Pandas lacks a dedicated mechanism for saving metadata to a DataFrame. However we have seen that recent libraries, such as Arrow and Parquet, do provide direct support for persisting DataFrames and metadata together in highly portable and performant files
Metadata is important — okay, perhaps not exactly a life & death issue for most Python data engineers, but its power and utility should not be overlooked. It enriches data with essential context, such as when, where and how it was created. Collection and storage of metadata should be considered a key feature to include in data processing applications. But how best to do this? If using the popular data analysis toolkit Pandas, how can metadata be stored with the ubiquitous DataFrame? Unfortunately Pandas doesn’t have great support for metadata. There’s no conventional way to attach metadata to a DataFrame, or for portable storage of combined data & metadata. The essential challenge is information coupling; how to ensure data & metadata remain linked together, not only during a single Python session, but also as they get persisted and passed from one system to the next. This latter question is our focus here; how can data & metadata be stored together in a portable and durable format? Naively we might try to save DataFrames as “pickle” files, perhaps adding metadata as a custom attribute or using the experimental attrs. This feels natural, straightforward and initially appears to work. But this approach has serious drawbacks. Pickles can be very specific to the particular version of Python and Pandas used to create them, rendering them unusable by different versions. They are not readily portable to non-Python programs, nor particularly optimised for performance.
In this post, we will learn about pandas’ data structures/objects. Pandas provide two type of data structures:- ### Pandas Series Pandas Series is a one dimensional indexed data, which can hold datatypes like integer, string, boolean, float...
Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.
In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.
Today you're going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates. We gonna use Python OS remove( ) method to remove the duplicates on our drive. Well, that's simple you just call remove ( ) with a parameter of the name of the file you wanna remove done.
In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.