Python is a great language for data analysis. A Data frame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns. In the previous parts of the “Python for data science” series, we looked at : Part 1: Basic in-built features in Python like functions, data types, date/time, map, reduce, filter, lambda functions etc.. Part 2: Numpy library for creating, accessing and manipulating arrays. Pandas DataFrame consists of three principal components, the data, rows, and columns. Like Series, DataFrame accepts many different kinds of input: Dict of 1D ndarrays, lists, dicts, or Series DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. import numpy as np A = np.array([[7,11,56,45], [20,21,74,12]]).T B = np.array([[42], [52]]).T C = np.array([[90,213,9], [101, 34, 45]]).T You can then create a three dimensional xarray.DataArray object like so: Series. Related course: Data Analysis with Python and Pandas: Go from zero to hero.

pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive.

... Data structures ¶ Pandas operates with three basic datastructures: Series, DataFrame, and Panel. You can think of it like a spreadsheet or SQL table, or a dict of Series objects. There are extensions to this list, but for the purposes of this material even the first two are more than enough. It is generally the most commonly used pandas object. Pandas is an essential data analysis library within Python ecosystem.

Pandas and xarray have strong conversion support, and panels have been deprecated in favour of using xarray. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. —In this paper we will discuss pandas, a Python library of rich data structures and tools for working with structured data sets common to statistics, finance, social sciences, and many other fields. A series can be seen as a one-dimensional array. Pandas DataFrame is two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). Pandas, a data analysis library, supports two data structures: Series: one-dimensional labeled arrays pd.Series(data) DataFrames: two-dimensional data structure with columns, much like a table. In this article, we will be looking at the most widely used library for data analysis — Pandas. Initial setup of the problem.