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MAD Architects, led by Ma Yansong, has completed “Chaoyang Park Plaza”, which includes the Armani apartment complex. Positioned on the southern edge of Beijing’s Chaoyang Park ─ the largest remaining park in Beijing’s central business district area ─ the 220,000 sqm complex includes 10 buildings which unfold as a classic Shanshui painting on an urban scale. Having a similar position and function as Central Park in Manhattan, but unlike the modern box-like buildings that only create a separation between the park and the city, “Chaoyang Park Plaza” instead is an expansion of nature. It is an extension of the park into the city, naturalizing the CBD’s strong artificial skyline, borrowing scenery from a distant landscape ─ a classical approach to Chinese garden architecture, where nature and architecture blend into one another.

“In modern cities, architecture as an artificial creation is seen more as a symbol of capital, power or technological development; while nature exists independently. It is different from traditional Eastern cities where architecture and nature are designed as a whole, creating an atmosphere that serves to fulfill one’s spiritual pursuits,” said architect Ma Yansong. “We want to blur the boundary between nature and the artificial, and make it so that both are designed with the other in mind. Then, the argument in the modern logic of humans to protect or to destroy nature will no longer exist if we understand and see humans and nature as co-existing. Human behavior and emotion is part of nature, and nature is where that originates and ends.”

Inspired by traditional Chinese landscape paintings, the design remodels the relationship of large-scale architecture within our urban centers. It introduces natural forms and spaces ─ “mountain, brook, creek, rocks, valley and forest” ─ into the city. The asymmetrical twin tower office buildings on the north side of the site, sit at the base of the park’s lake and are like two mountain peaks growing out of the water. The transparent and bright atrium acts like a “drawstring” that pulls the two towers together by a connecting glass rooftop structure.

The small-scale, low-rise commercial buildings appear as mountain rocks that have endured long-term erosion. They seem to be randomly placed, but their strategic relationship to one another forms a secluded, but open urban garden, offering a place where people can meet within nature in the middle of the city.

The two multi-story Armani apartments to the southwest continue this concept of “open air living” with their staggered balconies, offering each residential unit more opportunities to be exposed to natural sunlight, and ultimately feel a particular closeness to nature.

The overall environment is shaped by smooth, curved surfaces of black and white, creating a quiet and mysterious atmosphere. It is one that evokes the emotion and aesthetic resonance of a traditional Chinese ink painting, creating a tranquil escape from the surrounding, bustling urban environment. The landscape that weaves itself in between the buildings incorporates pine trees, bamboo, rocks and ponds ─ all traditional eastern landscape elements that imply a deeper connection between the architecture and classical space. Japanese graphic artist Kenya Hara led the design of the “simple” and “refined” signage system for the project.

The project has been awarded the LEED Gold Certification by the US Green Building Council, as the ideal of “nature” is not only embodied in the design concept, but in the innovation and integration of green technology as well. The vertical fins seen on the exterior glass façade emphasize the smoothness and verticality of the towers. They also function as the energy efficient ventilation and filtration system, drawing fresh air indoors. At the base of the towers, there is a pond, that while making them appear as if they are going into infinity, works as an air cooling system in the summer, decreasing the overall temperature of the interior.

“Chaoyang Park Plaza” completely transforms the model of building found in our cities’ central business districts. But even though it is located in the center of Beijing’s CBD, the intention is for it to have a dialogue with the traditional and classical city of Beijing – reflecting the interdependence between man and nature, both in urban planning, and the large-scale presentation of the Shanshui garden. In the painting of Wang Mingxian, an architectural historian, he juxtaposed “Chaoyang Park Plaza” into a classical landscape painting.

The architecture and the natural scenery seemed harmonious together, unlike how some might think the buildings do not fit into their urban context. Commenting on this contrast, Ma Yansong said: “I don’t think that’s our problem. The real question is when did the original cultural context of this city disappear? We have the opportunity to try and create a different kind of city, that on a spiritual and cultural level, can be compared to the classical cities of Eastern philosophy and wisdom.”

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Optimizing For Search Engines: By Language Or By Country?

When it comes to targeting search engines to reach international markets, we’re going to have to do things a little differently. As we expand our horizons we begin to form connections with those who not only speak different language but bring a whole set of different cultural expectations to their business dealings online.

There are two broad approaches to meeting the needs of our international customers. The first option is to focus on languages. Alternatively, we can consider our foreign markets on a country by country basis.

