. that you can include in your sources list. Simply check the box beside the PPA.

6. Lastly, scroll all the way down to the bottom and press the “Generate List” button.

7. On the next page, you should see three big boxes. The first box at the top contains the sources list that you have selected and you will need to copy/paste them into your chúng tôi file. In your terminal,

gksu gedit

/

etc

/

apt

/

sources.list

Paste the sources lists into the document (for a new slate, you might want to erase all the existing sources listed in the file before pasting the new sources list in). Save and exit.

8. If you have added third party software’s PPA, it will show you the PPA key that you need to add to your system. Run the commands in your terminal, line by line.

9. The third box is the alternate layout for Synaptic, which you can ignore most of the time.

To complete the process, you need to update and upgrade your system:

sudo

apt-get update

&&

sudo

apt-get upgrade

That’s it.

Damien

Damien Oh started writing tech articles since 2007 and has over 10 years of experience in the tech industry. He is proficient in Windows, Linux, Mac, Android and iOS, and worked as a part time WordPress Developer. He is currently the owner and Editor-in-Chief of Make Tech Easier.

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How To Generate All Combinations Of A List In Python

Working with lists is a common task in Python. Sometimes, you may want to generate all possible combinations of the items in a given list together, which is useful for solving combinatorial problems, creating permutations for machine learning algorithms, or simply exploring different arrangements of data.

In this article, we’ll explore how to use Python’s itertools module and other techniques to generate all possible combinations of a list efficiently. By understanding these methods, you can apply them to various scenarios and improve your problem-solving skills.

Let’s get into it!

Before we look at the Python code for generating combinations, let’s quickly refresh what a combination is in Python.

Combinations are a way to represent all possible selections of elements from a set or list without regard to the order of these elements.

an iterable (e.g. a list or set)

an integer r, representing the number of elements to select from the iterable.

It returns an iterator that produces all possible r-length combinations of elements from the input iterable above.

For instance:

import itertools # Define a list of numbers my_list = [1, 2, 3] # Generate all possible two-element combinations # Convert the resulting iterator to a list # Print the list of combinations to the console print(combinations) #Output: [(1, 2), (1, 3), (2, 3)]

This Python code is creating all possible two-element combinations of the values in my_list ([1, 2, 3]) using the combinations function from the itertools module.

The resulting combinations are converted into unique elements in a list and printed to the console.

To learn more about functions in Python, check the following video out:

The Python programming language offers a powerful, built-in library called itertools. The itertools module is useful for tasks such as generating combinations, permutations, and Cartesian products of iterable elements.

The itertools module provides a combinations function that allows you to generate all possible combinations of unique values of a list’s elements.

The following syntax is used for the function:

from itertools import combinations

In this syntax, the iterable parameter represents the list you want to generate possible combinations for, and r is the length of the individual combinations.

The returned object is an iterator, so you can convert it into a list using the list() function.

# Import itertools module from itertools import combinations # Define a list of five characters my_chars = ['a', 'b', 'c', 'd', 'e'] # The result is converted to a list # Print the list of combinations print(combinations)

The output will be:

[('a', 'b', 'c'), ('a', 'b', 'd'), ('a', 'b', 'e'), ('a', 'c', 'd'), ('a', 'c', 'e'), ('a', 'd', 'e'), ('b', 'c', 'd'), ('b', 'c', 'e'), ('b', 'd', 'e'), ('c', 'd', 'e')]

In some cases, you may want to generate combinations allowing repeated elements.

This can be achieved using another function called combinations_with_replacement.

You can use the following syntax for combination with replacement function:

from itertools import combinations from itertools import combinations # Define a list of three numbers lst = [1, 2, 3] # Use the combinations_with_replacement function from itertools to generate all 2-element combinations of lst # Convert the resulting iterable to a list and assign it to combs print(combs) # Output: [(1, 1), (1, 2), (1, 3), (2, 2), (2, 3), (3, 3)]

This code will output all possible combinations of a list, with replacement meaning that the same number can appear more than once in a combination.

For example, it’ll include both (1, 2) and (1, 1) in the output.

The itertools module offers a robust way to generate possible combinations of iterable elements.

In this section, we’ll explore different ways to generate all possible combinations of a list in Python.

We’ll primarily focus on two approaches:

Using for-loops

Creating a powerset combinations function

You can generate combinations by utilizing for-loops.

The idea is to iterate through the elements of the list and, for each element, append it to all possible subsets found so far.

The following is an example of using for loops to generate combinations:

# Define the list from which we want to generate combinations lst = ['a', 'b', 'c'] # Initialize a list with an empty subset, representing the start of our combinations combinations = [[]] # This loop structure is similar to the "combinations def powerset" concept, iterating through each element for element in lst: # For each existing combination, create a new combination that includes the current element for sub_set in combinations.copy(): new_sub_set = sub_set + [element] # Append the new combination to our list of combinations combinations.append(new_sub_set) # Print all combinations for combination in combinations: print(combination)

This code will output all the combinations of the elements in the list, including both the empty set and the entire set.

You can also generate all possible combinations of a list by creating a powerset function.

A powerset is the set of all subsets of a set. The length of the powerset would be 2^n, where n is the number of elements in the list.

You can use the binary representation of numbers from 0 to 2^n-1 as a template to form the combinations.

The following is a Python implementation of the powerset function for generating possible combinations of a list:

def combinations(original_list): # The number of subsets is 2^n num_subsets = 2 ** len(original_list) # Create an empty list to hold all the subsets subsets = [] # Iterate over all possible subsets for subset_index in range(num_subsets): # Create an empty list to hold the current subset subset = [] # Iterate over all elements in the original list for index in range(len(original_list)): # Check if index bit is set in subset_index if (subset_index & (1 << index)) != 0: # If the bit is set, add the element at this index to the current subset subset.append(original_list[index]) # Add the current subset to the list of all subsets subsets.append(subset) return subsets # Using the function to print generated combinations lst = ['a', 'b', 'c'] print(combinations(lst))

In this function, each element of the list has a position that corresponds to the bits in the numbers from 0 to 2^n – 1.

If a bit in the number is set, that means the corresponding element is included in the subset.

The function uses the bitwise AND operator (&) and bitwise shift operator (<<) to check if a bit is set in each number.

If the bit is set, it adds the corresponding element to the subset. It does this for all bits in all numbers from 0 to 2^n – 1, generating all possible combinations.

Specifically, we’ll look at the following:

Chaining Iterables with the Chain Function

Creating and Using Custom Itertools Functions

The itertools module in Python offers a variety of functions to manipulate iterables, one of which is the chainfunction.

This function allows you to combine all the elements into a single iterable.

It is perfect for use with functions that generate all possible combinations of a list such as combinations_with_replacement.

For instance, you have a list of numbers and want to create all possible combinations of those elements with replacements.

This is how you can do that:

import itertools lst = [1, 2, 3] # Output: [(1, 1), (1, 2), (1, 3), (2, 2), (2, 3), (3, 3)]

Now, suppose you have two lists and want to generate all possible combinations without replacement using the chain function:

from itertools import chain list1 = [1, 2, 3] list2 = [4, 5, 6] # Using the chain function to combine unique elements of list combined_list = list(itertools.chain(list1, list2)) # Generating all combinations of a list without replacement # This will create all possible combinations of two elements from the combined list

The itertools.chain() function will combine list1 and list2 into a single list: [1, 2, 3, 4, 5, 6].

The output will be:

[(1, 2), (1, 3), (1, 4), (1, 5), (1, 6), (2, 3), (2, 4), (2, 5), (2, 6), (3, 4), (3, 5), (3, 6), (4, 5), (4, 6), (5, 6)]

In some cases, you may need to create your own custom itertools functions to handle specific tasks.

This allows you to tailor your implementation to specific requirements, giving you more control over the process.

For instance, let’s create a custom itertools function that takes two lists and generates possible combinations of their elements.

The result doesn’t include possible combinations formed by elements from the same list:

import itertools def cross_combinations(list1, list2): # The function takes two lists as inputs and generates combinations of their elements # The result doesn't include combinations formed by elements from the same list # Using itertools.product to get all combinations of elements between the two lists return list(itertools.product(list1, list2)) # Test the function list1 = [1, 2, 3] list2 = [4, 5, 6] print(cross_combinations(list1, list2))

In this example, we use itertools.product, which returns the Cartesian product of input iterables.

It’s equivalent to nested for-loops.

For example, product(A, B) returns the same as ((x,y) for x in A for y in B).

The output of the function for these inputs will yield tuple values, where each tuple contains one element from input list 1 and one element from list2.

There will be no tuples containing two elements from the same original list.

The final list is given below:

[(1, 4), (1, 5), (1, 6), (2, 4), (2, 5), (2, 6), (3, 4), (3, 5), (3, 6)]

As you can see, creating custom itertools functions, combined with powerful built-in functions like product, can help you solve complex problems when generating possible combinations from lists or other iterables.

Understanding how to generate all combinations of a list in Python adds a powerful tool to your programming toolbox. It lets you solve complex problems with simplicity and elegance.

This skill is crucial across many fields.

In data analysis, generating combinations helps you explore possible scenarios or outcomes.

In machine learning, it helps in parameter tuning for model optimization. Even in game development, it’s useful for simulating different game states.

Moreover, learning about combinations in Python improves your grasp of iterative operations and helps you write more efficient code. It encourages you to think algorithmically, which is an essential part of problem-solving in programming.

What’s the best way to jumble up your list depends on what you’re working with and what you’re comfy with. Just remember, Python’s got your back with a tool for every job, so play around, find what works for you, and most importantly, have fun coding!

Mycointainer And How It Offers A Comfortable Way For Investors To Generate Profits

The creation of cryptocurrencies came along with impressive innovations such as blockchains, which continue to spearhead their functionalities. Through blockchains, the concept of transactional throughput is significantly improving on various digital currency platforms.

Where to Start

MyCointainer introduces a simplified way of starting your staking journey in the digital asset ecosystem. Firstly, users will have to select a cryptocurrency asset they prefer from a diverse number of assets hosted on MyCointainer’s system.

After that, users transfer a certain staking amount to the ecosystem’s staking wallet. Finally, the user will automatically receive regular profits as MyCointainer leverages the power of blockchains. Staking for more extended periods presents higher probabilities of garnering more incomes.

Moreover, the staking network doesn’t need any expert skill to navigate through it. Instead, the platform handles every technical detail and only aims at utilizing your hard-earned money for profit maximization.

Supported Assets

MyCointainer covers a wide range (100+) of PoS assets in the crypto-economy. Since there is no minimum or maximum deposit amount, you can choose from a variety of digital assets each carrying different reward fees. For instance, a virtual asset like Cardano (ADA) contains a reward fee of 0.43%.

Video Tutorials

Curious individuals and new members can watch MyCointainer’s Youtube channel for easy video guides. From the basics of the registration process to the activation of the Two-Factor Authenticator, the channel makes every activity as simple as possible. On top of these, 60+ special videos on how to stake and earn rewards are found to be valuable for all users.

In-Built Exchange

As an integrated feature, MyCointainer contains its own exchange platform. Therefore, users are able to make exchanges and receive their desired coin out of all those hosted on MyCointainer.

Designing an in-built exchange is an ideal way of ensuring users avoid going through the hassle of swapping coins from another exchange in order to stake with MyCointainer.

Safety Precautions

Without a doubt, many people may prefer dealing with a regulated platform because it instills a sense of security towards the platform’s participants. MyCointainer is a fully regulated staking ecosystem that displays all its legal details on the platform itself. MyCointainer is licensed to offer fiat-crypto transfers and e-wallet services.

Cold Staking & Delegations

With Cold Staking, you delegate your coins to the MyCointainer node that stakes your coins on your behalf. Since MyCointainer node is actively staking new blocks on the different blockchains, Cold Staking offers the same network security benefits as regular staking and therefore, the same rewards.

Thanks to the delegation to MyCointainer nodes, you can enjoy the security of your funds, but also the best bonus offer in the entire market.

Power Subscriptions

MyCointainer Power feature takes you to a world full of PoS and masternode coins. Under this function, there are three levels, each containing different subscription plans.

