Pandas for Everyone : Python Data Analysis

Daniel Chen

ISBN 10: 0134546938 / ISBN 13: 9780134546933

Published by Pearson Education Dez 2017, 2017

About this Item

Neuware - The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world problems. Chen gives you a jumpstart on using Pandas with a realistic dataset and covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes. Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability, and introduces you to the wider Python data analysis ecosystem. Work with DataFrames and Series, and import or export dataCreate plots with matplotlib, seaborn, and pandasCombine datasets and handle missing dataReshape, tidy, and clean datasets so they re easier to work withConvert data types and manipulate text stringsApply functions to scale data manipulationsAggregate, transform, and filter large datasets with groupbyLeverage Pandas advanced date and time capabilitiesFit linear models using statsmodels and scikit-learn librariesUse generalized linear modeling to fit models with different response variablesCompare multiple models to select the best Regularize to overcome overfitting and improve performanceUse clustering in unsupervised machine learning.

Seller Inventory # 9780134546933

Bibliographic Details

• Title: Pandas for Everyone : Python Data Analysis
• Publisher: Pearson Education Dez 2017
• Publication Date: 2017
• Language: English
• ISBN 10: 0134546938
• ISBN 13: 9780134546933
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 231W x 177H x 27D millimeters 231 millimeters width by 177 millimeters height by 27 millimeters depth
• Weight: 660 grams

About this title

Synopsis

The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python

 

Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.

 

Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world problems.

 

Chen gives you a jumpstart on using Pandas with a realistic dataset and covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes.

 

Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability, and introduces you to the wider Python data analysis ecosystem. 

• Work with DataFrames and Series, and import or export data
• Create plots with matplotlib, seaborn, and pandas
• Combine datasets and handle missing data
• Reshape, tidy, and clean datasets so they’re easier to work with
• Convert data types and manipulate text strings
• Apply functions to scale data manipulations
• Aggregate, transform, and filter large datasets with groupby
• Leverage Pandas’ advanced date and time capabilities
• Fit linear models using statsmodels and scikit-learn libraries
• Use generalized linear modeling to fit models with different response variables
• Compare multiple models to select the “best”
• Regularize to overcome overfitting and improve performance
• Use clustering in unsupervised machine learning

About the Author

Daniel Chen is a graduate student in the interdisciplinary PhD program in Genetics, Bioinformatics & Computational Biology (GBCB) at Virginia Tech. He is involved with Software Carpentry as an instructor and lesson maintainer. He completed his master’s degree in public health at Columbia University Mailman School of Public Health in Epidemiology, and currently works at the Social and Decision Analytics Laboratory under the Biocomplexity Institute of Virginia Tech where he is working with data to inform policy decision-making. He is the author of Pandas for Everyone and Pandas Data Analysis with Python Fundamentals LiveLessons.