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本次搜索耗时 0.447 秒,为您找到相关结果约 281 个.
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  • pdf文档 Rancher Kubernetes Cryptographic Library FIPS 140-2 Non-Proprietary Security Policy

    and distributed in its entirety without modification. Rancher Kubernetes Cryptographic Library FIPS 140-2 Non-Proprietary Security Policy Document Version 1.1 January 4, 2021 corsec.com +1 703.276.6050 FIPS 140-2 Security Policy Rancher Kubernetes Cryptographic Library Page 2 of 16 References Ref Full Specification Name Date [140] FIPS 140-2, Security Authentication Code (HMAC) 7/16/2008 FIPS 140-2 Security Policy Rancher Kubernetes Cryptographic Library Page 3 of 16 Acronyms and Definitions Term Definition AES Advanced Encryption Standard
    0 码力 | 16 页 | 551.69 KB | 1 年前
    3
  • pdf文档 pandas: powerful Python data analysis toolkit - 0.14.0

    extensions for pandas) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 595 21.2 Expression Evaluation via eval() (Experimental) . . . . . . . . . . . . . . . . . . . . . . . . . . . 599 time in learning about NumPy first. See the package overview for more detail about what’s in the library. 2 CONTENTS CHAPTER ONE WHAT’S NEW These are new features and improvements of note in each release variable that is not a column you must still refer to it with the ’@’ prefix. – You can have an expression like df.query(’@a < a’) with no complaints from pandas about am- biguity of the name a. – The
    0 码力 | 1349 页 | 7.67 MB | 1 年前
    3
  • pdf文档 pandas: powerful Python data analysis toolkit - 0.17.0

    . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 865 26.3 Expression Evaluation via eval() (Experimental) . . . . . . . . . . . . . . . . . . . . . . . . . . . 867 time in learning about NumPy first. See the package overview for more detail about what’s in the library. 2 CONTENTS CHAPTER ONE WHAT’S NEW These are new features and improvements of note in each release analysis toolkit, Release 0.17.0 • Development support for benchmarking with the Air Speed Velocity library (GH8361) • Support for reading SAS xport files, see here • Documentation comparing SAS to pandas
    0 码力 | 1787 页 | 10.76 MB | 1 年前
    3
  • pdf文档 pandas: powerful Python data analysis toolkit - 0.15

    extensions for pandas) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 745 25.2 Expression Evaluation via eval() (Experimental) . . . . . . . . . . . . . . . . . . . . . . . . . . . 749 time in learning about NumPy first. See the package overview for more detail about what’s in the library. 2 CONTENTS CHAPTER ONE WHAT’S NEW These are new features and improvements of note in each release days 00:00:00’, ..., ’2 days 00:00:02’] Length: 4, Freq: None Constructing a TimedeltaIndex with a regular range In [24]: timedelta_range(’1 days’,periods=5,freq=’D’) Out[24]:
    0 码力 | 1579 页 | 9.15 MB | 1 年前
    3
  • pdf文档 pandas: powerful Python data analysis toolkit - 0.15.1

    extensions for pandas) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 731 25.2 Expression Evaluation via eval() (Experimental) . . . . . . . . . . . . . . . . . . . . . . . . . . . 735 time in learning about NumPy first. See the package overview for more detail about what’s in the library. 2 CONTENTS CHAPTER ONE WHAT’S NEW These are new features and improvements of note in each release days 00:00:00’, ..., ’2 days 00:00:02’] Length: 4, Freq: None Constructing a TimedeltaIndex with a regular range In [24]: timedelta_range(’1 days’,periods=5,freq=’D’) Out[24]:
    0 码力 | 1557 页 | 9.10 MB | 1 年前
    3
  • pdf文档 pandas: powerful Python data analysis toolkit - 0.21.1

    . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 1.5.3 Reorganization of the library: Privacy Changes . . . . . . . . . . . . . . . . . . . . . . . . 64 1.5.3.1 Modules Privacy Has . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 743 15.5.6 String/Regular Expression Replacement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 744 15.5.7 Numeric Replacement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1149 26.3 Expression Evaluation via eval() . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1149
    0 码力 | 2207 页 | 8.59 MB | 1 年前
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  • pdf文档 pandas: powerful Python data analysis toolkit - 0.13.1

    extensions for pandas) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 535 21.2 Expression Evaluation via eval() (Experimental) . . . . . . . . . . . . . . . . . . . . . . . . . . . 539 time in learning about NumPy first. See the package overview for more detail about what’s in the library. 2 CONTENTS CHAPTER ONE WHAT’S NEW These are new features and improvements of note in each release for DataFrames Several experimental features are added, including: • new eval/query methods for expression evaluation • support for msgpack serialization • an i/o interface to Google’s BigQuery Their
    0 码力 | 1219 页 | 4.81 MB | 1 年前
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  • pdf文档 pandas: powerful Python data analysis toolkit - 0.20.3

    . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 1.3.3 Reorganization of the library: Privacy Changes . . . . . . . . . . . . . . . . . . . . . . . . 35 1.3.3.1 Modules Privacy Has . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 713 15.5.6 String/Regular Expression Replacement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 714 15.5.7 Numeric Replacement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1113 26.3 Expression Evaluation via eval() (Experimental) . . . . . . . . . . . . . . . . . . . . . . . . . . . 1113
    0 码力 | 2045 页 | 9.18 MB | 1 年前
    3
  • pdf文档 pandas: powerful Python data analysis toolkit - 0.20.2

    . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 1.2.3 Reorganization of the library: Privacy Changes . . . . . . . . . . . . . . . . . . . . . . . . 34 1.2.3.1 Modules Privacy Has . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 710 15.5.6 String/Regular Expression Replacement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 712 15.5.7 Numeric Replacement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1111 26.3 Expression Evaluation via eval() (Experimental) . . . . . . . . . . . . . . . . . . . . . . . . . . . 1111
    0 码力 | 1907 页 | 7.83 MB | 1 年前
    3
  • pdf文档 pandas: powerful Python data analysis toolkit - 0.19.0

    . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 640 16.5.6 String/Regular Expression Replacement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 641 16.5.7 Numeric Replacement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1013 27.3 Expression Evaluation via eval() (Experimental) . . . . . . . . . . . . . . . . . . . . . . . . . . . 1013 . . . . . . . . . . . . . . . . . . . . . . . . . . . 1020 27.3.8 Technical Minutia Regarding Expression Evaluation . . . . . . . . . . . . . . . . . . . . . 1021 28 Sparse data structures 1023 28.1
    0 码力 | 1937 页 | 12.03 MB | 1 年前
    3
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