Power Zero is the first subscription level going for a monthly fee of 7.90 EUR. The second and third levels are Max and Percent, each with a monthly subscription fee of 14.90 and 29.90 EUR, respectively.

Final Word

Since MyCointainer focuses majorly on staking, block validation will ultimately become a popular earning mechanism in the digital currency ecosystem. In the end, transaction operations will grow to become more transparent and fast, which meets the overall cryptocurrency goal of revolutionizing the traditional financial system.

List A Few Statistical Methods Available For A Numpy Array

In this article, we will show you a list of a few statistical methods of NumPy library in python.

Statistics is dealing with collecting and analyzing data. It describes methods for collecting samples, describing data, and concluding data. NumPy is the core package for scientific calculations, hence NumPy statistical Functions go hand in hand.

Numpy has a number of statistical functions that can be used to do statistical data analysis. Let us discuss a few of them here.

numpy.amin() and numpy.amax()

These functions return the minimum and the maximum from the elements in the given array along the specified axis.

Example

inputArray

=

np

.

array

(

[

[

2

,

6

,

3

]

,

[

1

,

5

,

4

]

,

[

8

,

12

,

9

]

]

)

print

(

‘Input Array is:’

)

print

(

inputArray

)

print

(

)

print

(

“Minimum element in an array:”

,

np

.

amin

(

inputArray

)

)

print

(

)

print

(

“Maximum element in an array:”

,

np

.

amax

(

inputArray

)

)

print

(

)

print

(

‘Minimum element in an array among axis 0(rows):’

)

print

(

np

.

amin

(

inputArray

,

0

)

)

print

(

‘Minimum element in an array among axis 1(columns):’

)

print

(

np

.

amin

(

inputArray

,

1

)

)

print

(

)

print

(

‘Maximum element in an array among axis 0(rows):’

)

print

(

np

.

amax

(

inputArray

,

0

)

)

print

(

)

print

(

‘Maximum element in an array among axis 1(columns):’

)

print

(

np

.

amax

(

inputArray

,

axis

=

1

)

)

print

(

)

Output

On executing, the above program will generate the following output −

Input Array is: [[ 2 6 3] [ 1 5 4] [ 8 12 9]] Minimum element in an array: 1 Maximum element in an array: 12 Minimum element in an array among axis 0(rows): [1 5 3] Minimum element in an array among axis 1(columns): [2 1 8] Maximum element in an array among axis 0(rows): [ 8 12 9] Maximum element in an array among axis 1(columns): [ 6 5 12] numpy.ptp() Example

The numpy.ptp() function returns the range (maximum-minimum) of values along an axis. The ptp() is an abbreviation for peak-to-peak.

inputArray

=

np

.

array

(

[

[

2

,

6

,

3

]

,

[

1

,

5

,

4

]

,

[

8

,

12

,

9

]

]

)

print

(

‘Input Array is:’

)

print

(

inputArray

)

print

(

)

print

(

‘The peak to peak(ptp) values of an array’

)

print

(

np

.

ptp

(

inputArray

)

)

print

(

)

print

(

‘Range (maximum-minimum) of values along axis 1(columns):’

)

print

(

np

.

ptp

(

inputArray

,

axis

=

1

)

)

print

(

)

print

(

‘Range (maximum-minimum) of values along axis 0(rows):’

)

print

(

np

.

ptp

(

inputArray

,

axis

=

0

)

)

Output

On executing, the above program will generate the following output −

Input Array is: [[ 2 6 3] [ 1 5 4] [ 8 12 9]] The peak to peak(ptp) values of an array 11 Range (maximum-minimum) of values along axis 1(columns): [4 4 4] Range (maximum-minimum) of values along axis 0(rows): [7 7 6] numpy.percentile()

Percentile (or a centile) is a measure used in statistics indicating the value below which a given percentage of observations in a group of observations fall.

It computes the nth percentile of data along the given axis.

