Data Analysis & Visualization: Python | Pandas | Jupyter

Data analysis is a process of inspecting, cleansing, transforming, and modelling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.

Data visualization  deals with the graphic representation of data.

pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.

Python is an interpreted high-level general-purpose programming language. Its design philosophy emphasizes code readability with its use of significant indentation. Its language constructs as well as its object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects.

The Jupyter Notebook is a web-based interactive computing platform. The notebook combines live code, equations, narrative text, visualizations

Python has a powerful and most popular package ‘Pandas’ built on top of Numpy which has the implementation of many data objects and data operations. Pandas is one of the most famous data science tools and it’s definitely a game-changer for cleaning, manipulating, and analyzing data.

Pandas provide tools for reading and writing data into data structures and files. It also provides powerful aggregation functions to manipulate data.

Pandas provide extended data structures to hold different types of labeled and relational data. This makes python highly flexible and extremely useful for data cleaning and manipulation.

Pandas is highly flexible and provides functions for performing operations like merging, reshaping, joining, and concatenating data.

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