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    Pandas for Everyone: Python Data Analysis

    Posted By: Grev27
    Pandas for Everyone: Python Data Analysis

    Daniel Y. Chen, "Pandas for Everyone: Python Data Analysis"
    English | ISBN: 0134546938 | 2018 | EPUB | 416 pages | 36 MB

    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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