Syntax numpy.percentile(a, q, axis) Parameters

a Input array

q The percentile to compute must be between 0-100

axis The axis along which the percentile is to be calculated

Example

inputArray

=

np

.

array

(

[

[

20

,

45

,

70

]

,

[

30

,

25

,

50

]

,

[

10

,

80

,

90

]

]

)

print

(

‘Input Array is:’

)

print

(

inputArray

)

print

(

)

print

(

‘Applying percentile() function to print 10th percentile:’

)

print

(

np

.

percentile

(

inputArray

,

10

)

)

print

(

)

print

(

’10th percentile of array along the axis 1(columns):’

)

print

(

np

.

percentile

(

inputArray

,

10

,

axis

=

1

)

)

print

(

)

print

(

’10th percentile of array along the axis 0(rows):’

)

print

(

np

.

percentile

(

inputArray

,

10

,

axis

=

0

)

)

Output

On executing, the above program will generate the following output −

Input Array is: [[20 45 70] [30 25 50] [10 80 90]] Applying percentile() function to print 10th percentile: 18.0 10th percentile of array along the axis 1(columns): [25. 26. 24.] 10th percentile of array along the axis 0(rows): [12. 29. 54.] numpy.median()

Median is defined as the value separating the higher half of a data sample from the lower half.

The numpy.median() function calculates the median of the multi-dimensional or one-dimensional arrays.

Example

inputArray

=

np

.

array

(

[

[

20

,

45

,

70

]

,

[

30

,

25

,

50

]

,

[

10

,

80

,

90

]

]

)

print

(

‘Input Array is:’

)

print

(

inputArray

)

print

(

)

print

(

‘Median of an array:’

)

print

(

np

.

median

(

inputArray

)

)

print

(

)

print

(

‘Median of array along the axis 0(rows):’

)

print

(

np

.

median

(

inputArray

,

axis

=

0

)

)

print

(

)

print

(

‘Median of array along the axis 1(columns):’

)

print

(

np

.

median

(

inputArray

,

axis

=

1

)

)

Output

On executing, the above program will generate the following output −

Input Array is: [[20 45 70] [30 25 50] [10 80 90]] Median of an array: 45.0 Median of array along the axis 0(rows): [20. 45. 70.] Median of array along the axis 1(columns): [45. 30. 80.] numpy.mean()

Arithmetic mean is the sum of elements along an axis divided by the number of elements.

The numpy.mean() function returns the arithmetic mean of elements in the array. If the axis is mentioned, it is calculated along it.

Example

inputArray

=

np

.

array

(

[

[

20

,

45

,

70

]

,

[

30

,

25

,

50

]

,

[

10

,

80

,

90

]

]

)

print

(

‘Input Array is:’

)

print

(

inputArray

)

print

(

)

print

(

‘Mean of an array:’

)

print

(

np

.

mean

(

inputArray

)

)

print

(

)

print

(

‘Mean of an array along the axis 0(rows):’

)

print

(

np

.

mean

(

inputArray

,

axis

=

0

)

)

print

(

)

print

(

‘Mean of an array along the axis 1(columns):’

)

print

(

np

.

mean

(

inputArray

,

axis

=

1

)

)

Output

On executing, the above program will generate the following output −

Input Array is: [[20 45 70] [30 25 50] [10 80 90]] Mean of an array: 46.666666666666664 Mean of an array along the axis 0(rows): [20. 50. 70.] Mean of an array along the axis 1(columns): [45. 35. 60.] numpy.average()

The numpy.average() function computes the weighted average along the axis of multidimensional arrays whose weights are specified in another array.

The function can have an axis parameter. If the axis is not specified, the array is flattened.

Example

inputArray

=

np

.

array

(

[

1

,

2

,

3

,

4

]

)

print

(

‘Input Array is:’

)

print

(

inputArray

)

print

(

)

print

(

‘Average of all elements in an array:’

)

print

(

np

.

average

(

inputArray

)

)

print

(

)

Output

On executing, the above program will generate the following output −

Input Array is: [1 2 3 4] Average of all elements in an array: 2.5 Standard Deviation & Variance Standard deviation

Standard deviation is the square root of the average of squared deviations from mean. The formula for standard deviation is as follows −

std = sqrt(mean(abs(x - x.mean())**2))

If the array is [1, 2, 3, 4], then its mean is 2.5. Hence the squared deviations are [2.25, 0.25, 0.25, 2.25] and the square root of its mean divided by 4, i.e., sqrt (5/4) is 1.1180339887498949.

