Data Analysis

Pandas: Merge

Combining Data with Pandas - Merge - Inner, Outer, Left and Right joins

Pandas: Quantile

Usage of the Pandas quantile function to analyze Fortune500 data. Data visualizations using Seaborn


Data Independence in SQL and DBMSs

An important advantage of database management systems is Data Independence. Data Independence refers to the insulation from applications programs from changes in the way the data is structured and stored. Levels of Abstraction in a DBMS graph TD F(fa:fa-table View 1)---E G(fa:fa-table View 2)---E H(fa:fa-table View 3)---E["fa:fa-database Conceptual Schema"] E---A["fa:fa-server Physical Schema"] A---B["fa:fa-HDD-o Disk"] Logical Independence If the structure of the data changes, applications and users can be insulated from this changes thanks to external schemas (in SQL, views).

PostgreSQL integer and floating point division

PostgreSQL integer and floating point division

SQL Temporary Tables

How to use SQL Temporary Tables

SQLite division: integer and floating point division

SQLite integer and floating point division

How to Describe and Store Data in a Database

A data model is a collection of descriptions of the data and it’s relationships that hides details of the storage implementation. A database management system such as PostgreSQL or MS SQL allows users to define data models. The relational data model is a particular type of data model. A more abstract data model is the semantic data model. It is usually used to develop an initial description of the data in a real world scenario, like a business or any other real world organization.

Advantages of Database Management Systems

DBMSs have the following advantages: Data Independece, Efficiency, Data Integrity, Centralized Administration and Reduced Application Development Time

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Data Independence

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