Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.
Aprende Visualizing the Dynamics Across Clusters | K-Means Algorithm
Cluster Analysis in Python

Desliza para mostrar el menú

book
Visualizing the Dynamics Across Clusters

The selective pair of months on the scatter plot looked good, didn't it? Maybe there were no key differences between 'areas' on the plot, but at least there were no outliers outside the respective groups, and in general, all groups were disjoint.

Finally, let's find out the yearly dynamics for each cluster, i.e. let's build the line plot representing the monthly averages for each group of points.

Tarea

Swipe to start coding

Table
  1. Extract the necessary columns (month's names and temperatures) within the col variable:
  • Firstly, extract the 2-13 column names as list type, and save them within the col variable.
  • Then add the 'prediction' string to the list col.
  1. Calculate the monthly average temperatures for each cluster, and save the result within monthly_data variable:
  • Firstly group the observations of col column of data by 'prediction'.
  • Then calculate .mean() of grouped table.
  • Then apply .stack() to stack the table (already done).
  • Finally reset the indices using .reset_index() method.
  1. Assign list ['Group', 'Month', 'Temp'] as column names for transformed data within monthly_data variable.

  2. Build the line plot 'Month' vs 'Temp' for each Group using monthly_data DataFrame.

Solución

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 1. Capítulo 8

Pregunte a AI

expand
ChatGPT

Pregunte lo que quiera o pruebe una de las preguntas sugeridas para comenzar nuestra charla

book
Visualizing the Dynamics Across Clusters

The selective pair of months on the scatter plot looked good, didn't it? Maybe there were no key differences between 'areas' on the plot, but at least there were no outliers outside the respective groups, and in general, all groups were disjoint.

Finally, let's find out the yearly dynamics for each cluster, i.e. let's build the line plot representing the monthly averages for each group of points.

Tarea

Swipe to start coding

Table
  1. Extract the necessary columns (month's names and temperatures) within the col variable:
  • Firstly, extract the 2-13 column names as list type, and save them within the col variable.
  • Then add the 'prediction' string to the list col.
  1. Calculate the monthly average temperatures for each cluster, and save the result within monthly_data variable:
  • Firstly group the observations of col column of data by 'prediction'.
  • Then calculate .mean() of grouped table.
  • Then apply .stack() to stack the table (already done).
  • Finally reset the indices using .reset_index() method.
  1. Assign list ['Group', 'Month', 'Temp'] as column names for transformed data within monthly_data variable.

  2. Build the line plot 'Month' vs 'Temp' for each Group using monthly_data DataFrame.

Solución

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 1. Capítulo 8
Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
Lamentamos que algo salió mal. ¿Qué pasó?
some-alt