Variance

Variance is the average of squared deviations, i.e., mean(abs(x – x.mean())**2). In other words, the standard deviation is the square root of variance.

Example

inputArray

=

[

1

,

2

,

3

,

4

]

print

(

“Input Array =”

,

inputArray

)

print

(

“Standard deviation of array = “

,

np

.

std

(

inputArray

)

)

print

(

“Variance of array = “

,

np

.

var

(

inputArray

)

)

Output

On executing, the above program will generate the following output −

Input Array = [1, 2, 3, 4] Standard deviation of array = 1.118033988749895 Variance of array = 1.25 Conclusion

By using examples, we studied some of the few statistical methods for a Numpy array in this article.

How To: Iptables List Rules

If you are working with Linux systems, then you are likely aware of iptables, a powerful tool for managing network traffic. iptables is a command-line utility that allows you to configure and manage firewall rules on your Linux system. In this article, we will explore iptables list rules, a command that allows you to view the current rules configured on your system.

What are iptables?

iptables is a firewall utility that allows you to configure rules to filter network traffic. It is a powerful tool that provides a great deal of control over network traffic, allowing you to block or allow traffic based on various criteria. iptables operates by creating a set of rules that are used to filter network traffic as it passes through your system. Each rule specifies a set of conditions that must be met for the traffic to be allowed or blocked.

iptables List Rules

iptables list rules is a command that allows you to view the current rules configured on your system. This can be useful for troubleshooting network issues or verifying that your firewall rules are working as expected. To use the iptables list rules command, simply enter the following command in your terminal:

sudo iptables -L

This will display a list of all the rules currently configured on your system. By default, the iptables list rules command displays the rules in a human-readable format, which can be useful for quickly understanding the rules that are in place.

iptables List Rules Output

The output of the iptables list rules command is divided into several sections, each of which provides information about a specific aspect of the firewall rules. The first section displays the default policy for each chain in the firewall. This section is followed by a list of the rules configured for each chain.

Each rule is displayed as a separate line, and includes information about the chain it applies to, the target of the rule (i.e. what action to take if the rule is matched), and the criteria that must be met for the rule to be applied. The criteria can include various elements such as source and destination IP addresses, port numbers, and protocols.

iptables List Rules Options

The iptables list rules command provides several options that can be used to customize the output. These options include:

-v: Displays more detailed information about each rule, including the number of packets and bytes that have matched the rule.

-n: Displays the output in numeric form, rather than resolving IP addresses and port numbers to their corresponding names.

-x: Displays the number of bytes that have matched each rule, in addition to the number of packets that have matched.

Conclusion

In this article, we have explored iptables list rules, a command that allows you to view the current rules configured on your system. We have learned that iptables is a powerful tool for managing network traffic, and that it operates by creating a set of rules that are used to filter network traffic as it passes through your system. We have also seen that the iptables list rules command provides a great deal of information about the firewall rules in place, and that it can be customized to provide even more detailed information.

By understanding iptables list rules, you can gain a better understanding of how your Linux system is configured to handle network traffic. This can be useful for troubleshooting network issues, securing your system against potential threats, and ensuring that your firewall rules are working as expected.

Top 7 Free Datasets Sources To Use For Data Science Projects

Free datasets sources for data science enthusiasts 

Data is preliminary for companies and corporations to analyze and obtain business intelligence. It helps in finding the correlations between the data and the unique insights for a better decision-making process. And for these  

Google Cloud Public Dataset

Most of us think that Google is just a search engine, right? But it is way beyond. Several datasets can be accessed through the Google cloud and analyzed to fetch new insights from the data. Google cloud has more than hundreds of

Amazon Web Services Open Data Registry

Amazon Web Services has the largest number of

Data.gov

The US government is also keen on data science, as most of the tech companies are located in Silicon Valley. chúng tôi is the main repository of the US government’s open datasets that can be used for research, developing data visualizations, mobile applications, and creating the web. This is an attempt of the government to become more transparent in terms of access without registering. But some of the datasets need permissions before downloading them. chúng tôi has diverse varieties of

Kaggle

Kaggle has more than 23,000 public datasets that can be downloaded for free. You can easily search for the dataset that you’re looking for and find them hassle-free ranging from health to cartoons. The platform also allows you to create new public datasets and can also earn medals along with the titles such as Expert, Master, and Grandmaster. The competitive Kaggle datasets are more detailed than the public datasets. Kaggle is the perfect place for data science lovers.  

