Smith JamesThe library is used to analyze and process tabular data. Pandas is like Excel, but more powerful. In Pandas, you can easily work with millions of rows of data.
That’s why Skypro’s Data Analyst course gives students the basics of Python. In a few months, you can learn the basic skills to process data faster and better. You can also use your Python knowledge to create visualizations so that data can be pulled from tables and updated automatically.
To work effectively with pandas, you need to master the most important data structures of the library: DataFrame and Series. Without understanding what they are, it is impossible to perform quality analysis in the future.
Series
The Series structure/object is an object similar to a one-dimensional array (a Python list, for example), but its distinctive feature is the presence of associated labels, so-called indexes, along each element in the list. This feature turns it into an associative array or dictionary in Python.
DataFrame
A DataFrame object is best thought of as a regular table, and rightly so, because a DataFrame is a tabular data structure. In any table, there are always rows and columns. Columns in a DataFrame object are Series objects, whose rows are their immediate elements.
Main features of Pandas
- Loading and saving data: Pandas has extensive capabilities for reading and writing data from a variety of sources, including CSV files, Excel, SQL databases, JSON, HTML, XML, and many others;
- Data Processing: Pandas provides many functions for data processing, including removing duplicates, handling missing values, changing data types, row and column processing, and more;
- Data Analysis: Pandas allows you to perform various types of data analysis, including descriptive statistics, data visualization, correlation calculations, and more;
- Data manipulation: Pandas provides powerful tools for data manipulation, including indexing, sorting, filtering, grouping, aggregating, merging, etc;
- Data Visualization: Pandas integrates with data visualization libraries such as Matplotlib and Seaborn, allowing you to create beautiful and informative charts and graphs directly from within a DataFrame.
Pandas is a powerful data manipulation tool for Python that provides high-level data structures and many tools for data processing, analysis, and visualization. With its ease of use and wide range of features, Pandas is becoming an integral part of the toolkit for data exploration, analysis, and visualization in a variety of fields including science, engineering, finance, medicine, and more.