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Articles related to "let"


A Practical Guide for Exploratory Data Analysis: Movies on Streaming Platforms

  • In this post, we will try to explore a dataset about the movies on streaming platforms.
  • We have data on movies and information about their availability on streaming platforms.
  • Let’s see how many missing values each column contains.
  • Missing values dominate “Age” and “Rotten Tomatoes” columns as expected.
  • Thus, we can drop the rows that contain any missing value.
  • We can check the average IMDb rating of directors.
  • The platforms contained in the dataset are Netflix, Hulu, Prime Video, and Disney+.
  • Let’s check how many movies each platform has and also the average IMDb ratings of platforms.
  • Netflix has 3152 movies and the average IMDb rating is 5.8.
  • We take the second row of what groupby function returns because second row contains the data on movies available (1) on that platform.
  • Prime Video has most movies by far and the lowest average IMDb rating.

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A Practical Guide for Exploratory Data Analysis: Movies on Streaming Platforms

  • In this post, we will try to explore a dataset about the movies on streaming platforms.
  • We have data on movies and information about their availability on streaming platforms.
  • Let’s see how many missing values each column contains.
  • Missing values dominate “Age” and “Rotten Tomatoes” columns as expected.
  • Thus, we can drop the rows that contain any missing value.
  • We can check the average IMDb rating of directors.
  • The platforms contained in the dataset are Netflix, Hulu, Prime Video, and Disney+.
  • Let’s check how many movies each platform has and also the average IMDb ratings of platforms.
  • Netflix has 3152 movies and the average IMDb rating is 5.8.
  • We take the second row of what groupby function returns because second row contains the data on movies available (1) on that platform.
  • Prime Video has most movies by far and the lowest average IMDb rating.

save | comments | report | share on


A Practical Guide for Exploratory Data Analysis: Movies on Streaming Platforms

  • In this post, we will try to explore a dataset about the movies on streaming platforms.
  • We have data on movies and information about their availability on streaming platforms.
  • Let’s see how many missing values each column contains.
  • Missing values dominate “Age” and “Rotten Tomatoes” columns as expected.
  • Thus, we can drop the rows that contain any missing value.
  • We can check the average IMDb rating of directors.
  • The platforms contained in the dataset are Netflix, Hulu, Prime Video, and Disney+.
  • Let’s check how many movies each platform has and also the average IMDb ratings of platforms.
  • Netflix has 3152 movies and the average IMDb rating is 5.8.
  • We take the second row of what groupby function returns because second row contains the data on movies available (1) on that platform.
  • Prime Video has most movies by far and the lowest average IMDb rating.

save | comments | report | share on


GitHub Classroom

  • Managing and organizing your class is easy with GitHub Classroom.
  • Track and manage assignments in your dashboard, grade work automatically, and help students when they get stuck— all while using GitHub, the industry-standard tool developers use.
  • Save time by using automated testing to grade assignments.
  • Tests run with every push, letting students see results immediately and make changes as necessary.
  • In group assignments, see each student's individual contribution in GitHub. Automatically distribute assignments to students and let automated testing do the heavy lifting for you.
  • Create individual and group assignments, prime them with starter code, and send them out to students.
  • Make student work private or public for any assignment with Educator’s benefits.
  • As many private assignments as you need, whenever you need them.
  • Create a new classroom, spin up assignments, and send them out to your students.

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Facebook now allows you to bulk-delete your most embarrassing posts

  • Facebook today announced a new feature that will finally let you get rid of your embarrassing old posts en masse.
  • Manage Activity essentially lets you select posts from your Activity Log and trash them if you wish.
  • Here’s how it’ll work: you find the feature in your Activity Log. Once you click on the option, you’ll be shown a list of all of your posts.
  • You can select each post you no longer want anyone to see.
  • Once you’ve selected all of the posts you’d rather no one saw, you have the option to Archive or Delete them.
  • Within that time, you can look in the Trash (it’ll be marked as an option on the Manage Activity menu) and restore any posts you’ve changed your mind about.