Optimizing by Language

While getting the words right is key to international communication, the SEO strategies at your disposal will not vary widely from one language to another. Bear in mind, though, that when you switch language you also gain the attention of those search engines designed with speakers of that language in mind. It’s worth taking some time to get to know which search engines you need to optimize for.

Approaching your SEO on a language basis can be an affordable and time-efficient option. You can focus on finding the best possible keywords, which may not be the direct equivalents of those that perform well for you in English.

On the other hand, you might not want to optimize by language if you have an interest in promoting your business to certain countries only. For instance, Brazil could be a key market for you while your business is not well-placed to serve Portugal.

You will want to consider too whether some languages benefit from being delivered on a regional rather than global basis. If you are selling fine leather purses, your customers in the US will expect to see a selection of bags but your British customers will be looking for somewhere to store coins. Similar miscommunication can happen with any of the major world languages and can be frustrating for customers.

Optimizing by Country

Opting for your content to be visible to a specific country is known as geo-targeting. Although SEO targeting of this kind can initially seem daunting, the fact that it is more specific can make it a more effective marketing strategy. Use geo-targeting to deliver your online content to the markets that matter to you, without having to worry about those that don’t.

It also gives you the flexibility to deliver locally relevant content to different countries who share a common language, without having to mirror your content on more than one top-level domain. With different pages geotargeted for different countries, linguistic and cultural differences can be allowed for and you can confidently quote local times, currencies and holidays.

Carefully geo-targeted content can help you by reducing the number of visitors who don’t find what they need and hit the back button. This will in turn bring down your bounce rate. Your overall visitor numbers may not increase but you are more likely to see visits that convert.

There will be times when geo-targeting is worthwhile but it is not going to meet the needs of every business or situation. Customers will only be delivered geo-targeted content if they opt to search for local results. Also remember that marking content as relevant to one specific country will mean that potential customers speaking that language in other countries are unlikely to see it.

Another consideration is that many countries have more than one official language, for instance if you want to target Switzerland you would be ignoring around a third of the population if you opted for German-only content.

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Drop Columns In Dataframe By Label Names Or By Index Positions

A pandas data frame is a 2D data structure consisting of a series of entities. It is very useful in the analysis of mathematical data. The data is arranged in a tabular manner with each row behaving as an instance of the data.

A Pandas data frame is special because it is empowered with numerous functions making it a very powerful programming asset. Each column in a data frame represents a series of information which is labelled. In this article, we will operate on these columns and discuss the various methods to drop columns in a pandas data frame.

Dropping of a single or multiple columns can be achieved by either specifying the column name or with the help of their index value. We will understand both of these method but firstly we have to prepare a dataset and generate a data frame.

Creating The Data Frame

While creating a data frame we can assign column names and row names to our table. This procedure is important as it specify the “label names” and “index values”.

Here, we imported the pandas library as “pd” and then passed the dataset using a dictionary of lists. Each key represents a column data and the value associated with it is passed in the form of a list. We created the data frame using pandas “DataFrame()” function. We assigned the row labels to the data frame with the help of “index” parameter. Now let’s drop the columns using column names.


import pandas as pd dataset = {"Employee ID":["CIR45", "CIR12", "CIR18", "CIR50", "CIR28"], "Age":[25, 28, 27, 26, 25], "Salary":[200000, 250000, 180000, 300000, 280000], "Role":["Junior Developer", "Analyst", "Programmer", "Senior Developer", "HR"]} dataframe = pd.DataFrame(dataset, index=["Nimesh", "Arjun", "Mohan", "Ritesh", "Raghav"]) print(dataframe) Output Employee ID Age Salary Role Nimesh CIR45 25 200000 Junior Developer Arjun CIR12 28 250000 Analyst Mohan CIR18 27 180000 Programmer Ritesh CIR50 26 300000 Senior Developer Raghav CIR28 25 280000 HR Using Column Names and Drop() Method

After generating the data frame, we used the “dataframe.drop” method to remove the “Salary” and “Role” columns from the data frame. We passed these column names in a list.

