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Articles related to "group"


Efficient Data Summarizing and Analysis Using Pandas’ Groupby Function

  • The way we can use groupby on multiple variables, using multiple aggregate functions is also possible.
  • This next example will group by ‘race/ethnicity and will aggregate using ‘max’ and ‘min’ functions.
  • Let’s make a DataFrame that contains the maximum and minimum score in math, reading, and writing for each group segregated by gender.
  • Group by ‘race/ethnicity’ and use max and mean on math score and median and min on reading score.
  • Apply aggregate functions on some nominal columns such as ‘lunch’ and ‘parental level of education’.
  • Map the mean reading score of each group and generate a new column.
  • There is a new column named ‘New’ at the end, that is containing the mean reading score of the corresponding group.
  • You learned to use aggregate functions, data transformation, filter, map, and visualization using groupby.

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Efficient Data Summarizing and Analysis Using Pandas’ Groupby Function

  • The way we can use groupby on multiple variables, using multiple aggregate functions is also possible.
  • This next example will group by ‘race/ethnicity and will aggregate using ‘max’ and ‘min’ functions.
  • Let’s make a DataFrame that contains the maximum and minimum score in math, reading, and writing for each group segregated by gender.
  • Group by ‘race/ethnicity’ and use max and mean on math score and median and min on reading score.
  • Apply aggregate functions on some nominal columns such as ‘lunch’ and ‘parental level of education’.
  • Map the mean reading score of each group and generate a new column.
  • There is a new column named ‘New’ at the end, that is containing the mean reading score of the corresponding group.
  • You learned to use aggregate functions, data transformation, filter, map, and visualization using groupby.

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An all-Black group is arming itself and demanding change. They are the NFAC

  • A spokeswoman for Louisville Mayor Greg Fischer said in a statement that city officials have worked hard to communicate with all groups, including NFAC, and have seen largely peaceful protests.
  • When the NFAC marched in Louisville, they were met by an armed, largely White extremist group called the "Three Percenters." The two groups yelled at one another but were kept apart by riot police.
  • The September 1 post on Higgins' campaign page, which has since been removed, included photos of Black armed demonstrators and warned that if such protesters came to Lafayette he would "drop 10 of you where you stand," according to CNN affiliate KATC.
  • There isn't one way to police armed groups because every state and city has its own rules but authorities tend to take a "very cautious, almost kid glove approach" with them, said Carolyn Gallaher, a professor and senior associate dean in the School of International Service at American University.

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Carlyle, PEP lob new Link proposal, secure more commitments

  • It is understood Carlyle and PEP have submitted a revised proposal for Link, which values the group at more than $5.20 a share.
  • The private equity firms also put the revised proposal to a bunch of Link's institutional investors, fund manager sources told Street Talk.
  • The revised proposal comes only days after Link dismissed Carlyle and PEP's initial $5.20 a share approach as "materially" undervaluing the group, and said it would consider pinching the suitors' idea to spin-off its 44 per cent PEXA stake.
  • The new offer, which fund managers said was indicative and non-binding like its offer disclosed last week, is sure to give Link and its investors plenty to think about - and may shift some of the focus back to Carlyle and PEP's interest in Link's core superannuation services businesses.

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Lee Kun-hee, who made Samsung a global powerhouse, dies at 78

  • Seoul | Lee Kun-hee, who built Samsung Electronics into a global powerhouse in smartphones, semiconductors and televisions, died on Sunday after spending more than six years in hospital following a heart attack, the company said.
  • Lee, who was 78, is the latest second-generation leader of a South Korean family-controlled conglomerate, or chaebol, to die, leaving potentially thorny succession issues for the third generation.
  • The death of Lee, with a net worth of $US20.9 billion ($29.3 billion) according to Forbes, is set to prompt investor interest in a potential restructuring of the group involving his stakes in key Samsung companies such as Samsung Life and Samsung Electronics.
  • Lee died with his family by his side, including Jay Y Lee, the Samsung Electronics vice-chairman, the conglomerate said.

