Contenido del Curso
Tweet Sentiment Analysis
Data Preprocessing
Data preprocessing refers to the techniques used to prepare raw data for further analysis or modeling. The goal of preprocessing is to clean, transform, and format the data so that it can be used effectively in an analysis or model.
Methods description
- The
.dropna()
method in Pandas is used to remove rows or columns with missing values (NaN). Settinginplace=True
modifies the DataFrame in place, meaning the changes are applied directly to the original DataFrame, and it returnsNone
; - The
.drop_duplicates()
method is used to remove duplicate rows from the DataFrame. Settinginplace=True
modifies the DataFrame in place, removing duplicate rows, and it returnsNone
.
Tarea
- Drop
NaNs
from our dataset. - Drop duplicates from our dataset.
Mark tasks as Completed
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?
¡Gracias por tus comentarios!
Data preprocessing refers to the techniques used to prepare raw data for further analysis or modeling. The goal of preprocessing is to clean, transform, and format the data so that it can be used effectively in an analysis or model.
Methods description
- The
.dropna()
method in Pandas is used to remove rows or columns with missing values (NaN). Settinginplace=True
modifies the DataFrame in place, meaning the changes are applied directly to the original DataFrame, and it returnsNone
; - The
.drop_duplicates()
method is used to remove duplicate rows from the DataFrame. Settinginplace=True
modifies the DataFrame in place, removing duplicate rows, and it returnsNone
.
Tarea
- Drop
NaNs
from our dataset. - Drop duplicates from our dataset.
Mark tasks as Completed
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
Sección 1. Capítulo 4
AVAILABLE TO ULTIMATE ONLY