We specified the “axis” value as 1 because we are operating on the column axis. At last, we stored this new data frame in a variable “colDrop” and printed it.


import pandas as pd dataset = {"Employee ID":["CIR45", "CIR12", "CIR18", "CIR50", "CIR28"], "Age":[25, 28, 27, 26, 25], "Salary":[200000, 250000, 180000, 300000, 280000], "Role":["Junior Developer", "Analyst", "Programmer", "Senior Developer", "HR"]} dataframe = pd.DataFrame(dataset, index=["Nimesh", "Arjun", "Mohan", "Ritesh", "Raghav"]) print(dataframe) colDrop = dataframe.drop(["Role", "Salary"], axis=1) print("After dropping the Role and salary column:") print(colDrop) Output Employee ID Age Salary Role Nimesh CIR45 25 200000 Junior Developer Arjun CIR12 28 250000 Analyst Mohan CIR18 27 180000 Programmer Ritesh CIR50 26 300000 Senior Developer Raghav CIR28 25 280000 HR After dropping the Role and salary column: Employee ID Age Nimesh CIR45 25 Arjun CIR12 28 Mohan CIR18 27 Ritesh CIR50 26 Raghav CIR28 25 Using Index Values and Drop() Method

We can use the index positions to lock the columns that we want to remove.


Here, we simply used the “dataframe.columns” method along with “dataframe.drop()” to specify the index positions of the columns to be dropped. We passed the “[[2,3]]” argument to drop the “Salary” and “role” columns.

Now that we have discussed both the basic methods for dropping columns, let’s discuss some extended concepts.

colDrop = dataframe.drop(dataframe.columns[[2, 3]], axis=1) print("After dropping salary and role: -") print(colDrop) Output After dropping salary and role: - Employee ID Age Nimesh CIR45 25 Arjun CIR12 28 Mohan CIR18 27 Ritesh CIR50 26 Raghav CIR28 25 Dropping a Range of Columns from the Data Frame

In the above discussed examples, we only dropped specific columns (Salary& Role) but as we all know pandas offers numerous facilities to the programmer and therefore we can use it to create a range of columns to be dropped. Let’s implement this logic.

Using iloc() Function

After generating the data frame, we used the “iloc() function” to select a range of columns and remove it from the data frame. The “iloc()” function takes an index range for both rows and columns. The range for rows was set to “[0:0]” and for columns it was “[1:4]”. Finally we use “dataframe.drop()” method to drop these columns.


import pandas as pd dataset = {"Employee ID":["CIR45", "CIR12", "CIR18", "CIR50", "CIR28"], "Age":[25, 28, 27, 26, 25], "Salary":[200000, 250000, 180000, 300000, 280000], "Role":["Junior Developer", "Analyst", "Programmer", "Senior Developer", "HR"]} dataframe = pd.DataFrame(dataset, index=["Nimesh", "Arjun", "Mohan", "Ritesh", "Raghav"]) print(dataframe) colDrop = dataframe.drop(dataframe.iloc[0:0, 1:4],axis=1) print("Dropping a range of columns from 'Age' to 'Role' using iloc() function") print(colDrop) Output

Employee ID Age Salary Role Nimesh CIR45 25 200000 Junior Developer Arjun CIR12 28 250000 Analyst Mohan CIR18 27 180000 Programmer Ritesh CIR50 26 300000 Senior Developer Raghav CIR28 25 280000 HR Dropping a range of columns from 'Age' to 'Role' using iloc() function Employee ID Nimesh CIR45 Arjun CIR12 Mohan CIR18 Ritesh CIR50 Raghav CIR28 Using loc() Function

If we want to use labels instead of indices for creating a range, we use “loc() function”.


We created a range with the help of “loc()” function. Unlike iloc(), it includes the last column. The “loc()” function selects the columns by taking the column names as the argument. At last, we printed the new data frame with the remaining columns.

colDrop = dataframe.drop(dataframe.loc[:, "Age": "Role"].columns, axis=1) print("Dropping a range of columns from Age to Role using loc() fucntion") print(colDrop) Output

Employee ID Age Salary Role Nimesh CIR45 25 200000 Junior Developer Arjun CIR12 28 250000 Analyst Mohan CIR18 27 180000 Programmer Ritesh CIR50 26 300000 Senior Developer Raghav CIR28 25 280000 HR Dropping a range of columns from Age to Role using loc() fucntion Employee ID Nimesh CIR45 Arjun CIR12 Mohan CIR18 Ritesh CIR50 Raghav CIR28 Conclusion

This article focuses on the simple operation of dropping columns from a pandas data frame. We discussed the two techniques i.e., “dropping by label names” and “dropping by index values”. We also used “loc()” and “iloc()” functions and acknowledged their application on a pandas data frame.