UCI Machine Learning Repository

If you are looking for interesting datasets then UCI Machine Learning Repository is a great place for you. It is one of the first and oldest data sources that are available on the internet since 1987. The datasets of the UCI are great for machine learning with their easy access and download options. Most of the datasets of UCI are contributed by different users so the data cleanliness is a little low. But UCI maintains the datasets for using them for ML algorithms.  

Global Health Observatory

If you are from a medical background then Global Health Observatory is a great option for creating projects on global health systems and diseases. The WHO has made all their data public on this platform. This is for the good quality health information available worldwide. The health data is characterized according to various communicable and noncommunicable diseases, mental health, morality, medicines for better access.  

Earthdata

Data is preliminary for companies and corporations to analyze and obtain business intelligence. It helps in finding the correlations between the data and the unique insights for a better decision-making process. And for these datasets sources are important to help you with your data science projects . But luckily there are many online data sources to fetch you free datasets to help with your projects by just downloading them absolutely free. Let’s learn more about the top 7 free dataset sources to use for data science projects in this chúng tôi of us think that Google is just a search engine, right? But it is way beyond. Several datasets can be accessed through the Google cloud and analyzed to fetch new insights from the data. Google cloud has more than hundreds of datasets that are hosted by BigQuery and cloud storage. Google’s machine learning can be helpful in analyzing datasets such as BigQuery ML, Vision AI, Cloud AutoML, etc. Also, Google’s Data Studio can be used to create data visualization and dashboards for better insights. These datasets have data from various sources such as GitHub, United States Census Bureau, NASA, and BitCoin, and many more. You can access these datasets free of cost.Amazon Web Services has the largest number of datasets on their registry. It is very easy to download these datasets and use them to analyze the data on the Amazon Elastic Compute Cloud. It also employs various tools such as Apache Spark, Apache Hive, and more. The Amazon Web Services is an open data registry that is part of the AWS Public Dataset Program that focuses on democratizing the access of data so that it is available to everybody. AWS open data registry is free but allows you to own a free AWS chúng tôi US government is also keen on data science, as most of the tech companies are located in Silicon Valley. chúng tôi is the main repository of the US government’s open datasets that can be used for research, developing data visualizations, mobile applications, and creating the web. This is an attempt of the government to become more transparent in terms of access without registering. But some of the datasets need permissions before downloading them. chúng tôi has diverse varieties of datasets relating to climate, agriculture, energy, oceans, and ecosystems.Kaggle has more than 23,000 public datasets that can be downloaded for free. You can easily search for the dataset that you’re looking for and find them hassle-free ranging from health to cartoons. The platform also allows you to create new public datasets and can also earn medals along with the titles such as Expert, Master, and Grandmaster. The competitive Kaggle datasets are more detailed than the public datasets. Kaggle is the perfect place for data science chúng tôi you are looking for interesting datasets then UCI Machine Learning Repository is a great place for you. It is one of the first and oldest data sources that are available on the internet since 1987. The datasets of the UCI are great for machine learning with their easy access and download options. Most of the datasets of UCI are contributed by different users so the data cleanliness is a little low. But UCI maintains the datasets for using them for ML chúng tôi you are from a medical background then Global Health Observatory is a great option for creating projects on global health systems and diseases. The WHO has made all their data public on this platform. This is for the good quality health information available worldwide. The health data is characterized according to various communicable and noncommunicable diseases, mental health, morality, medicines for better chúng tôi you are looking for data related to Earth or Space then, Earthdata is your place. This is created by NASA to provide datasets based on Earth’s atmosphere, oceans, cryosphere, solar flares, and tectonics. It is a part of the Earth Observing System Data and Information System that helps in collecting and processing the data from various NASA satellites, aircraft, and fields. Earthdata also has tools for handling, ordering, searching, mapping, and visualizing the data.

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