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K-Means Clustering in Python

  • K-means clustering is an unsupervised ML algorithm that we can use to split our dataset into logical groupings — called clusters.
  • We continue this process until the centroids stop moving — which means we have successfully clustered our dataset!
  • And with that, inside the variable X we now have our artificial data containing 500 datapoints, each consisting of 3 features/dimensions, and each belonging to one of 5 blobs.
  • Variable y tells us which cluster each datapoint belongs to — usually, we would not know this.
  • Now we can access our data classification and centroid coordinates using the labels_ and cluster_centers_ methods respectively.
  • SSE is calculated as the sum of the squared distance between each datapoint and its allocated cluster centroid.
  • It is important that we have enough clusters to match the clusters in our dataset, but not too many clusters than the SSE is minimized simply by assigning every datapoint it’s own cluster.

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Supervised Learning vs Unsupervised Learning

  • In unsupervised learning, we are trying to discover hidden patterns in data, when we don’t have any labels.
  • The machine learning problem is to predict whether a new label for a new email is spam or not spam.
  • You can imagine a teacher, or supervisor, telling you the label of each data point, which is whether each e-mail is spam or not spam.
  • Clustering them is the unsupervised learning problem where we take our data and assign each data point to exactly one group, or cluster.
  • In this example, we imagine the clusters correspond to three different countries that used to exists in the area (Figure 1.2).
  • On the other hand, clustering let’s find hidden groupings in data even when we are not able to run classification.
  • Before we can learn the k-means algorithm, we need to understand the set-up and assumptions that go into the k-means clustering problem.

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K-Means Clustering in Python

  • K-means clustering is an unsupervised ML algorithm that we can use to split our dataset into logical groupings — called clusters.
  • We continue this process until the centroids stop moving — which means we have successfully clustered our dataset!
  • And with that, inside the variable X we now have our artificial data containing 500 datapoints, each consisting of 3 features/dimensions, and each belonging to one of 5 blobs.
  • Variable y tells us which cluster each datapoint belongs to — usually, we would not know this.
  • Now we can access our data classification and centroid coordinates using the labels_ and cluster_centers_ methods respectively.
  • SSE is calculated as the sum of the squared distance between each datapoint and its allocated cluster centroid.
  • It is important that we have enough clusters to match the clusters in our dataset, but not too many clusters than the SSE is minimized simply by assigning every datapoint it’s own cluster.

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Supervised Learning vs Unsupervised Learning

  • In unsupervised learning, we are trying to discover hidden patterns in data, when we don’t have any labels.
  • The machine learning problem is to predict whether a new label for a new email is spam or not spam.
  • You can imagine a teacher, or supervisor, telling you the label of each data point, which is whether each e-mail is spam or not spam.
  • Clustering them is the unsupervised learning problem where we take our data and assign each data point to exactly one group, or cluster.
  • In this example, we imagine the clusters correspond to three different countries that used to exists in the area (Figure 1.2).
  • On the other hand, clustering let’s find hidden groupings in data even when we are not able to run classification.
  • Before we can learn the k-means algorithm, we need to understand the set-up and assumptions that go into the k-means clustering problem.

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Who Are the Boogaloo Bois - and What Do They Want?

  • A deeper look reveals that the Boogaloo Bois are a complicated, violent extremist group that’s still trying to figure itself out.
  • Ivan Harrison Hunter, a member of the Boogaloo Bois, has been charged with one count of interstate travel to incite a riot.
  • According to several reports, the Boogaloo Bois have one main goal: to start another civil war in America.
  • Just like the Proud Boys, they’re a far-right extremist group that’s usually heavily armed.
  • You may be starting to think that the Boogaloo Bois sound like a militia group similar to the Three Percenters.
  • The Three Percenters have started aligning with Donald Trump, which, according to some Boogaloo Bois, is a gross misstep.
  • In summary, the Boogaloo Bois are a complicated, new militia group that appears to still be figuring itself out.

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