Increasingly, packages are being vega of binary option on top of pandas to address specific needs in data preparation, analysis and visualization. This is encouraging because it means pandas is not only helping users to handle their data tasks but also that it provides a better starting point for developers to build powerful and more focused data tools.
For a list of projects that depend on pandas, see the libraries. We’d like to make it easier for users to find these projects, if you know of other substantial projects that you feel should be on this list, please let us know. Featuretools is a Python library for automated feature engineering built on top of pandas. Altair is a declarative statistical visualization library for Python. With Altair, you can spend more time understanding your data and its meaning. Bokeh is a Python interactive visualization library for large datasets that natively uses the latest web technologies.
D3, while delivering high-performance interactivity over large data to thin clients. Seaborn is a Python visualization library based on matplotlib. It provides a high-level, dataset-oriented interface for creating attractive statistical graphics. Hadley Wickham’s ggplot2 is a foundational exploratory visualization package for the R language. IPython Vega leverages Vega to create plots within Jupyter Notebook. Plotly’s Python API enables interactive figures and web shareability. IPython is an interactive command shell and distributed computing environment.
Jupyter Notebook is a web application for creating Jupyter notebooks. Note: HTML tables may or may not be compatible with non-HTML Jupyter output formats. It is based on functionality that was located in pandas. SDMX is a library to retrieve and acquire statistical data and metadata disseminated in SDMX 2. 1, an ISO-standard widely used by institutions such as statistics offices, central banks, and international organisations.
Geopandas extends pandas data objects to include geographic information which support geometric operations. N-dimensional variants of the core pandas data structures. Dask is a flexible parallel computing library for analytics. Odo provides a uniform API for moving data between different formats.
Its graph based approach is also extensible by end users for custom formats that may be too specific for the core of odo. The user does not need to know how many cores their system has, nor do they need to specify how to distribute the data. Engarde is a lightweight library used to explicitly state your assumptions about your datasets and check that they’re actually true. Cyberpandas provides an extension type for storing arrays of IP Addresses. This is for users to discover new accessors and for library authors to coordinate on the namespace.
This is the infamous equity structured product that wiped out the wealth of a lot of Asian High Net Worth individuals in 2007-2008. The product is also dubbed as “I kill you later” because what it did. In this situation, the contract keeps on accumulating stocks at a discount. We are however going to evaluate here the risks of the accumulator contract from the seller’s point of view. Lets assume that we have sold a contract that accumulates x stocks of company A for the investor everyday till maturity at price K0 unless the stock breaches a high barrier Kh.