Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text. It's a popular tool among data scientists, researchers, and educators for creating and sharing computational workflows, data analysis, and visualizations.
Jupyter Notebook supports multiple programming languages, including Python, R, Julia, and others. It allows you to write and execute code in cells, which can be rearranged and edited in any order. This makes it easy to experiment with code, test hypotheses, and explore data interactively.
In addition to code cells, Jupyter Notebook also supports Markdown cells, which allow you to add text, images, and equations using plain text formatting. This makes it easy to write documentation, tutorials, and reports that combine code and text in a single document.
To use Jupyter Notebook, you need to have it installed on your computer. You can install it using pip, Anaconda, or other package managers. Once installed, you can launch Jupyter Notebook from your command prompt or terminal, and it will open a web page in your default browser. From there, you can create new notebooks, open existing ones, and start writing code and text.
Pandas is an open-source Python library that is used for data manipulation and analysis. It is built on top of the NumPy library and provides a simple and efficient way to handle large datasets.
Some of the key features of Pandas include:
Data structures: Pandas provides two main data structures - Series (1-dimensional) and DataFrame (2-dimensional). These structures are designed to handle various types of data, such as numerical, categorical, and time-series data.
