Go to Python Big Data Analysis book notes on "Python for Data Analysis"-page 04th

Essential Python Lib

This section describes various types of libraries commonly used by Python for big data analysis.

Numpy Python-specific standard module library for numerical computation, including:

1. A powerful n-dimensional Array object Array;

2. Mature (broadcast) function libraries;

3. toolkit for integrating C/C ++ and Fortran code;

4. Practical linear algebra, Fourier transformation, and random number generation functions. The combination of numpy and scipy is more convenient.

5. the array of linear computing Numpy is a powerful advanced tool for data classification and management in Python.

Numpy is written in C language at the underlying layer. The Pandas module in Pandas Python can process big data quickly and efficiently. In subsequent chapters of this book, the Pandas module is used for big data processing and analysis. dataframe objects in pandas are often used. Similar to the two-dimensional form structure of Excel, output results are displayed in rows and columns. The combination of Pandas and Numpy enables pandas to process two-dimensional forms or database forms. Pandas is more handy in data processing in the financial industry. In fact, pandas was designed to handle financial data analysis at the beginning.

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