Today, analysts must manage data of exceptional diversity, speed and volume. With the Pandas open source library, you can use Python to quickly automate and perform any data analysis task, no matter how large or complex it is. Panda can help you ensure the accuracy of your data, visualize it for effective decision making, and reliably reproduce analytics across multiple datasets.
Chen gets you started using pandas with a realistic dataset right away, and covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualization. He demonstrates powerful data cleansing techniques, from basic string manipulation to the simultaneous application of functions across data frames.
Pandas for everyone Python data analysis by Daniel Y Chen
Once your data is ready, Chen walks you through fit models for forecasting, grouping, inference, and exploration. He provides tips on performance and scalability and introduces you to the broader Python data analytics ecosystem.
- He works with DataFrames and Series and imports or exports data
- Create textures with Matplotlib, Seaborn and Pandas
- Combine records and manage missing data
- Reshape, clean up, and purge datasets to make them easier to use
- Conversion of data types and modification of text strings
- Apply functions to scale data manipulation
- Aggregate, transform and filter large datasets with groupby
- Take advantage of panda's advanced date and time functions
- Customize linear models with statsmodel and scikit-learn libraries
- Use generalized linear modeling to fit models with different responses
- Compare different models to choose the "best" one
- Regularize to overcome overfitting and improve performance
- Use clustering in unsupervised machine learning
Daniel Chen biography
Daniel Chen is a graduate student in the interdisciplinary doctoral program in Genetics, Bioinformatics and Computational Biology (GBCB) at Virginia Tech. He is involved with Carpentry Software as an instructor and class maintainer. He completed his master's degree in public health at Columbia University's Mailman School of Public Health in Epidemiology and currently works in the Social and Decision Analysis Laboratory at 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.
Pandas for Everyone: Python Data Analysis Book reviews
I'm halfway through this book and I found it much better as an introduction to Pandas than the other two books I started reading: "Pandas Cookbook" by Petrou and "Python for Data Analysis" by Wes McKinney (the creator of Pandas) Both the latter are good books, but Chen's book is more concise: he explains a method, shows a good example or two, and then moves on. Perfect for the way I learn. (I have 14 years of Python experience.)
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I'm a data scientist at a large bank, and I'm currently doing a master's degree in DS. I've tried flipping through any number of Python / R / Data Science books and can usually barely decipher the first 100 pages. First of all, they all seem to have an obligation to drag me through the water. I understand. Everything is based on numpy. But I rarely use iRL numpy! In short, it takes most writers 600 pages to say what 200 can easily come up with and change.
Daniel's book is a breath of fresh air. He still manages to educate without delving into trifles or overly detailed theory, mathematics, etc., etc. libraries) that also provide bases applicable to data science (sorted data, visualizations, etc.).
Also worth mentioning is the accompanying information available in his Github repository. I've made very good use of Jupyter notebooks, adding numerous examples from the book. These will be my reference points in the future.
I can honestly say that I have never called a specialist book "turning the page", but that definitely fits that description. I wrote an average of three chapters a day and will read it again in about a month. For those who work in data science or want to become panda experts, I highly recommend this book.
I loved Jared Lander's "R for Everyone," the first entry in Addison-Wesley's "Data & Analytics" franchise, so I was looking forward to "Pandas for Everyone" - and I mentioned it in my November 2017 review of "Pandas Cookbook." "By Ted Petrou, as an alternative to waiting around the corner. Personally, I've wondered if Pandas Cookbook, a very decent book, would be completely eclipsed by Pandas for Everyone after just a month on the market."
Well I don't think that's going to happen. Between the two titles - and the two, along with (obviously) "Python for Data Analysis" by Wes McKinney, are, in my opinion, the only Panda titles worth considering - I prefer Petrous, there it is a book with more substance and clarity. . In any case, "Panda for Everyone" is a good original book, but it is clearly underpublished, making the presentation less effective than it could be. There are fewer pandas in it than in the "Pandas Cookbook" - on the other hand (and that could be a blessing for many readers) a few chapters are devoted to the illustration of statistical algorithms and machine learning. (Applied to pandas data frames - this is the pandas connection).
My advice to a Pandas student would be to get the Pandas Cookbook, Pandas for Everyone, and Python for Data Analysis, see which style you prefer, and leave 1-2 books for further study.
The author has a very well organized way of handling the data analysis concepts (i.e. the what) and tools (i.e. the what).
The best book on the market that bridges the gap between basic and intermediate levels. Very practical, concise and very engaging reading experience.
I buy other books more thoroughly and professionally and need more time to study, which is not my intention. For my purpose is that the pandas slim down quickly and use them to solve my problem. The arrangement and depth of the books are suitable for beginners. The book Let Me Master Pandas Quickly, but you don't have to go into every detail or the scientist's report is as important as the name of the book "For All". Thank you Daniele Y.
I've looked through dozens of data analysis books to get a clear understanding of data frames and data conflicts. This book is as succinct and perfect in its examples and explanations as I had always hoped it would be, but I could never have found it in a book; until now. I'm going to get the word out on this book because it needs more credit. Thank you Daniele, Lord!
Most Python data analysis textbooks start slowly and work your way up to Panda via NumPy. In contrast, Pandas for Everyone begins with an in-depth discussion of the steps to read and manipulate data. The chapters are mostly short and self-contained. The examples are basic, however, and I had to supplement this book with "Python for Data Analysis: Data Wrangling with Pandas, NumPy and IPython" by Wes Mckinney and "Python Data Science Handbook" by Jake Vanderplas to fully understand what was going on went the hood.