Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.
Aprenda Data Preparation | Clustering Demystified
Clustering Demystified

book
Data Preparation

Data preparation involves the process of transforming raw data into a format suitable for analysis or modeling. This includes tasks such as cleaning, handling missing values, encoding categorical variables, scaling, normalization, and feature engineering. The goal is to ensure that the data is accurate, complete, and structured in a way that facilitates effective analysis and modeling.

Methods description

  • .drop(): A DataFrame method used to remove columns or rows from a DataFrame. It takes the labels parameter to specify the columns or rows to be removed and the axis parameter to indicate whether to remove columns (axis=1) or rows (axis=0). The inplace parameter, if set to True, modifies the DataFrame in place;

  • .info(): A DataFrame method that prints a concise summary of a DataFrame, including the number of non-null values in each column and the data types of each column. It provides a quick overview of the DataFrame's structure and content.

Tarefa

Swipe to start coding

  1. Delete the following columns: "status_id", "status_published", "Column1", "Column2", "Column3", "Column4".

  2. Print the info of the dataset.

Solução

data.drop(["status_id", "status_published", "Column1", "Column2", "Column3", "Column4"], axis=1, inplace=True)


data.info()

Mark tasks as Completed
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 1. Capítulo 4
AVAILABLE TO ULTIMATE ONLY
some-alt