Tabular Data
Tabular data refers to information organized in rows and columns, resembling a table or spreadsheet. Each row typically represents an individual record or observation, while each column corresponds to a specific variable or attribute.
Tabular data is widely used in finance for tasks like portfolio analysis, risk assessment, and market trend analysis.
The structured nature of tabular data allows for efficient storage, querying, and manipulation. This makes tabular data a good choice when performing data analysis, data visualization, and statistical analysis, including machine learning.
Tabular data is also compatible with the CSV file format, spreadsheet software, and relational databases, further enhancing its versatility and accessibility.
Processing Tabular Data in Python with pandas
In Python, the pandas package makes it easy to work with CSV formatted (i.e. "tabular") data.
The pandas package provides two new super-charged datatypes of interest: the DataFrame and the Series. The DataFrame datatype represents the entire tabular data structure, a “matrix” of rows and columns. Whereas the Series datatype represents either a single row, or a single column in the dataset.
Let’s explore some common and practical ways of working with these objects.