Sql Group By Multiple Columns

Introduction to SQL GROUP BY Multiple Columns

SQL GROUP BY multiple columns is the technique using which we can retrieve the summarized result set from the database using the SQL query that involves grouping of column values done by considering more than one column as grouping criteria. Group by is done for clubbing together the records that have the same values for the criteria that are defined for grouping. When a single column is considered for grouping then the records containing the same value for that column on which criteria are defined are grouped into a single record for the resultset. Similarly, when the grouping criteria are defined on more than one column then all the values of those columns should be the same as that of other columns to consider them for grouping into a single record. In this article, we will learn about the syntax, usage, and implementation of the GROUP BY clause that involves the specification of multiple columns as its grouping criteria with the help of some of the examples.

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SELECT column1, column2,..., columnm, aggregate_function(columni) FROM target_table WHERE conditions_or_constraints GROUP BY criteriacolumn1 , criteriacolumn2,...,criteriacolumnj;

The syntax of the GROUP BY clause is as shown above. It is the optional clause used in the select clause whenever we need to summarize and reduce the resultset. It should always be placed after the FROM and WHERE clause in the SELECT clause. Some of the terms used in the above syntax are explained below –

column1, column2,…, column – These are the names of the columns of the target_table table that need to retrieved and fetched in the resultset.

aggregate_function(column) – These are the aggregate functions defined on the columns of target_table that needs to be retrieved from the SELECT query.

target_table – Name of the table from where the result is to be fetched.

conditions_or_constraints – If you want to apply certain conditions on certain columns they can be mentioned in the optional WHERE clause.

criteriacolumn1 , criteriacolumn2,…,criteriacolumnj – These are the columns that will be considered as the criteria to create the groups in the MYSQL query. There can be single or multiple column names on which the criteria need to be applied. We can even mention expressions as the grouping criteria. SQL does not allow using the alias as the grouping criteria in the GROUP BY clause. Note that multiple criteria of grouping should be mentioned in a comma-separated format.

Usage of GROUP BY Multiple Columns

When the grouping criteria are defined on more than one column or expression then all the records that match and have the same values for their respective columns mentioned in the grouping criteria are grouped into a single record. The group by clause is most often used along with the aggregate functions like MAX(), MIN(), COUNT(), SUM(), etc to get the summarized data from the table or multiple tables joined together. Grouping on multiple columns is most often used for generating queries for reports, dashboarding, etc.


Consider a table named educba_learning having the contents and structure as shown in the output of the following select query statement –

SELECT * FROM educba_learning;

The output of the execution of the above query statement is as follows showing the structure and contents of educba_learning table –

Now, we will group the resultset of the educba_learnning table contents based on sessions and expert_name columns so that the retrieved records will only a single record for the rows having the same values for sessions and expert_name collectively. Our query statement will be as follows –

SELECT sessions, expert_name FROM educba_learning GROUP BY sessions, expert_name ;

The output of the above query statement in SQL is as shown below containing the unique records for each of the session, expert name column values –

Note that while using the grouping criteria it is important to retrieve the records on which the grouping clause is defined. Using the above statement for retrieving all the records will give the following error if the SQL mode is set to only full group by –

SELECT * FROM educba_learning GROUP BY sessions, expert_name ;

The output of the above query statement in SQL is as shown below-

Let us execute the following query statement and study the output and confirm whether it results in output as discussed above –

SELECT SUM(sessions), expert_name FROM educba_learning GROUP BY sessions, expert_name ;

The output of the execution of the above query statement is as follows –

We can observe that for the expert named Payal two records are fetched with session count as 1500 and 950 respectively. Similar work applies to other experts and records too. Note that the aggregate functions are used mostly for numeric valued columns when group by clause is used.


We can group the resultset in SQL on multiple column values. When we define the grouping criteria on more than one column, all the records having the same value for the columns defined in the group by clause are collectively represented using a single record in the query output. All the column values defined as grouping criteria should match with other records column values to group them to a single record. Most of the time, group by clause is used along with aggregate functions to retrieve the sum, average, count, minimum or maximum value from the table contents of multiple tables joined query’s output.

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We hope that this EDUCBA information on “SQL GROUP BY Multiple Columns” was beneficial to you. You can view EDUCBA’s recommended articles for more information.

