Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.
Oppiskele Box Plots | Data Visualization
Gaining Insights with Data Visualization
course content

Kurssisisältö

Gaining Insights with Data Visualization

book
Box Plots

Box plots are useful for visualizing the distribution of a single numeric variable and identifying potential outliers. They are particularly effective for comparing the distribution of data across different categories or groups.

A box plot consists of a box, which encloses the first and third quartiles, and whiskers, which typically extend from the quartiles to the minimum and maximum values within 1.5 times the interquartile range. These components make box plots an excellent tool for summarizing data distributions clearly and concisely.

Tehtävä

Swipe to start coding

  1. Import the seaborn library with the sns alias.
  2. Import the pyplot module of the matplotlib library with the plt alias.
  3. Generate three arrays with 100 observations each, with standard deviations (std) ranging from 1 to 4, exclusive.
  4. Use the appropriate seaborn function to create a box plot.
  5. Display the resulting plot.

Ratkaisu

Mark tasks as Completed
Switch to desktopVaihda työpöytään todellista harjoitusta vartenJatka siitä, missä olet käyttämällä jotakin alla olevista vaihtoehdoista
Oliko kaikki selvää?

Miten voimme parantaa sitä?

Kiitos palautteestasi!

Osio 1. Luku 2

Kysy tekoälyä

expand
ChatGPT

Kysy mitä tahansa tai kokeile jotakin ehdotetuista kysymyksistä aloittaaksesi keskustelumme

course content

Kurssisisältö

Gaining Insights with Data Visualization

book
Box Plots

Box plots are useful for visualizing the distribution of a single numeric variable and identifying potential outliers. They are particularly effective for comparing the distribution of data across different categories or groups.

A box plot consists of a box, which encloses the first and third quartiles, and whiskers, which typically extend from the quartiles to the minimum and maximum values within 1.5 times the interquartile range. These components make box plots an excellent tool for summarizing data distributions clearly and concisely.

Tehtävä

Swipe to start coding

  1. Import the seaborn library with the sns alias.
  2. Import the pyplot module of the matplotlib library with the plt alias.
  3. Generate three arrays with 100 observations each, with standard deviations (std) ranging from 1 to 4, exclusive.
  4. Use the appropriate seaborn function to create a box plot.
  5. Display the resulting plot.

Ratkaisu

Mark tasks as Completed
Switch to desktopVaihda työpöytään todellista harjoitusta vartenJatka siitä, missä olet käyttämällä jotakin alla olevista vaihtoehdoista
Oliko kaikki selvää?

Miten voimme parantaa sitä?

Kiitos palautteestasi!

Osio 1. Luku 2
Pahoittelemme, että jotain meni pieleen. Mitä tapahtui?
some-alt