When you need to perform advanced data analysis, Python Pandas library is used for Data Analysis and Data Visualization. PANDAS is open source software for Data Science. It allows you to import data quickly from various sources, take it back to your machine to analyze it, and create compelling graphics.
Pandas library is used in data analysis and data visualization in Python. Data Analysis covers loading, cleaning, merging, grouping, aggregating, transforming, alignment and reshaping data in order to find structure within the data. Data Visualization is the process of representing data in an effective manner.
The Pandas library contains 2 modules, one for Data Structures and the other for Data Analysis and Visualization. The Data Structures module provides high-performance, clean and efficient data structures designed to make working with structured (tabular, multidimensional, potentially heterogeneous) and time series data both easy and intuitive. The Data Analysis and Visualization module provides high-performance, expressive tools for data analysis and visualization built on top of the high-performance data structures provided in the Data Structures module.
What is Python Pandas Series
Series is the primary concept that Pandas is built around. They allow for each row to be transformed into a Python object. Series support arithmetic operations amongst themselves and with scalars, as well as comparison operations. This allows for flexible manipulation of data across multiple axes.
Series is an important data structures of Pandas, similar to R dataframe. But what does series do? This article is divided into five parts.
Series in pandas is used to represent a single data set that is chained with other series. In this article, you will learn about Series from a different point of view.
Series are the most versatile type of data structure in Pandas. You can use a series to represent a time series, an ordered set, an indexed column, etc. In this section, we’ll start with the simplest usage of a series as an ordered set of unique values, and cover how to quickly convert between a regular Python list and a Pandas series.
Like most data structures in Python, the Series is a lot more than meets the eye. It provides a fixed-length list of timestamps that can be organized into time periods and even rolled up like a data frame. It provides indexing and modular arithmetic operations, and can be used as a decorator for transforming other Series objects. And if we dig even deeper, we’ll see that many of its methods and properties can be chained together to create new series data types with unique and powerful analytic capabilities.
What is Python Pandas DataFrame
DataFrame is the most important and frequently used object for data analysis in Python world. However, it indeed causes some challenges to new users who do not know how to use it. It can be compared to R’s data frame and Excel’s table. Once you get used to data frame in Pandas, your working efficiency will be boosted significantly when you are doing data analysis with Python. In this book, you are going to learn how to use Pandas DataFrame in 4 easy-to-follow steps:
DataFrame is the main data structure of a Pandas library, a powerful Python library for data analysis and scientific computing. It is a two-dimensional labeled array, conventionally used for data tables or matrix computations. In this post, we will see how to create DataFrame from scratch, from a python dictionary or from an existing CSV file. And we will talk about DataFrame methods operations to create new columns, drop columns, apply different functions over columns and set operations to merge 2 DataFrame objects.
pandas.DataFrame() is an object containing data organized into rows and columns. It is very similar to a spreadsheet or relational database table, with an intuitive (and mutable!) Python data structure that provides labeled axes (indexes), column names, and data types on access that can change depending on your explict inputs.
Example Pandas Dataframe