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Pandas for Everyone: Python Data Analysis
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
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
406 pages, Kindle Edition
Published December 15, 2017
140 people are currently reading
143 people want to read
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Displaying 1 - 5 of 5 reviews
December 9, 2019
I read this after reading "Python for Data Analysis" by Wes McKinney (creator of Pandas). This book actually made what I was reading from Wes stick. The examples here are more what you would encounter in a business setting doing analysis on large volume data sets. I have loaned this around the office with positive feedback as well. Best chapters for me were on the Generalized Linear Models and Model Diagnostics. Again, plenty of really useful examples.
April 18, 2021
Super helpful as a tool for pandas intro or refresher if you already know pandas.
Very didactic. Highly recommend.
I had the fortune of getting an autographed copy at the 2020 RStudio conference in SF.
Very didactic. Highly recommend.
I had the fortune of getting an autographed copy at the 2020 RStudio conference in SF.
September 17, 2022
When the author says this book is for everyone, he really means it. If you have basic experience with Python, perhaps there is a better book out there for you. In most chapters, the author details key methods as it relates to dataframes, etc. But, instead of showing the options in a methodical way, the author picks one or two that you may happen upon and uses them.
Frankly, the reader would benefit more from seeing pandas used in the context they want to use it. For instance, you can look up the youtube series about using SQL with Python from Bryan Cafferky, or try Python for Data Analysis, or even Python for Excel.
As an aside, I was also quite perplexed by how the author chose to describe the statistical methods. For somebody who knows statistics, the descriptions are too trivial to add much value in the book. For somebody who doesn't know stats well/the aspiring data analyst, please take a stats course designed for stats majors or math majors - you will be so much better off than trying to learn boot camp statistics to be a data analyst.
Frankly, the reader would benefit more from seeing pandas used in the context they want to use it. For instance, you can look up the youtube series about using SQL with Python from Bryan Cafferky, or try Python for Data Analysis, or even Python for Excel.
As an aside, I was also quite perplexed by how the author chose to describe the statistical methods. For somebody who knows statistics, the descriptions are too trivial to add much value in the book. For somebody who doesn't know stats well/the aspiring data analyst, please take a stats course designed for stats majors or math majors - you will be so much better off than trying to learn boot camp statistics to be a data analyst.
September 23, 2018
Материал, в принципе, не плох, но подается в очень сумбурном порядке. Автор перепрыгивает между разделами без всякой логики. Вдобавок, в книге огромное множество неудачных примеров. Так, чтобы показать возможности pandas по слиянию различных баз данных, автор предлагает скачать пять файлов, общим размеров в 500 мегабайт.
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December 18, 2022good book if you wanna learn data analysis python way, however analysis is more advanced now then this book so go for o'really data analysis with python that i am gonna read in few days i already bought it from amazon at 1800 rs inr.
Displaying 1 - 5 of 5 reviews