The pandas library has tools that support high-performance data analysis tasks. This library is useful for both commercial and scientific applications. The acronym "pandas" is partially derived from the econometric term "panel data" and Python data analysis. The five typical steps of data analysis and data processing are load, prepare, manipulate, model, and analyze.
pandas has added three new data structures to Python, namely Series, DataFrame, and Panel. These data structures are developed on top of NumPy. Let's discuss each of these data structures in detail.
Series is a one-dimensional object similar to an array, a list, or a column in a table. It can hold any of Python's data types, including integers, floating-point numbers, strings, and any Python object. It also assigns a labeled index to each item in a series. By default, it will assign labels from 0 to N to a series that have N-1 items. We can create a Series using the Series method, from an ndarrays, or...