Homepod White Ring Issue Confirmed By Apple

HomePod white ring issue confirmed by Apple [Update]

Apple’s HomePod may auto-tune itself to suit wherever it’s placed in a room, but there’s a physical reason why you should still be careful about where you put the smart speaker. The Siri-powered music player is one of Apple’s more cohesive designs of late, smaller than you might expect from photos, and clad in a textured – but acoustically transparent – fabric sheath.

However, it seems there are still some issues that Apple didn’t see fit to warn us about. HomePod sits on a silicone foot – indeed, the base is the only place you’ll actually find the Apple logo – but if you lift it up to look at it you might find there’s an ugly ring left behind on whatever furniture it was placed on. That, unfortunately, is what several owners are now discovering.

The Wirecutter spotted the issue, as did Pocket-lint, and Apple confirmed that it is, indeed, a known tendency of the HomePod. It depends on what the surface is it’s placed on, of course, with certain types of wood being problematic. After a period on a wooden side table, for example, or an oiled butcher-block countertop, white circular rings were found.

It’s worth noting that, in our own testing with HomePod in a number of places and on a range of different surfaces, we’ve not observed any issues with left-behind rings. That includes painted shelves, tile, glass, and sealed wood. All the same, it may give you pause as you consider not only the sound quality of a particular location, but whether HomePod might not play entirely nicely with the furniture there too.

It’s unclear at this stage what the exact problem is. Other devices – indeed, other Apple devices – have silicone feet, but don’t struggle with the same issue of leaving a mark. It could well be a chemistry issue with Apple’s supplier of the HomePod’s base, and thus something the company could correct on the production line.

Certainly, it’s not sufficient to dampen our enthusiasm for HomePod as a musical speaker. Siri’s performance still leaves us a little less impressed, at least when you roam outside controlling Apple Music by voice, as we noted in our HomePod review. All the same, it’s another thing to bear in mind as you decide where to situate your smart speaker.

Update: Apple has a new support document with guidance on where best to put HomePod, how to clean and maintain it, and other details. It specifically refers to the white mark issue, which Apple says “is not unusual” for silicone bases:

“It is not unusual for any speaker with a vibration-dampening silicone base to leave mild marks when placed on some wooden surfaces. The marks can be caused by oils diffusing between the silicone base and the table surface, and will often go away after several days when the speaker is removed from the wooden surface. If not, wiping the surface gently with a soft damp or dry cloth may remove the marks. If marks persist, clean the surface with the furniture manufacturer’s recommended cleaning process. If you’re concerned about this, we recommend placing your HomePod on a different surface.” Apple

Search Engine Results Protected By First Amendment

Although Google has been the subject of multiple antitrust investigations related to how they arrange search results and rank Web sites, a new 27-page report suggests that Google should be offered the same First Amendment rights as a newspaper. The report, which was commissioned by Google, makes a strong case that search engines are protected by the First Amendment and that the government cannot attempt to control the search results in any way.

Eugene Volokh, a UCLA law professor, First Amendment expert, and the author of the “First Amendment Protection for Search Engine Results” report said the following:

“Google, Microsoft’s Bing, Yahoo! Search and other search engine companies are rightly seen as media enterprises, much as the New York Times Company or CNN are media enterprises.”

Since Google and the other search engines are media enterprises, the report argues that they have a constitutional right to exclude or include certain Web sites and information from their results. In the report, Volokh also indicated that the search results are a direct product of an algorithmic “opinion” based on what is best for the end-user.  The report claimed that the same laws that protect news aggregators, such as the Drudge Report and the Huffington Post, will protect Google from antitrust legal action.

When Paid Content asked Google why they commissioned the report, the search engine stated, “we thought these issues were worth exploring in more depth by a noted First Amendment scholar.” However, with multiple antitrust investigations by the U.S. government, the European Union, and other foreign governments, Google is probably planning to use this report to bolster its legal positions.

When an Oklahoma ad agency sued Google in 2003 for decreased rankings, the federal judge ruled that the search engine’s actions were protected by free speech. In 2007, a California court ruled that Google’s rankings were private property and that they had the right to choose the businesses they feature in the search results.

While this report and the legal precedent related to free speech may further Google’s case in the U.S. court system, the prominent search engine is unlikely to find success with the First Amendment argument in Europe, South Korea, and other foreign countries.

Do you think that search engine results should be protected by the First Amendment or could this result in monopolistic control of information?

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