In a groundbreaking move, Microsoft has unveiled its latest feature for Excel – Python integration. This innovative development brings together the world of Excel and Python analytics, creating a powerful tool for data analysis and visualization. The integration of Python in Excel allows users to utilize potent Python libraries directly within their Excel workbooks. This opens up a world of possibilities, from creating captivating visualizations to building advanced machine learning models.
The integration of Python in Excel is woven directly into the fabric of the software, thanks to the new ‘py’ function. This allows users to use Python seamlessly within the grid, eliminating the need for any additional installations. In a strategic partnership with Anaconda, Microsoft is set to bring a secure and trusted Python distribution for analytics into Excel. This version will include popular Python libraries such as Pandas and Matplotlib, further enhancing the capabilities of Excel.
Excel receives Python support
Python in Excel empowers users to clean and manipulate their data, train machine learning models, create compelling plots, and much more. All of this can be done within the familiar, secure environment of Excel, using Excel’s built-in connectors and Power Query. This makes it easier than ever to bring external data into Python in Excel workflows.
Security and privacy are at the heart of Python in Excel’s design. The Python code runs on the Microsoft cloud as a compliant Microsoft 365 connected service, ensuring the utmost safety and privacy for users. Moreover, Python in Excel is compatible with all the beloved analytics features of Excel, such as formulas, charts, pivot tables, conditional formatting, and more.
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The integration of Python in Excel combines the flexibility of Excel with the power of Python, enabling users to make data-informed decisions and tell more compelling stories. With Python in Excel, the future of data analysis and visualization looks brighter than ever.
Pandas Python features
- A fast and efficient DataFrame object for data manipulation with integrated indexing;
- Tools for reading and writing data between in-memory data structures and different formats: CSV and text files, Microsoft Excel, SQL databases, and the fast HDF5 format;
- Intelligent data alignment and integrated handling of missing data: gain automatic label-based alignment in computations and easily manipulate messy data into an orderly form;
- Flexible reshaping and pivoting of data sets;
- Intelligent label-based slicing, fancy indexing, and subsetting of large data sets;
- Columns can be inserted and deleted from data structures for size mutability;
- Aggregating or transforming data with a powerful group by engine allowing split-apply-combine operations on data sets;
- High performance merging and joining of data sets;
- Hierarchical axis indexing provides an intuitive way of working with high-dimensional data in a lower-dimensional data structure;
- Time series-functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging. Even create domain-specific time offsets and join time series without losing data;
- Highly optimized for performance, with critical code paths written in Cython or C.
- Python with pandas is in use in a wide variety of academic and commercial domains, including Finance, Neuroscience, Economics, Statistics, Advertising, Web Analytics, and more.
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