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Books > Computing & IT > Computer programming
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Pandas for Everyone - Python Data Analysis (Paperback)
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Pandas for Everyone - Python Data Analysis (Paperback)
Series: Addison-Wesley Data & Analytics Series
Expected to ship within 12 - 17 working days
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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
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