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Ditch the Database

  • While Amazon S3 is AWESOME for storing datasets it, like other traditional cloud-based object storage solutions, doesn't allow retrieving subsets of a dataset (i.e. the entire dataset must be retrieved).
  • This makes it impractical to use Amazon S3 as the sole database for applications, particularly apps that constantly need to retrieve subsets of a larger dataset (i.e. web app retrieving customer records).
  • Amazon S3 Select lets you use simple SQL expressions to pull out only the data you need from an Amazon S3 object.
  • Instead, you can use classic SQL expressions to query data in place and retrieve only a subset, such as data between two dates or above a certain price.
  • This was the first web application I built where I didn’t use a classic database and instead stored and queried static and dynamic data directly from Amazon S3.

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Ditch the Database

  • While Amazon S3 is AWESOME for storing datasets it, like other traditional cloud-based object storage solutions, doesn't allow retrieving subsets of a dataset (i.e. the entire dataset must be retrieved).
  • This makes it impractical to use Amazon S3 as the sole database for applications, particularly apps that constantly need to retrieve subsets of a larger dataset (i.e. web app retrieving customer records).
  • Amazon S3 Select lets you use simple SQL expressions to pull out only the data you need from an Amazon S3 object.
  • Instead, you can use classic SQL expressions to query data in place and retrieve only a subset, such as data between two dates or above a certain price.
  • This was the first web application I built where I didn’t use a classic database and instead stored and queried static and dynamic data directly from Amazon S3.

save | comments | report | share on


Ditch the Database

  • While Amazon S3 is AWESOME for storing datasets it, like other traditional cloud-based object storage solutions, doesn't allow retrieving subsets of a dataset (i.e. the entire dataset must be retrieved).
  • This makes it impractical to use Amazon S3 as the sole database for applications, particularly apps that constantly need to retrieve subsets of a larger dataset (i.e. web app retrieving customer records).
  • Amazon S3 Select lets you use simple SQL expressions to pull out only the data you need from an Amazon S3 object.
  • Instead, you can use classic SQL expressions to query data in place and retrieve only a subset, such as data between two dates or above a certain price.
  • This was the first web application I built where I didn’t use a classic database and instead stored and queried static and dynamic data directly from Amazon S3.

save | comments | report | share on


Ditch the Database

  • While Amazon S3 is AWESOME for storing datasets it, like other traditional cloud-based object storage solutions, doesn't allow retrieving subsets of a dataset (i.e. the entire dataset must be retrieved).
  • This makes it impractical to use Amazon S3 as the sole database for applications, particularly apps that constantly need to retrieve subsets of a larger dataset (i.e. web app retrieving customer records).
  • Amazon S3 Select lets you use simple SQL expressions to pull out only the data you need from an Amazon S3 object.
  • Instead, you can use classic SQL expressions to query data in place and retrieve only a subset, such as data between two dates or above a certain price.
  • This was the first web application I built where I didn’t use a classic database and instead stored and queried static and dynamic data directly from Amazon S3.

save | comments | report | share on


Ditch the Database

  • While Amazon S3 is AWESOME for storing datasets it, like other traditional cloud-based object storage solutions, doesn't allow retrieving subsets of a dataset (i.e. the entire dataset must be retrieved).
  • This makes it impractical to use Amazon S3 as the sole database for applications, particularly apps that constantly need to retrieve subsets of a larger dataset (i.e. web app retrieving customer records).
  • Amazon S3 Select lets you use simple SQL expressions to pull out only the data you need from an Amazon S3 object.
  • Instead, you can use classic SQL expressions to query data in place and retrieve only a subset, such as data between two dates or above a certain price.
  • This was the first web application I built where I didn’t use a classic database and instead stored and queried static and dynamic data directly from Amazon S3.

save | comments | report | share on