OPERATOR 5.4. 基于 ANSIBLE 的 OPERATOR 5.5. 基于 HELM 的 OPERATOR 5.6. 基于 JAVA 的 OPERATOR 5.7. 定义集群服务版本（CSV） 5.8. 使用捆绑包镜像 5.9. 遵守 POD 安全准入 5.10. 云供应商上的 OPERATOR 的令牌身份验证 5.11. 使用 SCORECARD 工具验证 OPERATOR 5 OpenShift Container Platform 4.14 中。 Operator Registry Operator Registry 存储 ClusterServiceVersions (CSV) 和自定义资源定义 (CRD) 以便在集群中创建， 并存储有关软件包和频道的 Operator 元数据。它运行在 Kubernetes 或 OpenShift 集群中，向 OLM 提供这些 Operator 该捆绑包必须在注解中至少定义一个频道。 每个捆绑包都只有一个集群服务版本（CSV）。 如果 CSV 拥有自定义资源定义（CRD），则该 CRD 必须存在于捆绑包中。 2.2.1.1. 清 清单 捆绑包清单指的是一组 Kubernetes 清单，用于定义 Operator 的部署和 RBAC 模型。 捆绑包包括每个目录的一个 CSV，一般情况下，用来定义 CRD 所拥有的 API 的 CRD 位于 /manifest

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354 18 IO Tools (Text, CSV, HDF5, ...) 357 18.1 CSV & Text files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . axes (possible to have multiple labels per tick) • Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving / loading data from the ultrafast HDF5 format • Highlites include a consistent I/O API naming scheme, routines to read html, write multi-indexes to csv files, read & write STATA data files, read & write JSON format files, Python 3 support for HDFStore

values (GH28204). • Regression in to_csv() where writing a Series or DataFrame indexed by an IntervalIndex would incorrectly raise a TypeError (GH28210) • Fix to_csv() with ExtensionArray with list-like axes (possible to have multiple labels per tick) • Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving / loading data from the ultrafast HDF5 format • Release 0.25.3 3.2.12 Getting data in/out CSV Writing to a csv file. In [143]: df.to_csv('foo.csv') Reading from a csv file. In [144]: pd.read_csv('foo.csv') Out[144]: Unnamed: 0 A B C D 0 2000-01-01

. . . . . . . . . . . . . . . . . . . . . 12 1.3.1.5 Better support for compressed URLs in read_csv . . . . . . . . . . . . . . . . . 13 1.3.1.6 Pickle file I/O now supports compression . . . . . . . . . . . . . . . . . . . . . . . . 56 1.6.1.3 read_csv has improved support for duplicate column names . . . . . . . . . . . 58 1.6.1.4 read_csv supports parsing Categorical directly . . . . . . . 2.11 MultiIndex constructors, groupby and set_index preserve categorical dtypes 77 1.6.2.12 read_csv will progressively enumerate chunks . . . . . . . . . . . . . . . . . . 79 1.6.2.13 Sparse Changes

. . . . . . . . . . . . . . . . . . . . . 10 1.2.1.5 Better support for compressed URLs in read_csv . . . . . . . . . . . . . . . . . 11 1.2.1.6 Pickle file I/O now supports compression . . . . . . . . . . . . . . . . . . . . . . . . 54 1.5.1.3 read_csv has improved support for duplicate column names . . . . . . . . . . . 56 1.5.1.4 read_csv supports parsing Categorical directly . . . . . . . 2.11 MultiIndex constructors, groupby and set_index preserve categorical dtypes 75 1.5.2.12 read_csv will progressively enumerate chunks . . . . . . . . . . . . . . . . . . 77 1.5.2.13 Sparse Changes

. . . . . . . . . . . . . . . . . . . . . 41 1.5.1.5 Better support for compressed URLs in read_csv . . . . . . . . . . . . . . . . . 42 1.5.1.6 Pickle file I/O now supports compression . . . . . . . . . . . . . . . . . . . . . . . . 85 1.8.1.3 read_csv has improved support for duplicate column names . . . . . . . . . . . 87 1.8.1.4 read_csv supports parsing Categorical directly . . . . . . . 2.11 MultiIndex constructors, groupby and set_index preserve categorical dtypes106 1.8.2.12 read_csv will progressively enumerate chunks . . . . . . . . . . . . . . . . . . 108 1.8.2.13 Sparse Changes

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 452 19 IO Tools (Text, CSV, HDF5, ...) 455 19.1 CSV & Text files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . axes (possible to have multiple labels per tick) • Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving / loading data from the ultrafast HDF5 format • users upgrade to this version. Highlights include: • Added infer_datetime_format keyword to read_csv/to_datetime to allow speedups for homo- geneously formatted datetimes. • Will intelligently limit

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 508 19 IO Tools (Text, CSV, HDF5, ...) 511 19.1 CSV & Text files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . axes (possible to have multiple labels per tick) • Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving / loading data from the ultrafast HDF5 format • ftypes now return a series with dtype=object on empty containers (GH5740) • df.to_csv will now return a string of the CSV data if neither a target path nor a buffer is provided (GH6061) • pd.infer_freq()

. . . . . . . . . . . . . . . . . . . . . 8 read_csv has improved support for duplicate column names . . . . . . . . . . . . . . . . . 10 read_csv supports parsing Categorical directly . . . . . . . 29 MultiIndex constructors, groupby and set_index preserve categorical dtypes . . . . 30 read_csv will progressively enumerate chunks . . . . . . . . . . . . . . . . . . . . . . . . 31 Sparse Changes on groupby resampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 Changes in read_csv exceptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 to_datetime error changes

. . . . . . . . . . . . . . . . . . . . . 7 read_csv has improved support for duplicate column names . . . . . . . . . . . . . . . . . 9 read_csv supports parsing Categorical directly . . . . . . . . 28 MultiIndex constructors, groupby and set_index preserve categorical dtypes . . . . 28 read_csv will progressively enumerate chunks . . . . . . . . . . . . . . . . . . . . . . . . 30 Sparse Changes on groupby resampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 Changes in read_csv exceptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 to_datetime error changes