Data Analysis with Python Pandas 3 смотреть онлайн

Code available on github.com/abassolaiya
The Pandas library is one of the most important and popular tools for Python data scientists and analysts, as it is the backbone of many data projects. Pandas is an open-source Python package for data cleaning and data manipulation. It provides extended, flexible data structures to hold different types of labeled and relational data. On top of that, it is actually quite easy to install and use.

Pandas is often used in conjunction with other data science Python libraries. In fact, Pandas is built on the NumPy package, so a lot of the structure between them is similar. Pandas is also used in SciPy for statistical analysis or with Matplotlib for plotting functions. Pandas can be used on its own with a text editor or with Juptyer Notebooks, the ideal environment for more complex data modeling. Pandas is available for most versions of Python, including Python3.

Think of Pandas as the home for your data where you can clean, analyze, and transform your data, all in one place. Pandas is essentially a more powerful replacement for Excel. Using Pandas, you can do things like:

Easily calculate statistics about data such as finding the average, distribution, and median of columns
Use data visualization tools, such as Matplotlib, to easily create plot bars, histograms, and more
Clean your data by filtering columns by particular criteria or easily removing values
Manipulate your data flexibly using operations like merging, joining, reshaping, and more
Read, write, and store your clean data as a database, txt file, or CSV file