Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.
Motivation Analysis | Conducting Exploratory Data Analysis of Nobel Prizes
Conducting Exploratory Data Analysis of Nobel Prizes
course content

Contenido del Curso

Conducting Exploratory Data Analysis of Nobel Prizes

bookMotivation Analysis

In this section, our focus will be on examining the text to identify the most prevalent words in our dataset. Initially, we will eliminate all stopwords from the "motivation" column and modify our data accordingly.

Take, for instance, the sentence: "I like reading, so I read." It will be altered to: "Like Reading Read." Following this transformation, we will visualize these words in a word cloud, where the size of each word reflects its frequency in our dataset.

Tarea

  1. Apply a lambda function to remove stopwords from the 'motivation' column and store the processed text in the 'Filtered motivation' column.
  2. Concatenate all entries in the "Filtered motivation" column to form a single text string.
  3. Split the concatenated text into individual words and create a pandas DataFrame from the list of words.
  4. Calculate word frequency by counting occurrences of each word.
  5. Plot the 20 most common words using seaborn's barplot.

Mark tasks as Completed
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!

In this section, our focus will be on examining the text to identify the most prevalent words in our dataset. Initially, we will eliminate all stopwords from the "motivation" column and modify our data accordingly.

Take, for instance, the sentence: "I like reading, so I read." It will be altered to: "Like Reading Read." Following this transformation, we will visualize these words in a word cloud, where the size of each word reflects its frequency in our dataset.

Tarea

  1. Apply a lambda function to remove stopwords from the 'motivation' column and store the processed text in the 'Filtered motivation' column.
  2. Concatenate all entries in the "Filtered motivation" column to form a single text string.
  3. Split the concatenated text into individual words and create a pandas DataFrame from the list of words.
  4. Calculate word frequency by counting occurrences of each word.
  5. Plot the 20 most common words using seaborn's barplot.

Mark tasks as Completed
Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
Sección 1. Capítulo 5
AVAILABLE TO ULTIMATE ONLY
some-alt