Manage and Automate Data Analysis with Pandas 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 data sets.
Pandas for Everyone, 2nd Edition,
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 data science problems such as using regularization to prevent data overfitting, or when to use unsupervised machine learning methods to find the underlying structure in a data set.
New features to the second edition include:
Extended coverage of plotting and the seaborn data visualization library
Expanded examples and resources
Updated Python 3.9 code and packages coverage, including statsmodels and scikit-learn libraries
Online bonus material on geopandas, Dask, and creating interactive graphics with Altair
Chen gives you a jumpstart on using Pandas with a realistic data set and covers combining data sets, handling missing data, and structuring data sets 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 data sets and handle missing data
Reshape, tidy, and clean data sets 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 data sets 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” one
Regularize to overcome overfitting and improve performance
Use clustering in unsupervised machine learning