Python Data Cleaning Cookbook
portes grátis
Python Data Cleaning Cookbook
Detect and remove dirty data and extract key insights with pandas, machine learning and ChatGPT, Spark, and more
Walker, Michael
Packt Publishing Limited
05/2024
Mole
Inglês
9781803239873
Pré-lançamento - envio 15 a 20 dias após a sua edição
Descrição não disponível.
Table of Contents
Anticipating Data Cleaning Issues when Importing Tabular Data into Pandas
Anticipating Data Cleaning Issues when Importing HTML, JSON, and streaming into Pandas
Taking the Measure of Your Data
Identifying Missing Values and Outliers in Subsets of Data
Using Visualizations for the Identification of Unexpected Values
Cleaning and Exploring Data with Series Operations
Working with Missing Data
Fixing Messy Data When Aggregating
Addressing Data Issues When Combining Data Frames
Tidying and Reshaping Data
Automate Data Cleaning with User-Defined Functions and Classes
Anticipating Data Cleaning Issues when Importing Tabular Data into Pandas
Anticipating Data Cleaning Issues when Importing HTML, JSON, and streaming into Pandas
Taking the Measure of Your Data
Identifying Missing Values and Outliers in Subsets of Data
Using Visualizations for the Identification of Unexpected Values
Cleaning and Exploring Data with Series Operations
Working with Missing Data
Fixing Messy Data When Aggregating
Addressing Data Issues When Combining Data Frames
Tidying and Reshaping Data
Automate Data Cleaning with User-Defined Functions and Classes
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Table of Contents
Anticipating Data Cleaning Issues when Importing Tabular Data into Pandas
Anticipating Data Cleaning Issues when Importing HTML, JSON, and streaming into Pandas
Taking the Measure of Your Data
Identifying Missing Values and Outliers in Subsets of Data
Using Visualizations for the Identification of Unexpected Values
Cleaning and Exploring Data with Series Operations
Working with Missing Data
Fixing Messy Data When Aggregating
Addressing Data Issues When Combining Data Frames
Tidying and Reshaping Data
Automate Data Cleaning with User-Defined Functions and Classes
Anticipating Data Cleaning Issues when Importing Tabular Data into Pandas
Anticipating Data Cleaning Issues when Importing HTML, JSON, and streaming into Pandas
Taking the Measure of Your Data
Identifying Missing Values and Outliers in Subsets of Data
Using Visualizations for the Identification of Unexpected Values
Cleaning and Exploring Data with Series Operations
Working with Missing Data
Fixing Messy Data When Aggregating
Addressing Data Issues When Combining Data Frames
Tidying and Reshaping Data
Automate Data Cleaning with User-Defined Functions and Classes
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
