Python Data Processing Expansion pack: Introduction to NumPy and Pandas modules

Source: Internet
Author: User

One, NumPy module

The NumPy (Numeric python) module is an open-source computational extension of Python. This tool can be used to store and manipulate large matrices, which is much more efficient than Python's own nested list (nested list structure) structure, which is also useful for representing matrices (matrix). It is said that NumPy Python is the equivalent of becoming a free, more powerful MATLAB system.

The NumPy module provides a number of advanced numerical programming tools, such as matrix data types, vector processing, and sophisticated operational libraries.

1), a powerful n-dimensional array object;

2), more mature (broadcast) function library;

3), a toolkit for consolidating C/C + + and Fortran code;

4), Practical linear algebra, Fourier transform and random number generation function.

Second, Pandas module

The Pandas (Python data Analysis Library) is a numpy-based tool that is created to solve data analytics tasks. Pandas incorporates a number of libraries and a number of standard data models, providing the tools needed to efficiently manipulate large datasets. Pandas provides a number of functions and methods that enable us to process data quickly and easily.

There are several data structures in the pandas:

1, Series: one-dimensional arrays, similar to one-dimensional array in NumPy.   The two are similar to the Python basic data Structure list, the difference is that the elements in the list can be different data types, and the array and series only allow the same data types to be stored, so that more efficient use of memory, improve the efficiency of operations.   2, Time-series: Time-indexed Series. 3, DataFrame: two-dimensional tabular data structure. Many functions are similar to the Data.frame in R.   Dataframe can be understood as a container of series. 4, Panel: three-dimensional array, can be understood as dataframe container.

  

Python Data Processing Expansion pack: Introduction to NumPy and Pandas modules

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