Kubernetes for Edge Computing across Inter-Continental Haier Production Sites Jiyuan Tang & Xin Zhang zhangxin@caicloud.io tangjiyuan@caicloud.io 关于我们 • 开源技术创新者 • 从 Kubernetes 到 Kubeflow • Google

and much more. pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other 3rd party libraries. Here are just a few of the things that pandas principles are here to address the shortcomings frequently experienced using other languages / scientific research environments. For data scientists, working with data is typically divided into multiple pandas will soon become a dependency of statsmodels, making it an important part of the statistical computing ecosystem in Python. • pandas has been used extensively in production in financial applications

and much more. pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other 3rd party libraries. Here are just a few of the things that pandas principles are here to address the shortcomings frequently experienced using other languages / scientific research environments. For data scientists, working with data is typically divided into multiple pandas will soon become a dependency of statsmodels, making it an important part of the statistical computing ecosystem in Python. • pandas has been used extensively in production in financial applications

and much more. pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other 3rd party libraries. Here are just a few of the things that pandas principles are here to address the shortcomings frequently experienced using other languages / scientific research environments. For data scientists, working with data is typically divided into multiple pandas will soon become a dependency of statsmodels, making it an important part of the statistical computing ecosystem in Python. • pandas has been used extensively in production in financial applications

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 585 19.7 Computing indicator / dummy variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 585 and much more. pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other 3rd party libraries. Here are just a few of the things that pandas principles are here to address the shortcomings frequently experienced using other languages / scientific research environments. For data scientists, working with data is typically divided into multiple

as part of the Anaconda distribution, a cross platform distribution for data analysis and scientific computing. This is the recommended installation method for most users. Instructions for installing Anaconda, a cross-platform (Linux, Mac OS X, Windows) Python distribution for data analytics and scientific computing. After running the installer, the user will have access to pandas and the rest of the SciPy and much more. pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other 3rd party libraries. Here are just a few of the things that pandas

Window Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 684 14.2.8 Computing rolling pairwise covariances and correlations . . . . . . . . . . . . . . . . . . . 685 14.3 Aggregation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 806 18.8 Computing indicator / dummy variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 807 and much more. pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other 3rd party libraries. Here are just a few of the things that pandas

Window Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 682 14.2.8 Computing rolling pairwise covariances and correlations . . . . . . . . . . . . . . . . . . . 683 14.3 Aggregation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 802 18.8 Computing indicator / dummy variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 803 and much more. pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other 3rd party libraries. Here are just a few of the things that pandas

Window Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613 15.2.7 Computing rolling pairwise covariances and correlations . . . . . . . . . . . . . . . . . . . 614 15.3 Aggregation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 720 19.8 Computing indicator / dummy variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 720 and much more. pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other 3rd party libraries. Here are just a few of the things that pandas

Window Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615 15.2.7 Computing rolling pairwise covariances and correlations . . . . . . . . . . . . . . . . . . . 616 15.3 Aggregation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 722 19.8 Computing indicator / dummy variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 722 and much more. pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other 3rd party libraries. Here are just a few of the things that pandas