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:
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Extended coverage of plotting and the seaborn data visualization
library
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Expanded examples and resources
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Updated Python 3.9 code and packages coverage, including statsmodels
and scikit-learn libraries
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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
