"When it comes to working with data in a tabular form, most people reach for a spreadsheet. That's not a bad choice: Microsoft Excel and similar programs are familiar and loaded with functionality for massaging tables of data. But what if you want more control, precision, and power than Excel alone delivers? In that case, the open source Pandas library for Python might be what you are looking for."
"Pandas gives you two new data types: Series and DataFrame. The DataFrame represents your entire spreadsheet or rectangular data, whereas the Series is a single column of the DataFrame. In Python terms, you can think of the Pandas DataFrame as a dictionary or collection of Series objects. You'll also find later that you can use dictionary- and list-like methods for finding elements in a DataFrame."
Pandas is a third-party Python library that must be installed with pip and imported into Python. It provides two primary data structures: the Series (a single column) and the DataFrame (a table representing a spreadsheet). DataFrames behave like collections of Series and support dictionary- and list-like element access. Pandas excels at loading data from CSV files, aligning, merging, and processing tabular data at scale, and it supports quick manipulation and visualization of datasets such as the Gapminder sample. Common practice is to alias pandas as pd and to use pd.read_csv to load CSV files.
Read at InfoWorld
Unable to calculate read time
Collection
[
|
...
]