Python data processing with pandas

Pandas is a Python language package, which is used for data processing. This is a very common basic programming library when we use Python language for machine learning programming. This article is an introductory tutorial to it. Pandas provide fast, flexible and expressive data structures with the goal of making the work of “relational” or “marking” data simple and intuitive. It is intended to be a high-level building block for actual data analysis in Python.

Pandas is suitable for many different types of data, including:

• Table data with heterogeneous columns, such as SQL tables or Excel data.
• Ordered and unordered (not necessarily fixed frequency) time series data.
• Any matrix data with row and column labels (even type or different types)
• Any other form of observation/statistical data set.

The Pandas Index object contains metadata describing the axis. When creating a Series or DataFrame, the array or sequence of tags is converted to Index. You can get the Index object of the DataFrame column and row in the following way: