Over engineeringthe core of Kubernetes kops
---------------------------------- {{ range $zone := .Zones }} Kris Nova Over engineering the core of Kubernetes kops Kris Nova About Me Kris Nova “I work in the cloud...” Kris Nova In my free0 码力 | 75 页 | 4.56 MB | 1 年前3Leveraging Istio for Creating API Tests - Low Effort API Testing for Microservices
API Tests Low Effort API Testing for Microservices | CONFIDENTIAL • What has changed? – Migration to microservices triggering need for extensive API tests • Problem: – Creating API tests is effort effort intensive – Creating + maintainting E2E, service tests, component tests adds up very quickly • What happens if you do not address the problem? – Thorough test coverage can take a lot of time outcome: Just create E2E tests • What is our solution? – Leverage Istio sidecar to listen to API traffic data and create tests from the data – 10x speed in creating API tests • Can also be sped up0 码力 | 21 页 | 1.09 MB | 1 年前3pandas: powerful Python data analysis toolkit - 0.21.1
development/code writing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415 3.5.3.1 Writing tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415 3.5.3.2 Transitioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1180 30.6 Out-of-core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1180 . . . . . . . . . . . . 1903 34.15.1.1 pandas.core.window.Rolling.count . . . . . . . . . . . . . . . . . . . . . . . . . . 1904 34.15.1.2 pandas.core.window.Rolling.sum . . . . . . . . . . . . . .0 码力 | 2207 页 | 8.59 MB | 1 年前3pandas: powerful Python data analysis toolkit - 0.20.3
development/code writing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387 3.5.3.1 Writing tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387 3.5.3.2 Transitioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1144 30.6 Out-of-core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1144 . . . . . . . . . . . . 1766 34.12.1.1 pandas.core.window.Rolling.count . . . . . . . . . . . . . . . . . . . . . . . . . . 1766 34.12.1.2 pandas.core.window.Rolling.sum . . . . . . . . . . . . . .0 码力 | 2045 页 | 9.18 MB | 1 年前3pandas: powerful Python data analysis toolkit - 0.20.2
development/code writing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385 3.5.3.1 Writing tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385 3.5.3.2 Transitioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1142 30.6 Out-of-core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1142 . . . . . . . . . . . . 1635 34.12.1.1 pandas.core.window.Rolling.count . . . . . . . . . . . . . . . . . . . . . . . . . . 1636 34.12.1.2 pandas.core.window.Rolling.sum . . . . . . . . . . . . . .0 码力 | 1907 页 | 7.83 MB | 1 年前3pandas: powerful Python data analysis toolkit - 0.19.1
Test-driven development/code writing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 Writing tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 Running . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338 Running Google BigQuery Integration Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 Running the vbench performance test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1052 31.6 Out-of-core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10520 码力 | 1943 页 | 12.06 MB | 1 年前3pandas: powerful Python data analysis toolkit - 0.19.0
Test-driven development/code writing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335 Writing tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335 Running . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336 Running Google BigQuery Integration Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 Running the vbench performance test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1050 31.6 Out-of-core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10500 码力 | 1937 页 | 12.03 MB | 1 年前3pandas: powerful Python data analysis toolkit - 1.4.4
development environment. Running the test suite pandas is equipped with an exhaustive set of unit tests, covering about 97% of the code base as of this writing. To run it on your machine to verify that entities. Wes McKinney is the Benevolent Dictator for Life (BDFL). Development team The list of the Core Team members and more detailed information can be found on the people’s page of the governance repo columns] I’m interested in a technical summary of a DataFrame In [9]: titanic.info()core.frame.DataFrame'> RangeIndex: 891 entries, 0 to 890 Data columns (total 12 columns): # Column Non-Null 0 码力 | 3743 页 | 15.26 MB | 1 年前3pandas: powerful Python data analysis toolkit - 1.4.2
development environment. Running the test suite pandas is equipped with an exhaustive set of unit tests, covering about 97% of the code base as of this writing. To run it on your machine to verify that entities. Wes McKinney is the Benevolent Dictator for Life (BDFL). Development team The list of the Core Team members and more detailed information can be found on the people’s page of the governance repo columns] I’m interested in a technical summary of a DataFrame In [9]: titanic.info()core.frame.DataFrame'> RangeIndex: 891 entries, 0 to 890 Data columns (total 12 columns): # Column Non-Null 0 码力 | 3739 页 | 15.24 MB | 1 年前3pandas: powerful Python data analysis toolkit - 0.17.0
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 899 30.6 Out-of-core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 899 [6]: df['B'].dtype Out[6]: datetime64[ns, US/Eastern] In [7]: type(df['B'].dtype) Out[7]: pandas.core.dtypes.DatetimeTZDtype Note: There is a slightly different string repr for the underlying DatetimeIndex infer negative freq (GH11018) • Remove use of some deprecated numpy comparison operations, mainly in tests. (GH10569) • Bug in Index dtype may not applied properly (GH11017) • Bug in io.gbq when testing0 码力 | 1787 页 | 10.76 MB | 1 年前3
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