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PandaDoc includes features to create, track and execute documents, as well as functionality for electronic signatures. [15] It consists of features in the following categories: proposals, quotes, team management, content management, branding, tracking, workflow, productivity, etc. [16] It integrates with several CRMs, as well as ERP, payment, cloud storage, and other systems.
The input format with the most support is an extended version of Markdown. [10] Notwithstanding, pandoc can also read in the following formats: Creole; DocBook
PandaDoc was growing quickly and soon had more than 3000 clients. [7] [8] In 2013, Mikado returned to San Francisco and managed to raise $5 mln investments to PandaDoc from such prominent investors as Kima Ventures, Altair Capital, and Fabrice Grinda. [1] As of 2015, 16 employees worked in the Minsk office and 14 worked in San Francisco. [9]
Python 3.0, released in 2008, was a major revision not completely backward-compatible with earlier versions. Python 2.7.18, released in 2020, was the last release of Python 2. [37] Python consistently ranks as one of the most popular programming languages, and has gained widespread use in the machine learning community. [38] [39] [40] [41]
Python sets are very much like mathematical sets, and support operations like set intersection and union. Python also features a frozenset class for immutable sets, see Collection types. Dictionaries (class dict) are mutable mappings tying keys and corresponding values. Python has special syntax to create dictionaries ({key: value})
Python 2.6 was released to coincide with Python 3.0, and included some features from that release, as well as a "warnings" mode that highlighted the use of features that were removed in Python 3.0. [28] [10] Similarly, Python 2.7 coincided with and included features from Python 3.1, [29] which was released on June 26
It is an open-source cross-platform integrated development environment (IDE) for scientific programming in the Python language.Spyder integrates with a number of prominent packages in the scientific Python stack, including NumPy, SciPy, Matplotlib, pandas, IPython, SymPy and Cython, as well as other open-source software.
Filter feature selection is a specific case of a more general paradigm called structure learning.Feature selection finds the relevant feature set for a specific target variable whereas structure learning finds the relationships between all the variables, usually by expressing these relationships as a graph.