Cover art for Pandas for Everyone
Addison-Wesley, November 2017
Softcover, 416 pages

Pandas for Everyone Python Data Analysis

Ships today!
Fast $7.95 flat-rate shipping!
Only pay $7.95 per order within Australia, including end-to-end parcel tracking.
100% encrypted and secure
We adhere to industry best practice and never store credit card details.
Talk to real people
Contact us seven days a week – our staff are here to help.

Today, analysts must manage data characterised 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, visualise 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 visualisation. 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 generalised linear modeling to fit models with different response variables

Compare multiple models to select the "best"

Regularise to overcome overfitting and improve performance

Related books